H. Christopher Frey,a Nagui M. Rouphail,a,b Haibo Zhaia,c · 2000 Baseline No 1.23 39.0 4.36 995...
Transcript of H. Christopher Frey,a Nagui M. Rouphail,a,b Haibo Zhaia,c · 2000 Baseline No 1.23 39.0 4.36 995...
Measurement and Modeling of the Real-World Activity, Fuel Use, and Emissions of OnroadVehicles: Policy Implications of Fuels, Technologies, and Infrastructure
2010 TRB Energy and Environment Research ConferenceJune 6-9, 2010Raleigh, NC
H. Christopher Frey,a Nagui M. Rouphail,a,b
Haibo Zhaia,c
a Department of Civil, Construction, and
Environmental Engineeringb Institute for Transportation Research and
Education
North Carolina State University
Raleigh, NC 27695
c Now at Carnegie Mellon University
Key Questions
• What are the real-world energy use and
emissions of the transportation system?
• How sensitive are emissions to infrastructure,
vehicle technology, fuels, driving cycles, and
landuse?
• How can fuel consumption be decreased?
• How can emissions be reduced?
Estimating Vehicle Fuel Use Based on Vehicle Specific Power (VSP)
Where
a = vehicle acceleration (m/s2)
A = vehicle frontal area (m2)
CD = aerodynamic drag coefficient (dimensionless)
CR = rolling resistance coefficient (dimensionless, ~ 0.0135)
g = acceleration of gravity (9.8 m/s2)
m = vehicle mass (in metric tons)
r = road grade
v = vehicle speed (m/s)
VSP = Vehicle Specific Power (kw/ton)
ε = factor accounting for rotational masses (~ 0.1)
ρ = ambient air density (1.207 kg/m3 at 20 ºC)
m
ACvgCgravVSP D
R
3
2
11
Frey, H.C., K. Zhang, and N.M. Rouphail, “Vehicle-Specific Emissions Modeling
Based Upon On-Road Measurements,” Environmental Science and Technology,
in press (published online 4/10/10)
Portable Emission Measurement System
• OEM-2100 Montana System
– Clean Air Technologies International, Inc.
– Carry-on Luggage size
– Weight: 35 lbs.
– Global Positioning System (GPS)
• Gas Analyzer
– NO and O2 from electro-chemical sensors
– HC, CO, and CO2 from non-dispersive infrared (NDIR)
– PM from laser light scattering detection
• Global Positioning System (GPS)
– GPS system measures vehicle location
CO2 Emissions versus Vehicle Specific Powerfor a Typical Light Duty Gasoline Vehicle
0
2
4
6
8
10
12
14
16
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Vehicle Specific Power(kW/ton)
CO
2E
mis
sion (
g/s
ec)
VSP Mode Based Emissions Model for a 2005 Caravan
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
NO
x E
mis
sio
ns
(mg
/s)
0.01
0.1
1
10
NOx , Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
HC
Em
issi
ons
(mg/s
)
0.01
0.1
1
10
HC, Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
Em
issi
on
s (m
g/s
)
0.1
1
10
100
1000
CO, Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
2 E
mis
sio
ns
(g/s
)
0.0
5.0
10.0
15.0
20.0
CO2, Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
NO
x E
mis
sio
ns
(mg
/s)
0.01
0.1
1
10
NOx , Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
HC
Em
issi
ons
(mg/s
)
0.01
0.1
1
10
HC, Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
Em
issi
on
s (m
g/s
)
0.1
1
10
100
1000
CO, Caravan 3.3 L
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
2 E
mis
sio
ns
(g/s
)
0.0
5.0
10.0
15.0
20.0
CO2, Caravan 3.3 L
Frey, H.C., K. Zhang, and N.M. Rouphail, “Fuel Use and Emissions Comparisons for Alternative
Routes, Time of Day, Road Grade, and Vehicles Based on In-Use Measurements,” Environmental
Science and Technology, 42(7):2483–2489 (April 2008)
Example of a Real World Field Study:Multiple Routes and Roadway Types
Route A
Route B
Route C
Route 1
Route 2
Route 3
Six Forks Rd
Wake Forest
Rd
RTPNorth
Raleigh
NC State
O/D Pair: NC State to North RaleighRoutes A, B, C
O/D Pair: North Raleigh to RTPRoutes 1, 2, 3
Frey, H.C., K. Zhang, and N.M. Rouphail, “Fuel Use and Emissions Comparisons for
Alternative Routes, Time of Day, Road Grade, and Vehicles Based on In-Use
Measurements,” Environmental Science and Technology, 42(7):2483–2489 (April 2008).
Example: Quantifying Activity for Primary Arterials for a Speed Range of Average Speed
Distance (km)
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2
Sp
eed
(k
m/h
)
0
20
40
60
80
100
120Average Speed: 30-40 km/h
9 Runs
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Per
cen
tag
e o
f T
ime
(%)
0
10
20
30
40
50
60
70Average Speed: 30-40 km/h
9 Runs
Distance (km)
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2
Sp
eed
(k
m/h
)
0
20
40
60
80
100
120Average Speed: 30-40 km/h
9 Runs
VSP Bin
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Per
cen
tag
e o
f T
ime
(%)
0
10
20
30
40
50
60
70Average Speed: 30-40 km/h
9 Runs
Frey, H.C., N.M. Rouphail, and H. Zhai, “Speed- and Facility-Specific Emission
Estimates for On-Road Light-Duty Vehicles based on Real-World Speed Profiles,”
Transportation Research Record, 1987:128-137 (2006)
Link-based Average Emission Rates for Light Duty Gasoline Vehicles on Principal Arterials
Speed (km/h)
10-20 20-30 30-40 40-50 >50
CO
2 (
g/s
)
0.0
1.0
2.0
3.0
4.0
Speed (km/h)
10-20 20-30 30-40 40-50 >50
CO
(m
g/s
)
0
5
10
15
20
25
30
Speed (km/h)
10-20 20-30 30-40 40-50 >50
NO
(m
g/s
)
0.0
0.5
1.0
1.5
2.0
2.5
Speed (km/h)
10-20 20-30 30-40 40-50 >50
HC
(m
g/s
)
0.0
0.2
0.4
0.6
0.8
Speed (km/h)
10-20 20-30 30-40 40-50 >50
CO
2 (
g/s
)
0.0
1.0
2.0
3.0
4.0
Speed (km/h)
10-20 20-30 30-40 40-50 >50
CO
(m
g/s
)
0
5
10
15
20
25
30
Speed (km/h)
10-20 20-30 30-40 40-50 >50
NO
(m
g/s
)
0.0
0.5
1.0
1.5
2.0
2.5
Speed (km/h)
10-20 20-30 30-40 40-50 >50
HC
(m
g/s
)
0.0
0.2
0.4
0.6
0.8
Link-based Average Emission Rates of NOx for LDGVs for Selected Roadway Types and Speeds
* Vehicle technology: engine displacement <3.5 liter & odometer reading <50,000 miles.
0.0
1.5
3.0
4.5
6.0
Local &
Collector
Arterial Freeway Off-Ramp On-Ramp
NO
x (
mg
/s)
10-20 km/h
20-30 km/h
30-40 km/h
40-50 km/h
50-60 km/h
60-70 km/h
90-100 km/h
Real World CO2 Emission Rates (and Fuel Use) for Selected Roadway Types and Speeds
* Vehicle technology: engine displacement <3.5 liter & odometer reading <50,000 miles.
0
1
2
3
4
5
6
Local &
Collector
Arterial Freeway Off-Ramp On-Ramp
CO
2 (
g/s
)
10-20 km/h
20-30 km/h
30-40 km/h
40-50 km/h
50-60 km/h
60-70 km/h
90-100 km/h
FRAMEWORK
Trucks Cars Buses
Vehicle ClassLink Type Link Speed
Link-based Emission Factors (EF) per veh-sec
Link Volume
Emission Inventory =
Link Travel
Time
- Diesel
- Biodiesel
- Gasoline
- Diesel
- CNG
- Ethanol85
- Hybrid
- Electric
- Fuel cell
- Diesel
- CNG
Conventional Technology
Alternative Technology
Reg
ional T
ravel
Dem
and M
odels
VolumeTimeTravelEFlink classveh.
Link-based Emissions Model for a Pollutant
= basic emission rate (g/sec);
= cycle correction factor for real-world link-based cycle at FTP average speed versus FTP cycle
= emission factor (g/sec);
= relative humidity correction (dimensionless);
= pressure correction factor (dimensionless);
= link-based speed correction factor, ratio of emissions at speed V to a baseline speed;
= technology correction factor, ratio of emissions for technology T to conventional technology T’
(=1 for conventional fuels and technologies);
= temperature correction factor (dimensionless).
= facility type (freeway, arterial, ramp, local & collector);
= technology class (gasoline, diesel, E85, HEV, CNG cars, etc.);
= index of conventional fuels and technologies (gasoline or diesel);
= average driving cycle speed (19.6 mph for LDGV and 20.0 mph for HDDV);
= average link-based speed (mph);
= calendar year (CY2005, CY2030).
T
f
v
Y
EF
BER
SCF
TCF
Where:
V
Subscripts:
VfTTTTvfTYVfTY SCFTCFCCFPCFHCFTECFBEREF ,,,,,,,,,
HCF
PCF
TECF
CCF
'T
Parameter Database
Parameter Vehicle Fuel & Technology Source
Basic Emission
RatesLDGV, LDDV, HDDT, HDDB MOBILE6
Speed Correction
Factors
LDGV, HDDT NCSU PEMS
HDDB EPA PEMS
LDDV Portugal PEMS
Fuel Economy
LDGV EPA
LDDV, HEV, CNG CarsFuel Economy Guide by
EPA & DOE
Technology
Correction Factors
E85, HEV, CNG Cars EPA Certification Tests
B20 trucks, CNG Buses NCSU PEMS, Literature*
Traffic Demand Triangle Region Model ITRE, NCSU
* TCFs derived emission comparison studies for B20 versus diesel heavy-duty trucks, and from
reported comparisons of CNG versus diesel buses.
Example of Link-based Tailpipe Emission Factors: Light Duty Vehicles, Arterials, CY 2005
0.0
1.0
2.0
3.0
4.0
10-20 20-30 30-40 40-50 50-60
Speed (km/h)H
C (
mg
/s)
LDGV E85 CNG LDDV HEV
0
1
2
3
4
10-20 20-30 30-40 40-50 50-60
Speed (km/h)
CO
2(g
/s)
0
40
80
120
10-20 20-30 30-40 40-50 50-60
Speed (km/h)
CO
(m
g/s
)
0
2
4
6
8
10
10-20 20-30 30-40 40-50 50-60
Speed (km/h)
NO
x (
mg/s
)
0
1
2
3
4
10-20 20-30 30-40 40-50 50-60
Speed (km/h)
HC
(m
g/s
)
Emission Inventory Scenarios & Fleet Characterization
Vehicle
ClassFuel & Tech.
Fleet Penetration by Vehicle Class (%)
Present Scenario (2005) Future Scenario (2030)
Baseline Alternative Baseline Alternative
Car
LDGV 100 73 100 73
E85 0 9.9 0 9.9
HEV 0 9.9 0 9.9
LDDV 0 5.9 0 5.9
CNG 0 1.2 0 1.2
EV & Fuel
Cell0 0.1 0 0.1
TruckHDDT 100 73 100 73
B20 Trucks 0 27 0 27
Bus HDDB 100 73 100 73
CNG Bus 0 27 0 27
Effect of Vehicle Technology and Land-Use:Case Study for Mecklenburg County
Collaborative Project with UNC-CH Regional and Urban
Planning
Input-output model of Mecklenburg County’s economy with
12 sectors (UNC)
Cross-sectional land-market equilibrium model with three
sectors (UNC)
Multimodal behavioral travel forecasting, including non-
motorized modes and incorporating attributes of the built
environment (UNC and ITRE)
Modal approach to estimating emissions (NCSU)
Nominally looking at 2030 to 2050 time frame.
Vehicle Activity for Baseline and 2050 Future Scenarios
Roadway TypeBaseline
Scenario
Future Scenario
Business-as-
Usual
Smart
Growth
Freeways 649, 860 1,232,060 1,337,910
Arterials 1,470,760 2,930,120 2,640,750
Local roads 254,750 527,300 440,140
Ramps 65,260 130,400 136,900
Bus rapid
transit0 0 250
Light-rail 0 350 1,220
Commuter-rail 0 80 320
Entire network 2,440,640 4,820,310 4,557,480
Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, “Impact of Alternative Vehicle Technologies and Land
Use Patterns on Long-Term Regional On-Road Vehicle Emissions,” 12th World Congress on
Transportation Research, Lisbon, Portugal, July 11-15, 2010
Peak Hour Emissions for Baseline and 2050 Future Scenarios
Scenario Total emissions (tons)
Model
Year
Land use
Pattern
Alternative vehicle
TechnologiesHC CO NOx CO2
2000 Baseline No 1.23 39.0 4.36 995
2050
Business-
as-usualNo 0.26 16.0 0.63 1700
Business-
as-usualYes 0.25 14.2 0.60 1640
2050
Smart-
growthNo 0.24 15.0 0.60 1580
Smart-
growthYes 0.23 13.3 0.57 1530
Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, “Impact of Alternative Vehicle Technologies and Land
Use Patterns on Long-Term Regional On-Road Vehicle Emissions,” 12th World Congress on
Transportation Research, Lisbon, Portugal, July 11-15, 2010
Sensitivity of Emissions Reduction to Alternative Fuels and Technologies
Rouphail, N.M., H. Zhai, H.C. Frey, and B. Graver, “Impact of Alternative Vehicle Technologies and Land
Use Patterns on Long-Term Regional On-Road Vehicle Emissions,” 12th World Congress on
Transportation Research, Lisbon, Portugal, July 11-15, 2010
-50%
-40%
-30%
-20%
-10%
0%
0% 20% 40% 60% 80% 100%
Penetration Rate of Alternative Vehicle Technologies
HC
em
issio
n c
ha
ng
e r
ela
tive
to
BA
U
sce
na
rio
with
ou
t a
lte
rna
tive
ve
hic
le
tech
no
log
ies
BAU
SG
Estimated On-Road 2050 Tailpipe Emissions
Pollutant Vehicle Fleet Land Use Pattern
Trend TOD
Hydrocarbons100% conventional Benchmark -7.8%
73% conventional + 27% alt. -6.0% -11.6%
Carbon monoxide
(CO)
100% conventional Benchmark -6.3%
73% conventional + 27% alt. -11.6% -17.4%
NOx
100% conventional Benchmark -5.5%
73% conventional + 27% alt. -4.9% -9.9%
Carbon dioxide
(CO2)
100% conventional Benchmark -7.1%
73% conventional + 27% alt. -3.5% -10.2%
TOD = Transit-Oriented Development
Percent Different in Link-Based NOx Emissions for Mecklenburg County: NCSU Link-Based Model vs. Mobile6 for Baseline
% ChangeSource: UNC
Key Findings from Mecklenburg Case Study
Fleet turnover to all Tier 2 compliant vehicles will
substantially reduce emissions of Hydrocarbons, Carbon
Monoxide, and Nitrogen Oxides by 50 percent or more even
with growth in vehicle miles travelled.
Modest deployment of alternative vehicles may reduce these
emission by an addition 5 to 10 percent.
CO2 emissions increase by approximately 70 percent with
conventional vehicles and 64 percent with modest market
penetration of alternative vehicles.
Compared to Business as Usual land use, Smart Growth
landuse may reduce emissions of HC, CO, NOx and CO2
emissions between 5.5 and 7.8%, and slightly more with
modest market penetration of alternative vehicles.
Conclusions
Improvements in vehicle technology likely to enable
continued reductions in emissions of some pollutants (HC,
CO, NOx) despite growth in energy use and miles travelled.
Changes in landuse patterns may lead to incremental
reductions in these emissions
Modest penetration of alternative vehicle technologies is not
enough to make a substantial difference – more aggressive
diffusion of such technologies should be pursued.
However, CO2 emissions are not abated and instead grow
significantly under the scenarios considered.
Acknowledgments
• Disclaimer: The contents of this presentation reflect the views of the author
and not necessarily the views of the sponsors. The author is responsible for
the facts and accuracy of the data presented herein. The contents do not
necessarily reflect the official views or policies of US EPA. This presentation
does not constitute a standard, specification, or regulation.
Real-World Vehicle Activity, Fuel Use and Emissions Measurement Capability
• Portable Emission Measurement System (PEMS)
– Infrastructure Data: Vehicle location (GPS), road
grade (via altimeter and GPS, if applicable)
–Vehicle Technology and Fuels: Engine size, fuel
properties
–Behavior (Vehicle Dynamics): Speed,
Acceleration, Engine RPM
–Ambient conditions: temperature, humidity,
pressure
–Vehicle Fuel Use and Emissions: Gas analyzers
for NO, HC, CO, CO2 and opacity (Particulate
Matter)
Emission Rates Versus Vehicle Specific Power:CO2, NO, Hydrocarbons, and CO
0
2
4
6
8
10
12
14
16
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Vehicle Specific Power(kW/ton)
CO
2 E
mis
sion (
g/s
ec)
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Vehicle Specific Power(kW/ton)
NO
x E
mis
sion (
g/s
ec)
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Vehicle Specific Power(kW/ton)
HC
Em
issi
on (
g/s
ec)
0
0.5
1
1.5
2
2.5
3
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Vehicle Specific Power(kW/ton)
CO
Em
issi
on (
g/s
ec)
NCSU VSP Driving Modes
Frey, H.C., A. Unal, J. Chen, S. Li, and C. Xuan, Methodology for Developing Modal Emission Rates
for EPA’s Multi-Scale Motor Vehicle and Equipment Emission Estimation System, EPA420-R-02-027,
Prepared by NC State University for U.S. Environmental Protection Agency, Ann Arbor, MI, Oct. 2002
VSP Mode VSP (kW/ton) VSP Mode VSP (kW/ton)
1 VSP < -2 2 -2 ≤ VSP < 0
3 0 ≤ VSP < 1 4 1 ≤ VSP < 4
5 4 ≤ VSP < 7 6 7 ≤ VSP < 10
7 10 ≤ VSP < 13 8 13 ≤ VSP < 16
9 16 ≤ VSP < 19 10 19 ≤ VSP < 23
11 23 ≤ VSP < 28 12 28 ≤ VSP < 33
13 33 ≤ VSP < 39 14 VSP ≥ 39
Comparing Fuel Use and Emission Rates for Different Roadway Types
Arteria
l
Lo
cal R
oad
Freeway
On-Ram
p
Off-Ram
p
Arteria
l
Lo
cal R
oad
Freeway
On-Ram
p
Off-Ram
p
33
Synthesizing the Micro-Scale Models into a Larger Framework
Develop link-based emissions models to couple with
transportation models for emission inventory estimates.
Characterize regional on-road mobile source emissions.
Evaluate the potential reductions in air pollutant emissions
associated with real-world operation of advanced fuel and
technology vehicles in comparison to conventional
vehicles.
Speed Correction Factors (SCFs)
SCF = ratio of link average emission rate at any speed to link
average emission rate at baseline speed range (e.g. 30 to 40
km/h).
Link average emission rates for a given technology are
estimated using field-measured second-by-second speed
profiles and Vehicle Specific Power (VSP)-based emission
rates.Frey, H.C., N.M. Rouphail, and H. Zhai, “Speed- and Facility-Specific Emission Estimates for On-Road Light-
Duty Vehicles based on Real-World Speed Profiles,” Transportation Research Record, 1987:128-137
(2006)
Zhai, H., H.C. Frey, and N.M. Rouphail, “A Vehicle-Specific Power Approach to Speed- and Facility-Specific
Emissions Estimates for Diesel Transit Buses,” Environmental Science and Technology, 42(21):7985-7991
(2008).
Frey, H.C., N.M. Rouphail, and H. Zhai, “Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on
Real-World Data,” Transportation Research Record, 2058:23-32 (2008).
Coelho, M., H.C. Frey, N.M. Rouphail, H. Zhai, and L. Pelkmans, “Assessing Methods for Comparing
Emissions from Gasoline and Diesel Light-Duty Vehicles Based on Microscale Measurements,”
Transportation Research – Part D, 14D(2):91-99 (March 2009).
Frey, H.C., H. Zhai, and N.M. Rouphail, “Regional On-Road Vehicle Running Emissions Modeling and
Evaluation for Conventional and Alternative Vehicle Technologies,” Environmental Science and Technology,
43(21):8449–8455 (2009).
Speed Correction Factors: Example for Light Duty Gasoline Vehicles on Arterials
0.0
0.5
1.0
1.5
10 20 30 40 50 60
Speed (km/h)
Speed C
orr
ection F
acto
r
HC
CO
NOx
CO2
0.0
0.5
1.0
1.5
2.0
2.5
10-20 20-30 30-40 40-50 50-60
Speed (km/h)N
Ox (
mg/s
ec)
Technology Correction Factors (TCFs)
TCFs account for differences in emissions rates when replacing
conventional with alternative vehicle technology
For HC, CO and NOx, TCFs for E85, HEV and CNG are estimated
based on average FTP emission rates from EPA’s annual certification
tests for alternative fuel versus gasoline from 2001 through 2007 (e.g.,
Frey et al., 2009).
For CO2, TCFs for HEV and CNG are estimated based on fuel
economy comparisons for alternative fuel versus gasoline, and for E85
based on fuel combustion theoretical analysis.
For B20 biodiesel heavy-duty vehicles, TCFs are estimated from
previous studies at NCSU (e.g., Frey and Kim, 2006). Zhai, H., H.C. Frey, N.M. Rouphail, G. Goncalves, and T. Farias, “Comparison of Flexible Fuel Vehicle and
Life Cycle Fuel Consumption and Emissions of Selected Pollutants and Greenhouse Gases for Ethanol
85 Versus Gasoline,” Journal of the Air & Waste Management Association, 59(8):912-924 (August
2009).
Frey, H.C., and K. Kim, “Comparison of Real-World Fuel Use and Emissions for Dump Trucks Fueled with
B20 Biodiesel Versus Petroleum Diesel,” Transportation Research Record, 1987:110-117 (2006).
Frey, H.C., and K. Kim, “In-Use Measurement of Activity, Fuel Use, and Emissions of Cement Mixer Trucks
Operated on Petroleum Diesel and B20 Biodiesel,” Trans. Research – Part D. 14(8):585-592 (2009).
Emission Inventory
CT
ct
ctctct voltEFTE1
= combination of vehicle class and technology;
= link-based emission factor for vehicles of class / tech (ct) (g/sec);
= average link travel time for vehicles of class / tech (ct) (second);
= traffic volume on link for vehicles of class / tech (ct) (vehicles/hr);
= total emissions for a single link (g/hr).
ct
ctt
ctvol
ctEF
TE
Where TE reflects outputs for a SINGLE link
Vehicle activity (average speed, number and types of vehicles) for the RTP
road network estimated using ITRE’s Triangle Regional Model (TRM)
Data subsequently aggregated across all links in the network.
Triangle Regional Transportation Network
Present “Future”VMT growth (33%), average speed decrease (28%)
“Future”
No VMT growth
Durham
Chapel Hill
Raleigh
Regional Emissions on Weekday Morning Peak Hour
Scenario HC CO NOx CO2
Present: Baseline 0.85 34 4.6 1,380
Present: Alternative 0.79 30 4.5 1,330
Future, No Growth: Baseline 0.15 10 0.39 1,200
Future, No Growth: Alternative 0.15 8.4 0.37 1,170
Future, Growth: Baseline 0.24 14 0.60 1,850
Future, Growth: Alternative 0.24 13 0.56 1,780
Total Network Emissions (tons in peak hour)
Regional Emissions Relative Changes for Weekday Morning Peak (continued)
Scenario HC CO NOx CO2
Present: Alternative -8 -14 -3 -4
Future, No Growth: Baseline -82 -72 -92 -13
Future, No Growth: Alternative -83 -76 -92 -15
Future, Growth: Baseline -71 -58 -87 34
Future, Growth: Alternative -72 -64 -88 29
Difference in Emissions Relative to Present Baseline Scenario (%)
Change in Emissions for Future Alternative versus Future Baseline
(%)
Scenarios HC CO NOx CO2
Future, Growth, Alternative versus
Future, Growth, Baseline-3 -13 -7 -4
Spatial Characterization of Emissions During AM Peak Hour: Present Baseline Scenario
VMT Distribution
Total Network NOx
Emissions Distribution
0-121
121-328
>328
29%
20%
51%
35%
26%39%
grams/mile/hr
Normalized NOx Link Emissions
38.9%
32.3%
28.8%Freeway+ Ramp
Arterial
Local and Collector
Impact of Vehicle Fleet Distribution on Regional Network
Emissions for Present and Alternative Scenarios
VMT
Distr.
Present
Baseline
Future
Alternative
(Growth)
HC CO NOx CO2
1%
17%
83%
3%
67%
30%
59%34%
99%61%
36%
68%
31%
1%
17%
83%
car
truck
bus
1%
88%
11%
7%
45%48%
3% 1%7%
0.2%
98.0%
1.8%