EPA/60Q/R-09/OQ1 | December 2008 | www.epa.gov/ord
United States
Environmental Protection
Agency
Emission and Air Quality
Modeling Tools for
Near-Roadway Applications
ce of Research and Development
National Exposure Research Laboratory. Atmospheric Modeling Division
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EPA/600/R-09/001
December 2008
Emission and Air Quality Modeling Tools for
Near-Roadway Applications
Thomas Pierce,1 Vlad Isakov,1 Bernd Haneke,2 and James Paumier2
1Atmospheric Modeling Division, National Exposure Research Laboratory,
Office of Research and Development, U.S. Environmental Protection Agency
2MACTEC Engineering and Consulting, Inc.
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Disclaimer
The information in this document has been funded in part by the U.S. Environmental
Protection Agency (EPA) under contract number EP-D-05-096 to MACTEC Engineering and
Consulting, Inc. It has been subjected to the Agency's peer and administrative review and has
been approved for publication as an EPA document. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
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Acknowledgments
This work was sponsored by the U.S. Environmental Protection Agency (EPA) Office of
Research and Development's (ORD's) Near-Road Air Quality Research Initiative administered
by Dan Costa, EPA/ORD National Program Director for Air Quality Research.
The authors appreciate the editorial assistance, technical guidance, and reviews
provided by John Barton (SRA International, Inc.); Sue Kimbrough (EPA National Risk
Management Research Laboratory); Chad Bailey (EPA Office of Transportation and Air Quality);
and Donna Schwede, Alan Vette, and David Kryak (EPA National Exposure Research
Laboratory).
in
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Table of Contents
1. INTRODUCTION 1
2. EMISSION MODELS 1
2.1. Current Operational Emission Models 2
2.1.1. Consolidated Community Emissions Processing Tool (CONCEPT) 2
2.1.2. EMFAC2002/2007 4
2.1.3. MOBILE6.2 5
2.1.4. Motor Vehicle Emissions Simulator (MOVES) 8
2.2. Research-Grade and European Emission Models 9
3. DISPERSION MODELS 12
3.1. EPA Recommended Models 14
3.1.1. American Meteorological Society/EPA Regulatory Model (AERMOD) 14
3.1.2. CALINE-4 17
3.1.3. CAL3QHC/CAL3QHCR 18
3.1.4. California Puff Model (CALPUFF) 18
3.2. Miscellaneous Publicly Available Models 19
3.2.1. Canyon Plume Box Model, version 3.6a (CPB3) 19
3.2.2. Contaminants in the Air from a Road-Finnish Meteorological Institute
(CAR-FMI) 20
3.2.3. Hybrid Roadway Model (HYROAD) 20
3.2.4. Point, Area, Line (PAL) 21
3.2.5. Quick Urban & Industrial Complex (QUIC) 21
3.3. Proprietary Models 22
3.3.1. Atmospheric Dispersion Modeling System (ADMS)-Roads 22
3.3.2. Operational Street Pollution Model (OSPM) 23
3.3.3. PROKAS 23
3.4. Miscellaneous Research-Grade Models 24
4. SUMMARY 27
REFERENCES 28
List of Tables
Table 1. Summary of Emission Models 2
Table 2. Mobile Emission Type Classifications 7
Table 3. Summary of Dispersion Models 15
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1. INTRODUCTION
In the United States, more than 35
million people live within 100 m of a major
roadway. A growing body of literature
suggests that adverse health effects are
associated with populations living near major
roadways (e.g., Harrison et al., 1999; Brauer
et al., 2002; Hoek et al., 2002; Finkelstein
et al., 2004). According to U.S. Environmental
Protection Agency (EPA) estimates from
2006, highway sources contribute 22% of
volatile organic compounds (VOCs), 36% of
nitrogen oxides (NOX), and 54% of carbon
monoxide (CO) of all anthropogenic
emissions. Despite cleaner fuels and
improved onboard emission control
technology, the growth of vehicular miles
traveled and traffic congestion in urban areas
may further exacerbate the impact of
roadway emissions on air quality and human
health.
Air quality modeling of emissions from
roadways usually is applied either for
regulatory purposes or for supporting health
studies. From a regulatory perspective, State
and local authorities are required to consider
the impact of roadway emissions on air
quality as part of the State Implementation
Planning (SIP) process and to demonstrate
that transportation-related projects do not
cause or worsen air quality (formally defined
as a "transportation conformity" analysis).
Although epidemiological studies, as well as
toxicology studies, show associations
between exposure to traffic near major
roadways and elevated risks of adverse
health effects, such as asthma, impaired
cardiovascular function, and diminished life
expectancy, other epidemiological studies
highlight the need to resolve spatial gradients
near roadways, because, if concentration
profiles are spatially variable, the analysis of
particulate matter (PM) air pollution and
health data could be compromised by
exposure misclassification errors (U.S. EPA,
2004).
The typical set of tools for estimating
near-road air quality consists of estimates or
measures of traffic activity, calculation of
roadway emissions, and analysis of ambient
air concentrations with a numerical air quality
model using estimated emissions. To our
knowledge, a comprehensive review of
roadway emission and near-roadway air
quality models does not exist, and sources of
information on air quality and emission
modeling of roadways are diverse and
scattered. For example, Jungers et al. (2006)
provide a survey of dispersion models for use
in conformity analysis in California. In this
document, we review emission and air quality
modeling techniques for estimating airborne
emissions from roadways. Each model was
reviewed for the following attributes:
(1) model name, (2) developer name and
affiliation, (3) scope of application, (4) URL
addresses (if available), (5) summary of
model input requirements, (6) summary of
model technical formulation, (7) discussion of
model strengths and limitations, and
(8) references of supporting model
documentation.
Nine emission models (Section 2) and
21 air quality models (Section 3) are identified
and discussed in this document. The models
are described, along with a description of
their strengths and weaknesses. In Section 4,
the strengths and weaknesses of existing
operational modeling tools are summarized,
and areas of improvement to assist in
assessments of air pollutant impacts near
roadways are recommended. The intent of
this review is to provide a convenient
compendium of existing operational modeling
techniques and to provide guidance for
researchers interested in improving the
accuracy of air quality modeling for near-road
applications.
2. EMISSION MODELS
One of the first steps in performing a
near-roadway air quality assessment is to
estimate air pollutant emissions from the
roadway environment. Currently, modal and
nonmodal models provide emission
estimates. Modal models generate emission
factors to account for differences in vehicle
operation (i.e., idle, steady-state cruise,
acceleration/deceleration), whereas
nonmodal models generate emission factors
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for average vehicle speeds over different
driving cycles. The emission factors are
combined with vehicle activity data, usually in
the form of distance traveled, to estimate
emissions. Then, the resulting emission
inventory or emission factors are used as
inputs to air quality models, depending on the
input requirements of the air quality model.
This review of nine emission models is
separated into current operational models
and research-grade models. The emissions
models, their developers and reference web
sites are listed in Table 1.
Table 1. Summary of Emission Models
Model Name
Developer
URL
Current Operational Models
Consolidated
Community Emissions
Processing Tool
(CONCEPT)
Emission Factors
(EMFAC)
MOBILE6.2
Motor Vehicles
Emissions Simulator
(MOVES)
Lake Michigan Air
Director's Consortium/
Midwest Regional
Planning Organization
California Air
Resources Board
U.S. EPA
U.S. EPA
http://www.conceptmodel.org/
http://www.arb.ca.qov/msei/onroad/latest version.htm
http://www.epa.qov/otaq/m6.htm
http://www.epa.qov/otaq/nqm.htm
Research-Grade and European Models
Comprehensive Modal
Emissions Model
(CMEM)
COPERT
Microscale Emission
Factor (MicroFac)
Mobile Emissions
Assessment System
for Urban and
Regional Evaluation
(MEASURE)
Transportation
Analysis Simulation
System (TRANSIMS)
University of California,
Riverside
European Environment
Agency
Dr. R. Singh (University
ofWaterloo)/U.S. EPA
Georgia Institute of
Technology/U.S. EPA
Los Alamos National
Laboratory
http://pah.cert.ucr.edu/cmem/
http://lat.enq.auth.qr/copert/
http://dx.doi. orq/1 0.1 01 6/i.atmosenv.2006.04.01 2
http://qtresearchnews.qatech.edu/reshor/rh-spr99/tr-
emis.html
http://tmip.fhwa.dot.qov/transims/
2.1. Current Operational Emission Models
Currently, four models are used
widely in the United States for estimating
mobile source emissions for air quality
modeling applications, which may or may not
be applicable to near-road situations.
2.1.1. Consolidated Community Emissions
Processing Tool (CONCEPT)
CONCEPT is a suite of independent
models that use common supporting routines
and formats. It is an open-source model that
combines attributes of current emissions
modeling systems. Most, if not all of the
software, is in the public domain, and users
are encouraged to customize and share it.
CONCEPT has the following models that are
in various stages of development: (1) area
sources, (2) point sources, (3) onroad motor
vehicles, (4) nonroad motor vehicles,
(5) biogenic sources, and (6) process-based
livestock ammonia model.
The CONCEPT onroad motor vehicle
model combines vehicle activity data
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(volumes, speeds, and trip counts) with motor
vehicle emission factors derived from a
modified version of EPA's MOBILE6 model to
generate hourly, model-ready emissions
estimates. Estimates of emissions during
refueling are not modeled by CONCEPT.
CONCEPT'S onroad motor vehicle
model is designed to obtain activity data from
the Transportation Demand Model
Transformation Tool (T3). Users must provide
the following information: input data
describing the characteristics of the motor
vehicle fleet, spatial allocation of roadway
characteristics (roadway design/type of
roadway, number of lanes/capacity, speed
limit, etc.), chemical speciation mechanism
(i.e., CBIV, SAPRC), MOBILE6 cross-
reference data and execution parameters,
modeling episode, modeling grid definitions,
and the required air quality model output
format (CMAQ or CAMx). Other input data
inputs for CONCEPT include vehicle miles
traveled, number of trips, volumes, network
capacity, speeds, network definitions, speed
adjustments, and meteorological data.
The onroad motor vehicle source
model combines MOBILE6 emission factors
with link-based or county-level vehicle activity
data. CONCEPT uses T3 to generate link-
based vehicle activity data. The model
generates a MOBILE6 run for a number of
variables: representative county,
minimum/maximum temperature, calendar
year, season, roadway type, and speed bin.
Runs are made within CONCEPT for
freeway and arterial roadways. In addition,
speeds are "hard-coded" in MOBILE6 for
freeway ramps and local roads. Emissions
are allocated temporally by applying profiles
by State, county, roadway type, year, month,
and day of week. Temporal adjustments also
are applied to vehicle miles traveled, volume,
capacity, and trip counts. This is especially
critical when looking at heavy-duty diesel
vehicles because weekend/weekday
variations, including hourly variation, can
have a significant impact on emissions.
The T3 model disaggregates traffic
volumes for multihour periods into hourly
volumes, and the data are based on analyses
of calendar year 2002 automated traffic
recorder data. The hourly total volume
profiles are developed to correspond to the
facility class, month, and day of week
provided by the Department of
Transportation's Highway Performance
Monitoring System. The automated traffic
recorder data appears to be available for only
a handful of States, and an Internet query
indicates that the data are easily accessible
only for Minnesota. A presentation prepared
by Environ and the Lake Michigan Air
Directors Consortium (LADCO) indicates that
T3 analyses have been performed only for
Illinois, Michigan, Minnesota, and Wisconsin.
The current version of CONCEPT
provides emissions-related information for
hydrocarbons (HC), CO, and NOX. Because
CONCEPT makes use of MOBILE6, it is
being used to provide emissions estimates for
hazardous air pollutants (HAPs) and PM, as
well as sulfur dioxide (SO2). EPA currently is
customizing CONCEPT to provide hazardous
air pollutant emissions modeling capabilities.
One of the apparent strengths of
CONCEPT is that it relies on open-source
models. In addition, the model is designed to
be transparent and to allow multiple levels of
quality assurance (QA) analysis. The model
uses MOBILE6 to generate emissions
factors. In general, CONCEPT'S strength
appears to be its application to the
regional/urban scale.
A potential weakness of the
CONCEPT model is that it has not been
finalized. In addition, many of the
components of CONCEPT have not been
beta tested. However, discussions with the
model developers indicate that the mobile
source module is robust; it relies on the
MOBILE6 platform, which has been
evaluated and applied extensively. The most
significant problem with CONCEPT is that it is
relatively early in its development, and its
development has focused on QA and
transparency in lieu of development on
processing efficiency and model speed.
Another potential weakness is that
CONCEPT generally uses Bureau of Public
Records speed curves, which may not be the
most accurate approach to estimating vehicle
speed. This methodology assigns the same
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speed to all vehicles for a roadway link
independent of whether the vehicles are light
or heavy duty.
The CONCEPT model does not
appear to be adapted for microscale
applications, such as is the focus of EPA's
near-roadway research initiative. Output is
oriented toward the county level, and the
current version appears to have been applied
only to the Midwest (LADCO), where the
proper traffic data has been made available.
Although emissions factors are developed
using MOBILE6, they are combined with link-
based or county-level activity data. The motor
vehicle model is also perhaps overly input
demanding considering that some
applications require only a 1-km stretch of
urban interstate. This stretch of road does not
have any significant increases or decreases
in height, and it can be assumed that most
vehicles are traveling at fairly consistent
speeds; therefore, it is faster, easier, and
simpler to run MOBILE6 and apply vehicle-
miles-traveled (VMT) estimates to the
emissions factors to develop an emissions
inventory.
CONCEPT enables input of vehicle
activity data using any set of vehicle types.
However, the MOBILE6 emission factors are
generated in terms of the eight MOBILES
vehicle classes. Therefore, all incoming
activity data must be allocated to these eight
classes, which require a file that cross-
references the activity data to the MOBILES
classes.
Unlike other models, the CONCEPT
system requires a number of software
packages to be downloaded, installed, and
configured prior to the installation of the
CONCEPT model itself. Such packages
include PostgreSQL, PostGIS, PROJ.4,
GEOS, and ActivePerl. The user's guide also
recommends the installation of a FORTRAN
compiler and IO/API with National Center for
Atmospheric Research netCDF libraries.
2.1.2. EMFAC2002/2007
EMFAC2007 calculates emissions
inventories for pollutants from onroad motor
vehicles operating in California. EMFAC is a
FORTRAN computer model capable of
estimating both current year and back-cast
and forecasted inventories for calendar years
1970 to 2040. EMFAC estimates the
emission rates of 1965 and newer vehicles
powered by gasoline and diesel fuels.
Emissions estimates are made for over 100
different technology groups and are reported
for three distinct vehicle classes segregated
by usage and weight.
EMFAC calculates the emission rates
of total organic gas, reactive organic
compounds, HC, CO, NOX, PM, PM10, PM2.5,
lead, SO2, methane (CH4), and carbon
dioxide (CO2) for 45 model years for each
vehicle class within each calendar year; for
24 hourly periods; for each month of the year;
and for each county, air basin, and air
management district in California. EMFAC
can report the grams-per-mile emission rates
of a single technology group or the tons-per-
day inventory for the entire 28-million-vehicle
California fleet. With the exception of lead,
EMFAC does not calculate the emission rates
for hazardous air pollutants. A separate
"speciation" step therefore is required, using
factors provided by the Air Resources Board
(www.dot.ca.gov/hq/env/air/pages/msat.htm).
EMFAC2007, like previous versions of
the EMFAC modeling system, was designed
primarily as an emissions inventory tool for air
quality planning. EMFAC can be run over a
number of calendar years to establish
emissions reductions trends and determine
reaction of the inventory to increases or
decreases in population and VMT. Therefore,
the model is a useful tool for trend analyses,
an essential tool in assessing "progress
versus plan" for air quality planning in
California, and a vital tool for determining the
regulatory benefits and cost effectiveness of
specific emission reduction strategies or the
overall effects of growth and control.
The EMFAC modeling system is
tailored specifically to California in that
geographical inputs are specific to this State,
and the model covers California-specific light-
duty vehicle standards, as well as inspection
and maintenance programs. Several scenario
types can be modeled: (1) BURDEN (to
provide an area planning inventory in tons
per day), (2) EMFAC (to provide area fleet
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average emissions in grams per hour), and
(3) CALIMFAC (to provide detailed vehicle
emissions data in grams per mile). Model
inputs include geographical area, calendar
year, month/season, and beginning and final
model years of vehicles being modeled.
EMFAC2007 has a scenario-generating tool
that allows input of various model options and
scenarios, such as inspection and
maintenance assumptions, various correction
factors, outputs specific to Federal Test
Protocol bags, vehicle population data and
odometer accrual values, number of trips per
day and the accrued VMT, Reid vapor
pressure of the fuel, ambient temperature and
relative humidity profiles, speed fractions, and
idle times.
EMFAC is a FORTRAN model that is
constructed in a "bottom-up" fashion.
Therefore, the model is constructed from test
data with no preconceived assumption
regarding the end result. Special test
programs and research projects have been
conducted to isolate single variables such as
speed and temperature to determine their
relative effects on emissions. Multivariate
tests also have been run to determine
whether interactions exist among variables.
These data ultimately are reduced to
mathematical equations called "correction
factors," which are applied to a "basic
emission rate" or a base assumption of a
vehicle's emission characteristics.
Designed primarily as a planning tool,
the EMFAC modeling system is maintained
and updated by the California Air Resources
Board (CARB) as statewide and regional
SIPs are updated. Because EMFAC was
designed as a California-specific planning
tool, the model focuses on vehicles operating
in California at a statewide and regional level.
The model is not designed to estimate
subregional inventories on a link- or grid-
specific basis and is not designed for
conducting assessments on vehicle fleets
that do not operate in California.
EMFAC model outputs commonly are
used for project-level air quality assessments.
For example, CARB has conducted health
risk assessments of emissions generated at
ports and railyards throughout California
(http://www.arb.ca.qov/ports/ports.htm:
http://www.arb.ca.gov/railvard/railyard.htm).
In these studies, EMFAC emission rates were
used in conjunction with vehicle volumes and
speeds on a roadway network to estimate
emissions. EMFAC emission rates also are
used routinely to support air quality
assessments through the California
Environmental Quality Act. With new State-
level requirements to assess greenhouse gas
emissions associated with the transportation
and goods movement sectors, as well as new
regulations designed to reduce criteria
pollutant emissions from diesel vehicles and
new initiatives to assess subregional and
local-scale health risk, modeling requirements
on the EMFAC modeling system are
expanding. In recognition of these expanded
requirements, CARB is developing a toolkit of
next generation emissions models designed
to assess greenhouse gas, criteria, and toxic
air pollutants at statewide, regional, and local
scales that integrate VMT estimates from
EMFAC vehicle modeling and from
statewide/regional travel demand modeling
and that integrate statewide fuel usage
estimates with vehicle activity estimates.
A user's guide and training materials
are available from CARB's Web site.
2.1.3. MOBILE6.2
MOBILE6.2 is an emission factor
model designed by EPA to estimate emission
rates for the highway motor vehicle fleet
under a wide range of conditions. MOBILE6.2
is the latest in a series of MOBILE models
dating to 1978. One of the primary uses of
the MOBILE model is to develop emission
inventories for SIPs and for conformity
determinations. It has been used widely for
mobile source emission inventory
development efforts at many spatial
resolutions. MOBILE6.2 has a variety of
output formats, but, specifically, it provides
emissions factors by vehicle types. These
emissions factors, when combined with
activity data (VMT), provide emissions
estimates that can be used in the
development of emissions inventories or as
inputs to air quality models. MOBILE6.2
enables users to calculate and report
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emissions factors by category for some
pollutants. For example, evaporative HC
emissions from gasoline-fueled vehicles
include diurnal emissions, hot soak
emissions, running losses, resting losses,
and refueling emissions. Similarly,
MOBILE6.2 can report emissions by roadway
type, time of day, vehicle category, and other
characteristics that allow for very detailed
modeling of specific local situations.
MOBILE6.2 is not, however, a modal
emissions model. MOBILE6.2 is not designed
to produce second-by-second emission rates
or emission rates for individual vehicles in the
traffic stream that may have variable driving
patterns. Also, it is not applicable in situations
where automobiles are driven in transition
between two segments of roadway with
different average speeds.
MOBILE6.2 includes default values for
a wide range of conditions that affect
emissions. Worth noting is a correction for
aggressive driving behavior. The defaults are
designed to represent "national average"
input data values. Users who desire a more
precise estimate of local emissions can
substitute information that more specifically
reflects local conditions. Use of local input
data is particularly common when the
customization and development of emissions
inventories or other modeling efforts are
constructed from separate estimates of
roadways, geographic areas, or times of day
in which fleet or traffic conditions vary
considerably. MOBILE6.2 is used to develop
emission inventories on various geographic
scales.
MOBILE6.2 provides estimates of
current and future emissions from as many as
28 vehicle classifications of highway motor
vehicles. The model calculates average
in-use fleet emission factors and can be
programmed (via the input file) for the
following roadway types: freeway, arterial,
local, and freeway ramp.
MOBILE6.2 also calculates emissions
for 10 emissions scenarios. Table 2 provides
the emission type classifications and the
pollutants for which emission factors are
calculated.
MOBILE6.2 is designed to be used in
conjunction with data created by traffic
planners and, as such, is compatible with
planning tools. It also uses facility-specific
driving cycles to better differentiate speed
effects on highways and arterials. Input files
for the model can be developed that have
high levels of customization, or a user can
choose to use MOBILE6.2 default values.
Specific MOBILE6.2 input parameters include
the following: calendar year, month,
weekend/weekday flag, hourly temperature,
altitude, humidity, and solar input. The model
also requires vehicle fleet information
(registration distribution by vehicle class,
annual mileage accumulation by vehicle
class, diesel sales fractions by vehicle class
and model year, natural gas vehicle fractions,
average speed distribution by hour and
roadway, distribution of vehicle miles traveled
by roadway type and by vehicle class, and
average trip length distribution) and fuel
inputs (fuel characteristics, emissions factors
for PM and HAPs, and particle size cutoff).
MOBILE6.2 is FORTRAN based and
uses statistical relationships based on
thousands of emission tests performed on
both new and in-use vehicles. MOBILE6.2 is
available for downloading from EPA's Web
site. There is also ample documentation,
along with a detailed user's guide and sample
run data. EPA has produced 48 reports
explaining the technical formulation of
MOBILE6.2, which are available at
www.epa.gov/otaq/models/mobile6/m6tech.
htm. Unlike some of the other emission
models, obtaining a copy of the program is
straightforward.
MOBILE6.2 can generate emission
factors for 28 types of highway vehicle
classifications for criteria and HAPs: gaseous
HCs, CO, NOX, CO2, sulfate, organic carbon,
elemental carbon, total carbon portion of
gasoline exhaust particulate, lead, SO2,
ammonia, brake and tire wear particulate,
benzene, methyl tertiary butyl ether,
1,3-butadiene, formaldehyde, acetaldehyde,
and acrolein. In addition, PM emission factors
are based on algorithms from EPA's PM
model, PARTS. There are several
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Table 2. Mobile Emission Type Classifications
Number
1
2
3
4
5
6
7
8
9
10
Abbreviation
Running
Start
Hot Soak
Diurnal
Resting
Run Loss
Crankcase
Refueling
Brake Wear
Tire Wear
Description
Exhaust running emissions
Exhaust engine start emissions
(trip start)
Evaporative hot soak emissions
(trip end)
Evaporative diurnal emissions
(heat rise)
Evaporative resting loss
emissions (leaks and seepage)
Evaporative running loss
emissions
Evaporative crankcase emissions
(blow-by)
Evaporative refueling emissions
(fuel displacement and spillage)
Particulate matter from brake
component wear
Particulate matter from tire wear
Pollutants3
All except tire
and brake
wear
particulate
HC, CO, NOX,
toxics
HC, BZ, MTBE
HC, BZ, MTBE
HC BZ, MTBE
HC, BZ, MTBE
HC
HC, BZ, MTBE
Brake wear
particulate
Tire wear
particulate
Vehicle Classes
All
Light-duty
vehicles and
motorcycles
Gasoline,
including
motorcycles
Gasoline,
including
motorcycles
Gasoline,
including
motorcycles
Gasoline,
including
motorcycles
Gasoline,
including
motorcycles
Gasoline,
including
motorcycles
All
All
"The Additional HAPS command in MOBILE6.2 enables users to specify any compound either as a single emission rate or as a ratio
to volatile organic compounds (VOCs) or particulate matter. For example, quinine emissions, not explicitly modeled in MOBILE6.2,
can be modeled if the user has appropriate speciation data. In addition to the hazardous air pollutants specifically modeled by
MOBILE6.2(and included in this table), other VOCs can be modeled with the Additional HAPS command. This applies to all
evaporate processes.
Pollutants: BZ = benzene, CO = carbon monoxide, HC = hydrocarbons, MTBE = methyl tertiary butyl ether, NOX = nitrogen oxides
deficiencies in estimating PM emission
factors in MOBILE6.2, primarily the result of
carrying over the algorithms from PARTS, the
previous PM emission factor model. Delucchi
(2000) found that PARTS "underestimates
emissions from real on-road vehicles,
primarily because PARTS seems to be based
on low-mileage, properly functioning vehicles,
and takes little, if any account of super-
emitters." It also is thought that the database
used to develop emission factors does not
"include a representative number of old,
malfunctioning, poorly tuned, or inherently
high emitting vehicles." He also notes that
real PM emissions may be higher by a factor
of two than those estimated by PARTS. PM
7
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emission rates are not sensitive to vehicle
speed, road type, and temperature.
Emissions factors for fugitive dust are not
calculated because PARTS does not properly
account for unpaved roads. Newer tools for
calculating fugitive dust are available from
EPA.
Since MOBILE6.2's release in 2001,
there have been three studies sponsored to
evaluate the model: one by the Coordinating
Research Council (CRC), EPA, and LADCO;
a second by the American Association of
State Highway and Transportation Officials
(AASHTO); and a third by the National
Oceanic and Atmospheric Administration
(NOAA). A review of the PM emission factor
estimating algorithms module also is
presented. The results of these studies are
summarized in the following paragraphs.
The CRC/EPA/LADCO project (CRC,
2004) compared MOBILE6.2 HC, CO, and
NOX emission estimates with various real-
world data sources, including tunnel studies,
ambient pollutant concentration ratios,
emission ratios from remote sensing devices,
and heavy-duty vehicle emission data based
on chassis dynamometer testing. Compared
with tunnel studies at several sites in the
1990s, the CRC/EPA/LADCO study found
that MOBILE6.2 results vary with pollutant.
MOBILE6.2 overpredicts fleet average
emissions, with the overprediction being most
pronounced for CO (especially for newer
vehicles). Estimates of NOX emissions most
clearly matched the tunnel study data.
Compared with ambient data, the HC/NOX
ratios developed from MOBILE6.2 appear to
be reasonably accurate, and the CRC/EPA
data generally supported the HC deterioration
rates in MOBILE6.2.
AASHTO evaluated several
components of MOBILE6.2 (Heirigs et al.,
2004), including PM and HAP emission
factors, assessment of emission factors when
compressed natural gas is the fuel, and
methods to estimate CO2. It found that
MOBILE6.2 appears to overestimate exhaust
PM10 emissions from newer (1991 and later)
light-duty gasoline vehicles by about a factor
of two. For pre-1990 model years,
MOBILE6.2 predictions fall within the range of
recent test program expected values. The
AASHTO study also found that MOBILE6.2
may be underestimating PM10 exhaust
emissions from heavy-duty diesel trucks. It
also notes that MOBILE6 appears to
underestimate wintertime PM emissions,
possibly because of a lack of temperature
corrections for PM emissions. Finally, the
study found that MOBILE6.2 brake-wear
emission factors likely underestimate brake-
wear emissions from the heavier vehicle
classes.
NOAA's comparisons of emissions
inventories developed using the MOBILE
model and inventories developed using a
fuel-based approach tend to support three
conclusions. First, there is excellent
agreement in the total VOC and NOX
emissions. Second, CO estimates developed
using MOBILE are about 40% higher than
those developed using a fuel-based
approach. Third, although the total emissions
of the inventories agree well, the MOBILE
NOX inventory attributes a much smaller
fraction (approximately a factor of two) to
diesel-powered vehicles and a larger fraction
to gasoline-powered vehicles (Parrish et al.,
2002).
Outside of California, MOBILE6.2 is
probably the most widely used emissions
factor model for mobile sources in the United
States. Although it is not a modal model, it,
nevertheless, is the most tested and validated
model. With the exception of VMT, the model
provides all inputs required by the emissions
air quality models. In response to evolving
technology and knowledge, EPA currently is
developing a successor to MOBILE6.2, which
is discussed just below.
2.1.4. Motor Vehicle Emissions Simulator
(MOVES)
To keep pace with new analysis
needs, modeling approaches, and data,
EPA's Office of Transportation and Air Quality
developed MOVES2004. MOVES estimates
emissions for onroad and nonroad sources,
covers a broad range of pollutants, and
enables multiple-scale analysis from fine
scale to national emission inventory scale.
The foundation of the multiscale approach is
-------
a common set of modal emission rates
disaggregated by driving mode. These modes
then are reaggregated based on
representative activity data to estimate total
emissions at any scale over any driving
pattern. The MOVES2004 model uses a
binning approach to define modal emissions.
Vehicle-specific power and instantaneous
speed are used to identify driving modes.
This method produces 17 bins that segregate
idle and deceleration and splits the remaining
cruise and acceleration operation into 15 bins
defined by combinations of speed and
vehicle-specific power.
MOVES2004 is used to generate
national vehicle emissions inventories and
projections at the county level for energy
consumption and various pollutants from
highway vehicles. The model also generates
vehicle emission inventories on mesoscale
(regional travel) and microscale (individual
transportation facilities) levels. MOVES2004
includes the Argonne National Laboratory's
Greenhouse Gases, Regulated Emissions,
and Energy Use in Transportation (GREET)
model to estimate life cycle (i.e., well-to-
pump) effects in the estimate of energy
consumption and emissions. MOVES2004
also can be used to estimate pollutant
emissions from additional mobile sources,
such as aircraft, locomotives, and commercial
marine activity; nonroad mobile source
emissions; and criteria pollutant emissions.
MOVES2004 has an option of using
default data for estimating energy
consumption, nitrous oxides, and methane
from highway vehicles on a national basis.
Detailed information on the inputs for the
MOVES2004 model is available in its user's
guide (U.S. EPA, 2004). Inputs include
selection of scale (macroscale, mesoscale,
and microscale), although mesoscale and
microscale options are not currently available;
selection of geographic bounds for
macroscale (nation, State, or county);
selection of time spans (year, month, day, or
hour); selection of vehicles or equipment
(fuels and source use types); selection of
road type (off-network, rural interstate, rural
local, rural major collector, rural minor
arterial, urban collector, etc.); and selection of
pollutants and processes (current pollutants
include methane and nitrous oxide, and
processes include extended idle exhaust,
running exhaust, start exhaust, and well-to-
pump).
MOVES2004 is written in Java and
the MySQL relational database management
system. Principal user inputs and outputs and
several of the internal working storage
locations are MySQL databases. A default
input database covering 3,222 U.S. counties
is included. MOVES2004 interfaces with a
version of the GREET model, which is a
multidimensional Microsoft Excel
spreadsheet.
MOVES2004 has a master-worker
program architecture that enables multiple
computers to work together on a single model
run. A single computer can be used to
execute MOVES2004 runs by installing both
the master and worker components on the
same computer.
It is not necessary to create a detailed
input file for MOVES2004 (unlike MOBILE6),
and it is a modal model. These two attributes
should make this a user-friendly and
attractive model once it becomes fully
operational and functional.
Perhaps the single most important
weakness of MOVES2004 is that it only
models nitrous oxide and methane; therefore,
it currently is not possible to model other
criteria pollutants such as PM, NOx, gaseous
HCs, and CO. This limitation will be corrected
with the introduction of a new version,
MOVES Highway Vehicle Implementation
(MOVES-HVI). A demonstration model is
available to the general public. MOVES-HVI
can be used to estimate national inventories
and projections at the county level for energy
consumption (total, petroleum-based, and
fossil-based), nitrous oxide, and methane
from highway vehicles. The MOVES-HVI
demonstration model also performs runs for
HC, CO, and NOX.
2.2. Research-Grade and European
Emission Models
These models are included here as
they offer features that may benefit specific
-------
studies and may be incorporated into future
operational-grade emission models.
The Comprehensive Modal Emissions
Model (CMEM) is a modal model developed
to accurately relate light-duty vehicles
emissions as a function of the vehicle's
operating mode. CMEM was developed
initially using MATLAB, and two command-
line interface executables were created for
light-duty gasoline and heavy-duty diesel
vehicles. Data input is handled via a Java-
based graphical user interface (GUI). The
model is comprehensive in the sense that it is
able to predict emissions for a wide variety of
light-duty vehicles in various states of
condition (e.g., properly functioning,
deteriorated, malfunctioning). The model can
predict second-by-second tailpipe (and
engine-out) emissions and fuel consumption
for a wide range of vehicle and technology
categories. The principal strength of this
model is that it predicts vehicle emissions
modally and is easy to set up and use
because of its Java-based GUI. The model is
also transparent, and results are easily
dissected for evaluation. Because CMEM is
not restricted to steady-state emission
events, the transient operation of vehicles
can be modeled more appropriately. Potential
weaknesses with CMEM are the lack of
updates for heavy-duty vehicles. Because of
its intensive data requirements, CMEM
should be considered a research-grade
model.
The "COPERT" suite of models was
developed for use in Europe by the European
Environment Agency (2000). The model,
developed using Microsoft Access, is based
on methodologies from the European
Monitoring and Evaluation Program of long-
range transmission of air pollutants and the
Core Inventory of Air Emissions. COPERT
was designed to produce annual national
emission inventories for on- and offroad
mobile sources. The latest version,
COPERTIV, became available in late 2006.
Many of the inputs are Europe-specific and
probably not applicable to the United States.
Inputs for a typical COPERT run include
(1) country fuel, (2) country monthly
temperatures, (3) country Reid vapor
pressure, (4) country cold-start parameters,
(5) activity data fleet information, (6) activity
data circulation information, and (7) activity
data evaporation share. Outputs from
COPERT include the calculation of annual
emissions of pollutants for all CORINAIR road
traffic source categories at all defined
territorial units and road classes. Pollutants
include exhaust emissions of CO, NOX,
VOCs, CH4, CO2, nitrous oxide, NH3, sulfur
oxides, diesel exhaust particulates (PM),
polycyclic aromatic hydrocarbons (PAHs),
and persistent organic pollutants, dioxins,
furans, and heavy metals contained in the
fuel (lead, cadmium, copper, chromium,
nickel, selenium, and zinc). Nonmethane
VOCs are split into alkanes, alkenes, alkynes,
aldehydes, ketones, and aromatics. Although
it may be possible to customize COPERT for
use in the United States, that probably would
be too time and data intensive for most
operational applications.
MicroFac is a research-grade
emissions model developed by Dr. Rakesh
Singh with assistance from EPA. The main
application of MicroFac is to provide input to
air quality models and emission estimates at
small-temporal and fine-spatial scales.
According to the model's developer, MicroFac
is suited ideally for street-level air quality
modeling, identification of hot spots, human
exposure assessments, and project-level
analysis. The algorithm used in MicroFac
disaggregates emissions based on the
onroad vehicle fleet and calculates emission
rates from a real-time site-specific fleet.
MicroFac starts with geographically resolved
data, for example, modeling a traffic fleet on
an individual length of road. Emission factors
are calculated for site-specific on-road traffic
fleets (e.g., CO emissions in grams per
vehicle kilometers traveled). Total emissions
for a geographical area of interest can then
be obtained by summing contributions from
individual road segments. This approach
provides for a shorter and more accurate
averaging time, such as a single road during
a specific hour of the day. MicroFac requires
input variables that are necessary to
characterize the site-specific real-time fleet.
Input variables required to run the model
10
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include date and time, average fleet speed,
ambient temperature and relative humidity,
road gradient, gasoline fuel properties (such
as density, sulfur content, volatility,
aromatics, oxygen, olefins, fuel distillation,
and heavy metals content), diesel fuel
properties (such as density, sulfur, Reid
vapor pressure, cetane number, PAH
content, volatility, and heavy metals content),
length of trip (which is used to calculate the
fraction of distance traveled with cold running
emissions), and vehicle fleet composition
(such as age distribution and percentage of
high emitters). In a comparison to MOBILE,
Singh and Sloan (2006) suggest that
MicroFac is more appropriate than MOBILE
for estimating site-specific emission rates
from onroad vehicle fleets. An integrated
MicroFac and CALINE4 modeling system has
been used successfully to calculate vehicle-
generated contributions to PM2.s emissions in
Canada. However, the model developer has
indicated that there is currently not an easy
way of writing an input file for MicroFac; it
would require the model developer up to
4 days to create an input file for a specific
application. Furthermore, according to the
developer, the model is still in a research and
development phase, although a working
model, which is a series of spreadsheets, is
said to be available. It is our opinion that
MicroFac will require more development and
the availability of a user's guide before it is
truly operational.
The Mobile Emissions Assessment
System for Urban and Regional Evaluation
(MEASURE) Model, which was developed
with EPA's assistance by the Georgia
Institute of Technology, interfaces with a
travel demand model output in an ARC/INFO
environment to determine emissions on a
spatial basis. MEASURE is a research-grade
modal model that was developed in the late
1990s. Perhaps the greatest strength of
MEASURE is that it can display results
graphically using ARC/INFO. ARC/INFO also
allows users to define specific areas, thereby
allowing modeling at a very fine scale.
Because MEASURE is a modal model, it can
provide emission estimates for vehicles at
multiple speeds or operational modes. The
model is further refined by its ability to use
socioeconomic data to estimate differences in
vehicle fleet by geographic area. This feature
is significant because a neighborhood with
older vehicles may have higher levels of
emissions, which may, in turn, affect
geographic patterns of pollutants such as
ozone. MEASURE is supported by technical
documentation (U.S. EPA, 1998), including a
demonstration test case for Atlanta, GA. For
this example, a substantial amount of annual
data were needed: Georgia Department of
Motor Vehicles Registration Dataset, U.S.
Census Summary Tape File 3a, U.S. Census
TIGER file, Updated TIGER Road Database,
Atlanta Regional Commission's (ARC) Traffic
Analysis Zones, ARC'S Travel Demand
Forecasting Network, ARC'S Land Use Data,
and ARC'S ARCMAP Road Database.
MEASURE was developed based on
statistical distributions of a variety of vehicle
technologies and vehicle operating modes.
The core of the emission rate model relies on
hierarchical tree-based regression analyses.
MEASURE is quite modular and includes
11 different interlinking modules, such as
road environments, engine start activity, and
road activity. MEASURE predicts emissions
of CO, HC, and NOxwith a spatial resolution
determined by the user (e.g., grams per
kilometer or mile traveled). MEASURE does
not include nonautomobiles, emission
estimates for PM, evaporative emission
estimates, effects of vehicle deterioration,
effects of grade on engine and accessory
load, and intersection specific estimates.
Although speed-corrected emission factors
from the now-obsolete MOBILESa model are
used, they could be updated easily. For
operational use, MEASURE would have to be
updated with the latest version of MOBILE
and ARC/INFO software. Presumably, this is
not an inexpensive task, and the validation of
this updated software also could be time
consuming.
The Transportation Analysis
Simulation System (TRANSIMS) is actually
an integrated system of travel forecasting
models designed to give transportation
planners accurate and complete information
on traffic impacts, congestion, and pollution.
11
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TRANSIMS was developed at Los Alamos
National Laboratory (LANL) with funding from
the U.S. Department of Transportation, EPA,
and the U.S. Department of Energy as part of
the Travel Model Improvement Program.
TRANSIMS simulates the movement of
travelers and vehicles across the
transportation network in a metropolitan area
using multiple modes, including car, transit,
truck, bike, and walk, on a second-by-second
basis. This virtual world of travelers attempts
to mimic the traveling and driving behavior of
real people in the region. The interactions of
individual vehicles produce realistic traffic
dynamics from which analysts can judge the
overall performance of the transportation
system and estimate vehicle emissions. The
Emissions Estimator module requires
information from various other modules that
are part of TRANSIMS. For example, it needs
information from the Traffic Microsimulator.
Output from the Emissions Estimator is
aggregated on 30-m segments for a 1-h
period. The Emissions Estimator module is
designed to produce fleet average emissions
rather than emissions from individual
vehicles. The Emissions Estimator requires
information on fleet composition, vehicle
loads, and traffic patterns. The TRANSIMS
Emissions Estimator module is divided into
three submodules: tailpipe emissions from
light- and heavy-duty vehicles, and
evaporative emissions. For light-duty
vehicles, TRANSIMS uses the
Comprehensive Modal Emissions Model
(CMEM) described earlier). Evaporative
emissions are calculated using algorithms
that closely follow MOBILE6. Noncommercial
users can download Linux-based versions of
TRANSIMS for an approximately $1,000
licensing fee. However, commercial users are
required to contact IBM. Because of its
intensive data requirements, TRANSIMS is
currently considered a research-grade
emissions model.
3. DISPERSION MODELS
Several review papers on dispersion
of vehicular exhaust have been written over
the last several years. These review papers
cover a variety of model types and
methodologies, including Gaussian plume
models, puff models, box models, statistical
modeling, computational fluid dynamics
(CFD), geographical information systems
(GIS), and wind tunnel simulations. In
addition to the discussions, the review papers
also provide an extensive bibliography of
research and applications of dispersion
models and modeling techniques.
Holmes and Morawska (2006)
reviewed the suitability of nearly 30 models
for estimating the dispersion of particles,
several of which are included in this review.
Sharma et al. (2004) reviewed general
Gaussian-based highway models (such as
CALINE-4 and ROADWAY), dispersion
modeling in an urban environment (street
canyons and intersections), and recent
modeling trends, such as statistical modeling
tools, GIS, and, CFD. They also discussed
the current status of modeling in India and the
routine use of existing line-source models
(e.g., CALINE). Vardoulakis et al. (2003)
discussed air flow and pollutant dispersion in
street canyons, followed by discussions of
several operational and research models,
model input requirements, and field studies.
Nagendra and Khare (2002) presented
theoretical considerations of line-source
emission models. They reviewed current line-
source deterministic models (e.g.,
CALINE-4), numerical models, and stochastic
models. They also presented a short
discussion of artificial neural networks as
applied to line-source models and discussed
some of the limitations of line-source models.
The review by Sharma and Khare (2001) is
similar in nature to several of the review
papers identified above.
The focus of this effort is on Gaussian
plume models and puff models. However, a
brief discussion of other methods is
presented before presenting details on
emissions models and dispersion models.
Statistical modeling of air pollution can
be carried out by relating meteorological
parameters and other parameters after
developing a relationship between those
parameters and pollutant concentration
estimates. Techniques include regression,
time series analysis (e.g., Box-Jenkins
12
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methods), Markov chain-Monte Carlo
methods, and extreme value theory. Gokhale
and Khare (2004) reviewed deterministic,
stochastic, and hybrid methods of modeling
vehicular exhaust. Gokhale and Khare (2005)
developed a hybrid model that combined a
general finite line-source model
(a deterministic model) with a log-logistic
distribution model (statistical model) to predict
CO from vehicular exhaust. In more recent
studies, tools such as artificial neural
networks (Nagendra and Khare, 2004) and
fuzzy logic theory are being applied to
modeling vehicular pollution.
With the increase in affordable
computing power, the CFD field has become
more popular for dispersion modeling, with
the emphasis on the application of CFD
techniques on urban street canyons. CFD
techniques allow for a more detailed
examination of air flow and pollutant
dispersion and vehicle-induced turbulence in
areas with complex street canyon
geometries. Sahlodin et al. (2007) developed
a mathematical model that incorporated
vehicle-induced turbulence into a Gaussian
dispersion model using CFD to simulate the
roadway to modify the dispersion parameters.
Li et al. (2006) reviewed recent progress in
dispersion modeling within urban street
canyons in this rapidly developing field.
There are two types of CFD
techniques: (1) diagnostic and (2) prognostic.
Diagnostic techniques are basically
interpolation methods based on
measurements that are subject to physical
constraints (Li et al., 2006). CFD prognostic
techniques can be categorized in three ways:
(1) Reynolds-averaged Navier-Stokes
(RANS) theory, (2) direct numerical
simulation (DNS), and (3) large eddy
simulation (LES). The most common RANS
techniques are k-e turbulence closure and
renormalized group. These techniques are
the least computationally intensive of the
three categories and can be used to
investigate street aspect ratio, building
configurations, inflow (at the top of the
building canopy), and vehicle induced
turbulence. Sharma et al. (2004) and Li et al.
(2006) suggest that some uncertainty exists
in the ability of RANS models to simulate
urban street canyon pollution problems and,
therefore, are more appropriate as a
screening approach. DNS methods are the
most computationally intensive because the
"complete turbulent flow field is solved
directly without any form of time or length
averaging in the domain"
(http://www.fluent.com/elearninq/resources/
index-glossary.htm). Between the two are
large eddy simulation methods that include
subgrid scales to model energy-carrying
turbulent motions. LES models are more
appropriate if speed is of less concern, and
the goal is to investigate transient processes
and turbulence fields. Huber (2006) presents
a framework in which fine-scale CFD
modeling may complement a regional
modeling system to support human exposure
assessments.
Another method of estimating
concentrations resulting from vehicular
emissions is to use a GIS to map traffic-
related pollution. Although GIS does not
calculate the impacts resulting from vehicular
emissions, it can provide an integrated
framework that relates traffic data, emissions,
and other related parameters to asses the
impacts estimated by a dispersion model. For
example, Medina et al. (1994) integrated
computer-aided design and drafting (CADD)-
based roadway configurations using GIS and
traffic information to produce a database
appropriate for use in air dispersion models.
Gualtieri and Tartaglia (1998) developed a
comprehensive approach that includes traffic,
emissions, and dispersion modules. GIS can
output coordinates of sensitive receptors for
input to a dispersion model. Similarly, output
from a dispersion model can be input into GIS
to display hot spots. Hallmark and O'Neill
(1995) developed a model that combines a
transportation-based GIS with CAL3QHC
estimates. They also discussed problems that
arise when transferring data between air
quality models and GISs. Sharma et al.
(2003) reviewed several approaches that
have appeared in the literature and describe
a case study in India in which the impacts
estimated from CALINE-4 were integrated
13
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into a GIS specific for transportation
problems.
Another area where research has
focused is wind tunnel simulations. These
simulations are primarily studies of urban
street canyons and intersections and provide
insight into the complex flows introduced by
the presence of buildings, walls, and
vegetation and the dispersion of pollutants.
Unlike field observations in which there is
little or no control over the meteorological and
traffic parameters, wind tunnel experiments
provide a controlled environment. Parameters
can be held constant or changed to examine
the effect on dispersion. Ahmad et al. (2005)
reviewed the current state of wind tunnel
simulations of the urban environment.
The models presented in this review
are primarily Gaussian plume and puff
models. A limited number of CFD models and
research-based models that are not readily
available for public use or review also are
presented. Both older and more recent
models that can be used to estimate near-
roadway pollutant concentrations were
identified. This review presents four
categories of dispersion models for near-road
applications: (1) EPA-recommended models
acceptable for regulatory applications,
(2) other models freely available to the public,
(3) miscellaneous research-grade models,
and (4) proprietary models. The model
names, model developers, and reference
Web sites are listed in Table 3.
3.1. EPA Recommended Models
3.1.1. American Meteorological
Society/EPA Regulatory Model (AERMOD)
AERMOD is a steady-state Gaussian
plume model released by EPA that replaced
the Industrial Source Complex Short Term
(ISCST) model in 2006 as a "guideline" model
(Cimorelli et. al., 2005). It is used for
evaluating the dispersion of inert pollutants
from point, area, volume, and open pit
sources. If a roadway is simulated as multiple
area or volume sources, a single set of
coordinates defines the location of each
source. AERMOD is designed for transport
distance of 50 km or less. AERMOD includes
a photochemical option for nitrogen dioxide
(NO2) that accounts for the transformation of
NO2 to nitric oxide (NO) in the presence of
ozone, as well as dry and wet deposition
options.
AERMOD has a large number of input
requirements and requires running a
meteorological preprocessor (AERMET) and
a terrain preprocessor (AERMAP), assuming
local terrain is elevated. There are many
commercially available GISs to easily develop
the necessary inputs for AERMOD and its
associated preprocessors. One of the basic
inputs to AERMOD is the control setup file,
which contains the selected modeling
options, as well as source location and
parameter data, receptor locations,
meteorological data file specifications, and
output options. Another type of basic input
data needed to run the model is
meteorological data. AERMOD uses state-of-
the-art boundary layer parameterizations, and
it can utilize site-specific data in its
representation of the vertical structure of the
atmosphere. AERMOD requires two types of
meteorological data files that are provided by
the AERMET meteorological preprocessor
program. One file consists of surface scalar
parameters, and the other consists of vertical
profiles of meteorological data. For
applications involving elevated terrain effects,
the receptor and terrain data will need to be
processed by the AERMAP terrain
preprocessing program before input to the
AERMOD model. Further inputs to AERMOD
include the emissions rate per source. The
emission rates can be varied by hour of day,
but apply to the roadway and not to individual
vehicles. The number of receptors, discrete
or gridded, is not limited in AERMOD.
AERMOD has an urban option to
model urban areas (heat island effects) and
provides the capability of specifying sources
as urban sources. Because AERMOD is a
steady-state model, it does not estimate
concentration impacts when the winds are
calm. AERMOD includes a meander
component that enhances lateral dispersion.
Meander is the slow back and forth shifting of
the plume and is currently applicable to all but
the area sources. AERMOD estimates the
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Table 3. Summary of Dispersion Models
Model Name
Developer
URL
U.S. EPA Regulatory Models
AMS/EPA Regulatory Model
(AERMOD)
CALINE-4
CAL3QHC/CAL3QHCR
California Puff Model
(CALPUFF)
U.S. EPA, AMS
California Department of
Transportation
U.S. EPA
Sigma Research Corporation/
TRC Environmental Corporation
http://www.epa.qov/scram001/dispersion prefrec.htm
http://www.dot.ca.qov/hq/env/air/index.htm
http://www.epa.qov/scram001/dispersion prefrec.htm#ca!3qhc
http://src.com/calpuff/calpuff1.htm
Miscellaneous Publicly Available Models
Canyon Plume Box Model,
version 3.6a (CPB3)
Contaminants in the Air from a
Road-Finnish Meteorological
Institute (CAR-FMI)
Emissions and Dispersion
Modeling System (EDMS)
Hybrid Roadway Model
(HYROAD)
Point, Area, Line (PAL)
Quick Urban & Industrial
Complex (QUIC)
Atmospheric Dispersion
Modeling System (ADMS)-
ROADS
Operational Street Pollution
Model (OSPM)
PROKAS
Federal Highway Administration
Finnish Meteorological Institute
Federal Aviation Administration
SAI/ICF Consulting, Inc.
U.S. EPA
Los Alamos National Laboratory in
collaboration with the University of
Utah and the University of
Oklahoma
Cambridge Environmental
Research Consultants (CERC)
National Environmental Research
Institute of Denmark
Lohmeyer Consulting Engineers,
Inc. (German firm)
http://www.tfhrc.qov/structur/pubs/02036/intro.htm
http://www.fmi.fi/research air/air 14.html
http://www.faa.qov/about/office orq/headquarters offices/aep/models/
edms model/
http://www.epa.qov/scram001/dispersion alt.htm
http://www.epa.qov/scram001/models/other/altmodel.pdf
http://www.lanl.gov/proiects/quic/index.shtml
www.cerc.co.uk/software/admsroads.htm
Cost: Approximately $3,700 (annual, single user)
http://www2.dmu.dk/1 viden/2 Miljoe-
tilstand/3 luft/4 spredninqsmodeller/5 OSPM/5 description/default e
n.asp
Cost: Approximately $2,700
http://www.lohmever.de/air-eia/models/prokas.htm
Cost: Approximately $1 ,876 for PROKAS B and $4,020 for
PROKAS V
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Table 3. Summary of Dispersion Models (cont'd.)
Model Name
Developer
URL
Miscellaneous Research-Grade Models
Micro-Calgrid Model (MCG)
ROADWAY-2
PUFFER
TRAQSIM
UCD 2001
R. Stern and R. Yamartino
NOAA Air Resources Laboratory
University of Nottingham (UK)
University of Central Florida
University of California, Davis
http://www.ivu-umwelt.de/front content. php?idcat=5
http://www.sprinqerlink.com/index/N07515J23R1T6584.pdf
http://linkinqhub.elsevier.com/retrieve/pii/S01 6761 050000061 1
http://cee.ucf.edu/labs/air qualitv/SoftwareMain.html
http://pubs.its.ucdavis.edu/publication detail. php?id=243
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effect of meander on concentration estimates
by interpolating between two concentration
limits: (1) a coherent plume limit and (2) a
random plume limit.
As a regulatory model, AERMOD was
evaluated extensively using observational
field data and tracer study results. A total of
17 databases were used in the evaluation of
AERMOD to provide diagnostic as well as
descriptive information about the model
performance (U.S. EPA, 2003). Also,
AERMOD was evaluated with respect to
other models such as ADMS-Roads, ISCST3,
and CTDMPLUS. When considering only the
highest predicted and observed
concentrations, it was found that ISCST3
overpredicts by a factor of seven, on average,
whereas ADMS-Roads and AERMOD
underpredicted, on average, by about 20%. It
also was determined that ADMS-Roads
performance is slightly better than AERMOD
(Hannaetal., 1999). In complex terrain,
AERMOD consistently produced lower
regulatory design concentrations than
ISCST3, not an unexpected result because
ISCST3 uses algorithms from a screening
model (COMPLEX1) in its calculations. In
comparisons with CTDMPLUS and observed
data, AERMOD consistently performed better
than CTDMPLUS, a model approved by EPA
for regulatory applications in complex terrain.
The model has not been compared rigorously
for line-source applications. Because
AERMOD is used most commonly for
dispersion analyses of stationary point
sources, area sources, and volume sources,
there is no accommodation for different
roadway geometries (e.g., bridges and deep
roadway cuts).
AERMOD also is used as a part of the
Emissions and Dispersion Modeling System
(EDMS) developed by the U.S. Federal
Aviation Administration for assessing the
impacts of various emission sources at
airports (FAA, 2007). EDMS is EPA's
preferred guideline model for modeling
dispersion at civilian airports and military air
bases (www.faa.gov/about/office org/
headquarters offices/aep/models/
edms model/).
3.1.2. CALINE-4
CALINE-4 is a Gaussian plume
dispersion model that employs a mixing zone
concept to roadway sources. This version
updates CALINE-3, specifically by fine-tuning
the Gaussian method and the mixing zone
model. CALINE-4 can model roadways
at-grade, depressed, and filled (elevated);
bridges (flow under roadway); parking lots;
and intersections. Bluffs and canyons
(topographical or street) also can be
simulated.
CALINE-4 accepts composite vehicle
emission factors (expressed in grams per
vehicle) developed and input by the user for
each roadway link. The user inputs composite
emission factors by link. For intersections, the
required input parameters are the average
number of vehicles per cycle per lane, the
average number of vehicles delayed per
cycle per lane, hourly departure traffic
volume, composite idle emission factor,
vehicle idle time at stop line, and vehicle idle
time at end of queue. Users also enter hourly
information on traffic/sources by link. If a user
is modeling an intersection, information on
acceleration/deceleration and distance from
link end point to the stop line is required.
Additional inputs include wind direction
bearing, wind speed, atmospheric stability
class, mixing height, wind direction standard
deviation, and temperature.
CALINE-4 is a Gaussian model whose
formulations are based on steady-state
horizontally homogenous conditions. The
region directly over the highway is treated as
a zone of uniform emissions and turbulence.
An area equal to the traveled roadway plus
3 m on each side is referred to as the mixing
zone. Mechanical turbulence (from moving
vehicles) and thermal turbulence (from
vehicle exhausts) are the dominant dispersive
mechanisms. A modified version of the
Pasquill-Smith curves is used for the vertical
dispersion coefficient, oz. The vertical
dispersion parameter is assumed constant
over the mixing zone from the center of the
roadway link to a computed distance from the
link center and then follows a power curve
outside this distance. Dispersion is adjusted
for vehicular heat flux and surface roughness,
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which is assumed to be fairly uniform over the
study area. The horizontal dispersion is a
function of the horizontal standard deviation
of the wind direction, downwind distance,
diffusion time, and Lagrangian time scale.
CALINE-4 divides highway links into a
series of smaller elements. Each element is
modeled as an equivalent finite line source
(FLS) positioned perpendicular to the wind
direction. Each element is subdivided into
three subelements to distribute the
emissions. The downwind concentrations
from an element are modeled using the
crosswind FLS Gaussian formulation. The
concentration at individual receptors is a
series of incremental contributions from each
element FLS. The number of receptors that
can be modeled by CALINE-4 is limited to 20,
making it difficult to compare results to other
models that can handle many more receptor
locations. The control file is more involved if
more than a few days or hours are modeled
because the meteorology and vehicular
information alternate records in the control
file. In addition, there is no meteorological
processor available to develop the necessary
inputs for 1996 and later.
CALINE-4 is an older model with
1980s science. It is a plume model with
steady state, homogeneous conditions. The
roadway links cannot be more than 10m
above or below local topography. When
compared to measured data, CALINE-4
results show a lower correlation than ADMS-
Roads, and best fits lines are also closer to
the target line for ADMS-Roads than for
CALINE-4. Finally, CALINE-4 has been
shown to have a tendency to overpredict low
concentrations and underpredict high
concentrations of pollutants (Ellis et al.,
2001).
3.1.3. CAL3QHC/CAL3QHCR
CAL3QHC is a line-source model
used to estimate CO, other inert pollutants,
and PM from motor vehicles at signalized
intersections. It includes the CALINE-3 line-
source model to estimate dispersion using
worst-case screening meteorology.
CAL3QHC includes methods to estimate
queue lengths and emissions from idling
vehicles at intersections. CAL3QHCR is a
refined model that uses observed
meteorological data rather than screening
meteorology. In addition, calm winds are
excluded in multihour concentration
estimates.
These models require a number of
detailed inputs, including meteorological
variables, deposition velocities, roadway
coordinates and dimensions, receptor
coordinates, traffic variables (such as traffic
volume and speed by link, signal times,
clearance time, saturation flow time, and
arrival time), and emissions (composite
running and idling factors by link).
The dispersion component used in
CAL3QHC is CALINE-3, a line-source
dispersion model developed by the California
Department of Transportation. CALINE-3
estimates air pollutant concentrations
resulting from moving vehicles on a roadway
based on the assumptions that pollutants
emitted from motor vehicles traveling along a
segment of roadway can be represented as a
"line source" of emissions, and that pollutants
will disperse in a Gaussian distribution from a
defined "mixing zone" over the roadway being
modeled. CAL3QHC/CAL3QHCR only
simulates dispersion near intersections for
roads that are less than 10m above grade.
3.1.4. California Puff Model (CALPUFF)
CALPUFF is a non-steady-state
Lagrangian model that simulates pollutant
releases as a series of continuous puffs and
is most suitable for releases in the 50- to
200-km range. It has been adopted by EPA
as the preferred model for assessing long-
range transport of pollutants and their
impacts on Class I areas and on a case-by-
case basis for certain near-field applications
involving complex meteorological conditions.
It can model line sources with constant
emissions, as well as point, area, and volume
sources.
The inputs for the CALPUFF modeling
system can be created either through the GUI
available on the CALPUFF download page or
through the use of an American Standard
Code for Information Interchange (ASCII) text
editor.
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A meteorological preprocessor
(CALMET) generates gridded 3-D diagnostic
fields of the winds. An initial-guess wind field
is adjusted for kinematic effects of terrain,
slope flows, and terrain blocking effects to
produce an initial wind field. An objective
analysis procedure then utilizes any
observational data to refine the winds. The
model generates gridded fields of spatially
varying fields of temperature, mixing heights,
friction velocity, and other boundary layer
scaling parameters. Profiles of vertically and
horizontally varying turbulence rates also are
computed. To estimate turbulence, CALPUFF
can use measured values,
micrometeorologically scaled parameters
from CALMET, or Pasquill-Gifford dispersion
coefficients. The dispersion parameters are a
continuous function of height that responds to
changes in the underlying surface
characteristics. A puff-splitting option is
available to simulate the effects of vertical
wind shear.
CALPUFF can use the full
meteorology generated by CALMET, or it can
be run in a screening mode using the same
input meteorology that is input to the ISCST
model.
The CALPUFF developers
(http://www.src.com/calpuff/calpuff1.htm))
indicate that CALPUFF may be suitable for
the following applications: near-field impacts
in complex flow or dispersion situations; long-
range transport; visibility assessments and
Class I area impact studies; criteria pollutant
modeling, including application to SIP
development; secondary pollutant formation
and PM modeling; and buoyant area and line
sources. The developer of CALPUFF makes
the model available free of charge with the
signing of an end-user license agreement.
A potential weakness of CALPUFF
may be that it assumes hourly (or longer)
averaging periods and is designed primarily
for long-range transport (receptors more than
1 km from a source. Therefore, it may not be
ideally suited for modeling near-roadway
pollution dispersion.
3.2. Miscellaneous Publicly Available
Models
So-called miscellaneous models are
freely available to the user community and
may be applicable for operational use.
3.2.1. Canyon Plume Box Model,
version 3.6a (CPB3)
CPB3 is designed to simulate mobile
source impacts within an urban street canyon
and narrow highway cut sections (where the
surrounding topography is above the level of
the roadway) for complex site geometries.
The model was developed under the
auspices of the Federal Highway
Administration (FHWA). The current version
of the model is 3.6a. The model can handle a
variety of canyon geometries (width to height)
and has been tested ranging from about 1:4
to 6:1.
CPB3 requires two input files to run.
The first specifies the constants of the
application, and the second specifies the
variables of the model run. The input
constants include street heading, number of
lanes, position of lanes, height of vehicles,
width of lanes, and number of receptors (from
1 to 20). The input variables include wind
speed (miles per second), wind direction
(degrees), wind direction standard deviation
(degrees), global radiation (kilowatts per
square meter), traffic volumes (vehicles per
lane per second), traffic speeds (kilometers
per hour), emission densities (milligrams per
meter per vehicle), observed concentrations
(parts per million), and background
concentrations (parts per million).
The CPB3 dispersion model is
designed to simulate mobile source impacts
within urban street canyon or cut-section
highway environments. The model can
handle a wide variety of canyon/roadway
geometries, including curved geometries,
one-sided "canyons," and semipermeable
canyons (e.g., semiopen parking garages).
The canyon also may be of finite length and
terminated at either or both ends with an
intersection. Emissions for the model must be
provided separately using, for example, the
MOBILE emissions model.
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The CPB3 research dispersion model
is described in the FHWA report, Modification
of Highway Air Pollution Models for Complex
Site Geometries (FHWA, 2002). Although the
model can handle a wide range of canyon
geometries (e.g., canyon width-to-height
ratios from about 6:1 to 1:4 have been
tested), it is suggested that the model may
have limitations if the application geometry
differs appreciably from the simple
rectangular notch canyon having a width-to-
height ratio of 1. The model developers
consider it a research model, rather than a
regulatory model, because it is controlled by
a great many input variables, and all
combinations of these variables in their likely
ranges have not been evaluated to the extent
"usually expected" for a regulatory model.
Input to CPB3 is relatively simple; only
two control files containing time-varying and
time-invariant parameters are required. The
model was designed to predict pollutant
dispersion within a street canyon; therefore,
receptors are limited to the canyon
environment.
3.2.2. Contaminants in the Air from a
Road-Finnish Meteorological Institute
(CAR-FMI)
CAR-FMI models an open-road
network of finite line-source emissions for
inert and reactive (NOxand ozone [O3])
gases, as well as fine particulates (PM2.s)
from vehicle exhaust. Dry deposition is
included for particulates. There is limited
chemistry using the discrete parcel method
for CO, NO, NO2, NOX, O3, and PM2.5.
CAR-FMI requires inputs that are
typical of dispersion models. These include
location of line sources, hourly traffic volumes
for each road, hourly meteorology, hourly
background concentrations, and emission
coefficients.
CAR-FMI is a Gaussian plume model.
The general analytical solution of Luhar and
Patil (1989) (described in Section 3.4 as
GFLSM-LP) is used to solve the dispersion of
gases. Atmospheric boundary layer theory is
used for estimating turbulence parameters.
A finite length source algorithm is used to
estimate concentrations of inert and NOX
reactive pollutant and fine PM. A Windows-
based GUI is available to assist in developing
the inputs and running the model.
A limitation to CAR-FMI is that it is
only applicable to at-grade or near at-grade
roadways. In addition, it currently is not
known whether the model code is available.
One Web site (http://www.mi.uni-
hamburg.de/Car-fmi.336.0.html) indicated
that the code was obtainable from the
developers, but this could not be verified.
3.2.3. Hybrid Roadway Model (HYROAD)
HYROAD integrates three historically
individual modules that simulate the effects of
traffic, emissions, and dispersion. The traffic
module is a microscale transportation model
that simulates individual vehicle movement.
The emission module uses speed
distributions from the traffic module to
determine composite emission factors; spatial
and temporal distribution of emissions is
based on the vehicle operation simulations.
The model tracks vehicle speed and
acceleration distributions by signal phase per
10-m roadway segment for use in both
emissions distribution and for induced flows
and turbulence. The dispersion module uses
a Lagrangian puff formulation, along with a
gridded nonuniform wind and stability field
derived from traffic module outputs, to
describe near-roadway dispersion
characteristics. HYROAD is designed to
determine hourly concentrations of CO or
other gas-phase pollutants, PM, and air toxics
from vehicle emissions at receptor locations
that occur within 500 m of the roadway
intersections.
HYROAD requires simplified
meteorological inputs: wind speed and
direction, standard deviation of the horizontal
wind speed, Pasquill-Gifford stability class, a
mixing height, and ambient temperature. An
ambient background concentration also can
be entered. Multiple dispersion scenarios can
be run to simulate multiple hour simulations.
The model uses a Gaussian puff
approach in which dispersion processes are
affected by vehicle wakes. Methods
developed by EPA and incorporated into
EPA's ROADWAY model (in the mid-1980s),
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as well as some of the puff formulations from
CALPUFF are adapted into HYROAD. The
module creates a 2-D nonuniform wind field
that advects the puffs and enhances vertical
dispersion over the roadway.
HYROAD is a single package with
traffic, emissions, and dispersion components
contained in a single GUI. HYROAD is
primarily an intersection model, but can
simulate a highway link by creating a very
long link between intersections (on and off
ramps). The traffic module of HYROAD
appears to run only under Microsoft Windows
98. To run the complete package requires
installing software that emulates the Windows
98 operating system; therefore, HYROAD
may not be the best option for near-roadway
applications.
3.2.4. Point, Area, Line (PAL)
PAL is a multisource steady-state
Gaussian plume model for nonreactive
gaseous and suspended particulate
pollutants. Developed in the 1980s, its
application is primarily at the urban
microscale environment (up to several
hundred meters) and is included here for
historical perspective. Six source types can
be modeled with PAL: point, area, two types
of line sources (line and slant line), and two
types of curved path sources (curved and
special path). The slant line and curved
special path sources are for modeling
sources in which the end points are at
different heights above ground, such as a
freeway onramp. Options for dry deposition
and gravitational settling are included, but the
model does not perform any chemical
transformations.
There are 13 input "card" types for
PAL that define the program control options,
source data, meteorological data, and
receptors. Each source's emission rates are
constant, but PAL provides a means to vary
the emissions by hour of day for each source
type. Options for the diurnal variations of the
emissions can be input for each source type
for each hour. Up to 99 sources can be
entered in a single model run.
Meteorological data include wind
direction and speed, wind profile exponents,
anemometer height, stability class, mixing
height, and ambient air temperature. Winds
can vary as a function of height or can be
held constant, and the manner in which the
wind speed is varied can be specified by
source type. PAL can process up to 24 h of
meteorology in a single model run.
PAL is a relatively old Gaussian
plume model that assumes steady-state
conditions and nonvarying winds within the
modeling domain, and it does not perform
well in low-wind-speed situations. The model
does not have any provisions for building
downwash and is not appropriate for complex
terrain situations.
3.2.5. Quick Urban & Industrial Complex
(QUIC)
The QUIC modeling system consists
of three "modules:" (1) QUIC-URB, an urban
wind model; (2) QUIC-PLUME, a Lagrangian
dispersion model; and (3) QUIC-GUI, a GUI.
The QUIC system was developed to calculate
wind and concentration fields in cities with
complex clusters of buildings. It is a
diagnostic-empirical system that accounts for
building-induced circulations. This type of
model is not nearly as computationally
intensive as CFD models, which also are
used to simulate dispersion and transport in
cities.
The QUIC-URB wind model is based
on a mass-consistent diagnostic wind model
that computes the 3-D flow field around
buildings. In QUIC-URB, an initial wind field is
prescribed with flow effects associated with
the buildings superimposed on it. Empirical
algorithms are used to determine the initial
wind field at rooftops and upstream
recirculation zones, in the downwind cavity
and the wake of a single building, and in the
street canyons between buildings. The final
flow field is solved by ensuring mass
conservation.
The QUIC-PLUME dispersion model
tracks the movement of individual particles
using the mean wind field calculated by
QUIC-URB and produces the turbulent
dispersion using random-walk equations.
Additional drift terms are included to account
for the heterogeneous nature of turbulence
21
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around the various buildings. Gradient
transport and similarity theory are used to
estimate normal and shear stresses and the
turbulent dissipation. QUIC-PLUME includes
a nonlocal mixing formulation to better
describe the turbulent mixing in building
cavities and wakes. In their modeling using
QUIC, Kastner-Klein and Clark (2004)
implemented a vehicle-induced turbulence
(they use the term traffic-produced
turbulence) parameterization into QUIC-
PLUME.
Although not as accurate as a CFD
model, the QUIC model captures major flow
features with significantly less computational
resources. The model has been validated
against wind tunnel experiments,
observational field experiments (Salt Lake
City URBAN 2000 Tracer Experiment
[Gowhardhan et al., 2006]), and other models
(e.g., FLUENT) with promising results. The
model also comes with a GUI that makes
running the model easier for the user.
The model and documentation are not
readily available to the general public.
Several documents at LANL on the QUIC
system, including PLUME theoretical
formulation and user's guides, are "at
scanning," according to the LANL Web site.
3.3. Proprietary Models
This class of models, although
possessing many advanced features,
currently must be purchased. Because they
possess features and capabilities not
included in the other models, they are
included in this section.
3.3.1. Atmospheric Dispersion Modeling
System (ADMS)-Roads
ADMS-Roads is a "comprehensive
tool for investigating air pollution problems
because of small networks of roads that may
be in combination with industrial sites"
(McHugh et al., 1997). Roadway and
industrial sources (point, area, and volume)
can be modeled together. ADMS-Roads
includes a chemistry module for NOx-to-NO2
conversion and sulfate chemistry. A street
canyon module based on the Danish
Operational Street Pollution Model (OSPM;
see Section 3.3.2) is available. A module for
dispersion in complex terrain is included.
ADMS-Roads also can model the effects of
street canyons, noise barriers, and vehicle
induced turbulence.
ADMS-Roads requires an extensive
number of data inputs: source parameters
(including source locations, road widths,
building canyon heights, stack heights and
diameters, and up to 7,000 road links);
meteorological data; hourly traffic flow;
emission factors; and background
concentrations.
The model's Web site does not
contain detailed information on the technical
aspects of the model. According to
Cambridge Environmental Research
Consultants (CERC), the science of ADMS-
Roads is significantly more advanced than
that of most other air dispersion models (such
as CALINE and ISCST3) in that it
incorporates the latest understanding of the
boundary layer structure and goes beyond
the simplistic Pasquill-Gifford stability
categories method with explicit calculation of
important parameters. The model uses
advanced algorithms for the height
dependence of wind speed, turbulence, and
stability to produce improved predictions. In
addition, ADMS-Roads incorporates CERC's
FLOWSTAR model to calculate changes in
mean flow and turbulence because of terrain
and changes in land use. It has links to
ArcView and Maplnfo GIS packages, as well
as to the Surfer contour-plotting package.
The GIS link can be used to enter and display
input data and display output, usually as color
contour plots. From the brief discussion on
the ADMS-Roads Web site, the technical
formulation appears to be similar to that of
AERMOD.
Several models were compared to
ADMS-Roads for a variety of site conditions
(flat/complex terrain and rural/urban). For the
highest predicted and observed
concentrations, ISCST3 overpredicted by a
factor of seven, on average, whereas ADMS-
Roads and AERMOD underpredicted, on
average, by about 20%. It also was
determined that ADMS performance is
slightly better than AERMOD (Hanna et al.,
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1999). A comparison of ADMS-Roads and
CALINE-4 with measured concentrations
suggested that the ADMS-Roads "line-of-
best-fit" is closer to the target line than
CALINE-4. Both models tended to overpredict
low concentrations and underpredict high
concentrations (Ellis et al., 2001).
3.3.2. Operational Street Pollution Model
(OSPM)
OSPM is a practical street pollution
model that was developed by the National
Environmental Research Institute of
Denmark. Concentrations of exhaust gases
are calculated using a combination of a
plume model for the direct contribution and a
box model for the recirculating part of the
pollutants in the street. The model can be
used for streets with irregular buildings or
buildings on one side only but is best suited
for regular street-canyon configurations. The
model should not be used for crossings or for
locations far from traffic lanes.
The required input data are hourly
values of wind speed, wind direction,
temperature, and global radiation. The two
last parameters are used for calculation of the
chemical transformation of NO-NO2-O3. The
model also requires hourly values of urban
background concentrations of the modeled
pollutants. In addition to the hourly input
parameters, the model requires data on street
geometry and street traffic.
A Microsoft Windows version contains
a user-friendly interface that allows for online
preparation of all required input data and
files. The Windows version, which is
distributed under the name WinOSPM,
contains special modules for preparation and
visualization of traffic data and traffic
emissions.
Concentrations of exhaust gases are
calculated using a combination of a plume
model for the direct contribution and a box
model for the recirculating part of the
pollutants in the street. It is assumed that
both the traffic and emissions are uniformly
distributed across the canyon. It also is
assumed that the canyon vortex has the
shape of a trapezoid, with the maximum
length of the upper edge being half of the
vortex length. The ventilation of the
recirculation zone takes place through the
edges of the trapezoid, but the ventilation can
be limited by the presence of a downwind
building if the building intercepts one of the
edges. The concentration in the recirculation
zone is calculated assuming that the inflow
rate of the pollutants into the recirculation
zone is equal to the outflow rate, and that the
pollutants are well mixed inside the zone. The
turbulence within the canyon is calculated
taking into account the traffic-created
turbulence. The traffic-induced turbulence
plays a crucial role in determining pollution
levels in street canyons. During windless
conditions, the ambient turbulence vanishes,
and the only dispersion mechanism is
because of the turbulence created by traffic.
Therefore, the traffic-created turbulence
becomes the critical factor determining the
highest pollution levels in a street canyon.
The model has been used in a
number of studies and is well documented.
One study concluded that the use of
computed urban background concentrations
as input values to the OSPM model yields a
fairly good agreement with measured data
(Walleniusetal.,2001).
3.3.3. PROKAS
PROKAS consists of two modules:
PROKAS_V and PROKAS_B. PROKAS_V
provides the basic software module, and
PROKAS_B provides enhancements to the
model (e.g., street canyon capabilities).
PROKAS_V is based on the German
guideline VDI 3782/1 "Gaussian Dispersion
Model for Air Quality Management." Modeling
of up to 5,000 line sources (reproduced by
sets of point sources) of a network of streets
is possible. The model accounts for the
traffic-induced turbulence and the influence of
noise protection devices for each street.
Input requirements include street
coordinates, emissions for up to three
pollutants, street-specific dispersion
parameters (to account for near-field flow
disturbances, such as from traffic-induced
turbulence), dispersion parameters, receptor
coordinates, background concentrations,
hourly emissions, and meteorological
23
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statistics (such as 3-D winds and atmospheric
stability).
The model was developed according
to the German Emission Factors Handbook,
coupled with the Gaussian Dispersion Model.
The model also has an interface to
MOBILEV, a German emission factor model.
PROKAS is not applicable when the
wind field is not homogeneous in the area
under consideration or in areas with valley
drainage flows, when the influences of
buildings have to be considered in detail, or
at distances less than 10m from the line
source.
3.4. Miscellaneous Research-Grade
Models
There are various research-grade
dispersion models for near-road applications
developed by universities, private companies,
and government agencies. In this review, we
describe several types of models: Eulerian
grid models, puff models, and finite line-
source flume models.
The Micro-Calgrid model (MCG)
developed by R. Stern and R. Yamartino is a
microscale photochemical model for
applications in complex urban environments
such as street canyons. MCG is a
photochemical model that solves Eulerian
equations of motion with turbulence closure
based on two energy production-dissipation
equations, includes detailed treatment of
vehicle induced turbulence, and has three
chemistry schemes. MCG includes
MOBILEV, a traffic-induced emissions model
from the German Federal Environmental
Agency; MISKAM, a CFD microscale flow
model; and MCG, an Eulerian grid model.
The starting point for the development of
MCG was Calgrid, a second-generation
photochemical model with 3-D advection-
diffusion for each pollutant species. MCG
includes resistance-based dry deposition
rates, CBM-IV and SAPRC-93
photochemistry mechanisms, and a chemical
integration solver. Micro-Calgrid accounts for
vehicle-induced turbulence by considering
energy dissipated by a vehicle as it moves
through the ambient air. The
micrometeorological driver for MCG is
MIKSAM, which is a 3-D flow model for inert
pollutants. The MIKSAM flow model may not
perform well in stagnant or low wind
conditions. Emissions data come from the
German MOBILEV model. Yearly averages or
hourly emissions for on-street or a network of
streets are computed from emission factors
from the German Federal Environment
Agency, street characteristics, traffic activity,
and the composition of the vehicle fleet.
Because MCG is a street canyon model, it
will not be well suited for the generic near-
roadway modeling applications.
The ROADWAY-2 model developed
by NOAA Air Resources Laboratory (Rao,
2002) is a non-steady-state model that
incorporates an atmospheric boundary layer
model with turbulent kinetic energy closure
and up-to-date surface parameterizations to
derive mean and turbulence profiles.
ROADWAY-2 is based on EPA's ROADWAY
model developed by Eskridge and Catalano
(1987). Information on any other user inputs
was not obtainable in the absence of a review
of the source code. The concentration
equations are solved using a fractional-step
finite difference method. Vegetation canopy
flow theory was used to derive vehicle wake
parameterizations. Vehicle wake velocity
deficit is proportional to the square of the
relative wind speed, and the rate of vehicle
wake production of turbulent kinetic energy is
proportional to the cube of the relative wind
speed. ROADWAY-2 provides an advanced
treatment of atmospheric velocity and
turbulence fields. The model was evaluated
using tracer data from the 1975 General
Motors (GM) experiment in Milford, Ml. Model
predictions were in good agreement with the
observed data, with a tendency for slight
underprediction for all orientations of the wind
to the roadway. For winds parallel to the
roadway, which can be difficult to model,
ROADWAY-2 again was in good agreement
with the GM observations, with a tendency for
slight overprediction. The model requires
temperature, wind speed, and wind direction
from two heights, although it can run with
speed and direction from a single height (and
temperature from two levels). These data
should come from instrumentation located
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upwind of the roadway, therefore, data
routinely available from a single height, such
as National Weather Service airport data, are
not suitable for this model. Unfortunately,
there is no user's guide or manual for this
model to assist in developing the necessary
input files.
The PUFFER model was developed
as part of a doctoral dissertation at the
University of Nottingham (UK) to model
vehicular pollutants in an urban street
canyon. The dispersion is based on Gaussian
puff methods but with an extended range of
applicability. The model includes the explicit
effects of individual vehicles as sources of
pollution and turbulence over multiple lanes
of traffic. Each vehicle emits a puff at the start
of a time step, and each puff maintains its
independence of all other puffs (i.e., no
consideration for puffs crossing paths). Local
air flow is the superposition of the ambient
wind and movement of the vehicles. A puff is
influenced only by the wake of the vehicle
directly in front. Inputs to PUFFER include
specifying modeling options, canyon
geometry, number of lanes, meteorology
(wind speed, angle to canyon axis, roughness
length), vehicular data, time step and puff
frequency, and selection of output options. In
addition, emissions are input as part of the
puffer.dat input file. Puffer.dat includes the
number of vehicle types; length, width, and
height of each vehicle type; velocity control
parameter; headway parameters; idling
emission rate; speed-dependent emission
coefficient; and acceleration-dependent
emission coefficient. An advantage of this
model is that it is a puff model applied to
vehicular emissions. Real-time buildup of
traffic can be simulated using the traffic
submodule. An inherent weakness is that
only 1 h at a time can be modeled. Perhaps
the most limiting weakness is that the scripts
to set up the model runs have been lost. No
updates to PUFFER have been published,
and no additional work has been performed
on the model since the results were
published.
The TRAQSIM model was developed
by the University of Central Florida in support
of a doctoral dissertation. TRAQSIM is a puff
model for flat terrain (i.e., topography is not
addressed) that tracks vehicular exhaust
released as individual puffs using modal
emissions factors from CM EM that were
incorporated into a lookup table for
TRAQSIM. TRAQSIM combines traffic,
emissions and dispersion components into an
integrated, graphical framework. TRAQSIM is
applicable for emissions of CO and other
nonreactive pollutants. TRAQSIM has three
modules, each of which has a separate set of
inputs that are entered via GUI:
(1) Traffic/Sources, (2) Dispersion/
Meteorology, and (3) Emissions. The
dispersion module makes use of a Gaussian
puff model that tracks discrete moving
sources, rather than treating highway
sections as line sources. Atmospheric
turbulence is modeled as a function of
Pasquill-Gifford stability class. In an initial
model-to-model validation, Kim et al. (2007)
modeled a simple road link and compared
results from CAL3QHC and TRAQSIM. They
noted that TRAQSIM produced more
"intuitively correct" spatial allocation of
impacts when compared with CAL3QHC
(which used emission factors derived from
TRAQSIM). In a second validation, the two
models were compared using high-quality
field data collected in Denver, CO. A much
more complex roadway representation was
modeled. The results of this comparison
indicate that TRAQSIM performed on par with
CAL3QHC. The Visual Basic interface should
be considered a prototype as it has various
bugs in it that need to be worked out. As the
number of puffs increases, the simulation
slows considerably because no puffs are
removed (although there is an algorithm to
merge puffs). A 1-h simulation requires about
25 to 30 min on a 2.8-GHz Pentium desktop
computer for a free flow section of about
300 m.
The UCD 2001 dispersion model,
developed by the University of California,
Davis, is designed to estimate pollutant
concentrations near at-grade roadways. The
model is intended for use from 3 to 100 m
downwind of the edge of a roadway (Held,
et al., 2003). The UCD 2001 model was
calibrated with one-half of the GM sulfur
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hexafluoride tracer study database and
resulted in a selection of eddy diffusivity
parameters that did not vary with ambient
meteorology. This parameterization is
consistent with several independent studies
that indicate that the atmosphere is well
mixed and neutrally stratified immediately
downwind of a roadway with significant
vehicular activity. User input requirements for
the UCD 2001 model include roadway and
receptor geometry, vehicular data, and
meteorology. Because the application of this
model is limited to within 100 m of the
roadway, and mixing resulting from the
motion of the vehicles is intense near
roadways, atmospheric stability is ignored.
Applications further downwind of a roadway
likely would require rethinking some of the
model's internal parameters and, possibly, its
formulation. It is not known if the model can
be run for multiple hours or if it only runs 1 h
at a time. The UCD 2001 model is based on
the work of Huang (1979) who developed a
non-Gaussian model for turbulent shear flow
that uses "apparent" lateral diffusivity. UCD
2001 model performance was evaluated and
compared with the CALINE3 and CALINE4
dispersion models using the GM database.
UCD 2001 adequately simulates near
parallel, low-wind-speed (<0.5 m/s)
meteorological scenarios, whereas the
CALINE models significantly overpredict most
receptor concentrations for these conditions.
The UCD 2001 model results in
approximately 80% to 90% reduction in
squared residual error when compared with
the CALINE3 and CALINE4 models. In
addition, the UCD 2001 model exhibits better
agreement in simulating the top 40 observed
concentrations than either CALINE model.
Lastly, the UCD 2001 model requires less
user input and modeler expertise than most
roadway dispersion models and should result
in more consistent and robust pollutant field
estimations. The model is not publicly
available, but might be available from the
University of California, Davis. It is not known
if a user's guide or manual exists for this
model.
There are several general finite line-
source models described in multiple research
papers: Luharand Patil (1989), Esplin (1995),
Venkatram and Horst (2006). Chock (1978)
developed an infinite line-source model
(referred to as the GM model) to model
dispersion from roadways. The GM model
overpredicts concentration for upwind
roadway segments that are less than three
times the perpendicular distance from the
roadway to a receptor. Csanady (1972)
developed a model for a finite line source, but
it was applicable only when the wind was
perpendicular to the roadway. Calder (1973)
developed an infinite line-source model that
was more appropriate for winds at an angle to
the roadway.
Luhar and Patil (1989) developed a
simple general finite line-source model
(hereafter referred to as GFLSM-LP) to
overcome constraints imposed by modeling
of infinite line sources. According to the
authors, the GFLSM-LP can handle all
orientations of wind direction relative to a
roadway. Esplin (1995) extended Calder's
work to a finite line source. However, Esplin
reports that the model is not applicable if the
wind direction is within 15° of the orientation
of the roadway (i.e., nearly parallel to the
roadway). Venkatram and Horst (2006) also
report that these models perform poorly as
the wind direction approaches the orientation
of the roadway. None of these models have
chemical transformation capabilities.
In these research-grade models,
concentration estimates are assumed to
follow a generalized Gaussian plume model
formulation. The development of the line-
source formulation includes a transformation
between the line-source coordinate system in
the standard east-west and north-south
orientation and a wind coordinate system in
which the x axis is in the direction of the
mean wind. In this rotated system, the
horizontal and vertical dispersion parameters
(oy and oz) are functions of the downwind
distance and may not be known, so they are
transformed into forms that are functions of
the line-source coordinate system.
In their development, Luhar and Patil
(1989) account for the height of the receptor
above ground in the Gaussian equation. They
also include a ground reflection term, adjust
26
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the mean wind speed with a correction for
traffic wake effects to account for lateral
dispersion when the wind speed approaches
zero or the wind direction is nearly parallel to
the orientation of the roadway, and
incorporate the effects of plume rise in their
final form of the line-source model. In both
models, their form of the vertical dispersion
coefficient introduces a singularity when the
wind is parallel to the road. They use
expressions from the GM model (Chock,
1978) to avoid this complication. Esplin
developed a model that is valid for wind
directions from 0° to 75° when the Gaussian
plume model is used. In his formulation, he
uses a fraction of ground reflection term
rather than explicitly included a term in the
final equation. This fraction is incorporated
into a general term representing all terms that
are not dependent on cross-wind distance.
Venkatram and Horst (2006) derive a model
similar to Esplin (1995), with the main
difference being that the limits of integration
correspond to downwind distance from the
end points of the line segment to the
receptor. This results in oz being evaluated at
an effective distance between the line
segment and the receptor in the rotated
system but allows oy to be evaluated in the
unrotated system. Ganguly and Broderick
(2006) found that predictions from CALINE-4
and GFLSM-LP are very close to each other
and agreed well with monitored data. They
found the main advantage of a GFLSM-LP
"lies in the simplicity of its application as it is
an analytical solution of the Gaussian
equation." A sensitivity analysis conducted by
Ganguly and Broderick (2006) found that the
GFLSM-LP model performs well for neutral
conditions but worse for low wind speed
conditions. This model performs poorly for
winds that are parallel or nearly parallel to a
roadway. Validation tests on this model also
indicate the model underpredicts PM for all
size ranges. Finally, the model is applicable
only to CO and other inert pollutants and PM.
Venkatram et al. (2007) developed a
dispersion model and used it to analyze
measurements made during a field study
conducted by EPA in July and August 2006
to estimate the impact of traffic emissions on
air quality at distances of tens of meters from
an eight-lane highway located in Raleigh, NC.
This dispersion model for road emissions can
be incorporated readily into the current
generation of dispersion models typified by
AERMOD (Cimorelli et al., 2005). Unlike
CALINE (Benson, 1992), which uses stability-
based Pasquill-Gifford dispersion curves, this
model requires micrometeorological inputs
compatible with those of AERMOD. The most
important meteorological input is the standard
deviation of the vertical velocity fluctuations.
In principle, it can be estimated from the type
of measurements customarily required by
current models.
4. SUMMARY
This document has described a
number of emission and air quality models
available for near-road application. The focus
has been on models that are operational or
that have features that could be incorporated
into operational models. Of the emission
models listed above, MOBILE6.2 is the most
widely used, tested, and accepted model.
Although it is not a modal model, it has the
best representation of the vehicle fleet in the
United States. As a future replacement of
MOBILE6.2, EPA is developing MOVES,
which will handle modal emissions. In
addition to capturing modality in traffic activity
patterns, some other future directions are
becoming evident. For example, in comparing
two emission modeling approaches in
Philadelphia, PA, Cook et al. (2006) conclude
that more accurate estimates occur when
traffic demand model data are used at a link
level rather than at an aggregated county
level.
With the exception of including the
effects of vehicular-induced turbulence, near-
road dispersion models have advanced little
over the past two decades. Also, the
commonly applied dispersion model,
AERMOD, contains a very simplistic
algorithm for simulating line sources. For a
modest investment, near-road air quality
models could be upgraded to include a more
accurate line-source algorithm and could be
modified to account for features such as
noise barriers and vegetation, which can
27
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perturb near-field ground-level concentrations
of air pollutants. Furthermore, field,
laboratory, and advanced numerical modeling
studies (using models such as CFD and
QUIC) could be used to evaluate and
parameterize important physical processes
that are not captured in existing operational
models.
The need for further research on
emission and air quality models is further
bolstered by recommendations from a 2006
workshop on PM research needs organized
by FHWA (McCarthy et al., 2005). Two of the
three highest priorities for research are (1) to
evaluate "hot spot" air quality models and
(2) to develop and to evaluate emissions
models. Work on these two priorities arguably
would advance the application of near-road
emission and air quality models.
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