Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37...
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Studies of Air Pollutants and their Impact on Respiratory and
Cardiovascular Diseases in the El Paso North Region with Emphasis on
Mobile Emissions
Juan Gustavo Arias Ugarte
Master in Environmental Science
PhD In Environmental Science amp Engineering
OUTLINE
bull ABSTRACT
bull STUDY PROPOSE
bull SIGNIFICANCE OF THE RESEARCH
bull PROBLEM STATEMENT
bull METHODOLOGY
bull RESULT
bull CONCLUSION
bull BIBLIOGRAPHY
ABSTRACTThe focus of this research is the study of mobile emissions in the Paso del Norte (PdN) region with the
objective of establishing an association between mobile emissions and health
This association will allow a comparison of different exposure and risk situations relative to the emission
source (Exhaust and Non-Exhaust Emission) and to the particle size PM 10 (coarse) and PM25
(inhalable) with the adverse effects on respiratory (RD) and cardiovascular diseases (CVD)
The times selected for this study are periods in 2006 and 2010 Two areas associated with high traffic
density were selected highway I-10 in the Chamizal urban area zip code 79905 and Dona Ana
county urban areas zip codes 88021880278807288048 The mobile simulations were performed using
the Motor Vehicle Emission (MOVES model version 2014a) simulating weekly monthly and annual
data of mobile emissions
Estimates of the Health and Economic Effects of Air Pollution in the El PdN region were performed
using the Environmental Benefits Mapping and Analysis Program and GIS Software was used for
visualization purposes
In addition NOx is a major component of mobile emissions and is an ozone precursor Therefore we
have studied its concentrations during high episodes and analyzed the meteorological factors that
also contribute to high NOx and Ozone concentrations and performed corresponding air quality
simulations for the PdN region
Purpose
The purpose of this research was establishing an association between mobile emissions
and health This association will allow a comparison of different exposure and risk
situations relative to the location emission source (Exhaust and Non-Exhaust Emission)
and to the particle size PM 10 (coarse) and PM25 (inhalable) with the adverse effects
on respiratory (RD) and cardiovascular diseases (CVD)
Introduction
The Paso del Norte (PdN) region is
one of the largest metropolitan areas
along the US-Mexico border This
includes El Paso Ciudad Juarez
Mexico Each year over 18 million
vehicles cross the three international
ports of entry between El Paso and
Cuidad Juaacuterez
Approximately one-quarter of the
population in The Paso del Norte
(PdN) region lives close to major
roadways exposing the people to
risks of severe respiratory infections
and cardiovascular diseases (Trainer
M Parrish DD and Goldan PD 2000)
Significance of the Work
Traffic is a major source of mobile emissions causing air pollution
Mobile emissions such as
Exhaust Emissions (Carbon Monoxide Nitrogen Oxides and
Hydrocarbons) and Non-exhaust (Brake and Tire) Particulate
emissions can also be very damaging to health because their tiny
diameter can enter the respiratory system easily and possibly cause
adverse health impacts (pulmonary carcinogen inflammatory and
toxic effect on the lung (IARC 1989 Et al 2013) (Kelly and Fussell
2012) (iron(Fe) copper(Cu) Manganese(Mn) antimony(Sb)
barium(Ba) zinc(Zn)
Therefore the relevance of this research provides a key
knowledge to understand the impacts of traffic emissions on air
quality and the magnitude of public health risks associated with
exposure to mobile emissions
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
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)
Local time (Hrs)
C41 June 13-19 2006
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TCEQ_temperature
(⁰C)
01020304050
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d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
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June 18th June 19th
Performance Evaluation
01020304050
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ne
157
130
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Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
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June1
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June1
87
100
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0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
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10
20
30
40
50
June1
37
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130
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June1
57
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160
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87
100
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160
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190
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220
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0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
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20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 2: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/2.jpg)
OUTLINE
bull ABSTRACT
bull STUDY PROPOSE
bull SIGNIFICANCE OF THE RESEARCH
bull PROBLEM STATEMENT
bull METHODOLOGY
bull RESULT
bull CONCLUSION
bull BIBLIOGRAPHY
ABSTRACTThe focus of this research is the study of mobile emissions in the Paso del Norte (PdN) region with the
objective of establishing an association between mobile emissions and health
This association will allow a comparison of different exposure and risk situations relative to the emission
source (Exhaust and Non-Exhaust Emission) and to the particle size PM 10 (coarse) and PM25
(inhalable) with the adverse effects on respiratory (RD) and cardiovascular diseases (CVD)
The times selected for this study are periods in 2006 and 2010 Two areas associated with high traffic
density were selected highway I-10 in the Chamizal urban area zip code 79905 and Dona Ana
county urban areas zip codes 88021880278807288048 The mobile simulations were performed using
the Motor Vehicle Emission (MOVES model version 2014a) simulating weekly monthly and annual
data of mobile emissions
Estimates of the Health and Economic Effects of Air Pollution in the El PdN region were performed
using the Environmental Benefits Mapping and Analysis Program and GIS Software was used for
visualization purposes
In addition NOx is a major component of mobile emissions and is an ozone precursor Therefore we
have studied its concentrations during high episodes and analyzed the meteorological factors that
also contribute to high NOx and Ozone concentrations and performed corresponding air quality
simulations for the PdN region
Purpose
The purpose of this research was establishing an association between mobile emissions
and health This association will allow a comparison of different exposure and risk
situations relative to the location emission source (Exhaust and Non-Exhaust Emission)
and to the particle size PM 10 (coarse) and PM25 (inhalable) with the adverse effects
on respiratory (RD) and cardiovascular diseases (CVD)
Introduction
The Paso del Norte (PdN) region is
one of the largest metropolitan areas
along the US-Mexico border This
includes El Paso Ciudad Juarez
Mexico Each year over 18 million
vehicles cross the three international
ports of entry between El Paso and
Cuidad Juaacuterez
Approximately one-quarter of the
population in The Paso del Norte
(PdN) region lives close to major
roadways exposing the people to
risks of severe respiratory infections
and cardiovascular diseases (Trainer
M Parrish DD and Goldan PD 2000)
Significance of the Work
Traffic is a major source of mobile emissions causing air pollution
Mobile emissions such as
Exhaust Emissions (Carbon Monoxide Nitrogen Oxides and
Hydrocarbons) and Non-exhaust (Brake and Tire) Particulate
emissions can also be very damaging to health because their tiny
diameter can enter the respiratory system easily and possibly cause
adverse health impacts (pulmonary carcinogen inflammatory and
toxic effect on the lung (IARC 1989 Et al 2013) (Kelly and Fussell
2012) (iron(Fe) copper(Cu) Manganese(Mn) antimony(Sb)
barium(Ba) zinc(Zn)
Therefore the relevance of this research provides a key
knowledge to understand the impacts of traffic emissions on air
quality and the magnitude of public health risks associated with
exposure to mobile emissions
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 3: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/3.jpg)
ABSTRACTThe focus of this research is the study of mobile emissions in the Paso del Norte (PdN) region with the
objective of establishing an association between mobile emissions and health
This association will allow a comparison of different exposure and risk situations relative to the emission
source (Exhaust and Non-Exhaust Emission) and to the particle size PM 10 (coarse) and PM25
(inhalable) with the adverse effects on respiratory (RD) and cardiovascular diseases (CVD)
The times selected for this study are periods in 2006 and 2010 Two areas associated with high traffic
density were selected highway I-10 in the Chamizal urban area zip code 79905 and Dona Ana
county urban areas zip codes 88021880278807288048 The mobile simulations were performed using
the Motor Vehicle Emission (MOVES model version 2014a) simulating weekly monthly and annual
data of mobile emissions
Estimates of the Health and Economic Effects of Air Pollution in the El PdN region were performed
using the Environmental Benefits Mapping and Analysis Program and GIS Software was used for
visualization purposes
In addition NOx is a major component of mobile emissions and is an ozone precursor Therefore we
have studied its concentrations during high episodes and analyzed the meteorological factors that
also contribute to high NOx and Ozone concentrations and performed corresponding air quality
simulations for the PdN region
Purpose
The purpose of this research was establishing an association between mobile emissions
and health This association will allow a comparison of different exposure and risk
situations relative to the location emission source (Exhaust and Non-Exhaust Emission)
and to the particle size PM 10 (coarse) and PM25 (inhalable) with the adverse effects
on respiratory (RD) and cardiovascular diseases (CVD)
Introduction
The Paso del Norte (PdN) region is
one of the largest metropolitan areas
along the US-Mexico border This
includes El Paso Ciudad Juarez
Mexico Each year over 18 million
vehicles cross the three international
ports of entry between El Paso and
Cuidad Juaacuterez
Approximately one-quarter of the
population in The Paso del Norte
(PdN) region lives close to major
roadways exposing the people to
risks of severe respiratory infections
and cardiovascular diseases (Trainer
M Parrish DD and Goldan PD 2000)
Significance of the Work
Traffic is a major source of mobile emissions causing air pollution
Mobile emissions such as
Exhaust Emissions (Carbon Monoxide Nitrogen Oxides and
Hydrocarbons) and Non-exhaust (Brake and Tire) Particulate
emissions can also be very damaging to health because their tiny
diameter can enter the respiratory system easily and possibly cause
adverse health impacts (pulmonary carcinogen inflammatory and
toxic effect on the lung (IARC 1989 Et al 2013) (Kelly and Fussell
2012) (iron(Fe) copper(Cu) Manganese(Mn) antimony(Sb)
barium(Ba) zinc(Zn)
Therefore the relevance of this research provides a key
knowledge to understand the impacts of traffic emissions on air
quality and the magnitude of public health risks associated with
exposure to mobile emissions
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 4: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/4.jpg)
Purpose
The purpose of this research was establishing an association between mobile emissions
and health This association will allow a comparison of different exposure and risk
situations relative to the location emission source (Exhaust and Non-Exhaust Emission)
and to the particle size PM 10 (coarse) and PM25 (inhalable) with the adverse effects
on respiratory (RD) and cardiovascular diseases (CVD)
Introduction
The Paso del Norte (PdN) region is
one of the largest metropolitan areas
along the US-Mexico border This
includes El Paso Ciudad Juarez
Mexico Each year over 18 million
vehicles cross the three international
ports of entry between El Paso and
Cuidad Juaacuterez
Approximately one-quarter of the
population in The Paso del Norte
(PdN) region lives close to major
roadways exposing the people to
risks of severe respiratory infections
and cardiovascular diseases (Trainer
M Parrish DD and Goldan PD 2000)
Significance of the Work
Traffic is a major source of mobile emissions causing air pollution
Mobile emissions such as
Exhaust Emissions (Carbon Monoxide Nitrogen Oxides and
Hydrocarbons) and Non-exhaust (Brake and Tire) Particulate
emissions can also be very damaging to health because their tiny
diameter can enter the respiratory system easily and possibly cause
adverse health impacts (pulmonary carcinogen inflammatory and
toxic effect on the lung (IARC 1989 Et al 2013) (Kelly and Fussell
2012) (iron(Fe) copper(Cu) Manganese(Mn) antimony(Sb)
barium(Ba) zinc(Zn)
Therefore the relevance of this research provides a key
knowledge to understand the impacts of traffic emissions on air
quality and the magnitude of public health risks associated with
exposure to mobile emissions
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 5: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/5.jpg)
Introduction
The Paso del Norte (PdN) region is
one of the largest metropolitan areas
along the US-Mexico border This
includes El Paso Ciudad Juarez
Mexico Each year over 18 million
vehicles cross the three international
ports of entry between El Paso and
Cuidad Juaacuterez
Approximately one-quarter of the
population in The Paso del Norte
(PdN) region lives close to major
roadways exposing the people to
risks of severe respiratory infections
and cardiovascular diseases (Trainer
M Parrish DD and Goldan PD 2000)
Significance of the Work
Traffic is a major source of mobile emissions causing air pollution
Mobile emissions such as
Exhaust Emissions (Carbon Monoxide Nitrogen Oxides and
Hydrocarbons) and Non-exhaust (Brake and Tire) Particulate
emissions can also be very damaging to health because their tiny
diameter can enter the respiratory system easily and possibly cause
adverse health impacts (pulmonary carcinogen inflammatory and
toxic effect on the lung (IARC 1989 Et al 2013) (Kelly and Fussell
2012) (iron(Fe) copper(Cu) Manganese(Mn) antimony(Sb)
barium(Ba) zinc(Zn)
Therefore the relevance of this research provides a key
knowledge to understand the impacts of traffic emissions on air
quality and the magnitude of public health risks associated with
exposure to mobile emissions
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 6: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/6.jpg)
Significance of the Work
Traffic is a major source of mobile emissions causing air pollution
Mobile emissions such as
Exhaust Emissions (Carbon Monoxide Nitrogen Oxides and
Hydrocarbons) and Non-exhaust (Brake and Tire) Particulate
emissions can also be very damaging to health because their tiny
diameter can enter the respiratory system easily and possibly cause
adverse health impacts (pulmonary carcinogen inflammatory and
toxic effect on the lung (IARC 1989 Et al 2013) (Kelly and Fussell
2012) (iron(Fe) copper(Cu) Manganese(Mn) antimony(Sb)
barium(Ba) zinc(Zn)
Therefore the relevance of this research provides a key
knowledge to understand the impacts of traffic emissions on air
quality and the magnitude of public health risks associated with
exposure to mobile emissions
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 7: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/7.jpg)
Problem Statement
0
100
200
300
400
500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-10 measured in micrograms per cubic meter (μgm3)
2006 January
February
March
April
May
June
July
August
September
October
November
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region during
2006 2010 Analysis of Chamizal Area Zip Code 79905
These chart illustrates two high spikes that occurred which exceeds the 24 hour average
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
( μ
gm
3)
Days
Chamizal C 41
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter (μgm3)
2006 january
February
March
April
May
June
July
August
September
October
November
December
Chamizal C41Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM 10 and 25 was
exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is PM2535 microgmsup3 and PM10 50-
100 microgmsup3 The highest concentration occurred several times PM10(150200300450 microgmsup3)
PM25(4050607080120 microgmsup3 )
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 8: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/8.jpg)
Problem Statement
0
100
200
300
400
500
600
700
800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
10 (
μg
m3)
Days
Ascarate Park SE C37
Montly 24 Hours Avg PM 10 Exceedences and Maximum
Concentrations 2010 January
February
March
April
May
June
July
August
September
October
November
December
Graph 1 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
PM
25
UG
M3
DAYS
Ascarate Park SE C37
Montly 24 Hours Average
PM-25 measured in micrograms per cubic meter
(μgm3)
2010 JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
Graph 2 Source TCEQ ( Texas Commission on Environmental Quality) Ascarate Park monitoring station Data 2010
Ascarate Park C37 Monitoring Station The National Ambient Air Quality Standard (NAAQS) for PM
10 and 25 was exceeded but in 24 hour average The NAAQS for PM 25 for 24-hour average is
PM2535 microgmsup3 and PM10 50-100 microgmsup3 The highest concentration occurred several times
PM10(200250300400600720 microgmsup3) PM25(4090120microgmsup3)
The National Ambient Air Quality Standard (NAAQS) for PM was Exceeded in The PdN Region 2006 2010
Analysis of Ascarate Area Zip Code 79905
These chart is to illustrate there are two high spikes that occurred which exceeds the 24 hour average
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 9: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/9.jpg)
Fig Number of registered automobiles in Texas The Statistics show the
number of registered automobiles truck motorcycle in Texas Source
Texas department motor vehicles
Fig More than 61 percent of
US transportation emissionscome from cars and lighttrucks
Fig in US On-roadmobile sourcesconstitute the largestsingle source categoryof air pollution with(327)
Problem Statement
13000000140000001500000016000000170000001800000019000000
Millio
ns
of
Ve
hic
les
Years
Total Growth of Texas Registrations
2001-2012
Total
Vehicles
Registratio
ns
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 10: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/10.jpg)
Problem Statement
El Paso Texas and ciudad Juarez currently has commercialmovements through El PasondashCiudad Juarez gateways peaked at715000 trucks in the year 2010 and Approximately 40000 vehicles passthese roads each laborable day
Source US Department of Transportation Bureau of Transportation Statistics 2010
Bridge Number of US-bound
Truck Crossings year 2010
World Trade Bridge 1327479
Pharr ndash Reynosa International Bridge
452821
Ysleta ndash Zaragoza Bridge 379508
Laredo ndash Colombia Solidarity
Bridge
374781
Bridge of the Americas 337609
Veterans International Bridge 177986
Camino Real International Bridge 106423
Del Rio ndash Ciudad Acuna
International Bridge
62966
Progreso International Bridge 42605
Free Trade Bridge 30773
Source CBP
Fig Bridge of the Americas (Puente Cordova)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
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Ho
url
y o
zon
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pp
b]
0102030405060708090
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June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
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26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
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20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
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202
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202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 11: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/11.jpg)
Publications Review to examine the relationship between exposure to motor vehicle emissions and respiratory and cardiovascular disease
We have analyzed the current literature which have served as the basis for this project
Some studies determined that traffic-related emissions have been recognized as a major contributor to urban air pollution in particular major cities proximity to major roadways and exposure to motor vehicle exhaust and Non-exhaust emissions are associated with increased risks of cardiovascular respiratory and other diseases (Brugge et al 2007 Gan et al 2010 Gauderman et al 2007 Jerrett et al 2009 Laden et al 2007 McConnell et al 2006)
Effect of traffic pollution on respiratory and allergic disease in adults cross-sectional and longitudinal analyses Mar Pujades-Rodriacuteguez Tricia McKeever Sarah Lewis Duncan Whyatt John Britton and Andrea Venn
httpwwwsciencedailycomreleases200811081114081003htm April 2011
Traffic Pollution Increases Asthma in Adults Several research results revealed adults who suffer asthma and were exposed to heavy traffic pollution experienced an 80 per cent increase University of Melbourne Rev Public Health 15 (1994) pp 107ndash132 August 2010
Traffic Pollution Aggravates Symptoms In Asthmatic Children Traffic pollution especially in cities adversely affects respiratory health in children with asthma studies suggest that traffic pollution and diesel particles in particular may have a greater effect on respiratory health than other pollutants httpwwwsciencedailycomreleases200811081114081003htm September 2010
Traffic-related air pollution associated with prevalence of asthma and COPDchronic bronchitis A cross-sectional study in Southern Sweden Anna Lindgren Emilie Stroh Peter Montneacutemery September 2009
Acute respiratory health effects of air pollution on children with asthma in US inner cities Benjamin Vaughn MS Meyer Kattan MD Herman Mitchell PhD May 2008
Comparison of Air QualityndashRelated Mortality Impacts of two Different source of emission Transportation in the United States httpwwwtrborgMainBlurbs166402aspx January 2008
Long-term exposure to traffic-related air pollution and cardiovascular mortality Chen H Goldberg MS Burnett RT Jerrett M Wheeler AJ Villeneuve PJ Source Department of Epidemiology and Biostatistics McGill University Montreal Quebec Canada January 2011
heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect102(suppl 4)13ndash23 (1994)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 12: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/12.jpg)
Particulate Matter
PM is a mixture of solid and liquid particles suspended in the atmosphere
as atmospheric aerosols Mobile Pollutants are formed by chemical
reactions of gaseous components from burning of fossil fuel in vehicles
(Gasoline amp diesel motor vehicles)
Source EPA (2012) United States Environmental Protection Agency
Particulate matter basic information Cited October 30th 2012 Available
online at httpwwwepa govairqualityparticlepollutionbasichtml
Source EPA (2012) United States Environmental Protection Agency Particulate matter basic
information Cited October 30th 2012 Available online at httpwwwepa
govairqualityparticlepollutionbasichtml
PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
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PM10 25 Long Term Exposure ndashChronic Health Consequences
These particles are associated with a
variety of problems including
Aggravated asthma Chronic
bronchitis
Decreased lung function
Increased respiratory symptoms
coughing or difficulty breathing
Increased Cardiopulmonary Mortality
Increased Risk of Lung Cancer
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 14: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/14.jpg)
Sources of Particulate Matter Emissions from traffic
Diesel exhaust particulate matter
This particulate toxic potential has been associated with serious adverse Health
effects including respiratory symptoms Pulmonary function and cardiovascular
diseases (Geller et al 2006 Bernstein et al 2004)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 15: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/15.jpg)
Brake wear particulate matter
Brake wear particulate matter Chemical content (iron(Fe) copper(Cu) Antimony(Sb) barium(Ba) this type of particulate can cause severe respiratory and cardiovascular damages and it is also known that Sb has been classified as a possible human pulmonary carcinogen by the International Agency for Research on Cancer (IARC 1989 Et al 2013)
Fig images of brake wear particles generated at a road simulation study ( centre lt25 μm right lt10
μm) Source [Kukutschovaacute et al 2011]
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 16: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/16.jpg)
Tire wear particulate matter
The tire wear particles of the tires are generated either by frictional forces between the treadand the road pavement The chemical content Fe Cr Pb Ti Cr Cu Zn Sr Y Zr Sn Sb Ba LaCe and Pb)
Fig Examples of near spherical particles PM 10 and 25 which are originate from interaction of tires and pavements Source [Dahl et al 2006]
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 17: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/17.jpg)
Study Area
The Paso del Norte (PdN) region Selected two strategic areas of study El Paso County zip code 79905 Urban area and Dona Ana County Zip Code 880218802788072 Rural Restricted urban access (freeways and interstates)
Map of the area of the simulation by the MOVES model in the study area
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 18: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/18.jpg)
Geographic Domain Zip Code 79905 Urban Area (El Paso County)
Zip Codes 88021880278807288048 (Dona Ana County)
Urban road Delta Dr St 31o 45rsquo 5637- 106o 27rsquo 1212 10 miles
Cordova Bridge of the Americas 31o 45rsquo 4367- 106o 27rsquo 0506 15 miles
478 Highway interstate 31o 46rsquo 1326- 106o 27rsquo 0327 05 miles
110 Highway interstate 31o 46rsquo 1210- 106o 27rsquo 0066 10 miles
10 Interstate Gateway Blvd 31o 46rsquo 397- 106o 26rsquo 4769 05 miles
Chamizal Loop 478 31o 46rsquo 668- 106o 26rsquo 5456 05 miles
Alameda Loop 20 31o 46rsquo 1323- 106o 27rsquo 0328 10 miles
Paisano Dr St Patriot freeway
route 62
31o 46rsquo 0669- 106o 27rsquo 212 15 miles
St Piedras 31o 46rsquo 1326- 106o 27rsquo 0327 10 miles
Dona Ana County Miles
Interstate 25 32o 23rsquo 0561 -106 48
1054 32o 23rsquo 3434- -
106 48 1054 32o 23rsquo
5660 106 48 4314
50
Geographic domain To obtain the simulation mobile data from modelMOVES2010a was configurated the coordinates selected in the study areas
El Paso County
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 19: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/19.jpg)
PM25 Estimation from Aerosol Optical Depth (AOD) using
satellites
We examined the relationship
between Aerosol Optical Depth
(AOD) estimated from satellite data
at 5 km spatial resolution and the
mass of fine particles le25 μm in
aerodynamic diameter (PM25)
monitored on the ground in El PdN
area PM25 data was recorded from
air quality monitoring Chamizal
station (TCEQ) on different periods
Our analysis shows a significant
positive association between AOD
and PM25
Time-series and scatter plots of AOD estimated PM25 and surface PM25
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
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01
30
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40
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01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
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00
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00
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00
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00
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00
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00
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00
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23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 20: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/20.jpg)
Analysis of the meteorological factors and synoptic conditions leading to a local high
ozone event episode June 12-21 2006
Meteorology plays a critical role in determining atmospheric ozone concentrations A good synoptic scale weather forecast is essential to accurately forecast tropospheric and surface ozone concentrations
Figure 1 The domain configuration and Landuse categories
resolution 25 km domainFigure 2 The Geopotential height at 500 mb Figure 3 depicts the 850 mb at a lower altitude The
white regions observed in the graph correspond to
mountainous areas
Figure 4 Temperature at 2m height
above surface at 22 UTC June 18 2006
36 km resolution
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 21: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/21.jpg)
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
01020304050
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4
Win
d s
pee
d (
ms
Local time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
We evaluate the WRF modelrsquos performance
by comparing against corresponding
experimental TCEQ data and performing the
required statistical analysis
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
00
30
04
00
50
06
00
70
08
00
90
01
00
01
10
01
20
01
30
01
40
01
50
01
60
01
70
01
80
01
90
02
00
02
10
02
20
02
30
0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
01
00
20
03
00
40
05
00
60
07
00
80
09
00
10
00
11
00
12
00
13
00
14
00
15
00
16
00
17
00
18
00
19
00
20
00
21
00
22
00
23
00
Ho
url
y o
zon
e [
pp
b]
June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 22: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/22.jpg)
HYSPLIT Model
HYSPLIT was simulated and showed that the ozone is mostly transported to El Paso from north western and south western US and Mexican regions during the 72 hours duration of the simulation
0
20
40
60
80
100
1200
00
10
02
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08
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90
02
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0
Ho
url
y o
zon
e [
pp
b]
0102030405060708090
00
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40
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60
07
00
80
09
00
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Ho
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e [
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June 18th June 19th
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 23: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/23.jpg)
Performance Evaluation
01020304050
Ju
ne
137
130
0
190
0
10
0
Ju
ne
157
130
0
190
0
10
0
Ju
ne
187
130
0
190
0
10
0
Tem
pe
ratu
re (⁰C
)
Local time (Hrs)
C41 June 13-19 2006
WRF_temperature
(⁰C)
TCEQ_temperature
(⁰C)
0
50
100
150
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0Ozo
ne
(pp
b)
Local time (Hrs)
C41 June 13-19 2006
CAMx_ozone (ppbV) TCEQ_ozone (ppb)
0
10
20
30
40
50
June1
37
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
57
100
0
130
0
160
0
190
0
220
0
10
0
40
0
June1
87
100
0
130
0
160
0
190
0
220
0
10
0
40
0
Rel
ativ
e h
um
idit
y (
)
Local time (Hrs)
C41 June 13-19 2006
WRF_rh () TCEQ_relative humidity ()
0
5
10
15
20
June
137 10
13
16
19
22 1 4
June
157 10
13
16
19
22 1 4
June
187 10
13
16
19
22 1 4W
ind
sp
eed
(m
sLocal time(Hrs)
C12 June 13-192006
WRF_wind speed (ms) TCEQ_ wind speed (ms)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 24: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/24.jpg)
Methodology
Medical Data The data by weekly monthly and year for daily emergency room and hospital admission for the respiratory
infections (RD) and cardiovascular diseases (CVD) was extracted from hospitals records located in the El PdN
the system includes data on type of admission primary and secondary causes of admissionRESPIRATORY DISEASE
Bronquitis amp Chronic Bronchitis 466 - 4661 491 - 4919
Asthma 463 - 4939
Emphysema 492
Respiratory failure 51881
HEART DISEASE
Accute Myocardial Infarction 410
Coronary Athersclerosis 4140
Other Ischemic Heart Disease 411-4139 4141-4149
Cardiac Dyshythmias 427
Essential hypertension 401
Acute myocardial infarction 410
Congestive Heart Failure 4280 4282-4284
Hypertensive heart disease 402
Table 1 ICD -9 Codes for Respiratory and Cardiovascular Diseases
0
50
100
150
200
250
300
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Mo
rb
idit
y D
isch
arg
e fo
r a
ll R
esp
ira
tory
Dis
ease
IC
D-9
(
Ast
hm
aB
ro
nch
itis
)
Weekly Morbidity Discharges of all the Listed Diagnoses for All
Respiratory Disease During WinterSpring 2010
Asthma-Bronchitis
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 25: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/25.jpg)
EPA MOVES Model for Estimating Emission
Inventories
Motor Vehicle Emission Simulator (MOVES 2010b) will be configuredin The El Paso North Region for Calculate five categories of PM10
PM25 total Emission inventories from all on-road traffic over a
variety of scales within periods of time selected weeks months
and years
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 26: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/26.jpg)
Moves (Motor Vehicle Emission
Simulator)
The emission model is develop using the current equation to calculate direct traffic emissions from
vehicles in the form of PM is
Eext annual emissions are measured in grams of miles traveled by the vehicle (gVMT )
SL is the road surface loading in (gm2)
W is the average weight in tons of the vehicles on the road
K is the particle size multiplier for particle size range (eg gVMT for PM10 when using grams and miles)
P is the 7 number of wet days with at least 0254 mm of precipitation during the averaging period
N is the number of years considered in the averaging period
C is the particle size specific emissions factor in units of interest vehicle direct exhaust brake wear and tirewear
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 27: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/27.jpg)
Modeling Simulation Domain and Emissions Scenarios
For simulations of mobile air quality emission inventories a motor vehicle emission simulator EPA (MOVES 2014a) was used
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 28: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/28.jpg)
The modeling use input factors divided into seven section
parameters
On-Road Inventory local data
input
Scale
Geographic Bounds
Traffic intensity
Vehicle Type
Pollutants
Road Type
Fuel Types
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 29: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/29.jpg)
EPA Moves 2014a The modeling used input factors divided into six section
parameters On-Road Inventory local data input
Geographic Domains CountyNonattainment Area
Vehicle-Miles Traveled (VMT) is the total number of miles driven by all vehicles withina given time period and geographic area
Average speed range from 25 miles per
hour to 65 miles per hour
Vehicle Classes Motorcycle PassengerCarTruks light-duty Commercial Truck(6001-8500 lbs) Buses heave-duty trucksRefuse trucks School Bus Single UnitShort Haul-truck Single Unit Long Haul-truck Long Haul Truck Heavy-DutyVehicles (26001-33000 lbs)
Pollutants
Road TypeRural restricted Acces(Freeways and interstates) Rural and Restricted Acces Urban Restricted Acces Off-Network
Fuel Types Gasoline Diesel Fuel
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 30: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/30.jpg)
Specific Road Types
Two road types for running emission from the MOVES can be
selected
(freeways and interstates)
Interior urban Urban
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 31: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/31.jpg)
VehiclesEquipment
This panel show which on-road vehicles will be modeled
Figure Source EPA US Environmental Protection Agency 2012- Moves 2010b Model
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 32: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/32.jpg)
Long haul truck Short haul truck Inercity bus Ligth comercial truck
Passenger car Passenger truck Refuse truck
School bus Single unit long haul-truck
Single unit short haul-truck
Transit bus
Motor home
Motorcycle
Based on Department of Transportation Highway MOVES will Estimates Traffic
Emissions from Cars Trucks amp Motorcycles
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 33: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/33.jpg)
Output Data Moves 2014a
Inventories of PM 10 25 Mobile Emission from all
on-Road Traffic
Exhaust Emission PM 10 25 gramsmile
Brake Wear Emission PM 10 25 gramsmile
Tire wear Emission PM 10 25 gramsmile
Date of Vehicle miles traveled (VMT) for
Day Week Month Year
Total Vehicle population
Vehicle age distribution
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 34: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/34.jpg)
Simulation of Mobile Emission in the El PdN Region Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
0
10000
20000
30000
40000
50000
PM
10 k
gM
on
th
Months
Monthly PM 10 Exhaust and Non-Exhaust Mobile Emissions 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 (kg) Primary PM 10 Brakewear (kg)
Primary PM 10 Tirewear (kg)
05000
100001500020000250003000035000400004500050000
PM
25
kg
Mo
nth
Months
Monthly PM 25 Exhaust and Non-Exhaust Mobile Emission 2010
On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 25 (kg) Primary PM 25 Brakewear (kg) Primary PM 25Tirewear (kg)
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM25
Exhaust and Non exhaust emission in kilogramsmonth (Source EPA MOVES 2014a)
Total Emissions from Road Traffic in El Paso County PM1025 Exhaust and Non exhaust Emission in kilogramsmonth
Taking the interests to the health risks related to PM 1025 mobile emission were simulated
Exhaust and Non-exhaust emissions contribute almost equally to total traffic-related PM1025 Emissions (Querol et al 2004 Bukowiecki et al 2009a Amato et al 2011)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 35: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/35.jpg)
Monthly PM 10 25 Exhaust and Non-Exhaust (BrakewearTirewear) Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Figure Total Emissions from road traffic in El Paso TX Area were estimated for PM10 25 Exhaust and Non Exhaust mobile
emission in kilograms per month (Source EPA MOVES)
0
10000
20000
30000
40000
50000
January February March April May June July August September October November December
PM
10
25
(K
ilog
ram
s)
Months
Summary- Monthly PM 10 25 Exhaust and Non-Exhaust Mobile
Emission 2010 On-Road Urban Restricted Area Zip Code 79905
Primary Exhaust PM 10 Primary PM 10 Brakewear Primary PM 10 Tirewear
Primary Exhaust PM 25 Primary PM 25 Brakewear Primary PM 25Tirewear
Exhaust Emissions PM1025 is reaching maximum emission 48000-42000 thousand of Kgmonth followedby Brake wear 29000-5000 thousand kgmonth and Tire wear with 9000-2000 kgmonth The results showthat the emissions of PM derived mainly from Exhaust and Brake are significantly higher in Urban Area
Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
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Annual Contribution of Mobile Emission PM 1025 (Kg) Zip Code 79905 El
Paso County 2010
Total Primary
Exhaust _PM10
39
Total Primary
Exhaust _PM25
36
Brakewear
particulate_PM10
18
Tirewear
particulate_PM10
4
Brakewear
particulate_PM25
2
Tirewear
particulate_PM25
1
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Fig Annual Average PM10 Concentrations at the
Chamizal C41 Station and PM1025 mobile
Concentration Zip code 79905
PM10 TCEQ (μgm3) Total Mobile Emission PM10 (Ugm3)
Fig Exhaust Emission is Contributing Approximately 74 of total
PM1025 Mobile Emissions following of Brake wear with 20 and Tire
wear with 5
Annual Average PM1025 ugm3 Mobile Emissions (Moves) 26 Chamizal C41
Monitoring Station (TCEQ Chamizal Station )
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 37: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/37.jpg)
Predicting Mobile Emission and VTM
Estimation The PdN Region 2010
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
50000
100000
150000
200000
250000
300000
350000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and VTM
Estimation
2010 - 2030 e l PdN Area
Total_ Exhaust PM10 Total_ Exhaust PM25 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM Estimation 2010
- 2030 el PdN Area
44E+09
46E+09
48E+09
5E+09
52E+09
54E+09
56E+09
58E+09
6E+09
62E+09
0
5000000
10000000
15000000
20000000
25000000
30000000
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
EX
HA
US
T
EM
ISS
ION
MO
BIL
E (
KG
YE
AR
)
YEARS
Predict ing Mobile Exhaust Emission and
VTM Estimation
2010 - 2030 e l PdN Area
CO NO2 NO NOx Distance
Fig Predicting Mobile Exhaust Emission and VTM Estimation
2010 - 2030 el PdN Area in El Paso TX Area were estimated for
Total CO NOx NO2 NO emission in kilograms per year
(Source EPA MOVES 2014a model)
The simulated Exhaust emissions in the future years shows a trend of reduction of this type of emissions in the
future years one factor should be the new hybrid technology is increasing by 476 since 2010 to 2015 and
use of catalytic converters diesel particulate filters and improved fuels and engines Non-exhaustive sources
will increase in the coming years because there is no control in the material of manufacture composed by Fe
Cu and Mn also not to be able to reduce the number of events of braking (Accelerations and decelerations)
45E+09
5E+09
55E+09
6E+09
65E+09
0
50000
100000
150000
200000
250000
201
0
201
1
201
2
201
3
201
4
201
5
201
6
201
7
201
8
202
1
202
2
202
3
202
4
202
5
202
6
202
7
202
8
202
9
203
0
NO
N-
EX
HA
US
T
EM
ISS
ION
MO
BIL
E
(KG
YE
AR
)
YEARS
Predicting Non Exhaust Mobile
Emission and VTM Estimation
2010 - 2030 el PdN Area
Brake_PM10 Tire_PM10 Distance (Miles)
Fig Predicting Mobile Exhaust PM1025 (kgMonth) Emission and VTM
Estimation 2010 - 2030 el PdN Area
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 38: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/38.jpg)
Daily Avg PM1025
Levels of PM Mobile Emission Non-Exhaust -Rush Hour Periods Fall 2010 Zip Code
79905
00
100
200
300
400
500
600
Mo
bil
e P
M1
0 (k
g)
Rush Hours
Daily Avg PM10
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
Periods 2010 Zip Code 79905
Total Primary Exhaust
_PM10 WinterSpring
Brakewear
particulate_PM10
WinterSpringTirewear particulate_PM10
WinterSpring
Total Primary Exhaust
_PM10 Summer
Brakewear
particulate_PM10 Summer
Tirewear Particulate_PM10
Summer
Total Primary Exhaust
_PM10 Fall
Brakewear
particulate_PM10 Fall
Tirewear particulate_PM10
Fall
00
50
100
150
200
250
300
350
Mo
bil
e P
M2
5 (k
g)
Rush hours
Daily Avg PM25
Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush
Hours Periods 2010 Zip Code 79905
Total Primary Exhaust _PM25
WinterSpring
Brakewear Particulate_PM25
WinterSpring
Tirewear particulate_PM25
WinterSpring
Total Primary Exhaust _PM25
Summer
Brakewear particulate_PM25
Summer
Tirewear particulate_PM25
Summer
Total Primary Exhaust _PM25
Fall
Brakewear particulate_PM25
Fall
Tirewear particulate_PM25
Fall
Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours Figure Seasonal Levels of PM25 from Mobile Emission Non-Exhaust -Rush Hours
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 39: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/39.jpg)
GIS EMISSIONS INVENTORY SYSTEM A geographic information system (GIS) was used to established geographic scenarios with processed images of seasonal mobile
emissions in the PdN region Seasonal mapping is visualized for different types of pollutants Brakewear Exhaust and Tyrewear
Fg Graphics of GIS and Seasonal mobile Emission of particulate matter emission inventory Seasonal emission simulations were performed in kilograms and five contributing inputs factors were identified for
modeling 1) On the composition of the particulate material from different sources such as Exhaust and Non exhaust (Brake wear Tirewear) 2) The Vehicles Equipment clasification were selected according to
the use of gasoline and diesel fuel (motorcycle passenger car Light Duty Vehicles Trucks refuse truck Light Duty Vehicles Light Commercial Truck Light Duty Vehicles School Bus)
Winter Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
120354 67860 19065
Spring Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
139767 86702 24463
Summer Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
94747 62481 17630
Fall Primary Exhaust PM 10 (kg)Primary PM 10 Brakewear
(kg) Primary PM 10 Tirewear (kg)
75510 45542 12596
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 40: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/40.jpg)
251 Descriptive Statistics Data Analysis
Weekly Correlation Analysis of Mobile Emission PM10 (Exhaust-Non-Exhaust) and Respiratory
Diagnoses WinterSpring Summer and Fall 2010
0
2
4
6
8
10
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(A
sth
ma
-Bro
nc
hitis
)
WinterSpring Week
Weekly Avg Mobile PM10 Concentrations during
WinterSpring 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
0
1
2
3
4
5
6
7
0
20
40
60
80
100
Mo
rbid
ity
Dis
ch
arg
e
for
all R
esp
ira
tory
Dis
ea
se
ICD
-9
(Ast
hm
aB
ron
ch
itis
)
Weekly Avg Mobile PM10 Concentrations during
Summer 2010 and its Influence on Respiratory
Diagnoses
Summer 2010 Zip Code 79905
Total Mobile Emission PM10
Hospital Admission (Asthma-Bronchitis)
0
2
4
6
8
10
12
0
20
40
60
80
100
120
140
160
180
200
Mo
rbid
ity
dis
ch
arg
es
for
all r
esp
ira
tory
dis
ea
se
Weekly Avg Mobile PM10 Concentrations
during Fall 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Total Medical data ZIP Code 79902
Total Mobile Emission PM10 (Ugm3)
Mobile(Exhaust-Non-
Exhaust) PM10 and
Asthma-Bronchitis)
Linear R
Square
WinterSpring 734 ndash 39
Summer 614 ndash 32
Fall 548 -29
PM10 Mobile emission (Exhaust-Non-Exhaust)contributed significantly
association to the incidence of RD
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 41: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/41.jpg)
Weekly Correlation Analysis of PM 25 Mobile Emission (Exhaust-Non-Exhaust)
and Cardiovascular Diagnoses WinterSpring Summer and Fall 2010
0010203040
00500
100015002000
Wee
k 1
Wee
k 2
Wee
k 3
Wee
k 4
Wee
k 5
Wee
k 6
Wee
k 7
Wee
k 8
Wee
k 9
Wee
k 1
0
Wee
k 1
1
Wee
k 1
2
Wee
k 1
3
Wee
k 1
4
Wee
k 1
5
Wee
k 1
6
Wee
k 1
7
Wee
k 1
8
Wee
k 1
9
Wee
k 2
0
Wee
k 2
1
Morb
idit
y d
isch
arg
es f
or
all
card
iovasc
ula
r d
isea
se
Weekly Avg PM25 Mobile Concentrations during
WinterSpring 2010 and its Influence on all Heart
Diagnoses
Zip Code 79905
Total All Heart Disease PM 25 mobile (μgm3)
0010203040
00
500
1000
1500
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
Weeks
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS
SUMMER SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Figure Statistical Analyses of the Relationship Between PM25 Caused by Traffic Emission( 26 of total Emission) with Heart diseases Source Hospital Admissions for Asthma Acute Bronchitis and Heart diseases in El Paso County zip code 79905 from Texas Health Care
Information Council in Austin Texas (Texas Health Care Information Council 2000)
00
10
20
30
40
0
50
100
150
We
ek 3
6
We
ek 3
7
We
ek 3
8
We
ek 3
9
We
ek 4
0
We
ek 4
1
We
ek 4
2
We
ek 4
3
We
ek 4
4
We
ek 4
5
We
ek 4
6
We
ek 4
7
We
ek 4
8
We
ek 4
9
We
ek 5
0
We
ek 5
1
We
ek 5
2
We
ek 5
3
Mo
rbid
ity
dis
ch
arg
es
for
all
ca
rdio
va
scu
lar
dis
ea
se
CORRELATION BETWEEN MOBILE PM25 ugm3
EXPOSURE TO HEART DISEASES SYMPTOMS FALL
SEASON 2010
Total All Heart Disease PM 25 mobile (μgm3)
Mobile(Exhaust-
Non-Exhaust)
PM10 and
Asthma-
Bronchitis)
Linear R
Square
WinterSpring 833 -42
Summer 620 -32
Fall 501-26
PM10 Mobile
emission (Exhaust-
Non-
Exhaust)contribut
ed significantly
association to the
incidence of CVD
Diseases
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 42: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/42.jpg)
Monthly Brake Mobile Emission PM1025 kg During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
00200040006000800010000
010000200003000040000
PM
10
Mo
bile
(K
g)
Months
Monthly Brake Mobile Emission PM10 kg during year
2010 and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 10
Brakewear 00100020003000400050006000
0
1000
2000
3000
4000
PM
25
Mo
bil
e(K
g)
Months
Monthly Brake Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 25
Brakewear
Heart Diseases
Hospital
Admission
Brake Mobile Emission
PM10 kg Asthma-
Bronchitis)
Brake Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=969 Linear Square R=856
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 43: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/43.jpg)
Monthly Mobile Exhaust Emission PM1025 During year 2010 and its Influence on
Respiratory Diagnoses Zip Code 79905
0
200
400
600
800
1000
0
10000
20000
30000
40000
50000
1 3 5 7 9 11
PM
10
(K
g m
on
th)
Months
Monthly Mobile Exhaust Emission PM10 during
year 2010 and its Influence on Respiratory
Diagnoses
Zip Code 79905
Primary Exhaust
PM 10
Respiratory Disease
(Asthma
Bronchitis) Hospital
Admission
0
100
200
300
400
500
600
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 101112
PM
25
(K
g m
on
th)
Months
Monthly Exhaust Mobile Emission PM25 during
year 2010 and its Influence on Heart Diagnoses
Zip Code 79905
Primary Exhaust PM
25
Heart Diseases
Hospital Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=929 Linear Square R=831
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 44: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/44.jpg)
Monthly Tire wear Mobile Emission PM1025 During year 2010 and its Influence on Heart
Diagnoses Zip Code 79905
0
100
200
300
400
500
600
700
800
900
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10 11 12
PM
10
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM10 during year 2010
and its Influence on Heart Diagnoses
Zip Code 79905
Primary PM 10 Tirewear
Mobile Emission
Respiratory Disease
(Asthma Bronchitis)
Hospital Admission
0
100
200
300
400
500
600
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12
PM
25
(K
g m
on
th)
Months
Monthly Tirewear Mobile Emission PM25 during year 2010
and its Influence on Respiratory Diagnoses
Zip Code 79905
Primary PM 25Tirewear
Mobile Emission
Heart Diseases Hospital
Admission
Exhaust Mobile Emission
PM10 kg Asthma-
Bronchitis)
Exhaust Mobile Emission
PM25 kg and Heart
Diagnoses
Linear Square R=769 Linear Square R=715
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 45: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/45.jpg)
Dona Ana County Zip Codes
88021880278807288048
Primary
Exhaust
PM 10 Zip
code
79905
Total
Primary
Exhaust
PM10
Dona Ana
Primary
Exhaust
PM 25 Zip
Code
79905
Total
Primary
Exhaust
PM25
Dona Ana
Series1 430378 111773 391915 102123
0
100000
200000
300000
400000
500000
AX
IS T
ITLE
Annual Comparison of the Mobile
Exhaust Emissions El Paso County
and Dona Ana 2010
Primary
PM 10
Brakewea
r Zip code
79905
Brakewea
r
particulat
e_PM10
Dona Ana
Primary
PM 10
Tirewear
Zip code
79905
Tirewear
particulat
e_PM10
Dona Ana
Series1 262585 48573 73754 11864
050000
100000150000200000250000300000
AX
IS T
ITLE
Annual Comparison of the Mobile
Brakewear Emissions El Paso County
and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10 Non-Exhauts (Brake ) from Dona
Ana County New Mexico and El Paso Texas Units
in KgMonth
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Primary
PM 25
Brakewe
ar Ziphellip
Brakewe
ar
particula
te_PMhellip
Primary
PM
25Tirewe
ar Ziphellip
Tirewear
particula
te_PM25
Donahellip
Series1 32645 6071 10919 1779
0
10000
20000
30000
40000
AX
IS T
ITLE
Annual Comparison of the
Mobile Exhaust Emissions El Paso
County and Dona Ana 2010
Figure Comparison of the Annual Mobile Emissions
of Primary PM10-Exhauts Emission from Dona Ana
County New Mexico and El Paso Texas Units in
KgMonth
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 46: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/46.jpg)
Monthly Mobile Emission PM1025 kg During year 2006
and its Influence on Respiratory Diagnoses Dona Ana
County Zip Code 880218802788072
0
100
200
300
0
5000
10000
15000
Axi
s Ti
tle
Axis Title
Monthly Mobile Exhaust Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Total PrimaryMobile Exhaust_PM10
All Respiratory ICD-9 (AsthmaBronchitis)
050100150200250300
010002000300040005000
Axi
s Ti
tle
Axis Title
Monthly Mobile Brake Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Brakewearparticulate_PM10
All RespiratoryICD -9 (AsthmaBronchitis)
050100150200250300
0200400600800
10001200
Axi
s Ti
tle
Axis Title
Monthly Mobile Tirewear Emission PM10 during year 2006 and its Influence on Respiratory Diagnoses
Zip Code 880218802788072
Tirewearparticulate_PM10
All Respiratory ICD -9 (AsthmaBronchitis)
Mobile Exhauts
Emission PM10
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM10
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM10
kg and Heart
Diagnoses
Linear Square
R= 951
Linear Square
R=625
Linear Square
R=525
Mobile Exhauts
Emission PM25
kg Asthma-
Bronchitis)
Brake Mobile
Emission PM25
kg and Heart
Diagnoses
Tirewear Mobile
Emission PM25
kg and Heart
Diagnoses
Linear Square
R= 986
Linear Square
R=691
Linear Square
R=601
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 47: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/47.jpg)
Results
The results showed that mobile PM emissions in the urban areas close to urban Road and highways is putting at risk public health
and the proportion of diseases attributable to this exposure is potentially large
Brake wear emissions is much lower in freeways due to significantly reduced number of braking events while tire wear is much
higher in areas where studded tires are used The emission of exhaust gases are much higher in areas where there is a high
vehicular traffic
Brake Mobile
Emission PM10 kg
Asthma-Bronchitis)
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=969
Linear Square
R=856
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Exhaust Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square
R=929
Linear Square
R=831
Tirewear Mobile
Emission PM10 kg
Asthma-Bronchitis)
Tirewear Mobile
Emission PM25 kg
and Heart Diagnoses
Linear Square R=769 Linear Square R=715
Brake Mobile
Emission PM10 kg
and Heart
Diagnoses
Brake Mobile
Emission PM25 kg
and Heart
Diagnoses
Exhaust Mobile
Emission PM10 kg
Asthma-Bronchitis)
Linear Square
R=625
Linear Square
R=691
Linear Square R=
951
Exhaust Mobile
Emission PM25 kg
Asthma-
Bronchitis)
Tire wear Mobile
Emission PM10 kg and
Heart Diagnoses
Tirewear Mobile
Emission PM25 kg
and Heart
Diagnoses
Linear Square R=
986
Linear Square R=525 Linear Square
R=601
El Paso County
Dona Ana
County
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 48: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/48.jpg)
BenMAP-CE
In this analysis the Program of Mapping and Analysis of Environmental Benefits-Community Edition
(BenMAP-CE) was used to Estimate the number and economic value of health impacts resulting from changes
in air pollution PM 25 Ugm3 in The El Paso Area
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 49: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/49.jpg)
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010
Number of Mortalities in 2010 Base Year Based on El Paso County Texas Department of State Health Services
Number of Mortalities in 2010 Base Year Based on El Paso County Texas
Department of State Health Services
cause of death
year
2010
Populatio
n
Affected
Backgro
und
Concentr
ation Pollutant
Rollback
Type
Avoided
Deaths
(
Populatio
n)
alzheimers disease 2010 40716 125 PM25
25
microgmAcircsup3
Rollback 9
diseases of the
heart 2010 15834 125 PM25
25
microgmAcircsup3
Rollback 35
influenza and
neumonia 2010 81432 125 PM25
25
microgmAcircsup3
Rollback 18
acute bronchitis
and bronchiolitis 2010 117624 125 PM25
25
microgmAcircsup3
Rollback 26
chronic lower
respiratory diseases 2010 54288 125 PM25
25
microgmAcircsup3
Rollback 12
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 50: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/50.jpg)
The Basin-wide decrease in 4 ugm3 PM25 concentration after this
reduction control
Cause of death year 2010
Populatio
n
Affected
Background
Concentrati
on
Reduc
cion
scena
rio Polluta
nt
Rollback
Type
Avoi
ded
Deat
hs
(
Pop
ulati
on)
economic
benefits
analysis $
Alzheimers
disease 2010 121 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 9 12000
Diseases of the
heart 2010 470 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 35 35000
Influenza and
neumonia 2010 242 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 18 42500
Acute bronchitis
and bronchiolitis 2010 349 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 26 95000
Chronic lower
respiratory
diseases 2010 161 125
25
ugm
3 PM25
25
microgmAcircsup3
Rollback 12 4500
$1 295000
PM25-related Mortalities Based on BenMAP-CE Estimate in 2010 Base Year with change in the concentrations levels
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 51: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/51.jpg)
Health and Economic Benefits associated with the Response of PM25 Emission between
Base Year 2010 Increasing 7 Ugm3
Cause of death year 2010
Population
Affected
Deaths
avoided by
attaining 195
μgm3
standard
Background
Concentrati
on
Pollut
ant
Rollback
Type
Avoided
Deaths (
Populatio
n)
econo
mic
Unben
efits
analysi
s
Alzheimers
disease 2010 4524 195 PM25
25
microgmAcircsup3
Rollback 19 150000
Diseases of the
heart 2010 20358 195 PM25
25
microgmAcircsup3
Rollback 135 450000
Influenza and
neumonia 2010 126672 195 PM25
25
microgmAcircsup3
Rollback 38 525000
Acute bronchitis
and bronchiolitis 2010 162864 195 PM25
25
microgmAcircsup3
Rollback 36 122000
Chronic lower
respiratory
diseases 2010 99528 195 PM25
25
microgmAcircsup3
Rollback 12 650000
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 52: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/52.jpg)
Conclusions
An excellent Pearson Correlation was established to analyze the relationship in the relative risks of hospitalization of Respiratory(RD) and cardiovascular (CVD) associated with ambient exposure caused by traffic emission in relation to their size andspecific sources of emission on different scenarios interior a border Urban Rural Urban restricted access (freeways andinterstates)
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) andcardiovascular (CVD) diagnoses was established The results showed that PM emissions in urban restricted access (freeways andinterstates) is mainly caused by traffic Therefore people living close to highways are more exposed higher vehicular emissions
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 53: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/53.jpg)
Conclusions
We found statistically significant associations between mobile PM25 caused by traffic emission with Respiratory and Cardiovascular Diseases in the study areaSubsequently a correlation between cardio-respiratory mortality is established
A significant correlation of PM1025 exposure from mobile emission and hospital admissions for selected Respiratory (RD) and cardiovascular (CVD) diagnoses isestablished The results showed that PM emissions in urban restricted access (freeways and interstates) is mainly caused by traffic Therefore people living close tohighways receive higher vehicular emissions
The table shows the percentage of PM mobile exposure in the analyzed areas contributing to increase the risk of Respiratory and Cardiovascular Diseases
An examination was conducted of the synoptic and local meteorology for the June 2006 ozone episode using the WRF model to determine how meteorological parametersinfluence the formation transport and dispersion of ozone in the PdN The predominant synoptic feature of the ozone event day was the expansion intensification and slowprogression of an upper-level ridge of high pressure The high ozone episodes are associated with highly stable atmospheres strong temperature inversions with low mixingheights and therefore low mixing volumes Under these conditions emissions such as NOx lead to highly polluted conditions that favor ozone formation
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 54: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/54.jpg)
BenMAP Conclusions
We have estimated the number and economic value of health impacts resulting
from changes in air pollution concentrations of PM 25 in the PdN region
Reduction of Pm 25 4 UGm3= Avoided 213 cases respiratory cardiovascular
diseses and Economic Benefits associated with the Response of PM25 Emission + $
1295000
Increase of PM25 7 ugm3 = increasing Dies= 326 economic Unbenefits= 2345000$
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 55: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/55.jpg)
Bibliography
1 Dockery DW Pope CAI Xu X Spengler JD Ware JH Fay ME Ferris BG Speizer FE An association between air pollution and mortality in six US cities N Engl J Med 3291753ndash1759 (1993)
2 Samet JM Dominici F Curriero FC Coursac I Zeger SL Fine particulate air pollution and mortality in 20 US cities 1987ndash1994 N Engl J Med 3431742ndash1749 (2000)
3 Nel AE Diaz-Sanchez D Li N The role of particulate pollutants in pulmonary inflammation and asthma evidence for the involvement of organic chemicals and oxidative stress Curr Opin Pulm Med 20017 20ndash26
4 Westerholm R Karl-Erik E Exhaust emissions from lightand heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
5 Bascom R Bromberg P Costa D Delvin R Dockery D Frampton M Lambert W Samet J Speizer F Utell
6 Health effects of outdoor air pollution Am J Respir Crit Care Med 1533ndash50 (1996)
7 Chow JC Watson JG Frazier CA Egami RT Goodrich A Ralph C Spatial and temporal source contributions to
8 PM10 and PM25 in Reno NV In PM-10 Implementation of Standards Transactions An APCAEPA International Speciality Conference San Francisco CA (Mathal CV Stonefield DH eds) PittsburghAPCA 1988439ndash457
9 overview In Particulate Matter Health and Regulatory Issues VIP-49 Proceedings of an International Specialty Conference 4ndash6 April 1995 Pittsburgh PA PittsburghAir and Waste Management Association 1995577ndash598
10 Kunzli N Kaiser R Medina S Studnicka M Chanel O
11 Filliger P Herry M Horak F Jr Puybonnieux-Texier V Quenel P et al Public-health impact of outdoor and traffic- related air pollution a European assessment Lancet 356795ndash801 (2000)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337
![Page 56: Studies of Air Pollutants and their Impact on Respiratory and ...€¦ · Ascarate Park C37 Monitoring Station: The National Ambient Air Quality Standard (NAAQS) for PM 10 and 2.5](https://reader035.fdocuments.net/reader035/viewer/2022062603/5f3b22c7550ca55afa06bb16/html5/thumbnails/56.jpg)
Bibliography
1 Sioutas C Delfino RJ Singh M Exposure assessment for atmospheric ultrafine particles (UFP) and implications in epidemiological research Environ Health Perspect 2005113947ndash955
2 Mosleh M Blau PJ Dumitrescu D (2004) Characteristics and morphology of wear particles from laboratory testing of disc brake materials Wear 2561128ndash1134
3 Kuumlnzli N et al (2000) Public-health impact of outdoor and traffi c-related air pollution a European assessment Lancet 356(9232)795ndash801
4 Dora C Phillips M eds (2000) Transport environment and health Copenhagen WHO Regional Office for Europe (WHO Regional Publications European Series No 89 httpwwweurowhointdocumente72015pdf accessed 26 November 2004)
5 Westerholm R Karl-Erik E Exhaust emissions from lightand
6 heavy-duty vehicles chemical composition impact of exhaust after treatment and fuel parameters Environ Health Perspect 102(suppl 4)13ndash23 (1994)
7 Fauser Tjell Mosbaek pigegaard Quantification of tire-Tread particles using extractable Organic Zinc as a Tracer Rubber Chemistry and Technology 1999 969Table shows some literature estimates of the contribution of tire wear to PM10 in air (Panko et al 2013) Gustafsson M Blomqvist G Gudmundsson A Dahl A Swietlicki E Bohgard M Lindbom J Ljungman A
8 Chen Y Shah N Huggins FE and Huffman GP (2006) Microanalysis of Ambient Particles from Lexington Ky by Electron Microscopy Atmos Environ 40 651ndash663
9 Donaldson K Tran CL Jimenez LA Duffin R Newby DE and Mills N (2005) Combustion-derived Nanoparticles A Review of Their Toxicology Following Inhalation Exposure Part Fibre Toxicol 2 10ndash23
10 Su D Serafino A Muller JO Jentoft RE Schlogl R and Fiorito S (2008) Cytotoxicity and Inflammatory Potential of Soot Particles of Low-emission Diesel Engines Environ Sci Technol 42 1761ndash1765
11 Maier KL Alessandrini F Beck-Speier I Hofer TPJ Diabatґe S Bitterle E Stoger T and Jakob T (2008) Health Effects of Ambient Particulate Matter-Biological Mechanisms and Inflammatory Responses to in Vitroand in Vivo Particle Exposures Inhalation Toxicol 20 319ndash337