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 

1

Atmospheric Modeling Division, National Exposure Research Laboratory, 

Office of Research and Development, U.S. Environmental Protection Agency 2

MACTEC Engineering and Consulting, Inc. 
 

 ii

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. 
 

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). 

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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 

1
 
for average vehicle speeds over different input requirements of the air quality model. 

driving cycles. The emission factors are This review of nine emission models is 

combined with vehicle activity data, usually in separated into current operational models 

the form of distance traveled, to estimate and research-grade models. The emissions 

emissions. Then, the resulting emission models, their developers and reference web 

inventory or emission factors are used as sites are listed in Table 1. 

inputs to air quality models, depending on the 

Table 1. Summary of Emission Models 

Model Name Developer URL 

Current Operational Models 

Consolidated 

Community Emissions 

Processing Tool 

(CONCEPT) 

Lake Michigan Air 

Director’s Consortium/ 

Midwest Regional 

Planning Organization 

http://www.conceptmodel.org/ 

Emission Factors 

(EMFAC) 

California Air 

Resources Board 

http://www.arb.ca.gov/msei/onroad/latest_version.htm 

MOBILE6.2 U.S. EPA http://www.epa.gov/otaq/m6.htm 

Motor Vehicles 

Emissions Simulator 

(MOVES) 

U.S. EPA http://www.epa.gov/otaq/ngm.htm 

Research-Grade and European Models 

Comprehensive Modal 

Emissions Model 

(CMEM) 

University of California, 

Riverside http://pah.cert.ucr.edu/cmem/ 

COPERT European Environment 

Agency http://lat.eng.auth.gr/copert/ 

Microscale Emission 

Factor (MicroFac) 

Dr. R. Singh (University 

of Waterloo)/U.S. EPA http://dx.doi.org/10.1016/j.atmosenv.2006.04.012 

Mobile Emissions 

Assessment System 

for Urban and 

Regional Evaluation 

(MEASURE) 

Georgia Institute of 

Technology/U.S. EPA 

http://gtresearchnews.gatech.edu/reshor/rh-spr99/tremis.html 

Transportation 

Analysis Simulation 

System (TRANSIMS) 

Los Alamos National 

Laboratory http://tmip.fhwa.dot.gov/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 crossreference 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 linkbased 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 linkbased 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 vehiclemiles-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 MOBILE5 

vehicle classes. Therefore, all incoming 

activity data must be allocated to these eight 

classes, which require a file that crossreferences the activity data to the MOBILE5 

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-perday 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 lightduty 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 gridspecific 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.gov/ports/ports.htm; 

http://www.arb.ca.gov/railyard/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 Statelevel 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, PART5. There are several 

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Table 2. Mobile Emission Type Classifications 

Number Abbreviation Description Pollutantsa Vehicle Classes 

1 Running Exhaust running emissions 

All except tire 

and brake 

wear 

particulate 

All 

2 Start Exhaust engine start emissions 

(trip start) 

HC, CO, NOx, 

toxics 

Light-duty 

vehicles and 

motorcycles 

3 Hot Soak Evaporative hot soak emissions 

(trip end) HC, BZ, MTBE 

Gasoline, 

including 

motorcycles 

4 Diurnal Evaporative diurnal emissions 

(heat rise) HC, BZ, MTBE 

Gasoline, 

including 

motorcycles 

5 Resting Evaporative resting loss 

emissions (leaks and seepage) HC BZ, MTBE 

Gasoline, 

including 

motorcycles 

6 Run Loss Evaporative running loss 

emissions HC, BZ, MTBE 

Gasoline, 

including 

motorcycles 

7 Crankcase Evaporative crankcase emissions 

(blow-by) HC 

Gasoline, 

including 

motorcycles 

8 Refueling Evaporative refueling emissions 

(fuel displacement and spillage) HC, BZ, MTBE 

Gasoline, 

including 

motorcycles 

9 Brake Wear Particulate matter from brake 

component wear 

Brake wear 

particulate All 

10 Tire Wear Particulate matter from tire wear Tire wear 

particulate All 

a

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 and takes little, if any account of superfactors in MOBILE6.2, primarily the result of emitters.” It also is thought that the database 

carrying over the algorithms from PART5, the used to develop emission factors does not 

previous PM emission factor model. Delucchi “include a representative number of old, 

(2000) found that PART5 “underestimates malfunctioning, poorly tuned, or inherently 

emissions from real on-road vehicles, high emitting vehicles.” He also notes that 

primarily because PART5 seems to be based real PM emissions may be higher by a factor 

on low-mileage, properly functioning vehicles, of two than those estimated by PART5. PM 

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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 PART5 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 realworld 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 brakewear 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 

8 
 

 

 

 

 

 

 

 

 

 

 2

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-topump) 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-topump). 

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 

9 
 3

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 commandline interface executables were created for 

light-duty gasoline and heavy-duty diesel 

vehicles. Data input is handled via a Javabased 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 longrange 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 
 

 

 

 

 

 

 4

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 vehiclegenerated contributions to PM2.5 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 NOx with 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 MOBILE5a 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 
 

 

 

 

 

 

 

 

 

 

 

 5

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 linesource 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 
 

 

 

 6

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-ε 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/elearning/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 trafficrelated 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 
 7

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 nearroadway 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-ofthe-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 

14 
 015 

Table 3. Summary of Dispersion Models 

Model Name Developer URL 

U.S. EPA Regulatory Models 

AMS/EPA Regulatory Model (AERMOD) U.S. EPA, AMS http://www.epa.gov/scram001/dispersion_prefreCALINE-4 California Department of 

Transportation http://www.dot.ca.gov/hq/env/air/index.htm 

CAL3QHC/CAL3QHCR U.S. EPA http://www.epa.gov/scram001/dispersion_prefreCalifornia Puff Model (CALPUFF) 

Sigma Research Corporation/ TRC Environmental Corporation http://src.com/calpuff/calpuff1.htm 

Miscellaneous Publicly Available Models 

Canyon Plume Box Model, version 3.6a (CPB3) Federal Highway Administration http://www.tfhrc.gov/structur/pubs/02036/intro.h

Contaminants in the Air from a Road-Finnish Meteorological Institute (CAR-FMI) 

Finnish Meteorological Institute http://www.fmi.fi/research_air/air_14.html 

Emissions and Dispersion Modeling System (EDMS) Federal Aviation Administration http://www.faa.gov/about/office_org/headquarte

edms_model/ 

Hybrid Roadway Model (HYROAD) SAI/ICF Consulting, Inc. http://www.epa.gov/scram001/dispersion_alt.htm Point, Area, Line (PAL) U.S. EPA http://www.epa.gov/scram001/models/other/altm

Quick Urban & Industrial Complex (QUIC) 

Los Alamos National Laboratory in collaboration with the University of Utah and the University of Oklahoma 

http://www.lanl.gov/projects/quic/index.shtml 

Atmospheric Dispersion Modeling System (ADMS)­ROADS 

Cambridge Environmental Research Consultants (CERC) 

www.cerc.co.uk/software/admsroads.htm Cost: Approximately $3,700 (annual, single use

Operational Street Pollution Model (OSPM) 

National Environmental Research Institute of Denmark 

http://www2.dmu.dk/1_viden/2_Miljoetilstand/3_luft/4_spredningsmodeller/5_OSPM/5n.asp Cost: Approximately $2,700 

PROKAS Lohmeyer Consulting Engineers, Inc. (German firm) http://www.lohmeyer.de/air-eia/models/prokas.h

Cost: Approximately $1,876 for PROKAS_B and PROKAS_V 
 116 

Table 3. Summary of Dispersion Models (cont’d.) 

Model Name Developer URL 

Miscellaneous Research-Grade Models 

Micro-Calgrid Model (MCG) R. Stern and R. Yamartino http://www.ivu-umwelt.de/front_content.php?idcROADWAY-2 NOAA Air Resources Laboratory http://www.springerlink.com/index/N07515J23RPUFFER University of Nottingham (UK) http://linkinghub.elsevier.com/retrieve/pii/S0167

TRAQSIM University of Central Florida http://cee.ucf.edu/labs/air_quality/SoftwareMain

UCD 2001 University of California, Davis http://pubs.its.ucdavis.edu/publication_detail.ph
 

 

 

 

 

 

 

 

 

 

 

 0

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 

(Hanna et al., 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, σz. 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, 

17 
 

 

 

 

 

 

 

 

 

 1

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 10 m 

above or below local topography. When 

compared to measured data, CALINE-4 

results show a lower correlation than ADMSRoads, 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 linesource 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 10 m 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 longrange transport of pollutants and their 

impacts on Class I areas and on a case-bycase 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. 

18 
 

 

 

 

 

 

 

 2

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; longrange 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. 

19 
 

 

 

 

 

 3

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-toheight 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 (PM2.5) 

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 Windowsbased 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.unihamburg.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), 

20 
 

 

 

 

 

 4

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 
 

 

 

 

 

 

 

 

 

 

 5

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 QUICPLUME. 

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 ADMSRoads 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 MapInfo 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 ADMSRoads and AERMOD underpredicted, on 

average, by about 20%. It also was 

determined that ADMS performance is 

slightly better than AERMOD (Hanna et al., 

22 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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1999). A comparison of ADMS-Roads and 

CALINE-4 with measured concentrations 

suggested that the ADMS-Roads “line-ofbest-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 

(Wallenius et al., 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 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 7

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 10 m 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 linesource 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 advectiondiffusion 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 nearroadway 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, MI. 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 

24 
 

 

 

 

 8

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 CMEM 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 

25 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 9

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 linesource models described in multiple research 

papers: Luhar and 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 linesource 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 

(σy and σz) 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 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 10

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 σz being evaluated at 

an effective distance between the line 

segment and the receptor in the rotated 

system but allows σy 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 stabilitybased 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, nearroad 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 
 11

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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