1 

# 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, 20092 

# 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 applications3 

# 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!4 

# “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 1997Upwind–Downwind PM**μ**g/m3 Raleigh, NC, 2006 Lamoree & Turner, 1999 Hagler et al., 2009Ultrafine particle count (#/cm3)5 

# Multiple Pollutants NO 2 and air toxics Particle number BTEX6 

# 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 2009Number of Published Studies *

* Projected based on studies published to date (dark shade).7 

# 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 standards8 

### • “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 endpoints9 

# 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% Vacant Total Owner Renter Characteristics Total housing

units Seasonal Year-round New construction 4 years Manufactured/ mobile homes Total Occupied10 

# 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 NAAQS11 

# Transportation Conformity Guidance for Modeling Impacts of Transportation Projects12 

# 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 (NO 2

### ) • 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 Administration13 

# 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 12/200914 

# 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 characterization15 

# National Environmental Policy Act16 

# 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 generally17 

# 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 months18 

# 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 impacts19 

# 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 organizations20 

# Modeling Issues for Transportation Projects21 

# Key Issues • Traffic characterization • Emission characterization • Air quality model choice • Meteorological data • Land use / surface characteristics • Variables most influential on modeled concentrations22 

# 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 factors23 

# 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 quality24 

# Air Quality Models for Transportation Projects25 

# 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 PM 2.5 or PM 10 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” exercise26 

# 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” modeling27 

# 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?28 

# 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 albedo29 

# 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)30 

# 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 region31 

# Impacts of Highway on Household Growth within Metropolitan Area Residential Units per Acre Waddell et al., 200332 

# 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?33 

# 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 terminals34 

# 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, 197735 

# 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!36 

# 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 characteristics37 

# Questions? bailey.chad@epa.gov 734-214-4954