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Developing a High-Resolution Vehicular Emission
Inventory by Integrating an Emission Model and a
Traffic Model: Part 1—Modeling Fuel Consumption and
Emissions Based on Speed and Vehicle-Specific Power
Haikun Wang
a
& Lixin Fu
a
a
Department of Environmental Science and Engineering , Tsinghua University , Beijing ,
People’s Republic of China
Published online: 24 Jan 2012.
To cite this article: Haikun Wang & Lixin Fu (2010) Developing a High-Resolution Vehicular Emission Inventory by
Integrating an Emission Model and a Traffic Model: Part 1—Modeling Fuel Consumption and Emissions Based on
Speed and Vehicle-Specific Power, Journal of the Air & Waste Management Association, 60:12, 1463-1470, DOI:
10.3155/1047-3289.60.12.1463
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2. Developing a High-Resolution Vehicular Emission Inventory
by Integrating an Emission Model and a Traffic Model:
Part 1—Modeling Fuel Consumption and Emissions Based
on Speed and Vehicle-Specific Power
Haikun Wang and Lixin Fu
Department of Environmental Science and Engineering, Tsinghua University, Beijing, People’s
Republic of China
ABSTRACT
To improve the accuracy and applicability of vehicular
emission models, this study proposes a speed and vehicle-
specific power (VSP) modeling method to estimate vehic-
ular emissions and fuel consumption using data gathered
by a portable emissions monitoring system (PEMS). The
PEMS data were categorized into discrete speed-VSP bins
on the basis of the characteristics of vehicle driving con-
ditions and emissions in Chinese cities. Speed-VSP modal
average rates of emissions (or fuel consumption) and the
time spent in the corresponding speed-VSP bins were then
used to calculate the total trip emissions (or fuel con-
sumption) and emission factors (or fuel economy) under
specific average link speeds. The model approach was
validated by comparing it against measured data with
prediction errors within 20% for trip emissions and link-
speed-based emission factors. This analysis is based on
the data of light-duty gasoline vehicles in China; how-
ever, this research approach could be generalized to
other vehicle fleets in other countries. This modeling
method could also be coupled with traffic demand
models to establish high-resolution emissions invento-
ries and evaluate the impacts of traffic-related emission
control measures.
INTRODUCTION
On-road traffic-related emissions have become the domi-
nant source of air pollution in the urban area of China,
which has undergone dramatic growth of its vehicle pop-
ulation in recent years.1–3 In addition to improving the
emission control technologies of individual vehicles, cur-
rent efforts are also directed toward strengthening trans-
portation management to reduce vehicular emissions and
fuel consumption.4 Capabilities are needed to establish
high-resolution emission inventories and to evaluate the
effectiveness of traffic-related measures to reduce air pol-
lution.5,6 To achieve this result, the integration of vehicle
emission models and travel demand models (TDMs) are
necessary. However, because neither TDM nor vehicle
emission models were originally developed to address
conformity practice, specific issues need to be addressed
to ensure these two modeling processes are combined in a
compatible way.7
Vehicle emission models based on average speed
(e.g., MOBILE and COPERT) were widely applied to re-
gional and national emissions inventories in the world.
They use the assumption that average emissions factors
for a given pollutant and vehicle type vary according to
the average speed during a trip. Thus, they can be inte-
grated with macroscopic TDM, the output of which is
average link or grid speeds.6–10 However, average-speed
models cannot account for the fact that trips may have
very different driving characteristics, and therefore differ-
ent emission levels, while attaining the same average
speed. Furthermore, these models could not be used to
calculate vehicular emissions in detailed spatial resolution
and therefore may not be coupled with microscopic TDM.
Microscopic emission models (e.g., comprehensive
modal emissions model [CMEM] and the Virginia Tech
microscopic model [VT-Micro]), estimate instantaneous
vehicular emissions using engine or speed/acceleration
data.11,12 The CMEM, which can output emission rates
(ERs), vehicle speed, and acceleration on a second-by-
second level, was coupled with microscopic traffic models
to present high spatial resolution emission invento-
ries.13–15 However, because of the substantial amounts of
input data, the integration of microscopic emission mod-
els and traffic models was mainly applied to estimate
small-scale traffic emissions.
Power demand is a key variable that explains the
vehicle fuel consumption rate (FCR) and ER. Thus vehicle-
specific power (VSP), a surrogate for power demand orig-
inally reported by Jimenez-Palacios in his Ph.D. thesis16
that has been found to be highly correlated with fuel
consumption and emissions, was used in many studies on
IMPLICATIONS
Because on-road vehicles have become a dominant source
of air pollution and energy consumption (and greenhouse
gas emissions) in many cities in China, it is necessary to
develop methods to accurately estimate vehicular emis-
sions. However, emission inventories typically used in
China were based on the MOBILE model or equivalent
macroscale models, which could cause significant biases
compared with the real situations. In this study, a speed-
VSP-based model was developed using PEMS data, which
will enable the integration of vehicular emissions models
with traffic demand models to more precisely evaluate
traffic-related emissions.
TECHNICAL PAPER ISSN:1047-3289 J. Air & Waste Manage. Assoc. 60:1463–1470
DOI:10.3155/1047-3289.60.12.1463
Copyright 2010 Air & Waste Management Association
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3. vehicular emissions modeling.17–20 VSP is also the core
parameter of driving condition in MOVES,21 which will
substitute for MOBILE as the U.S. Environmental Protec-
tion Agency (EPA)’s next-generation vehicle emissions
model. However, some studies also indicated that a bias is
likely when using VSP as the only parameter of driving
conditions to simulate vehicular emissions, particularly
under high- or low-speed driving conditions.22 In addi-
tion, VSP is a proxy parameter combining speed, acceler-
ation, and road grade, which does not intuitively reflect
road traffic conditions such as speed or acceleration. It
cannot directly correlate with the TDM, which usually
outputs the average link or grid speed. Although vehicle
speed was introduced and divided into three levels (⬍25
mph, ⬃25–50 mph, and ⬎50 mph) in MOVES, the three
speed categories are too broad to integrate with the TDM.
Portable emissions monitoring systems (PEMS), which
can produce instantaneous data and characterize variability
in emissions and fuel consumption under real-world vehicle
activities and traffic conditions,20,23,24 were applied here to
develop an instantaneous model for calculating vehicular
fuel consumption and emissions. The method for this
model based on the combination of speed and VSP was
developed using available PEMS data that the authors col-
lected in four Chinese cities from 2004 to 2008.25 The mod-
eling method was also validated. In part 2 of this research, a
case study applying the emission model developed in this
part and a TDM to establish high-resolution vehicular emis-
sion inventory for Beijing city will be discussed.
METHODOLOGY
This section describes the analysis of PEMS data, applies
the speed and VSP modal approach to estimate vehicular
ER and FCR, and validates this modal approach from
various aspects.
PEMS Data
The PEMS used in this study consists of four major sys-
tems: the Corrys–Datron Microstar noncontact velocity
sensor, the Corrys–Datron DFL-2 fuel flow meter, an OTC
Microgas 5-gas exhaust analyzer, and a global positioning
system (GPS). They were used to measure the vehicle
driving parameters, FCR, tailpipe ER, and geographic co-
ordinates. This instrument has been widely used in the
authors’ previous research, and a more detailed descrip-
tion of it may be found in the published papers.26–28 The
PEMS reported data for vehicle speed, location, and ER on
a second-by-second basis. Road grade is computed based
on the difference in altitude and distance between two
consecutive readings.
It should be noted that the 5-gas analyzer was cali-
brated with calibrating gases before the test started, and
this process was repeated every 20 working hours. The
errors of the analyzer were ⬍3% after the calibration and
⬍5% after 20 working hours for all pollutants examined.
The correlations between the 5-gas analyzer and labora-
tory dynamometer measurements were high for carbon
monoxide (CO), carbon dioxide (CO2), nitric oxide (NO),
and hydrocarbons (HCs) as indicated in correlation coef-
ficients (R2
), which ranged from 0.91 to 0.97.
There were 71 light-duty gasoline vehicles (LGVs)
from Beijing, Guangzhou, Shenzhen, and Chengdu cities
in China tested in this study, including privately owned
and business-owned cars and taxis. As mentioned in the
authors’ previous study,3 China implemented Euro I, Euro
II, and Euro III emission standards in 2000, 2004, and
2007 and will implement Euro IV emission standard in
2010 nationwide, so the major technologies in the cur-
rent Chinese vehicle fleet include carburetor; electronic-
injection without three-way catalytic converter; and ad-
ditional technologies used meet Euro I, Euro II, and Euro
III emission standards. In this study, the vehicles were
selected to cover the most commonly used engines and
exhaust control technologies in typical cities to represent
the in-use fleet in China. The model years of these vehi-
cles ranged from 1993 to 2008 with the accumulated
vehicle miles traveled (VMT) from 5000 to 600,000 km.
The vehicles were operated on regular routes in the
urban area under different driving conditions, which cov-
ered common activities (e.g., commuting, shopping, and
entertainment) in different functional areas (e.g., com-
mercial area and residential areas) and different times
(e.g., peak and nonpeak hours of city traffic). The PEMS
dataset for the 71 vehicles has a total of 74 hr of records,
including second-by-second FCR, ER, vehicle speed, and
GPS coordinates.
These data were subsequently divided into a calibra-
tion dataset and a validation dataset. The calibration data-
set was used to develop the speed and VSP-based emis-
sions model (SVEM), and the validation dataset was
applied to evaluate the predictive ability of this model.
Five hundred seconds of continuous second-by-second
data selected from each test vehicle were combined to
produce a 35,500-sec validation dataset; the remaining
230,900 sec were retained for the development of the
SVEM.
VSP and Demarcation
VSP is defined as the engine power output per unit mass
of the vehicle and is expressed as the function of speed,
acceleration, and road grade. The detailed derivation pro-
cedures of VSP could be found in Jimenez-Palacios’s doc-
toral thesis.16 This study applied the following simplified
expression29 to calculate VSP (eq. 1):
VSP(kW/t) ⫽ ␯ ⫻ 共a ⫻ 共1 ⫹ εi兲 ⫹ g ⫻ grade ⫹ g ⫻ CR兲
⫹
1
2
␳a
CD ⫻ A
m
共␯ ⫹ ␯w兲2
䡠 v ⫹ Cif ⫻ g ⫻ ␯
⫽ ␯ ⫻ 关1.1a ⫹ 9.81 ⫻ 共a tan共sin共grade兲兲兲
⫹ 0.132兴 ⫹ 0.000302 ⫻ ␯3
(1)
where VSP is the VSP (kW/t); v is the vehicle speed (m/
sec); a is the vehicle acceleration (m/sec2
); m is the vehicle
mass (kg); and εi is the “mass factor,” which is the equiv-
alent translational mass of the rotating components
(wheels, gears, shafts, etc.) of the powertrain (unitless).
The suffix i indicates that εi is gear dependent; grade
indicates road grade (degrees); g is the acceleration of
gravity (9.8 m/sec2
); CR is the coefficient of rolling resis-
tance (unitless); CD is the drag coefficient (unitless); A is
the frontal area of the vehicle (m2
); ␳a is the ambient air
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4. density (1.207 kg/m3
at 20 °C); and vw is the headwind
into the vehicle (m/sec).
On the basis of second-by-second PEMS data, VSP
were calculated and then grouped into discrete bins.
When determining the number of VSP bins, two consid-
erations17,19 were taken into account: (1) bins should have
statistically different average FCR and ER from each other;
and (2) no single bin should dominate the estimate of
total fuel consumption or emissions.
Speed-VSP-Based Fuel Consumption
and Emission Estimates
Vehicle speed was divided into different bins and com-
bined with the VSP bins to simulate the impact of driving
conditions on fuel consumption and emissions. Thus, the
amount of speed-VSP bins in this study should be the
arithmetic product of numbers of speed bins and VSP
bins. Modal average of FCR and ER were estimated for
each speed-VSP bin, and the total trip-based emissions
and fuel consumption could be estimated as follows (eqs.
2 and 3):
Ei 共or FCi兲 ⫽ 冘
k
K
ERi,k 共or FCRi,k兲 ⫻ ti,k (2)
EFi 共or FEi兲 ⫽ Ei 共or FCi兲/TL (3)
where i is the vehicle type; k is the speed-VSP bin index, 1,
2,…, K (K ⫽ number of speed-VSP bins); Ei is the total trip
emissions of vehicle type i (g); FCi is the total trip fuel
consumption for vehicle type i (L); FCRi,k is the modal
average FCR in k speed-VSP bin for vehicle type i (L/hr);
ERi,k is the modal average ER in k speed-VSP bin for
vehicle type i (g/hr); ti,k is the trip time spent of vehicle
type i in speed-VSP bin k (hr); EFi is the trip-based emis-
sion factors (EFs) (g/km); FEi is the trip-based fuel econ-
omy (FE) (km/L); and TL is the trip length (km).
The road link- or grid-based EF and FE under a specific
average speed are estimated as follows (eq. 4):
EFm 共or FEm兲 ⫽ 冘
j
J
ERn,j 共or FCRn,j兲 ⫻ Rn,j/␯m (4)
where m is the link (or grid) index; n is the speed bin
index; j is the VSP bin index, 1, 2,…, J (J ⫽ number of VSP
bins); EFm is the EF on link (or in grid) m (g/km); FEm is
the FE on link (or in grid) m (L/km); ERn,j is the ER in
speed bin n and VSP bin j (g/hr); FERn,j is the FCR in speed
bin n and VSP bin j (L/hr); Rn,j is the time proportion
spent in speed bin n and VSP bin j (%); and vm is the
average speed on link (or in grid) m, which located within
speed bin n (km/hr).
RESULTS AND DISCUSSION
Exploratory Data Analysis
According to the analysis of VSP distributions for LGV
and previous research results,25,28,30,31 most of the vehi-
cle’s VSP was concentrated in the vicinity of 0 kW/t, and
Figure 1. Average (a) FCR and ER for (b) NOx, (c) HCs, and (d) CO vs. VSP on the basis of test vehicle data. The bars show 95% confidence
intervals of the mean for each VSP bin.
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5. more than 95% of the driving conditions on China’s
urban roads are located in the VSP range from ⫺20 to 20
kW/t. Therefore, this study focuses on the VSP at approx-
imately ⫺20 to 20 kW/t to simulate the emissions and fuel
consumptions for LGV in China’s urban area. The rela-
tionships between VSP and FCR and ER were investigated
as illustrated in Figure 1.
Figure 1 shows similar characteristics for FCR and ER
of various pollutants as VSP changes, and a VSP of 0 kW/t
is the inflection point. When the VSP value is positive,
FCR and ER indicate a monotonic rise with the increase of
VSP. FCR and ER tend to be very low and almost invari-
able for negative VSP. Therefore, define driving condi-
tions could be defined into fewer VSP bins during the
negative range while the VSP was used as an explanatory
variable to estimate vehicular fuel consumption and emis-
sions. This not only reduces the number of VSP bins and
simplifies the modeling process, but it also reflects the
impact of key VSP bins on vehicular fuel consumption or
emissions and ensures the accuracy of model results.
The ERs of CO and HCs were found to be more
sensitive to the variation of VSP than the FCRs or ERs of
NOx, especially in the ranges of the high absolute value of
VSP, which correspond to the driving conditions of rapid
acceleration or deceleration. During these driving pat-
terns, the engine operation is unstable and significant
fuel is injected during acceleration, which can lead to
incomplete combustion and result in high CO and HC
emissions.
It should be noted that there is an apparent decrease
for FCR and ER, especially for HCs and CO, in the very
high VSP range (i.e., VSP ⬎ 20 kW/t). This may be caused
by the fact that vehicles driven in very high VSP ranges
are rare in China’s cities. Thus, the average FCR and ER in
the high VSP bins are based on a relatively small amount
of PEMS data. Reliability of the model results for high VSP
Figure 2. Impacts of speed and VSP on vehicular (a) FCR and ER for (b) CO, (c) HCs, and (d) NOx. The data represent the LGV fleet meeting
Euro II emission standards.
Table 1. Definition of speed-VSP bins for the LGVs in Chinese cities.
Speedb
VSP Binsa
<ⴚ2 (ⴚ2, 0] (0,1) [1,3) [3,5) [5,7) [7,9) [9,11) [11,13) >13
Idle 0
(0, 10) 101 102 103 104 105 106 107 108 109 110
[10, 20) 201 202 203 204 205 206 207 208 209 210
[20, 30) 301 302 303 304 305 306 307 308 309 310
[30, 40) 401 402 403 404 405 406 407 408 409 410
[40, 50) 501 502 503 504 505 506 507 508 509 510
[50, 60) 601 602 603 604 605 606 607 608 609 610
[60, 70) 701 702 703 704 705 706 707 708 709 710
[70, 80) 801 802 803 804 805 806 807 808 809 810
[80, 100) 901 902 903 904 905 906 907 908 909 910
ⱖ100 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010
Notes: a
VSP, kW/t; a
Speed, km/hr.
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6. bins is in question. Furthermore, bumps or holes in the
road may also impact speed measurement when using
GPS units, which could influence the VSP values. This
may be another reason for the FCR and ER decrease in
high VSP bins. However, some researchers32 also found
results similar to this study. Therefore, the possibility
cannot be ruled out that FCR and ER may actually
decrease on average in the very high VSP bins.
This study also examined the impact of vehicle speed
on FCR and ER estimations during various VSP ranges, as
shown in Figure 2. FCR and ER differed significantly even
in the same VSP ranges because of their various speed
profiles. The ratios of high-speed to low-speed average
FCR and ER within the same VSP ranges even exceeded 10
(e.g., ER of NOx in the VSP bin from 13 to 16 kW/t).
Therefore, considerable deviation will be produced when
VSP is applied as the only parameter to model vehicular
fuel consumption and emissions.
Speed-VSP Modes and Model Average FCR and ER
VSP in Chinese cities was divided into 10 bins according
to the analysis in previous sections and the authors’ rel-
evant studies.28,29 To overcome the defect of the VSP
modeling approach, speed was introduced and combined
with VSP to estimate the vehicular fuel consumption and
emissions. The introduction of speed can also facilitate
the integrating of fuel consumption and emissions model
with TDM. Vehicle speed was divided into 10 bins in this
study. Then, a total of 100 speed-VSP bins of driving
conditions were defined for the vehicle operating-exhaust
process as shown in Table 1. Zero speed was defined as the
idle mode.
The LGVs were classified into 12 fleets (as shown in
Table 2) in this study on the basis of the engine displace-
ment and emissions standards, which is similar to the
rules of the COPERT model.33
Average FCR and ER in every speed-VSP bin were
initially calculated for each test vehicle. Fleet average FCR
and ER were estimated as the averages from vehicles in
this fleet. All of the calculation processes were performed
using Python programming language. Figure 3 illustrates
the FCR and ER of NOx in each speed-VSP bin for the fleet
of LGV 12.
Figure 3 shows that the impact of driving condi-
tions on FCR and ER varies for their different formation
mechanisms. However, in general, during the lower
speed and VSP bins, the FCR and ER of NOx are also
relatively low and will rise rapidly with the increment
of speed and VSP. FCR and ER were found to be signif-
icantly different (even exceeding a factor of 20 times) in
various speed-VSP bins.
Validation of Speed-VSP Modeling Approach
Instantaneous FCR and ER. A comparison of the measured
FCR and ER of HCs, CO, CO2, and NOx against modeled
values on a second-by-second level was performed for the
test vehicles in this study, as illustrated in Figure 4.
In general, the modeled second-by-second FCR and
ER show good agreement with measured data. The
model prediction lines follow the measured lines, in-
cluding all of the valleys and peaks. However, some
points show large differences because of the abnormal
noise in the measurements, which are difficult to con-
trol and inevitable for real-world PEMS tests.4 These
points were treated as invalid data and screened out
Table 2. Classifications of LGVs in this study.
Fleet Engine Size Emission Standard
LGV01 ⬍1.4 l Pre-Euro
LGV02 Euro I
LGV03 Euro II
LGV04 Euro III
LGV11 1.4–2.0l Pre-Euro
LGV12 Euro I
LGV13 Euro II
LGV14 Euro III
LGV21 ⬎2.0l Pre-Euro
LGV22 Euro I
LGV23 Euro II
LGV24 Euro III
Figure 3. (a) FCR and (b) ER of NOx in each speed-VSP bin modeled by SVEM for the LGV 13 fleet meeting Euro II emission standards.
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7. during the development of SVEM. In this study, the
abnormal data must meet either of the following two
conditions: (1) the negative data of vehicle emissions
and FCRs or (2) the points that were over 50 times the
average value in each VSP bin.
The sensitivity of SVEM to the instantaneous varia-
tions of speed and acceleration offers a unique tool for
assessing the environmental impact of traffic manage-
ment projects, such as traffic signals adjustment and in-
telligent transportation system (ITS) technologies.
EFs under Various Speeds. Under a combination of vehicle
driving conditions, SVEM can be applied to simulate ve-
hicular FE and EFs for various speed bins through eq 4.
Figure 5 illustrates the comparisons of modeled and mea-
sured data for the LGV 11 fleet.
Figure 5 shows a good fit between the modeled and
measured data, and all of the modeled EFs under various
speed bins lie within the 95% confidence intervals. Fur-
thermore, SVEM modeled values generally follow the
mean EF calculated by measured data. Specifically, the
Figure 4. Comparisons of instantaneous (a) FCR and ER for (b) CO, (c) HCs, and (d) NOx between measured and modeled values for a test
vehicle produced to meet the Euro I emission standard.
Figure 5. Model validation for the LGV 11 fleet under various speed bins. The bars show 95% confidence intervals on the mean of all of the
test vehicles in the LGV 11 fleet for each speed bin. EFs for (a) CO, (b) CO2, (c) HCs, and (d) NOx.
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8. errors in the EFs of CO, CO2, HCs, and NOx do not exceed
20% within any of the speed bins.
Because TDM always outputs the average traffic flow
and speed on the road links (or in the grids), SVEM can be
easily integrated with these TDM through the parameter
of speed to evaluate the environmental effect of traffic
management, land use, and traffic planning.
Trip-Based Fuel Consumption and Emissions. Vehicular trip
fuel consumption and emissions of CO, CO2, HCs, and
NOx may be calculated using eq 2. Figure 6 shows the
modeled and measured trip fuel consumptions and emis-
sions for the test vehicles in this study.
The modeled and measured trip values of fuel con-
sumptions and emissions for the test vehicles are in good
agreement, and the differences between them are all
within 20%. For fuel consumption and CO2 emissions,
the differences are no more than 10%.
It should be noted that vehicle emission models have
inherent uncertainties because they simplify complex
real-world processes.34 Even for the same vehicle with the
same driving parameters, the emissions simulated by the
model also differ from the measurement.4 The differences
between the modeled and measured values in this study
are mainly due to the following reasons. First, the vehicles
tested in this study were primarily driven on urban roads.
Thus, some speed-VSP bins of SVEM, especially within
high-speed VSP regions, were based on few data and
their reliability is low. Second, the FCR and ER in each
speed-VSP bin in the SVEM model were based on the
average PEMS data of test vehicles in the same fleet.
However, the FCR and ER for individual vehicles are
subject to different conditions because of factors such
as different manufacturers, maintenance status, and
weight. These problems could be addressed in the fu-
ture when more vehicle tests are conducted under var-
ious driving conditions in China.
SUMMARY
A vehicular fuel consumption and emissions model based
on speed and VSP has been developed for LGV with on-
road measurement data. It can reasonably simulate vehic-
ular instantaneous or aggregate fuel consumption and
emissions. As more and more PEMS data for specific ve-
hicles are obtained in the future, the accuracy of this
model will be improved. The SVEM methodology may
also be extended to other types of on-road vehicles (e.g.,
buses and trucks) when PEMS data for these vehicles are
available.
Because SVEM applies speed and VSP as the proxy
parameters of vehicle driving conditions, it has the char-
acteristics of micro- and macroscale emission models. It
enables the integration of real-world ERs with a traffic
simulation model, which usually provides links or grid
levels of vehicle activities.
ACKNOWLEDGMENTS
This work was supported by the China National Nature
Science Foundation (project no. 51008155). The au-
thors thank Yu Zhou, Xin Lin, He Li, and Tingkun Yan
in the Department of Environmental Science and Engi-
neering at Tsinghua University for their contributions
in the on-road vehicular emissions measurement. The
authors also acknowledge Chuck Freed, formally of
EPA, and three anonymous reviewers whose comments
greatly helped improve the manuscript. The contents of
this paper are solely the responsibility of the authors
and do not necessarily represent official views of the
sponsors.
Figure 6. Comparisons of trip (a) fuel consumption and emissions between measured and modeled
values for (b) CO, (c) HC, and (d) NOx emissions.
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About the Authors
Dr. Haikun Wang is now an assistant professor in the
School of the Environment at Nanjing University. He partic-
ipated in this project while he was a Ph.D. candidate in the
Department of Environmental Science and Engineering at
Tsinghua University. Lixin Fu is a professor in the Depart-
ment of Environmental Science and Engineering at Tsing-
hua University. Please address correspondence to: Haikun
Wang, School of the Environment, Nanjing University, Nan-
jing 210093, People’s Republic of China; phone: ⫹86 25
89680533; fax: ⫹86 25 89680533; e-mail: wanghk@nju.
edu.cn.
Wang and Fu
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