Application of a Microscale Emission Factor Model for
Particulate Matter (MicroFacPM) to Calculate Vehicle
Generated Contribution of PM2.s Emissions
Proceedings of the 95th Annual Conference of the A&WMA, Baltimore, MD, June 2002
Paper #42825
i 2 i
Rakesh B. Singh , Alan H. Huber and James N. Braddock
1. National Research Council Research Associate at the National Exposure Research
Laboratory, United States Environmental Protection Agency, Research Triangle Park, NC
27711, Currently, working as an independent contractor (rbsingh00@yahoo,com).
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,
Research Triangle Park, NC 27711 (huber.alan@epa.gov).
3. United States Environmental Protection Agency, National Exposure Research Laboratory,
Research Triangle Park, NC 27711 (braddock.james@epa.gov),
ABSTRACT
The United States Environmental Protection Agency's (EPA) National Exposure Research
Laboratory is developing improved methods for modeling the source through the air pathway to
human exposure in significant microenvironments of exposure. As a part of this project, we
developed a microscale emission factor model for predicting real-world real-time motor vehicle
particulate matter (PMio and PM2.5) (MicroFacPM) emissions, which uses available information
on the vehicle fleet composition. This paper presents the use of MicroFacPM to calculate the
contribution of PM2.5 per vehicle class, age-wise, gasoline, diesel, brake wear and tire wear
sources. The contribution of emission factors is presented for two scenarios: first the Tuscarora
Mountain Tunnel, on the Pennsylvania Turnpike, PA and second for Capital Boulevard, in
Raleigh, NC. In the Tuscarora Tunnel, average contributions of PM2.5 emission factors were 2.4
percent from 58.7 percent LDGV&T, 2.9 percent from 0.4 percent LDDV&T, 0.04 percent from
0.8 percent HDGV, 3.6 percent from 1.5 percent HDDV45,1.1 percent from 0.9 percent
HDDV6, 14.7 percent from 6.5 percent HDDV7, 20.0 percent from 9.4 percent HDDV8A, 51.6
percent from 21.8 percent HDDV8B, and 3.7 percent from tire wear emissions. For the Capital
Boulevard, Raleigh, NC, scenario the largest PM 2.5 contribution was from light-duty diesel
trucks (37% emissions from 2% vehicles) followed by heavy-duty trucks class 8 (22% from 1%
vehicles),
INTRODUCTION
In response to request from Congress, the National Research Council established a Committee to
Review Environmental Protection Agency's Mobile Source Emission Factor (MOBILE)' Model
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in October 1998. The Committee's findings were published recently,2 A few of the concerns
raised by the Committee included the limitations of MOBILE models by stating "Originally,
MOBILE was developed to estimate overall emissions levels, trends over time, and the
effectiveness of mobile-source emissions control strategies." This report indicated "An emission
factor model is fundamental for assessing the nature and magnitude of on-road motor vehicle
emissions and their impacts on ambient air quality." The report followed the earlier published
NRC recommendations, which identified outdoor measures versus actual human exposure,
characterization of emission sources, air-quality-model development and testing as among the
top 10 research areas of highest priority.3
The mobile source emission models, such as MOBILE (used in the United States except
California) and EMFAC4 (used in California only), are suitable for supporting regional scale
modeling and emission inventories. These emission models have not been designed to estimate
real-time emissions needed to support human exposure studies near roadways. Hitherto, in the
absence of microscale emission models, these models are used for microenvironmental modeling
applications. The site-specific real-time real-world modeling is necessary for assessing human
exposures in different roadway microenvironments, such as in-vehicles and near roadways; and
to understand complex relationships between roadway fixed-site ambient monitoring data and
actual human exposure.
The mutagenic and carcinogenic effects of particulate matter, especially from diesel-fueled
vehicles are is well known. ,6,7'8'9 The United States Environmental Protection Agency's (EPA)
National Exposure Research Laboratory has an ongoing project to improve the methodology for
modeling human exposures to motor vehicle emissions. The overall project goal is to develop
improved methods for modeling from the source through the air pathway to human exposure,
within significant microenvironments of exposure. Roadway dispersion models use the source
strength of particles or gases in terms of concentration per unit distance (e.g. milligrams per mile,
mg/mi) as an input to predict particle or gas concentrations in space or time. Detailed and correct
knowledge of the emission characteristics is therefore an essential prerequisite to developing a
reliable human exposure model.
In view of the above need, a microscale emission factor model for predicting real-world real-
time motor vehicle particulate matter (MicroFacPM) emissions for TSP (total suspended
particulate matter), PMio (particulate matter less than 10 /im aerodynamic diameter) and PM2.5
(particulate matter less than 2.5 /im aerodynamic diameter) has been developed.10,11 The
sensitivity analysis and evaluation of MicroFacPM has shown very encouraging results.12
This paper presents an application of MicroFacPM to calculate the contribution of motor vehicle
generated PM2.5 emissions per vehicle class, year-wise and sources.
MicroFacPM MODEL
The algorithm used to calculate emission factors in MicroFacPM is disaggregated based on the
on-road vehicle fleet, and calculates emission rates from a real-time site-specific fleet. The
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model requires only a few input variables that are necessary to characterize the real-time fleet.
The main input variables required are the description or characterization of on-road vehicle fleet,
time and day of the year, ambient temperature, relative humidity and percentage of smoking
vehicles. The speed correction factor is calculated for speeds other than 19.6 mi/h for heavy-duty
diesel vehicles. The fuel additive correction factor is accounted for if oxygenated fuel is used
with gasoline vehicles. The cold engine correction factor is calculated for the vehicles running
with cold engines based on their trip length and ambient temperature. The air conditioning
correction factor for light-duty gasoline vehicles is applied for the ambient temperatures (heat
index) greater than 65°F.
The primary emission rates were calculated per vehicle type and model year based on their
emission categories (normal and non-normal). MicroFacPM first calculates the fraction of
vehicles in each category for a 25-year age-wise distribution and then groups these into either the
normal and non-normal emitting categories. Then the vehicle miles accumulated for each vehicle
are calculated based on the model year. The vehicle miles accumulated are used to calculate
primary normal emission rates in mg/mi for heavy-duty diesel vehicles (>8500 lbs) and buses.
MicroFacPM then calculates various correction factors based on the 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. The sum of these
yields composite emission factor for the on-road vehicle fleet.
CEF=S(ERiljxVEHi>i)
i.j
Where,
CEF = Composite emission factor,
ERi( j = Composite emission rate for vehicle type i and model year j, and
VEHi, j = Fraction of vehicles for vehicle type i and model year j.
The schematic diagram of MicroFacPM are shown in Figure 1. The vehicle classification and
symbols used in MicroFacPM is listed in Table 1.
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Figure 1. The schematic diagram of the MicroFacPM general model structure
Speed
Correction Factor
Oxygenated Fuel
Correction Factor
Particle Size
PM2.5 or PM10
Cold Engine
Correction Factor
Air Conditioning
Correction Factor
Ambient Temperature
Correction Factor
I—
V
Primary Individual
Normal Exhaust
Emission Rate
Primary Individual
Non-Normal Exhaust
Emission Rate
JSsjL
Individual
Normal Exhaust
Emission Rate
Iz
V
Individual
Non-Normal Exhaust
Emission Rate
Xz:
On Road Vehicle Fleet
Tire and
Brake Wear
Emission Rate
Composite
Emission Factor
in mg/mi
Re-entrained
Road Dust
Emission Rate
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Table I. Vehicle classifications used in MicroFacPM
SN
DESCRIPTION
Gross Vehicle Weight (lbs) I Symbol
Light-duty vehicles (LD)
Gaso
ine vehicles
1
Light-duty gasoline vehicles (cars)
0-6000
LDGV
2
Light-duly gasoline tracks 1
0-3750
LDGT1
3
Light-duty gasoline tracks 2
3750-6000
LDGT2
4
Light-duty gasoline tracks 3
6001-7250
LDGT3
5
Light-duty gasoline trucks 4
7251-8500
LDGT4
6
Motor cycles
All
MC
Diesel vehicles
7
Light-duty diesel vehicles (cars)
0-6000
LDDV
8
Light-duty diesel trucks 1
0-3750
LDDT1
9
Light-duty diesel trucks 2
3750-6000
LDDT2
10
Light-duty diesel trucks 3
6001-7250
LDDT3
11
.Light-duty diesel trucks 4
7251-8500
LDDT4
Heavy-duty vehicles (IID)
Gaso
ine vehicles
12
Heavy-duty gasoline vehicles class 2B
8501-10000
HDGV2B
13
Heavy-duty gasoline vehicles class 3
10001-14000
HDGV3
14
Heavy-duty gasoline vehicles class 4
14001-16000
HDGV4
15
Heavy-duty gasoline vehicles class 5
16001-19500
HDGV5
16
Heavy-duty gasoline vehicles class 6
19501-26000
HDGV6
17
Heavy-duty gasoline vehicles class 7
26001-33000
HDGV7
18
Heavy-duty gasoline vehicles class 8A
33001-60000
HDGV8A
19
Heavy-duty gasoline vehicles class 8B
>60000
HDGV8B
20
Heavy-duty gasoline school bus
All
HDGSB
21
Heavy-duty gasoline transit bus
AH
HDGTB
Diesel vehicles
22
Heavy-duty diesel vehicles class 2B
8501-10000
HDDV2B
23
Heavy-duty diesel vehicles class 3
10001-14000
HDDV3
24
Heavy-duty diesel vehicles class 4
14001-16000
HDDV4
25
Heavy-duty diesel vehicles class 5
16001-19500
HDDV5
26
Heavy-duty diesel vehicles class 6
19501-26000
HDDV6
27
Heavy-duty diesel vehicles class 7
26001 -33000
HDDV7
28
Heavy-duty diesel vehicles class 8A
33001-60000
HDDV8A
29
Heavy-duty diesel vehicles class 8B
>60000
HDDV8B
30
Heavy-duty diesel school bus
All
HDDSB
31
Heavy-duty diesel transit bus
All
HDDTB
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APPLICATION OF MicroFacPM
The contribution to PM2,5 emissions is discussed for Tuscarora Mountain Tunnel, Pennsylvania
Turnpike, PA (scenario 1); and along Capital Boulevard Road in Raleigh, NC (scenario 2).
SCENARIO l:TUSCARORA MOUNTAIN TUNNEL, PENNSYLVANIA TURNPIKE, PA
The Tuscarora Mountain Tunnel is along Interstate 76 (1-76), also called the Pennsylvania
Turnpike, running east-west through the Tuscarora Mountain in south central Pennsylvania. It is
a two-bore tunnel, two lanes per bore and 1.01 mi long. The tunnel is flat (grades +0.3% towards
the middle from the either end) and straight. Studies were conducted between May 18 and 22,
1999. All experimental runs were of one hour duration except the last which was for a two hour
duration. Average vehicle speed was determined using a radar gun. The detailed traffic fleet
composition and age-wise distribution were determined visually on a run-by-run basis.13,14
Tables 2 summarizes the traffic fleet data and speeds for 18 runs during the study period.
Table 2. Tuscarora Mountain Tunnel study during evaluation time
Run No.
Date
Day
Start Time
Flow
Speed
LD
(No.)
(mi/h)
(%)
1
5/18/99
Tue
12:00
532
54.9
62.78
2
5/18/99
Tue
20:00
385
54.8
45.97
3
5/18/99
Tue
22:00
293
57.0
35.49
4
5/19/99
Wed
2:00
190
55.1
13.68
5
5/19/99
Wed
19:00
452
57.7
53.10
6
5/19/99
Wed
21:00
357
54.4
41.46
7
5/19/99
Wed
23:00
249
53.6
28.11
8
5/20/99
Thu
1:00
201
55.0
21.39
9
5/20/99
Thu
16:00
726
53.2
69.56
10
5/21/99
Fri
5:00
247
58.1
35.63
11
5/21/99
Fri
9:00
574
53.8
63.76
12
5/21/99
Fri
17:00
814
56.9
86.73
13
5/22/99
Sat
11:00
553
57.0
88.61
14
5/22/99
Sat
13:00
536
56.5
82.84
15
5/22/99
Sat
15:00
489
57.0
83.03
16
5/22/99
Sat
17:00
440
59.5
85.68
17
5/23/99
Sun
10:00
530
58.1
82.08
18
5/23/99
Sun
12:00
1678
61.7
83.19
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The speeds varied from 53.2 to 61.7 mi/h and the percentage of LD (Light Duty) vehicles ranged
from 13.7 to 88.6 percent. The age-wise distributions of the fleet per vehicle class for each run
were known. The vehicles operated mostly in the hot-stabilized mode. The LD fleet consisted
mostly of new vehicles, comprised of about 64 percent Tier 1 (1994+) vehicles (ranged from
50.0 to 68.8%), 35 percent Tier 0 (1981-1993) vehicles (ranged from 29.6 to 48.1%), and 1
percent Pre-1981 vehicles (ranged from 0.0 to 3.2%). For the distribution of LDGV, LDGT,
LDDV and LDDT, we assumed national default values, i.e. 67.25, 32.01, 0.49 and 0.25 percent,
respectively.
The yearly age distributions were available for heavy-duty vehicles except for classes 7 and 8,
which were grouped into 1993+, 1991-93 and Pre-1990. In the absence of a precise split between
class 8A and class 8B vehicles, we assumed the national average for the breakdown of class 8A
and 8B vehicles, i.e. 30.2% class 8A vehicles and 69.8% class 8B vehicles. The age-wise
distribution for vehicles classes 7 and 8 was grouped in to 1993+, 1991-93 and Pre-1990. The
HD age-wise distribution for Run 4 (May 19, Wednesday, Start Time 2:00) could not be found,
therefore we assumed an age-wise distribution for this run similar to Run 8 (May 20, Thursday,
Start Time 1:00).
Observed Versus Modeled PM2.5 Emission Factors
The following input values are required to run MicroFacPM:
• Date
• Time
• Vehicle fleet characteristics
• Ambient temperature
• Atmospheric relative humidity
• Average speed
• Cold mileage option (Yes or No)
• Fuel type (Oxygenated or Non oxygenated)
• Smoking vehicles percentage
MicroFacPM was run assuming that these were no smoking (not high emitting) vehicles in the
fleet. In view of the large percentage of diesel heavy-duty vehicles in classes 7 and 8 (HDDV7,
HDDV8A and HDDV8B) and vehicles that were operating in the hot-stabilized mode,
MicroFacPM results will not be very sensitive to ambient temperature and relative humidity
changes. Since the Tuscarora Tunnel is relatively flat, brake wear emissions are assumed to be
negligible. A comparison of the observed emission factor and a calculated MicroFacPM
emission factor is shown in Figures 2. Note the modeled emission factors do not include the re-
entrained road dust. The average observed facior and MicroFacPM values are 100 and 97 mg/mi,
respectively. The higher observed values (in comparison to MicroFacPM estimated values) may
be due to presence of a few smoking vehicles in the fleet, which is not accounted for in running
the mode] due to the absence of any specific information.
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Figure 2. Comparison between the observed and
MicroFacPM estimated PM2.5 emission factors for the
Tuscarora Mountain Tunnel, PA in 1999.
300
¦Observed MicroFacPM —Heavy Duty(%)
100
mavnraRfl
>6-
8 8 8
§ 8
M o) r
i r*
888 8888 88
r fi li) N O «
Ifl Ol N
O t-
May19
May20
May21 May22
May23
o
>.
>
a
0)
z
Contribution of PM2,s Emission Factors by Vehicle Class
The contribution of emission factors for the MicroFacPM estimated values and percentage of
vehicle classes are shown in Figure 3 and 4, respectively. The largest contribution of exhaust
PM2.5 came from the heavy-duty diesel vehicles in class 8B. If the fleet were dominated by light
duty vehicles, then not only would the tailpipe emissions increase but tire wear emissions would
also increase significantly.
The average contributions (average of 18 runs) of PM2.5 emission factors are as follows: 2.4
percent from 58.7 percent I.DGV&T, 2.9 percent from 0.4 percent LDDV&T, 0.04 percent from
0.8 percent HDGV, 3.6 percent from 1.5 percent HDDV45,1.1 percent from 0.9 percent
HDDV6, 14.7 percent from 6.5 percent HDDV7, 20.0 percent from 9.4 percent HDDV8A, 51.6
percent from 21.8 percent HDDV8B, and 3.7 percent from tire wear emissions.
-------
The contribution of emissions ranged from 0.2 (Run 4) to 6.5 (Run 13) percent for LDGV&T,
0.2 (Run 4) to B.3 (Run 13) percent for LDDV&T, 0.0 (Run 10] to 0.2 (Rmti 13) percent for
HDGV, 0.0 (Run 7) to 9,0 (Run 15) percent for HDDV45,0.2 (Run 7) to 3.7 (Run 6) percent far
HDDV6,6.6 (Run 2) to 28 0 (Rim 17) percent for HDD V 7, 13.4 (Run 13) to 24.2 (Run 7}
percent for JLDDV8A, 33.2 (Run 13) to 63,2 (Run 7) percent for KDDV80 and 1.9 (Run 4) to
7,5 (Run 13) percent for tire-wear,
Figure 3, Conribiilicll of MicroFacPM estimated PM2.S
emission tadors per venrcle class (including tire wear} for the
TuacarorB Moimfsin Tunnel, PA in 1999,
ieo.%
so%
BO-%
TD%
eo%
§n%
4o*
J.0'%
.80%
10%
0%
fsLBSViT ¦LD0U&T
jQKDDVfiA (| HDD Vtf£ aiTlrft
j»*11 i! M!
x—rmif - -tiMm ¦ i in> . aw' - -«IStmpv - nJIBr • .mmmn- "«JWhw«- -r- ¦ <
C C C C S C CT C C »-
3 3 3 3 .3 a l» 9- -3 S
« ic. m m .k be « « or .=>
CL
nilDDV<5 ~ H DDV6 IHOOV7
-------
Figure 4, I'etteniage of v e hieles for Mio TuscairorB Mountain
Tunnei, PA in 1938
(stDGVat HLDDV&T QHOfiV ~ H DDV45 BHC>DV6 »HDDV7 DHDDVBA HHDDVBB ' :
100%
Contribution vf FM2.1 Emission Faquirs by Yehide Age
In the Tuscarora Tunnel, the majority of PMj^ emissions came from tieuvy-dufy diesel vehicles.
The MicroFacPM emission rales for heavy-duty vehicles were derived from MOBILE6 sources
and divided into mainly 1993+, 1991-93 and pre-1991 category, Therefore, '.he age-wise
conlrihutfon of emission faclore presented here is divided into three main categories. The
contribution of emission fad or? for tlte MicroF^icPM estimated values per age-wise arid
percentage of vehicle ages are shown in Figure 5 and <5, respectively.
The average conlributions {average of 18 runs) of PMj.j emission fetors for 1993+ velucleS.
1991-93 and pre-1991 are 37,9 from 69,3 percent vehicles,. 39.5 from 19 7 percent vehicles and
22.6 from ] 1,0 percent vehicles, respectively.
The contribution of emissions ranged from 30.0 (Kun 1) 10 56.4 (Run 10) percent for 1993+
vehicles* from 18.9 (Run 113 to 48.6 (Run 3} peirenr for 1991 -93 vehicles, and from 17.1 (Run
4) to 32.33 (Run 17) percent for pre-1991 vehicles.
-------
Figure 5. Contributen of MicfoFacPM estimated PM 2.5
emission factors per vehiole age for the Tuscdrora
Mountain Tunnel. PA in 199$
!gj 1®93+ ¦ 1 691-93 pPr*-*!
Figure 6. Percentage of vehicles per age lor the Tu-icarore
Mountain Tunnel PA in 1 999
jUJIBW* B1991-93 DP re-Si
100%
40%
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-------
SCENARIO 2: CAPITAL BOULEVARD, RALEIGH, NC
In a test case, MicroFacPM was applied in conjunction with a line-source dispersion model,
CALINE4, for a typical urban roadway setting to predict hourly average roadside concentration
ofPM2 5.15 The test case was run on Capital Boulevard in Raleigh, NC for 24 hrs staring at 8:00
AM on July 10, 2001. MicroFacPM was run with the cold mileage option, oxygenated fuel and
the assumption of no smoking vehicles in the fleet. The light duty vehicle fleet composition and
age-wise distribution for vehicles less than8500 lbs were assumed to be as registered in the
Research Triangle Park area (Wake and Durham Counties), while for heavy-duty vehicle fleet
(>8500 lbs) we used the average default US vehicle fleet.
Contribution of PM2.5 Emission Factors by Vehicle Class
The average contribution of tailpipe PM2.5 emission factors for the MicroFacPM estimated
values and percentage of vehicle classes are shown in Figure 7. The largest contribution is from
light-duty diesel trucks (37% emissions from 2% vehicles) followed by heavy-duty trucks class 8
(22% from 1% vehicles). About 92 percent light-duty gasoline vehicles and trucks (<8500 lbs)
contributed only about 20 percent emissions.
-------
Figure 7. Contribution of tailpipe PM 2.5 emission
factor per vehicle class
ID Vehicles ¦Emissions
If we compare the contribution per source, (hen 66 percent of PM2.5 emissions came from tf iesel
vehicles, 18 percent Iron! gasoline, 8 percent from lire wear und 8 percent from "brake wear
(Figure 8). Note that rtiesel vehicles comprised only 5 percent of the fleet. If during certain lime
intervals the traffic is relatively free flowing, then the brake wear emission factors can be
accordingly reduced. This value was calculated lor the typical urban driving cycle.
-------
Figure 8. Contribution of PM2.5
source
emission factor per
Diesel
66%
Contribution of PM2.S Emission Factors by Vehicle Age
Contribution to PM2.5 exhaust emissions per age of the vehicle fleet is presented in Figure 9. The
vehicles up to 5 year old constitute about 34 percent of the fleet, but contribute only 19 percent
of the total exhaust PM2.5 emissions, while vehicles more than 20 year old (5 percent of the fleet)
contribute about 19 percent of total exhaust PM2.5 emissions. Vehicles older than 15 years
comprise about 87 percent of the fleet and contribute 67 percent of the total exhaust PM2.5
emissions.
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Figure 9. Contribution of tailpipe PM2.5 emission
factor per age-wise
missions | Veftlc tes
1 a 3 t 5 e 7 E 9 lil 11 12 13 15 16 1: IS 19 20 3? 23 ii £5
Year
CONCLUSIONS
A microscale emission factor Tiiodel for predicting real-world real-time motor vehicle particulate
matter (MicroFacPM) emissions has been developed, MicroFacPM requires only a few input
variables, which are. necessary to characterize the local real xi me fleet. MicrrFacPM calculates
the contribution of PM emissions from different vehicle categories and sources. MicroFacPM
emission estimations are suitable for modeling air quality and human exposure in
rnitro^nviiOEmexits near roadways.
A CKNOYV LEDG EMENT
Tiit authors guilefully acknowledge Dr. Alan Gertler of the Desert Research Institute for
providing a well -prepared report (Gerfler. A.W.; Gillies. J.A,; Pierson, W.R.; Rogers, C.F.;
S^gcbi^lt J.C; Abu-AIiaban, M.; Couloir be, W.; Tamay. L.; Cahil! T.A. Ambient Sampling of
Diesel P&nicmite Matter, Draft Final Report; Prepared by Desert Research Institute: Reno, NV,
2000) and detailed informiifjur! on the traffic fleet for lJjc Tusearora Tunnel.
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DISCLAIMER
The U.S. Environmental Protection Agency through its Office of Research and Development
funded the research described here. It has been subjected to Agency review and approved for
publication. Mention of trade names or commercial products does not constitute an endorsement
or recommendation for use.
REFERENCES
1. United States Environmental Protection Agency, Office of Transportation and Air Quality,
MOBILE6 Vehicle Emission Modeling Software Home Page
http://www.epa.gov/otaq/m6.htm. Home Page http://www.epa.gov/otaq/models.htm.
2. Modeling Mobile-Source Emissions; National Academy Press 2101 Constitution Ave., N.W.,
Washington, D.C. 20418, International Standard Book No. 0-309-07086-0,2000.
3. Research Priorities for Airborne Particulate Matter: II. Evaluating Research Progress and
Updating the Portfolio; National Academy Press 2101 Constitution Ave., N.W., Washington,
D.C., International Standard Book No. 0-309-06638-7, 1999.
4. California Air Resources Board, On-Road Motor Vehicle Emission Inventory Models Home
Page http://www.arb.ca.gov/msei/mvei/mvei.htm.
5. Carraro, E., Locatelli, A.L., Ferrero, C., Fea, E. and Gilli, G. (1995) Biological-activity of
exhaust emissions from 2 aftertreatment device-equipped light-duty diesel-engines. Journal
of Environmental Science and Health, Part A - Environmental Science and Engineering &
Toxic and Hazardous Substance Control, 30 (7), 1503-1514.
6. Heinrich, U. (1989) Exhaust-specific carcinogenic effects of polyaromatic hydrocarbon and
their significance for the estimation of the exhaust-related lung cancer risk. Assessment of
Inhalation Hazards, Integration and Extrapolation Using Diverse data (Ed. Mohr U. et ah),
Springer-Verlag, Berlin, 301-313.
7. Mauderly, J.L., Barr, E.B., Griffith, W.C., Henderson, R.F., Nikula, K.J. and Spines, M.B.
(1993) Current assessment of the carcinogenic hazard of diesel exhaust. Third International
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8. McClellan, R.O. (1987) Health effects of exposure to diesel exhaust particles. Annual Review
of Pharmacology Toxicology, 27, 279-300.
9. Rudell, B, Sandstrom, T,, Sternberg, N. and Kolmodin-Hedman, B. (1995) Controlled diesel
exhaust exposure in an atmosphere chamber: pulmonary effects investigated with
bronchoalveolar lavage. Journal of Aerosol Science, 21 (Si), S 411-S 414.
10. Singh, R.B.; Huber, A.H.; Braddock, J.N. Development of a Microscale Emission Factor
Mode] for Particulate Matter (MicroFacPM) for Predicting Real-Time Motor Vehicle
Emissions, Proceedings of the Air & Waste Management Association's 94th Annual
Conference & Exhibition, Orlando, FL June 24-28, 2001.
11. Singh, R.B.; Huber, A.H.; Braddock, J.N. Development of a Microscale Emission Factor
Model for Particulate Matter (MicroFacPM) for Predicting Real-Time Motor Vehicle
Emissions, J. Air & Waste Manage. Assoc., Submitted for Publication.
12. Singh, R.B.; Huber, A.H.; Braddock, J.N, Sensitivity Analysis and Evaluation of
MicroFacPM: A Microscale Motor Vehicle Emission Factor Model for PM Emissions, J. Air
& Waste Manage. Assoc., Submitted for Publication.
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13. Gertler, A.W.; Gillies, J.A.; Pier son, W.R.; Rogers, C.F.; Sagebiel, J.C.; Abu-Allaban, M.;
Coulombe, W.; Tarnay, L.; Cahill T.A. Ambient Sampling of Diesel Particualte Matter, Draft
Final Report; Prepared by Desert Research Institute: Reno, NV, 2000.
14. Gertler, A.W. Desert Research Institute; Reno, NV. Personal Communication, 2001.
15. Singh, R.B.; Huber, A.H. Application of a Microscale Emission Factor Model for Particulate
Matter (MicroFacPM) in conjunction with CALINE4 Model, To be presented at the Air &
Waste Management Association's 95th Annual Conference & Exhibition, Baltimore
Maryland, June 24-28, 2002.
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TECHNICAL REPORT DATA
1. report no.
2 .
3 .
4. TITLE AND SUBTITLE
Application of a Microscale Emission Factor Model for Particulate Matter
(MicroFacPM) to Calculate Vehicle Generated Contribution PM:3 Emissions
5.REPORT DATE
6.PERPORMING ORGANIZATION CODE
7. AUTHOR(S)
'Rakesh B. Singh, 2Alan H. Huber, 'James N. Braddock
8.PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
'National Research Council Research Associate
USEPA/NERL
R I P, NC 27711
2Same as block 12
3USEPA/NERL
RTP.NC 27711
10.PROGRAM ELEMENT NO.
11. 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
13,TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA/600/9
15. SUPPLEMENTARY NOTES
16. ABSTRACT
The United States Environmental Protection Agency's (EPA) National Exposure Research Laboratory is developing
improved methods for modeling the source through the air pathway to human exposure in significant microenvironments of
exposure. As a part of this project, we developed a microscale emission factor model for predicting real-world real-time
motor vehicle particulate matter (PM.U and PM, 5) (MicroFacPM) emissions, which uses available information on the vehicle
fleet composition. This paper the use of MicroFacPM to calculate the contribution of PM2 5 per vehicle class, age-wise,
gasoline, diesel, brake wear and tire wear sources. The contribution of emission factors is presented for two scenarios: first
the Tuscarora Mountain Tunnel, on the Pennsylvania Turnpike, PA ans second for Capital Boulevard, in Raleigh, NC. In the
Tuscarora Tunnel, average contributions of PM: 5 emission factors were 2.4 percent from 58.7 percent LDGV&T, 2.9 percent
from 0.4 percent LDDV&T, 0.04 percent from 0.8 percent HDGV, 3.6 percent from 1.5 percent HDDV45, 1.1 percent from
0.9 percent HDDV6, 14.7 percent from 6.5 percent HDDV7, 20.0 percent from 9.4 percent HDDvSA, 5.16 percent from 21.8
percent HDDV8B, and 3.7 percent from tire wear emissions. For the Capital Boulevard, Raleigh, NC, scenario the largest
PM;, contribution was from light-duty diesel trucks (37% emissions from 2% vehicles) followed by heavy-duty trucks class
8 (22% from 1% vehicles).
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
b IDENTIFIERS./OPEN ENDED TERMS
c.COSATl
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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