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Dispersion Modeling for 

Mobile Source Applications

Chad Bailey

EPA Office of Transportation and Air Quality

Regional, State, and Local Air Modelers’ Workshop

Philadelphia, PA – May 14, 2009
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Overview

• Upcoming dispersion model applications

– Modeling guidance for highway and transit 

projects under transportation conformity

– National Environmental Policy Act

• Implications for the modeling community

• Issues to address in these applications
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The Nutshell

• Regulatory and NEPA applications of dispersion 

models to roadways and other transportation 

projects are likely to increase over the next 12-

18 months

• EPA is producing guidance this year on 

modeling PM impacts of transportation projects

• The public (and NGOs) are increasingly 

requesting dispersion modeling of traffic-related 

air pollutants as part of NEPA

• Modeling issues that might be coming your way!
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“Near Road” Air Quality

• Concentrations of primary 

pollutants are elevated in 

proximity of major 

roadways, etc.

• “Zone of influence” can 

reach 300-500 meters 

downwind

– Some studies suggest over 

1 km downwind during 

stable conditions

– Steepest gradient closest 

to road

St. Louis, MO 1997

Upwind 

– Downwind PM 

μg/m3

Raleigh, NC, 2006

Lamoree & Turner, 1999

Hagler et al., 2009

Ultrafine particle count (#/cm3)
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Multiple Pollutants

NO2 and air toxics

Particle number

BTEX
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Where We Are:

Growing Body of Health Research

Studies With Keywords "Traffic, Pollution, Epidemiology"

in PubMed Medical Database

0

10

20

30

40

50

60

70

80

90

100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Number of Published Studies

*

* Projected based on studies published to date (dark shade).
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Health Effects

• General agreement in public health literature

– Populations near roadways experience a wide range of adverse 

health outcomes

• Several recent survey articles

– Samet (2007): general public health 

– Adar and Kaufmann (2007): cardiovascular

– Salam et al. (2008): asthma

– Brabeck and Forsberg (2009): allergies

• Remaining questions

– Relevant pollutants / sources

– “Safe distance”

– Effect of standards
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• “Sufficient” evidence to infer a likely causal relation

– Exacerbation of respiratory symptoms in asthmatic children

• “Sufficient” OR “Suggestive but not sufficient” 

– New onset asthma incidence and asthma prevalence in children

• “Suggestive but not sufficient”

– All-cause mortality

– Cardiovascular mortality

– Cardiovascular morbidity

– Lung function

• Inadequate/insufficient evidence to infer the presence or 

absence of a likely causal relation for other endpoints
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2007 American Housing Survey

“Description of Area within 300 Feet”

U.S. Census Bureau, National Data, December 2008

All Housing Unitss 128,203 4,402 123,801 110,692 75,647 35,045 13,109 7,188 8,705

4-or-more-lane highway, railroad, or airport 20,016 265 19,751 17,864 9,361 8,503 1,887 759 1,220

% Near Mobile Sources 15.6% 6.0% 16.0% 16.1% 12.4% 24.3% 14.4% 10.6% 14.0%

Suburbs 58,941 1,720 57,221 52,062 38,164 13,899 5,159 530 2,445

4-or-more-lane highway, railroad, or airport 9,156 89 9,068 8,305 4,660 3,645 763 29 393

% Near Mobile Sources 15.5% 5.2% 15.8% 16.0% 12.2% 26.2% 14.8% 5.5% 16.1%

Central Cities 35906 333 35573 31602 16889 14713 3971 915 701

4-or-more-lane highway, railroad, or airport 6987 45 6942 6213 2596 3617 729 183 195

% Near Mobile Sources 19.5% 13.5% 19.5% 19.7% 15.4% 24.6% 18.4% 20.0% 27.8%

Outside MSAs 33,356 2,349 31,007 27,028 20,594 6,434 3,979 5,743 5,559

4-or-more-lane highway, railroad, or airport 3,873 131 3,742 3,346 2,106 1,241 395 547 631

% Near Mobile Sources 11.6% 5.6% 12.1% 12.4% 10.2% 19.3% 9.9% 9.5% 11.4%

Total Owner Renter Vacant

Characteristics

Total 

housing 

units Seasonal

Year-round

New 

construction 

4 years

Manufactured/ 

Total mobile homes

Occupied
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Upcoming Model Applications

• Transportation conformity

– Modeling guidance for analysis of PM “hot 

spot” impacts from transportation projects

• National Environmental Policy Act

– Air toxics

– Criteria pollutants in areas meeting NAAQS
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Transportation Conformity 

Guidance for Modeling Impacts 

of Transportation Projects
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Transportation Conformity

• Section 176(c) of Clean Air Act

• Applies in nonattainment and maintenance areas for:

– Ozone

– Particulate matter: PM2.5 and PM10

– Carbon monoxide (CO)

– Nitrogen dioxide (NO2)

• Applies to:

– Long-term regional transportation plans

– Shorter-term transportation improvement programs

– Highway and transit projects funded or approved by 

Federal Highway Administration or Federal Transit 

Administration
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Project-level Modeling

• Conformity requires that projects will not cause 

or contribute to a new violation of the NAAQS or 

worsen the frequency or severity of existing 

violations

– Historically, CO dispersion modeling for roadside 

receptors has been widespread

– Following 2006 rulemaking, PM modeling guidance 

for projects to be released to coincide with final 

release of EPA’s new emission model, MOVES

• Scheduled for 12/2009
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Issues to Address

• How to estimate emissions from a project

– MOVES (US) or EMFAC (California-specific)

– Developing local input data

– Focus on “projects of air quality concern”

• Use of dispersion models for projects

– Model recommendations

– Meteorological inputs

– Source characterization
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National Environmental Policy 

Act
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NEPA

• Requires that Federal agencies document 

and consider the environmental impacts of 

major projects

– Environmental Assessments

– Environmental Impact Statements

• Traditionally, has been a place where air 

issues are addressed for transportation 

projects generally
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FHWA Interim Guidance on Air 

Toxics in NEPA

• FHWA issued interim guidance to division offices 

to its field in February 2006 

• Acknowledged that MSATs must be addressed

• 3-tiered approach based on size of project

– No analysis, qualitative analysis, or emissions-only for 

>140k-150k AADT

– Describes dispersion models as limited in 

applicability, performance, and utility for population 

exposure assessment

• FHWA suggests it may be revising interim 

guidance in coming months
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NEPA

• EPA Regions have been commenting on air toxics and 

PM language in NEPA documents

– NEPA / Air Toxics workgroup meets bimonthly

• With increased attention to near-roadway exposure and 

health, public and NGOs expressing greater desire to 

understand impacts of transportation projects

– Highways

– Ports

– Airports

– Railroads

• EPA is working on recommendations for how to best 

model air toxics impacts
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Implications for State, Local, and 

Regional Modelers

• Increased level of modeling activity related to 

transportation projects and roads in general

• Modelers may be asked to

– Review and comment on modeling protocols

– Provide information for analyses (e.g. background)

• In advance of increased activity, could be useful to

– Familiarize state air managers with near-road applications

– Establishing channels of communication in advance of increased 

activity may be useful

• EPA Regional mobile source staff

• State transportation and transit agencies

• Metropolitan planning organizations
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Modeling Issues for 

Transportation Projects
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Key Issues

• Traffic characterization

• Emission characterization

• Air quality model choice

• Meteorological data

• Land use / surface characteristics

• Variables most influential on modeled 

concentrations
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Traffic Characterization

• Often, traffic data lack hourly detail

– Usually includes peak hour traffic flow and annual average daily 

traffic (AADT)

– Often includes 4 periods: AM peak/mid-day/PM peak/night

– Hourly emissions may require additional assumptions

• Focus generally on total traffic, not heavy-duty trucks

– Could be important for diesel-related pollutants

• Projected future travel demand can depend on land use 

changes in response to project

– May be outside the scope (and authority) of project sponsor

– Typically this is a metropolitan planning organization function, 

but affected by many factors
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Emission Characterization

• Current emission factor model is MOBILE6.2

– Pollutants: HC, CO, NOx, PM, Toxics, CO2

– Emission factors (g/mi) depend on:

• Vehicle type (e.g., light-duty gas vehicle, heavy-duty diesel truck)

• Vehicle model year distribution

• Average speed of traffic / Roadway class

• Fuel type and characteristics

• Meteorology (i.e., temperature, humidity)

• Local emission inspection & maintenance programs

• EPA released a draft version of its new emission model, MOVES in 

April 2009, with final release planned by end of 2009

– PM emissions higher

– Much greater flexibility in inputs, including driving patterns

– High degree of flexibility will call for greater attention to data quality
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Air Quality Models for 

Transportation Projects
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Air Quality Models

• Current guideline models

– Appendix W lists CALINE-3 as the preferred model for roadways

– CAL3QHC is specified for modeling CO from intersections

– Accept 1 hour of meteorological input

• Recent policy and health considerations require 

consideration of longer-term averages

– For instance, annual average exposure for air toxics or relevant 

design values for PM2.5 or PM10 NAAQS

– CAL3QHCR accepts one year’s surface meteorology, and has 

been used in research publications, but employs model code 

from ISCST2

– AERMOD can be used to model line sources, but treatment of 

traffic-induced turbulence is presently an “off-model” exercise
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Common Model Applications for 

Mobile Sources

• CAL3QHC/R, CALINE3/4

– Roadway and intersections

• AERMOD (or ISCST3 before it)

– Intermodal freight terminals

– Bus garages

– Rail terminals and rail lines

– Complex source mixtures

• Terminals, ports, and roadways together

• Large highway corridors

• “Urban scale” modeling
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Meteorological Data Issues

• Representativeness of surface stations

• Large transportation projects can induce 

changes to land use and population maps

– Changes in surface characteristics?

– Changes in urban heat island?
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AERMOD Sensitivity to Surface 

Characteristics

• Recent study by Brode et al. (2008) 

suggests that for non-buoyant, short 

stacks, AERMOD predictions at nearby 

receptors are sensitive to surface 

characteristics

– In particular, roughness length

– Lower sensitivity to Bowen Ratio and albedo
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Road Network Influences on 

Surface Characteristics

• Satellite-based land cover 

information may not 

detect road network 

impacts on land

• RDU example, where 

road maps superimposed 

on NLCD

– 8.7% less forest area

– 11.4% more forest edge

– 42.3% more forest patches

– 35.9% reduction in 

average forest patch area

• How important is this?

Riiters et al. (2004)
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Transportation Project Impacts on 

Land Use

• Phenomenon not frequently included in urban or 

project planning, but increasing in frequency

• Example

– Application of UrbanSIM model in Salt Lake City, UT 

metropolitan area (Wasatch Front Regional Council)

– Model simulates changes in land prices, developer 

decisions, and residential and business location 

choices resulting from existing land use and travel 

time throughout a region
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Impacts of Highway on Household 

Growth within Metropolitan Area

Residential Units per Acre

Waddell et al., 2003
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Thoughts on Land Use

• Even in current years, roads influence interpretation of 

land cover information

• In future, transportation projects may exert significant 

impacts on land use

– “Before and after” changes on surface characteristics?

– Changes in population affect extent of urban heat island?

• Appreciate your thoughts

– Is “tweaking” land use data feasible and/or easy?

– Is prognostic meteorological modeling for individual 

transportation projects appropriate or overkill?

– Could AERSCREEN provide a first-order way to identify when 

land use impacts on dispersive properties of the atmosphere 

could be significant?
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Source Characterization in 

AERMOD

• Concentrations can 

depend on choice of area 

or volume source

– New York DEC (2007 

workshop)

– Receptor location 

determine sensitivity of 

concentration to choice of 

volume/area source 

• May be relevant in 

representation of roads, 

parking lots, freight 

terminals
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Source Characterization

• Accounting for traffic 

induced turbulence

– CAL3QHC: simple “mixing 

zone” algorithm

– AERMOD: “off-model” 

exercise with area or 

volume source 

specification

• GM sulfate experiment in 

1975 shown on right

– 5462 cars per hour

– 80 km/h

– All light duty

Chock, 

1977
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As of Today...

• For simpler, roadway-only sources, 

CALINE/CAL3QHCR series of models explicitly 

addresses traffic-induced turbulence

• For AERMOD applications, need to determine

– Source characterization that results in best model 

performance

– Recommended method for addressing vehicleinduced turbulence using current versions

• Thoughts and studies welcome!
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Influential Inputs and Assumptions

• Overall, understanding inputs to which 

model results are most sensitive will help 

in understanding value of information from 

various inputs, increase model 

performance, and analyst efficiency

– Inputs in traffic characterization

– Inputs in emissions characterization

– Meteorological data

– Surface characteristics
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Questions?

bailey.chad@epa.gov

734-214-4954