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Dispersion Modeling for Mobile Source Applications
Chad BaileyEPA Office of Transportation and Air Quality
Regional, State, and Local Air Modelers’ WorkshopPhiladelphia, 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
Upw
ind
–D
ownw
ind
PM
µg/
m3
Raleigh, NC, 2006Lamoree & Turner, 1999
Hagler et al., 2009
Ultr
afin
e pa
rticl
e co
unt (
#/cm
3 )
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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
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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
Nu
mb
er o
f P
ub
lish
ed S
tud
ies
*
* 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 8,7054-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 2,4454-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 7014-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 5,5594-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%
VacantTotal Owner Renter
CharacteristicsTotal
housing units Seasonal
Year-round
New construction
4 yearsManufactured/ mobile homesTotal
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 vehicle-
induced 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
