Application of a Microscale Emission Factor Model for
Particulate Matter (MicroFacPM) in Conjunction with
CALINE4 Model
Proceedings of the 95th Annual Conference of the A&WMA, Baltimore, MD, June 2002
Paper # 42816
Rakesh B. Singh1 and Alan H. Huber2
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 (rbsinghOO@yahoo.con).
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).
ABSTRACT
The United States Environmental Protection Agency's (EPA) National Exposure Research
Laboratory is developing improved methods for modeling the pollutant sources through the air
pathway to human exposure in significant microenvironments of exposure. As a part of this
project, we developed MicroFacPM, a microscale emission factor model for predicting real-
world real-time motor vehicle particulate matter (PMio and PM^j) emissions. MicioFacPM uses
available information on the vehicle fleet composition. The main input variables required are the
characterization of on-road vehicle fleet, time and day of the year, ambient temperature, relative
humidity and percentage of smoking vehicles. Using the fleet information, MicroFacPM
estimates a Composite Emission Factor (milligrams per mile). This paper presents the use of
MicroFacPM to calculate the contribution of PMi.s from motor vehicle sources along an example
roadway as input to a roadway air dispersion model. The contribution of PM2.3 is presented per
vehicle class (light, heavy duty), vehicle age, fuel type (gasoline, diesel), brake wear and tire
wear sources.
INTRODUCTION
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/ini) as an input
to predict particle or gas concentrations in space or time. Detailed and correct knowledge of
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emission characteristics is therefore an essential prerequisite to developing a reliable human
exposure model.
In response lo the request from Congress, the National Research Council established the
Committee to Review The Environmental Protection Agency's Mobile Source Emission Factor
(MOBILE)1 Model in October 1998. The Committee's findings are published recently.2 The
report followed the earlier published NRC recommendations, which identified outdoor
concentrations 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 inventory. These emission models have not been designed to estimate real-time
emission needed to support human exposure studies near roadways. In the absence of microscale
emission models, these models are being used for microenvironmental modeling applications.
Site-specific real-time modeling is necessary for assessing human exposures in different roadway
microenvironmcnts, such as in-vehicles and near roadways; and to understand complex
relationships between roadway fixed-site ambient monitoring data and actual human exposure.
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), PMjo (particulate matter less than 10 fim aerodynamic diameter) and PM2.5
(particulate matter less than 2.5 /un aerodynamic diameter) has been developed.5,6 The
sensitivity analysis and evaluation of MicroFacPM has shown very encouraging results.7
This paper presents the application of MicroFacPM in conjunction with CALINE4 :o calculate
roadside concentrations of PM2.J.
MicroFacPM MODEL
The MicroFacPM model provides a composite emissions factor (mg/mi) for a specified site-
spccific real-time vehicle fleet. The schematic diagram of MicroFacPM is shown in Figure 1.
The vehicle classification and symbols used in MicroFacPM is listed in Table 1. The algorithm
used to calculate emission factors in MicroFacPM is disaggregated based on the on-road vehicle
fleet, and calculates emission rates from a site-specific fleet over any selected averaging time.
The model requires only a few input variables that are necessary to characterize the real-time
fleet. The main input variables required are the 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. 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 apparent temperatures (heat index) greater than 65°F.
2
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The primary emission rates are calculated per vehicle type and model year based on their
emission categories (normal and non-normal high emitters). MicroFacPM first calculates the
fraction of vehicles in each category for a 25-year vehicle age distribution and then groups these
into either the normal or non-normal emitting categories. Then the vehicle miles accumulated for
each vehicle are calculated based on the model year. The vehicle miles accumulated without age
are used to calculate primary normal emission rates in mg/mi for heavy-duty diesel vehicles
(>8500 lbs) and buses. MicroFacPM then calculates correction factors based on the vehicle type,
model year and emission rates. 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 a composite emission factor for the on-road vehicle fleet.
C E F = X (E R j j x V E H, o)
i.j
Where,
CEF = Composite emission factor; mg/mi
ERi. j = Composite emission rate for vehicle type i and model year j,; mg/mi
VEHi. j = Fraction of vehicles for vehicle type i and model year j.
3
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Figure 1. The schematic diagram of the MicroFacPM general model structure
Speed
Correction Factor
Oxygenated Fuel
Correction Factor
Particle Size:
TSP, PM10, PM2.5
Cold Engine
Correction Factor
Air Conditioning
Correction Factor
Ambient Temperature
Correction Factor
Primary Individual
Normal Exhaust
Emission Rate
Primary Individual
Non-Normal Exhaust
Emission Rate
Individual
Normal Exhaust
Emission Rate
oz
Individual
Non-Normal Exhaust
Emission Rate
Xz:
On Road Vehicle Fleet
:Ts:
Tire and
Brake Wear
Emission Rate
I
7\
Composite
Emission Factor
in mg/mi
Re-entrained
Road Dust
Emission Rate
4
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Table 1. Vehicle classification used in MicroFacPM
SN
DESCRIPTION
Gross Vehicle Weight (lbs)
Symbol
Light-duty vehicles (LD)
Gaso
ine vehicles
]
Light-duty gasoline vehicles (cars)
0-6000
LDGV
2
Light-duty gasoline trucks 1
0-3750
Lixrri
3
Light-duty gasoline trucks 2
3750-6000
LX3T2
4
Light-duty gasoline trucks 3
6001-7250
LX5T3
5
Light-duty gasoline trucks 4
7251-8500
LXJT4
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 dicscl trucks 2
3750-6000
LDDT2
10
Light-duty dicscl trucks 3
6001-7250
LDDT3
11
Light-duty diesel trucks 4
7251-8500
LDDT4
Heavy-duty vehicles (1ID)
Gaso
ine vehicles
12
Heavv-duty gasoline vehicles class 2B
8501-10000
HIX3V2B
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
H1XJV8B
20
Heavy-duty gasoline school bus
All
HDGSB
21
Heavy-duty gasoline transit bus
All
HDGTB
DieseJ vehicles
22
Heavy-duty diesel vehicles class 2B
| 8501-10000
HDDV2B
23
Heavy-duty diesel vehicles class 3
10001-14000
HDDV3
24
Heavy-duly 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 c lass 8B
>60000
HDDV8B
30
Heavy-duty diesel school bus
j All
HDDSB
31
! Heavy-duty diesel transit bus
I All
HDDTB
5
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APPLICATION OF MicroFacPM
ROADWAY DISPERSION MODEL
Roadway dispersion models require emission factors, traffic flow information, meteorological
data and site geometry to predict the air pollutant concentration at a receptor location for certain
time averages. Most of the flat terrain roadway air pollution assessment models in use today are
of the Gaussian plume variety, 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 perform as well or Detter than
more sophisticated numerical approaches.8 A literature review by Yamartino, Strim litis and
Messier of the existing flat terrain dispersion models, such as EPA-HIWAY9 and HIWAY-210,
PAL11, CALINE 312, and ALSM13, and the model inter-comparison study by Martir.ez et al.14
revealed CAL1NE3 as the best model. CALINE41516, which uses the Gaussian diffi sion equation
and mixing zone concept for pollutant dispersion near roads, is the most recent in th e series and
is an updated and expanded version of CAL1NE3. It can predict pollutant concentrations (CO,
NO2 and Suspended particles) for receptors located within 500 meters of the roadway. In
complex terrain, the use of CAL1NE4 should be restricted to receptors immediately adjacent to
the primary source of emission.17
We applied MicroFacPM in conjunction with CALINE4 for a typical urban setting to predict
hourly average roadside concentration of PM2. The trial was run on Capital Boulevard in
Raleigh, NC for 24 hrs staring at 8:00 AM on July 10, 2001.
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 with the cold mileage option, oxygenated fuel and assuming no smoking
vehicles in the fleet. The hourly vehicle volume, volume/capacity ratio, assumed vehicle speed,
ambient temperature and relative humidity used as input to the MicroFacPM predicted
composite emission factor for the fleet in grams per mile per hour (g/mi/h) are shewn in Table 2.
The meteorological data were obtained from a nearby meteorological site. Another input "mixing
6
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ght" was modeled for the local area. The values given in Table 2 are modeled vjilues for the
cal 1995 vehicle fleet. North Carolina Department of Transportation has maps on their website
.vith traffic count information (http://www.dot.state.nc ns/planning/statewide/trafficsurvey/).18
Unfortunately, at the time of this study the information was temporarily unavailable.. The posted
speed limit on Capital Boulevard is 45 mi/h. Vehicles speeds were assumed based on a Volume
to Capacity ratio on the road.
Table 2. The hourly vehicle volume, volume/capacity ratio, assumed vehicle speed, ambient
temperature, relative humidity and emission factor for the fleet in grams per mile per hour
(g/mi/h) on Capital Boulevard Raleigh, NC
Date
Time |
Vehicles/
Volume/ |
Assumed
Ambient
Relative
Composite
Ending
(lhour)
hour
Capacity j
Ratio '
Speed
mi/h
Temperature
op
Humidit
y
Emission
Factor
g/mi/h
7/10/01
9:00
2971
0.578
30
60.0
56
81.9
7/10/01
10:00
1574
0.306
40
64.5
48
42.4
7/10/01
11:00
1326
0.258
40
67.6
49
34.6
7/10/01
12:00
1814
0.353
40
69.5
36
48.7
7/10/01
13:00
2915 ' 0.567
30
71.5
28
79.9
7/10/01
14:00
2223
0.432
40
72.4
28
59.8
, 7/10/01
15:00
2075
0.404
40
71.6
28
55.8
j 7/10/01
16:00
2609
0.507
30
71.2
28
71.4
I 7/10/01
17:00
2766
0.538
30
67.0
38
75.8
: 7/10/01
18:00
3904
0.760
20
59.5
64
120.7
j 7/10/01
19:00
2919
0.568
30
54.8
80
80.9
7/10/01
20:00
2144
0.417
40
52.3
86
58.5
7/10/01
21:00
1138
0.221
50
53.1
80
30.7
7/10/01
22:00
970
0.189
50
54.1
74
25.9
7/10/01
23:00
465
0.091
50
52.2
80
12.5
7/10/01
24:00
367
0.071
50
53.8
76
9.8
7/11/01
1:00
81
0.016
50
47.7
82
2.2
7/11/01
2:00
66
0.013
50
; 48.5
78
1.8
7/11/01
3:00
41
0.008
50
48.6
75
1.1
7/11/01
4:00
12
0.002
50
47.7
77
0.3
7/11/01
5:00
45
0.009
50
47.3
77
1.2
7/11/01
6:00
292
0.057
50
! 42.7
91
7.9
7/11/01
7:00
1534
0.298
40
48.6
77
42.0
7/11/01
8:00
4314
0.839
20
55.5
67
133.8
7
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The vehicle fleet composition used to run the model is shown in Figure 2. The vehicle fleet
composition and vehicle age distribution for a light-duty fleet (<8500 lbs) was assumed to be that
in Wake and Durham Counties, while for a heavy-duty vehicle fleet (>8500 lbs) the average
default US vehicle fleet were used because local information was not available.
Figure 2: The vehicle fleet composition (or Capital Boulevard, Raleigh, NC !
|
I
picni c>
~\
,»V\
DlidW
• 0.T* ^
¦ LD0T12
14%
~ 100712
J0J%
~ LDGV
• LOW U 7X
OJX
LINK COORDINATES AND RECEPTOR POSITIONS
The coordinates of the Capital Boulevard were obtained by using G1S software (vertical axis in
Figure 3 is in South to North direction). Capital Boulevard is not a straight road and can be
divided into 9 major links (Figure 3). The road width on Capital Boulevard was assumed to be
constant approximately 30 meters, [i.e. CALINE4 estimated mixing zone width of 36 m].
Ambient PM2 5 concentrations were calculated for the receptors, assumed to be located near the
middle of the each roadlink and about 8 meters away from the edge of the road, i.e. 5 meters
away from the mixing zone width on both sides of the road (height 1.8 m). When traveling
Northward on Capital Boulevard receptors R1 to R9 are located on the left side of the road and
RIO to R18 are on the right side of the road.
8
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Figure 3. Receptor positions for the Capital
Boulevard, Raleigh
R O AI)S 1DE CON CENTRATIONS
The roadside concentrations were generated at 5 meters outside of the mixing zone of each road
segment using CALINE4 for 24 one-hour time-periods starting al 8:00 AM on July 10, 2001.
PM2j roadside ambient concentrations modeled here shows a very large variation in roadside
concentrations (Figure 4). The estimated roadside concentrations are very low late at night after
the traffic volumes become low. Within the daytime period of high emissions there is significant
variation of the estimated concentrations due to wind speed and especially orientation of the
wind direction relative to the roadway segment. Roadside concentrations on the downwind side
are naturally high relative to the upwind side.
This example has demonstrated the usefulness of real-time modeling of MicroFacI'M. Some
other applications are also discussed in other presentation.19 The scope of this modeling approach
can be enhanced to include the network of roads including intersections. This modeling
9
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framework is a very useful too) to generate the emission factors and identify hoi spots as we are
planning.
10
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Figure 4a. Roadside PM2.5 ambient
concentartions on leftside of the Capital
Boulevard when moving towards North-East
—•— R1 -=®— R2 R3 ¦- ¦ R4 -+-R7 — TO
July 10,2001 July 11,2001
]]
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• R10
Figure 4b. Roadside PM2.5 ambient
concentartions on rightside of the Capital
Boulevard when moving towards North-East
R11
R12
R13
R14
— R15
R16
- R18
8 0«-(No^rtn
8 5 S S 3 § 8
July 11,2001
3
12
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CONCLUSION
A microscale emission factor model for predicting real-world real-time motor vehicle particulate
matter (MicroFacPM) emission has been developed. MicroFacPM requires a few input variables,
which are necessary to characterize the local real-time fleet. MicroFacPM calculates the
contribution of PM emissions from different vehicle categories. MicroFacPM emission
estimations are suitable for modeling air quality in real-time at a microscale level and useful for
improving human exposure estimates for mkroenvironments near roadways. MicroFacPM
should be applied where local site specific information is available or good estimates can be
assumed to support the model application.
ACKNOWLEDGEMENT
The authors gratefully acknowledge Robert Gilliam (State Climate Office of North Carolina,
Marine Earth and Atmospheric Sciences Department, North Carolina State University, Raleigh,
NC 27695) for providing coordinates for the Capital Boulevard and modeling mixing height
needed to run CALINE4. Thanks are also due to Chuck Mann and Sue Kimbrough (United
States Environmental Protection Agency. National Risk Management Research Laboratory) for
providing traffic fleet information for Wake-Durham County and traffic volume information for
Capital Boulevard, Raleigh, NC.
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 Stales Environmental Protection Agency, Office of Transportation and Air
Quality, M0BILE6 Vehicle Emission Modeling Software Home Page
http://www.epa.gov/otaq/m6.htm. Home Page http://www.epa.gov/otaq/models.htm
(accessed on January 15, 2002).
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.
13
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5. Singh, R.B.; Hubcr, A.H., Braddock, J.N. Development of a Microscale Emission Factor
Model 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.
6. 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.
7. 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.
8. Yamartino, R.J.; Strimaitis, D. G.; Messier, T.A. Modifications of Highway Pollution
Models for Complex Site Geometries, Vol. J: Data analysis and development of the CPB
model. FHWA-RD-89-112, Federal Highway Administration, Washington, USA 1989.
9. Zimmerman, J.R.; Thomson, R.S. User's guide for IJIWAY - A highway air pollution
model. United States Environmental Protection Agency, EPA-650/4-74-008.L6., USA
1975.
10. Petersen, W.B. User's guide for HIWAY-2 - A highway air pollution model. United States
Environmental Protection Agency, EPA-600/8-80-018, Research Triangle Park, NC,
USA, 1980.
11. Petersen, W.B. User's guide for PAL - A plume algorithm for point, area and line
sources. United States Environmental Protection Agency, EPA-600/4-78-013, Research
Triangle Park, NC, USA, 1978.
12. Benson, P.E. CALlhlE 3 - A versatile dispersion model for predicting air pollutant
concentrations near roadways. Report No. FHWA/CA/TL-79/23 State of California,
Department of Transportation, Division of New Technology and Research, Sacramento,
California, USA, 1979.
13. Wang, I.T.; Rote, D.M. A finite line source dispersion model for mobile source air
pollution. Journal of the Air Pollution Control Association 1975, 25, 730-733.
14. Martinez, J.R.; Javitz, H.S.; Ruff, R.E.; Valdcs, A.; Nitz, K.C.; Dabberdt, W.F.
Methodology for evaluating highway dispersion models. National Cooperative Highway
Research Program Report 245, Transport Research Board, National Research Council,
Washington, DC, 1981.
15. Benson, P.E. CALINE 4 - A dispersion model for predicting air pollutant concentrations
near roadways. Report No. FHWA/CA/TL-84/15 Stale of California, Department of
Transportation, Division of New Technology and Research, Sacramento, California,
USA, 1984.
16. Benson, P.E. CAUNE 4 - A dispersion model for predicting air pollutant concentrations
near roadways. Report No. FHWA/CA/TL - 84/15 (Modified) State of California,
Department of Transportation, Division of New Technology and Research, Sacramento,
California, USA, 1989.
17. Benson, P.E.; Nokes, W.A.; Cramer, R.E. Evaluation of the CALINE4 line source
dispersion model for complex terrain application. Transport Research Record 1986,
1058,7-13.
14
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18. North Carolina Department of Transportation Home Page
hitD://www.dot.state.nc.us/plannino/siate\vi(ie/iraffiC survey/ (accessed on January 15,
2002).
19. Singh, R.B.; Huber, A.H. Application of a Microscale Emission Factor Model for
Particulate Matter (MicroFacPM) to Calculate Vehicle Generated Contribution of PM2.5
Emissions, To be presented at the Air & Waste Management Association's 95th Annual
Conference & Exhibition, Baltimore Maryland, June 24-28, 2002.
15
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TECHNICAL RF.PORT DATA
:. REPORT NC
2 .
i
i. TITLE AND SUBTITLE
Application of a Microscalc Emission Factor Model lor Particulate Matter
(MicroFacPM» in Conjunction with CALINH4-Model
:.REPORT DATE
!. PERI:OKMIN( i ORGANIZATION CODF
' ADi HOR(S)
Rakesh B. Singh. Alan H. Huber
8 PERhORMING OKGAM/AI ION RiiPORT NO
V. PEREORMINU ORGANI/A; KJN NAME AND ADDRESS
'National Research Council Research Associate
USKPA'NF.Rl
RTP. NC 27711
:Same as block 12
10 PROGRAM 1:1 EM EN'. NO
11 CONTRACT'CiRANT NO
\2 SPONSORING AGENCY NAME AND ADDRESS
National Exposure Research Laboratory
Office of Research and Development
li S. Environmental Protection Agency
Research Triangle Park. NC 27711
1* rvpl:0( REPORI AND PERIOD COVLREi)
14 SPONSORING AGENCY CODE
EPA 600/9
iS SUPPIJAIENTARY NOTES
16 ABSIRACt
ITie United Stales Environmental Protection Agency's (EPA) National Exposure Research Laboratory is developing
improved methods lor modeling the pollutant sources through the air pathway to human exposure in significant
microenvironmcnts of exposure. As a part of this project, we developed MicroFacPM. a microscalc emission factor model
for predicting real-world real-time motor vehicle particulate matter (PM.„ and PMj,) emissions, MicioFacPM uses available
information on the vehicle fleet composition. The main input variables required are the characterization of on-road vehicle
fleet, time and day of the year, ambient temperature, relativ e humidity and percentage of smoking vehicles. Using the fleet
information, MicroEacI'.M estimates a 1 ompo.Mtc Emission Factor (milligrams per mile) This paper presents the use of
MicroFacPM to calculate the contribution of PM:. from motor vehicle sources along an example roadway as input to a
roadway air dispersion model. The contribution of FM>, is presented per vehicle class (light, heavy duty), vehicle age, fuel
type (gasoline, diesel), brake wear and tire wear sources.
17 KEY WORDS AND DOOl MHNT ANALYSIS
a DESCRIPTORS
b IDEN 1IHLKS OPEN ENDED TERMS
c COSAT1
18. DIS . RI13U 1 ;ON SiAEEMEM
RELEASE TO PUBLIC
,') StCURI 1 V CLASS r77ns Report,
UNCLASSIFIED
21 NO. or- PAGES
20 S?CMJK:TYn,.\SS {l~ht\
UNCI ASS1FIFD
22 PRICE
EPA 2220
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