Best Practices in Air Quality Monitoring - BAQ) · PDF fileProf. Shun‐cheng LEE ... Judith...
Transcript of Best Practices in Air Quality Monitoring - BAQ) · PDF fileProf. Shun‐cheng LEE ... Judith...
12/17/2012
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Best Practices in Air Quality Monitoring
Prof. Shun‐cheng LEEDr. Wing‐tat HUNG
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University
1. URBAN AIR QUALITY MANAGEMENT STRATEGY IN ASIA GUIDEBOOK By Steinar Larssen, Knut Erik Grønskei, Norwegian Institute for Air Research
2. Kjeller, Norway and M. C. Hanegraaf, Huib Jansen, O. J. Kuik, F. H. Oosterhuis , Xander A. Olsthoorn, Institute for Environmental Studies, The Free Unversity
3. Amsterdam, the Netherlands.4 Air Quality Monitoring Programme Design By Bjarne Sivertsen Norwegian
KEY REFERENCES
4. Air Quality Monitoring Programme Design By Bjarne Sivertsen, Norwegian Institute for Air Research, Kjeller, Norway. NILU: F 2/2002 REFERENCE:
5. O‐101128 DATE: FEBRUARY 20026. Guidelines for Ambient Air Quality Monitoring By CENTRAL POLLUTION CONTROL
BOARD (Ministry of Environment & Forests, Govt. of India), NATIONAL AMBIENT AIR QUALITY MONITORING SERIES : NAAQMS/ ... /2003‐04
7. GUIDELINES FOR DEVELOPING NATIONAL STRATEGIES TO USE AIR QUALITY8. MONITORING AS AN ENVIRONMENTAL POLICY TOOL, United Nations Economic
and Social Council, ECE/CEP/2009/10, 14 October 2009and Social Council, ECE/CEP/2009/10, 14 October 20099. National Air Pollution Surveillance Network Quality Assurance and Quality
Control Guidelines, Environment Canada, Environmental Protection Service, Environmental Technology Advancement Directorate, Analysis and Air Quality Division, Environmental Technology Centre, Report No. AAQD 2004‐ 1
10. GUIDANCE FOR NETWORK DESIGN AND OPTIMUM SITE EXPOSURE FOR PM2,5 AND PM10, PREPARED BY John G. Watson, Judith C. Chow, David DuBois, Mark Green, Neil Frank, Marc Pitchford, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency
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Essential Elements of Good Air Quality Monitoring System
ATTRIBUTE/CATEGORY INDICATORS SUPPORTING DOCUMENTS
A Ability to properly plan and implement a AQM network to a compatible international standard
Evidence of proper planning documents (for example guidelines) and planning process for AQM system (in particular, number and locations of AQM stations
Evidence of a proper equipment sourcing and procurement procedure (for example, tendering documents)
Evidence of qualification requirements for personnel (including responsible officers and technicians)
Guidelines for siting and planning of AQM station
Lists of AQ monitoring equipment, equipment specifications
B Ability to plan and implement a QA/QC process
Evidence of a QA/QC process (for example, a published requirements and guides for data quality, i.e., acceptable accuracy and
lid d t t i l l )
QA/QC guidelines for AQM system
Summary of Essential Attributes of Good Practices in implementing AQM t valid data capturing levels)
Evidence of remedial measures in case of un-reliable / uncertain data records (for example, a model to rectify the faulty data)
Evidence of qualification requirements for independent QA/QC agent
C Ability to disseminate AQM data and analytical results to stakeholders
Evidence of published data and reports (hardcopy of electronic forms)
Evidence of formal established channels to disseminate data and results to stakeholders
Evidence of channels receiving feedbacks from stakeholders
Samples of air quality data record sheets
D Ability to utilize the AQM results to improve AQ control policy
Evidence of changes in policy resulting from AQM results (for example, changes in legislation and implementation of clean air programme)
Evidence of stakeholders using the AQM
Local air quality standards
AQM system
data records and resultsE Ability to provide
manpower and financial resources to sustain the AQM system
Evidence of staff development and training programmes for personnel in AQM system
Evidence of funding commitment for sustaining the expenses of the AQM system
Evidence of continuous review exercise to improve AQM system
Manpower structure, personnel qualification requirements
Costs : i) initial site capital cost; ii) costs of spare parts and consumables; iii) Operating costs ,electricity, calibration gases, filter papers and iv) maintenance cost
Monthly/ Annual running costs for AQM system
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World Health Organization Guidelines
The Role of Monitoring in Air Quality Management
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AQM NETWORK SIZE DESIGN
Good practice to check the necessity of station, the types of pollutant monitoring, the appropriate equipment to use
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US ENVIRONMENTAL PROTECTION AGENCYSummarized below is the minimum number of stations per metropolitan statistical area (MSA) for PM10, PM2.5 and O3 under the US EPA monitoring guidelines.PM10 Minimum Monitoring RequirementsMSA Population HIGH
CONCENTRATION (exceeds NAAQS by 20% or more)
MEDIUM CONCENTRATION(exceeds NAAQS
by 80%)
LOW CONCENTRATION(less than 80% of
NAAQS)
>1,000,000 6 ‐ 10 4 ‐ 8 2 ‐ 4500,000 ‐ 1,000,000 4 ‐ 8 2 ‐ 4 1 ‐ 2250,000 ‐ 500,000 3 ‐ 4 3 ‐ 4 0 ‐ 1100 000 250 000 1 2 1 2 0100,000 ‐ 250,000 1‐ 2 1 ‐ 2 0Source: Federal Register / Vol. 71, No. 200 / October 17, 2006 / Rules and Regulations
PM2.5 Minimum Monitoring Requirements MSA Population Most recent 3‐
year design value 85% of any PM2.5 NAAQS
Most recent 3‐year design value
<85% of any PM2.5 NAAQS
>1,000,000 3 2
500,000 ‐ 1,000,000 2 1
50,000 ‐ 500,000 1 0
Source: Federal Register / Vol. 71, No. 200 / October 17, 2006 / Rules and Regulations
O3 Minimum Monitoring Requirements
MSA Population Most recent 3 Most recent 3MSA Population Most recent 3‐year design value 85% of any PM2.5 NAAQS
Most recent 3‐year design value
<85% of any PM2.5 NAAQS
>10 million 4 2
4 ‐ 10 million 3 1
350,000 ‐ 4 million 2 1
50,000 ‐ 350,000 1 0
Source: Federal Register / Vol. 71, No. 200 / October 17, 2006 / Rules and Regulations
Australian Specification
• The Peer Review Committee (PRC), Australian National Environment Protection Council (NEPC) – National Environment Protection (Ambient Air Quality) Measure Technical Paper No. 4 on Screening Procedures, May
2001 – “Fewer performance monitoring stations may be needed where it can be demonstrated that pollutant levels are reasonably expected to be consistently lower than the standards mentioned in this Measure.”
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Equipment Considerations
The case of California Air Resources Board (CARB)
AQM Management and manpower training
TRAINING ‐ The ARB has recruitment and screening procedures to ensure that station operators are experienced and qualified instrument technicians. On‐the‐jobtraining is completed by all new station operators before they are allowed to independently operate field stations.
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The primary purpose of the QAPP is to provide an overview of the project, describe the need for the measurements, and define QA/QC activities to be applied to the project, all within a single document.
Common Issues and Problems in Asian Cities
1. Inadequate political drive – deficiency of public awareness and no champion in government senior management
2. Insufficient funds to sustain the recurrent and replacement costs of air monitoring system
3. Deficiency in data analysis and especially in QA/QC requirement and processing –insufficient fund and technical ability
4 Di i i d di i i4. Discrepancies in data dissemination –stakeholders have no access to raw data
5. Insufficient use of data to make policy changes to improve air quality
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THE CASE OF HONGTHE CASE OF HONG KONG AND PEARL RIVER DELTA AQM NETWORK
Problems/ Issues in early 1980s
1. Serious air pollution from factories2. Inadequate public awareness on air pollution3. Inadequate funds to set up AQM system4. No political leader(s) to advocate abatement of air pollution.
How the problems were tackled ?
1. Air pollution control work started in late 1980s when the British Hong Kong Government implemented the international obligation on Hong Kong; the political drive came from the UK government and expatriates working in HK
2 The AQM network started with a few stations and gradually expanded2. The AQM network started with a few stations and gradually expanded3. The planning, operation, QA/QC systems were borrowed directly from UK
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Air quality monitoring network in Hong Kong
• Hong Kong has 14 air quality monitoring stations for measuring concentrations of
Land Use Type
Land Use Characteristics
Air Monitoring Stations
Urban Densely populated residential areas mixed with some commercial
1. Central/ western2. Eastern3 K i Chmajor air pollutants.
• It consists of 11 general stations for monitoring ambient air quality and 3 roadside stations for measuring street level air quality since 1999
with some commercial and/ or industrial area
3. Kwai Chung4. Kwun Tong5. Sham Shui Po6. Tsuen Wan
New Town Mainly residential area 7. Sha Tin8. Tai Po9. Tung Chung10. Yuen Long
Rural Rural area 11. Tap Mum (background station)q y (background station)
Roadside Urban roadside in mixed residential/ commercial area with heavy traffic
and surrounded by many tall buildings
12. Causeway Bay13. Central14. Mong Kok
Air quality monitoring network in Hong Kong
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General stations and Roadside stations
Contents General station Roadside station
Purposes Represent an area of mixed residential and commercial/industrial activities or only residential activities
Monitor street-level emissions from nearby vehicle exhaust and road dustonly residential activities road dust
Stations cost US$ 385000 for capital and US$ 220000 for Annual operation/ development
US$ 510000 for capital and US$ 220000 for Annual operation/ development
Height above ground level
From 11 meters (Tap Mun) to 27.5 meter (Tung Chung)
Quite low, at 2 to 3 meters
Distance to nearest major roadway
From 20 meter (Sham Shui Po) to 200 meters (Sha Tin)
All less than 5 meters
roadway
Parameter SO2, NOx, NO, NO2, CO, O3, RSP, TSP
SO2, NOx, NO, NO2, CO, RSP
Maintenance Schedule
Less frequent More frequent
API Comes from measurements at 11 general AQM stations
Comes from measurements at 3 roadside AQM stations
Hok Tsui Station
Hok Tsui was established by the Civil& Structural Engineering Departmentof Hong Kong Polytechnic Universityin 1993. It is the first long‐termbackground site
Equipment
• CO analyze;
• O3 analyzer;
• Multi‐gas calibrator; Standard gas;
UV h t t i O lib t Parameter: ozone, solar ultraviolet
radiation, aerosol, CO and NOx, PM2.5,Rn
• UV photometric O3 calibrator;
• Brewer Spectrophotometer; external UVB lamp;
• Data logger; temperature and Humidity data logger;
• Three fine particle collecting instruments (belong to EPD);
• Radon analyzer; Radon calibration unit; ;
• Telephone lines;
• Pump; dehumidifier control equipment; air condition….
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With the rapid economic development in the Mainland, air quality in HK is With the rapid economic development in the Mainland, air quality in HK is severely affected, there is a great problem to identify responsibilities, Pearl severely affected, there is a great problem to identify responsibilities, Pearl River Delta AQM network was set up ten years ago to identify our source River Delta AQM network was set up ten years ago to identify our source apportioningapportioning
Mobile stationMobile station
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How to use the data?1.Determine air‐pollution levels and trend2 Determine source region for measured2.Determine source region for measured plume using wind and back trajectories
3.Improving emission inventories4.Determine what precursors control photochemical production of ozone
5.Modelling – Emission Based models and Observation Based models
10‐day back trajectories arriving HK for days with PM2.5
samples(November 2000‐October 2001)
(Wang, Final Report to EPD, 2003)
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Local vs. regional contribution to PM2.5 in coastal air mass
(PM2.5 data courtesy of
HKEPD)
35
40) Rural (Hok Tsui)
15
20
25
30
35
Com
posi
tion
(μg/
m3) Rural (Hok Tsui)
Urban (Tsuen Wan)
0
5
10
15
Sulfate Nitrate Ammonium EC OC PM 2.5
Che
mic
al C
Source apportionments of NMHCs
36.4%19.4%
5.2%0.1%
Use of solvent
Vehicle emission
LPG or natural gas leakage
Industrial sources
Biogenic emissions
3%
17%
9%
8% 2%
Gasoline evaporation
Biomass burning
Natural gas leakage
Industrial sources
Biogenic emissions
Urban Hong Kong Rural Lin’an
38.9%
Biogenic emissions 61% LPG leakage
Guo et al., EP, 2003, AE 2004
Vehicle and solvent Biofuel/biomass
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Contribution of different pollutants to light extinction (Bext)
Bext (Mm‐1) = 3f(rh) [Sulfate]+Hygroscopic species growth function
7( ) ( )
3f(rh)[Nitrate]+
4 [Organic] +
1[Soil]+
0.6[Coarse Mass]+ 0123456
0 20 40 60 80 100
f(rh
)
10[EC] +
0.175[NO2] +
10
Relative Humidity (%)
[Sulfate] = (NH4)2SO4
[Nitrate] = NH4NO3
[Organic] = 1.4[OC][Soil] = 2.2[Al] + 2.19[Si] + 1.63[Ca] + 2.42[Fe] + 1.94[Ti][Coarse Mass] = [RSP]-[FSP]f(rh)= hygroscopic species growth function
Inverse model result
(Y. X. Wang et al., JGR, 2004)
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Roadside emissions problems: unclear characteristics and factors influences the roadside pollution concerntrationsSolution: carried out a detailed study and analaysis with a Roadside Supersite at PolyU
Supersite Objectives• Evaluate measurement methods (Inter‐comparisons, practicality, operating procedures)
• Understand atmospheric processes and source contributions (Chemical/size characteristics, Influencing factors, effects of meteorology)
• Establish PM/health relationships (epidemiology, human exposure, toxicological responses, practical
d f h l h ff )PM indicators of health effects)
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Traffic Patterns at Supersite
2030
3030
4030
5030
6030
c co
unts
(# h
our-1
)
30
1030
2030
Tra
ffi
Diesel fueled vehicle Gasoline fueled vehicle Taxis Total
Sun Mon Tue Wed Thu Fri Sat
Sun Mon Tue Wed Thu Fri Sat
Research‐Grade Supersite Measurements
• Continuous, high‐time‐resolution measurements of , gparticle mass, number, and chemical components
• Ultrafine, fine, and coarse particle size distributions
• Standard and advanced chemical and morphological characterization
• Precursor gases, co‐pollutants, and meteorology
• Collocated instrument testing
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Observation and Method Avg Time Frequency Filter Filter Mass and Chemistry PM2.5 mass, ions, carbon (RP sequential /Quartz filter)
24-hr Daily Quartz (47mm)
PM1 mass, ions, carbon (RP sequential /Quartz filter)
24-hr Daily Quzrts (47mm)
PM1, PM2.5, PM10 Mass, ions, Elements, carbon including water soluble organic carbon (URG sampler with Quartz/Teflon filters)
24-hr Every six days Teflon (47mm) Quartz (47mm)
Positive OC artifact for PM1 and PM2.5 (URG sampler with Quartz/Teflon filters)
24-hr Intensive Teflon (47mm) Quartz (47mm)
Diurnal variation of PM2.5 and major chemical composition (RP sequential /Quartz filter)
2-hr Intensive Quartz (47mm)
Particle Sizes Mass, ion and elemental size distribution (MOUDI 0.056-18 m in 11 fraction with Teflon & IC, AC, XRF; and Aluminum & OC/EC analyser)
48-hr to 120-hr
intensive
Teflon (47mm and 37 mm) Quartz (47mm and 37 mm)
Mass, ion and elemental size distribution (MOUDI 0.01-2.5 m in 11 fraction with Aluminum foil & IC, OC/EC analyzer)
48-hr to 72-hr Intensive
Aluminium foil (47mm) Quartz (37mm) Teflon (47 mm)
Carbon size distribution (Anderson Impactor 0.43-10 m in 8 fraction with Quartz & IC, OC/EC analyzer)
24-hr Weekly Quartz (81mm)
Continuous Particle Mass and Chemistry PM (TEOM) 30 i E d C dPM2.5 mass (TEOM) 30-min Every day Connected to a computer EC (Aethalometer Model AE-30) 5-min Every day Kimoto PMcoarse/fine/OBC 1-hr Every day Total Particulate PAH 1-min Every day Particle number Concentration 0.007-0.217 μm size distribution (TSI 3936 SMPS)
30-min Every day Connected to a computer
0.1-2 μm size distribution (PMS 1003) 30-min Every day Morphology Particles on Nucleapore filter (SEM) 30-min - Nuclepore (47 mm) Gases NO-NO2-NOx 1-min Every day Connected to a computer NH3 15-min Every day
PM Measurements‐filterBGI frmOMNITM Ambient Air Sampler
Airmetrics Mini‐Vol portable samplers
URG‐3000ABC multi‐channel samplers
Partisol‐Plus Sequential Air Sampler (2025)
Roadside Medium‐Vol Air Sampler (PM2.5)MSP MOUDI model 110
and model 115 (Nano‐MOUDI)
High‐Vol Air Sampler (PM2.5)
MOUDI)
High‐Vol PUF sampler (PAHs)
Eight Stage Non‐Viable Impacor(0.4‐10 m)
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Aerosol Physical Properties
• Near‐real‐time automated measurements
Sizing sensitivities from 0.1 ‐2.0 microns (0.1, 0.2, 0.3, 0.4, 0.5, 0.7,
OPC = optical particle counter, SMPS = scanning mobility particle spectrometer
SMPS ‐ 3080 Electrostatic Classifiers and 3022A Condensation Particle Counters (CPCs), measuring high‐resolution size distributions of ultrafine particles. display data using 162 size channels from 10 to 1000 nm
microns (0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1.0 and 2.0 µm)
Continuous Measurements
Li‐Cor CO2 and H2ONOx and NH3
BC Single‐ and seven‐wavelength aethalometer
BC Kimoto SPM‐613D Dichotomous Monitor
PAH EcoChem R&P TEOM
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i fConsistency Tests for PM Measurements
Chemical Analyses
• consistency tests include: (1) comparisons between mass concentrations and the weighted sum of chemical species; (2) comparisons between concentrations of the same species measured by different analysis methods (e.g., sulfate by IC versus total sulfur by XRF; soluble potassium by IC versus total potassium by XRF; and chloride by IC versus chlorine by XRF); (3) charge balances between anions and cations; and (4) comparisons between mass and chemical concentrations in different size fractions (e.g., PM2 5 concentrations must always ( g 2.5 ybe less than or equal to PM10 concentrations).
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Mass Concentrations versus the Sum of Chemical Species
60
90
120
y=0.80(0.03)+10.50(1.67)
R2=0.96n=40y/x=1.09(0.13)
es o
f P
M1.
0 (ug
m-3
)
1:1 line
0 30 60 90 1200
30
Sum
of
spec
ie
Teflon particulate mass (ug m-3)
Different Analytical Methods
24
32
40
3
4
5
y=2.67(0.06)x-0.21(0.32)
R2=0.95n=120
g m
-3)
y=0.89(0.02)x-0.06(0.03)
R2=0.93n=120
um (
ug m
-3)
0 2 4 6 8 10 12 140
8
16
0 1 2 3 4 50
1
2
4
5
25
30
Sul
fate
(ug
Sulfur (ug m-3)
Sol
uble
pot
assi
u
Total potassium (ug m-3)
y=0.80(0.03)x+0.25(0.03)2
Ammonium sulfate y=1 87(0 06)x+0 44(0 18)
g m
-3)
0 1 2 3 4 50
1
2
3
0 5 10 15 20 25 300
5
10
15
20R2=0.90n=86
Chl
orid
e (u
g m
-3)
Chlorine (ug m-3)
Ammonium bisullfatey=1.07(0.04)x+0.32(0.16)
R2=0.87, n=120
y=1.87(0.06)x+0.44(0.18)
R2=0.90, n=120
Cal
cula
ted
amm
oniu
m (
ug
Measured ammonium (ug m-3)
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Different Size Fractions
60
90
120
s co
ncen
trat
ion
(ug
m-3
)
y=1.30(0.05)-1.53(1.77)2
0 30 60 90 1200
30
PM
2.5 m
ass
PM1.0
mass concentration (ug m-3)
R2=0.96PM
1.0/PM
2.5=0.81(0.18)
Inter‐comparisons of Different Instruments
• Measurement methods developed by different organizations may give different results when sampling the same atmosphere even though the techniques appear to be similar (e g Hitzenberger et al 2004)similar (e.g., Hitzenberger, et al., 2004).
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Comparison of different PM2.5 samplers
Instrument BGI High Volume
Mini Volume
URG Y-Shape Kimoto Rel. Ref.*
Mass Concentration, μgm-3
56.0 54.4 56.7 56.4 59.0 51.5 56.5
Diff. to Rel. Ref.*
-0.9 -3.7 0.3 -0.2 4.5 -8.8
*Rel Ref represents the Relative Reference Value Relative Reference Value is the average mass*Rel. Ref. represents the Relative Reference Value, Relative Reference Value is the average mass
concentrations obtained by different filter-based samplers.
Time Series Analysis
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Inter‐comparisons of hourly PM2.5 between Kimoto (beta‐gauge) and TEOM (weighing)
60
90
120o
hour
ly P
M2
.5 (μ
g m
-3)
y = 0.96(0.01)x + 11.40(0.51)
R2 = 0.72
P<0.001, n=2658
0
30
0 30 60 90 120
TEOM hourly PM2.5 (μg m-3
)
Kim
ot
Results:
The Characteristics of Fine (PM2.5) and
Coarse (PM2.5‐10) Particles
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Seasonal Variations
10
20
30
40
50
con
cent
rati
on (u
g m
-3)
PMcoarse at PU roadside
20
40
60
80
100
120
con
cent
rati
on (u
g m
-3)
PM2.5 at PU roadside
0
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Se
p
Oct
Nov Dec
MonthPM0
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Se
p
Oct
Nov Dec
Month
PM
The PM data was determined by Kimoto SPM‐613D Dichotomous Monitor
30
50
70
90
110
S M T W d Th F i S
PM c
once
ntra
tion
(ug
m-3
)
PM2.5
PM10Weekly cycle
Sun Mon Tue Wed Thu Fri Sat
5
13
21
29
37
Sun Mon Tue Wed Thu Fri Sat
Con
cent
rati
on (u
g m-3
) PMcoarse
BCR PM2.5 PMcoarse BC
Diesel-fueled vehicle 0.80 0.48 0.94
Gasoline-fueled vehicle 0.73 0.46 0.65
30
1030
2030
3030
4030
Sun Mon Tue Wed Thu Fri Sat
Tra
ffic
cou
nts
(# h
our-1
)
Diesel fueled vehicle
Gasoline fueled vehicle
Taxis
Taxis -0.30 0.05 -0.39
The PM data were determined by Kimoto SPM‐613D Dichotomous Monitor
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24
Median concentrations of PMcoarse for each 0.4 m s‐1 wind speed bin .
16
18
20
22
PMcoarse
PM
coar
se
0 2 4 6 812
14coarse
R=0.98, P<0.0001
Wind speed (m s-1)
The PM data was determined by Kimoto SPM‐613D Dichotomous Monitor
Chemical Composition
PM2.5 (55.5±25.6 μg m‐3) PMcoarse (25.9±15.5 μg m‐3)
27%
25%
6%
6%5% 2%
8%
8%
17%
32%11%
13%
29% 11%
2 7 %
4 2 %
1 9 %
2 %7 %3 %0 %
OM( OC*1. 4)ECAmmoni um sul f at eAmmoni um ni t r at eSea- sal tMi ner al mat er i al and t r ace el ementUni dent i f i ed
URG‐3000ABC multi‐channel samplers
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The diurnal patterns of OC/EC ratios
0 5
1.0
1.5
2.0
2.5
3.0
OC
/EC
Rat
io
1:00-4:00
12:0020:00 6:00 14:0022:00 8:00 16:00 0:00 10:0018:00 4:00 12:0020:00 6:00 14:0022:000.0
0.5O
Time (h)
8:00-10:00
The samples were collected by RP2025
Average OC/EC ratio: ~0.6 during 8:00‐10:00; ~1.6 during 1:00‐4:00
Results:
Pollution Episodes
the air pollution in Hong Kong has close relations to synoptic
systems, especially continental high pressure in cold season
(Pathak et al., 2003; Wang et al., 1997; 2003; Louie et al.,
2005b) and tropical storms in the warm season (Cheng et al.,
2006; Wang et al., 2006)
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Aerosols during Pollution EpisodesJan 21 – May 31, 2004
0
50
100
150
PM1.0 at PU roadside station
50
100
150
PM2.5 at PU roadside station
Episode days: Jan 30 Feb 14 15 23 26 Apr 19 20
The samples were collected by RP2025
0
On episode days, the average OC concentration in PM1.0 and PM2.5 increased 70% and 100%, respectively, compared to average values. EC showed only a 20–30% increase.
1000
2000
3000
4000Case I Case II Case III
ffic
cou
nts
(# h
-1) Diesel vehicleLPG Taxi Gasoline vehicle
Aerosols during Pollution EpisodesJul 20 – Jul 28, 2005
0
30
60
90
120
1500
1000
10
15
20
25
entr
atio
n (u
g m
-3)
PM2.5
EC OC
(b)
Tra
f
(a)
Sulfate Ammnonium Nitrate
(c)
0
5
10
14:00 0:00 12:00 22:00 10:00 20:00 8:00 18:00 6:00 16:00 4:00 14:00 0:000
5
10
15Mas
s co
nce
Time (hour)
SOC
(d)
The samples were collected by RP2025
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Results: Size distributions
SMPS OPC
MSP MOUDI model 110 and model 115 (Nano‐MOUDI)
Size Distributions of EC and Ions
15
20
25
dlog
Dp
Sample 6
Sample 7
Sample 8
EC
30
40
50
60
dlog
Dp
Sample 6
Sample 7
Sample 8
SO42-
0
5
10
0.001 0.01 0.1 1 10
Diameter (µm)
dM/d
0
10
20
30
0.001 0.01 0.1 1 10
Diameter (µm)
dM/d
8
10Sample 6
Sample 7
NH4+
8
10Sample 6
Sample 7
NO3-
0
2
4
6
0.001 0.01 0.1 1 10
Diameter (µm)
dM/d
logD
p Sample 7
Sample 8
0
2
4
6
0.001 0.01 0.1 1 10
Diameter (µm)
dM/d
logD
p Sample 7
Sample 8
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Size Distributions of Metals
0
1
2
3
4
5
0.001 0.1 10
Si
0
1
2
3
0.001 0.1 10
K
0.002
0.004 Ni
0 2
0.4
0.6
0.8Cl
0
1
2
3
0.001 0.1 10
Al
0.0
0.3
0.6
0.9
0.001 0.1 10
dM/d
logD
p Na
2
3
4
5Fe
0.01
0.02
M/d
logD
p V
0.000
0.001 0.1 10
0.00
0.05
0.10
0.15
0.20
0.001 0.1 10
dM/d
logD
p Cu
0.0
0.5
1.0
1.5
0.001 0.1 10
Zn
0.00
0.02
0.04
0.06
dM/d
logD
p Br
0.00
0.05
0.10
0.15
0.20Ba
0.000
0.008
0.016 Rb
0.00
0.08
0.16
0.24Pb
0.0
0.2
0.001 0.1 10
0.00
0.02
0.04
0.06
0.08
0.001 0.1 10
Sn
0.00
0.01
0.02
0.03
0.001 0.1 10
Sb
0
1
0.001 0.1 10
0.00
0.001 0.1 10
dM
0.001 0.1 10
Diameter (µm)
0.001 0.1 10
Diameter (µm)
0.001 0.1 10
Diameter (µm)
0.001 0.1 10
Diameter (µm)
Elements with similar size distributions:
1. Al, Si… ‐ Crustal elements
2. V and Ni ‐ ship emissions (Yu et al., 2003)
3. Cu, Zn, and Ba – brake dust
4. Br, Rb, and Pb ‐ incinerator (Louie et al., 2005a)
Typical size distribution of particle numbers (7‐2000
nm) in roadside environment in winter70000
(a) winter
30000
40000
50000
60000
SMPS
/log
Dp
(# c
m-3)
( )
Lasair OPC
1 10 100 10000
10000
20000dN/
Midpoint diameter (nm)
12/17/2012
30
The relationship between ultrafine/accumulation mode particle numbers and PM mass
0 40 80 120 160100000
3 )
R2=0 26
R2 0 27
0
20000
40000
60000
80000
8000
Ultr
afin
e pa
rtic
le (
# cm
-3R =0.26
R2=0.73
R =0.27
R2=0.74
(# c
m-3
)
0 40 80 120 1600
2000
4000
6000
PM10
(µg m-3)
Acc
umul
atio
n m
ode
part
icle
PM2.5
(µg m-3)
Evolution of ultrafine particle size distribution in a typical day
217
rib u
tion
dN/d
logD
p Si
z e d
i st
7
Time of day (hour)
12/17/2012
31
Diurnal patterns of ultrafine particle, BC, total traffic number and meteorological parameters
20
24
120000
140000
BC Ultrafine particle )# cm
-3)
4
8
12
16
0
20000
40000
60000
80000
100000
500
1000
1500
2
4
6
B
C (
µg c
m-3)
Ultr
afin
e pa
rtic
le (
#
Jan 6 Jan 11 Jan 20 Jan 21
Mix
ing
heig
ht (
m)
MixingHeight
Win
d sp
eed
(m s
-1)
Wind speed
R2=0.73
0
10
20
30
40
50
6000
4:00 9:00 14:00 19:00 0:00 5:00 10:00 15:00 20:00 1:00 6:00 11:00 16:00 21:00 2:00 7:00 12:00 17:00 22:00
2000
4000
6000
Sol
ar r
adia
tion
(m
W c
m-2)
Solar radiation
MW
Tot
oal v
ehic
le n
umbe
r Total vehicle number
Summary
• Continuous mass and chemical monitors are available and practical for long term measurement programspractical for long‐term measurement programs
• Fine particles were mainly from vehicle exhausts, while coarse particles were mainly from the resuspension of road dust by wind and vehicle‐generated turbulence
• Continuous particle number measurements indicate ultrafine concentrations are fresh emissions from vehicles
• The seasonal variations of PM is influenced by Asian monsoon
• For day‐to‐day variations, regional or long‐range transport have significant impact on the loading of some chemical species during pollution episode days
• Besides traffic, wind speed and mixing height influence the loading of hourly PM