Modeling and Measurement of Real-Time CO
Concentrations in Roadway Microenvironments
Rakesh B. Singh1, Alan H. Huber2 and James N. Braddoek3
1. National Research Council Research Associate at the National Exposure Research
Laboratory, United States Environmental Protection Agency, Mail Drop 56, Research
Triangle Park, NC 27711.
2. Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic
and Atmospheric Administration, Research Triangle Park, NC 27711. On assignment to
the National Exposure Research Laboratory, United States Environmental Protection
Agency, Mail Drop 56, Research Triangle Park, NC 27711,
3. United States Environmental Protection Agency, National Exposure Research
Laboratory, Mail Drop 46, Research Triangle Park, NC 27711.
ABSTRACT
Although emission standards for motor vehicles continue to be tightened, tailpipe emissions
continue to be a major source of human exposure to air toxics. The United States
Environmental Protection Agency's National Exposure Research Laboratory has initiated a
project to improve the methodology for modeling motor vehicle emissions from source
through the pathway to human exposure. A real-time microscale automobile emission factor
model for CO (MicroFacCO) virtually capturing all the information in the real world has
been developed for United States vehicles. The model was developed for CO because of the
available information to support its development. The goal is to use this CO model as a
surrogate for other tailpipe air toxic emissions. The emission model is being used in
conjunction with roadway dispersion models (e.g., CALINE4), and being evaluated in the
roadways around Research Triangle Park, North Carolina in a range of traffic fleet and
meteorological conditions. Modeled concentrations are being compared with measured
concentrations inside a moving vehicle and parked vehicle along the roadside.
This paper discusses the new emission model, demonstrates the use of the emission model in
modeling roadway air concentrations through an example, and discusses the issues and
research needs for improving the methodology of modeling human exposures to mobile
source emissions. This paper provides the status of a comprehensive study on modeling and
measurement in real-time roadway microenvironments.
INTRODUCTION
Motor vehicles are an integral part of our society and everyone is exposed to their emissions.
The Clean Air Act Amendments of 1990 require continued reduction in motor vehicle
emissions.1 The United States Environmental Protection Agency (EPA) estimates that motor
vehicle derived air toxics account for a significant portion of cancers attributable to outdoor
sources of air toxics.2 Motor vehicles are the primary source of urban carbon monoxide (CO),
and are an important source of volatile organic compounds (VOCs) and nitrogen oxides
(NOX) responsible for the formation of photochemical smog and ground level ozone (Oi).3
Their emissions vary significantly among the different types of vehicles and can be
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categorized by engine type, type of emission control system, vehicle weight, engine capacity,
emission standard to which the vehicle was initially certified, age (which determines the level
of emission control technology to which it was built and indicative of the wear on its
engines) and type of fuel used. >:s Roadway air pollutant dispersion models use the source
strength of particles or gases in terms of concentrations per unit distance (e.g., g km"1) as an
input to predict the local particle or gas concentrations in space and time. Detailed accurate
knowledge of emission characteristics is therefore an essential prerequisite to developing a
reliable human exposure model.
The EPA's National Exposure Research Laboratory (NERL) is pursuing a project to improve
the methodology for modeling urban-scale human exposure to air toxics. The overall project
goal is to develop improved methods for modeling the source through the air pathway to
human exposure in significant microenvironments of exposure. Current human exposure
models using simplified assumptions based on fixed air monitoring stations and regional
scale emission models do not represent the actual human exposures and should be improved.
The modeling project has started by considering the need for an emission model that is
structured to support human exposure assessment. Presently, the MOBILE (used in the
United States except California) and EMFAC (used in California only) mobile source
emission models are suitable for supporting regional scale modeling and emission inventory.
These emission models have not been designed to estimate real-time emission needed to
support human exposure studies near roadways. Our developments for "microscale"
applications refer to spatial scales from the size of an individual vehicle to the order of 1 km.
A number of independent studies have found that these emission factor models are not
reliable for estimating microscale emissions and are therefore inappropriate for use with
roadway dispersion models and microenvironmental modeling necessary to estimate human
exposures near roadways. The schematic diagram of the planned modeling system from
source through the air pathway to human exposure is shown in Figure 1.
The Figure 1 schematic diagram shows that the emissions model feeds a dispersion model to
support microenvironmental modeling. The roadway dispersion model will provide ambient
air concentrations resulting from transport and dispersion of the roadway emissions. The
microenvironmental model considers factors more refined than the air dispersion model,
which are specific to the particular exposure microenvironment (e.g., standing by the
roadside or actually inside the vehicle, inside the moving vehicle). Estimates of how
emissions inside the vehicle would contribute directly to the in-vehicle microenvironment
can be made without need for an air dispersion model. The roadside microenvironmental air
pollutant concentrations are impacted both by a general contribution from emissions upwind
along the roadway which may be modeled by an air dispersion model and a localized specific
complex flow generated by the local terrain and vehicles. The local model may need to use a
more refined than can be specified by a general dispersion model. Refined modeling using
Computational Fluid Dynamics (CFD) models will be used to develop refined dispersion
models and a microenvironmental model. Measurement databases from past research and
special new studies as part of this project will be used to
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Figure 2. EPA NERL Mobile Monitoring Van
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both support and evaluate the emission, dispersion and microenvironmental models. The
goal is to have a modeling system that can be used to simulate potential exposure factors to
mobile source emissions within mobile source microenvironments. These generalized
factors would be appropriately incorporated into population-based human exposure models.
This paper presents discussion on the planned system and presents pilot study applications.
A new generation emission model for CO designed to estimate real-time site-specific
emission factors needed for human exposure studies in microenvironments has been
completed and is summarized here. Measurements supporting this project are being taken
using the EPA NERL Mobile Monitoring Van instrumented to continuously monitor total
hydrocarbons, oxides of nitrogen, carbon monoxide, polycyclic aromatic hydrocarbons, and
ozone (Figure 2). Particulate Matter measurements will be added this year. Modeled
concentrations are being compared with the measured concentrations inside a moving vehicle
and also inside a parked vehicle along the roadside of Interstate-40 and Highway-70 in
Research Triangle Park, NC to assist in the development and evaluation of the modeling
system. Two example applications at a location along Interstate 40 in Research Triangle
Park, NC are presented here. This emission model is being developed first for CO because of
the available information to support its development and model evaluation. The goal is to
use this CO model as a surrogate for other tailpipe air toxic emissions when appropriate. A
separate emission model is being developed for particulate matter since its emission
characteristics are different than for those gases.
MODELING SYSTEM
Emission Modeling
The MOBILE (used in the United States except California) and EMFAC (used in California
only) emission factor models are based on vehicle miles traveled, and aggregated temporally
and spatially in support for determining emission inventories. These emission models
calculate the composite emission factors for each vehicle class by weighting the emission
factors calculated for each model year by the travel fraction for that model year and then
summing the various weighted factors. The MOBILE method is suitable for larger regional
(county) scale emission estimates and for emission inventories, but not for emission factor
estimates in microenvironments critical to human exposure studies. A number of
independent studies6"9 have found that these emission factor models are not reliable for
estimating microscale emissions and are therefore inappropriate to be used with roadway
dispersion models and micro-environmental modeling necessary to estimate human
exposures near roadways. Therefore, it has become necessary to design a real-time site-
specific emission factor model capable of estimating emission factors at microscale level
helpful in establishing complex source-to-dose relationships. Researchers have shown that
disaggregated emission models are necessary to reliably estimate ambient concentrations in
small temporal and spatial scales.
In view of the above, a Microscale emission Factor model for predicting real-time motor
vehicle Carbon Monoxide (MicroFacCO)1 ''' emissions has been developed and is
summarized here. The schematic diagram of MicroFacCO is shown in Figure 3. The
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algorithm used to calculate emission rates in MieroFaeCO is disaggregated based on the
observed on-road real-time vehicle fleet. The model requires only a few input variables that
are necessary to characterize the real-time fleet. The main input variables required are the
on-road vehicle fleet, time and day of the year, ambient temperature, relative humidity and
percentage of vehicles exceeding emission standards set by the regulatory agency. Primary
emission rates are based on calculations for 1550 vehicle types considering their type of fuel
use, weight and emission categories (normal and non-normal). MieroFaeCO first calculates
the fraction of vehicles in each category using a 25-year age-wise distribution, assigned to
both normal and non-normal emitting categories. After that, the vehicle miles accumulated
for each vehicle type are calculated for the model year as a function of the date of the
modeling application. The vehicle miles accumulated are then used to assign the normal and
non-normal emission rates in g mi"1. MieroFaeCO then calculates various adjustments in
accordance to vehicle type, model year and emission level. Finally, corrected emission rates
for individual vehicles are calculated, and multiplied by the fraction of vehicles of each
model year and vehicle class (550 light duty (< 8500 Ib) vehicle classes, and 1000 heavy duty
(>8500 Ib) vehicle classes). The sum of these yields the composite CO emission rate for the
on-road vehicle fleet that is being modeled.
Dispersion Modeling
Pollutant transport, dispersion, and deposition affect ambient air concentrations. The
concentration of pollutants associated with moving vehicles is determined by the vehicles'
emission rate, mixing induced by vehicle motion, wind speed and direction relative to the
highway axis, intensity of ambient atmospheric turbulence, reactions to or from other
species, rate of removal to the ground surface, and more.22 After estimating emission rates
for the motor vehicle fleet, the next important step is to select and apply the appropriate
dispersion model to be used in conjunction with the emission model. Most of the flat terrain
roadway air pollution assessment models in use today are of the Gaussian plume type,
because Gaussian models are easy to formulate and code, inexpensive computationally,
moderately flexible in terms of including a wide variety of phenomena, have simple
meteorological input requirements and for some situations perform as well or better than
more sophisticated numerical approaches.23 A literature review by Yamartino, Strimaitis and
Messier (1989)23 of the existing flat terrain dispersion models, such as EPA-HIWAY24 and
HTWAY-225, PAL26, CALINE 327, and ALSM28; and the model inter-comparison study by
Martinez etal (1981)29 reveals CALINE3 as the best model. CALINE430'31 is the most
recent in the series and is an updated and expanded version of CALINE3. In complex terrain,
the use of CALINE4 should be restricted to receptors immediately adjacent to the primary
source of emission.32 In view of the above literature; we decided to use CALINE4 for our
example application here. We will be further evaluating the applicability of these and other
models in the future.
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Input variables for MicroFacCO
Job Title (Alpha-numeric), County
Number of Roads
Year, Month, Day, Time, Fuel Type, Emission Standard Failure
Ambient Temperature, Relative Humidity, Vehicle Fleet Type,
Output Option, Road Type, Number of Lanes
Average Speed on Lanes
Detailed for Vehicle Fleet (Options 1 to 6)
Check for data entry
Calculations for individual vehicles
Calculation for Vehicle Fleet Fraction }
Calculation for Vehicle Miles Accumulated
I
Calculation for Normal and Basic Emission Rates |
Calculation for Cold Mileage Percentage J
Calculation for Temperature Correction Factors
Calculation for Speed Correction Factors j
Calculation for Air Conditioning Use
Calculation for Oxygenated Fuel
1
Calculation for Corrected Running Emission Factors
Calculation for Composite Emission Factors |
Composite Emission Factors j
Figure 3. The schematic diagram of MicroFacCO data input
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Our project will be evaluating issues related to modeling concentrations on-the-road and
alongside a major roadway in RTF, NC. The vehicle type and travel density will be fully
characterized by examining video recordings of actual traffic conditions during the
experiments. Proper meteorology is needed to support the dispersion model. Routine
National Weather Service data is available from nearby Raleigh-Durham International
Airport. The North Carolina State Climate Office maintains several nearby meteorological
stations. In addition, a diagnostic wind field model will be applied to provide better
estimates of winds at the roadway. Performance of the diagnostic model can be evaluated
using EPA's portable 10 m tower that will be used to provide roadside measurements at
elevations of 2 m, 5 m, and 10m and a portable SODAR (SOund Detection And Ranging)
system that will provide wind measurements at 5 m intervals at elevations from 15 m up to
200 m. A specific wind field model will be selected after evaluation. These systems will
provide the general meteorology needed to run a dispersion model. It is also planned to use
these systems to develop improved local wind field models in conjunction with the
application of Computational Fluid Dynamics (CFD) models. Wind patterns along roadways
are greatly affected by the road grade and the terrain/structures near the roadway. The goal is
to use refined measurements and modeling to develop improved simpler models for the local
roadway. The possibilities for modifying a diagnostic wind field model to adjust and extend
the application of "limited" nearby meteorology measurements to accurately represent
meteorology near the roadway will be examined and developed to support this application.
Mieroenvironmental and Human Exposure Modeling
Human exposure modeling generally simulates population exposure by compartmentalizing
peoples daily activities into time spent in a series of microenvironments, (for example:
commuting, street canyons, office buildings, shopping malls, parking garages, and home).
Accurate assessments of population exposures to air pollutants must include simulations of
the temporal and spatial patterns of air pollution concentrations in urban microenvironments.
This project is developing models for estimating air pollutant concentrations in roadway
related microenvironments. Presently, we are pilot studying the in-vehicle and roadside
microenvironment. The goal is to have a modeling system that can be used to simulate
potential exposure factors to mobile source emissions within most of the mobile source
related microenvironments. These generalized factors would be appropriately incorporated
into population-based human exposure models. The in-vehicle microenvironmental air
concentrations are being estimated by first simulating roadway concentrations and then
treating the vehicle as a single compartment that exchanges air from outside the vehicle.
CFD simulation will be used to study the roadway pollutant mixing and air exchange with
the vehicle compartment. Simplified factors for characterizing the envelope of pollutant
concentration around the vehicle and air exchange will be developed to estimate the in-
vehicle concentrations. The development of a reliable model for the roadside
microenvironment presents challenges. First, to understand the effects on the mixing of
tailpipe emissions the complex interactions of the vehicle generated complex flow, vehicle
heat generation, and roadway heat generation must be understood. Then this complex
process is strongly influenced by the ambient wind. CFD simulation will be used to study
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the roadway mixing process and its interaction with the ambient wind. Simplifying factors
for characterizing the roadway mixing will be developed for estimating concentrations both
in the roadway and alongside the roadway.
PILOT STUDY EXAMPLE APPLICATION
EPA NERL Mobile Monitoring Van
Modeled concentrations are being compared with measured concentrations inside a moving
vehicle and a parked vehicle along the roadside of Interstate-40 and Highway-70 in the
Research Triangle Park, NC to assist in pilot testing and development of the modeling
system. An example is presented in this paper. A more comprehensive presentation of the
measurement project and data collected shall be presented in another paper to be prepared.
This paper is principally limited to presenting our model development and measurement
plan. Pilot study applications are helping us better understand and refine our modeling and
measurement plans. CO measurements were taken using the Langen Model T15 carbon
monoxide analyzer. This uses an electrochemical technique to measure CO in the ambient
range of 0 to 10 ppm. The van also includes a Trimble GeoExplorer II global positioning
system, which gives the position of the moving vehicle; a fifth wheel, which provides vehicle
speed and distance traveled per unit time; a video camera, which provides traffic density and
composition; a laser range finder, which allows the van to travel at a specific distance behind
a lead vehicle (traffic and conditions permitting); and a portable computer, which interfaces
with on-board instrumentation and acquires data.
Roadside Concentrations
An example case study of roadside concentrations between 7:00 and 10:00 AM on March 10
and 11,1999 at the roadside of 1-40 in the Research Triangle Park, NC was conducted. The
van was parked along the edge of the road pullover lane. The traffic fleet during the
measurement period was recorded on videotape, and classified as either light duty (<8500 Ib)
or heavy duty (>8500 Ib) vehicles separately. Figure 4 shows lane-by-lane westward traffic
flow (receptor side) and total eastward traffic flow without distinguishing lane-by-lane.
Westward traffic flow into the Research Triangle Park is higher than eastward traffic flow
during the morning as workers commute to work. The afternoon commuting pattern is
opposite. The default distribution of vehicle ages for the vehicle fleet operating in Research
Triangle Park was used to run the MicroFacCO model. The emission rates were calculated
from MicroFacCO at 15-minute intervals on a lane-by-lane basis for the receptor side and on
road basis for the other side (Table 1). The Table shows the lowest emission in
grams/mile/second for lane 1 (slowest lane) in comparison to other lanes. Meteorological
data were obtained from the National Weather Service at Raleigh-Durham International
Airport located only a few miles away. Using local meteorology and upper air data from the
National Weather Service ETA model, the CALMET diagnostic meteorology model was
used to calculate the local mixing height. Standard deviation of wind angle was assumed to
be 5° for this example. The emission rates in grams/mile were estimated on a lane-by-lane
basis using MicroFacCO. The CALINE4 dispersion model was used to predict roadside
concentrations using the reported National Weather Service wind (marked estimated in
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Figure 5), assumed erosswind to the roadway (marked crosswind in Figure 5), and assumed
parallel (marked parallel on Figure 5) to the roadway wind conditions. In this case hourly-
averaged meteorological data were used to run CALINE4 for 15-minute periods
Measured and estimated roadside concentrations together with crosswind and parallel-wind
conditions are shown in Figure 5, Note that only the assumed wind direction is different in
each case. In general, March 10 was over-predicted and March 11 was under-predicted. The
crosswind conditions include the emission contribution from the other road lanes and do not
reflect potential minimum concentrations. However, these limited examples highlight the
need to understand the local micrometeorology, both temporally and spatially.
In view of the uncertainty associated with input variables, these experiments do not reflect
the best conditions for evaluating the emission model. A sensitivity and evaluation of the
emission model based on tunnel studies is discussed separately in the paper entitled "
Sensitivity Analysis and Evaluation of MicroFacCO: A Microscale Motor Vehicle Emission
Factor Model for CO Emissions".12
In-Vehicle Concentrations
Two case study examples of in-vehicle CO concentrations for February 2 traveling along
Interstate-40 (12.7 miles) are shown in Figure 6. Figure 6(a) shows first commute towards
the east (Raleigh) starting near Highway 55 and ending at the ramp exit onto Blue Ridge
Road. This is a 12.7-mile trip traveling east from suburban-like Research Triangle Park up to
the western edge of the city of Raleigh. Figure 6(b) shows a westward example. Note that
distance traveled at any point in these figures represents the same location. The trip towards
the east showed less in-vehicle CO concentrations (0.9 ppm) in comparison to commuting
towards the west (2.2 ppm), as expected during the morning commute period. The average
speeds were 63.6 and 43.2 miles/hour for the eastward and westward trip, respectively. The
times spent to cover the same distance were 13 and 19 minutes for eastward and westward
commute, respectively. Exposure estimates being a product of the concentration multiplied
by the time period are 1.17 and 4.18 ppm-minutes for the eastward and westward trip,
respectively
In the roadway being studied along Interstate-40, there are now 8 video cameras operated by
the NC Department of Transportation. These cameras will be used to estimate traffic
volume. Then emission rates will be calculated lane-by-lane for the on-road vehicle fleet
along 12.7 miles of roadway being studied. We are also examining the use of the in-vehicle
video camera to identify traffic patterns directly in front of a vehicle (i.e., a gross polluter) as
it travels along the roadway. In some situations, a nearby single high emitting vehicle may
contribute more to the in-vehicle concentration than from the general fleet. However, the in-
vehicle peak concentrations are time-lagged and damped relative to the roadway
concentration by the air exchange processes. The goal of the project is to integrate a series of
models and factors to estimate air concentrations from tailpipe to in-vehiele human exposure.
10
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• Lanel(W) ™H~ Lane2 {W) -*- LaneS (W) -«- Lane4 (W) -»K-TotaI West •
•Total EastJ
2000
1800
Figure 4. Traffic fleet on March 10
11
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Time
Lanel
Lane2
LaneS
Lane4
Other Side
March 10, 1999
7:30
7:45
8:00
8:15
8:30
8:45
9:00
9:15
9:30
9:45
10:00
10:15
706.3
737.0
729.7
738.0
698.2
705.4
636.6
384.4
462.9
335.8
301.4
278.6
1696.8
1638.3
1572.5
1741.7
1654.4
1568.2
1480.4
1292.8
1311.1
1256.1
1226.3
991.7
1889.7
1906.9
1795.2
1876.7
1934.6
1770.2
1701.1
1617.8
1657.2
1424.8
1621.6
1275.6
1753.4
1883.3
1752,7
1812.1
1875.4
1498.1
1178.7
1023.3
957.0
954.2
863.8
648.8
4511.8
5022.6
5111.5
4816.5
4700.1
4548.5
4100.0
3526.2
3472.8
3203.3
3431.1
2910.0
March 11, 1999
7:30
7:45
8:00
8:15
8:30
8:45
9:00
9:15
9:30
9:45
10:00
808.8
804.1
777.8
753.5
796.8
752.4
888.4
600.0
455.4
292.2
267.6
1632.8
1492.6
1474.7
1568.2
1486.8
1523.7
1490.9
1261.0
1185.8
1108.6
1149.4
1948.6
1759.6
1690.2
1716.2
1748.0
1871.1
1849.1
1761.8
1524.9
1484.1
1705.2
1908.3
1759.7
1879.3
1758.9
1801.8
1723.4
1759.7
1161.8
983.7
853.7
757.9
4314.2
5120.1
5482.8
4815.4
5008.6
4917.7
4272.0
3587.6
2988.3
3214.8
2940.2
Table 1. On-road estimated emission rates in grams per mile for 15-minute traffic fleet
intervals. Lane 1 to 4 is for 1-40 West. Lane 1 is slowest lane while other side
represents average emission rates over 1-40 East.
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•Measured -1-Esimated -*-Cross-wind -M-Parallel-wind
O «? O ifl O
o • r n f o
NNWWCOCOOOOOOO
r r
March 10,1999
larch 11,1985
Figure 5. Measured and estimated roadside CO concentrations on March 10 and 11, 1999
13
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-EH Inside Measured
-Distance Traveled
3.5
(a) East-ward
—*— Distance Traveled -B- Inside Measured
r 14
0.0
N. pw. N. N. N.
(b) West-ward
Figure 6. In-vehicle CO concentrations along Interstate-40 on February 2, 1999
14
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CONCLUDING REMARKS
This paper discusses a comprehensive measurement and modeling study to develop a
modeling system for estimating concentrations from source to human exposure both inside
vehicles and alongside roadways. Example results from a few of the present pilot case
studies are presented. The development of a new generation emission model for CO
designed to estimate real-time site-specific emission factors needed to support human
exposure studies has already been completed and is summarize here.
Knowing the real-time emissions is the first critical step in modeling human exposure in a
roadway microenvironment. A real-time emission factor model (MicroFacCO), which
virtually captures all the real-world information for the on-road motor vehicle fleet has been
developed. The methodology for MicroFacCO uses the same databases that are being used
to develop MOBILE6"". No new emission measurement databases are developed in this
study. MicroFacCO has been developed with the intention of being a complement to the
MOBILE (now MOBILES34 which will be upgraded to MOBILE6) model that has been
successfully used to estimate emission for supporting regional (county) scale modeling and
emission inventory. The algorithm used to calculate emission factors in MicroFacCO is
disaggregated. MicroFacCO calculates emission factors in real-time from an on-road vehicle
fleet, not for an aggregated fleet-wide average estimated by vehicle miles traveled (VMT).
MicroFacCO has been designed to estimate emission factors from on-road traffic and can be
directly used to support reliable modeling of human exposures near roadways and inside
vehicles traveling along the roadways. The model is developed for CO because of the
available information to support its development. The goal is to use this CO model as a
surrogate for other tailpipe air toxic emission, where a reliable relationship can be
established.
The local meteorology and terrain/structures near the roadway, as well as the real-time
individual vehicle emission, vehicle induced mixing and the traffic density affect air
pollutant concentrations in roadway microenvironments. More research is planned to fully
examine these issues. This modeling and measurement project should lead to the
development of a modeling system for developing better exposure factors for mobile source
related microenvironments that can be used to support population-based human exposure
models.
ACKNOWLEDGMENTS
The field measurements reported in this paper were collected in part by the U.S.
Environmental Protection Agency under a contract with Clean Air Vehicle Technology
Center (CAVTC). Appreciation is extended to the CAVTC team. The meteorological
information reported in this paper was provided in part under a contract with the North
Carolina State Climate Office. This paper has been reviewed in accordance with the U.S.
Environmental Protection Agency's peer and administrative review policies. Mention of
trade names or commercial products does not constitute endorsement or recommendation for
use.
15
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NEIL-RTP-AMD-00-060
TECHNICAL REPORT DATA
1. REPORT NO.
EPA/600/A-00/013
2.
3,RECIPIENT'S ACCESSION NO,
4, TITLE AHD SUBTITLE
5.REPORT DATE
Modeling and Measurement of Real-Time CO Concentrations in Roadway
Microenv ironmen ts
6.PERFORMING ORGANIZATION CODE
?. AUTHOR(S)
'Rakesh B. Singh, 2Alan H. Huber, and 'James N. Braddock
8.PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
'National Research Council Research Associate
USEPA/NERL, RTF, NC
2Sameas block 12
3USPEA/NERL/MD-46
RTP.NC 27711
10.PROGRAM ELEMENT NO.
1 i. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
! 3.TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA/600/9
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Although emission standards for motor vehicles continue to be tightened, tailpipe emissions continue to be a major source of
human exposure to air toxics. The United States Environmental Protection Agency's National Exposure Research Laboratory
has initiated a project to improve the methodology for modeling motor vehicle emissions from source through the pathway to
human exposure. A real-time microscale automobile emission factor model for CO (MicroFacCO) virtually capturing all the
information in the real world has been developed for United States vehicles. The model was developed for CO because of
the available information to support its development. The goal is to use this CO model as a surrogate for other tailpipe air
toxic emissions. The emission model is being used in conjunction with roadway dispersion models (e.g., CALINE4), and
being evaluated in the roadways around Research Triangle Park, North Carolina in a range of traffic fleet and meteorological
conditions. Modeled concentrations are bing compared with measured concentrations inside a moving vehicle and parked
vehicle along the roadside. This paper discusses the new emission model., demonstrates the use of the emission model in
modeling roadway air concentrations through an example, and discusses the issue and research needs for improving the
methodology of modeling human exposures to mobile source emissions.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/ OPEN ENDED TERMS
c.COSATI
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21.NO. OF PAGES
20. SECURITY CLASS (This Page)
UNCLASSIFIED
22. PRICE
EPA-2220
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