Regulatory Impact Analysis
 

Control of Hazardous Air Pollutants from
 

Mobile Sources 
 

Chapter 3
 

Air Quality and Resulting Health and
 

Welfare Effects of Air Pollution from 
 

Mobile Sources
 

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

EPA420-R-07-002 

February 2007 
Final Regulatory Impact Analysis 

Chapter 3: Table of Contents 

Chapter 3: Air Quality and Resulting Health and Welfare Effects of Air Pollution from Mobile 
 

Sources............................................................................................................................................ 3 
 

3.1 Air Quality and Exposure Measurements ...................................................................... 3 
 

3.1.1 Ambient Monitoring.................................................................................................. 3 
 

3.1.2 Population-Based (Representative) Exposure Measurements................................... 9 
 

3.1.3 Elevated Concentrations and Exposures in Mobile Source-Impacted Areas.......... 12 
 

3.1.3.1 Concentrations Near Major Roadways............................................................... 14 
 

3.1.3.1.1 Particulate Matter................................................................................. 14 
 

3.1.3.1.2 Gaseous Air Toxics.............................................................................. 15 
 

3.1.3.2 Exposures Near Major Roadways ...................................................................... 16 
 

3.1.3.2.1 In Vehicles ........................................................................................... 16 
 

3.1.3.2.2 In Homes and Schools.......................................................................... 19 
 

3.1.3.2.3 Pedestrians and Bicyclists.................................................................... 21 
 

3.1.3.3 Concentrations and Exposure in Homes with Attached Garages....................... 22 
 

3.1.3.4 Concentrations and Exposure in Parking Garages ............................................. 25 
 

3.1.3.5 Concentrations and Exposure at Service Stations .............................................. 28 
 

3.1.3.6 Occupational Exposure....................................................................................... 29 
 

3.1.4 	 Uncertainties in Air Toxics Measurements............................................................. 31 
 

3.2 Modeled Air Quality, Exposures, and Risks for Air Toxics........................................ 31 
 

3.2.1 National-Scale Modeled Air Quality, Exposure, and Risk for Air Toxics.............. 31 
 

3.2.1.1 Air Quality Modeling ......................................................................................... 34 
 

3.2.1.1.1 Methods................................................................................................ 34 
 

3.2.1.1.2 Air Quality Trends for Air Toxics: Reference Case ............................ 35 
 

3.2.1.1.3 	 Distributions of Air Toxic Concentrations across the U. S.: Reference 
 

Case 38 
 

3.2.1.1.4 Impacts of Controls on Ambient Concentrations................................. 39 
 

3.2.1.2 Exposure and Risk Modeling ............................................................................. 44 
 

3.2.1.2.1 Methods................................................................................................ 44 
 

3.2.1.2.2 Exposure and Risk Trends for Air Toxics: Reference Case ................ 48 
 

3.2.1.2.3 Distributions of Air Toxics Risk across the U. S.: Reference Case..... 57 
 

3.2.1.2.4 	 Impacts of Controls on Average Inhalation Cancer Risks and 
 

Noncancer Hazards........................................................................................ 67 
 

3.2.1.3 Strengths and Limitations................................................................................... 74 
 

3.2.1.4. Perspective on Cancer Cases ............................................................................. 77 
 

3.2.2 Local-Scale Modeling ............................................................................................. 79 
 

3.3 Ozone ................................................................................................................................. 83 
 

3.3.1 	 Science of Ozone Formation ................................................................................... 84 
 

3.3.2 	 Health Effects of Ozone .......................................................................................... 85 
 

3.3.3 	 Current 8-Hour Ozone Levels................................................................................. 86 
 

3.3.4 	 Projected 8-Hour Ozone Levels .............................................................................. 88 
 

3.3.4.1 CAIR Ozone Air Quality Modeling ................................................................... 88 
 

3.3.4.2 Ozone Response Surface Metamodel Methodology .......................................... 93 
 

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Final Regulatory Impact Analysis 

3.3.4.3 Ozone Response Surface Metamodel Results .................................................... 96 
 

3.3.5 Environmental Effects of Ozone Pollution ............................................................. 97 
 

3.3.5.1 Impacts on Vegetation........................................................................................ 97 
 

3.4 Particulate Matter............................................................................................................... 99 
 

3.4.1 Science of PM Formation........................................................................................ 99 
 

3.4.2 Health Effects of Particulate Matter...................................................................... 100 
 

3.4.2.1 Short-Term Exposure Mortality and Morbidity Studies .................................. 100 
 

3.4.2.2 Long-Term Exposure Mortality and Morbidity Studies................................... 101 
 

3.4.2.3 Roadway-Related Pollution Exposure.............................................................. 102 
 

3.4.3 Current and Projected PM Levels ......................................................................... 102 
 

3.4.3.1 Current PM2.5 Levels ....................................................................................... 102 
 

3.4.3.2 Current PM10 Levels........................................................................................ 103 
 

3.4.3.3 Projected PM2.5 Levels ..................................................................................... 104 
 

3.4.3.3.1 PM Modeling Methodology............................................................... 104 
 

3.4.3.3.2 Areas at Risk of Future PM2.5 Violations........................................... 104 
 

3.4.4 Environmental Effects of PM Pollution ................................................................ 107 
 

3.4.4.1 Visibility Impairment ....................................................................................... 107 
 

3.4.4.1.1 Current Visibility Impairment............................................................ 108 
 

3.4.4.1.2 Current Visibility Impairment at Mandatory Class I Federal Areas.. 108 
 

3.4.4.1.3 Future Visibility Impairment ............................................................. 109 
 

3.4.4.1.4 Future Visibility Impairment at Mandatory Class I Federal Areas.... 109 
 

3.4.4.2 Atmospheric Deposition................................................................................... 110 
 

3.4.4.2.1 Heavy Metals ..................................................................................... 111 
 

3.4.4.2.2 Polycyclic Organic Matter ................................................................. 112 
 

3.4.4.3 Materials Damage and Soiling ......................................................................... 112 
 

3.5 Health and Welfare Impacts of Near-Roadway Exposure ......................................... 113 
 

3.5.1 Mortality................................................................................................................ 114 
 

3.5.2 Non-Allergic Respiratory Symptoms.................................................................... 116 
 

3.5.3 Development of Allergic Disease and Asthma ..................................................... 117 
 

3.5.4 Cardiovascular Effects .......................................................................................... 118 
 

3.5.5 Birth Outcomes ..................................................................................................... 119 
 

3.5.6 Childhood Cancer.................................................................................................. 120 
 

3.5.7 	 Summary of Near-Roadway Health Studies ......................................................... 121 
 

Appendix 3A: Influence of Emissions in Attached Garages on Indoor Air Benzene 
 

Concentrations and Human Exposure......................................................................................... 124 
 

Appendix 3B: 8-Hour Ozone Nonattainment ............................................................................. 144 
 

Appendix 3C: PM Nonattainment .............................................................................................. 159 
 

Appendix 3D: Visibility Tables.................................................................................................. 162 
 

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Final Regulatory Impact Analysis 

Chapter 3: Air Quality and Resulting Health and Welfare Effects of 

Air Pollution from Mobile Sources 

3.1 Air Quality and Exposure Measurements 

3.1.1 Ambient Monitoring 

Ambient air toxics data are useful for identifying pollutants of greatest concern, areas of 

unhealthy ambient air toxics concentrations, and air toxics trends; evaluating and improving 

models; and assessing the effectiveness of air toxics reduction strategies. Ambient air toxics data 

though have limitations for use in risk assessments. While EPA, states, tribes, and local air 

regulatory agencies collect monitoring data for a number of toxic air pollutants, both the 

chemicals monitored and the geographic coverage of the monitors vary from state to state.1 In 

recent years, the US EPA and states have initiated more extensive monitoring of air toxics to 

assist in air pollution management through measurement and mitigation.2 EPA is working with 

its regulatory partners to build upon the existing monitoring sites to create a national monitoring 

network for a number of toxic air pollutants. The goal is to ensure that those compounds that 

pose the greatest risk are measured. In 2004, EPA published a draft National Air Toxics 

Monitoring Strategy to advance this goal.3 The National Air Toxics Trends Station (NATTS) 

monitoring network is currently in place, consisting of 23 sites in 22 urban areas nationally.4 

The available monitoring data help air pollution control agencies track trends in toxic air 

pollutants in various locations around the country. EPA conducted a pilot city monitoring 

project in 2001 that included sampling in four urban areas and six small city/rural areas (see 

Figure 3.1-1). This program helped answer several important national network design questions 

(e.g., sampling and analysis precision, sources of variability, and minimal detection levels). 

Figure 3.1-1. Map of Ten Cities in Monitoring Pilot Project 

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Final Regulatory Impact Analysis 

Building on the pilot program, the US EPA and states established a national air toxics 

monitoring program beginning with a 10-city pilot program, which now consists of the NATTS, 

and numerous community-scale monitoring studies.5 To guide development of the monitoring 

program, a qualitative data analysis project was begun in 2001 and the first phase was completed 

in 2004. The analysis showed that typical urban concentration ranges for most VOCs are 

approximately an order of magnitude (or more) higher than the background concentrations. 

Because air toxics concentrations vary spatially, other monitoring networks are needed to 

provide additional, especially rural, concentrations. Extrapolation for most air toxics beyond the 

urban scale is not recommended without a network of rural measurements capable of capturing 

gradients between urban and rural areas. For the latest information on national air toxics 

monitoring, see www.epa.gov/ttn/amtic/airtxfil.html. 

Figure 3.1-2 shows measurements of benzene taken from 95 urban monitoring sites 

around the country. These urban areas generally have higher levels of benzene than other areas 

of the country. Measurements taken at these sites show, on average, a 47% drop in benzene 

levels from 1994 to 2000. During this period, EPA phased in new (so-called “tier 1”) car 

emission standards; required many cities to begin using cleaner-burning gasoline; and set 

standards that required significant reductions in benzene and other pollutants emitted from oil 

refineries and chemical processes. 

Figure 3.1-2. Ambient Benzene, Annual Average Urban Concentrations, Nationwide, 1994­

2000 

Following is a summary of analyses recently performed on ambient measurements of air 

toxics to identify pollutants and geographic areas of concern and to evaluate trends. Use of 

monitoring data to evaluate and improve models is discussed in Section 3.2. 

EPA recently completed a study of the spatial and temporal trends in ambient air toxics 

data within the NATTS and other networks from 1990-2003.6

 Most data came from urban 

monitors. Nationally, citywide average annual concentrations of benzene, formaldehyde, and 

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Final Regulatory Impact Analysis 

acetaldehyde, varied by about a factor of five, and 1,3-butadiene by more than 10 times. The 

coefficient of variationa of annual average concentrations between different monitors within the 

same city averaged 0.37 for benzene, 0.45 for 1,3-butadiene. Between cities, the coefficient of 

variation could vary substantially. Different pollutants showed different seasonal trends, with 

average concentrations of benzene and 1,3-butadiene being highest in colder seasons, while 

average concentrations of formaldehyde and acetaldehyde were higher during warm seasons, 

reflecting the high photochemical production of aldehydes. The concentrations of benzene, 

butadiene, and acetaldehyde fell substantially over different time periods. From 1990-2003, 

benzene concentrations fell by 57%. Insufficient data existed in earlier years to analyze 1,3­

butadiene and acetaldehyde. Formaldehyde increased by 134% over this period, although 

changes in sampling methodology at some sites around 1995 make this quantification suspect. 

From 1995-2003, the average annual changes in benzene, 1,3-butadiene, formaldehyde, and 

acetaldehyde were -47%, -54%, +11%, and -12%. From 1998-2003, the changes were -21%, ­

46%, +17%, and -4%, respectively. For benzene, these trends were statistically significant, but 

formaldehyde and acetaldehyde trends after 1995 were not. 

One recent publication evaluated the trends in ambient concentrations of benzene and 

1,3-butadiene in the Houston, TX metropolitan area.7 Using data from two air monitoring 

networks, a state-based network and the Photochemical Assessment Monitoring Systems, the 

study constructed a statistical model, controlling for meteorology and seasonality, to evaluate 

trends in ambient toxics over the 1997-2004 time period. Averaged over state monitoring sites 

with data across the time period, the model estimated 1.7% and 3.7% average annual decrease in 

ambient benzene and 1,3,-butadiene, respectively. Mobile source and point source emission 

reductions contributed roughly equally to this change. Examining long-term average 

concentration differences across monitoring sites, benzene varied by roughly two-fold across 

monitors while 1,3-butadiene varied roughly six-fold across monitors. This may be attributable 

to the substantial contribution of industrial sources to the local 1,3-butadiene inventory, while the 

benzene inventory is dominated by mobile sources. The study also evaluated differences in 

weekday and weekend concentration, with the model predicting significant meteorologicallyadjusted concentration weekday increases relative to weekend only during the 6-9 A.M. morning 

rush hour period. 

A recent study from San Francisco, CA evaluated trends in ambient benzene emissions 

and air quality throughout the 1990’s.8 The study noted substantial decreases in benzene 

emissions and ambient concentrations. Unique to the study was the attribution of components of 

these reduction to specific regulatory programs related to vehicles and fuels. In particular, the 

study attributed a 1-year drop of 54% in benzene emission rates to a combination of the 

introduction of California phase 2 RFG (attributed a 50% decrease) and fleet turnover (attributed 

a 4% decrease). During the same year (1995-1996), a 42% reduction in the ambient 

concentration of benzene was also observed. Fleet turnover effects were shown to be cumulative 

over time. The study indicates that in San Francisco both fuel and vehicle effects are important 

a

 A “coefficient of variation” is a measure of variability, and for a set of data is defined as the standard deviation 

over the mean. 

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Final Regulatory Impact Analysis 

contributors to changes in emissions and ambient concentrations of benzene. 

New York State has a systematic program in place that has been measuring air toxics 

since the 1990s.9

 The network of monitors is located throughout urban, industrial, residential 

and rural locations. The New York State Department of Environmental Conservation recently 

examined the spatial and temporal characteristics of benzene by analyzing five of the 32 total 

network sites across the state (see Table 3.1-1). Spatial trends show a wide range of annual 

average benzene concentrations, with the lowest value at a rural site and the highest at an 

industrial site. The recent 3-year period of 2001-2003 was also compared with the longer 1990­

2003 period. The 3-year period exhibits a decrease in mean concentration compared to the entire 

period, indicating that benzene concentrations are decreasing over New York State throughout 

this period. The mean annual rate of change in the period 1990 to 2003 was determined using 

linear regression and moving average (KZ filter) on the concentration data. The analysis 

indicated that site-specific ambient concentration levels of benzene decreased by 50% or more 

during 1990 to 2003. These decreases occurred in ozone nonattainment areas that had 

reformulated gasoline (RFG) requirements as well as in the rest of the state. The downward 

trend can be attributed to regulatory measures aimed at reducing toxic emissions from industrial 

sources, replacement of older higher emitting vehicles with vehicles meeting more stringent EPA 

standards for hydrocarbon emissions, as well as the adoption of RFG in 1995 and 1999 for the 1­

hour ozone nonattainment areas in New York State. Since trends were observed for sites that 

were not part of the RFG program, decreases may also be attributed to the improvement in 

vehicle emissions technology and the state-wide adoption of the California Low Emission 

Vehicle program. 

A similar downward trend was observed in California. In California, the Air Resources 

Board (ARB) maintains an Almanac of Emissions and Air Quality.10 The Almanac summarizes 

statewide emissions, statewide annual average concentrations (calculated as a mean of monthly 

means), and statewide average health risks for selected air toxics. Currently there are data 

available for ten air toxics in California, including benzene. The ARB network consists of 18 air 

quality monitoring stations. The data collected, analyzed, and reported reflect a spatial average; 

therefore, ambient concentrations for individual locations may be higher or lower. Estimates 

show that approximately 84% of the benzene emitted in California comes from motor vehicles, 

including evaporative leakage and unburned fuel exhaust. The predominant sources of total 

benzene emissions in the atmosphere are gasoline fugitive emissions and gasoline motor vehicle 

exhaust. Approximately 49% of the statewide benzene emissions can be attributed to on-road 

motor vehicles, with an additional 35% attributed to other mobile sources such as recreational 

boats, off-road recreational vehicles, and lawn and garden equipment. Currently, the benzene 

content of gasoline is less than 1%. Some of the benzene in the fuel is emitted from vehicles as 

unburned fuel. Benzene is also formed as a partial combustion product of larger aromatic fuel 

components. Industry-related stationary sources contribute 15% and area-wide sources 

contribute 1% of the statewide benzene emissions. The primary stationary sources of reported 

benzene emissions are crude petroleum and natural gas mining, petroleum refining, and electric 

generation. The primary area-wide sources include residential combustion of various types such 

as cooking and water heating. The primary natural sources are petroleum seeps that form where 

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Final Regulatory Impact Analysis 

oil or natural gas emerge from subsurface sources to the ground or water surface. The statewide 

benzene levels have shown generally steady improvement since 1990. To examine the trend in 

benzene while minimizing the influences of weather on the trend, the statewide average benzene 

concentration for 1990-1992 was compared to that for 2001-2003. The result was a 72% 

decrease in benzene concentration. These downward trends for benzene and other air toxics are 

a result of many control measures implemented to reduce emissions. 

Another recent evaluation of hazardous air pollutant (HAP) trends was conducted for 

selected metropolitan areas.11 Researchers retrieved historical concentration and emissions data 

from the US EPA for Boston, New York City, Philadelphia, Tampa Bay, Detroit, Dallas, St. 

Louis, Denver, Los Angeles, and Seattle, chosen for each of EPA’s ten regions. Annual and 

seasonal trends were generated to evaluate reductions in HAP emissions and ambient 

concentrations during the time period 1990-2003. Several air toxics were targeted, including 

benzene. To evaluate the trends, average concentrations from 1990-1994 were compared to 

2002-2003 (these time periods were chosen due to availability of data). The results showed that 

over 85% of the metropolitan area-HAP combinations decreased in their HAP concentrations, 

while less than 15% realized an increase. For example, Table 3.1-2 shows that benzene 

concentrations decreased in seven of the ten metropolitan areas (range 19 to 79%). 

Each of these analyses consistently illustrates the significant reductions in national annual 

average concentrations of benzene and other air toxics. The air pollution management efforts of 

the US EPA and states have been effective in reducing ambient concentrations of air toxics over 

time. Additional reductions are expected with the implementation of additional regulatory 

measures such as this one. It should be noted that due to the limited spatial and temporal 

coverage of air toxics monitoring networks, using ambient monitors to represent exposure adds 

substantial uncertainty in exposure assessment. 

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Final Regulatory Impact Analysis 

Table 3.1-1. Site Descriptions of the Monitoring Stations Along with Mean Benzene Concentration from 

2003, for Monitoring Stations in New York State. 

Lackawanna 

Eastern District 

High School Troy Niagara Falls 

Site Character Industrial Urban Small Urban Urban Industrial Location Area Buffalo Brooklyn Hudson Valley Niagara 2000 Population (thousands) 950 2465 153 220 Annual Vehicle Miles Traveled (million miles) 

8250 4246 1413 1546 

Period 1990-2003 Mean Concentration (μg/m3) 

5.09 2.85 2.31 1.80 

Period 2001-2003 Mean Concentration (μg/m3) 

2.26 2.05 1.68 1.08 

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Final Regulatory Impact Analysis 

3.1.2 Population-Based (Representative) Exposure Measurements 

In addition to measurements of outdoor concentrations, an important component of 

understanding human exposure to air toxics is the body of studies that employ survey techniques 

to assess microenvironmental and representative populations’ exposures. Typically, these 

studies are designed to represent a discrete geographic area. The personal exposure 

concentration summaries from these studies are shown in Table 3.1-3. 

The National Human EXposure Assessment Survey (NHEXAS) was a series of 

population-based exposure studies. The states in EPA Region 5 were the focus of one NHEXAS 

study, which was conducted in mid-1990.12 Nearly 400 personal and indoor air samples were 

obtained from both smokers and non-smokers, along with a smaller number of outdoor air 

samples in residential areas. Measurements took place over 6 days per subject. Overall, average 

personal exposure to benzene was 7.52 μg/m3, with indoor air concentrations averaging 7.21 

μg/m3. Outdoor air concentrations averaged 3.61 μg/m3. Personal air concentrations were 

significantly associated with indoor air concentrations, as well as blood concentrations. The 

preliminary results of the NHEXAS pilot study in Arizona, another study area, indicate that 

among the 179 statistically-sampled homes, median indoor concentrations were 1.3 μg/m3 during 

the mid-1990’s, while outdoor concentrations were 1.0 μg/m3.

13 Furthermore, reported results 

from the Arizona study indicate that fuel-related VOCs are elevated in homes with attached 

garages. 

In another study based on a random population-based sample of an urban population, 37 

non-smoking residents of South Baltimore, MD were equipped with passive monitors to assess 

3-day average personal exposure to VOCs, in addition to indoor and outdoor air.14 Monitoring 

took place in 2000 and 2001. Modeled air quality data from the ASPEN dispersion model, 

employed in EPA’s National Air Toxics Assessment for 1996, were also obtained for the study 

area. Overall, median outdoor modeled concentrations of benzene and other fuel-related VOCs 

corresponded well with measured data in the area (correlation coefficient of median VOC 

concentrations = 0.97). Average personal exposure to benzene was 4.06 μg/m3, while 95th 

percentile values were 7.30 μg/m3

. For indoors, the respective values were 3.70 and 8.34 μg/m3, 

while for outdoors the values were 1.84 and 3.14 μg/m3. Overall, the study provides evidence 

that modeling outdoor benzene concentrations using ASPEN, as is done in this rule, provides 

adequate representation of outdoor values. However, indoor and personal exposures are also 

influenced by other sources, as is described in the section on attached garages. 

While not a population-based study, the recently-completed Relationships of Indoor, 

Outdoor and Personal Air (RIOPA) study provides a depiction of indoor, outdoor, and personal 

concentrations of benzene and other toxics in three regions with differing source mixtures.15 100 

non-smoking homes in each of Los Angeles, CA, Houston, TX, and Elizabeth, NJ were selected 

for sampling in areas representing locations dominated by emissions from mobile sources, 

stationary sources, and a mixture of sources, respectively. In the adult sample, average personal 

exposures to benzene were 3.64 μg/m3, with a 95th percentile of 10.7 μg/m3. Respective statistics 

3-9
 
Final Regulatory Impact Analysis 

for indoor air were 3.50 μg/m3 and 10.0 μg/m3, while outdoor statistics were 2.15 and 5.16 

μg/m3. 

Few studies have systematically addressed exposures among representative samples of 

children. Several have been done in Minnesota, with others in New York, Los Angeles, and 

Baltimore areas. 

For the Minnesota Children’s Pesticide Exposure Study (MNCPES), conducted in urban 

and rural areas in the vicinity of Minneapolis-St. Paul, MN,16 all monitoring used the same 6-day 

monitoring duration as used in the Region 5 NHEXAS study. In the first phase of the study, a 

statistically representative sample of 284 homes with children underwent air monitoring for 

VOCs. Low-income and minority homes were over sampled to ensure representation. Indoor 

benzene concentrations averaged 4.6 μg/m3, with the data skewed toward higher concentrations. 

The 95th percentile concentration was 12.7 μg/m3. Homes with attached garages had 

significantly higher concentrations of benzene indoors (p < 0.0001). In the second phase of the 

study, a subset of 100 children underwent intensive monitoring of personal, indoor, and outdoor 

air as well as activity tracking via diary. Overall personal exposures were 4.8 μg/m3, with a 95th 

percentile of 9.1 μg/m3. Indoor concentrations in the intensive period averaged 3.9 μg/m3 and 

outdoor averaged 3.3 μg/m3. Regression analysis indicated that personal exposures generally 

were higher than the time-weighted average of indoor and outdoor air. Furthermore, personal 

exposures to benzene and toluene were elevated for children living in a home with an attached 

garage, but only the relationship for toluene was significant at the 95% confidence level. 

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Final Regulatory Impact Analysis 

Table 3.1-2. Benzene Emission (Tons Per Year) and Concentration (μg/m3) Compariso

Metropolitan Area 

1990 

Emissions 

2002 

Emissions 

% Change 

in 

Emissions 

1990-1994 

Average 

Concentration 

2002-2003 

Average 

Concentration 

C

Boston 6262 2229 -64.4 3.93 0.81 New York City 16653 7512 -54.9 3.24 1.35 Philadelphia 5961 2577 -56.8 3.60 1.26 Tampa Bay 3103 2408 -22.4 NA NA Detroit 6480 4388 -32.3 4.19 3.40 Dallas 7933 2832 -64.3 1.21 0.78 St. Louis 4358 2304 -47.1 5.16 1.43 Denver 2800 1913 -31.7 NA 2.75 Los Angeles 19762 4168 -78.9 8.97 2.34 Seattle 5844 4315 -26.2 NA 1.39 

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Final Regulatory Impact Analysis 

In another study, students recruited from an inner-city school in Minneapolis, MN 

participated in an exposure study called SHIELD.17 Students were recruited using stratified 

random sampling, with a total of 153 children participating between two seasons. Home and 

personal samples were collected and averaged over two continuous days of sampling using 

passive methods. School measurements took place during school hours only, over the course of 

5 days, and outdoor measurements were set up to run continuously outside the school through 

each week sampled (Monday through Friday). The study reported median, 10th, and 90th 

percentile concentrations. In personal samples, median benzene concentrations were 1.5 μg/m3 

in spring and 2.1 μg/m3

 in winter.18 

The TEACH exposure study tracked inner-city high school students’ exposures in New 

York, NY and Los Angeles, CA. In the New York City study, 42 students underwent personal, 

indoor home, and outdoor home air quality monitoring during two seasons.19 Average winter 

benzene personal concentrations were 4.70 μg/m3

, while indoor and outdoor concentrations 

averaged 5.97 and 2.55 μg/m3. Average indoor concentrations exceeding average personal 

concentrations is unique to the TEACH winter results. Summer values were 3.09, 1.75, and 1.31 

μg/m3, respectively. The authors noted that VOC concentrations within the city tracked traffic 

patterns. There was no substantial evidence for indoor sources of benzene.20 In a subsequent 

publication, personal exposure concentrations for both cities were reported, averaged across both 

seasons. New York City average exposure concentrations were 3.82 μg/m3, while Los Angeles 

average exposure concentrations were 4.64 μg/m3.

21 

Overall, these studies show that personal and indoor concentrations of benzene and other 

VOCs are substantially higher than those found outdoors (see Table 3.1-3). In general, these 

differences are statistically significant. Some of the factors leading to these elevated 

concentrations are likely a result of motor vehicle impacts such as exhaust and evaporative 

emissions in attached garages, exposures during on-road commutes and exposures during vehicle 

re-fueling. These and other factors are discussed in more detail in Section 3.1.3. This suggests 

that risk reductions from the controls in this proposal will be greater than can currently be 

estimated using national-scale modeling tools. 

3.1.3 Elevated Concentrations and Exposures in Mobile Source-Impacted Areas 

Air quality measurements near roads often identify elevated concentrations of air toxic 

pollutants at these locations. The concentrations of air toxic pollutants near heavily trafficked 

roads, as well as the pollutant composition and characteristics, differ from those measured distant 

from heavily trafficked roads. Thus, exposures for populations residing, working, or going to 

school near major roads are likely different than for other populations. Following is an overview 

of concentrations of air toxics and exposure to air toxics in areas experiencing elevated pollutant 

concentrations due to the impacts of mobile source emissions. 

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Final Regulatory Impact Analysis 

Table 3.1-3. Personal Exposure to Benzene from Population-Based Studiesa 

Location Year(s) 

Includes 

Smokers 

Personal 

Average 

(μg/m3) 

“Upper 

Bound” 

(μg/m3) 

Indoor 

Average 

(μg/m3) 

Outdoor 

Average 

(μg/m3) Reference 

EPA Region 

5 

1995­

1996 Yes 7.52 13.71b 7.21 3.61 Clayton et al. (1999) 

Baltimore, 

MD 

2000­

2001 No 4.06 7.30c

 3.70 1.84 

PayneSturges et 

al. (2004) 

Elizabeth, 

NJ, 

Houston, TX, 

Los Angeles 

1999­

2001 No 3.64 10.7c 

, 

27.4g 3.50 2.15 Weisel et 

al. (2005) 

CA 

Elizabeth, 

NJ, 

Houston, TX, 

Los Angeles 

CA 

1999-

2001 No 4.16 12.0c 

, 

43.6g N/Rh

 N/Rh Weisel et 

al. (2005) 

Minneapolis 

-

St. Paul, MN 

1997 Yese 4.8 9.1 3.9 3.3 Adgate et 

al. (2004a) 

Minneapolis, 

MN 2000 Yese 

2.1 

Winter 

1.5 

Spring 

6.5 

Winterb 

4.2 

Springb 

2.2 

Winter 

2.1 

Spring 

1.3 

Winter 

1.1 

Spring 

Adgate et 

al. (2004b) 

New York, 

NY 

1999-

2000 No 

4.7 

Winter 

3.1 

Summer 

3.8 

Total 

11.4 

Winterd 

7.0 

Summerd 

12.3 

Totalf 

6.0 

Winter 

1.8 

Summer 

3.6 

Total 

2.5 

Winter 

1.3 

Summer 

1.8 

Total 

Kinney et 

al. (2002); 

Sax et al. 

(2006) 

Los Angeles, 

CA 

1999-

2000 No 4.64 11.27 3.87 3.32 Sax et al. (2006) a

 Children’s studies in italics 

b

 90th percentile c

 95th percentile 

d

 Mean +2 standard deviations 

e

 Smoking in homes 

f

 Maximum measured value 

g

 99th percentile 

h

 Not reported 

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Final Regulatory Impact Analysis 

3.1.3.1 Concentrations Near Major Roadways 

3.1.3.1.1 Particulate Matter 

Mobile sources influence temporal and spatial patterns of criteria pollutants, air toxics, 

and PM concentrations within urban areas. Motor vehicle emissions may lead to elevated 

concentrations of pollutants near major roads. Since motor vehicle emissions generally occur 

within the breathing zone, near-road populations may be exposed to “fresh” primary emissions as 

well as combustion pollutants “aged” in the atmosphere. For particulate matter, these fresh 

versus aged emissions can result in the presence of varying particle sizes near roadways, 

including ultrafine, fine, and coarse particle modes. 

The range of particle sizes of concern is quite broad and is divided into smaller 

categories. Defining different size categories is useful since particles of different sizes behave 

differently in the atmosphere and in the human respiratory system. Table 3.1-4 lists the four 

terms for categorizing particles of different sizes as defined by the US EPA.22 

Table 3.1-4. Descriptions and Particle Sizes of Each Category of Particles 

Description Particle Size, dp (μm) 

Supercoarse dp > 10 

Coarse (or Thoracic Coarse Mode) 2.5 < dp ≤ 10 

Fine (or Accumulation Mode) 0.1 < dp ≤ 2.5 

Ultrafine (or Nuclei Mode)

a dp ≤ 0.1 a Nuclei Mode has also been defined as dp ≤ 0.05 μm elsewhere. 

Other particle classifications of interest include total suspended particulate matter (TSP). 

TSP includes a broad range of particle sizes including fine, coarse, and supercoarse particles. 

PM10 is defined as particulate matter with an aerodynamic diameter of less than or equal to 10 

μm. PM10 is regulated as a specific type of "pollutant" because this size range is considered 

respirable and can penetrate into the lower respiratory tract. PM2.5 is particulate matter with an 

aerodynamic diameter less than or equal to 2.5 μm. PM2.5 settles quite slowly in the atmosphere 

relative to coarse and supercoarse particles. Normal weather patterns can keep PM2.5 airborne for 

several hours to several days and enable these particles to transport hundreds of miles. PM2.5 can 

cause health problems due to widespread exposures and efficiency at reaching deep into the 

lungs. 

The size distribution of particles can be defined as a function of number, surface area, 

volume, and mass.23,24 Typically, on a number basis, emissions from mobile sources are heavily 

dominated by ultrafine mode particles, which tend to be comprised of volatile carbon. On a 

surface area basis, the average diameter of particles emitted by mobile sources is 0.1 μm. On a 

volume and mass basis, the size distribution of particles emitted from mobile sources has an 

average particle diameter of approximately 0.2 μm. 

Evidence of the large number of ultrafine mode particles emitted by motor vehicles can 

be found in the near-road environment. Roadside and ambient on-road measurements show that 

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Final Regulatory Impact Analysis 

ultrafine mode particles dominate the number concentration in close proximity to the roadway, 

while fine mode dominates farther from the road. Particle size distributions, mass and elemental 

carbon concentrations have been examined near roads in Los Angeles.25,26 Researchers observed 

a four-fold increase in particle number concentrations, when comparing measurements 300 m 

and 20 m from LA highways. Other studies have similarly shown that ultrafine mode particles 

show a sharp decrease in particle number concentrations as the distance from major roadways 

increases.27,28 Evidence was recently found of increased exposures to ultrafine particles near 

roads when it was discovered that children living near major roads had elevated levels of 

particle-containing alveolar macrophages.29 Additionally, roadside monitoring has shown that 

particle number varies with vehicle type and vehicle operating conditions. For example, elevated 

ultrafine mode particle concentrations have been identified when operating speeds on the road 

increase as well as when the proportion of heavy-duty diesel vehicles increases.30,31 

An increase in coarse particles near roads could originate from engine deterioration, 

brake and tire wear, and secondary aerosol formation.32,33,34,35 Engine deterioration is generally 

a function of vehicle age and maintenance condition. Brake wear emissions are highly 

dependent on brake pad materials.36 Secondary aerosol formation is dependent on fuel 

composition, emission rates, atmospheric chemistry, and meteorology. Re-entrained road dust, 

as well as brake and tire wear will also contribute to increased concentrations of coarse PM. 

Meteorological factors can affect exposures to motor vehicle emissions near the road. 

Researchers have noted that particle number concentrations changed significantly with changing 

wind conditions, such as wind speed, near a road.37 Studies suggest that ambient temperature 

variation can also affect particle number gradients near roads substantially.38 Wind direction 

also affects traffic-related air pollution mass concentrations inside and outside of schools near 

motorways.39,40 Diurnal variations in mixing layer height will also influence both near-road and 

regional air pollutant concentrations. Decreases in the height of the mixing layer (due to 

morning inversions, stable atmosphere, etc.) will lead to increased pollutant concentrations at 

both local and regional scales. 

3.1.3.1.2 Gaseous Air Toxics 

Concentrations of mobile source air toxics have been estimated by a number of different 

methods such as the NATA National-Scale Assessment, local-scale modeling assessments, and 

from air quality monitoring in locations in immediate proximity to busy roadways. Each 

approach offers a different level of representation of the concentrations of air toxics near 

roadways. 

Air quality monitoring is one way of evaluating pollutant concentrations at locations near 

sources such as roadways. Ambient VOC concentrations were measured around residences in 

Elizabeth, NJ, as part of the Relationship among Indoor, Outdoor, and Personal Air (RIOPA) 

study. Data from that study was analyzed to assess the influence of proximity of known ambient 

emission sources on residences.41 The ambient concentrations of benzene, toluene, 

ethylbenzene, and xylene isomers (BTEX) were found to be inversely associated with: distances 

from the sampler to interstate highways and major urban roads; distance from the sampler to 

gasoline stations; atmospheric stability; temperature; and wind speed. The data indicate that 

3-15
 
Final Regulatory Impact Analysis 

BTEX concentrations around homes within 200 m of roadways and gas stations are 1.5 to 4 

times higher than urban background levels. In a subsequent study, proximity to major roadways, 

meteorology, and photochemistry were all found to be significant determinants of ambient 

concentration of a range of aldehyde species, including formaldehyde, acetaldehyde, acrolein, 

and others. For most aldehydes, spring and summer concentrations were significantly higher 

than those from colder seasons.42 However, formaldehyde concentrations were significantly 

lower in summertime, suggesting greater photochemical destruction than production. On colder 

days, when photochemical activity was lower, concentrations of formaldehyde, acetaldehyde, 

acrolein, and other aldehydes were significantly higher with increasing proximity to high-traffic 

roads. 

Several other studies have found that concentrations of benzene and other mobile source 

air toxics are significantly elevated near busy roads compared to “urban background” 

concentrations measured at a fixed site.43,44,45,46,47,48 For example, measurements near a 

tollbooth in Baltimore observed mean benzene concentrations to vary by time of day from 3 to 

22.3 μg/m3

 depending on traffic volume, vehicle type, and meteorology.49 In comparison with 

ambient levels, Maryland’s Department of Environment reported the range of benzene annual 

averages measured at seven different monitoring sites in 2000 between 0.27-0.71 μg/m3.

50 

Another study measured the average benzene concentration in a relatively high traffic density (~ 

16000 automobiles/day) sampling area at 9.6 μg/m3 and in rural areas with hardly any traffic (< 

50 automobiles/day) at 1.3 μg/m3.

51 The concentration of benzene, along with several other air 

toxics (toluene and the isomeric xylenes), in the urban area far exceeded those in the rural area. 

According to Gaussian dispersion theory, pollutants emitted along roadways will show 

highest concentrations nearest a road, and concentrations exponentially decrease with increasing 

distance downwind. These near-road pollutant gradients have been confirmed by measurements 

of both criteria pollutants and air toxics.52,53,54,55,56 Researchers have demonstrated exponential 

reductions in concentrations of CO, as well as PM number, and black carbon (as measured by an 

aethalometer), with increasing downwind distance from a freeway in Los Angeles.57,58 These 

pollutants reached background levels approximately 300 m downwind of the freeway. 

3.1.3.2 Exposures Near Major Roadways 

The modeling assessments and air quality monitoring studies discussed above have 

increased our understanding of ambient concentrations of mobile source air toxics and potential 

population exposures. Results from the following exposure studies reveal that populations 

spending time near major roadways likely experience elevated personal exposures to motor 

vehicle related pollutants. In addition, these populations may experience exposures to differing 

physical and chemical compositions of certain air toxic pollutants depending on the amount of 

time spent in close proximity to motor vehicle emissions. Following is a detailed discussion on 

exposed populations near major roadways. 

3.1.3.2.1 In Vehicles 

Several studies suggest that people may experience significant exposures while driving in 

vehicles. A recent in-vehicle monitoring study was conducted by EPA and consisted of in3-16
 
Final Regulatory Impact Analysis 

vehicle air sampling throughout work shifts within ten police patrol cars used by the North 

Carolina State Highway Patrol (smoking not permitted inside the vehicles).59 Troopers operated 

their vehicles in typical patterns, including highway and city driving and refueling. In-vehicle 

benzene concentrations averaged 12.8 μg/m3

, while concentrations measured at an “ambient” site 

located outside a nearby state environmental office averaged 0.32 μg/m3

. The study also found 

that the benzene concentrations were closely associated with other fuel-related VOCs measured. 

The American Petroleum Institute funded a screening study of “high-end” exposure 

microenvironments as required by section 211(b) of the Clean Air Act.60 The study included 

vehicle chase measurements and measurements in several vehicle-related microenvironments in 

several cities for benzene and other air toxics. In-vehicle microenvironments (average 

concentrations in parentheses) included the vehicle cabin tested on congested freeways (17.5 

μg/m3

), in parking garages above-ground (155 μg/m3) and below-ground (61.7 μg/m3), in urban 

street canyons (7.54 μg/m3

), and during refueling (46.0 μg/m3). It should be noted that sample 

sizes in this screening study were small, usually with only one to two samples per 

microenvironment. The final report of this study is expected to be released in 2007. 

In 1998, the California Air Resources Board published an extensive study of 

concentrations of in-vehicle air toxics in Los Angeles and Sacramento, CA.61 The data set is 

large and included a variety of sampling conditions. On urban freeways, in-vehicle benzene 

concentrations ranged from 3 to 15 μg/m3 in Sacramento and 10 to 22 μg/m3 in Los Angeles. In 

comparison, ambient benzene concentrations ranged from 1 to 3 μg/m3 in Sacramento and 3 to 7 

μg/m3 in Los Angeles. 

Studies have also been conducted in diesel buses, such as the one recently conducted of 

LA school buses.62,63 In the study, five conventional diesel buses, one diesel bus equipped with 

a catalytic particle filter, and one natural gas bus were monitored for benzene, among other 

pollutants. These buses were driven on a series of real school bus routes in and around Los 

Angeles, CA. Average benzene concentrations in the buses were 9.5 μg/m3, compared with 1.6 

μg/m3 at a background urban fixed site in west Los Angeles. Type of bus, traffic congestion 

levels, and encounters with other diesel vehicles contributed to high exposure variability between 

runs. 

The same researchers additionally determined the relative importance of school busrelated microenvironments to children’s pollutant exposure.64 Real-time concentrations of black 

carbon (BC), particle-bound PAH, nitrogen dioxide (NO2), particle counts (0.3-0.5 μm size 

range), and PM2.5 mass were measured inside school buses during long commutes, at bus stops 

along the routes, at bus loading and unloading zones, and at nearby urban background sites. 

Across all the pollutants, mean concentrations during bus commutes were higher than in any 

other microenvironment. Mean exposures in bus commutes were 50 to 200 times more than for 

loading and unloading zones at the school, and 20 to 40 times more than for bus stops along the 

route, depending on the pollutant. The in-cabin exposures were dominated by the effect of 

surrounding traffic when windows were open and by the bus’ own exhaust when the windows 

were closed. The mean pollutant concentrations in the three school bus commute-related 

environments and background air are presented in the Table 3.1-5. 

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Final Regulatory Impact Analysis 

Table 3.1-5. Mean Concentrations of Black Carbon (BC), Particle Bound PAH, NO2, 
 

Particle Count (PC), and PM2.5 in Three School Bus Commute Microenvironments and 
 

Background Air
 

Mean Concentrations 

Background (Un)Loading 

Zone Bus Stops Bus Commutesa 

BC (μg/m3) 2 ± 0.1 2 ± 0.3 4 ± 0.4 3-19 (8) 

Particle 

Bound -PAH 

(μg/m3) 

0.027 ± 0.0015 0.015 ± 0.0003 0.044 ± 0.0045 0.064-0.400 

(0.134) 

NO2 (ppb) 49 ± 1.0 35 ± 0.2 54 ± 1.9 34-110 (73) 

PC 

(count/cm3) 83 ± 3.1 Not collected 62 ± 1.8 77-236 (130) 

PM2.5 (μg/m3) 20 ± 2.4 Not collected 25b 21-62 (43) 

a Ranges are associated with different bus types and window positions. Values in 

parenthesis are the mean for all runs.

b Not enough data to establish a confidence interval. 

In another recent study of commuter buses, concentrations of benzene and other VOCs 

were measured in buses on several routes in Detroit, MI.65 The average in-bus concentration of 

benzene was 4.5 μg/m3, while the average concentrations at three fixed sites taken during the 

study period ranged from 0.9-2.0 μg/m3. In this study, daily bus/ambient concentration ratios 

were reported, and ranged from 2.8-3.3 on the three reported study days. The in-bus 

concentrations were found to be most influenced by local traffic sources. A number of other 

studies similarly observe that passenger car commuters are exposed to elevated pollutant 

concentrations while driving on busy roads.66,67,68,69,70,71 

Older studies that examine in-vehicle concentrations in older model year vehicles are 

difficult to apply for regulatory analyses, due to the relatively rapid changes in vehicle emission 

controls over the last 15 years. In general, these studies indicate that concentrations in vehicles 

are significantly higher than ambient concentrations.72,73,74 The average benzene measurements 

of these older in-vehicle studies (Raleigh, NC and CA South Coast Air Basin) are in Table 3.1-6 

along with the more recent studies for comparison. 

Overall, these studies show that concentrations experienced by commuters and other 

roadway users are substantially higher than ambient air measured in typical urban air. As a 

result, the time a person spends in a vehicle will significantly affect their overall exposure. 

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Final Regulatory Impact Analysis 

Table 3.1-6. Benzene Concentrations (μg/m3) Measured in Vehicles and in Ambient Air 

Study In-Vehicle Ambient Air 

Mean Max Mean Max 

Raleigh, NC (1989) a 11.6 42.8 1.9 8.5 

CA South Coast Air Basin (1989) b 42.5 267.1 9.3-16.9 -- 

Boston, MA (1991) c 17.0 64.0 -- -- 

Los Angeles, CA (1998) 10-22 -- 3-7 -- 

Sacramento, CA (1998) 3-15 -- 1-3 -- 

Detroit, MI (2000) d 4.5 10.8 0.9-2.0 -- 

API Gasoline Screening (2002) 17.5 -- -- -- 

LA, CA School Buses (2003) 9.5 -- 1.6 -- 

NC State Highway Patrol (2003) 12.8 43.1 0.32 1.92 a A one-hour measurement was taken for each experimental trip. 

b The estimated sampling time period was 1.5 hours/round-trip. n=191. c In-vehicle measurement includes both interstate and urban driving, n=40. 

d Measurements taken from interiors of urban buses. 

3.1.3.2.2 In Homes and Schools 

The proximity of schools to major roads may result in elevated exposures for children 

due to potentially increased concentrations indoors and increased exposures during outdoor 

activities. Here we discuss international studies in addition to the limited number of US studies, 

because while fleets and fuels outside the U.S. can be much different, the spatial distribution of 

concentrations is relevant. 

There are many sources of indoor air pollution in any home or school. These include 

indoor sources and outdoor sources, such as vehicle exhaust. Outdoor air enters and leaves a 

house by infiltration, natural ventilation, and mechanical ventilation. In infiltration, outdoor air 

flows into the house through openings, joints, and cracks in walls, floors, and ceilings, and 

around windows and doors. In natural ventilation, air moves through opened windows and doors. 

Air movement associated with infiltration and natural ventilation is caused by air temperature 

differences between indoors and outdoors and by wind. Finally, there are a number of 

mechanical ventilation devices, from outdoor-vented fans that intermittently remove air from a 

single room, such as bathrooms and kitchen, to air handling systems that use fans and duct work 

to continuously remove indoor air and distribute filtered and conditioned outdoor air to strategic 

points throughout the house. The concentrations of outdoor pollutants can therefore influence 

indoor concentrations. A review of the literature determined that approximately 100% of 

gaseous compounds, such as benzene, and 80% of diesel PM can penetrate indoors.75,76 

In the Fresno Asthmatic Children’s Environment Study (FACES), traffic-related 

pollutants were measured on selected days from July 2002 to February 2003 at a central site, and 

inside and outside of homes and outdoors at schools of asthmatic children.77 Preliminary data 

indicate that PAH concentrations are higher at elementary schools located near primary roads 

than at elementary schools distant from primary roads (or located near primary roads with 

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Final Regulatory Impact Analysis 

limited access). PAH concentrations also appear to increase with increase in annual average 

daily traffic on nearest major collector. 

The East Bay Children’s Respiratory Health Study studied traffic-related air pollution 

outside of schools near busy roads in the San Francisco Bay Area in 2001.78 Concentrations of 

the traffic pollutants PM10, PM2.5, black carbon, total NOx, and NO2 were measured at ten school 

sites in neighborhoods that spanned a busy traffic corridor during the spring and fall seasons. 

The school sites were selected to represent a range of locations upwind and downwind of major 

roads. Differences were observed in concentrations between schools nearby (< 300 m) versus 

those more distant (or upwind) from major roads. Investigators found spatial variability in 

exposure to black carbon, NOx, NO, and (to a lesser extent) NO2 associated with roads with 

heavy traffic within a relatively small geographic area. 

A study to assess children’s exposure to traffic-related air pollution while attending 

schools near roadways was performed in the Netherlands.79 Investigators measured PM2.5, NO2 

and benzene inside and outside of 24 schools located within 400 m of roadways. The indoor 

average benzene concentration was 3.2 μg/m3, with a range of 0.6-8.1 μg/m3. The outdoor 

average benzene concentration was 2.2 μg/m3, with a range of 0.3-5.0 μg/m3. Overall results 

indicate that indoor pollutant concentrations are significantly correlated with traffic density and 

composition, percentage of time downwind, and distance from major roadways. 

In another study performed in the Netherlands, investigators measured indoor 

concentrations of black smoke, PM10, and NO2 in twelve schools between the periods of May 

and August 1995.80 The schools were located at varying distances from the motorway (35-645 

m). Results indicate that black smoke and NO2 concentrations inside the schools were 

significantly correlated with truck and/or car traffic intensity as well as percentage of time 

downwind from the motorway and distance of the school from the motorway. PM10 

concentrations measured in classrooms during school hours were highly variable and much 

higher than those measured outdoors, but they did not correlate with any of the distance or traffic 

parameters. 

In another Dutch study, researchers monitored children’s personal exposure 

concentrations, and home indoor and home outdoor levels of “soot” (particle blackness), NO, 

and NO2.

81 Four-month average concentrations were calculated for each pollutant. Personal 

exposure to “soot” was 35-38% higher in students living within 75 meters of roads with 10,000 

average annual daily traffic, a statistically significant result. Nonsignificant elevations in 

personal exposure to NO, NO2, and NOx were also found. 

The TEACH study (Toxic Exposure Assessment – Columbia/Harvard) measured the 

concentrations of VOCs, PM2.5, black carbon, and metals outside the homes of high school 

students in New York City.82 The study was conducted during winter and summer of 1999 on 46 

students and in their homes. Average winter (and summer) indoor concentrations exceeded 

outdoor concentrations by a factor of 2.3 (1.3). In addition, spatial and temporal patterns of 

MTBE concentrations, used as a tracer for motor vehicle pollution, were consistent with traffic 

patterns. 

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Final Regulatory Impact Analysis 

Average benzene concentrations were determined in a recent evaluation of the exposure 

of urban inhabitants to atmospheric benzene in Athens, Greece.83 Home and personal levels of 

50 non-smokers in six monitoring campaigns varied between 6.0-13.4 and 13.1-24.6 μg/m3, 

respectively. Urban levels varied between 15.4 and 27.9 μg/m3 with an annual mean of 20.4 

μg/m3

. The highest values were observed during the first two sampling periods in fall and 

winter, when wind speed was low. The low summer values were attributed to decreased vehicle 

traffic. Among home factors, only proximity to busy roads was determined to be an important 

influence on indoor benzene levels. 

Children are exposed to elevated levels of air toxics not only in their homes, classrooms, 

and outside on school grounds, but also during their commute to school. See above discussion of 

in-vehicle (school bus and passenger car) concentrations of air toxics for one method of 

commuting. The discussion below also presents potential exposures to children from another 

commuting method. 

3.1.3.2.3 Pedestrians and Bicyclists 

Researchers have noted that pedestrians and cyclists along major roads experience 

elevated exposures to motor vehicle related pollutants. Although commuting near roadways 

leads to higher levels of exposure to traffic pollutants, the general consensus is that exposure 

levels of those commuting by walking or biking is lower than for those who travel by car or bus, 

(see discussion on in-vehicle exposure in previous section above). For example, investigators 

found that personal measurements of exposure to PM10 concentrations were 16% higher inside 

the car than for the walker on the same route, but noted that a walker may have a larger overall 

exposure due to an increase in journey time.84 Similarly, researchers found that traffic-related 

pollutant exposure concentrations of car drivers were higher than for cyclists.85 Cyclists are 

typically on the border of the road or on dedicated bike paths and therefore further away from the 

vehicle emissions and are less delayed by traffic jams. However, after accounting for cyclists’ 

higher ventilation, the uptake of CO, benzene, toluene, and xylenes by cyclists sometimes 

approached that of car drivers, and for NO2 it was significantly higher. 

In the early 1990’s, researchers studied the in-vehicle concentrations of a large number of 

compounds associated with motor vehicle use and the exposure to VOCs of a pedestrian on an 

urban sidewalk (50 m from roadways) in Raleigh, NC.86 The mean concentration of benzene in 

the six pedestrian sidewalk samples was 6.8 μg/m3. This concentration was lower than the invehicle measurement (11.6 μg/m3

), but higher than the fixed-site measurement (1.9 μg/m3) on 

urban roadways 100-300 m from streets. 

The same researchers studied the exposure of commuters in Boston to VOCs during car 

driving, subway travel, walking, and biking.87 For pedestrians, mean time-weighted 

concentrations of benzene, toluene, and xylenes of 10.6, 19.8, and 16.7 μg/m3, respectively, were 

reported. For cyclists, the time-weighted concentrations were similar to those of pedestrians, at 

9.2, 16.3, and 13.0 μg/m3

, respectively. In-vehicle exposure concentrations were higher as 

discussed above. 

Numerous other studies which were conducted in Europe and Asia yield similar results. 

3-21
 
Final Regulatory Impact Analysis 

A survey of CO concentration was conducted for various transport modes along heavy traffic 

routes in Athens, Greece.88 Results showed that mean CO levels for trips of 30 min were 21.4 

ppm for private car, 10.4 ppm for bus, and 11.5 ppm for pedestrians. In Northampton, UK 

during the winter 1999, personal measurements of exposure to PM10, PM2.5, and PM1 were made 

during walking and in-car journeys on two suburban routes.89 In-car measurements were highest 

(43.16, 15.54, and 7.03 μg/m3 for PM10, PM2.5, and PM1, respectively) followed by walking 

(38.18, 15.06, and 7.14 μg/m3, respectively). Background levels were only available for PM10 

(26.55 μg/m3), but were significantly lower than the walking exposure levels. Researchers found 

similar results for CO exposure levels of schoolchildren commuters.90 So although personal 

exposures are greater for in-vehicle commutes, pedestrians and bicyclists in proximity to heavy 

traffic are exposed to elevated pollutant levels relative to background. 

3.1.3.3 Concentrations and Exposure in Homes with Attached Garages 

Residential indoor air quality is a major determinant of personal exposure, with most 

people spending the majority of their time indoors at home. According to the National Human 

Activity Pattern Survey, nationally, people spend an average of 16.68 hours per day indoors in a 

residence.91 The large fraction of time spent in this microenvironment implies that sources that 

impact indoor air are likely to have a substantial effect on personal exposure. 

Indoor air quality is in large part determined by ventilation of indoor spaces. Natural 

ventilation occurs as a result of two factors: wind-induced pressure and the “stack effect.” The 

latter occurs when hot air rises in a home, causing a pressure drop in the lower part of the home, 

which then creates airflow into the home from higher-pressure locations outside the home. 

Natural ventilation can also be influenced by opening of windows and doors. Mechanical 

ventilation employs fans and sometimes ductwork to manage ventilation within a home. 

Air can be drawn into a home from either outdoors, or in a home with an attached garage, 

from the garage. Air from the garage can have higher concentrations of VOCs and other 

pollutants as a result of the storage of vehicles, other engines and equipment, fuel (gasoline in 

gas cans), solvents, or cleaning products. As a result, homes with a greater fraction of airflow 

from the garage are more susceptible to air quality decrements from in-garage emissions. 

Several studies have examined homes with attached garages to determine the fraction of 

residential air intake from the garage. A recent study from Fairbanks, Alaska used 

perfluorocarbon tracer (PFT) gases to estimate that 12.2% of air entering a mechanically 

ventilated energy efficient home and 47.4% of the air entering the living spaces of an older 

passively ventilated home originated in the homes’ attached garages.92 In an Ann Arbor, 

Michigan home, researchers used PFT gases to estimate that 16% of the air entering the home 

entered through the garage.93 A recent study of a representative sample of homes in Anchorage, 

Alaska employing PFT estimated that in homes with a forced air furnace in an attached garage, 

36.7% of indoor air originated in the garage.94 In homes that had forced air furnaces indoors or 

hytronic heat, 17.0% and 18.4% of indoor air originated in the garage, respectively. A study 

from Minnesota examined homes constructed in 1994, 1998, and 2000.95 Homes built in 1994 

had 17.4% of airflow originating in the garage. Homes built in 1998 and 2000 had 10.5% and 

9.4% of airflow from the garage, respectively. In another study conducted in Ottawa, Ontario, an 

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Final Regulatory Impact Analysis 

average of 13% of home air intake came from the garage.96 That study also found that the 

house-garage interface area was as leaky as the rest of the building envelope. In another study 

from Washington, D.C., the house-garage interface was found to be 2.5 times as permeable as 

the rest of the house.97 This discrepancy may indicate that homes built in colder climates are 

built more tightly than homes in warmer regions as a result of weather-sealing. However, there 

is no evidence that in regions with cold weather, colder temperatures lead to elevated indoor 

concentrations of VOCs.98 

Several studies have examined the influence of attached garages on indoor air and 

personal exposure. In the 1980’s researchers identified attached garages as a major source of 

benzene and other VOCs in residences. The Total Exposure Assessment Methodology (TEAM) 

Study was completed in 1985.99 The goal of this study was to develop methods to measure 

individual total exposure (through air, food and water) and resulting body burden to toxic and 

carcinogenic chemicals, and then to apply these methods with a probability-based sampling 

framework to estimate the exposures and body burdens of urban populations in several U.S. 

cities. The study measured personal exposures of 600 people to a number of air toxics. The 

subjects were selected to represent residents of cities in New Jersey, North Carolina, North 

Dakota, and California. In the study, a large fraction of an average nonsmoker’s benzene 

exposure originated from sources in attached garages.100 Work done as part of the TEAM study 

also identified stored gasoline as an important source of elevated benzene levels indoors.101 This 

stored gasoline can be found primarily in gas cans as well as the fuel tanks of lawn and garden 

equipment, such as lawn mowers and string trimmers. Lawn and garden equipment fuel tank 

emissions, however, are significantly lower than evaporative emissions from gas cans, because 

the fuel tanks are much smaller than gas cans, typically 0.3 to 0.4 gallons. Emissions are also 

higher from gas cans because vents and spouts are left open. 

 These early studies have highlighted the role of evaporative emissions within the garage 

as contributors to indoor air pollution. Since then, major changes have affected emissions from 

vehicles, including additional controls on evaporative emissions, on- board diagnostics, and state 

inspection and maintenance programs addressing evaporative emission controls. Several 

researchers have subsequently conducted air measurements in homes and in attached garages to 

evaluate the effects on indoor air. 

Garage concentrations of benzene and other VOCs are generally much higher than either 

indoor or outdoor air, and constitute one of the highest-concentration microenvironments to 

which a person might typically be exposed outside the occupational setting. The garage also 

supplies contaminated air to the home to which it is attached. One recent study from Michigan 

found average garage benzene concentrations of 36.6 μg/m3, with a standard deviation of 38.5 

μg/m3

, compared to mean and standard deviation concentrations of 0.4 μg/m3 and 0.12 μg/m3 in 

ambient air.102 In Alaska, where fuel benzene levels tend to be very high and homes may be 

built very airtight, garage concentrations have been measured at even higher levels. One study 

from Anchorage measured average garage benzene concentrations of 103 μg/m3

, with a standard 

deviation of 135 μg/m3.

103 More recently, a two-home study in Fairbanks found garage benzene 

average concentrations of 119 μg/m3

 during summer and 189 μg/m3 during winter in one wellventilated home with an air-to-air heat exchanger.104 In an older home with passive ventilation 

summer and winter garage benzene concentrations were 421 and 103 μg/m3

, respectively. 

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Final Regulatory Impact Analysis 

Other studies have studied the effect of garages or the sources within them on indoor air 

quality. Most prominently, a group of Canadian investigators conducted source apportionment 

of indoor non-methane hydrocarbons (NMHC) in 16 Ontario homes in the late 1990’s.105 They 

also assembled source profiles from hot soak and cold start emissions, which they used to 

conduct source apportionment of total indoor air NMHC. All emissions samples and house 

testing were conducted using the same 1993 model year vehicle. Overall, while the vehicle was 

hot-soaking in the garage over a four hour sampling period, between 9 and 71% of the NMHC 

inside the house could be attributable to that vehicle’s emissions. Similarly, in the two hours 

following a cold start event, between 13 and 85% of indoor NMHC could be attributed to the 

vehicle cold start. Prior to the hot soak testing, average indoor benzene concentrations were 3.77 

μg/m3

, while during the hot soak, concentrations averaged 13.4 μg/m3. In the garage, 

concentrations averaged 121 μg/m3 during the cold start. Prior to a cold start, indoor benzene 

concentrations averaged 6.98 μg/m3

, while for the two hours following cold start, concentrations 

averaged 25.9 μg/m3

. In the garage, concentrations averaged 422 μg/m3 over the two hours 

following cold start. 

The study also conducted real-time monitoring of CO and total hydrocarbons (THC) 

within the house and garage. Overall, concentrations of CO and THC were relatively constant 

during hot-soaks, but following a cold start, indoor concentrations of CO and THC tended to rise 

sharply, and fall over the next two hours. This study provides direct evidence that a high fraction 

of indoor NMHC (or VOCs) are directly attributable to emission events occurring in the garage. 

Other studies have examined the influence of attached garages by comparing homes with 

and without attached garages. In another study from Alaska, 137 Anchorage homes underwent 

indoor air quality monitoring for benzene and other VOCs.106 Homes with attached garages had 

significantly higher concentrations of indoor benzene compared to homes without attached 

garages (70.8 μg/m3 vs. 8.6 μg/m3). In addition, elevated benzene indoors was also associated 

with the presence of a vehicle in the garage, fuel being opened in the garage, and the use of 

forced-air heaters. 

In another Alaska study, concentrations of benzene and toluene in indoor air were found 

to be not significantly associated with their urinary biomarkers, but indoor concentrations were 

associated with the number of gasoline-powered engines stored in the garage.107 In a recent 

follow-up to the study, ventilation patterns in two homes were evaluated using perfluorocarbon 

tracers and a multi-zone indoor air quality model.108 In the study, average garage concentrations 

were consistently elevated relative to the home. Furthermore, the study calculated the “virtual” 

source strengths for benzene and toluene within the garage, and the garage was the only major 

source of benzene within the home. Median garage source strengths for benzene ranged from 

14-126 mg/h. 

Several population-based surveys have also found evidence of the influence of attached 

garages. The National Human Exposure Assessment Survey (NHEXAS) Phase I pilot study in 

Arizona was a representative exposure survey of the population. It found that in non-smoking 

homes with attached garages, distribution of toluene concentrations indoors was shifted 

significantly higher in homes with attached garages.109 Homes with attached garages had 

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Final Regulatory Impact Analysis 

median toluene levels of 24 μg/m3, while homes without garages had median concentrations of 5 

μg/m3. The NHEXAS study in EPA Region 5 states was of similar design, but covering the 

states of the upper Midwest. Using multivariate statistics, investigators found that VOCs 

including benzene were associated with the storage of gasoline-powered equipment in an 

attached garage.110 

In one study from New Jersey, investigators evaluated the indoor air effects of a vehicle 

fueled with “M85” – an 85% methanol, 15% gasoline blend – parking in the garage of a single 

home.

111 Testing was undertaken with both normally-functioning and malfunctioning 

evaporative emissions controls, as well as with the HVAC system on and off. Garage benzene 

concentrations exceeded indoor concentration by approximately 10-fold. Furthermore, the room 

adjacent to the garage had substantially higher concentrations than a room on the opposite side of 

the house. This study provides evidence that the garage is a major source of benzene inside the 

house. 

Appendix 3A presents an EPA analysis of the effect of attached garages on indoor air 

under various scenarios. This study was undertaken to evaluate the magnitude of exposure 

underestimation using the national-scale exposure modeling techniques discussed above. Using 

a mass balance model, steady-state concentrations of benzene were calculated as a function of 

the concentration of air in the garage, the concentration of outdoor air, and the fraction of house 

air intake from a garage. Data were obtained from studies discussed above. Because it is 

unclear how well the homes studied to date represent the housing nationally, it is not currently 

feasible to provide a highly precise estimate of the effect of attached garages on benzene 

exposure nationally. Depending on how the available data are summarized, overall modeled 

exposure concentrations would be expected to increase between 1.2 and 6.6 μg/m3

 above average 

inhalation exposure concentrations to benzene from ambient sources (1.4 μg/m3, as discussed in 

Section 3.2). It should be noted that there is considerable uncertainty associated with this 

estimated range, as discussed in Appendix 3A. 

Proposed reductions in fuel benzene content, new standards for cold temperature exhaust 

emissions during vehicle starts, and reduced emissions from gas cans are all expected to 

significantly reduce this major source of exposure. 

3.1.3.4 Concentrations and Exposure in Parking Garages 

Relatively limited air quality data for parking garages is available in the literature. The 

following are results of air quality studies performed in parking garages, all of which indicate 

that air toxics and criteria pollutants measured in these environments are substantially higher 

than found in outdoor air. Because of the limited amount of data, we include results from some 

non-U.S. studies, although differences in fuels and control technology limited their applicability 

to the U.S. 

In November 1990, a study of microenvironments, partially funded by the US EPA, 

evaluated the potential range in concentrations of selected air toxics.112 Ten parking garages, 

along with gasoline stations and office buildings, were randomly chosen for sampling since they 

were among the least studied of the potentially important exposure microenvironments. The 

principal air contaminants monitored were benzene, formaldehyde, and CO. Additional 

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Final Regulatory Impact Analysis 

compounds included toluene, xylenes, 1,2-dichloroethane, chloroform, carbon tetrachloride, 

perchloroethylene, 1,1,1-trichloroethane, 1,3-butadiene, and trichloroethylene. The majority of 

the compounds measured were significantly higher inside the garage compared to the ambient 

sample. For example, the median 5-minute concentration of benzene was 67.1 μg/m3

 in the 

parking garage and 12.8 μg/m3 in ambient air. CO was 11000 ppb in the parking garage and 

2000 ppb in ambient air. The researchers identified elevated levels of selected air toxics in 

parking garages and pointed out the potential contribution from cold starts at the end of the work 

day. 

A more recent 2002 study was funded by The American Petroleum Institute to screen 

“high-end” exposure microenvironments as required by section 211(b) of the Clean Air Act.113 

An interim report is available. The study included measurements at underground parking 

garages and surface parking lots in several cities. Air toxics quantified included hydrocarbons 

(HCs), carbonyl compounds, BTEX, total VOC, and CO. When sampling at parking lot exits, 

spikes in pollutant concentrations were observed when vehicles accelerated out of the parking 

lot, while presumably prior to full catalyst warm-up. In underground garages, the levels of 

BTEX and other compounds of interest varied with traffic level and reached concentrations that 

were significantly higher than ambient levels outside the garage. The final report of the 211(b) is 

expected in 2007. 

A comparative study of indoor air quality in Hong Kong showed that the levels of CO, 

NOx, and nonmethane hydrocarbons (NMHC) detected in a local park garage were the highest 

among 13 other indoor sampling locations.114 The study did not specify the type or size of the 

chosen parking garage, but indicated that it was located in an urban commercial area. High 

indoor/outdoor ratios indicated that the air quality was mainly affected by indoor sources, 

namely the vehicle exhaust. They also concluded that the pollution generated might cause health 

hazards to the users and workers using such an environment. 

Another assessment of the air quality in indoor park garages was performed in Hong 

Kong in August through December 2000.115 Air samples were collected in two different garages 

(an enclosed and semi-enclosed parking garage) as well as outdoors (within 10 m of each 

parking garage) and analyzed for one hundred different C3-C12 VOCs. Other compounds 

measured included CO, CO2, PM10, and PM2.5. The CO levels in the enclosed garage were more 

than in the semi-enclosed garage, and double the levels of the outdoor air. The PM10 and PM2.5 

concentrations were also found to be higher in the parking garage environments than outdoors. 

High mass fractions of aliphatic and aromatic compounds detected in the enclosed garage 

showed that fuel evaporation and motor vehicular exhaust were the major contributors to the 

VOCs. The total concentrations of NMHC in the enclosed and semi-enclosed garages ranged 

from 580 to 4610 μg/m3 and 43.1 to 175 μg/m3, respectively. The mean concentration of NMHC 

measured in the enclosed garage (1910 μg/m3) was about 17 times higher than in the semienclosed garage (94.6 μg/m3

), and 3 times higher than measured at the outdoor sites. Not only 

was the level of VOCs higher in the enclosed garage, but also the abundance of species 

identified. The most abundant species in similar ranking order for both garages was toluene, 2­

methylbutane, m/p-xylenes, n-pentane, 2-methylpentane, n-hexane, and n-butane. Other major 

gasoline components such as benzene, xylenes, and C4-C7 saturated HCs were also very high in 

the enclosed garage. The difference between the two sites could be associated with the 

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Final Regulatory Impact Analysis 

ventilation and location, since the occupancy rates and fleet mixes were similar. The authors 

also noted that the absence of sunlight in the enclosed garage would result in a slower or 

negligible photochemical depletion rate of unsaturated hydrocarbons, and consequently an 

increased abundance of the species observed. 

In another study of multi-level parking garages in an Athens urban area, CO levels were 

characterized in autumn 1999.116 Samples were collected at the exit sites (ramp where the flow 

of vehicles was concentrated), the indoor site (first underground level where the majority of cars 

parked), and immediately outside of each garage. Results indicate that CO levels varied 

significantly over site, time, and day of measurement. The peak 1-hour value at the indoor sites 

ranged from 22.9 to 109.3 ppm. At the indoor site, levels showed little variation and remained 

high over time. The peak 1-hour value at the exit sites ranged from 8.9 to 57.3 ppm. At the exit 

sites, 15-minute maximum concentrations were 5-15 times higher than the maximum recorded 

CO level immediately outside the garage. CO levels on Saturday were much lower than a typical 

weekday due to the reduced traffic, and weekday values were highest during the afternoon 

sampling times (12:00-16:00 hour) corresponding with peak traffic volumes. 

In Mumbai, India, ambient levels of benzene were determined during different seasons at 

several different locations, including two parking areas.117 Parameters of the parking areas were 

not specified, but 24-hour geometric means of benzene measured 117.4 and 74.2 μg/m3 during 

the summer, 94.5 and 75.4 μg/m3 during the monsoon, and 148.0 and 703.0 μg/m3 during the 

winter seasons, respectively. These values were considerably higher in comparison to less 

heavily trafficked residential locations. The mean benzene concentrations of four different 

residential locations ranged from 4.7 to 32.9 μg/m3, 1.9 to 33.5 μg/m3, and 4.7 to 18.8 μg/m3, 

respectively, for the summer, monsoon, and winter seasons. The high concentrations in parking 

areas were attributed to cold start-up emissions of engines. 

A study in the UK of twelve underground parking garages identified high pollutant levels 

of NOx, CO, CO2, BTEX, and PM.118 The parking garages selected covered a cross-section of 

sizes (1 to 8 decks), ventilation system (natural and mechanical), designs (50 to 690 spaces), and 

usages (business, shopping, and/or residential). Monitoring sites were located inside and at the 

exit of the parking garage. The highest 15-minute average CO levels were measured at the exit 

of parking garages, but a number of the parking garages had CO levels consistently higher inside 

than at their exit. The NO2 measurements showed similar trends. Weekday benzene 

concentration measurements averaged over one hour inside the parking garage and at the exit 

ranged from 60 to 870 μg/m3 and 10 to 350 μg/m3, respectively. 

In Madrid, Spain, atmospheric pollution produced by vehicles in parking garages was 

studied.119 Two parking garages of different design were chosen for measurements of PM10, 

lead, 12 PAHs, and CO. In both garages, CO, NO, TSP, and lead concentrations directly 

correlated with vehicle traffic flow into and out of the garage. Also, higher values were observed 

on the weekdays than during the weekend, for CO, NO, PAHs, and TSP in both garages. For 

example, in one garage, the average daily TSP concentrations were 78-122 μg/m3 on the 

weekdays versus 39 μg/m3 on the weekend, which was similar to outdoor city average 

measurement (50 μg/m3

). The researchers conclude that maximum concentrations for NO were 

observed during maximum parking garage exits and therefore due to vehicle cold-starts. They 

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Final Regulatory Impact Analysis 

also conclude that the mechanical ventilation used in both garages was not sufficient to disperse 

the pollutants emitted by the vehicles. 

3.1.3.5 Concentrations and Exposure at Service Stations 

Although there is relatively limited air quality data for service stations available currently 

in the literature, the general consensus is that exposures to air toxics at service stations 

significantly exceed ambient background levels. The studies below measure personal exposures 

and concentrations during refueling either inside or outside of vehicles throughout the United 

States. Several studies conducted outside of the United States chronicle similar results but are 

not presented here due to differences in fuels and control technologies. 

The TEAM study from the 1980’s, described above, pumping gas and exposure to auto 

exhaust were significantly associated with elevated benzene exposure. People who filled their 

tanks with gasoline had twice as much benzene in their breath as people who did not. Estimated 

concentrations at the breathing zone could exceed 1000 μg/m3 (100 times the ambient level), 

based on the median breath benzene value measured (n=67) for those who had worked at or been 

in a service station during the past 24 hours. Since this study, implementation of fuel controls, 

onboard vapor recovery, and Stage II vapor recovery have changed emission and concentration 

levels as discussed in Section 3.1.1. 

In March 1990, another study randomly sampled 100 self-service filling stations 

throughout Southern California along with samples at 10 parking garages and 10 offices nearby 

those garages.120 The study took five-minute samples of 13 motor vehicle air pollutants (CO, 

formaldehyde, and VOCs) in each microenvironment and in the ambient environment. The 

median benzene concentration measured at the service stations was 28.8 μg/m3 with the 

maximum reported value of 323 μg/m3. The median benzene concentration in ambient air was 

significantly lower at 12.8 μg/m3. 

A 1993 National Institute for Occupational Safety and Health (NIOSH) study assessed 

benzene and MTBE concentrations and service station attendant exposures at service stations 

with and without Stage II vapor recovery in Cincinnati, Phoenix, and Los Angeles.121 The mean 

(and maximum) benzene exposure measurements were 96 (927), 160 (1662), and 192 (607) 

μg/m3, respectively. The study found that Stage II vapor recovery did not significantly reduce 

exposure to benzene during refueling. However, the efficiency of Stage II vapor recovery has 

improved over the years. Northeast States for Coordinated Air Use Management (NESCAUM) 

has suggested that Stage II vapor recovery systems are greater than 90% effective at capturing 

MTBE and benzene vapors during refueling.122 These systems would therefore be expected to 

reduce exposure beyond that shown in the NIOSH exposure assessment. 

In March 1996 to July 1997, concentrations of MTBE, benzene, and toluene were 

determined inside automobile cabins during fueling.123 Air samples were collected at service 

stations in New Jersey, and the mean benzene in-cabin concentration was 54.3 μg/m3 (n=46). 

The background concentration at the pump island measured 9.6 μg/m3 (n=36). The highest incabin concentrations for all three pollutants occurred in a car that had a malfunctioning vapor 

recovery system and in a series of cars sampled on an unusually warm, calm winter day when the 

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Final Regulatory Impact Analysis 

fuel volatility was high, the evaporation maximal, and the wind dispersion minimal. The incabin concentrations were also typically higher when the car window was opened during the 

entire fueling process. 

In a study conducted between summer 1998 and spring 1999, self-service gas station 

customers took part in a study to measure personal and breath concentrations of benzene at gas 

stations in New Jersey.124 Benzene exposure concentrations during refueling (with a median 

duration of three minutes) averaged 2.9 mg/m3 (SD = 5.8 mg/m3). Breath concentrations 

averaged 160 μg/m3

 (SD = 260 μg/m3). Breath benzene concentrations were significantly 

correlated with refueling exposure concentrations, which was itself significantly associated with 

refueling duration, time of year, and fuel octane grade. 

Most recently, as discussed in the section on in-vehicle and parking garage exposure and 

concentrations, a screening study of “high-end” exposure microenvironments was performed by 

the American Petroleum Institute.60 The study included several vehicle-related 

microenvironments in Houston and Atlanta during summer 2002. Among the various 

microenvironments examined, the highest short-term concentrations occurred during refueling. 

The in-vehicle average concentration of benzene measured during refueling was 46.0 μg/m3. 

3.1.3.6 Occupational Exposure 

Occupational settings can be considered a microenvironment in which exposure to 

benzene and other air toxics can occur. Occupational exposures to benzene from mobile sources 

or fuels can be several orders of magnitude greater than typical exposures in the nonoccupationally exposed population. Several key occupational groups are discussed below. 

Occupations that involve fuel distribution, storage, and tank remediation lead to elevated 

exposure to mobile-source related air toxics. Researchers published a review of benzene and 

total hydrocarbon exposures in the downstream petroleum industry, including exposure data 

from the past two decades among workers in the following categories: refinery, pipeline, marine, 

rail, bulk terminals, tank truck drivers, service stations, underground storage tanks, tank cleaning, 

and site remediation.125 The studies reviewed indicate that benzene exposure can range from <1 

to more than 10 mg/m3

, which is approximately three orders of magnitude higher than typical 

non-occupational exposures (although there are occurrences of high benzene exposures in nonoccupational settings as well). This review is relevant because of the potential for fuel benzene 

reductions to reduce their exposures as well. This statement is echoed by researchers in the 

occupational literature.126 Occupational exposures in this range have been associated with 

increased risk of certain leukemias in occupational epidemiology studies (Section 1.3.1). 

Handheld and non-handheld equipment operators may also be exposed to elevated 

concentrations of air toxics. As discussed below, several studies were conducted in work 

categories employing small engine equipment, such as lawn and garden workers, workers in 

construction/demolition, and others. Many of these occupations require the use of personal 

protective equipment to prevent high exposures to carbon monoxide or other species. At present, 

there are no representative samples of exposures among these categories. Non-occupational 

exposures from these equipment types may also be important contributors to overall exposure. 

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Final Regulatory Impact Analysis 

EPA recently conducted a study of occupational exposures among lawn and garden workers 

using riding tractors, walk-behind lawn mowers, string trimmers, and chainsaws.127 Results 

demonstrated that equipment operators can experience highly variable exposures, with shortterm personal concentrations of CO and PM2.5 ranging over two orders of magnitude. The study 

also reported operator breathing-zone concentrations of formaldehyde and acetaldehyde that 

were higher than background levels in all tests. This study illustrated the role of operator’s 

activity in affecting exposure levels to fuel-related air toxics. 

Another study provides some insight into the possible range of benzene exposures in 

workers who operate gasoline-powered engines, particularly those with 2-stroke engine 

cycles.128 A study of snowmobile rider exposures in Sweden found benzene concentrations 

ranging from under 10 μg/m3 to 2.5 mg/m3, a range of at least two orders of magnitude. 

Exposures measured on riders on the back of the vehicle ranged from 0.7-0.8 mg/m3. These 

measurements illustrate the potential for relatively high exposures when operating 2-stroke 

equipment, as used in this study. Yellowstone National Park commissioned a study in 2002 to 

examine occupational exposures of park employees to benzene, other VOCs, PM10, and CO.129 

Work shift benzene concentrations at a snowmobile entry gate 176.7 μg/m3, while snowmobilebound mobile patrol officers’ exposure concentrations averaged 137.20 μg/m3

. The highest 

observed work shift concentration in the study was 514.1 μg/m3. At major sites of tourist interest 

where snowmobiles parked, such as the Old Faithful geyser, concentrations averaged 41.3 to 

48.8 μg/m3. 15-minute “peak” samples of workers’ personal air ranged from 46.8 μg/m3 to 842.8 

μg/m3. This study provides an indication of the variability of occupational benzene exposure 

concentrations with time, and highlights the potential for elevated work shift exposures over 

several hours. 

A preliminary report published by the Northeast States for Coordinated Air Use 

Management further illustrates the occupational impact of nonroad heavy-duty diesel 

equipment.130 In-cabin and work site perimeter measurements were collected for diesel 

equipment emissions from the agricultural, construction (building and roadway), and lumber 

industries in the Northeast. Initial results indicate that PM2.5 concentrations were 1-16 times 

greater than the average ambient concentrations in each monitoring area. In-cabin exposures to 

PM2.5 for operators ranged from 2 μg/m3 to over 660 μg/m3

. Additionally, measured 

concentrations of acetaldehyde, benzene, and formaldehyde were found to be significantly 

elevated, although concentrations were not presented. 

In one recently-published study of diesel exhaust exposures in a representative sample of 

trucking terminals nationally, investigators applied structural equation modeling to data on 

personal exposure to diesel exhaust (as elemental carbon).131 The study found that worker 

exposure to elemental carbon depended on work area concentrations and worker tobacco use. 

Work area concentrations depended on the size and type of the trucking terminal, whether the 

work site was a mechanical shop, work site ventilation, and terminal yard concentrations. 

Terminal yard concentrations in turn were related to local meteorology, the proximity of 

interstate highways, surrounding industrial land uses, and region of the country. This study is 

valuable in showing how personal occupational exposures are a complicated function of many 

factors. Sophisticated statistical methods are needed to properly estimate models with highly 

complex covariance structures. 

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Final Regulatory Impact Analysis 

In addition, some occupations require that workers spend considerable time in vehicles, 

which increases the time they spend in a higher-concentration microenvironment. In-vehicle 

concentrations are discussed in Section 3.1.3.2.1 above. 

3.1.4 Uncertainties in Air Toxics Measurements 

A number of uncertainties limit our ability to fully describe the impacts of motor vehicle 

emissions. As described above, most people in the U.S. experience some level of exposure to 

emissions from motor vehicles. Thus, proper characterization of the level of these exposures is 

critical. However, the exposure assessment techniques used may not adequately represent the 

populations’ true exposures to motor vehicle emissions. 

Air quality and exposure measurements are expensive and therefore are limited. The 

high costs of measurement techniques affect the quantity of samples that can be collected and 

quantity of compounds that can be identified. As a result, measurements may only occur at 

central monitoring sites, rather than in microenvironments impacted by motor vehicle emissions 

or in personal breathing zones. Air quality monitoring at these central sites often do not 

represent actual exposures, especially for populations living near roads or with substantial 

occupational exposure. 

Monitoring samples are often integrated and therefore lack time resolution. This can 

result in difficulty in determining source contributions. Additionally, some compounds are hard 

to measure accurately. For example, 1,3-butadiene is very reactive in the ambient atmosphere 

and has a short atmospheric lifetime, estimated to be only two hours.132 Thus, this compound 

can easily break down before samples are analyzed. Also, a vapor pressure of 3.3 atm at 25oC 

makes it a very volatile compound. Secondary reactions are a confounding factor in air quality 

measurements and can add additional uncertainty to measured ambient concentrations. 

Personal exposure monitoring provides greater realism in describing a person’s actual 

exposure to air toxics. However, given the limitations on size of equipment, detection limits in 

personal exposure monitoring studies are sometimes greater than those found in studies using 

other techniques. 

3.2 Modeled Air Quality, Exposures, and Risks for Air Toxics 

3.2.1 National-Scale Modeled Air Quality, Exposure, and Risk for Air Toxics 

EPA assesses human health impacts from outdoor, inhalation, chronic exposures to air 

toxics in the National-Scale Air Toxics Assessment (NATA). It assesses lifetime risks assuming 

continuous exposure to levels of air toxics estimated for a particular point in time. The most 

recent NATA was done for the year 1999.133 It had four steps: 

1) Compiled a national emissions inventory of air toxics emissions from outdoor sources. 

The 1999 National Emissions Inventory is the underlying basis for the emissions 

information in the 1999 assessment. 

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Final Regulatory Impact Analysis 

2) Estimated ambient concentrations based on emissions as input to an air dispersion 

model (the Assessment System for Population Exposure Nationwide, or ASPEN 

model).134 

3) Estimated population exposures based on a screening-level inhalation exposure model 

(Hazardous Air Pollutant Exposure Model, version 5, or HAPEM5) and the estimated 

ambient concentrations (from the ASPEN model) as input to the exposure model.135 

4) Characterized 1999 potential public health risks due to inhalation of air toxics. This 

included cancer and noncancer effects, using available information on air toxics health 

effects, current EPA risk assessment and risk characterization guidelines, and estimated 

population exposures.136 

For this final rule, we have conducted air quality, exposure and risk modeling for the 

years 1999, 2015, 2020, and 2030, using the same general approach as the 1999 NATA. We 

modeled all the pollutants in Table 2.2-1 for both the reference case, which includes all control 

programs currently planned by EPA in regulations, and the control case, which includes the 

cumulative impacts of the standards proposed in this rule. These pollutants 

• Are on EPA’s list of hazardous air pollutants in Section 112 of the Clean Air Act 

• Are emitted by mobile sources 

• Are included in the National Emissions Inventory 

• Are included in the 1999 NATA 

Note that the modeling did not include diesel PM and diesel exhaust organic gases. EPA has 

previously done future-year projections of the mobile source contribution to air toxics 

concentrations, exposure, and risk for selected air toxics,137, 138, 139, 140 but prior to the proposal 

for this rule, had never done a comprehensive assessment that includes projections for all mobile 

source air toxics, as well as the stationary source contribution for those pollutants. It should be 

noted that the reference case assessment results developed for the proposal have been published 

in a peer reviewed journal article.141 

As discussed in Chapter 2, a number of major revisions to inventory methodology have 

been made relative to what was done for both the 1999 NATA, and air quality exposure and risk 

modeling for the proposal. These include revisions to cold start emissions, use of NMIM2005 

for nonroad equipment, addition of portable fuel container emissions, and changes to gasoline 

distribution inventories. Also, this final rule modeling for 1999 does not include data submitted 

by States for the 1999 NEI. In addition, the modeling for the final rule relied on an updated 

version of the HAPEM model, HAPEM6.142 HAPEM6 improves on HAPEM5 by accounting 

for the spatial variability of outdoor concentrations of air toxics within a census tract due to 

higher outdoor concentrations at locations near major roadways. Other improvements to 

HAPEM are discussed in section 3.2.1.2.1. This modeling work is discussed in more detail in an 

EPA technical report, “National Scale Modeling of Air Toxics for the Final Mobile Source Air 

Toxics Rule; Technical Support Document,” Report Number EPA-454/R-07-002. It should be 

noted that the control case modeling accounted only for the 0.62 percent standard, but not the 1.3 

vol% maximum average. Thus, the emission reductions from highway vehicles and other 

sources attributable to the fuel benzene standard are underestimated in many areas of the 

country, particularly in areas where fuel benzene levels were highest without control, such as the 

Northwest. 

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Final Regulatory Impact Analysis 

The NATA modeling framework has a number of limitations which prevent its use as the 

sole basis for setting regulatory standards. Even so, this modeling framework is very useful in 

identifying air toxic pollutants and sources of greatest concern, setting regulatory priorities, and 

informing the decision making process. 

Among the significant limitations of the framework is that it cannot be used to identify 

ambient “hot spots,” as mobile sources are not represented explicitly as roads or other locations 

of mobile source activity. In addition, this kind of modeling assessment cannot address the kinds 

of questions an epidemiology study might allow, such as the relationship between asthma or 

cancer risk, and proximity of residences to point sources, roadways and other sources of air 

toxics emissions. The framework also does not account for risk from potentially significant 

sources of air toxics originating indoors, such as stoves or out-gassing from building materials or 

evaporative benzene emissions from cars in attached garages. The ASPEN model performs well 

for some pollutants, but has also been shown to systematically underestimate pollutant 

concentrations relative to measured levels for certain pollutants such as metals and some reactive 

compounds. The cancer unit risk estimates for most pollutants are “upper bound,” meaning they 

probably lead to overestimates of risk. It should be noted, however, that the unit risk estimate for 

benzene is a maximum likelihood estimate, which is a best scientific estimate. The above 

limitations are discussed in detail in Section 3.2.1.4. 

Although we do not use it in this modeling, another tool that EPA uses to assess 

distributions of concentrations of air toxics at the national scale is the Community Multiscale Air 

Quality (CMAQ) modeling system.

143 CMAQ can account for photochemical destruction and 

production, deposition and regional transport of toxic air pollutants, and thus can be used to 

predict the concentrations of HAPs with significant atmospheric production. In general, 

predicted concentrations of air toxics from CMAQ were within a factor of 2 of measured values, 

with a tendency to underpredict measured ambient concentrations.144 CMAQ underpredicts 

monitored benzene levels more than ASPEN, because ASPEN values contain a large, added-on 

concentration based on monitored values of benzene. CMAQ has sophisticated photochemistry, 

but does not yet have the spatial resolution of dispersion models such as ASPEN, and thus 

accounts for less of the total variability in levels of air toxics with localized concentration 

gradients, such as benzene.145 Finally, CMAQ is requires more computational resources, which 

makes it more difficult to use for evaluating trends in a large number of air toxics over many 

years or impacts of control scenarios. 

Details of the methods used and presentation of key results are discussed in the following 

sections. Results do not account for other potentially significant sources of inhalation exposure, 

such as benzene emissions from sources in attached garages (such as vehicles, snowblowers, 

lawnmowers and gas cans). 

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Final Regulatory Impact Analysis 

3.2.1.1 Air Quality Modeling 

3.2.1.1.1 Methods 

Prior to performing air quality modeling of the projected emissions, the emissions from 

the stationary and mobile inventories (discussed in Chapter 2) are processed in the Emissions 

Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3 to create the emissions 

input files used by ASPEN to calculate air quality concentrations.146 In addition to projecting 

stationary and area source emissions to future years for some source categories, EMS-HAP 

spatially allocates emissions inventoried at the county level to the census tract level, and 

temporally allocates them to eight three-hour time periods throughout the day. Once the 

emissions are processed, they are input into ASPEN to calculate air quality concentrations. In 

addition to the emissions, ASPEN uses meteorological parameters and census tract centroid 

locations for concentration calculations. ASPEN estimates do not account for day-of-week or 

seasonal variations in emissions. The ASPEN model takes into account important determinants 

of pollutant concentrations, such as: rate of release, location of release, the height from which the 

pollutants are released, wind speeds and directions from the meteorological stations nearest to 

the release, breakdown of the pollutants in the atmosphere after being released (i.e., reactive 

decay), settling of pollutants out of the atmosphere (i.e., deposition), and transformation of one 

pollutant into another. The model first estimates concentrations at receptors arranged in rings 

around emission sources up to 50 kilometers away. The model then interpolates concentrations 

to census tract centroids. For 1999, meteorological conditions in 1999 and 2000 census tract 

data were used. 

In using ASPEN to estimate projected concentrations in 2015, 2020, and 2030 for this 

final rule, the same meteorology and census tract locations were used as for the 1999 NATA. 

Details of how ASPEN processed emissions data are provided in the technical document, 

“National-Scale Modeling of Mobile Source Air Toxic Emissions, Air Quality, Exposure and 

Risk for the Mobile Source Air Toxics Final Rule.” ASPEN only accounts for sources within a 

50-kilometer radius of each source when calculating ambient concentrations. Thus, the 

contribution to ambient levels of air toxics from sources further away than 50-kilometers, as well 

as the contribution of uninventoried sources, is addressed through the addition of a “background” 

term.

147 Mobile source pollutants which include a background component are 1,3-butadiene, 

acetaldehyde, benzene, formaldehyde, and xylenes. Each of the three projection years used the 

same 1999-based background. However, background levels are likely to change with emissions. 

Thus, for the proposal, a sensitivity analysis was done to evaluate the potential impact of not 

changing the background concentration (see Section 3.2.1.4). 

It should be noted that in the control case scenarios, we have modeled the cumulative 

impacts on air quality, exposure, and risk for all of the programs finalized today, not the impacts 

of individual programs. Were we to model each program individually, we anticipate that 

changes in air quality, exposure, and risk would track the patterns of emission changes closely. 

Also, for the final rule, we estimated the contribution of secondary formation to ambient 

concentrations of MSATs by applying ratios of secondary to primary concentrations from 1999 

NATA to the modeled primary concentrations for this rule. This is different from the approach 

3-34
 
Final Regulatory Impact Analysis 

used in the proposal where we projected precursor emissions and then modeled secondary 

formation. When we applied the ratio approach to the proposal’s primary concentrations, the 

results were very similar to the full modeling approach (see Section 3.2.1.3). The comparisons 

are discussed in the technical document cited above. 

We estimated the contributions to ambient concentrations for the following source 

sectors: major, area and other, onroad, nonroad, and background.b 

3.2.1.1.2 Air Quality Trends for Air Toxics: Reference Case 

Table 3.2-1 summarizes nationwide mean census tract ambient concentrations, without 

the controls being finalized in this rule, of mobile source air toxics in 1999 and projection years 

for the following source sectors: major sources, area and other sources, highway vehicles, 

nonroad sources, and background. The behavior of benzene is typical of the projected trends. 

Over 90% of the mobile source contribution to ambient benzene levels is attributable to gasoline 

vehicles and engines. Figure 3.2-1 depicts the trend in nationwide average census tract 

concentrations of benzene over this time period. The mobile source contribution to ambient 

benzene concentrations is projected to decrease over 40% by 2015, with a decrease in ambient 

benzene concentration from all sources of about 25%. Subsequently, increases in vehicle miles 

traveled (VMT) are projected to produce increasing concentrations. Summary tables providing 

data by State, and for reformulated and non-reformulated (i.e., conventional) gasoline areas, can 

be found in the docket for this rule. Due to greater population and vehicle activity, the average 

ambient benzene concentration in 1999 is much higher for counties in reformulated gasoline 

areas than non-reformulated gasoline areas – about 1.9 μg/m3 versus 1.2 μg/m3. However the 

percent reduction in average 2015 ambient concentration is similar regardless of fuel type – 22% 

for non-reformulated gasoline counties versus 29% for reformulated gasoline counties. 

b

 Major and “area and other” are stationary source emission sectors. Major sources, as defined by the Clean Air Act, 

are those stationary facilities that emit or have the potential to emit 10 tons of any one toxic air pollutant or 25 tons 

of more than one toxic air pollutant per year. Area and other sources include sources that generally have smaller 

emissions on an individual basis than "major sources" and are often too small or ubiquitous in nature to be 

inventoried as individual sources. "Area sources" include facilities that have air toxics emissions below the major 

source threshold as defined in the air toxics sections of the Clean Air Act and thus emit less than 10 tons of a single 

toxic air pollutant or less than 25 tons of multiple toxic air pollutants in any one year. Area sources include smaller 

facilities, such as dry cleaners. "Other sources" include sources such as wildfires and prescribed burnings that may 

be more appropriately addressed by other programs rather than through regulations developed under certain air 

toxics provisions (section 112 or 129) in the Clean Air Act. For example, wildfires and prescribed burning are being 

addressed through the burning policy agreed to by the Interim Federal Wildland Policy. “Background” includes 

emissions from transport and uninventoried sources. 

3-35
 
Final Regulatory Impact Analysis 

Table 3.2-1. Mean Ambient Concentrations of Mobile Source Air Toxics in 1999, 2015, 2020, and 2030, W

this Rule. 

1999 average concentrations (μg m-3) 2015 annual average conce

total 

Pollutant 

background 

(μg m-3) major 

area & 

other onroad nonroad 

(including 

background) major 

area & 

other onroad 

1,3-Butadiene 5.10E-02 1.97E-03 2.05E-02 5.20E-02 1.81E-02 1.44E-01 2.17E-03 2.05E-02 2.28E-02 2,2,4-Trimethylpentane 0.00E+00 2.16E-02 2.32E-02 7.29E-01 1.96E-01 9.70E-01 1.09E-02 2.69E-02 3.66E-01 Acetaldehyde 5.17E-01 2.94E-02 5.49E-02 6.78E-01 1.47E-01 1.43E+00 2.97E-02 5.71E-02 3.86E-01 Acrolein 0.00E+00 3.21E-03 2.93E-02 5.63E-02 2.27E-02 1.11E-01 3.53E-03 2.62E-02 2.42E-02 Benzene 3.94E-01 2.20E-02 1.40E-01 6.89E-01 1.77E-01 1.42E+00 1.55E-02 1.63E-01 3.79E-01 Chromium III 0.00E+00 8.22E-04 4.53E-04 3.22E-05 5.53E-05 1.36E-03 1.04E-03 6.16E-04 4.40E-05 Chromium VI 0.00E+00 1.07E-04 1.98E-04 2.15E-05 1.25E-05 3.39E-04 1.36E-04 2.72E-04 2.94E-05 Ethyl Benzene 0.00E+00 1.84E-02 9.00E-02 2.73E-01 9.73E-02 4.79E-01 1.24E-02 1.19E-01 1.35E-01 Formaldehyde 7.62E-01 3.99E-02 8.77E-02 4.65E-01 2.21E-01 1.58E+00 4.98E-02 9.82E-02 1.92E-01 Hexane 0.00E+00 6.68E-02 4.30E-01 2.34E-01 8.56E-02 8.17E-01 5.94E-02 5.21E-01 1.16E-01 MTBE 0.00E+00 1.30E-02 6.04E-02 4.00E-01 4.04E-01 8.77E-01 1.38E-02 6.52E-02 1.05E-01 Manganese 0.00E+00 2.71E-03 2.22E-03 1.73E-05 5.46E-06 4.95E-03 3.23E-03 2.92E-03 2.36E-05 Naphthalene 0.00E+00 4.56E-03 4.11E-02 1.46E-02 4.36E-03 6.46E-02 3.97E-03 5.01E-02 7.90E-03 Nickel 0.00E+00 7.76E-04 1.42E-03 3.96E-05 9.98E-05 2.33E-03 8.87E-04 1.62E-03 5.43E-05 POM 0.00E+00 4.93E-03 1.61E-02 1.73E-03 8.60E-04 2.37E-02 3.79E-03 1.86E-02 9.13E-04 Propionaldehyde 0.00E+00 1.01E-02 2.33E-02 1.68E-01 4.27E-02 2.45E-01 9.31E-03 2.39E-02 8.24E-02 Styrene 0.00E+00 2.52E-02 1.40E-02 2.98E-02 3.65E-03 7.27E-02 3.00E-02 1.89E-02 1.50E-02 Toluene 0.00E+00 2.03E-01 8.05E-01 1.81E+00 4.18E-01 3.24E+00 1.43E-01 1.06E+00 9.00E-01 Xylenes 1.70E-01 9.98E-02 5.59E-01 1.01E+00 3.99E-01 2.23E+00 8.22E-02 7.60E-01 4.98E-01 

3-36
Final Regulatory Impact Analysis 

Table 3.2-1 (cont’d). Mean Ambient Concentrations of Mobile Source Air Toxics in 1999, 2015, 2020, a

Controls in this Rule. 

2020 annual average concentrations (μg m-3) 2030 annual average conce

total 

Pollutant 

background 

(μg m-3) major 

area & 

other onroad nonroad 

(including 

background) major 

area & 

other onroad 

1,3-Butadiene 5.10E-02 2.34E-03 2.05E-02 2.37E-02 1.14E-02 1.09E-01 2.34E-03 2.05E-02 2.78E-02 2,2,4-Trimethylpentane 0.00E+00 1.17E-02 2.84E-02 3.66E-01 1.14E-01 5.20E-01 1.17E-02 2.84E-02 4.24E-01 Acetaldehyde 5.17E-01 3.10E-02 5.83E-02 3.98E-01 1.09E-01 1.11E+00 3.10E-02 5.83E-02 4.69E-01 Acrolein 0.00E+00 3.96E-03 2.54E-02 2.50E-02 1.91E-02 7.34E-02 3.96E-03 2.54E-02 2.94E-02 Benzene 3.94E-01 1.70E-02 1.69E-01 3.88E-01 1.18E-01 1.09E+00 1.70E-02 1.69E-01 4.54E-01 Chromium III 0.00E+00 1.17E-03 6.96E-04 4.84E-05 5.90E-05 1.97E-03 1.17E-03 6.96E-04 5.94E-05 Chromium VI 0.00E+00 1.54E-04 3.07E-04 3.23E-05 1.34E-05 5.07E-04 1.54E-04 3.07E-04 3.96E-05 Ethyl Benzene 0.00E+00 1.39E-02 1.31E-01 1.35E-01 5.78E-02 3.38E-01 1.39E-02 1.31E-01 1.57E-01 Formaldehyde 7.62E-01 5.65E-02 1.03E-01 1.97E-01 1.64E-01 1.28E+00 5.65E-02 1.03E-01 2.31E-01 Hexane 0.00E+00 6.53E-02 5.62E-01 1.07E-01 6.13E-02 7.96E-01 6.53E-02 5.62E-01 1.18E-01 MTBE 0.00E+00 1.55E-02 6.67E-02 8.48E-02 1.12E-01 2.79E-01 1.55E-02 6.67E-02 8.42E-02 Manganese 0.00E+00 3.59E-03 3.21E-03 2.60E-05 6.83E-06 6.83E-03 3.59E-03 3.21E-03 3.19E-05 Naphthalene 0.00E+00 4.46E-03 5.32E-02 7.86E-03 4.80E-03 7.03E-02 4.46E-03 5.32E-02 9.11E-03 Nickel 0.00E+00 9.61E-04 1.78E-03 5.97E-05 1.20E-04 2.92E-03 9.61E-04 1.78E-03 7.34E-05 POM 0.00E+00 4.21E-03 1.90E-02 9.47E-04 7.71E-04 2.49E-02 4.21E-03 1.90E-02 1.12E-03 Propionaldehyde 0.00E+00 9.35E-03 2.45E-02 8.45E-02 2.78E-02 1.46E-01 9.35E-03 2.45E-02 9.84E-02 Styrene 0.00E+00 3.44E-02 2.09E-02 1.57E-02 2.21E-03 7.32E-02 3.44E-02 2.09E-02 1.85E-02 Toluene 0.00E+00 1.60E-01 1.16E+00 9.11E-01 2.50E-01 2.48E+00 1.60E-01 1.16E+00 1.06E+00 Xylenes 1.70E-01 9.29E-02 8.38E-01 5.04E-01 2.18E-01 1.82E+00 9.29E-02 8.38E-01 5.86E-01 

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Final Regulatory Impact Analysis 

Figure 3.2-1. Nationwide Average Benzene Concentration, 1999-2030, Without Controls in 

this Rule. 

0.00 

0.20 

0.40 

0.60 

0.80 

1.00 

1.20 

1.40 

1.60 

ug/

m3 

background 

nonroad 

highway 

area and other 

major 

1999 2015 2020 2030 

Year 

3.2.1.1.3 Distributions of Air Toxic Concentrations across the U. S.: Reference Case 

Table 3.2-2 gives the distribution of census tract concentrations, summed across all 

source sectors and background, for mobile source air toxics across the nation in 2020, absent the 

controls being finalized in this rule. Distributions for other years are similar. Summary tables 

providing distributions for other years, as well as distributions by State and for reformulated and 

non-reformulated gasoline areas, can be found in the docket for this rule. From this table, it can 

be seen that 95th percentiles of average census tract concentrations for mobile-source dominated 

pollutants such as benzene and 1,3-butadiene are typically two to five times higher than the 

median of census tract concentrations, even though mobile source emissions are widely 

dispersed. For pollutants with large major source contributions (e.g., manganese), the 95th 

percentile of census tract averages can be much higher than the median. In addition, average 

census tract concentrations can span one to several orders of magnitude. Thus, there is 

considerable variation in average concentrations across the U.S. 

Figure 3.2-2 depicts the geographic distribution of county median concentrations of 

benzene in 2020. Relatively high levels are seen in the Northeast, Southern California, Florida, 

parts of Texas, and the Great Lakes Region, where there is high population density and thus high 

vehicle and nonroad equipment activity. Relatively high levels are also seen in the Pacific 

Northwest, parts of Alaska, and the upper Great Lakes region. Analysis of fuel survey data 

3-38
 
Final Regulatory Impact Analysis 

Table 3.2-2. National Distribution of Census Tract Concentrations for Mobile Source Air 

Toxics in 2020, Without Controls in this Rule. 

2020 concentration (μg m-3) distribution 

5th 10th 25th 75th 90th 95th 

Pollutant percentile percentile percentile Median percentile percentile percentile 

1,3-Butadiene 3.03E-03 5.60E-03 3.12E-02 8.36E-02 1.30E-01 1.98E-01 3.28E-01 

2,2,4-Trimethylpentane 3.83E-02 7.00E-02 1.74E-01 3.79E-01 6.80E-01 1.12E+00 1.50E+00 

Acetaldehyde 5.45E-01 5.82E-01 6.99E-01 9.41E-01 1.29E+00 1.84E+00 2.49E+00 

Acrolein 6.04E-03 9.78E-03 2.09E-02 4.41E-02 8.64E-02 1.71E-01 2.71E-01 

Benzene 3.42E-01 4.15E-01 6.33E-01 9.37E-01 1.32E+00 1.90E+00 2.36E+00 

Chromium III 5.73E-06 1.52E-05 6.40E-05 2.41E-04 7.31E-04 2.34E-03 4.89E-03 

Chromium VI 3.52E-06 8.79E-06 3.56E-05 1.22E-04 3.32E-04 9.08E-04 1.55E-03 

Ethyl Benzene 2.04E-02 3.79E-02 1.01E-01 2.30E-01 4.06E-01 6.70E-01 9.60E-01 

Formaldehyde 4.08E-01 5.29E-01 8.08E-01 1.16E+00 1.52E+00 2.12E+00 2.67E+00 

Hexane 3.27E-02 6.16E-02 1.90E-01 4.76E-01 8.93E-01 1.70E+00 2.81E+00 

MTBE 3.34E-03 7.88E-03 2.39E-02 7.22E-02 2.44E-01 8.80E-01 1.30E+00 

Manganese 1.33E-05 4.35E-05 2.04E-04 8.68E-04 3.53E-03 1.42E-02 2.10E-02 

Naphthalene 2.88E-03 5.91E-03 1.86E-02 4.48E-02 8.82E-02 1.67E-01 2.37E-01 

Nickel 1.38E-05 3.80E-05 1.67E-04 6.65E-04 2.01E-03 4.78E-03 8.17E-03 

POM 1.72E-03 2.94E-03 5.73E-03 1.19E-02 2.08E-02 3.62E-02 5.78E-02 

Propionaldehyde 1.24E-02 2.13E-02 4.81E-02 1.07E-01 1.93E-01 3.26E-01 4.33E-01 

Styrene 2.52E-03 4.88E-03 1.23E-02 2.70E-02 5.39E-02 1.06E-01 1.75E-01 

Toluene 1.54E-01 2.83E-01 7.34E-01 1.64E+00 2.96E+00 5.31E+00 7.43E+00 

Xylenes 2.66E-01 3.43E-01 6.35E-01 1.22E+00 2.06E+00 3.61E+00 5.38E+00 

indicate higher than average fuel benzene levels in these areas. These areas also have higher 

benzene emissions in winter due to cold starts. Higher benzene levels in Idaho are not due to 

fuel benzene levels, but are primarily due to wildfire emission estimates, which were determined 

to be an error in the 1999 National Emissions Inventory and the subsequent projections. 

Similar benzene median county concentration maps for 1999, 2015, and 2030 can be 

found in the docket for this rule, along with maps for other mobile source air toxics and tables of 

concentration distributions. 

3.2.1.1.4 Impacts of Controls on Ambient Concentrations 

The standards being finalized in this rule will substantially reduce ambient concentrations 

of air toxics across the United States. As noted above, these results reflect the cumulative effects 

of all of the programs finalized in today’s rule, not the individual programs. Table 3.2-3 shows 

the reduction in nationwide average census tract concentrations of MSATs from all sources in 

2015, 2020 and 2030. Table 3.2-4 shows the reduction in the highway vehicle contribution to 

nationwide average census tract concentrations of MSATs. Table 3.2-5 shows that in 2030, the 

highway vehicle portion of ambient benzene concentrations will be reduced almost 45% across 

the U.S., the nonroad equipment contribution will be reduced about 10%, and 

3-39
 
Final Regulatory Impact Analysis 



 

Figure 3.2-2. Geographic Distribution of County Median Concentrations (μg/m3) of 

Benzene in 2020 Without Controls in this Rule. 

Concentrations 

0.063 - 0.338 

0.339 - 0.567 

0.568 - 0.880 

0.881 - 1.316 

1.317 - 2.114 

2.115 - 4.929
Final Regulatory Impact Analysis 

Table 3.2-3. Nationwide Average Census Tract Concentrations of MSATs, With and Without Controls 

2020, and 2030. 

Reference 

2015 

Control % Reduction Reference 

2020 

Control % Reduction Reference 

1,3-Butadiene 1.07E-01 1.03E-01 3.6 1.09E-01 1.03E-01 5.7 1.15E-01 2,2,4-Trimethylpentane 5.19E-01 4.53E-01 12.7 5.20E-01 4.19E-01 19.5 5.88E-01 Acetaldehyde 1.10E+00 1.04E+00 5.8 1.11E+00 1.01E+00 9.1 1.19E+00 Acrolein 7.20E-02 6.79E-02 5.7 7.34E-02 6.69E-02 8.9 8.05E-02 Benzene 1.07E+00 9.56E-01 10.3 1.09E+00 9.38E-01 13.6 1.17E+00 Chromium III 1.76E-03 1.76E-03 0.0 1.97E-03 1.97E-03 0.0 1.98E-03 Chromium VI 4.50E-04 4.50E-04 0.0 5.07E-04 5.07E-04 0.0 5.15E-04 Ethyl Benzene 3.24E-01 2.99E-01 7.5 3.38E-01 3.01E-01 11.1 3.66E-01 Formaldehyde 1.27E+00 1.24E+00 2.3 1.28E+00 1.24E+00 3.6 1.33E+00 Hexane 7.56E-01 7.37E-01 2.5 7.96E-01 7.70E-01 3.2 8.14E-01 MTBE 2.93E-01 2.82E-01 3.5 2.79E-01 2.66E-01 4.6 2.92E-01 Manganese 6.17E-03 6.17E-03 0.0 6.83E-03 6.83E-03 0.0 6.84E-03 Naphthalene 6.65E-02 6.65E-02 0.0 7.03E-02 7.03E-02 0.0 7.23E-02 Nickel 2.67E-03 2.67E-03 0.0 2.92E-03 2.92E-03 0.0 2.95E-03 POM 2.40E-02 2.40E-02 0.0 2.49E-02 2.49E-02 0.0 2.52E-02 Propionaldehyde 1.44E-01 1.33E-01 7.8 1.46E-01 1.28E-01 12.2 1.62E-01 Styrene 6.61E-02 6.33E-02 4.3 7.32E-02 6.87E-02 6.2 7.63E-02 Toluene 2.35E+00 2.18E+00 7.1 2.48E+00 2.22E+00 10.4 2.65E+00 Xylenes 1.73E+00 1.64E+00 5.3 1.82E+00 1.68E+00 7.8 1.93E+00 

3-41
Final Regulatory Impact Analysis 

Table 3.2-4. Nationwide Highway Vehicle Contribution to Average Census Tract Concentrations of M

Without Controls in this Rule, 2015, 2020, and 2030. 

Reference 

2015 

Control 

% 

Reduction Reference 

2020 

Control % Reduction Reference 

1,3-Butadiene 2.28E-02 1.89E-02 17.0 2.37E-02 1.74E-02 26.3 2.78E-02 2,2,4-Trimethylpentane 3.66E-01 3.06E-01 16.3 3.66E-01 2.71E-01 25.9 4.24E-01 Acetaldehyde 3.86E-01 3.22E-01 16.5 3.98E-01 2.97E-01 25.4 4.69E-01 Acrolein 2.42E-02 2.01E-02 17.0 2.50E-02 1.85E-02 26.2 2.94E-02 Benzene 3.79E-01 2.83E-01 25.3 3.88E-01 2.55E-01 34.2 4.54E-01 Chromium III 4.40E-05 4.40E-05 0.0 4.84E-05 4.84E-05 0.0 5.94E-05 Chromium VI 2.94E-05 2.94E-05 0.0 3.23E-05 3.23E-05 0.0 3.96E-05 Ethyl Benzene 1.35E-01 1.14E-01 16.0 1.35E-01 1.01E-01 25.6 1.57E-01 Formaldehyde 1.92E-01 1.62E-01 15.3 1.97E-01 1.50E-01 23.6 2.31E-01 Hexane 1.16E-01 1.05E-01 9.8 1.07E-01 8.89E-02 16.9 1.18E-01 MTBE 1.05E-01 1.01E-01 4.3 8.48E-02 7.77E-02 8.3 8.42E-02 Manganese 2.36E-05 2.36E-05 0.0 2.60E-05 2.60E-05 0.0 3.19E-05 Naphthalene 7.90E-03 7.90E-03 0.0 7.86E-03 7.86E-03 0.0 9.11E-03 Nickel 5.43E-05 5.43E-05 0.0 5.97E-05 5.97E-05 0.0 7.34E-05 POM 9.13E-04 9.13E-04 0.0 9.47E-04 9.47E-04 0.0 1.12E-03 Propionaldehyde 8.24E-02 7.12E-02 13.6 8.45E-02 6.66E-02 21.1 9.84E-02 Styrene 1.50E-02 1.22E-02 18.8 1.57E-02 1.12E-02 28.8 1.85E-02 Toluene 9.00E-01 7.47E-01 17.1 9.11E-01 6.66E-01 26.9 1.06E+00 Xylenes 4.98E-01 4.14E-01 16.9 5.04E-01 3.69E-01 26.7 5.86E-01 

3-42
Final Regulatory Impact Analysis 

Table 3.2-5. Contributions of Source Sectors to Nationwide Average Census Tract Concentrations of B

Without Controls in this Rule, 2015, 2020, and 2030. 

2015 annual average concentrations (μg m-3) 2020 annual average concentrations (μg m-3) 2030 annual av

major 

area & 

other 

highway 

vehicles nonroad 

total 

(including 

background) major area & other 

highway 

vehicles nonroad 

total 

(including 

background) major 

area & 

other 

Reference 1.55E-02 1.63E-01 3.79E-01 1.14E-01 1.07E+00 1.70E-02 1.69E-01 3.88E-01 1.18E-01 1.09E+00 1.70E-02 1.69E-01 

Control 1.54E-02 1.61E-01 2.83E-01 1.02E-01 9.56E-01 1.69E-02 1.67E-01 2.55E-01 1.05E-01 9.38E-01 1.69E-02 1.67E-01 % Difference 0 -1 -25 -10 -10 0 -1 -34 -10 -14 0 -1 Average Nationwide Difference in Ambient Benzene Concentration -- Non RFG Areas Reference 1.08E-02 1.43E-01 2.96E-01 8.15E-02 8.93E-01 1.20E-02 1.48E-01 3.06E-01 8.34E-02 9.11E-01 1.20E-02 1.48E-01 Control 1.08E-02 1.41E-01 2.17E-01 6.82E-02 7.99E-01 1.20E-02 1.46E-01 2.00E-01 6.95E-02 7.89E-01 1.20E-02 1.46E-01 % Difference 0 -2 -27 -16 -11 0 -2 -35 -17 -13 0 -2 Average Nationwide Difference in Ambient Benzene Concentration -- RFG Areas Reference 2.39E-02 1.99E-01 5.29E-01 1.72E-01 1.38E+00 2.58E-02 2.08E-01 5.34E-01 1.79E-01 1.40E+00 2.58E-02 2.08E-01 Control 2.38E-02 1.97E-01 4.02E-01 1.63E-01 1.24E+00 2.58E-02 2.05E-01 3.54E-01 1.70E-01 1.21E+00 2.58E-02 2.05E-01 % Difference 0 -1 -24 -5 -10 0 -1 -34 -5 -14 0 -1 

3-43
Final Regulatory Impact Analysis 

the area source contribution will be reduced about 1 to 2%. The reduction for area sources is due 

to the impacts of fuel benzene control on gasoline distribution emissions, and reductions in 

portable fuel container (PFC) emissions from PFC and fuel benzene controls. Reductions in 

non-reformulated gasoline areas are even larger. It should be noted that the estimated total 

reductions in ambient concentrations from all sources are probably significantly underestimated, 

since we could not account for the impacts of controls on background levels, which includes 

transport of emissions from these sources. Figure 3.2-3 presents the distribution of percent 

reductions in median ambient benzene concentrations for U.S. counties with the controls being 

finalized in 2030. Again, since the 1.3% maximum average fuel benzene standard is not 

included in the modeling, reductions in some parts of the country, including the Pacific 

Northwest, are underestimated. Summary tables providing data by State, as well as maps of 

MSAT concentrations with controls and percent reductions with controls, can be found in the 

docket for the rule.


 

Figure 3.2-3. Distribution of Percent Reductions in Median Ambient Benzene 
 

Concentrations, 2030, for U. S. Counties with the Controls in this Rule. 
 

Percent difference 

-13.547% - -6.407% 

-6.406% - -4.241% 

-4.240% - -2.863% 

-2.862% - -1.821% 

-1.820% - -0.956% 

-0.955% - -0.008%

3.2.1.2 Exposure and Risk Modeling 

3.2.1.2.1 Methods 

The HAPEM6 exposure model used in this assessment is the most recent version in a 

series of models that the EPA has used to model population exposures and risks at the urban and 

national scale in a number of assessments.148, 149, 150 HAPEM6 is designed to assess average 

3-44
 
Final Regulatory Impact Analysis 

long-term inhalation exposures of the general population, or a specific sub-population, over 

spatial scales ranging from urban to national. HAPEM6 uses the general approach of tracking 

representatives of 6 specified age groups as they move among indoor and outdoor 

microenvironments and among geographic locations (a total of 14, HAPEM5 had 37). The 

estimated pollutant concentrations in each microenvironment visited are combined into a timeweighted average concentration, which is assigned to members of the demographic group. 

HAPEM calculates 30 replicates with different exposures for each demographic group. These 

data can be used to develop a distribution of exposures for the entire U. S. population. 

HAPEM6 uses five primary sources of information: year 2000 population data from the 

U.S. Census, population activity data, air quality data, roadway locations, and 

microenvironmental data. The population data used are obtained from the U.S. Census. Two 

kinds of activity data are used: activity pattern data and commuting pattern data. The activity 

pattern data quantify the amount of time individuals spend in a variety of microenvironments and 

come from EPA’s Consolidated Human Activity Database (CHAD).151 The commuting data 

contained in the HAPEM6 default file were derived from the year 2000 U.S. Census, and 

includes the number of residents of each tract that work in that tract and every other U.S. Census 

tract, as well as data on commuting times and distances. The air quality data come from ASPEN 

(after background has been added). The road locations are determined from geographic 

information system files from the U.S. Census. The microenvironmental data consist of factors 

that estimate air toxic concentrations in specific microenvironments, based on penetration of 

outdoor air into the microenvironment, proximity of the microenvironment to the emission 

source, and emission sources within the microenvironment. These factors vary among 

pollutants.152 

New to HAPEM6 are algorithms which account for the gradient in concentrations of 

primary (directly emitted) mobile source air toxics within 200 meters of major roadways. 153 

HAPEM6 adjusts ambient concentrations generated by ASPEN for each census tract using 

concentration gradients developed with the CALPUFF dispersion model.154 For locations within 

75 meters and from 75 to 200 meters from major roads, ambient concentrations are adjusted 

upward, while locations further from major roadways are adjusted downward. These 

adjustments are consistent with results from prior modeling studies that explicitly accounted for 

concentration gradients around major roads within census tracts.155 These adjusted 

concentrations are then employed in microenvironmental concentration calculations. 

HAPEM6 has a number of other technical improvements over the previous version of 

HAPEM. These improvements, along with other details of the model, are described in the 

HAPEM6 User’s Guide.156 In short, HAPEM6 reduces the number of demographic groups to 6 

age-based groups from 10 age-gender groups in HAPEM5, and reduces the number of 

microenvironments modeled, from 37 to 14. This reduces modeling run time significantly with 

little impact on results. HAPEM6 also accounts for commuting time better, basing commute 

times and travel modes for each census tract on distributions reported in the 2000 Census. The 

HAPEM runs used year 2000 census data. Average lifetime exposure for an individual in a 

census tract was calculated from data for individual demographic groups using a post-processing 

routine. We estimated the contributions to ambient concentrations for the following source 

sectors: major, area and other, onroad, nonroad, and background. 

3-45
 
Final Regulatory Impact Analysis 

Once HAPEM runs were completed, cancer risk and noncancer risk were calculated for 

each of the mobile source air toxic pollutants, based on population exposure distributions. In the 

HAPEM6 output, for each source category, there are 30 replicate exposure concentrations for 

each of the six demographic groups (180 concentrations per census tract for each source 

category). For each source category and each of the 30 replicates, a lifetime exposure 

concentration was calculated. A risk estimate was then calculated for each of the 30 replicates. 

The resulting data were used to develop distributions of population risks at various summary 

levels (census tract, county, state, national). More detail is provided in the technical support 

document. Table 3.2-6 lists the pollutants with their respective unit risk estimates (UREs) for 

cancer calculations and reference concentrations (RfCs) for noncancer calculations. These are 

the same values used in the 1999 NATA, and more detailed information on how dose-response 

values were selected is provided at the website for that assessment. Also listed are the cancer 

weight of evidence classifications and target organ system(s) for noncancer calculations. 

Table 3.2-6. Dose-Response Values Use in Risk Modeling (Concentrations in μg/m3) 

HAP Carcinogen 

Class 

URE 

(per μg/m3) 

Source Organ 

Systems 

RfC (mg/ 

m3) 

Source 

1,3-Butadiene A 3.0x10-5 IRIS Reproductive 2.0x10-3 

2,2,4­

Trimethylpentane 

N/A N/A N/A N/A 

Acetaldehyde B2 2.2x10-6 IRIS Respiratory 9.0x10-3 IRIS 

Acrolein 0 Respiratory 2.0x10-5 IRIS 

Benzene A 7.8x10-6* IRIS Immune 3.0x10-2 IRIS 

Chromium III N/A N/A N/A N/A 

Chromium VI A 1.2x10-2 IRIS Respiratory 1.0x10-4 IRIS 

Ethyl Benzene 0 Developmental 1.0 IRIS 

Formaldehyde B 5.5x10-9 CIIT Respiratory 9.8x10-3 ATSDR 

Hexane N/A Respiratory, 

Neurological 

2.0x10-1 IRIS 

Manganese N/A Neurological 5.0x10-5 IRIS 

MTBE N/A Liver, Kidney, 3.0 IRIS 

Ocular 

Naphthalene C 3.4x10-5 CAL Respiratory 3.0x10-3 IRIS 

Nickel A 1.6x10-4 EPA/ 

OAQPS 

Respiratory, 

Immune 

6.5x10-5 CAL 

POM1 B2 5.5x10-5 OAQPS N/A 

POM2 B2 5.5x10-5 OAQPS N/A 

POM3 B2 1.0x10-1 OAQPS N/A 

POM4 B2 1.0x10-2 OAQPS N/A 

POM5 B2 1.0x10-3 OAQPS N/A 

POM6 B2 1.0x10-4 OAQPS N/A 

POM7 B2 1.0x10-5 OAQPS N/A 

POM8 B2 2.0x10-4 OAQPS N/A 

Styrene N/A Neurological 1.0 IRIS 

Toluene N/A Respiratory, 

Neurological 

4.0x10-1 IRIS 

Xylenes N/A Neurological 1.0x10-1 IRIS 

*represents upper end of a range of MLE values 

3-46 
Final Regulatory Impact Analysis 

The weight of evidence classifications provided in this table were developed under EPA’s 1986 

risk assessment guidelines where: 

A = Known human carcinogen 

B1 = Probable human carcinogen, based on incomplete human data 

B2 = Probable human carcinogen, based on adequate animal data 

C = Possible human carcinogen 

Dose-response values were selected using the following hierarchy: 

1) EPA IRIS assessments. 
 

2) Agency for Toxic Substances and Disease Registry (ATSDR) minimum risk levels
 

(MRLs) for noncancer effects – used as RfC. 
 

3) California Office of Environmental Health Hazard Assessment (OEHHA) values.
 

There are a number of exceptions to this hierarchy: 

1) Formaldehyde -- EPA no longer considers the formaldehyde URE reported in IRIS, 

which is based on a 1987 study, to represent the best available science in the peerreviewed literature. Accordingly, the 1999 risk estimates for formaldehyde are based on a 

dose-response value developed by the CIIT Centers for Health Research (formerly the 

Chemical Industry Institute of Toxicology) and published in 1999. This issue is 

discussed in Chapter 1 of the RIA. 

2) Nickel -- The IRIS URE for nickel inhalation shown in Table 3.2.-6 was derived from 

evidence of the carcinogenic effects of insoluble nickel compounds in crystalline form. 

Soluble nickel species, and insoluble species in amorphous form, do not appear to 

produce genotoxic effects by the same toxic mode of action as insoluble crystalline 

nickel. Nickel speciation information for some of the largest nickel-emitting sources 

(including oil combustion, coal combustion, and others) suggests that at least 35% of 

total nickel emissions may be soluble compounds. The remaining insoluble nickel 

emissions are not well-characterized, however. Consistent with this limited information, 

this analysis has conservatively assumed that 65% of emitted nickel is insoluble, and that 

all insoluble nickel is crystalline. On this basis, the nickel URE (based on nickel 

subsulfide, and representative of pure insoluble crystalline nickel) was adjusted to reflect 

an assumption that 65% of the total mass of nickel may be carcinogenic. The ATSDR 

MRL in Table 3.2.-6 was not adjusted, however, because the noncancer effects of nickel 

are not thought to be limited to the crystalline, insoluble form. 

3) POM -- POM was divided into eight toxicity categories to cover the range of unit 

risks of the individual POM species and POM groups contained in the 1999 NEI. The 

unit risks for those eight categories were based on the midpoint of the range of unit risks 

defining the toxicity category. More details on the development of these unit risks can be 

found on the website for the 1999 NATA and in Appendix H of the 2001 EPA draft 

report to the Science Advisory Board on the 1996 National-Scale Assessment.157 

Individual cancer risk estimates (the product of unit risk estimates and exposure levels) 

for various pollutants were assumed to be additive, since there was no evidence of non-additive 

3-47
 
Final Regulatory Impact Analysis 

interactions for any of the pollutants. Most of the estimates are based on the statistical upper 

confidence limit (UCL) of the fitted dose-response curve, but the estimates for hexavalent 

chromium, nickel, and benzene are based on the statistical best fit (“maximum likelihood 

estimate,” or MLE). Except for benzene and chromium, where risks are based on maximum 

likelihood dose-response values, risks from mobile source air toxics should all be considered 

upper-bound values. True risks could be greater, but are likely to be lower, and could be zero. 

To express chronic noncancer hazards, we used the RfC as part of a calculation called the 

hazard quotient (HQ), which is the ratio between the concentration to which a person is exposed 

and the RfC. A value of the HQ less than one indicates that the exposure is lower than the RfC 

and that no adverse health effects would be expected. A value of the HQ greater than one 

indicates that the exposure is higher than the RfC. However, because many RfCs incorporate 

protective assumptions in the face of uncertainty, an HQ greater than one does not necessarily 

suggest a likelihood of adverse effects. Furthermore, the HQ cannot be translated to a probability 

that adverse effects will occur and is not likely to be proportional to risk. A HQ greater than one 

can best be described as indicating that a potential exists for adverse health effects. However 

one should evaluate the weight of evidence supporting the RfC value for a particular chemical 

before determining potential risks. Following the approach used in the 1999 NATA, combined 

noncancer hazards were calculated using the hazard index (HI), defined as the sum of hazard 

quotients for individual air toxics compounds that affect the same organ or organ system. The HI 

is only an approximation of the combined effect, because some of the substances may affect the 

target organs in different (i.e., non-additive) ways. As with the HQ, a value of the HI below 1.0 

will likely not result in adverse effects over a lifetime of exposure. However, a value of the HI 

greater than 1.0 does not necessarily suggest a likelihood of adverse effects. Furthermore, the HI 

cannot be translated to a probability that adverse effects will occur and is not likely to be 

proportional to risk. An HI greater than one can be best described as indicating that a potential 

may exist for adverse health effects. 

3.2.1.2.2 Exposure and Risk Trends for Air Toxics: Reference Case 

Tables 3.2-7 and 3.2-8 summarize nationwide averages of median and 90th percentile 

census tract exposure concentrations of mobile source air toxics in 1999, 2015, 2020, and 2030, 

without the controls being finalized in this rule. It should be noted that all the other noninventoried sources, as well as the contribution from transport, contribute to background levels. 

Overall, exposure to ambient concentrations tends to be less than ambient concentrations because 

penetration rates to indoor microenvironments are typically less than one.c However, highway 

vehicles make a larger contribution to overall average population exposures than they do to 

ambient levels. This is largely because of elevated exposures experienced inside vehicles. 

c In the exposure monitoring studies discussed in section 3.1.2, average measured personal exposure concentrations 

are greater than those in both indoor and outdoor air. These differences may be attributable to several factors. First, 

HAPEM6 does not include pollution sources within indoor microenvironments, such as attached garages, 

environmental tobacco smoke, and solvent storage. Second, measured personal breathing zone concentrations are 

integrated measurements that account for time-weighted average (TWA) concentrations that incorporate every 

source, activity, and location with which a monitor comes into contact. Microenvironmental models like HAPEM6 

simplify individual time budgets so they fit within the microenvironments modeled or monitored. 

3-48
 
Final Regulatory Impact Analysis 

Table 3.2-9 summarizes national average population cancer risk across census tracts for 

these years by pollutant, as well as total cancer risk across pollutants. The total cancer risk from 

mobile source air toxics (including the stationary source contribution) was about 25 in a million 

in 1999. 

In all projection years, benzene emissions are by far the largest contributor to cancer risk 

from mobile sources (see Figure 3.2-4). Other significant contributors to cancer risk from 

mobile source air toxics include 1,3-butadiene, acetaldehyde, naphthalene, and hexavalent 

chromium. It should be noted, however, that we have no actual measurements of hexavalent 

chromium emissions from mobile sources, and that the risk estimate for this pollutant is based on 

an assumption that forty percent of the chromium from highway vehicles and eighteen percent of 

the chromium from nonroad sources was assumed to be the highly toxic hexavalent form. The 

estimate for highway vehicles is based on data from utility boilers,158 and the estimate for 

nonroad equipment is, based on combustion data from stationary combustion turbines that burn 

diesel fuel.159 Thus there is a great deal of uncertainty in estimates for this pollutant. 

Despite significant reductions in risk from mobile source air toxics, average inhalation 

cancer risks for these pollutants in 2030, accounting for both mobile and stationary source 

contributions, remain well above 20 in 1,000,000 (Figure 3.2-5). In addition, average risk from 

exposure to benzene remains above 9 in 1,000,000. 

3-49
 
Final Regulatory Impact Analysis 

Table 3.2-7. National Means of Census Tract Median Population Exposure Concentrations of Mobile S

1999, 2015, 2020, and 2030, Without Controls in this Rule. 

1999 annual average concentrations (μg m-3) 2015 annual averag

total 

Pollutant 

background 

(μg m-3) major 

area & 

other onroad nonroad 

(including 

background) major 

area & 

other onr

1,3-Butadiene 3.96E-02 1.54E-03 1.66E-02 6.39E-02 1.64E-02 1.38E-01 1.72E-03 1.69E-02 2.82,2,4-Trimethylpentane 0.00E+00 1.70E-02 1.86E-02 8.23E-01 1.57E-01 1.02E+00 8.68E-03 2.18E-02 4.1Acetaldehyde 4.00E-01 2.34E-02 4.33E-02 8.08E-01 1.18E-01 1.39E+00 2.41E-02 4.60E-02 4.7Acrolein 0.00E+00 2.56E-03 2.35E-02 6.62E-02 1.83E-02 1.10E-01 2.91E-03 2.14E-02 2.9Benzene 3.05E-01 1.76E-02 1.16E-01 8.08E-01 1.51E-01 1.40E+00 1.25E-02 1.37E-01 4.5Chromium III 0.00E+00 3.23E-04 1.79E-04 1.93E-05 2.21E-05 5.43E-04 4.11E-04 2.43E-04 2.6Chromium VI 0.00E+00 4.25E-05 7.94E-05 1.30E-05 5.06E-06 1.40E-04 5.40E-05 1.09E-04 1.7Ethyl Benzene 0.00E+00 1.45E-02 7.49E-02 3.22E-01 8.02E-02 4.91E-01 9.92E-03 1.00E-01 1.6Formaldehyde 6.12E-01 3.29E-02 7.20E-02 5.78E-01 1.88E-01 1.48E+00 4.15E-02 8.26E-02 2.4Hexane 0.00E+00 5.50E-02 3.60E-01 2.85E-01 7.13E-02 7.71E-01 4.94E-02 4.41E-01 1.4MTBE 0.00E+00 1.05E-02 4.84E-02 4.61E-01 3.40E-01 8.59E-01 1.26E-03 1.17E-03 1.4Manganese 0.00E+00 1.05E-03 8.93E-04 1.08E-05 2.40E-06 1.96E-03 1.13E-02 5.35E-02 1.2Naphthalene 0.00E+00 3.82E-03 3.37E-02 1.79E-02 3.85E-03 5.92E-02 3.37E-03 4.18E-02 9.8Nickel 0.00E+00 3.02E-04 5.78E-04 2.38E-05 4.17E-05 9.46E-04 3.47E-04 6.50E-04 3.2POM 0.00E+00 2.87E-03 1.00E-02 1.56E-03 5.48E-04 1.50E-02 2.26E-03 1.16E-02 8.3Propionaldehyde 0.00E+00 7.73E-03 1.80E-02 1.93E-01 3.35E-02 2.52E-01 7.24E-03 1.89E-02 9.5Styrene 0.00E+00 2.04E-02 1.14E-02 3.40E-02 3.03E-03 6.88E-02 2.40E-02 1.56E-02 1.7Toluene 0.00E+00 1.61E-01 6.57E-01 2.14E+00 3.42E-01 3.30E+00 1.16E-01 8.80E-01 1.09Xylenes 1.28E-01 8.08E-02 4.66E-01 1.21E+00 3.33E-01 2.22E+00 6.79E-02 6.43E-01 6.1

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Final Regulatory Impact Analysis 

Table 3.2-7 (cont’d). National Means of Census Tract Median Population Exposure Concentrations of 

Toxics in 1999, 2015, 2020, and 2030, Without Controls in this Rule. 

2020 annual average concentrations (μg m-3) 2030 annual average 

total 

Pollutant 

background 

(μg m-3) major 

area & 

other onroad nonroad 

(including 

background) major 

area & 

other onr

1,3-Butadiene 3.96E-02 1.86E-03 1.69E-02 2.98E-02 1.07E-02 9.88E-02 1.86E-03 1.69E-02 3.492,2,4-Trimethylpentane 0.00E+00 9.37E-03 2.31E-02 4.16E-01 9.21E-02 5.41E-01 9.37E-03 2.31E-02 4.8Acetaldehyde 4.00E-01 2.52E-02 4.70E-02 4.85E-01 9.01E-02 1.05E+00 2.52E-02 4.70E-02 5.68Acrolein 0.00E+00 3.27E-03 2.07E-02 2.99E-02 1.58E-02 6.97E-02 3.27E-03 2.07E-02 3.5Benzene 3.05E-01 1.37E-02 1.42E-01 4.64E-01 1.02E-01 1.03E+00 1.37E-02 1.42E-01 5.40Chromium III 0.00E+00 4.59E-04 2.74E-04 2.90E-05 2.37E-05 7.86E-04 4.59E-04 2.74E-04 3.56Chromium VI 0.00E+00 6.14E-05 1.23E-04 1.96E-05 5.45E-06 2.09E-04 6.14E-05 1.23E-04 2.40Ethyl Benzene 0.00E+00 1.11E-02 1.10E-01 1.62E-01 4.83E-02 3.32E-01 1.11E-02 1.10E-01 1.87Formaldehyde 6.12E-01 4.71E-02 8.68E-02 2.52E-01 1.38E-01 1.14E+00 4.71E-02 8.68E-02 2.94Hexane 0.00E+00 5.44E-02 4.77E-01 1.33E-01 5.19E-02 7.17E-01 5.44E-02 4.77E-01 1.46MTBE 0.00E+00 1.27E-02 5.48E-02 1.01E-01 9.25E-02 2.61E-01 1.27E-02 5.48E-02 1.00Manganese 0.00E+00 1.40E-03 1.29E-03 1.62E-05 3.00E-06 2.71E-03 1.40E-03 1.29E-03 1.99Naphthalene 0.00E+00 3.78E-03 4.44E-02 9.84E-03 4.31E-03 6.23E-02 3.78E-03 4.44E-02 1.14Nickel 0.00E+00 3.77E-04 7.15E-04 3.62E-05 5.02E-05 1.18E-03 3.77E-04 7.15E-04 4.45POM 0.00E+00 2.51E-03 1.18E-02 8.63E-04 5.01E-04 1.57E-02 2.51E-03 1.18E-02 1.02Propionaldehyde 0.00E+00 7.27E-03 1.94E-02 9.81E-02 2.25E-02 1.47E-01 7.27E-03 1.94E-02 1.14Styrene 0.00E+00 2.74E-02 1.72E-02 1.80E-02 1.87E-03 6.45E-02 2.74E-02 1.72E-02 2.13Toluene 0.00E+00 1.30E-01 9.68E-01 1.10E+00 2.09E-01 2.41E+00 1.30E-01 9.68E-01 1.28Xylenes 1.28E-01 7.68E-02 7.10E-01 6.18E-01 1.87E-01 1.72E+00 7.68E-02 7.10E-01 7.17

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Final Regulatory Impact Analysis 

Table 3.2-8. National Means of Census Tract 90th Percentile Population Exposure Concentrations of M

Toxics in 1999, 2015, 2020, and 2030, Without Controls in this Rule. 

1999 annual average concentrations (μg m-3) 2015 annual averag

total 

Pollutant 

background 

(μg m-3) major 

area & 

other onroad nonroad 

(including 

background) major 

area & 

other onr

1,3-Butadiene 5.88E-02 2.03E-03 2.23E-02 1.00E-01 2.49E-02 2.08E-01 2.15E-03 2.16E-02 4.12,2,4-Trimethylpentane 0.00E+00 2.65E-02 3.12E-02 1.42E+00 2.65E-01 1.75E+00 1.30E-02 3.56E-02 7.0Acetaldehyde 5.82E-01 3.48E-02 6.34E-02 1.27E+00 1.80E-01 2.13E+00 3.32E-02 6.27E-02 6.8Acrolein 0.00E+00 3.68E-03 3.36E-02 1.07E-01 2.82E-02 1.72E-01 3.72E-03 2.79E-02 4.3Benzene 4.50E-01 2.57E-02 1.71E-01 1.24E+00 2.28E-01 2.12E+00 1.70E-02 1.91E-01 6.5Chromium III 0.00E+00 4.55E-04 2.59E-04 2.88E-05 3.15E-05 7.74E-04 5.81E-04 3.51E-04 3.9Chromium VI 0.00E+00 6.16E-05 1.15E-04 1.92E-05 7.20E-06 2.03E-04 7.88E-05 1.57E-04 2.6Ethyl Benzene 0.00E+00 2.33E-02 1.19E-01 5.49E-01 1.35E-01 8.27E-01 1.51E-02 1.51E-01 2.6Formaldehyde 8.03E-01 4.21E-02 9.22E-02 7.89E-01 2.52E-01 1.98E+00 4.93E-02 9.74E-02 3.0Hexane 0.00E+00 7.54E-02 5.11E-01 4.32E-01 1.07E-01 1.13E+00 6.51E-02 5.95E-01 2.0MTBE 0.00E+00 1.46E-02 7.13E-02 7.22E-01 5.16E-01 1.32E+00 1.45E-02 7.24E-02 1.9Manganese 0.00E+00 1.44E-03 1.18E-03 1.47E-05 3.25E-06 2.64E-03 1.72E-03 1.55E-03 2.0Naphthalene 0.00E+00 4.81E-03 4.39E-02 2.44E-02 5.09E-03 7.83E-02 4.07E-03 5.13E-02 1.2Nickel 0.00E+00 4.25E-04 8.25E-04 3.52E-05 6.04E-05 1.35E-03 4.94E-04 9.09E-04 4.7POM 0.00E+00 3.68E-03 1.21E-02 2.04E-03 7.05E-04 1.85E-02 2.89E-03 1.38E-02 1.0Propionaldehyde 0.00E+00 1.30E-02 2.79E-02 3.36E-01 5.58E-02 4.33E-01 1.15E-02 2.72E-02 1.6Styrene 0.00E+00 2.87E-02 1.78E-02 5.90E-02 5.23E-03 1.11E-01 3.31E-02 2.31E-02 2.8Toluene 0.00E+00 2.52E-01 1.05E+00 3.61E+00 5.66E-01 5.48E+00 1.70E-01 1.32E+00 1.7Xylenes 2.04E-01 1.23E-01 7.05E-01 1.95E+00 5.25E-01 3.50E+00 9.59E-02 9.14E-01 9.1

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Final Regulatory Impact Analysis 

Table 3.2-8 (cont’d). National Means of Census Tract 90th Percentile Population Exposure Concentratio

Air Toxics in 1999, 2015, 2020, and 2030, Without Controls in this Rule. 

2020 annual average concentrations (μg m-3) 2030 annual averag

total 

Pollutant 

background 

(μg m-3) major 

area & 

other onroad nonroad 

(including 

background) major 

area & 

other onr

1,3-Butadiene 5.88E-02 2.32E-03 2.16E-02 4.28E-02 1.48E-02 1.40E-01 2.32E-03 2.16E-02 5.12,2,4-Trimethylpentane 0.00E+00 1.40E-02 3.75E-02 7.09E-01 1.54E-01 9.14E-01 1.40E-02 3.75E-02 8.2Acetaldehyde 5.82E-01 3.47E-02 6.42E-02 7.14E-01 1.28E-01 1.52E+00 3.47E-02 6.42E-02 8.5Acrolein 0.00E+00 4.15E-03 2.70E-02 4.53E-02 2.23E-02 9.87E-02 4.15E-03 2.70E-02 5.3Benzene 4.50E-01 1.86E-02 1.99E-01 6.68E-01 1.44E-01 1.48E+00 1.86E-02 1.99E-01 7.9Chromium III 0.00E+00 6.51E-04 3.97E-04 4.37E-05 3.39E-05 1.13E-03 6.51E-04 3.97E-04 5.4Chromium VI 0.00E+00 8.98E-05 1.77E-04 2.90E-05 7.78E-06 3.04E-04 8.98E-05 1.77E-04 3.5Ethyl Benzene 0.00E+00 1.68E-02 1.65E-01 2.62E-01 7.71E-02 5.21E-01 1.68E-02 1.65E-01 3.0Formaldehyde 8.03E-01 5.60E-02 1.02E-01 3.11E-01 1.67E-01 1.44E+00 5.60E-02 1.02E-01 3.7Hexane 0.00E+00 7.12E-02 6.39E-01 1.86E-01 7.27E-02 9.69E-01 7.12E-02 6.39E-01 2.0MTBE 0.00E+00 1.61E-02 7.34E-02 1.52E-01 1.37E-01 3.78E-01 1.61E-02 7.34E-02 1.5Manganese 0.00E+00 1.92E-03 1.71E-03 2.21E-05 4.07E-06 3.65E-03 1.92E-03 1.71E-03 2.7Naphthalene 0.00E+00 4.55E-03 5.44E-02 1.24E-02 5.33E-03 7.66E-02 4.55E-03 5.44E-02 1.4Nickel 0.00E+00 5.39E-04 1.00E-03 5.25E-05 7.19E-05 1.66E-03 5.39E-04 1.00E-03 6.4POM 0.00E+00 3.21E-03 1.42E-02 1.08E-03 6.21E-04 1.91E-02 3.21E-03 1.42E-02 1.2Propionaldehyde 0.00E+00 1.16E-02 2.78E-02 1.65E-01 3.52E-02 2.39E-01 1.16E-02 2.78E-02 1.9Styrene 0.00E+00 3.78E-02 2.55E-02 2.99E-02 3.07E-03 9.64E-02 3.78E-02 2.55E-02 3.5Toluene 0.00E+00 1.88E-01 1.44E+00 1.73E+00 3.23E-01 3.68E+00 1.88E-01 1.44E+00 2.04Xylenes 2.04E-01 1.08E-01 1.01E+00 9.22E-01 2.74E-01 2.51E+00 1.08E-01 1.01E+00 1.09

3-53
Final Regulatory Impact Analysis 

Table 3.2-9. National Average Cancer Risk Across Census Tracts for 1999, 2015, 2020, and 2030 by P

Controls in this Rule. 

1999 average individual risk 2015 annual average individual risk 

Pollutant major 

area & 

other onroad nonroad 

total 

(including 

background) major 

area & 

other onroad nonroad 

(in

bac

1,3-Butadiene 4.36E-08 4.85E-07 2.06E-06 5.39E-07 4.43E-06 4.62E-08 4.50E-07 8.69E-07 3.20E-07 Acetaldehyde 5.65E-08 1.10E-07 1.96E-06 2.89E-07 3.39E-06 5.59E-08 1.16E-07 1.08E-06 2.12E-07 Benzene 1.49E-07 9.82E-07 6.79E-06 1.30E-06 1.18E-05 1.00E-07 1.13E-06 3.66E-06 8.25E-07 Chromium VI 5.32E-07 9.43E-07 1.69E-07 7.18E-08 1.72E-06 6.67E-07 1.25E-06 2.29E-07 8.11E-08 Formaldehyde 1.81E-10 4.51E-10 3.36E-09 1.11E-09 8.69E-09 2.10E-10 5.18E-10 1.35E-09 7.69E-10 Naphthalene 1.21E-07 1.22E-06 6.38E-07 1.37E-07 2.11E-06 1.01E-07 1.46E-06 3.43E-07 1.39E-07 Nickel 4.81E-08 9.79E-08 4.17E-09 6.20E-09 1.56E-07 5.53E-08 1.07E-07 5.65E-09 6.87E-09 POM 1.77E-07 1.06E-06 1.05E-07 3.62E-08 1.38E-06 1.46E-07 1.25E-06 5.39E-08 3.25E-08 

2020 annual average individual risk 2030 annual average individual risk 

Pollutant major 

area & 

other onroad nonroad 

total 

(including 

background) major 

area & 

other onroad nonroad 

(in

bac

1,3-Butadiene 4.95E-08 4.38E-07 8.92E-07 3.39E-07 3.00E-06 4.82E-08 4.19E-07 1.03E-06 3.86E-07 Acetaldehyde 5.80E-08 1.19E-07 1.10E-06 2.08E-07 2.46E-06 5.75E-08 1.19E-07 1.28E-06 2.23E-07 Benzene 1.09E-07 1.17E-06 3.71E-06 8.54E-07 8.45E-06 1.08E-07 1.16E-06 4.29E-06 9.59E-07 Chromium VI 7.53E-07 1.40E-06 2.50E-07 8.34E-08 2.49E-06 7.48E-07 1.38E-06 3.05E-07 8.78E-08 Formaldehyde 2.34E-10 5.47E-10 1.38E-09 7.63E-10 6.49E-09 2.28E-10 5.54E-10 1.59E-09 8.22E-10 Naphthalene 1.12E-07 1.54E-06 3.39E-07 1.48E-07 2.14E-06 1.09E-07 1.52E-06 3.91E-07 1.69E-07 Nickel 6.02E-08 1.16E-07 6.19E-09 7.10E-09 1.90E-07 6.01E-08 1.15E-07 7.55E-09 7.60E-09 POM 1.61E-07 1.30E-06 5.54E-08 3.27E-08 1.55E-06 1.61E-07 1.31E-06 6.52E-08 3.59E-08 

3-54
Final Regulatory Impact Analysis 

Figure 3.2-4. Contributions to Average Inhalation Cancer Risk from Air Toxics Emitted 

by Mobile Sources, 2020 (Not Including Diesel PM and Diesel Exhaust Organic Gases), 

Without Controls in this Rule. 

1,3-Butadiene 

15% 

Acetaldehyde 

16% 

Benzene 

58% 

Nickel 

0%

Naphthalene 

6% 

Chromium VI 

4% 

POM 

1% 

Formaldehyde 

0% 

Figure 3.2-5. Average Nationwide Cancer Risk from Emissions of Mobile Source Air 
 

Toxics from both Mobile and Stationary Sources across Census Tracts, 1999 to 2030 (Not 
 

Including Diesel PM and Diesel Exhaust Organic Gases), Without Controls in this Rule. 
 

3.0E-05
 

2.5E-05
 

2.0E-05
 

All MSATs 

1.5E-05 

Benzene 

1.0E-05 

5.0E-06 

0.0E+00
 

1999 2015 2020 2030
 

Year 

A

verag

e N

atio

n

wide Can

cer

Ri

sk 

3-55
 
Final Regulatory Impact Analysis 

It should also be noted that because of population growth projected to occur in the United 

States, the number of Americans above cancer risk benchmarks will increase. Figure 3.2-6 

depicts the U. S. population at various risk benchmarks for mobile source air toxics in 1999, 

2015, 2020, and 2030, using population projections from EPA’s BenMAP model, a tool the EPA 

uses to estimate benefits of air pollution control strategies, and average census tract exposures. 

(BenMAP was recently used for EPA’s Clean Air Interstate Air Quality Rule (CAIR),160 and is 

also discussed in Chapter 12 of the RIA). These statistics do not include populations in Alaska 

and Hawaii; thus populations in these States were assumed to remain at year 2000 levels. More 

details on the methodology used to project the U. S. population above various cancer risk 

benchmarks are provided in the technical support document “National-Scale Modeling of Mobile 

Source Air Toxic Emissions, Air Quality, Exposure and Risk for the Final Mobile Source Air 

Toxics Rule.” From Figure 3.2-6 it can be seen that, based on average census tract risks, the vast 

majority of the population experiences risks between one in a million (1x10-6) and one in ten 

thousand (1x10-4). However, the number of people experiencing risks above one in a hundred 

thousand (1x10-5) increases from 223 million in 1999 to 272 million in 2030. 

Figure 3.2-6. U. S. Population at Various Cancer Risk Benchmarks due to Exposure to 
 

Mobile Source Air Toxics, 1999 – 2030, Without Controls in this Rule. 
 

0 

50 

100 

150 

200 

250 

300 

350 

400 

1999 2015 2020 2030 

M illio

n s 

Year 

Popu la tion 

Risk < 1E-06 

1E-06 <= Risk < 1E-05 

1E-05 <= Risk < 1E-04 

Risk >= 1E-04 

Tables 3.2-10 and 3.2-11 summarize national average population hazard quotients for 

chronic non-cancer effects across census tracts for these years by pollutant, as well as the 

respiratory hazard index across pollutants. The respiratory system is the only target organ 

system where the hazard index exceeds one. Although the average respiratory hazard index for 

mobile source air toxics decreases by almost 33% between 1999 and 2030 (Figure 3.2-7), it is 

still over 4 in 2030, indicating a potential for adverse health effects. The reduction in hazard 

index occurs despite large increases in activity for highway and nonroad sources. In addition, 

3-56
 
Final Regulatory Impact Analysis 

about 90% of this non-cancer risk is attributable to acrolein in all projection years. It should be 

noted that the confidence in the RfC for acrolein is medium. About 25% of primary acrolein 

emissions are from mobile sources, and about 70% of ambient concentrations of acrolein (and 

about 75% of exposure) are attributable to mobile sources. The mobile source contribution to 

concentrations and exposure is largely attributable to the contribution from mobile source 1,3­

butadiene, which is transformed to acrolein in the atmosphere. Moreover, projected growth in 

the U. S. population and increasing vehicle miles traveled will increase the number of Americans 

with a respiratory hazard index for mobile source air toxics above one, from 258 million in 1999 

to 307 million in 2030 (Figure 3.2-8). 

Detailed summary tables presenting cancer risk, hazard quotients and hazard indices by 

State, and for reformulated and non-reformulated (i.e., conventional) gasoline areas, can be 

found in the docket for this rule, along with statistics on number of individuals above various 

cancer and non-cancer benchmarks, by source sector. 

3.2.1.2.3 Distributions of Air Toxics Risk across the U. S.: Reference Case 

Table 3.2-12 gives the distribution of nationwide individual cancer risks for mobile 

source air toxics in 2020, absent the controls being finalized in this rule. Summary tables 

providing distributions for other years, as well as distributions by State and for reformulated and 

non-reformulated gasoline areas, can be found in the docket for this rule. Risk distributions are 

broader than the distributions of ambient concentrations in Table 3.2-2. For instance, while the 

95th percentile benzene concentration is about twice the median value, the 95th percentile cancer 

risk is roughly three times the median risk. A key reason for this is the variability in activity 

patterns, concentrations among microenvironments, and commuting patterns. Figures 3.2-9 

through 3.2-12 depict the geographic distributions of median county cancer risks in 2020 for all 

mobile source air toxics, and separately for benzene, acetaldehyde and 1,3-butadiene. These 

geographic distributions closely track distributions of ambient concentrations, with the highest 

risks in major population centers of the country where mobile source activity is the greatest. 

Relatively high benzene risks are also seen in areas of the country where fuel benzene levels are 

higher, such as the Pacific Northwest, parts of Alaska, and the upper Great Lakes region, since 

higher fuel benzene levels lead to higher benzene emissions and higher exposures. Higher risks 

are also seen in States with colder winters, due to elevated cold start emissions. 

Previously discussed changes to the HAPEM exposure model, to account for near road 

impacts, can impact distributions of risk. In order to evaluate the effect of switching to 

HAPEM6 from HAPEM5 on individual risks nationally, we conducted model runs using 

identical input data. Figure 3.2-13 depicts the national distribution of individual cancer risks 

from benzene, comparing HAPEM6 and HAPEM5. Note that the graph is on a logarithmic 

scale. As the graph illustrates, when HAPEM6 is used, there are fewer individuals with lower 

benzene cancer risk levels (e.g. <1x10-6) in 1999. The population with higher benzene risk levels 

(e.g. >1x10-4) is higher with HAPEM6 than HAPEM5. In general, the distribution of cancer 

risks shifts slightly higher when comparing HAPEM6 to HAPEM5, but the largest effects are 

observed in the populations with the highest and lowest risk levels, which are generally small 

fractions of the total population. 

3-57
 
Final Regulatory Impact Analysis 

Table 3.2-13 gives the distribution of nationwide individual hazard quotients for acrolein, 

and hazard indices for the respiratory target system in 2020. Patterns for other years are similar. 

The average respiratory hazard index at the 95th percentile is over 20 times that at the 5th 

percentile, and about 4 times the median. Thus, some populations are experiencing much higher 

hazard indices than others. Figure 3.2-14 depicts the geographic distribution of median county 

respiratory hazard indices in 2020. The high hazard indices in Idaho are the result of high 

inventory estimates for wildfires and reflect a known error in the Idaho inventory for this source. 

This error was discovered at too late a date to produce and update emissions inventories for use 

in the analyses undertaken for this rule. The errors are not expected to affect the analyses of the 

impacts of controls undertaken for this rule. 

3-58
 
Table 3.2-10. National Average Population Hazard Quotient for Chronic Noncancer Effects Across Cen

2030, Without Controls in this Rule.

1999 average Hazard Quotient 2015 ave

Pollutant Target System major 

area & 

other onroad nonroad 

total 

(including 

background) major 

area & 

other 

1,3-Butadiene Reproductive 7.27E-04 8.08E-03 3.43E-02 8.98E-03 7.39E-02 7.69E-04 7.49E-03 Acetaldehyde Respiratory 2.86E-03 5.54E-03 9.92E-02 1.46E-02 1.71E-01 2.82E-03 5.84E-03 Acrolein Respiratory 1.44E-01 1.28E+00 3.70E+00 1.03E+00 6.16E+00 1.58E-01 1.13E+00 Benzene Immunological 6.35E-04 4.20E-03 2.90E-02 5.55E-03 5.06E-02 4.29E-04 4.83E-03 Chromium VI Respiratory 4.43E-04 7.86E-04 1.41E-04 5.98E-05 1.43E-03 5.56E-04 1.04E-03 Ethyl Benzene Developmental 1.60E-05 8.09E-05 3.60E-04 9.17E-05 5.48E-04 1.05E-05 1.05E-04 Formaldehyde Respiratory 3.36E-03 8.37E-03 6.23E-02 2.05E-02 1.61E-01 3.90E-03 9.62E-03 Hexane Neurological, Respiratory 2.76E-04 1.89E-03 1.55E-03 3.95E-04 4.11E-03 2.43E-04 2.21E-03 MTBE Liver, Kidney, Ocular 3.86E-06 1.76E-05 1.72E-04 1.28E-04 3.21E-04 3.94E-06 1.88E-05 Manganese Neurological 2.04E-02 1.93E-02 2.27E-04 4.59E-05 3.99E-02 2.65E-02 2.56E-02 Naphthalene Respiratory 1.19E-03 1.19E-02 6.25E-03 1.35E-03 2.07E-02 9.88E-04 1.43E-02 Nickel Respiratory, Immunological 4.62E-03 9.42E-03 4.01E-04 5.96E-04 1.50E-02 5.32E-03 1.03E-02 Styrene Neurological 2.38E-05 1.28E-05 3.77E-05 3.46E-06 7.78E-05 2.85E-05 1.76E-05 Toluene Respiratory, Neurological 4.55E-04 1.82E-03 5.96E-03 9.69E-04 9.20E-03 3.12E-04 2.39E-03 Xylenes Neurological 8.47E-04 5.00E-03 1.32E-02 3.72E-03 2.43E-02 6.85E-04 6.69E-03 

3-59
Final Regulatory Impact Analysis 

Table 3.2-10 (cont’d). National Average Population Hazard Quotient for Chronic Noncancer Effects Ac

Without Controls in this Rule. 

2020 average Hazard Quotient 2030 aver

Pollutant Target System major 

area & 

other onroad nonroad 

total 

(including 

background) major 

area & 

other 

1,3-Butadiene Reproductive 8.25E-04 7.30E-03 1.49E-02 5.64E-03 5.00E-02 8.03E-04 6.98E-03 Acetaldehyde Respiratory 2.93E-03 5.99E-03 5.58E-02 1.05E-02 1.24E-01 2.90E-03 6.02E-03 Acrolein Respiratory 1.78E-01 1.09E+00 1.57E+00 8.52E-01 3.69E+00 1.78E-01 1.08E+00 Benzene Immunological 4.67E-04 4.99E-03 1.58E-02 3.65E-03 3.61E-02 4.63E-04 4.96E-03 Chromium VI Respiratory 6.28E-04 1.17E-03 2.09E-04 6.95E-05 2.07E-03 6.23E-04 1.15E-03 Ethyl Benzene Developmental 1.17E-05 1.14E-04 1.72E-04 5.44E-05 3.52E-04 1.15E-05 1.12E-04 Formaldehyde Respiratory 4.34E-03 1.02E-02 2.55E-02 1.42E-02 1.20E-01 4.23E-03 1.03E-02 Hexane Neurological, Respiratory 2.66E-04 2.37E-03 6.92E-04 2.82E-04 3.61E-03 2.65E-04 2.32E-03 MTBE Liver, Kidney, Ocular 4.36E-06 1.91E-05 3.53E-05 3.28E-05 9.16E-05 4.26E-06 1.90E-05 Manganese Neurological 2.99E-02 2.80E-02 3.37E-04 5.59E-05 5.83E-02 3.08E-02 2.81E-02 Naphthalene Respiratory 1.09E-03 1.51E-02 3.33E-03 1.45E-03 2.10E-02 1.07E-03 1.49E-02 Nickel Respiratory, Immunological 5.78E-03 1.12E-02 5.95E-04 6.83E-04 1.83E-02 5.78E-03 1.10E-02 Styrene Neurological 3.29E-05 1.96E-05 1.90E-05 2.09E-06 7.36E-05 3.32E-05 1.97E-05 Toluene Respiratory, Neurological 3.47E-04 2.63E-03 2.89E-03 5.78E-04 6.45E-03 3.44E-04 2.63E-03 Xylenes Neurological 7.69E-04 7.35E-03 6.39E-03 2.04E-03 1.80E-02 7.59E-04 7.26E-03 

3-60
Final Regulatory Impact Analysis 

Table 3.2-11. National Respiratory Hazard Index for Chronic Noncancer Effects across 
 

Census Tracts, Without Controls in this Rule. 
 

Respiratory System Average Hazard Index 

Year background major area & other onroad nonroad 

total (including 

background) 

1999 0.12 0.16 1.32 3.88 1.07 6.54 

2015 0.12 0.17 1.17 1.63 0.84 3.92 

2020 0.12 0.19 1.14 1.66 0.88 3.99 

2030 0.11 0.19 1.13 1.92 0.99 4.35 

Figure 3.2-7. Average Respiratory Hazard Index for U.S. Population (Aggregate of Hazard 

Quotients for Individual Pollutants), Without Controls in this Rule. 

0 

1 

2 

3 

4 

5 

6 

7 

1999 2015 2020 2030 

Year 

H 

az 

ard In d 

e

x

mobile 

stationary 

background 

3-61
 
Final Regulatory Impact Analysis 

Figure 3.2-8. U. S. Population at Various Non-Cancer Hazard Benchmarks due to 
 

Exposure to Mobile Source Air Toxics, 1999 – 2030, Without Controls in this Rule. 
 

0 

50 

100 

150 

200 

250 

300 

350 

400 

1999 2015 2020 2030 

Millio

ns 

Year 

Popul

ation 

HQ < 0.1 

0.1 <= HQ < 1 

1 <= HQ < 10 

HQ >= 10 

Table 3.2-12. Distribution of Individual Cancer Risks for Mobile Source Air Toxics in 

2020, Without Controls in this Rule. 

2020 risk distribution 

Pollutant 

5th 

percentile 

10th 

percentile 

25th 

percentile Median 

75th 

percentile 

95th 

percentile 

Total Risk: All HAPs 4.71E-06 6.08E-06 9.78E-06 1.53E-05 2.37E-05 3.79E-05 4.93E-05 

1,3-Butadiene 1.52E-07 2.96E-07 1.06E-06 2.30E-06 3.60E-06 5.47E-06 7.70E-06 

Acetaldehyde 1.09E-06 1.19E-06 1.46E-06 1.96E-06 2.81E-06 4.20E-06 5.35E-06 

Benzene 2.72E-06 3.36E-06 4.84E-06 6.93E-06 1.00E-05 1.48E-05 1.86E-05 

Chromium VI 3.85E-08 7.93E-08 2.38E-07 7.01E-07 1.81E-06 4.54E-06 7.29E-06 

Formaldehyde 2.29E-09 2.89E-09 4.12E-09 5.75E-09 7.67E-09 1.05E-08 1.29E-08 

Naphthalene 1.59E-07 2.80E-07 6.72E-07 1.39E-06 2.61E-06 4.73E-06 6.68E-06 

Nickel 1.84E-09 4.09E-09 1.39E-08 4.60E-08 1.31E-07 3.04E-07 5.06E-07 

POM 1.26E-07 1.90E-07 3.48E-07 6.78E-07 1.19E-06 1.99E-06 3.07E-06 

3-62
 
Final Regulatory Impact Analysis 

Figure 3.2-9. 2020 County Median Cancer Risk for All Mobile Source Air Toxics, Without 

Controls in this Rule. 


 

Risk (N in a million) 

1.4 - 6.4 

6.5 - 12.0 

12.1 - 21.2 

21.3 - 41.9 

42.0 - 101.8

Figure 3.2-10. 2020 County Median Cancer Risk for Benzene, Without Controls in this 

Rule.

Risk (N in a million) 

0.5 - 2.8 

2.9 - 5.0 

5.1 - 8.2 

8.3 - 15.4 

15.5 - 51.6

3-63
 
Final Regulatory Impact Analysis 


 

Figure 3.2-11. 2020 County Median Cancer Risk for Acetaldehyde, Without Controls in 
 

this Rule.

Risk (N in a million) 

0.8 - 1.4 

1.5 - 1.9 

2.0 - 2.8 

2.9 - 4.6 

4.7 - 10.2

Figure 3.2-12. 2020 County Median Cancer Risk for 1,3-Butadiene, Without Controls in 

this Rule.

Risk (N in a million) 

0.0 - 0.9 

1.0 - 2.1 

2.2 - 4.1 

4.2 - 9.8 

9.9 - 21.4

3-64
 
Population 

Final Regulatory Impact Analysis 

Figure 3.2-13. 1999 Comparison Between HAPEM6 and HAPEM5 Nationwide Individual 

Benzene Cancer Risk, Without Controls in this Rule. 

100,000,000 

10,000,000 

1,000,000 

100,000 

10,000 

1,000 

100 

10 

1 

0 

1.E-072.E-073.E-074.E-075.E-076.E-077.E-078.E-079.E-071.E-062.E-063.E-064.E-065.E-066.E-067.E-068.E-069.E-061.E-052.E-053.E-054.E-055.E-056.E-057.E-058.E-059.E-051.E-042.E-043.E-04 

Population with Risk > Risk bin 

HAPEM6 HAPEM5 

3-65
 
Final Regulatory Impact Analysis 

 Table 3.2-13. Distribution of Individual Hazard Quotients/Hazard Indices for Mobile 

Source Air Toxics (from both Mobile and Stationary Sources) in 2020, Without Controls in 

this Rule.

2020 average Hazard Quotient or Hazard Index 

5th 10th 25th 75th 90th 95th 

Pollutant percentile percentile percentile Median percentile percentile percentile 

Acrolein 0.41 0.61 1.18 2.31 4.47 8.05 11.3 

Respiratory System 0.53 0.75 1.36 2.57 4.83 8.54 11.9 

Figure 3.2-14. 2020 County Median Non-Cancer Hazard Index Respiratory Mobile Source 

Air Toxics, Without Controls in this Rule. 

Hazard Index 

0.050 - 1.268 

1.269 - 3.113 

3.114 - 6.543 

6.544 - 13.225 

13.226 - 31.131

3-66
 
Final Regulatory Impact Analysis 

3.2.1.2.4 Impacts of Controls on Average Inhalation Cancer Risks and Noncancer Hazards 

The standards being finalized in this rule will substantially reduce inhalation cancer and 

noncancer risk from exposure to air toxics emitted by mobile sources across the United States. 

Table 3.2-14 shows that in 2030, the highway vehicle contribution to MSAT cancer risk will be 

reduced on average 36% across the U.S., and the nonroad equipment contribution will be 

reduced about 6%. In 2030, the highway vehicle contribution to benzene cancer risk will be 

reduced on average by 43% across the U.S., and the nonroad contribution will be reduced by 

11%. Table 3.2-15 summarizes the change in median and 95th percentile inhalation cancer risks 

from benzene and all MSATs attributable to all outdoor sources in 2015, 2020, and 2030, with 

the controls being finalized in this rule. Reductions are significantly larger for individuals in 

the 95th percentile than in the 50th percentile. Thus, this rule is providing bigger benefits to 

individuals experiencing the highest levels of risk. In states with high fuel benzene levels and 

high cold start emissions, the cancer risk reduction from total MSATs is about 40% or higher 

(Table 3.2-16).d Figure 3.2-15 depicts the impact on the mobile source contribution to 

nationwide average population cancer risk from all MSATs and benzene in 2030. Nationwide, 

the cancer risk attributable to total MSATs would be reduced by 30%, and the risk from mobile 

source benzene would be reduced by 37%. Figures 3.2-16 and 3.2.-17 present the distribution of 

percent reductions in average MSAT and benzene cancer risk, respectively, from all sources in 

2030 with the controls being finalized in 2030. Table 3.2-17 shows reductions in hazard 

quotients and hazard indices for acrolein and respiratory effects, respectively. Nationwide, the 

mobile source contribution to the acrolein hazard quotient and respiratory hazard index would 

both be reduced about 23%, and the highway vehicle contribution will be reduced about 35%. 

Summary tables providing exposure and risk data by State, as well as maps of cancer risks and 

noncancer hazards with controls and percent reductions with controls, can be found in the docket 

for the rule. 

It should be noted that the estimated total relative reductions are significant 

underestimates, since we could not account for further reductions in emissions from transport, 

i.e., background sources. In Section 3.2.1.4, we provide a quantitative estimate of the expected 

reductions in background concentrations in future years. Again, as noted previously, since this 

modeling did not include the 1.3 vol% maximum average fuel benzene level, reductions in risk 

for some parts of the country, such as the Pacific Northwest, are underestimated. 

d

 Reductions are likely to be higher than estimated by this modeling, due to the 1.3% maximum average fuel 

benzene level. 

3-67
 
Final Regulatory Impact Analysis 

Table 3.2-14. Contributions of Source Sectors to Nationwide Average Cumulative MSAT Cancer Risk, With an

2015, 2020, and 2030 

2015 Average Risks 2020 Average Risks 

total total (including total total (including 

major area & other total onroad nonroad background) major area & other total onroad nonroad background) major area & 

Total MSATs 

Reference 1.17E-06 5.76E-06 6.24E-06 1.62E-06 1.97E-05 1.30E-06 6.08E-06 6.35E-06 1.67E-06 2.03E-05 1.29E-06 6.02Control 1.17E-06 5.74E-06 4.98E-06 1.53E-06 1.83E-05 1.30E-06 6.06E-06 4.58E-06 1.58E-06 1.84E-05 1.29E-06 6.01% Difference 0.0 0.3 20.2 5.3 6.9 0.0 0.3 27.9 5.5 9.3 0.0 

Benzene 

Reference 1.00E-07 1.13E-06 3.66E-06 8.25E-07 8.33E-06 1.09E-07 1.17E-06 3.71E-06 8.54E-07 8.45E-06 1.08E-07 1.16Control 1.00E-07 1.12E-06 2.73E-06 7.38E-07 7.30E-06 1.09E-07 1.15E-06 2.45E-06 7.62E-07 7.09E-06 1.08E-07 1.15% Difference 0.3 1.3 25.4 10.5 12.3 0.3 1.3 34.0 10.8 16.2 0.3 

3-68
Table 3.2-15. Change in Median and 95th Percentile Inhalation Cancer Risk from Benzene 

and all MSATs Attributable to Outdoor Sources in 2015, 2020, and 2030 with the Controls 

Being Finalized in this Rule. 

2015 2020 2030 

median 95th median 95th median 95th 

All 

MSATs 

Without 1.50x10-5 4.75x10-5 1.53x10-5 4.93x10-5 1.61x10-5 5.28x10-5 

Controls 

With 

Controls 

1.41x10-5 4.37x10-5 1.40x10-5 4.40x10-5 1.42x10-5 4.49x10-5 

Percent 

Change 

6 8 8 11 12 15 

Benzene 

Without 6.86x10-6 1.82x10-5 6.93x10-6 1.86x10-5 7.37x10-6 2.06x10-5 

Controls 

With 

Controls 

6.17x10-6 1.53x10-5 6.02x10-6 1.47x10-5 6.06x10-6 1.49x10-5 

Percent 

Change 

10 16 13 21 18 28 

Table 3.2-16. States with Highest Reductions in Average Benzene Cancer Risk Resulting 

from Mobile Source Emissions, 2030. 

State Average Risk – 

Reference Case 

Average Risk – 

Control Case Percent Difference 

Alaska 1.01x10-5 4.23x10-6 -58% 

North Dakota 2.92x10-6 1.68x10-6 -42 

Washington 1.39x10-5 8.10x10-6 -42 

Minnesota 1.21x10-5 7.08x10-6 -42 

Wyoming 2.38x10-6 1.39x10-6 -41 

Montana 3.12x10-6 1.87x10-6 -40 

Idaho 5.03x10-6 3.02x10-6 -40 

Michigan 1.09x10-5 6.55x10-6 -40 

South Dakota 2.73x10-6 1.66x10-6 -39 

Oregon 1.01x10-5 6.17x10-6 -39 

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Figure 3.2-15. Contribution to Nationwide Average Population Cancer Risk from Mobile 
 

Source MSATs and Benzene Emitted by Mobile Sources in 2030, Without and With 
 

Controls in this Rule. 

1.E-05 

9.E-06 

8.E-06 

7.E-06 

6.E-06 

5.E-06 

Without Controls 

With Controls 

4.E-06 

A

3.E-06 

verage Cancer

Risk 

2.E-06 

1.E-06 

0.E+00 

Total MSATs Benzene 

Figure 3.2-16. Distribution of Percent Reductions in Median MSAT Cancer Risk, 2030, for 
 

U.S. Counties with Controls in this Rule. 


 

Differences 

-39.369% - -7.938% 

-7.937% - -5.693% 

-5.692% - -3.923% 

-3.922% - -2.087% 

-2.086% - 1.135%

3-70 
Figure 3.2-17. Distribution of Percent Reductions in Median Benzene Cancer Risk, 2030, 

for U.S. Counties With Controls in this Rule.


 

Differences 

-46.519% - -21.861% 

-21.860% - -15.843% 

-15.842% - -10.893% 

-10.892% - -5.932% 

-5.931% - 0.747%

As a result of the controls being finalized in this rule, the number of people above the 1 in 

100,000 cancer risk level due to exposure to all mobile source air toxics from all sources will 

decrease by over 11 million in 2020 and by about 17 million in 2030. The number of people 

above the 1 in 100,000 increased cancer risk level from exposure to benzene from all sources 

decreases by about 30 million in 2020 and 46 million in 2030 (Table 3.2-18). 

3-71 
Table 3.2.-17. Reductions in Hazard Quotients and Hazard Indices for Acrolein and Respiratory Effects Due 

2015 Average Hazard Index/ Quotient 2020 Average Hazard Index/ Quotient 2030 Average Haz

area & 

other total onroad 

total 

nonroad 

total (including 

background) major 

area & 

other total onroad 

total 

nonroad 

total 

(including 

background) major 

area & 

other total on

1.17 1.63 0.84 3.92 0.19 1.14 1.66 0.88 3.99 0.19 1.13 11.17 1.35 0.84 3.65 0.19 1.14 1.24 0.88 3.56 0.19 1.13 1

0.0 16.7 0.0 6.9 0.0 0.0 25.6 0.0 10.7 0.0 0.0 3

1.13 1.54 0.81 3.63 0.18 1.09 1.57 0.85 3.69 0.18 1.08 1

3-72
Table 3.2-18. Decrease in Number of People with Inhalation Exposure above the 1 in 

100,000 Cancer Risk Level due to Inhalation Exposure from Ambient Sources, With 

Controls in this Rule. 

Year Benzene 

All Mobile Source Air 

Toxics 

2015 21,697,000 8,149,000 

2020 30,031,000 11,257,000 

2030 46,360,000 16,737,000 

The standards being finalized will also impact on the number of people above various respiratory 

hazard index levels (Table 3.2-19). 

Table 3.2-19. Decrease in Number of People with Inhalation Exposure above a Respiratory 

Hazard Index of One due to Inhalation Exposure from Ambient Sources, With Controls in 

this Rule. 

Year Decrease in Population 

with Respiratory HI > 1 

2015 5,639,000 

2020 10,227,000 

2030 16,919,000 

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3.2.1.3 Strengths and Limitations 

Air quality, exposure, and risk were assessed using the best available suite of tools for 

national-scale analysis of air toxics. The same general suite of tools was used in 1996 and 1999 

NATA. The 1996 NATA was reviewed by EPA’s Science Advisory Board, and the analyses 

done for 1999 incorporate several changes in response to comments made in this peer review. 

Among the improvements were: 

•	 Improved emission inventory with detailed characterization of source categories within 

the onroad and nonroad source sectors and more speciated data for some pollutant groups 

(POM) within particular source categories. 

•	 Speciation of chromium to hexavalent form based on emission sources rather than a 

single number applied across all sources 

•	 Improved surrogates for spatial allocation in EMS-HAP. 

•	 Improved estimation of “background” concentrations for many pollutants. These 
 

background levels were previously uniform across the country. Now, for many 
 

pollutants, background levels are based on recent monitor data and spatially vary 
 

depending on county population density.161
 

•	 Improved version of HAPEM, which includes more recent census data, commuting 

algorithms and better characterization of exposure distributions through improvements in 

modeling long-term activity patterns and variability in concentration levels in 

microenvironments. 

In addition to the improvements for the 1999 NATA, improvements were made in analyses 

for this rule, including inventory improvements and updates to HAPEM discussed earlier. 

The SAB expressed their belief that due to the limitations inherent in the analysis, the 1996 

NATA should not be used to support regulatory action. However, the use of the improved 

analyses in this rule does provide useful insight on the nature of the mobile source air toxics 

problem and the possible public health improvements associated with this rule. 

In addition to the strengths listed above, there are limitations due to uncertainty. The 

inventory uncertainties are discussed in Chapter 2. There are a number of additional significant 

uncertainties associated with the air quality, exposure and risk modeling. These uncertainties 

result from a number of parameters including: development of county-level estimates from 

broader geographic data (i.e., state, regional or national), surrogates used to allocate emissions to 

census tracts, parameters used to characterize photochemical processes, long range transport, 

terrain effects, deposition rates, human activity pattern parameters, assumptions about 

relationships between ambient levels in different microenvironments, and dose-response 

parameters. Uncertainties in dose-response parameters are discussed in Chapter 1 of the RIA. 

The modeling also has certain key limitations: results are most accurate for large geographic 

areas, exposure modeling does not fully reflect variation among individuals, non-inhalation 

exposure pathways and indoor sources are not accounted for; and for some pollutants, the 

ASPEN dispersion model may underestimate concentrations. Also, the 1999 NATA does not 

3-74
 
include default adjustments for early life exposures recently recommended in the Supplemental 

Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens.162 If 

warranted, incorporation of such adjustments would lead to higher estimates of lifetime risk. 

EPA will determine as part of the IRIS assessment process which substances meet the criteria for 

making adjustments, and future assessments will reflect them. 

As part of the 1999 NATA, EPA compared ASPEN-modeled concentrations with 

available, but geographically limited, ambient air quality monitoring data for 1999. For each 

monitor-pollutant combination, EPA compared the annual average concentration estimated by 

the ASPEN model at the exact geographical coordinates of the monitor location with the annual 

average monitored value to get a point-to-point comparison between the model and monitor 

concentrations. The agreement between model and monitor values for benzene was very good, 

with a median model to monitor ratio of 0.95, and 74% of sites within a factor of 2. Agreement 

for acetaldehyde was almost as good as benzene, but data suggest that ASPEN could be 

underpredicting for other mobile source air toxics (see Table 3.2-20). 

More detailed discussion of modeling limitations and uncertainties can be found on the 1999 

NATA website. 

Table 3.2-20. Agreement of 1999 Model and Monitors by Pollutant on a Point-to-Point 
 

Basis Pollutants listed were Monitored in at least 30 Sites and in a Broad Geographical 
 

Area (Several States) 
 

Pollutant 

No. of 

Sites 

Median of 

Ratios 

Within 

Factor of 2 

Within 

30% Underestimated 

Acetaldehyde 68 0.92 74% 44% 56% 

Benzene 115 0.95 72% 43% 52% 

Formaldehyde 68 0.64 60% 28% 76% 

Chromium 42 0.29 26% 5% 95% 

Manganese 34 0.4 44% 15% 91% 

Nickel 40 0.53 48% 18% 75% 

In addition to the limitations and uncertainties associated with modeling the 1999 base 

year, there are additional ones in the projection year modeling. For instance, the modeling is not 

accounting for impacts of demographic shifts that are likely to occur in the future. Assumptions 

about future-year meteorology introduce additional uncertainty in ambient concentrations and 

resulting exposures. Another limitation is the use of 1999 “background” levels to account for 

mid-range to long-range transport. However, since background is related to emissions far away 

from receptors, these levels should decrease as those emissions decrease. For the proposed rule 

we performed a sensitivity analysis for benzene, formaldehyde, acetaldehyde and 1,3-butadiene 

to evaluate the potential bias introduced by this assumption. We used background estimates 

scaled by the change in the proposed rule inventory for a future year relative to 1999. The 

scaling factors applied to the background level for an individual county were based on emissions 

for counties within 300 kilometers of that county’s centroid. Our analysis indicated that using a 

scaled background reduced benzene concentrations about 15% on average across the U. S in 

2015, 2020, and 2030. Table 3.2-21 compares national average total concentrations from the 

3-75
 
proposed rule using 1999 versus scaled backgrounds. More details are provided in the technical 

support document for the proposed rule.163 

Table 3.2-21. National Average Total Concentrations (All Sources and Background) for 

2015, 2020, and 2030 using both the 1999 Background and the Scaled Backgrounds (Data 

from Proposed Rule). 

HAP 

Total Concentrations (μg m-3) using 1999 

Background 

Total Concentrations (μg m-3) using Scaled 

Concentrations 

2015 2020 2030 2015 2020 2030 

1,3-Butadiene 9.81×10-2 9.77×10-2 1.00×10-1 7.57×10-2 7.50×10-2 7.86×10-2 

Acetaldehyde 9.66×10-1 9.36×10-1 9.56×10-1 7.77×10-1 7.47×10-1 7.78×10-1 

Benzene 9.13×10-1 9.02×10-1 9.24×10-1 7.57×10-1 7.40×10-1 7.71×10-1 

Formaldehyde 1.22 1.22 1.25 9.56×10-1 9.68×10-1 1.01 

Xylenes 1.55 1.61 1.65 1.50 1.56 1.60 

The largest impacts were in the Midwest as can be seen in Figure 3.2-19, which depicts 

ratios of the ASPEN-modeled ambient benzene concentrations with an adjusted background 

versus the 1999 background in 2020. Data tables with results of the sensitivity comparison by U. 

S. County, along with maps of pollutant concentrations with and without an adjusted background 

can be found in the docket for the rule. 

While accounting for impacts of emission reductions on background levels would reduce 

estimated population risks, it would increase estimated reductions in risk of control strategies in 

a given year, since background levels would be reduced. Also, if the modeling accounted for 

equipment and fuels in attached garages and increased risks from early lifetime exposures, 

estimated risks and risk reductions from fuel benzene control would be larger. 

3-76
 

 

Figure 3.2-19. Ratios of Benzene Concentrations with and without an Adjusted 
 

Background, 2020 (from modeling done to support proposed rule). 
 

Legend 

0.516 - 0.638 

0.639 - 0.716 

0.717 - 0.786 

0.787 - 0.893 

0.894 - 1.068

3.2.1.4. Perspective on Cancer Cases 

We have not quantified the cancer-related health benefits of expected MSAT reductions 

in terms of avoided cancer cases or dollars. The EPA Science Advisory Board (SAB) 

specifically commented in their review of the 1996 National Air Toxics Assessment (NATA) 

that these tools were not yet ready for use in a national-scale benefits analysis, because they did 

not consider the full distribution of exposure and risk, or address sub-chronic health effects.164

While EPA has since improved many of these tools, there remain critical limitations for 

estimating cancer incidence. For the MSATs of greatest concern, for example, we are currently 

unable to estimate cessation lag, which is the time between reduction in exposure and decline in 

risk to “steady state level.”165 We have also not resolved the analytical challenges associated 

with quantifying partial lifetime probabilities of cancer for different age groups or estimating 

changes in survival rates over time. Indeed, some of these issues are likely to remain highly 

uncertain for the foreseeable future. 

We can, however, present some perspective on how average individual risks could 

translate into cumulative excess cancer cases across the U.S. population over a lifetime, 

assuming continuous exposure at a given level for 70 years. Cancer cases were estimated by 

summing the distribution of individual cancer risks from the national-scale modeling done to 

support this rule. 

To estimate annual incidence, this would be divided by 70. However, without knowing 

when within a lifetime cancer is more likely to occur, and without accounting for time-varying 

3-77 
exposure, any estimate of incidence for a given calendar year is highly uncertain. We also note 

that a proper calculation would entail the use of a life table of incidence rates within discrete age 

ranges and a dose-response formulation expressing rate ratios as a function of benzene inhalation 

exposure concentration. 

In 2030, the cumulative excess average individual cancer risk from outdoor emissions of 

mobile source air toxics is estimated at 2.1x10-5. If the entire U. S. population (projected to be 

about 364 million)166 were exposed to this level of risk over a 70-year lifetime, it would result in 

about 7700 cancer cases, which translates into 110 annual cancer cases. 

In its review of the 1996 NATA, SAB recommended that if cancer cases were calculated 

for benefits assessment, a “best estimate” of risk (rather than an upper bound), should be used. 

We believe that the maximum likelihood unit risk range for benzene represents a best estimate. 

In our analyses, we have used the upper end of this range, as did the 1999 NATA. If we used the 

lower end of this range, incidence estimates would be lower by a factor of about 3.5. Following 

is a discussion related to benzene specifically, including a discussion of the potential 

implications of the limitations of our national-scale modeling, which were noted in Section 

3.2.1.4. 

In 2030, the national average inhalation individual cancer risk from outdoor mobile and 

stationary sources of benzene, in the absence of the standards being finalized in this rule, is 

estimated at approximately 9.1x10-6, based on the modeling done for this rule. If the entire U. S. 

population were exposed to that level of risk over a 70-year lifetime, it would result in 

approximately 47 excess cancer cases per year (Equation 1). 

(1) Excess Cancer Cases at 2030 Exposure Level =

(Average Individual Cancer Risk )× (2030 Population)

= 9.1×10−6 × 3.64 ×108 ≈ 3300
 

Annual Cancer Cases = 3300 / 70 = 47 

As discussed in Section 3.1.3.3, EPA’s estimate of risk due to exposure to benzene could 

increase significantly if the influence of attached garages were included. When the exposures for 

people with attached garages are averaged across the population, time-weighted average 

individual exposures to benzene could increase by roughly 1.2 to 6.6 μg/m3

 (Appendix 3A). 

There is a great deal of uncertainty associated with these estimates. This could result in about 

another 3400 to 18700 excess cancer cases (equation 3). The numerical ranges expressed here 

may not fully address all sources of uncertainty involved in making these projections. 

(3) Attached Garage Excess Cancer Cases =

(Average Exposure) x (BenzeneURE) × (Population)

3 3 8 = (1.2 − 6.6 μg / m ) x (7.8 x 10 / μg / m ) x (3.64 x 10 )= 3400 −18700 −6 

Annual Cancer Cases = 49 − 268 

3-78
 
Thus, including attached garages would increase the number of benzene-related excess cancer 

cases to somewhere between 96 and 315 annually. This estimate would still not include higher 

exposure levels from occupational exposures, vapor emissions from leaking underground storage 

tanks, or other accidental releases into the environment. Any population risk characterization 

that does not account for these factors underestimates the excess cancer related to benzene. 

With the controls being finalized in this rule, average individual risk, not including 

attached garage exposures, is reduced to 7.3x10-6 , which results in approximately 37 cancer 

cases per year. Thus, excess leukemia cases would be reduced by 10 annually. A roughly 40% 

reduction in overall benzene emissions could reduce attached garage exposures by approximately 

0.5-2.6 μg/m3 as well, thus reducing excess annual cancer cases from this source of exposure by 

another estimated 20 to 100 excess cancer cases. Thus, this rule would prevent roughly 30 to 

110 benzene-related excess cancer cases annually, assuming continuous lifetime exposure to 

2030 levels, given the assumptions of population size and lifetime above, and not including 

excess leukemia from occupational exposure or from leaking underground storage tanks. 

Emission reductions in 2030 would reduce cancer cases not just in 2030, but also well beyond 

this period. There would also be further unquantified reductions in incidence due to the other air 

toxics reductions. 

Such estimates should be interpreted with extreme caution since they could imply an artificial 

sense of precision. Serious limitations include: 

•	 As discussed in Chapter 1, the current unit risk estimate for benzene may underestimate 

risk from leukemia, because some recent epidemiology data, including key studies 

published after the most recent IRIS assessment, suggest a supralinear rather than linear 

dose-response at low doses. However, the studies published after the most recent IRIS 

assessment have not yet been formally evaluated by EPA as part of the IRIS review 

process, and it is not clear whether these data provide sufficient evidence to reject a linear 

dose-response curve. A better understanding of the biological mechanism of benzeneinduced leukemia is needed. 

•	 Geographically heterogeneous percentage emissions reductions do not translate directly 

into changes in ambient levels, exposure, and risk. 

•	 The U.S. population would have experienced higher average exposures in previous years, 

but this is not accounted for. 

•	 The extent to which available studies of indoor air homes in with attached garage are 

representative of the national housing stock is unknown. 

•	 Cessation lag between reduction in exposure and reduction in risk is not accounted for. 

•	 Differences in risk among various age groups are not known, and the age structure of the 

U.S. population is expected to change over time. 

3.2.2 Local-Scale Modeling 

Modeling at the national or regional scale, such the modeling done for the NATA 

National-Scale Assessment described in Section 3.2.1, is designed to identify and prioritize air 

toxics, emission source types and locations which are of greatest potential concern in terms of 

contributing to population risk. Such assessments also help elucidate patterns of exposure and 

risk across broad geographic areas, and can help characterize trends in air toxics risk and 

3-79
 
potential impacts of controls at a broad geographic scale, as demonstrated above. However, 

more localized assessments are needed to characterize and compare risks at local levels, and 

identify potential “hotspots.” 

National or regional-scale assessments typically rely on a “top down” approach to 

estimate emissions. Under a “top down” approach, emissions are estimated at the county level, 

typically starting from more aggregated information (e.g., state or national level) on activity. 

Spatial surrogates are then used to allocate emissions to grid cells or census tracts for modeling. 

Use of more local data can greatly improve the characterization of the magnitude and distribution 

of air toxic emissions. Air quality modeling can also be conducted with better spatial resolution 

than is computationally feasible in a regional or national-scale assessment. As a result, spatial 

gradients of air toxic concentrations and locations where the highest risks are likely to occur can 

be more accurately identified. 

Local-scale modeling is typically done using steady-state plume dispersion models, such 

as the Integrated Source Complex (ISC) Model, the newly promulgated AERMOD (AMS/EPA 

Regulatory Model), or non-steady-state puff models such as CALPUFF. These models have a 

limited ability to simulate chemical reactions in the atmosphere. As discussed in Section 3.2.1, 

grid-based models, such as CMAQ, which better simulate chemical processes, do not yet have 

the spatial resolution of dispersion models. Significant advances are being made, however, in 

combining features of grid-based models and plume/puff models. These advances are described 

in a recent paper.167 A case study of diesel exhaust particulate matter in Wilmington, CA was 

recently conducting employing some of these advances.168 The researchers combined Gaussian 

and regional photochemical grid models. They found that local data, when modeled, provided a 

much more refined picture of the magnitude and distribution of possible community “hot spots” 

than more traditional, regional data, which rely on more default assumptions. An evaluation of 

the approach determined that spatial allocation and emission rates contribute most to uncertainty 

in model results, and this uncertainty could be substantially reduced through the collection and 

integration of site specific information about the location of emission sources, and the activity 

and emission rates of key sources affecting model concentrations. They conclude that for 

neighborhood assessments, incorporating site-specific data can lead to improvement in modeled 

estimates of concentrations, especially where site-specific data are lacking in regulatory 

databases. 

The Wilmington study discussed above also allocated motor vehicle emissions to 

individual road “links,” rather than using spatial surrogates to allocate county level vehicle 

emissions to grid cells. In using spatial surrogates to allocate emissions, high local 

concentrations may not be captured for environments near major roadways, which are often 

clustered in urban centers. One local-scale assessment done in the Minneapolis-St. Paul area of 

Minnesota, using such an inventory with the ISC model, found that the model tended to 

overpredict at low monitored benzene concentrations and underpredict at high monitored 

concentrations.169 Local-scale modeling using activity data for individual road links can better 

characterize distributions of concentrations, and differentiate between locations near roadways 

and those further away, as observed in the following studies. 

As discussed in Section 3.1.3.2, local-scale modeling in Houston assigned emissions to 

3-80
 
individual road links. 170 Researchers at US EPA developed a methodology which utilized a 

Geographic Information System (GIS) to allocate benzene emissions in Houston to major road 

segments in an urban area and model the segments as elongated area sources. The Industrial 

Source Complex Short Term (ISCST) dispersion model used both gridded and link-based 

emissions to evaluate the effect of improved spatial allocation of emissions on ambient modeled 

benzene concentrations. Allocating onroad mobile emissions to road segments improved the 

agreement between modeled concentrations when compared with monitor observations, and also 

resulted in higher estimated concentrations in the urban center where the density of 

neighborhood streets is greater and the largest amount of traffic found. The calculated annual 

average benzene model concentrations at monitor sites are compared to the observed annual 

average concentrations in Figure 3.2-20. Most of the gridded model emissions show lower 

benzene concentrations than both the link-based and observed monitor concentrations. 

Allocating the onroad mobile emissions to road segments resulted in an increase in the average 

benzene concentration, resulting in values that more closely match concentrations reported by 

monitors. 

Recent air quality modeling in Portland, OR using the CALPUFF dispersion model 

assigned emissions to specific roadway links.171 The resulting data were used to develop a 

regression model to approximate the CALPUFF predicted concentrations, determine the impacts 

of roadway proximity on ambient concentration of three hazardous air pollutants (1,3-butadiene, 

benzene, and diesel PM), and to estimate the zone of influence around roadways. Concentrations 

were modeled at several distances from major roadways (0-50, 5-200, 200-400, and > 400 

meters). For benzene, the resulting average concentrations were 1.29, 0.64, 0.40, and 0.12 

μg/m3, respectively, illustrating the steep concentration gradient along roadways. There was a 

zone of influence between 200 and 400 meters, with concentrations falling to urban background 

levels beyond this distance. The overall mean motor vehicle benzene concentration modeled in 

Portland was about 0.21 μg/m3, with concentrations increasing to 1.29 μg/m3 at model receptor 

sites within 50 meters of a road. The results indicate that in order to capture localized impacts of 

hazardous air pollutants in a dispersion model, there is a need to include individual roadway 

links. 

3-81
 
Figure 3.2-20. Model to Monitor Comparisons of Houston Benzene Concentrations 

A recent review of local-scale modeling studies concluded that:172 

1) Significant variations in air toxic concentrations occurred across the cities, with 

highest concentrations occurring near the highest emitting sources, illustrating the need 

for modeling on a local scale. 

2) Increasing the receptor density near high emission sources changes the location of 

maximum concentrations, illustrating the concentration gradients that can occur near high 

emission sources and the importance of receptor placement and density for model 

performance. 

3) Allocating on-road mobile emissions to road segments improved the agreement 

between modeled concentrations when compared with the observations, and also resulted 

in higher estimated concentrations in the urban center. 

4) It is important to refine the national emissions inventory for input into local air quality 

model applications. 

In another US EPA study, researchers provide a comparison of “top down” and “bottom 

up” approaches to developing a motor vehicle emissions inventory for one urban area, 

Philadelphia, in calendar year 1999.173 Under the “top down” approach, emissions were 

estimated at the county level, typically starting from more aggregated information. Data on 

vehicle miles traveled (VMT) in the metropolitan statistical area were allocated to counties using 

population information. Default national model inputs (e.g. fleet characteristics, vehicle speeds) 

rather than local data were also used. The “bottom up” approach utilizes vehicle activity data 

from a travel demand model (TDM), and this “bottom up” approach estimates emission rates 

using more local input data to better estimate levels and spatial distribution of onroad motor 

vehicle emissions. TDM data can include information on the spatial distribution of vehicle 

activity, speeds along those roads (which can have a large impact on emissions), and the 

distribution of the VMT among vehicle classes for different speed ranges. These data can be 

3-82
 
used to more accurately estimate the magnitude of toxic emissions at the local scale and where 

they occur. Both the spatial distribution of emissions and the total county emissions in the 

Philadelphia area differed significantly between the top-down and the bottom-up methodologies 

as shown in Table 3.2-22. 

Table 3.2-22. Comparison of Annual 1999 Benzene Emissions from Two Approaches in 

Philadelphia Area Counties 

County Local (TDM) 

Based 

National 

(NEI) 

Percent 

Difference 

Camden 165 210 -27% 

Delaware 162 160 1% 

Gloucester 110 104 6% 

Montgomery 333 209 59% 

Philadelphia 255 467 -45% 

Total 1,025 1,150 -12% 

In the case of Philadelphia County, using local registration distribution data resulted in 

significantly lower air toxics emission factors and resultant emissions, while Montgomery 

County showed higher emissions. In the 1999 National Air Toxics Assessment, higher countylevel emissions were generally associated with higher county-level average concentrations, so it 

is anticipated that county-level concentrations will follow similar trends. However, in 

microscale settings near specific road links, these results may not apply. 

Local-scale modeling could also be improved by using local data on nonroad equipment 

activity for lawn and garden, recreational, construction and other sectors. EPA’s county-level 

inventories used in NATA and other modeling are developed using activity allocated from the 

national or state level using surrogates. 

The use of more spatially refined emission inventories, in conjunction with other refined 

air quality modeling techniques, improve the performance of air quality models. They also 

enable better characterization of the magnitude and distribution of air toxic emissions, exposure 

and risk in urban areas, including risks associated with locations heavily impacted by mobile 

sources. 

In conclusion, local scale modeling studies indicated higher concentrations of air toxics 

than predicted by National scale analysis, particularly in near-source microenvironments such as 

near roads. Thus, National scale analyses such as 1999 NATA are likely underestimating high 

end exposures and risks. 

3.3 Ozone 

In this section we review the health and welfare effects of ozone. We also describe the 

air quality monitoring and modeling data which indicate that people in many areas across the 

country continue to be exposed to high levels of ambient ozone and will continue to be into the 

3-83
 
future. Emissions of volatile organic compounds (VOCs) from the gas cans subject to this final 

rule have been shown to contribute to these ozone concentrations. Information on air quality was 

gathered from a variety of sources, including monitored ozone concentrations, air quality 

modeling forecasts conducted for this rulemaking, and other state and local air quality 

information. 

3.3.1 Science of Ozone Formation 

Ground-level ozone pollution is formed by the reaction of VOCs and nitrogen oxides 

(NOx) in the atmosphere in the presence of heat and sunlight. These pollutants, often referred to 

as ozone precursors, are emitted by many types of pollution sources such as highway and 

nonroad motor vehicles, gas cans, power plants, chemical plants, refineries, makers of consumer 

and commercial products, industrial facilities, and smaller area sources. 

The science of ozone formation, transport, and accumulation is complex.174 Groundlevel ozone is produced and destroyed in a cyclical set of chemical reactions, many of which are 

sensitive to temperature and sunlight. When ambient temperatures and sunlight levels remain 

high for several days and the air is relatively stagnant, ozone and its precursors can build up and 

result in more ozone than typically would occur on a single high-temperature day. Ozone also 

can be transported into an area from pollution sources found hundreds of miles upwind, resulting 

in elevated ozone levels even in areas with low VOC or NOx emissions. 

The highest levels of ozone are produced when both VOC and NOx emissions are present 

in significant quantities on clear summer days. Relatively small amounts of NOx enable ozone to 

form rapidly when VOC levels are relatively high, but ozone production is quickly limited by 

removal of the NOx. Under these conditions NOx reductions are highly effective in reducing 

ozone while VOC reductions have little effect. Such conditions are called “NOx-limited”. 

Because the contribution of VOC emissions from biogenic (natural) sources to local ambient 

ozone concentrations can be significant, even some areas where man-made VOC emissions are 

relatively low can be NOx -limited.

 When NOx levels are relatively high and VOC levels relatively low, NOx forms inorganic 

nitrates (i.e., particles) but relatively little ozone. Such conditions are called “VOC-limited.” 

Under these conditions, VOC reductions are effective in reducing ozone, but NOx reductions can 

actually increase local ozone under certain circumstances. Even in VOC-limited urban areas, 

NOx reductions are not expected to increase ozone levels if the NOx reductions are sufficiently 

large. 

Rural areas are usually NOx-limited, due to the relatively large amounts of biogenic VOC 

emissions in many rural areas. Urban areas can be either VOC- or NOx -limited, or a mixture of 

both, in which ozone levels exhibit moderate sensitivity to changes in either pollutant. 

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide with 

ozone, forming nitrogen dioxide (NO2); as the air moves downwind and the cycle continues, the 

NO2 forms additional ozone. The importance of this reaction depends, in part, on the relative 

concentrations of NOx, VOC, and ozone, all of which change with time and location. 

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The Clean Air Act (CAA) requires EPA to set National Ambient Air Quality Standards 

(NAAQS) for wide-spread pollutants from diverse sources considered harmful to public health 

and the environment. The CAA established two types of NAAQS: primary standards to protect 

public health, secondary standards to protect public welfare. The primary and secondary ozone 

NAAQS are identical. The 8-hour ozone standard is met when the 3-year average of the annual 

4th highest daily maximum 8-hour ozone concentration is less than or equal to 0.08 ppm. (62 FR 

38855, July 18, 1997) 

3.3.2 Health Effects of Ozone 

Exposure to ambient ozone contributes to a wide range of adverse health effects.e These 

health effects are well documented and are critically assessed in the EPA ozone Air Quality 

Criteria Document (ozone AQCD) and EPA staff paper.175,176 We are relying on the data and 

conclusions in the ozone AQCD and staff paper, regarding the health effects associated with 

ozone exposure. 

Ozone-related health effects include lung function decrements, respiratory symptoms, 

aggravation of asthma, increased hospital and emergency room visits, increased asthma 

medication usage, inflammation of the lungs, and a variety of other respiratory effects and 

cardiovascular effects. People who are more susceptible to effects associated with exposure to 

ozone include children, asthmatics and the elderly. There is also suggestive evidence that certain 

people may have greater genetic susceptibility. Those with greater exposures to ozone, for 

instance due to time spent outdoors (e.g., outdoor workers), are also of concern. 

Based on a large number of scientific studies, EPA has identified several key health 

effects associated with exposure to levels of ozone found today in many areas of the country. 

Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to higher ambient ozone 

concentrations have been linked to lung function decrements, respiratory symptoms, increased 

hospital admissions and emergency room visits for respiratory problems.177, 178, 179, 180, 181, 182 

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung 

inflammation and can aggravate preexisting respiratory diseases, such as asthma.

183, 184, 185, 186, 187 

Repeated exposure to sufficient concentrations of ozone can also cause inflammation of the lung, 

impairment of lung defense mechanisms, and possibly irreversible changes in lung structure, 

which over time could lead to premature aging of the lungs and/or chronic respiratory illnesses, 

such as emphysema and chronic bronchitis.188, 189, 190, 191 

Children and adults who are outdoors and active during the summer months, such as 

construction workers and other outdoor workers, are among those most at risk of elevated ozone 

exposures.192 Children and outdoor workers tend to have higher ozone exposures because they 

typically are active outside, working, playing and exercising, during times of day and seasons 

(e.g., the summer) when ozone levels are highest.193 For example, summer camp studies in the 

Eastern United States and Southeastern Canada have reported significant reductions in lung 

e

 Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people 

move between locations which have notable different ozone concentrations. Also, the amount of ozone delivered to 

the lung is not only influenced by the ambient concentration but also by the individuals breathing route and rate. 

3-85
 
function in children who are active outdoors.194, 195, 196, 197, 198, 199, 200, 201 Further, children are 

more at risk of experiencing health effects from ozone exposure than adults because their 

respiratory systems are still developing. These individuals (as well as people with respiratory 

illnesses such as asthma, especially asthmatic children) can experience reduced lung function 

and increased respiratory symptoms, such as chest pain and cough, when exposed to relatively 

low ozone levels during prolonged periods of moderate exertion.202, 203, 204, 205 

3.3.3 Current 8-Hour Ozone Levels 

The gas can emission reductions will assist 8-hour ozone nonattainment areas in reaching 

the standard by each area’s respective attainment date and assist 8-hour ozone maintenance areas 

in maintaining the 8-hour ozone standard in the future. In this section and the next section we 

present information on current and model-projected future 8-hour ozone levels. 

A nonattainment area is defined in the CAA as an area that is violating a NAAQS or is 

contributing to a nearby area that is violating the NAAQS. EPA designated nonattainment areas 

for the 8-hour ozone NAAQS in June 2004. The final rule on Air Quality Designations and 

Classifications for the 8-hour Ozone NAAQS (69 FR 23858, April 30, 2004) lays out the factors 

that EPA considered in making the 8-hour ozone nonattainment designations, including 2001­

2003 measured data, air quality in adjacent areas, and other factors.f 

As of October 26, 2006, approximately 157 million people live in the 116 areas that are 

currently designated as nonattainment for either failing to meet the 8-hour ozone NAAQS or for 

contributing to poor air quality in a nearby area. There are 461 full or partial counties that make 

up the 116 8-hour ozone nonattainment areas. Figure 3.3-1 illustrates the widespread nature of 

these problems. Shown in this figure are counties designated as nonattainment for the 8-hour 

ozone NAAQS, also depicted are PM2.5 nonattainment areas and the mandatory class I federal 

areas. The 8-hour ozone nonattainment areas, nonattainment counties and populations are listed 

in Appendix 3B to this RIA. 

f

 An ozone design value is the concentration that determines whether the ozone levels recorded at a monitoring site 

meet the NAAQS for ozone. The level of a design value is determined based on three consecutive-year monitoring 

periods. For example, an 8-hour design value is the fourth highest daily maximum 8-hour average ozone 

concentration measured over a three-year period at a given monitor. Greater detail on how these values are 

determined (including how to account for missing values and other complexities) is given in Appendices H and I of 

40 CFR Part 50. Due to the precision with which the standards are expressed (0.08 ppm for the 8-hour NAAQS 

value), a violation of the 8-hour standard is defined as any design value greater than or equal to 0.085 ppm, or 85 

ppb. For any particular county, the design value is the highest design value from amongst all the monitors having 

valid design values within that county. If there are no ozone monitors located in a particular county, that county is 

not assigned a design value. However, readers should note that ozone design values represent air quality over a 

broad area and the absence of a design value for a specific county does not imply that that county is in compliance 

with the NAAQS for ozone. Therefore, our analysis may underestimate the number of counties with ozone levels, 

i.e., design values, which are above the level of the ozone NAAQS. 

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Figure 3.3.-1. 8-Hour Ozone and PM2.5 Nonattainment Areas and Mandatory Class I 
 

Federal Areas 
 

Counties designated as 8-hour ozone nonattainment were categorized, on the basis of 

their one-hour ozone design value, as Subpart 1 or Subpart 2 (69 FR 23951, April 30, 2004). 

Areas categorized as Subpart 2 were then further classified, on the basis of their 8-hour ozone 

design value, as marginal, moderate, serious, severe or extreme. The maximum attainment date 

assigned to an ozone nonattainment area is based on the area’s classification. 

Table 3B-1 presents the 8-hour ozone nonattainment areas, their 8-hour design values, 

and their category or classification. States with 8-hour ozone nonattainment areas are required to 

take action to bring those areas into compliance prior to the ozone season in the attainment year. 

Based on the final rule designating and classifying 8-hour ozone nonattainment areas, most 8­

hour ozone nonattainment areas will be required to attain the 8-hour ozone NAAQS in the 2007 

to 2013 time frame and then be required to maintain the 8-hour ozone NAAQS thereafter.g The 

gas can emission standards being finalized in this action will become effective in 2009. Thus, 

g

 The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area will have to attain before June 15, 2021. 

3-87
 
the expected ozone precursor emission inventory reductions from the standards finalized in this 

action will be useful to States in attaining and/or maintaining the 8-hour ozone NAAQS. 

EPA’s review of the ozone NAAQS is currently underway and a proposed decision in 

this review is scheduled for June 2007 with a final rule scheduled for March 2008. If the ozone 

NAAQS is revised then new nonattainment areas could be designated. While EPA is not relying 

on it for purposes of justifying this rule, the emission reductions from this rulemaking would also 

be helpful to states if there is an ozone NAAQS revision. 

3.3.4 Projected 8-Hour Ozone Levels 

Recent air quality modeling predicts that without additional local, regional or national 

controls there will continue to be a need for reductions in 8-hour ozone concentrations in some 

areas in the future. In the following sections we describe recent ozone air quality modeling from 

the CAIR analysis as well as results of the ozone response surface metamodel (RSM) analysis 

we completed to assess the potential ozone impacts resulting from the VOC emissions controls 

for gas cans. 

3.3.4.1 CAIR Ozone Air Quality Modeling 

Recently ozone air quality analyses were performed for the Clean Air Interstate Rule 

(CAIR), which was promulgated by EPA in 2005. The Comprehensive Air Quality Model with 

Extension (CAMx) was used as the tool for simulating base and future year concentrations of 

ozone in support of the CAIR ozone air quality assessment. The CAIR analysis included all final 

federal rules up to and including CAIR controls. Details on the air quality modeling are 

provided in the Air Quality Modeling Technical Support Document for the Final Clean Air 

Interstate Rule, included in the docket for this final rule.206 

Air quality modeling performed for CAIR indicates that in the absence of additional 

controls, counties with projected 8-hour ozone concentrations greater than or equal to 85 ppb are 

likely to persist in the future. The CAIR analysis provided estimates of future ozone levels 

across the country. For example, in 2010, in the absence of controls beyond those relied on for 

the CAIR modeling, we project that 24 million people would live in 37 Eastern counties with 8­

hour ozone concentrations at and above 85 ppb, see Table 3.3-1.h Table 3.3-1 also lists the 148 

Eastern counties, where 61 million people are projected to live, with 2010 projected design 

values that do not violate the 8-hour ozone NAAQS but are within ten percent of it, in the 

absence of emission reductions beyond those considered in the CAIR modeling. These are 

counties that are not projected to violate the standard, but to be close to it. The rule may help 

ensure that these counties continue to maintain their attainment status and the emission 

reductions from this final rule will be included by the states in their baseline inventory modeling 

for their ozone maintenance plans. 

h

 Counties forecast to remain in nonattainment may need to adopt additional local or regional controls to attain the 

standards by dates set pursuant to the Clean Air Act. The emissions reductions associated with this proposed rule 

would help these areas attain the ozone standard by their statutory date. 

3-88
 
Table 3.3-1. Eastern Counties with 2010 projected 8-hour Ozone Concentrations 

Above and within 10% of the 8-hour Ozone Standard 

2010 Projected 

8-hour Ozone 

State County Concentration (ppb)

a 2000 popb 2010 popc 

Arkansas Crittenden Co 80.8 50,866 52,889 

Connecticut Fairfield Co 92.2 882,567 891,694 

Connecticut Hartford Co 80.1 857,183 859,080 

Connecticut Middlesex Co 90.6 155,071 164,202 

Connecticut New Haven Co 91.3 824,008 829,181 

Connecticut New London Co 83.4 259,088 267,199 

Connecticut Tolland Co 82.7 136,364 142,988 

D.C. Washington Co 85.0 572,058 554,474 

Delaware Kent Co 78.7 126,697 139,376 

Delaware New Castle Co 84.7 500,264 534,631 

Delaware Sussex Co 80.3 156,638 181,962 

Georgia Bibb Co 80.0 153,887 158,291 

Georgia Cobb Co 79.4 607,750 744,488 

Georgia Coweta Co 76.6 89,215 111,522 

Georgia De Kalb Co 81.9 665,864 698,335 

Georgia Douglas Co 78.7 92,174 114,380 

Georgia Fayette Co 76.7 91,263 117,580 

Georgia Fulton Co 85.1 816,005 855,826 

Georgia Henry Co 80.3 119,341 153,957 

Georgia Rockdale Co 80.4 70,111 87,977 

Illinois Cook Co 81.8 5,376,739 5,363,464 

Illinois Jersey Co 77.0 21,668 22,905 

Illinois Lake Co 76.8 644,356 731,690 

Illinois McHenry Co 76.6 260,077 307,400 

Indiana Boone Co 78.1 46,107 54,035 

Indiana Clark Co 78.4 96,472 107,096 

Indiana Hamilton Co 81.7 182,740 230,565 

Indiana Hancock Co 80.4 55,391 65,282 

Indiana La Porte Co 81.8 110,106 111,566 

Indiana Lake Co 82.8 484,563 489,220 

Indiana Madison Co 78.6 133,358 137,710 

Indiana Marion Co 79.6 860,453 879,932 

Indiana Porter Co 81.1 146,798 165,350 

Indiana Shelby Co 81.6 43,445 46,565 

Indiana St Joseph Co 77.8 265,559 275,031 

Kentucky Campbell Co 81.5 88,616 92,109 

Louisiana Bossier Parish 77.0 98,310 110,838 

Louisiana East Baton Rouge Parish 80.6 412,852 465,411 

Louisiana Iberville Parish 79.4 33,320 33,089 

Louisiana Jefferson Parish 78.6 455,466 493,359 

Louisiana Livingston Parish 77.8 91,814 124,895 

Louisiana West Baton Rouge Parish 78.8 21,601 22,672 

Maine Hancock Co 80.5 51,791 53,886 

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2010 Projected 

8-hour Ozone 

State County Concentration (ppb)

a 2000 popb 2010 popc 

Maine York Co 80.2 186,742 201,082 

Maryland Anne Arundel Co 88.6 489,656 543,785 

Maryland Baltimore Co 83.7 754,292 792,284 

Maryland Carroll Co 80.0 150,897 179,918 

Maryland Cecil Co 89.5 85,951 96,574 

Maryland Charles Co 78.7 120,546 145,763 

Maryland Frederick Co 78.1 195,277 234,304 

Maryland Harford Co 92.8 218,590 268,207 

Maryland Kent Co 85.8 19,197 20,233 

Maryland Montgomery Co 79.3 873,341 940,126 

Maryland Prince Georges Co 84.2 801,515 842,221 

Massachusetts Barnstable Co 83.6 222,230 249,495 

Massachusetts Bristol Co 83.0 534,678 558,460 

Massachusetts Essex Co 81.7 723,419 747,556 

Massachusetts Hampden Co 80.2 456,228 452,718 

Massachusetts Hampshire Co 78.0 152,251 158,130 

Massachusetts Middlesex Co 79.1 1,465,396 1,486,428 

Massachusetts Suffolk Co 78.1 689,807 674,179 

Michigan Allegan Co 82.1 105,665 121,415 

Michigan Benzie Co 77.9 15,998 17,849 

Michigan Berrien Co 78.1 162,453 164,727 

Michigan Cass Co 78.2 51,104 53,544 

Michigan Genesee Co 76.7 436,141 441,196 

Michigan Macomb Co 85.4 788,149 838,353 

Michigan Mason Co 78.9 28,274 30,667 

Michigan Muskegon Co 82.0 170,200 175,901 

Michigan Oakland Co 80.7 1,194,155 1,299,592 

Michigan Ottawa Co 76.6 238,314 277,400 

Michigan St Clair Co 80.6 164,235 178,391 

Michigan Washtenaw Co 81.0 322,895 344,398 

Michigan Wayne Co 84.7 2,061,161 1,964,209 

Missouri Clay Co 76.5 184,006 213,643 

Missouri Jefferson Co 76.7 198,099 230,539 

Missouri St Charles Co 80.5 283,883 341,686 

Missouri St Louis City 79.4 348,188 324,156 

Missouri St Louis Co 80.5 1,016,315 1,024,964 

New Hampshire Hillsborough Co 76.6 380,841 412,071 

New Jersey Atlantic Co 80.4 252,552 269,754 

New Jersey Bergen Co 86.0 884,118 898,450 

New Jersey Camden Co 91.6 508,932 509,912 

New Jersey Cumberland Co 84.4 146,438 149,595 

New Jersey Gloucester Co 91.3 254,673 278,612 

New Jersey Hudson Co 84.3 608,975 607,256 

New Jersey Hunterdon Co 88.6 121,989 139,641 

New Jersey Mercer Co 95.2 350,761 359,912 

New Jersey Middlesex Co 92.1 750,162 805,537 

3-90
 
2010 Projected 

8-hour Ozone 

State County Concentration (ppb)

a 2000 popb 2010 popc 

New Jersey Monmouth Co 86.4 615,301 670,971 

New Jersey Morris Co 85.5 470,212 500,033 

New Jersey Ocean Co 100.3 510,916 572,364 

New Jersey Passaic Co 79.7 489,049 495,610 

New York Bronx Co 79.7 1,332,649 1,298,206 

New York Chautauqua Co 81.8 139,750 139,909 

New York Dutchess Co 81.0 280,150 291,098 

New York Erie Co 86.9 950,265 953,085 

New York Essex Co 77.6 38,851 39,545 

New York Jefferson Co 80.5 111,738 113,075 

New York Monroe Co 76.9 735,343 745,350 

New York Niagara Co 82.3 219,846 220,407 

New York Orange Co 77.1 341,367 371,434 

New York Putnam Co 82.3 95,745 107,967 

New York Queens Co 78.3 2,229,379 2,239,026 

New York Richmond Co 87.1 443,728 488,728 

New York Suffolk Co 90.8 1,419,369 1,472,127 

New York Westchester Co 84.7 923,459 944,535 

North Carolina Mecklenburg Co 81.4 695,453 814,088 

North Carolina Rowan Co 80.1 130,340 143,729 

North Carolina Wake Co 77.2 627,846 787,707 

Ohio Allen Co 76.8 108,473 106,900 

Ohio Ashtabula Co 83.5 102,728 104,850 

Ohio Butler Co 78.0 332,806 384,410 

Ohio Clermont Co 78.0 177,977 205,365 

Ohio Clinton Co 81.4 40,543 47,137 

Ohio Cuyahoga Co 77.3 1,393,977 1,348,313 

Ohio Delaware Co 77.3 109,989 136,125 

Ohio Franklin Co 81.9 1,068,977 1,142,894 

Ohio Geauga Co 86.6 90,895 102,083 

Ohio Hamilton Co 78.6 845,302 843,226 

Ohio Knox Co 76.5 54,500 59,435 

Ohio Lake Co 82.2 227,511 237,161 

Ohio Lorain Co 78.5 284,664 292,040 

Ohio Lucas Co 80.0 455,053 447,302 

Ohio Medina Co 76.5 151,095 173,985 

Ohio Portage Co 79.8 152,061 162,685 

Ohio Summit Co 82.4 542,898 552,567 

Ohio Trumbull Co 79.7 225,116 226,157 

Ohio Warren Co 80.0 158,383 186,219 

Ohio Wood Co 77.4 121,065 129,124 

Oklahoma Tulsa Co 79.2 563,299 610,536 

Pennsylvania Allegheny Co 81.9 1,281,665 1,259,040 

Pennsylvania Armstrong Co 79.7 72,392 72,829 

Pennsylvania Beaver Co 79.6 181,412 183,693 

Pennsylvania Berks Co 81.7 373,637 388,194 

3-91
 
2010 Projected 

8-hour Ozone 

State County Concentration (ppb)

a 2000 popb 2010 popc 

Pennsylvania Bucks Co 94.3 597,635 648,796 

Pennsylvania Cambria Co 76.9 152,598 146,811 

Pennsylvania Chester Co 85.4 433,501 478,460 

Pennsylvania Dauphin Co 80.8 251,798 265,019 

Pennsylvania Delaware Co 84.0 550,863 543,169 

Pennsylvania Erie Co 79.1 280,843 284,835 

Pennsylvania Franklin Co 80.2 129,313 135,088 

Pennsylvania Lancaster Co 83.6 470,657 513,684 

Pennsylvania Lehigh Co 82.1 312,090 323,215 

Pennsylvania Mercer Co 78.1 120,293 122,546 

Pennsylvania Montgomery Co 87.6 750,097 772,849 

Pennsylvania Northampton Co 81.8 267,066 279,797 

Pennsylvania Philadelphia Co 89.9 1,517,549 1,420,803 

Pennsylvania Washington Co 77.3 202,897 205,153 

Pennsylvania Westmoreland Co 76.7 369,993 372,941 

Pennsylvania York Co 79.4 381,750 404,807 

Rhode Island Kent Co 86.2 167,090 174,126 

Rhode Island Providence Co 81.2 621,602 621,355 

Rhode Island Washington Co 84.2 123,546 137,756 

South Carolina Richland Co 76.9 320,677 349,826 

Tennessee Sevier Co 76.5 71,170 96,097 

Tennessee Shelby Co 76.7 897,471 958,501 

Texas Brazoria Co 84.1 241,767 281,960 

Texas Collin Co 82.5 491,675 677,868 

Texas Dallas Co 82.2 2,218,899 2,382,657 

Texas Denton Co 86.8 432,976 554,033 

Texas Galveston Co 84.6 250,158 283,963 

Texas Gregg Co 79.1 111,379 121,241 

Texas Harris Co 97.4 3,400,577 3,770,129 

Texas Jefferson Co 85.0 252,051 260,847 

Texas Johnson Co 78.2 126,811 157,545 

Texas Montgomery Co 81.2 293,768 413,048 

Texas Tarrant Co 87.2 1,446,219 1,710,920 

Virginia Alexandria City 80.9 128,283 130,422 

Virginia Arlington Co 86.0 189,453 193,370 

Virginia Charles City Co 77.7 6,926 7,382 

Virginia Fairfax Co 85.4 969,749 1,085,483 

Virginia Hampton City 78.7 146,437 153,246 

Virginia Hanover Co 80.9 86,320 98,586 

Virginia Henrico Co 78.2 262,300 294,174 

Virginia Loudoun Co 78.6 169,599 214,469 

Virginia Suffolk City 77.5 63,677 69,003 

Wisconsin Door Co 82.1 27,961 30,508 

Wisconsin Kenosha Co 91.0 149,577 166,359 

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State County 

2010 Projected 

8-hour Ozone 

Concentration (ppb)

a 2000 popb 2010 popc 

Wisconsin 

Wisconsin 

Wisconsin 

Wisconsin 

Wisconsin 

Wisconsin 

 Kewaunee Co 

Manitowoc Co 

Milwaukee Co 

Ozaukee Co 

Racine Co 

Sheboygan Co 

79.9 

80.0 

82.1 

85.8 

83.9 

87.7 

20,187 

82,887 

940,164 

82,317 

188,831 

112,646 

20,538 

83,516 

922,943 

 95,549 

199,178 

118,866 

Number of Violating Counties 

Population of Violating Counties 

Number of Counties within 10% 

Population of Counties within 10% 

37 

148 

22,724,010 

58,453,962 

24,264,574 

61,409,062 

a) Bolded concentrations indicate levels above the 8-hour ozone standard. 
 

b) Populations are based on 2000 census data. 
 

c) Populations are based on 2000 census projections. 
 

3.3.4.2 Ozone Response Surface Metamodel Methodology 

We performed ozone air quality modeling simulations for the Eastern United States using 

the ozone RSM. The ozone RSM is a screening-level air quality modeling tool that allows users 

to quickly assess the estimated air quality changes over the modeling domain. The ozone RSM 

is a model of a full-scale air quality model and is based on statistical relationships between 

model inputs and outputs obtained from the full-scale air quality model. In other words, the 

ozone RSM uses statistical techniques to relate a response variable to a set of factors that are of 

interest, e.g., emissions of precursor pollutants from particular sources and locations. The 

following section describes the modeling methodology, including the development of the multidimensional experimental design for control strategies and implementation and verification of 

the RSM technique. Additional detail is available in the Air Quality Modeling Technical 

Support Document (AQMTSD) for this rule.207 

The foundation for the ozone response surface metamodeling analyses was the CAMx 

modeling done in support of the final Clean Air Interstate Rule (CAIR). The CAIR modeling is 

fully described in the CAIR Air Quality Modeling Technical Support Document, but a brief 

description is provided below.208 The modeling procedures used in the CAIR analysis (e.g., 

domain, episodes, meteorology) have been used for several EPA rulemaking analyses over the 

past five years and are well-established at this point. 

The ozone RSM uses the 2015 controlled CAIR emissions inventory as its baseline.209 

This inventory does not include the gas can emissions that are being controlled in this rule. The 

uncontrolled and controlled gas can emissions have been incorporated into the base and control 

runs of the ozone RSM (see Section 2.1 for more detail about the gas can emissions inventory). 

The inventory also does not include the higher estimates of cold temperature emissions for 

gasoline vehicles developed for this rule; however, these emissions are not likely to have a 

significant impact on ozone formation. Finally, the inventory includes an error in mobile source 

NOx for 13 Northeastern states. The impact of this error is minimized as the model is used in a 

relative way. Because the base years of our air quality modeling analysis are 2020 and 2030, we 

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extrapolate the model from 2015 to 2020 and 2030. Additional detail on how the model was 

extrapolated to reflect gas can emissions and various projection years is included in the 

AQMTSD for this final rule.210 

The modeling simulations that comprised the metamodeling were conducted using 

CAMx version 3.10. It should be noted that because the ozone RSM is built from CAMx air 

quality model runs, it therefore has the same strengths and limitations of the underlying model 

and its inputs. CAMx is a non-proprietary computer model that simulates the formation and fate 

of photochemical oxidants including ozone for given input sets of meteorological conditions and 

emissions. The gridded meteorological data for three historical episodes were developed using 

the Regional Atmospheric Modeling System (RAMS), version 3b.211 In all, 30 episode days 

were modeled using frequently-occurring, ozone-conducive, meteorological conditions from the 

summer of 1995. Emissions estimates were developed for the evaluation year (1995) as well as a 

future year (2015). 

The CAMx model applications were performed for a domain covering all, or portions of, 

37 States (and the District of Columbia) in the Eastern U.S., as shown in Figure 3.3-2. The 

domain has nested horizontal grids of 36 km and 12 km. However, the output data from the 

metamodeling is provided at a 12 km resolution (i.e., cells from the outer 36 km cells populate 

the nine finer scale cells, as appropriate). Although the domain of the ozone RSM is the 37 

Eastern states, the gas can controls are a nationwide program. Section 2.1.3 describes the 

nationwide inventory reductions that could be achieved by the gas can controls. Section 2.1.1.2 

also details the states that have their own gas can control programs and how the controls 

finalized here impact states which already have gas can control programs. 

3-94
 
Figure 3.3-2. Map of the CAMx Domain used for MSAT Ozone Metamodeling 

The ozone RSM used for assessing the impacts of gas can emission reductions was 

developed broadly to look at various control strategies with respect to attaining the 8-hour ozone 

NAAQS. The experimental design for the ozone RSM covered three key areas: type of precursor 

emission (NOx or VOC), emission source type (i.e., onroad vehicles, nonroad vehicles, area 

sources, electrical generating utility (EGU) sources, and non-utility point sources), and location 

in or out of a 2015 model-projected residual ozone nonattainment area. This resulted in a set of 

14 emissions factors. Since some of the spillage emissions associated with gas cans are currently 

included in the NONROAD emissions model, for the purposes of the ozone RSM we have 

included gas can emissions as part of the nonroad factor in our air quality modeling. 

The 14 emission factors were randomly varied and used as inputs to CAMx. The 

experimental design for these 14 factors was developed using a Maximin Latin Hypercube 

method. Based on a rule of thumb of 10 runs per factor, we developed an overall design with 

154 runs (a base case plus 139 control runs plus 10 evaluation runs plus 4 boundary condition 

runs). The range of emissions reductions considered within the metamodel ranged from 0 to 120 

percent of the 2015 CAIR emissions. This experimental design resulted in a set of CAMx 

simulations that serve as the inputs to the ozone response surface metamodel. Because the 

metamodeling was going to be used to assess the impacts of the gas can standards, the 

3-95
 
experimental design also included oversampling in the range of 0 to 10 percent control for the 

nonroad VOC sector, as well as CAMx runs that only included VOC controls. 

To develop a response surface approximation to CAMx, we used a multidimensional 

kriging approach, implemented through the MIXED procedure in SAS. We modeled the 

predicted changes in ozone in each CAMx grid cell as a function of the weighted average of the 

modeled responses in the experimental design. A response-surface was then fit for the ozone 

design value metric. Validation was performed and is summarized in the AQMTSD. The 

validation exercises indicated that the ozone RSM replicates CAMx response to emissions 

changes very well for most emissions combinations and in most locations. 

The assessment of gas can controls conducted for this analysis involved adjusting the 

nonroad mobile source VOC emissions both in and out of ozone nonattainment areas and looking 

at the impact on the 8-hour ozone design value metric. We created an input or adjustment factor 

for the nonroad mobile source VOC emission factor by adding future year gas can emission 

estimates to the projected CAIR emission inventory and then relating the future year emissions 

estimate to 2015. For this assessment the future years modeled are 2020 and 2030. 

3.3.4.3 Ozone Response Surface Metamodel Results 

This section summarizes the results of our modeling of ozone air quality impacts in the 

future with and without the reductions in gas can emissions. Based upon our previous CAIR air 

quality modeling, we anticipate that without emission reductions beyond those already required 

under promulgated regulations and approved SIPs, ozone nonattainment will likely persist into 

the future. 

The inventories that underlie the ozone modeling conducted for this rulemaking included 

emission reductions from all current or committed federal, state, and local controls, including the 

recent CAIR. There was no attempt to examine the prospects of areas attaining or maintaining 

the 8-hour ozone standard with possible additional future controls (i.e., controls beyond current 

or committed federal, State, and local controls). 

According to the ozone response surface metamodel (RSM), the gas can controls are 

projected to result in a very small population-weighted net improvement in future ozone. The 

net improvement is generally so small as to be rendered insignificant when presenting design 

values. The model changes are smaller than the precision with which the ozone standard is 

expressed (0.08 parts per million (ppm)) and to which 8-hour ozone data is reported.i 

Nonetheless, there are some areas where the ozone improvement is more significant. These 

areas include Chicago, Milwaukee, Detroit and New York City. It is also important to note that 

the ozone RSM results indicate that the counties which are projected to experience the greatest 

improvement in ozone design values are generally also those that are projected to have the 

highest ozone design values. Those counties that are projected to experience an extremely small 

increase in ozone design values generally have design values that are lower, below 70 ppb. The 

results from the metamodeling projections indicate a net overall improvement in future 8-hour 

i Appendix I of 40 CFR Part 50. 

3-96
 
ozone design values due to the gas can controls, when weighted by population. The AQMTSD, 

contained in the docket for this final rule, includes additional detail on the ozone RSM results. 

3.3.5 Environmental Effects of Ozone Pollution 

There are a number of public welfare effects associated with the presence of ozone in the 

ambient air.212 In this section we discuss the impact of ozone on plants, including trees, 

agronomic crops and urban ornamentals. 

3.3.5.1 Impacts on Vegetation

 The ozone AQCD notes that “ozone affects vegetation throughout the United States, 

impairing crops, native vegetation, and ecosystems more than any other air pollutant.”213 Like 

carbon dioxide (CO2) and other gaseous substances, ozone enters plant tissues primarily through 

apertures (stomata) in leaves in a process called “uptake.” To a lesser extent, ozone can also 

diffuse directly through surface layers to the plant's interior.214 Once sufficient levels of ozone, a 

highly reactive substance, (or its reaction products) reaches the interior of plant cells, it can 

inhibit or damage essential cellular components and functions, including enzyme activities, 

lipids, and cellular membranes, disrupting the plant's osmotic (i.e., water) balance and energy 

utilization patterns.215,216 This damage is commonly manifested as visible foliar injury such as 

chlorotic or necrotic spots, increased leaf senescence (accelerated leaf aging) and/or reduced 

photosynthesis. All these effects reduce a plant’s capacity to form carbohydrates, which are the 

primary form of energy used by plants.217 With fewer resources available, the plant reallocates 

existing resources away from root growth and storage, above ground growth or yield, and 

reproductive processes, toward leaf repair and maintenance. Studies have shown that plants 

stressed in these ways may exhibit a general loss of vigor, which can lead to secondary impacts 

that modify plants' responses to other environmental factors. Specifically, plants may become 

more sensitive to other air pollutants, more susceptible to disease, insect attack, harsh weather 

(e.g., drought, frost) and other environmental stresses. Furthermore, there is some evidence that 

ozone can interfere with the formation of mycorrhiza, essential symbiotic fungi associated with 

the roots of most terrestrial plants, by reducing the amount of carbon available for transfer from 

the host to the symbiont.218 

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate over 

the growing season of the plant, so that even lower concentrations experienced for a longer 

duration have the potential to create chronic stress on sensitive vegetation. Not all plants, 

however, are equally sensitive to ozone. Much of the variation in sensitivity between individual 

plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via 

leaf stomata (e.g., avoidance of O3 uptake through closure of stomata).219,220,221 Other resistance 

mechanisms may involve the intercellular production of detoxifying substances. Several 

biochemical substances capable of detoxifying ozone have been reported to occur in plants 

including the antioxidants ascorbate and glutathione. After injuries have occurred, plants may be 

capable of repairing the damage to a limited extent.222 Because of the differing sensitivities 

among plants to ozone, ozone pollution can also exert a selective pressure that leads to changes 

in plant community composition. Given the range of plant sensitivities and the fact that 

3-97
 
numerous other environmental factors modify plant uptake and response to ozone, it is not 

possible to identify threshold values above which ozone is consistently toxic for all plants. The 

next few paragraphs present additional information on ozone damage to trees, ecosystems, 

agronomic crops and urban ornamentals. 

Ozone also has been shown conclusively to cause discernible injury to forest trees.223,224 

In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the 

greatest potential for regional-scale forest impacts.225 Studies have demonstrated repeatedly that 

ozone concentrations commonly observed in polluted areas can have substantial impacts on plant 

function.226,227 

Because plants are at the center of the food web in many ecosystems, changes to the plant 

community can affect associated organisms and ecosystems (including the suitability of habitats 

that support threatened or endangered species and below ground organisms living in the root 

zone). Ozone impacts at the community and ecosystem level vary widely depending upon 

numerous factors, including concentration and temporal variation of tropospheric ozone, species 

composition, soil properties and climatic factors.228 In most instances, responses to chronic or 

recurrent exposure in forested ecosystems are subtle and not observable for many years. These 

injuries can cause stand-level forest decline in sensitive ecosystems.229,230,231 It is not yet 

possible to predict ecosystem responses to ozone with much certainty; however, considerable 

knowledge of potential ecosystem responses has been acquired through long-term observations 

in highly damaged forests in the United States. 

Laboratory and field experiments have also shown reductions in yields for agronomic 

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and 

wheat). The most extensive field experiments, conducted under the National Crop Loss 

Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN 

results show that “several economically important crop species are sensitive to ozone levels 

typical of those found in the Unites States.”232 In addition, economic studies have shown 

reduced economic benefits as a result of predicted reductions in crop yields associated with 

observed ozone levels.233,234,235 

Urban ornamentals represent an additional vegetation category likely to experience some 

degree of negative effects associated with exposure to ambient ozone levels and likely to impact 

large economic sectors. It is estimated that more than $20 billion (1990 dollars) are spent 

annually on landscaping using ornamentals, both by private property owners/tenants and by 

governmental units responsible for public areas.236 This is therefore a potentially costly 

environmental effect. However, in the absence of adequate exposure-response functions and 

economic damage functions for the potential range of effects relevant to these types of 

vegetation, no direct quantitative analysis has been conducted. Methods are not available to 

allow for plausible estimates of the percentage of these expenditures that may be related to 

impacts associated with ozone exposure. 

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3.4 Particulate Matter 

In this section we review the health and welfare effects of particulate matter (PM). We 

also describe air quality monitoring and modeling data that indicate many areas across the 

country continue to be exposed to levels of ambient PM above the NAAQS. Emissions of PM 

and VOC from the vehicles subject to this rule contribute to these PM concentrations. 

Information on air quality was gathered from a variety of sources, including monitored PM 

concentrations, air quality modeling done for recent EPA rulemakings and other state and local 

air quality information. 

3.4.1 Science of PM Formation 

Particulate matter (PM) represents a broad class of chemically and physically diverse 

substances. It can be principally characterized as discrete particles that exist in the condensed 

(liquid or solid) phase spanning several orders of magnitude in size. PM is further described by 

breaking it down into size fractions. PM10 refers to particles generally less than or equal to 10 

micrometers (μm) in diameter. PM2.5 refers to fine particles, those particles generally less than 

or equal to 2.5 μm in diameter. Inhalable (or “thoracic”) coarse particles refer to those particles 

generally greater than 2.5 μm but less than or equal to 10 μm in diameter. Ultrafine PM refers to 

particles with diameters generally less than 100 nanometers (0.1 μm). Larger particles (>10 μm) 

tend to be removed by the respiratory clearance mechanisms, whereas smaller particles are 

deposited deeper in the lungs. 

Fine particles are produced primarily by combustion processes and by transformations of 

gaseous emissions (e.g., SOx, NOx and VOCs) in the atmosphere. The chemical and physical 

properties of PM2.5 may vary greatly with time, region, meteorology and source category. Thus, 

PM2.5, may include a complex mixture of different pollutants including sulfates, nitrates, organic 

compounds, elemental carbon and metal compounds. These particles can remain in the 

atmosphere for days to weeks and travel through the atmosphere hundreds to thousands of 

kilometers. 

The vehicles that will be covered by the standards contribute to ambient PM levels 

through primary (direct) and secondary (indirect) PM. Primary PM is directly emitted into the 

air, and secondary PM forms in the atmosphere from gases emitted by fuel combustion and other 

sources. Along with primary PM, the vehicles controlled in this action emit VOC, which react in 

the atmosphere to form secondary PM2.5, namely organic carbonaceous PM2.5. The gas cans that 

will be covered by the standards also emit VOC which contribute to secondary PM2.5. Both 

types of directly and indirectly formed particles from vehicles and gas cans are found principally 

in the fine fraction. 

EPA has recently amended the PM NAAQS (71 FR 61144, October 17, 2006). The final 

rule, signed on September 21, 2006 and published on October 17, 2006, addressed revisions to 

the primary and secondary NAAQS for PM to provide increased protection of public health and 

welfare, respectively. The primary PM2.5 NAAQS include a short-term (24-hour) and a longterm (annual) standard. The level of the 24-hour PM2.5 NAAQS has been revised from 65μg/m3 

to 35 μg/m3 to provide increased protection against health effects associated with short-term 

3-99
 
exposures to fine particles. The current form of the 24-hour PM2.5 standard was retained (e.g., 

based on the 98th percentile concentration averaged over three years). The level of the annual 

PM2.5 NAAQS was retained at 15μg/m3, continuing protection against health effects associated 

with long-term exposures. The current form of the annual PM2.5 standard was retained as an 

annual arithmetic mean averaged over three years, however, the following two aspects of the 

spatial averaging criteria were narrowed: (1) the annual mean concentration at each site shall be 

within 10 percent of the spatially averaged annual mean, and (2) the daily values for each 

monitoring site pair shall yield a correlation coefficient of at least 0.9 for each calendar quarter. 

With regard to the primary PM10 standards, the 24-hour PM10 NAAQS was retained at a level of 

150 μg/m3

 not to be exceeded more than once per year on average over a three-year period. 

Given that the available evidence does not suggest an association between long-term exposure to 

coarse particles at current ambient levels and health effects, EPA has revoked the annual PM10 

standard. 

With regard to the secondary PM standards, EPA has revised these standards to be 

identical in all respects to the revised primary standards. Specifically, EPA has revised the 

current 24-hour PM2.5 secondary standard by making it identical to the revised 24-hour PM2.5 

primary standard, retained the annual PM2.5 and 24-hour PM10 secondary standards, and revoked 

the annual PM10 secondary standards. This suite of secondary PM standards is intended to 

provide protection against PM-related public welfare effects, including visibility impairment, 

effects on vegetation and ecosystems, and material damage and soiling. 

3.4.2 Health Effects of Particulate Matter 

As stated in the EPA Particulate Matter Air Quality Criteria Document (PMAQCD), 

available scientific findings “demonstrate well that human health outcomes are associated with 

ambient PM.”j We are relying primarily on the data and conclusions in the PM AQCD and PM 

staff paper, which reflects EPA’s analysis of policy-relevant science from the PM AQCD, 

regarding the health effects associated with particulate matter.237,238 We also present additional 

recent studiesk published after the cut-off date for the PM AQCD.239 Taken together this 

information supports the conclusion that PM-related emissions such as those controlled in this 

action are associated with adverse health effects. 

3.4.2.1 Short-Term Exposure Mortality and Morbidity Studies 

As discussed in the PM AQCD, short-term exposure to PM2.5 is associated with 

premature mortality from cardiopulmonary diseases (PM AQCD, p. 8-305), hospitalization and 

j

 Personal exposure includes contributions from many different types of particles, from many sources, and in many 

different environments. Total personal exposure to PM includes both ambient and nonambient components; and 

both components may contribute to adverse health effects. k

 These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects of 

Particulate Matter Exposure. The provisional assessment did not and could not (given a very short timeframe) 

undergo the extensive critical review by EPA, CASAC, and the public, as did the PM AQCD. The provisional 

assessment found that the “new” studies expand the scientific information and provide important insights on the 

relationship between PM exposure and health effects of PM. The provisional assessment also found that the “new” 

studies generally strengthen the evidence that acute and chronic exposure to fine particles and acute exposure to 

thoracic coarse particles are associated with health effects. 

3-100
 
emergency department visits for cardiopulmonary diseases (PMAQCD, p. 9-93), increased 

respiratory symptoms (PM AQCD, p. 9-46), decreased lung function (PM AQCD Table 8-34) 

and physiological changes or biomarkers for cardiac changes (PM AQCD, Section 8.3.1.3.4). In 

addition, the PM AQCD describes a limited body of new evidence from epidemiologic studies 

for potential relationships between short-term exposure to PM and health endpoints such as low 

birth weight, preterm birth, and neonatal and infant mortality (PM AQCD, Section 8.3.4). 

Among the studies of effects from short-term exposure to PM2.5, several studies 

specifically address the contribution of mobile sources to short-term PM2.5 effects on daily 

mortality. These studies indicate that there are statistically significant associations between 

mortality and PM related to mobile source emissions (PM AQCD, p.8-85). The analyses 

incorporate source apportionment tools into daily mortality studies and are briefly mentioned 

here. Analyses incorporating source apportionment by factor analysis with daily time-series 

studies of daily death indicated a relationship between mobile source PM2.5 and mortality.240,241 

Another recent study in 14 U.S. cities examined the effect of PM10 exposures on daily hospital 

admissions for cardiovascular disease. They found that the effect of PM10 was significantly 

greater in areas with a larger proportion of PM10 coming from motor vehicles, indicating that 

PM10 from these sources may have a greater effect on the toxicity of ambient PM10 when 

compared with other sources.242 These studies provide evidence that PM-related emissions, 

specifically from mobile sources, are associated with adverse health effects. 

3.4.2.2 Long-Term Exposure Mortality and Morbidity Studies 

Long-term exposure to elevated ambient PM2.5 is associated with mortality from 

cardiopulmonary diseases and lung cancer (PM AQCD, p. 8-307), and effects on the respiratory 

system such as decreased lung function or the development of chronic respiratory disease (PM 

AQCD, pp. 8-313, 8-314). Of specific importance to this rule, the PM AQCD also notes that the 

PM components of gasoline and diesel engine exhaust represent one class of hypothesized likely 

important contributors to observed ambient PM-related increases in lung cancer incidence and 

mortality (PM AQCD, p. 8-318). 

The PM AQCD and PM Staff Paper emphasize the results of two long-term studies, the 

Six Cities and American Cancer Society (ACS) prospective cohort studies, based on several 

factors – the inclusion of measured PM data, the fact that the study populations were similar to 

the general population, and the fact that these studies have undergone extensive reanalysis (PM 

AQCD, p. 8-306, Staff Paper, p.3-18).243,244,245 These studies indicate that there are significant 

associations for all-cause, cardiopulmonary, and lung cancer mortality with long-term exposure 

to PM2.5. A variety of studies have been published since the completion of the AQCD. One such 

study, which was summarized in EPA’s provisional assessment, was an analysis of a subset of 

the ACS cohort data, which was published after the PM AQCD was finalized but in time for the 

2006 Provisional Assessment, found a larger association than had previously been reported 

between long-term PM2.5 exposure and mortality in the Los Angeles area using a new exposure 

estimation method that accounted for variations in concentration within the city.246 EPA is 

assessing the significance of this study within the context of the broader literature. 

As discussed in the PM AQCD, the morbidity studies that combine the features of cross3-101
 
sectional and cohort studies provide the best evidence for chronic exposure effects. Long-term 

studies evaluating the effect of ambient PM on children’s development have shown some 

evidence indicating effects of PM2.5 and/or PM10 on reduced lung function growth (PM AQCD, 

Section 8.3.3.2.3). A variety of studies have been published since the completion of the AQCD. 

One such study, which was summarized in EPA’s provisional assessment, reported the results of 

a cross-sectional study of outdoor PM2.5 and measures of atherosclerosis in the Los Angeles 

basin.247 The study found significant associations between ambient residential PM2.5 and carotid 

intima-media thickness (CIMT), an indicator of subclinical atherosclerosis, an underlying factor 

in cardiovascular disease. EPA is assessing the significance of this study within the context of 

the broader literature. 

3.4.2.3 Roadway-Related Pollution Exposure 

A recent body of studies reinforces the findings of these PM morbidity and mortality 

effects by looking at traffic-related exposures, PM measured along roadways, or time spent in 

traffic and adverse health effects. While many of these studies did not measure PM specifically, 

they include potential exhaust exposures which include mobile source PM because they employ 

indices such as roadway proximity or traffic volumes. One study with specific relevance to 

PM2.5 health effects is a study that was done in North Carolina looking at concentrations of PM2.5 

inside police cars and corresponding physiological changes in the police personnel driving the 

cars. The authors report significant elevations in markers of cardiac risk associated with 

concentrations of PM2.5 inside police cars on North Carolina state highways.248 A number of 

studies of traffic-related pollution have shown associations between fine particles and adverse 

respiratory outcomes in children who live near major roadways.249,250,251 Additional information 

on near-roadway health effects is included in Section 3.5 of this RIA. 

3.4.3 Current and Projected PM Levels 

The emission reductions from this rule will assist PM nonattainment areas in reaching the 

standard by each area’s respective attainment date and assist PM maintenance areas in 

maintaining the PM standards in the future. In this section we present information on current 

and future attainment of the PM standards. 

3.4.3.1 Current PM2.5 Levels 

A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating 

an ambient standard or is contributing to a nearby area that is violating the standard. In 2005, 

EPA designated 39 nonattainment areas for the 1997 PM2.5 NAAQS based on air quality design 

values (using 2001-2003 or 2002-2004 measurements) and a number of other factors.l

 (70 FR 

943, January 5, 2005; 70 FR 19844, April 14, 2005). These areas are comprised of 208 full or 

partial counties with a total population exceeding 88 million. The 1997 PM2.5 nonattainment 

areas and populations, as of October 2006, are listed in Appendix 3C to this RIA. As mentioned 

in Section 3.4.1, the 1997 PM2.5 NAAQS was recently revised and the 2006 PM2.5 NAAQS 

became effective on December 18, 2006. Nonattainment areas will be designated with respect to 

the 2006 PM2.5 NAAQS in early 2010. Table 3.4-1 presents the number of counties in areas 

l

 The full details involved in calculating a PM2.5 design value are given in Appendix N of 40 CFR Part 50. 

3-102
 
currently designated as nonattainment for the 1997 PM2.5 NAAQS as well as the number of 

additional counties which have monitored data that is violating the 2006 PM2.5 NAAQS. 

Table 3.4-1. PM2.5 Standards: Current Nonattainment Areas and Other Violating Counties 

 Number of 

Counties 

Population1 

1997 PM2.5 Standards: 39 areas currently designated 208 88,394,000 

2006 PM2.5 Standards: Counties with violating monitors2 49 18,198,676 

Total 257 106,592,676 

1) Population numbers are from 2000 census data. 

2) This table provides an estimate of the counties violating the 2006 PM2.5 NAAQS based on 2003-05 air quality 

data. The areas designated as nonattainment for the 2006 PM2.5 NAAQS will be based on 3 years of air quality data 

from later years. Also, the county numbers in the summary table includes only the counties with monitors violating 

the 2006 PM2.5 NAAQS. The monitored county violations may be an underestimate of the number of counties and 

populations that will eventually be included in areas with multiple counties designated nonattainment. 

 States with PM2.5 nonattainment areas will be required to take action to bring those areas 

into compliance in the future. Most PM2.5 nonattainment areas will be required to attain the 1997 

PM2.5 NAAQS in the 2010 to 2015 time frame and then be required to maintain the 1997 PM2.5 

NAAQS thereafter.m The attainment dates associated with the potential nonattainment areas 

based on the 2006 PM2.5 NAAQS would likely be in the 2015 to 2020 timeframe. The emission 

standards being finalized in this action will become effective between 2009 and 2015. The 

expected PM2.5 and PM2.5 precursor inventory reductions from the standards finalized in this 

action will be useful to states in attaining or maintaining the PM2.5 NAAQS. 

3.4.3.2 Current PM10 Levels 

EPA designated PM10 nonattainment areas in 1990.n As of October 2006, approximately 

28 million people live in the 46 areas that are designated as PM10 nonattainment, for either 

failing to meet the PM10 NAAQS or for contributing to poor air quality in a nearby area. There 

are 46 full or partial counties that make up the PM10 nonattainment areas. The PM10 

nonattainment areas and populations are listed in Appendix 3C to this RIA. 

As mentioned in Section 3.4.1, the 1997 PM NAAQS was recently revised and the 2006 

PM NAAQS became effective on December 18, 2006. The annual PM10 NAAQS was revoked 

and the 24 hour PM10 NAAQS was not changed. The projected reductions in emissions from the 

controls finalized in this action will be useful to states to maintain the PM10 NAAQS. 

m The EPA finalized PM2.5 attainment and nonattainment areas in April 2005. The EPA proposed the PM 

Implementation rule in November 2005 (70 FR 65984). n

 A PM10 design value is the concentration that determines whether a monitoring site meets the NAAQS for PM10. 

The full details involved in calculating a PM10 design value are given in Appendices H and I of 40 CFR Part 50. 

3-103
 
3.4.3.3 Projected PM2.5 Levels 

Recent air quality modeling predicts that without additional controls there will continue 

to be a need for reductions in PM concentrations in the future. In the following sections we 

describe the recent PM air quality modeling and results of the modeling. 

3.4.3.3.1 PM Modeling Methodology 

Recently PM air quality analyses were performed for the PM NAAQS final rule, which 

was promulgated by EPA in 2006. The Community Multiscale Air Quality (CMAQ) model was 

used as the tool for simulating base and future year concentrations of PM, visibility and 

deposition in support of the PM NAAQS air quality assessment. The PM NAAQS analysis 

included all final federal rules up to and including Clean Air Interstate Rule (CAIR) and all final 

mobile source rule controls as of October 2006. Details on the air quality modeling are provided 

in the Regulatory Impact Analysis (RIA) for the Final PM NAAQS Rule, included in the docket 

for this final rule.252 

3.4.3.3.2 Areas at Risk of Future PM2.5 Violations 

Air quality modeling performed for the final PM NAAQS indicates that in the absence of 

additional local, regional or national controls, there will likely continue to be counties that will 

not attain some combination of the annual 2006 PM2.5 standard (15 μg/m3) and the daily 2006 

PM2.5 standard (35 μg/m3). The PM NAAQS analysis provides estimates of future PM2.5 levels 

across the country. For example, in 2015 based on emission controls currently adopted or 

expected to be in placeo, we project that 53 million people will live in 52 counties with projected 

PM2.5 design values at and above the 2006 standard, see Table 3.4-2.p The rule will assist these 

counties in attaining the PM2.5 NAAQS. Table 3.4-2 also lists the 54 counties, where 27 million 

people are projected to live, with 2015 projected design values that do not violate the PM2.5 

NAAQS but are within ten percent of it. The rule may help ensure that these counties continue 

to maintain their attainment status. 

Table 3.4-2. Counties with 2015 Projected Annual and Daily PM2.5 Design Values 
 

Above and within 10% of the 2006 PM2.5 Standarda
 

2015 2015 

Projected Projected 

Annual Daily 

PM2.5 PM2.5 

Design Design 

Value Value 2015 

State County (μg/m3) (μg/m3) Populationb 

o

 Counties forecast to remain in nonattainment may need to adopt additional local or regional controls to attain the 

standards by dates set pursuant to the Clean Air Act. The emissions reductions associated with this rule will help 

these areas attain the PM standards by their statutory date. p

 Note that this analysis identifies only counties projected to have a violating monitor; the number of counties to be 

designated and the associated population would likely exceed these estimates. 

3-104 
Alabama Jefferson Co 

California Alameda Co 

California Butte Co 

California Colusa Co 

California Contra Costa Co 

California Fresno Co 

California Imperial Co 

California Inyo Co 

California Kern Co 

California Kings Co 

California Los Angeles Co 

California Merced Co 

California Orange Co 

California Placer Co 

California Riverside Co 

California Sacramento Co 

San Bernardino 

California Co 

California San Diego Co 

California San Francisco Co 

California San Joaquin Co 

San Luis Obispo 

California Co 

California San Mateo Co 

California Santa Clara Co 

California Solano Co 

California Sonoma Co 

California Stanislaus Co 

California Sutter Co 

California Tulare Co 

California Ventura Co 

California Yolo Co 

Connecticut Fairfield Co 

Georgia Bibb Co 

Georgia Clayton Co 

Georgia DeKalb Co 

Georgia Floyd Co 

Georgia Fulton Co 

Georgia Muscogee Co 

Georgia Wilkinson Co 

Idaho Ada Co 

Idaho Bannock Co 

Idaho Canyon Co 

Idaho Power Co 

Idaho Shoshone Co 

Illinois Cook Co 

Illinois Madison Co 

Illinois St. Clair Co 

Illinois Will Co 

Indiana Clark Co 

Indiana Lake Co 

15.9 

13.3 

13.4 

9.5 

12.6 

20.1 

14.8 

6.1 

21.3 

17.2 

23.7 

15.8 

20.0 

11.4 

27.8 

12.2 

24.6 

15.8 

11.3 

15.4 

9.4 

10.5 

10.7 

11.7 

10.0 

16.6 

11.2 

21.2 

14.1 

10.2 

11.0 

13.7 

13.9 

13.6 

14.0 

15.5 

13.4 

13.6 

8.9 

9.1 

9.2 

10.5 

12.4 

15.5 

15.2 

14.6 

13.2 

13.6 

13.4 

36.9 669,850 

59.4 1,628,698 

50.7 242,166 

33.5 23,066 

61.3 1,155,323 

73.0 960,934 

45.7 173,482 

38.1 19,349 

81.4 804,940 

70.6 161,607 

62.2 9,910,805 

54.4 250,152 

41.1 3,467,120 

38.1 403,624 

73.5 2,015,955 

49.8 1,488,456 

65.7 2,157,926 

40.7 3,489,368 

52.5 765,846 

51.1 675,362 

35.8 304,079 

41.9 785,949 

48.5 1,899,727 

57.7 529,784 

38.9 569,486 

61.9 547,041 

39.3 99,716 

77.2 441,185 

38.8 923,205 

33.0 206,388 

31.6 893,629 

27.0 160,468 

28.7 280,476 

31.5 715,947 

30.9 97,674 

32.2 877,365 

34.2 197,634 

29.3 11,259 

32.2 397,456 

40.2 88,033 

32.6 154,137 

36.6 8,932 

36.2 15,646 

37.1 5,362,931 

35.5 271,854 

30.4 251,612 

32.0 634,068 

31.1 112,523 

40.8 490,795 

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Indiana Marion Co 

Kentucky Jefferson Co 

Maryland Anne Arundel Co 

Maryland Baltimore city 

Maryland Baltimore Co 

Massachusetts Hampden Co 

Michigan Kalamazoo Co 

Michigan Kent Co 

Michigan Oakland Co 

Michigan St. Clair Co 

Michigan Wayne Co 

Montana Lincoln Co 

Montana Missoula Co 

New Jersey Camden Co 

New Jersey Hudson Co 

New Jersey Union Co 

New York Bronx Co 

New York New York Co 

Ohio Cuyahoga Co 

Ohio Franklin Co 

Ohio Hamilton Co 

Ohio Jefferson Co 

Ohio Lucas Co 

Ohio Scioto Co 

Ohio Trumbull Co 

Oregon Jackson Co 

Oregon Klamath Co 

Oregon Lane Co 

Oregon Washington Co 

Pennsylvania Allegheny Co 

Pennsylvania Beaver Co 

Pennsylvania Berks Co 

Pennsylvania Dauphin Co 

Pennsylvania Lancaster Co 

Pennsylvania Lehigh Co 

Pennsylvania Mercer Co 

Pennsylvania Northampton Co 

Pennsylvania Philadelphia Co 

Pennsylvania York Co 

Tennessee Knox Co 

Utah Box Elder Co 

Utah Cache Co 

Utah Salt Lake Co 

Utah Utah Co 

Utah Weber Co 

Washington Clark Co 

Washington King Co 

Washington Pierce Co 

Washington Snohomish Co 

Washington Thurston Co 

13.5 

13.8 

11.1 

13.0 

11.3 

11.6 

12.8 

12.0 

13.0 

12.5 

17.4 

15.0 

10.6 

11.1 

12.0 

12.2 

12.8 

14.0 

15.4 

13.7 

14.3 

14.2 

12.5 

15.6 

12.1 

10.9 

10.1 

12.9 

9.0 

16.5 

12.1 

12.0 

11.0 

12.2 

10.5 

11.0 

10.9 

13.3 

12.3 

13.6 

8.6 

12.5 

12.6 

9.3 

9.1 

9.2 

10.8 

11.1 

11.3 

8.9 

33.1 889,645 

33.4 710,231 

33.2 574,322 

35.5 596,076 

32.6 810,172 

32.9 452,055 

32.7 257,817 

31.9 654,449 

33.2 1,355,670 

32.5 185,970 

39.0 1,921,253 

42.4 19,875 

32.1 118,303 

32.1 512,135 

32.8 604,036 

32.8 525,096 

33.2 1,283,316 

33.2 1,551,641 

40.0 1,325,507 

33.5 1,181,578 

34.2 841,858 

34.2 68,909 

32.2 443,230 

34.3 81,013 

34.2 227,546 

37.6 250,169 

39.1 69,423 

53.6 387,237 

32.0 639,839 

53.4 1,245,917 

33.2 184,648 

35.5 396,410 

33.3 272,748 

33.7 535,622 

34.7 328,523 

31.6 123,577 

35.0 286,838 

35.2 1,372,037 

35.9 417,408 

29.6 448,931 

39.0 49,878 

51.9 114,729 

49.3 1,133,410 

36.7 508,106 

36.2 229,807 

34.3 479,002 

34.0 2,013,808 

43.0 879,363 

40.1 782,319 

34.9 264,364 

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Washington Yakima Co 9.6 34.9 261,452 

West Virginia Berkeley Co 12.0 32.7 99,349 

West Virginia Hancock Co 13.4 32.7 30,857 

West Virginia Kanawha Co 13.9 28.9 196,498 

Wisconsin Milwaukee Co 12.1 32.1 908,336 

Wisconsin Waukesha Co 11.8 32.4 441,482 

Wyoming Sheridan Co 10.5 31.8 28,623 

Number of Violating Counties 52 

Population of Violating Counties 53,468,515 

Number of Counties within 10% 54 

Population of Counties within 10% 26,896,926 

a) Bolded concentrations indicate levels above the PM2.5 standard. 

b) Populations are based on 2000 census projections. 

3.4.4 Environmental Effects of PM Pollution 

In this section we discuss public welfare effects of PM and its precursors including 

visibility impairment, atmospheric deposition, and materials damage and soiling. 

3.4.4.1 Visibility Impairment 

Visibility can be defined as the degree to which the atmosphere is transparent to visible 

light.253 Visibility impairment manifests in two principal ways: as local visibility impairment 

and as regional haze.q Local visibility impairment may take the form of a localized plume, a 

band or layer of discoloration appearing well above the terrain as a result from complex local 

meteorological conditions. Alternatively, local visibility impairment may manifest as an urban 

haze, sometimes referred to as a “brown cloud.” This urban haze is largely caused by emissions 

from multiple sources in the urban areas and is not typically attributable to only one nearby 

source or to long-range transport. The second type of visibility impairment, regional haze, 

usually results from multiple pollution sources spread over a large geographic region. Regional 

haze can impair visibility over large regions and across states. 

Visibility is important because it has direct significance to people’s enjoyment of daily 

activities in all parts of the country. Individuals value good visibility for the well-being it 

provides them directly, where they live and work, and in places where they enjoy recreational 

opportunities. Visibility is also highly valued in significant natural areas such as national parks 

and wilderness areas, and special emphasis is given to protecting visibility in these areas. For 

more information on visibility see the 2004 PMAQCD as well as the 2005 PM Staff Paper.254,255 

Fine particles are the major cause of reduced visibility in parts of the United States. To 

address the welfare effects of PM on visibility, EPA set secondary PM2.5 standards which would 

q See discussion in U.S. EPA , National Ambient Air Quality Standards for Particulate Matter; Proposed Rule; 

January 17, 2006, Vol71 p 2676. This information is available electronically at http://epa.gov/fedrgstr/EPAAIR/2006/January/Day-17/a177.pdf. 

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act in conjunction with the establishment of a regional haze program. In setting this secondary 

standard, EPA concluded that PM2.5 causes adverse effects on visibility in various locations, 

depending on PM concentrations and factors such as chemical composition and average relative 

humidity. The secondary (welfare-based) PM2.5 NAAQS was established as equal to the suite of 

primary (health-based) NAAQS. Furthermore, Section 169A of the Act provides additional 

authority to address existing visibility impairment and prevent future visibility impairment in the 

156 national parks, forests and wilderness areas categorized as mandatory class I federal areas 

(62 FR 38680-81, July 18, 1997).r

 In July 1999 the regional haze rule (64 FR 35714) was put in 

place to protect the visibility in mandatory class I federal areas. Visibility can be said to be 

impaired in both PM2.5 nonattainment areas and mandatory class I federal areas. 

Data showing PM2.5 nonattainment areas and visibility levels above background at the 

Mandatory Class I Federal Areas demonstrate that visibility impairment is experienced 

throughout the U.S., in multi-state regions, urban areas, and remote mandatory Federal class I 

areas. The PM and PM precursor emissions from the vehicles and gas cans subject to this 

proposed rule contribute to these visibility effects. 

3.4.4.1.1 Current Visibility Impairment 

The need for reductions in the levels of PM2.5 is widespread. Currently, high ambient 

PM2.5 levels are measured throughout the country. Fine particles may remain suspended for days 

or weeks and travel hundreds to thousands of kilometers, and thus fine particles emitted or 

created in one county may contribute to ambient concentrations in a neighboring region.256 

As mentioned above the secondary PM2.5 standards were set as equal to the suite of 

primary PM2.5 standards. Recently designated PM2.5 nonattainment areas indicate that almost 90 

million people live in 208 counties that are in nonattainment for the 1997 PM2.5 NAAQS, see 

Appendix 3C. Thus, at least these populations (plus others who travel to these areas) would 

likely be experiencing visibility impairment. 

3.4.4.1.2 Current Visibility Impairment at Mandatory Class I Federal Areas 

Detailed information about current and historical visibility conditions in mandatory class 

I federal areas is summarized in the EPA Report to Congress and the 2002 EPA Trends 

Report.257,258 The conclusions draw upon the Interagency Monitoring of Protected Visual 

Environments (IMPROVE) network data. One of the objectives of the IMPROVE monitoring 

network program is to provide regional haze monitoring representing all mandatory class I 

federal areas where practical. The National Park Service report also describes the state of 

national park visibility conditions and discusses the need for improvement.

259 

The regional haze rule requires states to establish goals for each affected mandatory class 

I federal area to improve visibility on the haziest days (20% most impaired days) and ensure no 

degradation occurs on the cleanest days (20% least impaired days). Although there have been 

r These areas are defined in Section 162 of the Act as those national parks exceeding 6,000 acres, wilderness 

areas and memorial parks exceeding 5,000 acres, and all international parks which were in existence on August 7, 

1977. 

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general trends toward improved visibility, progress is still needed on the haziest days. 

Specifically, as discussed in the 2002 EPA Trends Report, without the effects of pollution a 

natural visual range in the United States is approximately 75 to 150 km in the East and 200 to 

300 km in the West. In 2001, the mean visual range for the worst days was 29 km in the East 

and 98 km in the West. 260 

3.4.4.1.3 Future Visibility Impairment 

Recent modeling for the final PM NAAQS rule was used to project PM2.5 levels in the 

U.S. in 2015. The results suggest that PM2.5 levels above the 2006 NAAQS will persist in the 

future. We predicted that in 2015, there will be 52 counties with a population of 53 million 

where annual PM2.5 levels will exceed the 2006 PM2.5 NAAQS, see Table 3.4-1. Thus, in the 

future, a percentage of the population may continue to experience visibility impairment in areas 

where they live, work and recreate. 

The PM and PM precursor emissions from the vehicles and gas cans subject to the 

proposed controls contribute to visibility impairment. These emissions occur in and around areas 

with PM2.5 levels above the annual 1997 PM2.5 NAAQS. Thus, the emissions from these sources 

contribute to the current and anticipated visibility impairment and the emission reductions 

finalized here may help improve future visibility impairment. 

3.4.4.1.4 Future Visibility Impairment at Mandatory Class I Federal Areas 

Achieving the PM2.5 NAAQS will help improve visibility across the country, but it will 

not be sufficient to meet the statutory goal of no manmade impairment in the mandatory class I 

federal areas (64 FR 35714, July 1, 1999 and 62 FR 38652, July 18, 1997). In setting the 

NAAQS, EPA discussed how the NAAQS in combination with the regional haze program, is 

deemed to improve visibility consistent with the goals of the Act. In the East, there are and will 

continue to be areas with PM2.5 concentrations above the PM2.5 NAAQS and where light 

extinction is significantly above natural background. Thus, large areas of the Eastern United 

States have air pollution that is causing and will continue to cause visibility impairment. In the 

West, scenic vistas are especially important to public welfare. Although the PM2.5 NAAQS is 

met in most areas outside of California, virtually the entire West is in close proximity to a scenic 

mandatory class I federal area protected by 169A and 169B of the CAA. 

Recent modeling for CAIR was also used to project visibility conditions in mandatory 

class I federal areas across the country in 2015. The results for the mandatory class I federal 

areas suggest that these areas are predicted to continue to have visibility impairment above 

background on the 20% worst days in the future. 

The overall goal of the regional haze program is to prevent future visibility impairment 

and remedy existing visibility impairment in mandatory class I federal areas. As shown by the 

future visibility estimates in Appendix 3D it is projected that there will continue to be mandatory 

class I federal areas with visibility levels above background in 2015. 261 Additional emission 

reductions will be needed from the broad set of sources that contribute, including the vehicles 

and gas cans subject to this rule. The reductions being finalized in this action are a part of the 

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overall strategy to achieve the visibility goals of the Act and the regional haze program. 

3.4.4.2 Atmospheric Deposition 

Wet and dry deposition of ambient particulate matter delivers a complex mixture of 

metals (e.g., mercury, zinc, lead, nickel, aluminum, cadmium), organic compounds (e.g., POM, 

dioxins, furans) and inorganic compounds (e.g., nitrate, sulfate) to terrestrial and aquatic 

ecosystems. The chemical form of the compounds deposited is impacted by a variety of factors 

including ambient conditions (e.g., temperature, humidity, oxidant levels) and the sources of the 

material. Chemical and physical transformations of the particulate compounds occur in the 

atmosphere as well as the media onto which they deposit. These transformations in turn 

influence the fate, bioavailability and potential toxicity of these compounds. Atmospheric 

deposition has been identified as a key component of the environmental and human health hazard 

posed by several pollutants including mercury, dioxin and PCBs.262 

Adverse impacts on water quality can occur when atmospheric contaminants deposit to 

the water surface or when material deposited on the land enters a waterbody through runoff. 

Potential impacts of atmospheric deposition to waterbodies include those related to both nutrient 

and toxic inputs. Adverse effects to human health and welfare can occur from the addition of 

excess particulate nitrate nutrient enrichment which contributes to toxic algae blooms and zones 

of depleted oxygen, which can lead to fish kills, frequently in coastal waters. Particles 

contaminated with heavy metals or other toxins may lead to the ingestion of contaminated fish, 

ingestion of contaminated water, damage to the marine ecology, and limited recreational uses. 

Several studies have been conducted in U.S. coastal waters and in the Great Lakes Region in 

which the role of ambient PM deposition and runoff is investigated.263,264,265,266,267 

Adverse impacts on soil chemistry and plant life have been observed for areas heavily 

impacted by atmospheric deposition of nutrients, metals and acid species, resulting in species 

shifts, loss of biodiversity, forest decline and damage to forest productivity. Potential impacts 

also include adverse effects to human health through ingestion of contaminated vegetation or 

livestock (as in the case for dioxin deposition), reduction in crop yield, and limited use of land 

due to contamination. 

In the following subsections, atmospheric deposition of heavy metals and particulate 

organic material is discussed. 

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3.4.4.2.1 Heavy Metals 

Heavy metals, including cadmium, copper, lead, chromium, mercury, nickel and zinc, 

have the greatest potential for influencing forest growth (PM AQCD, p. 4-87).268 Investigation 

of trace metals near roadways and industrial facilities indicate that a substantial burden of heavy 

metals can accumulate on vegetative surfaces. Copper, zinc, and nickel have been documented 

to cause direct toxicity to vegetation under field conditions (PM AQCD, p. 4-75). Little research 

has been conducted on the effects associated with mixtures of contaminants found in ambient 

PM. While metals typically exhibit low solubility, limiting their bioavailability and direct 

toxicity, chemical transformations of metal compounds occur in the environment, particularly in 

the presence of acidic or other oxidizing species. These chemical changes influence the mobility 

and toxicity of metals in the environment. Once taken up into plant tissue, a metal compound can 

undergo chemical changes, accumulate and be passed along to herbivores or can re-enter the soil 

and further cycle in the environment. 

Although there has been no direct evidence of a physiological association between tree 

injury and heavy metal exposures, heavy metals have been implicated because of similarities 

between metal deposition patterns and forest decline (PM AQCD, p. 4-76).269 Contamination of 

plant leaves by heavy metals can lead to elevated soil levels. Some trace metals absorbed into 

the plant and can bind to the leaf tissue (PM AQCD, p. 4-75). When these leaves fall and 

decompose, the heavy metals are transferred into the soil.270,271 

The environmental sources and cycling of mercury are currently of particular concern due 

to the bioaccumulation and biomagnification of this metal in aquatic ecosystems and the potent 

toxic nature of mercury in the forms in which is it ingested by people and other animals. 

Mercury is unusual compared with other metals in that it largely partitions into the gas phase (in 

elemental form), and therefore has a longer residence time in the atmosphere than a metal found 

predominantly in the particle phase. This property enables a portion of emitted mercury to travel 

far from the primary source before being deposited and accumulating in the aquatic ecosystem. 

Localized or regional impacts are also observed for mercury emitted from combustion sources. 

The major source of mercury in the Great Lakes is from atmospheric deposition, accounting for 

approximately eighty percent of the mercury in Lake Michigan.272,273 Over fifty percent of the 

mercury in the Chesapeake Bay has been attributed to atmospheric deposition.274 Overall, the 

National Science and Technology Council (NSTC, 1999) identifies atmospheric deposition as the 

primary source of mercury to aquatic systems. Forty-four states have issued health advisories for 

the consumption of fish contaminated by mercury; however, most of these advisories are issued 

in areas without a mercury point source. 

Elevated levels of zinc and lead have been identified in streambed sediments, and these 

elevated levels have been correlated with population density and motor vehicle use.275,276 Zinc 

and nickel have also been identified in urban water and soils. In addition, platinum, palladium, 

and rhodium, metals found in the catalysts of modern motor vehicles, have been measured at 

elevated levels along roadsides.277 Plant uptake of platinum has been observed at these 

locations. 

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3.4.4.2.2 Polycyclic Organic Matter 

Polycyclic organic matter (POM) is a byproduct of incomplete combustion and consists 

of organic compounds with more than one benzene ring and a boiling point greater than or equal 

to 100 degrees centigrade.278 Polycyclic aromatic hydrocarbons (PAHs) are a class of POM that 

contains compounds which are known or suspected carcinogens. 

Major sources of PAHs include mobile sources. PAHs in the environment may be 

present as a gas or adsorbed onto airborne particulate matter. Since the majority of PAHs are 

adsorbed onto particles less than 1.0 μm in diameter, long range transport is possible. However, 

studies have shown that PAH compounds adsorbed onto diesel exhaust particulate and exposed 

to ozone have half lives of 0.5 to 1.0 hours.279 

Since PAHs are insoluble, the compounds generally are particle reactive and accumulate 

in sediments. Atmospheric deposition of particles is believed to be the major source of PAHs to 

the sediments of Lake Michigan.280,281 Analyses of PAH deposition to Chesapeake and 

Galveston Bay indicate that dry deposition and gas exchange from the atmosphere to the surface 

water predominate.282,283 Sediment concentrations of PAHs are high enough in some segments 

of Tampa Bay to pose an environmental health threat. EPA funded a study to better characterize 

the sources and loading rates for PAHs into Tampa Bay.284 PAHs that enter a waterbody 

through gas exchange likely partition into organic rich particles and be biologically recycled, 

while dry deposition of aerosols containing PAHs tends to be more resistant to biological 

recycling.285 Thus, dry deposition is likely the main pathway for PAH concentrations in 

sediments while gas/water exchange at the surface may lead to PAH distribution into the food 

web, leading to increased health risk concerns. 

Trends in PAH deposition levels are difficult to discern because of highly variable 

ambient air concentrations, lack of consistency in monitoring methods, and the significant 

influence of local sources on deposition levels.286 Van Metre et al. (2000) noted PAH 

concentrations in urban reservoir sediments have increased by 200-300% over the last forty years 

and correlates with increases in automobile use.287 

Cousins et al. (1999) estimates that greater than ninety percent of semi-volatile organic 

compound (SVOC) emissions in the United Kingdom deposit on soil.288 An analysis of 

polycyclic aromatic hydrocarbon (PAH) concentrations near a Czechoslovakian roadway 

indicated that concentrations were thirty times greater than background.289 

3.4.4.3 Materials Damage and Soiling 

The deposition of airborne particles can also reduce the aesthetic appeal of buildings and 

culturally important articles through soiling, and can contribute directly (or in conjunction with 

other pollutants) to structural damage by means of corrosion or erosion.290 Particles affect 

materials principally by promoting and accelerating the corrosion of metals, by degrading paints, 

and by deteriorating building materials such as concrete and limestone. Particles contribute to 

these effects because of their electrolytic, hygroscopic, and acidic properties, and their ability to 

3-112
 
sorb corrosive gases (principally sulfur dioxide). The rate of metal corrosion depends on a 

number of factors, including the deposition rate and nature of the pollutant; the influence of the 

metal protective corrosion film; the amount of moisture present; variability in the 

electrochemical reactions; the presence and concentration of other surface electrolytes; and the 

orientation of the metal surface. 

3.5 Health and Welfare Impacts of Near-Roadway Exposure 

Over the years there have been a large number of studies that have examined associations 

between living near major roads and different adverse health endpoints. These studies generally 

examine people living near heavily-trafficked roadways, typically within several hundred meters, 

where fresh emissions from motor vehicles are not yet fully diluted with background air. 

As discussed in Chapter 3.1.3, many studies have measured elevated concentrations of 

pollutants emitted directly by motor vehicles near large roadways, as compared to overall urban 

background levels. These elevated concentrations generally occur within approximately 200 

meters of the road, although the distance may vary depending on traffic and environmental 

conditions. Pollutants measured with elevated concentrations include benzene, polycyclic 

aromatic hydrocarbons, carbon monoxide, nitrogen dioxide, black carbon, and coarse, fine, and 

ultrafine particles. In addition, resuspended road dust, and wear particles from tire and brake use 

also show concentration increases in proximity of major roadways. 

As noted in section 3.2, HAPEM6 estimates the changes in time-weighted exposures 

associated with proximity to roadways for individual pollutants. The studies discussed in this 

section address exposures and health effects that are at least partially captured by our modeling, 

but there may be additional exposures and health effects associated with pollutants, singly or in 

combination, that are not explicitly quantified. However, because the studies discussed in this 

section often employ exposure estimation metrics associated with multiple pollutants, exposureresponse information from these studies may not be suitable for risk assessment geared around 

one or several chemicals. 

At this point, there exists no exposure metric specific to “traffic,” although as noted 

above, a wide variety of gaseous, particulate, and semi-volatile species are elevated near 

roadways. As a result, the exposure metrics employed generally indicate the presence and/or 

intensity of a mixture of air pollutants for exposure assessment. Many of the health studies 

discussed below employ non-specific exposure metrics, including traffic on roads nearest home 

or school, distance to the nearest road, measured outdoor nitrogen dioxide concentrations, air 

quality dispersion modeling of specific traffic-generated chemicals, and exposure assignment 

based on land use. These exposure metrics represent the mixture of traffic-generated pollutants, 

rather than individual pollutants. Accordingly, such results are not directly comparable with 

community epidemiology studies that employ ambient measurements of particulate matter or 

ozone over a fixed time period, or to toxicological studies employing a single pollutant to 

evaluate responses in humans or animals. 

A wide range of health effects are reported in the literature related to near roadway and 

in-vehicle exposures. This is not unexpected, given the chemical and physical complexity of the 

3-113
 
mixture to which people are exposed in this environment. These effects overlap with those 

identified in our discussion of the effects of PM and ozone. The discussion below addresses the 

studies in detail. However, in general terms, the near-roadway health studies provide stronger 

evidence for some health endpoints than others. Epidemiologic evidence of adverse responses to 

traffic-related pollution is strongest for non-allergic respiratory symptoms, and several wellconducted epidemiologic studies have shown associations with cardiovascular effects, premature 

adult mortality, and adverse birth outcomes, including low birth weight and size. Traffic-related 

pollutants have been repeatedly associated with increased prevalence of asthma-related 

respiratory symptoms in children, although epidemiologic evidence remains inconclusive for a 

hypothesized link between traffic and the development of allergies and new onset asthma. 

For childhood cancer, in particular childhood leukemia, epidemiologic studies have 

shown less ability to detect the risks predicted from toxicological studies. Several small studies 

report positive associations, though such effects have not been observed in two larger studies. As 

described above in Chapter 1.3, benzene and 1,3-butadiene are both known human leukemogens 

in adults from occupational exposures. As previously mentioned, epidemiologic studies have 

shown an increased risk of leukemia among children whose parents have been occupationally 

exposed to benzene. While epidemiologic studies of near-roadway exposures have not always 

shown a statistically significant association with childhood leukemias, the results are consistent 

with the risks predicted from the studies at higher exposure levels. As a whole the toxicology 

and epidemiology are consistent with a potentially serious children's health concern and 

additional research is needed. 

Significant scientific uncertainties remain in research on health effects near roads, 

including the exposures of greatest concern, the importance of chronic versus acute exposures, 

the role of fuel type (e.g. diesel or gasoline) and composition (e.g., percent aromatics), and 

relevant traffic patterns. Furthermore, in these studies, it is often difficult to understand the role 

of co-stressors including noise and socioeconomic status (e.g., access to health care, nutritional 

status), and the role of differential susceptibility. 

3.5.1 Mortality 

The quantifiable effects of this rule on premature mortality associated with exposure to 

PM2.5 are assessed as part of the benefits estimates for this rule. In addition to studies that have 

documented the relationship between ambient PM and premature mortality, a few recent studies 

have investigated the relationship between premature mortality and broader indicators of 

transportation emissions, such as residence near traffic. The extent to which these studies are 

detecting any additional effects not accounted for in the ambient PM-premature mortality 

relationship is unclear. 

Living near major roads has been investigated in both long-term and short-term mortality 

studies. Long-term studies track subjects over time and investigate the mortality rates among 

groups with different levels of exposure to ambient pollutants. Short term studies employ daily 

variation in ambient concentrations to estimate the daily deaths attributable to air pollution. 

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A total of three cohort studies have examined premature mortality in relation to residence 

near traffic, another examined county-level traffic density, while one other has examined stroke 

mortality. In addition, one study accounted for the effect of residence along a major road on 

associations with daily deaths in a time-series study. These studies constitute all of the studies 

examining mortality with reference to proximity to traffic. 

Premature mortality in adults in association with living near high-traffic roadways has 

been studied in three recent cohort studies for all-cause and cardiopulmonary mortality from the 

Netherlands, Ontario, Canada, and most recently, Germany.291,292,293 Canadian vehicles and 

emission standards largely mirror the U.S. vehicle fleet. Both studies defined living near a major 

road as having a residence within 100 meters of a highway or within 50 meters of a major urban 

roadway. In the first study, involving approximately 5,000 people over 55 years old living 

throughout the Netherlands, residence near major roadways was associated with a 41% increase 

in the mortality rate from all causes and a 95% increase in the cardiopulmonary mortality rate.294 

The second study involved over 5,200 subjects aged 40 years or more, all living in the 

Hamilton, Ontario area. This study examined total mortality, finding a statistically significant 

18% increase associated with living near a major roadway. No difference in response was found 

among those with pre-existing respiratory illness. The study also calculated “rate advancement 

periods,” which describe the effect of an exposure in terms of the time period by which exposed 

persons reach prematurely the same disease risk as unexposed persons reach later on. The rate 

advancement period for total mortality was 2.5 years. The rate advancement periods were also 

calculated for other risk factors for mortality, including chronic pulmonary disease excluding 

asthma (3.4 years), chronic ischemic heart disease (3.1 years), and diabetes mellitus (4.4 years). 

A subsequent follow-up study found elevated mortality rates from circulatory causes in the 

Canadian study population. 

Most recently, German investigators followed up a series of cross-sectional studies on 

women age 50-59 living in the North Rhine-Westphalia region during the late 1980’s and 

1990’s, tracking vital status and migration to the years 2002-2003.295 In total, the cohort 

consisted of approximately 4800 women. Exposures were categorized using ambient NO2 and 

PM10 (estimated from TSP), and an indicator of residence within 50 m of a “major road”, defined 

at ≥10,000 cars/day. Overall, living within 50 meters of a major road was associated with a 

significant 70% increase in the rate of cardiopulmonary mortality. Nearest-monitor NO2 and 

PM10 were also associated with a 57% and 34% increase in the rate of cardiopulmonary 

mortality. Exposure to NO2 was also associated with a 17% increase in all-cause mortality. 

Despite differences in the vehicle fleets of Europe and Canada, whose emission standards 

largely mirror those of the U.S., the results of these studies are similar. 

In another study evaluating a cohort of older, hypertensive male U.S. veterans, countylevel traffic index and pollution estimates were employed in estimating exposure to traffic 

activity and other air pollutants.296 Area-based traffic density was significantly associated with 

increased mortality rates, as were constituents of motor vehicle exhaust, such as elemental 

carbon. 

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One cohort study conducted in the United Kingdom examined cardiocerebral (stroke) 

mortality in relation to living near traffic.297 Those living in census areas near roadways had 

significantly higher stroke mortality rates. In a study involving nearly 190,000 stroke deaths in 

1990-1992, Maheswaran and Elliott (2002) examined stroke mortality rates in census districts 

throughout England and Wales. Census districts closest to major roads showed significant 

increases in stroke mortality rates for men and women. Compared to those living in census 

districts whose center was greater than 1000 m from a main road, men and women living in 

census regions with centers less than 200 m away had stroke mortality rates 7% and 4% higher, 

respectively. 

One study from the Netherlands used time-series analysis to evaluate the change in the 

magnitude of the association between daily concentrations of black smoke, an air metric related 

to black carbon, and daily deaths, for populations living along roads with at least 10,000 vehicles 

per day.298 Compared with the population living elsewhere, the traffic-exposed population had 

significantly higher associations between black smoke and daily mortality. 

Although the studies of mortality have employed different study designs and metrics of 

exposure, they provide evidence for increased mortality rates in proximity of heavy traffic. In 

evaluating the generalizability of these study results, questions remain regarding differences in 

housing stock, residential ventilation, vehicle type and fuel differences, personal activity 

patterns, and the appropriate exposure metric. Furthermore, in the cohort studies, although 

controls for income level were incorporated based on postal code or census area, it is possible 

that other unmeasured covariates explain the associations with traffic. 

3.5.2 Non-Allergic Respiratory Symptoms 

Our analysis of the benefits associated with reduced exposure to PM2.5 includes chronic 

bronchitis, hospital admissions for respiratory causes, emergency room visits for asthma, acute 

bronchitis, upper and lower respiratory symptoms and exacerbation of asthma. In addition, 

studies in Europe, Asia and North America have found increased risk of respiratory symptoms 

such as wheeze, cough, chronic phlegm production, and dyspnea (shortness of breath) in children 

and adults with increased proximity to roadways and/or associated with local traffic density. 

Most of these studies were cross-sectional and relied solely on questionnaire assessments of 

health outcomes, in combination with simple exposure indicators. There are a large number of 

studies available, but for the sake of brevity, only studies conducted in the United States are 

discussed here. European studies reach similar conclusions, as summarized in a recent review of 

the European literature.299 The discussion below covers all studies conducted in the United 

States. EPA has not formally evaluated the extent to which these studies may be documenting 

health effects that are already included in the benefits analysis associated with PM. 

Most recently, a study from Cincinnati, OH examined the prevalence of wheezing in a 

group of infants less than one year of age.300 Infants with at least one atopic parent qualified for 

enrollment. The study compared infants living near stop-and-go truck traffic with others living 

near smoothly-flowing truck traffic, and others further from traffic. Infants with wheeze were 

significantly more likely to live near stop-and-go traffic than either those living near smoothlyflowing traffic or those living away from traffic. Truck volume was not associated with wheeze. 

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A respiratory health study in the east San Francisco Bay area looked at a series of 

community schools upwind and downwind of major roads along a major transportation corridor, 

where ambient air quality was monitored.301 Over 1,100 children in grades three through five 

attending the schools were assessed for respiratory symptoms and physician’s diagnosis of 

asthma. Overall, concentrations of traffic-related air pollutants measured at each school were 

associated with increased prevalence of bronchitis symptoms and physician confirmed asthma, 

both within the last 12 months. 

A case-control study in Erie County, NY compared home proximity to traffic among 

children admitted into local hospitals for asthma with those admitted for non-respiratory 

conditions.302 Overall, children hospitalized for asthma were more likely to live within 200 

meters of roads above the 90th percentile of daily vehicle miles traveled, and to have trucks and 

trailers passing within 200 meters of their residences. However, hospitalization for asthma was 

not associated with residential distance from major state routes. 

A study in San Diego County, CA compared the residential location of asthmatic children 

with children having a non-respiratory diagnosis within the state Medicaid system.

303 Traffic 

volumes on streets nearby the home were not associated with the prevalence of asthma. 

However, among asthmatic children, high street volumes on the nearest street were associated 

with an increased annual frequency of medical visits for asthma. 

In the only U.S. study examining adult respiratory symptoms, Massachusetts veterans 

were evaluated for traffic-health relationships.304 In the study, living within 50 m of a major 

roadway was associated with increased reporting of persistent wheeze. This trend held only for 

roads with at least 10,000 vehicles per day. Patients experiencing chronic phlegm were also 

more likely to live within 50 meters of roads with at least 10,000 vehicles per day. However, 

chronic cough was not associated with living near traffic. 

The studies described above employ different exposure metrics and health endpoints, 

making evaluation difficult. However, numerous other studies from around the world also 

provide evidence for increased prevalence of respiratory symptoms among people living near 

major roads. For a detailed listing, refer to the docket of this rule. Taken together, these studies 

provide evidence that respiratory symptoms may be associated with living near major roadways, 

particularly in children, upon whom the preponderance of studies have focused. 

3.5.3 Development of Allergic Disease and Asthma 

A significant number of studies have examined evidence of a role of traffic-generated 

pollution in the development (e.g. new onset) of atopic illnesses (i.e., hypersensitivity to 

allergens), such as asthma, allergic rhinitis, and dermatitis. A critical review of evidence, 

primarily generated in European studies, was recently published.305 Overall, the review 

concluded that there is some limited evidence of an association between traffic-generated 

pollutants and asthma incidence. More recent studies have also found significant associations 

between prevalent asthma and living near major roads.306 Toxicological evidence provides some 

evidence that particles from diesel engine exhaust may serve as adjuvants to IgE-mediated 

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immune responses. EPA’s Health Assessment Document for Diesel Engine Exhaust addresses 

many of the toxicological studies on diesel exhaust. However, in community epidemiology 

studies, the evidence remains tentative. The potential for these effects is not taken into account 

in the benefits analysis for PM because EPA’s various scientific advisors have argued that the 

literature is not strong enough to support a causal association. 

3.5.4 Cardiovascular Effects 

Cardiovascular effects are currently seen as a potentially important set of mechanisms 

whereby PM2.5 may be leading to premature mortality. In Chapter 12, we estimate the 

quantifiable benefits of PM-related non-fatal acute myocardial infarction and cardiovascular 

hospital admissions. The studies described in Section 3.5.1 found higher relative risks for 

cardiopulmonary causes of death. 

In addition to cardiopulmonary mortality, some studies have looked at morbidity. A 

recent study from Germany also found significant increased odds of coronary heart disease 

(CHD) in a cohort of approximately 3400 participants.307 Residents living within 150 meters of 

major roads were compared to those living further ways. Overall, controlling for background air 

pollution and individual risk factors, the adjusted odds ratio for CHD prevalence was 

significantly elevated (1.85). Subgroup analyses indicated stronger effects in men, in 

participants under 60 year of age, and in never-smokers. 

Several additional studies have provided suggestive evidence that exposure to fresh 

emissions from traffic predispose people to adverse cardiovascular events. Studies have focused 

on both short-term variations in exposure, as well as long-term residential history. As discussed 

in the summary section below, there are stressors in the roadway environment in addition to 

ambient air pollutants (e.g., noise, anxiety) that also have an impact on cardiovascular activity. 

The potential role of these co-stressors has not been adequately investigated. 

A study from Augsburg, Germany interviewed survivors of myocardial infarction (MI) 

shortly after they had recovered to examine ambient pollution and activities that might 

predispose someone to having a heart attack.308 Survivors of MI were nearly three times as 

likely to be in a car, in transit, or on a bicycle in the hour prior to the event as they were to be in 

traffic at other times. Ambient air pollutants measured in the hour prior to MI at a central site in 

the city were not associated with the risk of MI. 

A study of healthy young North Carolina state patrolmen conducted by EPA’s Office of 

Research and Development monitored in-vehicle concentrations of PM2.5, VOCs, and metals.309 

In-vehicle PM2.5 concentrations were associated with altered heart rate variability, an indicator of 

cardiac stress. In-vehicle concentrations were also associated with increased concentrations of 

factors in the blood associated with long-term cardiac risk, such as C-reactive protein, an 

indicator of inflammation. This study provides information on possible mechanisms by which 

cardiac stress could be induced by exposures to traffic-generated air pollution. 

Heart rate variability has also been measured in a study of elderly residents of the Boston 

area.310 In the study, ambient PM2.5 was associated with changes consistent with reduced 

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autonomic control of the heart. Black carbon, often a more reliable index of traffic-related 

pollution, was also associated with these changes. In a related study, ST-segment depression, a 

cardiographic indicator of cardiac ischemia or inflammation, was associated with black carbon 

levels as well.311 These studies further document a hypothesized mechanism associated with 

motor vehicle emissions, but do not necessarily suggest effects independent of those identified in 

our discussion of PM health effects. 

3.5.5 Birth Outcomes 

A few studies examining birth outcomes in populations living near major traffic sources 

have found evidence of low birth weight, preterm birth, reduced head circumference and heart 

defects among children of mothers living in close proximity to heavy traffic. Our discussion of 

PM health effects also quantitatively accounts for premature mortality effects in infants and 

qualitatively accounts for low birth weight. 

One measure of exposure to traffic-generated pollution is “distance-weighted traffic 

density,” where traffic volume is treated as a measure that “disperses” along a Gaussian bellshaped curve evenly on both sides of a roadway. This approach captures some of the patterns of 

dispersion from line sources, but does not account for micrometeorology. One study from Los 

Angeles County, California employed this metric in a study of birth outcomes for births from 

1994 to 1996. The study showed associations between distance-weighted traffic volume near 

women’s residences during pregnancy and premature birth and low birth weight in their 

babies.312 The elevated risks occurred primarily for mothers whose third trimesters fell during 

fall or winter months. 

The same researchers had conducted an earlier study of births occurring between 1989 

and 1993. In that study, consisting of over 125,000 births, exposures to ambient carbon 

monoxide (CO), an indicator of traffic pollution, during the third trimester were significantly 

associated with increased risk of low birth weight.313 In another study, preterm birth was 

associated with ambient PM10 and CO.314 These authors have also reported in a separate study 

on the increase in cardiac ventricular septal defects with increasing CO exposure during the 

second month of pregnancy.315 The role of socioeconomic status and factors associated with it 

should be investigated in future study design. 

Although the exposure metrics employed in these studies are based on surrogate 

approaches to exposure estimation, other researchers have shown associations between New 

York mothers’ measured personal exposure to polycyclic aromatic hydrocarbons (PAHs) during 

pregnancy and an increased risk of low birth weight and size.316 Subsequent follow-up of the 

same birth cohort to age three found evidence of neurodevelopmental deficits associated with 

maternal exposure to PAHs during pregnancy, particularly in cognitive development.317 

Overall, although the number of studies examining perinatal exposures is small, there is 

some evidence that exposure to traffic-related pollutants may be associated with adverse birth 

outcomes, including low birth weight and preterm birth. However, given the variety of exposure 

metrics employed and the relatively limited geographic extent of studies, the generalization of 

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the conclusions requires a better understanding of relevant sources, pollutants, susceptibility, and 

local factors. 

3.5.6 Childhood Cancer 

Several MSATs are associated with cancer in adult populations. However, children have 

physical and biochemical differences that may affect their susceptibility to and metabolism of 

MSATs. Particularly in the first year or two after birth, infants’ liver enzyme profiles undergo 

rapid change. As such, children may respond to MSATs in different ways from adults. Some 

evidence exists that children may face different cancer risks from adults as a result of exposure to 

certain MSATs and other components of motor vehicle exhaust. EPA recently recommended 

default adjustments to cancer risk estimates for compounds with a mutagenic mode of action to 

account for early life exposures in the Supplemental Guidance for Assessing Susceptibility from 

Early-Life Exposure to Carcinogens.318 

Evidence from human and animal studies suggests that increases in childhood leukemia 

may be associated with in utero exposures to benzene and maternal and paternal exposure prior 

to conception. Furthermore, there is some evidence that key changes related to the development 

of childhood leukemia occur in the developing fetus.319 

In the last 15 years, several studies have evaluated the association between maternal or 

childhood residence near busy roads and the risk of cancer in children. Most studies to date have 

been ecological in nature, with several employing individual-level exposure estimates within 

cohort designs. The studies employed widely varying exposure metrics, including modeled air 

quality, proximity to sources, and distance-weighted traffic volumes. Positive studies tend to 

have used small population sizes, although one recent positive study used a large population. 

Due to differences in ages studied, study design, exposure metrics, and study location (e.g. 

Europe vs. U.S.), a systematic comparison between studies is difficult. A description of several 

key studies from this literature follows. 

One early study from Colorado showed significant elevated risk of childhood leukemia in 

children under age 15 associated with living near roads with higher traffic volumes. The 

strongest associations were with roads with at least 10,000 vehicles per day.320 The study was 

reanalyzed using an approach to combine traffic volume with residential distance from major 

roads to assess “distance-weighted traffic volume.”321 The study found that the significant, 

monotonically increasing risks associated with increased distance-weighted traffic volume. 

NO2 has been used as an indicator of traffic emissions in some studies; however, it is 

important to note that NO2 is not implicated as causing cancer. For instance, a study used a 

dispersion model of NO2 from traffic to conduct a case-control study of childhood cancer in 

Sweden.322 The study found that in the highest-exposed group, risk of any cancer was 

significantly elevated. Risks in the most-exposed group were also elevated for leukemia and 

central nervous system tumors, but were not statistically significant. 

These earlier studies were based on relatively small populations of children with cancer. 

In response, subsequent studies focused on either replicating the earlier studies or studying larger 

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groups of children. A study in Los Angeles, California applied the same distance-weighted 

traffic volume approach as the earlier Colorado study, but found no elevation in risk in a larger 

group of children.323 A large study of nearly 2,000 Danish children with cancer found no 

association between modeled concentrations of benzene and NO2 at home and the risk of 

leukemia, central nervous system tumors, or total cancers.324 However, the study did find a 

dose-dependent relationship between Hodgkin’s disease and modeled air pollution from traffic. 

Several large studies were conducted in California using a statewide registry of cancer. 

These studies employed study sizes of several thousand subjects. In one cross-sectional study, 

the potency-weighted sum of concentrations of 25 air toxics modeled using EPA’s ASPEN 

model was not associated with mobile source emissions, but increased rates of childhood 

leukemia were found when accounting for all sources of air toxics together, and for point sources 

separately.325 Another study from the same researchers found that roadway density and traffic 

density within 500 meters of children’s homes was not associated with risk of cancer.326 

Most recently, a novel approach to assessing childhood leukemia in relation to early life 

exposures was employed in the United Kingdom. The study examined all children dying of 

cancer between 1955 and 1980, consisting of over 22,000 cases. Birth and death addresses of 

children with cancer who moved before death were compared with regard to proximity to nearby 

sources and emissions of specific chemicals.327 An excess of births near sources, relative to 

deaths, was used to indicate sources in early life associated with greatest cancer. Greater risks 

were associated with birth addresses within 300 meters of high emissions of benzene, 1,3­

butadiene, NOx, PM10, dioxins, and benzo[a]pyrene. In addition, births within 1.0 km of bus 

stations, hospitals, freight terminals, railways, and oil installations were associated with elevated 

risk. Overall, locations with the highest emissions of 1,3-butadiene and carbon monoxide 

showed the greatest risk. 

In summary, the lack of consistency in results between large studies and the multiplicity 

of study designs makes it difficult to draw firm conclusions. Epidemiologic methods for 

detection of childhood cancer risks may lack sufficient power to detect risks with precision. 

However, given the well-established carcinogenicity of benzene and 1,3-butadiene in the 

toxicological and occupational epidemiologic literature, and data suggesting exposure to benzene 

prior to conception and in utero can lead to increased risk of childhood leukemia, the potential 

for public health concern is present. The standards proposed in this rule will reduce such 

exposures. 

3.5.7 Summary of Near-Roadway Health Studies 

Taken together, the available studies of health effects in residents near major roadways 

suggest a possible public health concern. These studies’ exposure metrics are reflective of a 

complex mixture from traffic, and the standards will reduce a broad range of pollutants present in 

higher concentrations near roadways. It is unclear to what extent these health effects are 

attributable to PM versus other components of the complex mixture. Note that the benefits 

associated with the direct PM reductions from the cold temperature vehicle standards are 

presented in Chapter 12 of this RIA. 

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3.5.8 Size and Characteristics of Populations Living near Major Roads 

In assessing the public health implications of near-roadway health concerns, some 

understanding of the population living near major roads is required. Those living near major 

roadways are a subpopulation of the total population included in quantitative analysis, and to the 

extent that there may be additional exposures and health effects not captured in analyses for the 

total population, we enumerate the size and characteristics of the subpopulation. A study of the 

populations nationally using geographic information systems indicated that more than half of the 

population lives within 200 meters of a major road (see file USbytract.txt in the docket for this 

rule).s

 It should be noted that this analysis relied on the Census Bureau definition of a major 

road, which is not based on traffic volume. Thus, some of the roads designated as 

"major" may carry a low volume of traffic. Detailed analyses of data were conducted in three 

states, Colorado, Georgia, and New York. In Colorado, 22% live within 75 meters of a major 

road, while an additional 33% live between 75 and 200 meters of major roads. In Georgia, the 

respective percentages are 17% living within 75 meters and an additional 24% living between 75 

and 200 meters. In New York, the percentages are 31% and 36%.328 

To date, the only source of national data on populations living in close proximity to major 

transportation sources is the American Housing Survey, conducted by the U.S. Census 

Bureau.329 This study characterizes the properties and neighborhood characteristics of housing 

units throughout the U.S. According to the Census Bureau’s summaries of the 2003 survey, 

among approximately 120,777,000 housing units in the nation, 15,182,000 were within 300 feet 

of a “4-or-more-lane highway, railroad, or airport.” This constitutes 12.6% of total U.S. housing 

units. A simple assumption that the U.S. population is uniformly distributed among all types of 

housing leads to the conclusion that approximately 37.4 million people live in what might be 

considered a “mobile source hot spot.” 

According to the American Housing Survey’s summary tables, occupied housing units in 

central cities are 35% more likely to be close to major transportation sources than housing units 

in suburban areas.330 Furthermore, nationally, housing units that are renter-occupied are 2.3 

times more likely to be close to major transportation sources, compared to housing units that are 

owner-occupied. In the 2003 American Housing Survey, median household income for owneroccupied units was $52,803, while only $26,983 for renter-occupied units. These statistics imply 

that those houses sited near major transportation sources are likely to be lower in income than 

houses not located near major transportation sources. 

A few population-based epidemiology studies have also examined whether discrete 

groups of people live close to major roadways. In one study of veterans living in southeastern 

Massachusetts, 23% lived within 50 meters of a “major road,” 33% lived within 100 meters, and 

51% within 200 meters. 331 In examining traffic volumes, 13% lived within 50 meters of a road 

with annual average daily traffic of 10,000 vehicles or more, while other distances were not 

analyzed. 

In another study using 150 meters as a definition of “near” a road, 2.3% of California 

s

 Major roads are defined as those roads defined by the U.S. Census as one of the following: “limited 

access highway,” “highway,” “major road (primary, secondary and connecting roads ),” or “ramp.” 

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public schools were found to be near a road with more than 50,000 vehicles per day, while 7.2% 

were near roads with between 25,000 and 49,999 vehicles per day.332 This corresponded to 2.6% 

and 9.8% of total enrollment, respectively. In that study, traffic exposure increased, the fractions 

of school populations comprised of black and Hispanic students also increased, as did the 

fraction of children in government-subsidized meal programs. 

Another study in California defined the issue differently, examining the child population 

living in census block groups and traffic density.333 The study found that approximately 3% of 

the state child population resided in the highest traffic density census tracts. Furthermore, block 

groups with lower income were more likely to have high traffic density. Children of color were 

more likely than white children to live in high traffic density areas. 

In summary, a substantial fraction of the U.S. population lives within approximately 200 

meters of major roads. 

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Appendix 3A: Influence of Emissions in Attached Garages on 
 

Indoor Air Benzene Concentrations and Human Exposure
 

Introduction 

Measurement studies provide strong evidence that VOC sources in attached garages can 

significantly increase VOC concentrations inside homes.334 Preliminary analyses of data from a 

pilot study for the National Human Exposure Assessment Survey (NHEXAS) in Arizona also 

found indoor concentrations of mobile source-related VOC compounds significantly higher in 

homes with attached garages than in homes without them.

335 This population-based exposure 

study included measurements from 187 homes. A study in 50 Alaska residences found that in 

homes with attached garages, indoor benzene levels averaged 70.8 μg/m3, while in homes 

without attached garages, concentrations averaged 8.6 μg/m3

.

336 Multiple factors, including 

house architecture, ventilation design, garage configuration, and climate can all play roles as 

well. 

National-scale air toxics modeling efforts, such as those discussed in RIA Section 3.2.1.2, 

employ Gaussian dispersion models in combination with human exposure models to calculate the 

concentrations of air toxics in various microenvironments. Exposure models calculate an 

average exposure resulting from the movement of a simulated population through a time-activity 

pattern that brings them into contact with air in the various microenvironments. 

At this point, the NATA and the analyses performed for this rulemaking have only 

included exposures from outdoor sources. Although the HAPEM6 exposure model is capable of 

addressing indoor sources, more thorough analyses of the prevalence and use of emission sources 

within attached garages are required to develop quantitative estimates of model parameters to 

address attached garage contributions across the U.S. population. 

This appendix addresses the potential impact of all benzene sources within an attached 

garage on residential indoor air quality. 

Methods 

Calculation of Within-garage Source Emission Rate 

Emission rates for indoor sources of VOCs can be derived by several methods. Most 

accurately, the actual emission rates of an indoor VOC source can be measured through the use 

of a Sealed Housing for Evaporative Determination (SHED). However, test conditions must be 

representative of real world applications. Short of SHED-based measurement, several surrogate 

approaches may be employed. For evaporative losses from a sealed container, the change in 

weight of a container over time may be used to calculate a total mass loss rate, which can be 

assumed to be in the form of VOC. Alternatively, if the air concentrations and ventilation 

conditions of a defined indoor space are known, mass balance equations can be employed to 

derive a “virtual” emission rate for all sources within the space. 

This appendix employs the latter approach in calculating source emission factors. The 

general approach of a mass balance equation is to calculate the change in mass over a given time, 

accounting for the mass of a pollutant transported into a space, the mass of pollutant transported 

out of a space, the emission rate of a source within the space, and the decay of any pollutants 

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within the space, which can be treated as a first-order decay. A simple space like a garage can be 

treated as a single zone. The differential equation representing this mass balance is as follows: 

dM t,i dV dM i dV (1) 	 = C k + − C

dt o dt dt i dt 

Here, dMt,i/dt represents the rate of change of total indoor mass, Ci is the indoor concentration, 

Co is the outdoor concentration, dV/dt is the volumetric air flow through the space, k is the 

penetration fraction indicating the proportion of mass that passes through the wall of the 

compartment, and dMi/dt represents the mass emission rate inside the space. Note that all air 

entering the garage is assumed to enter from outdoors. 

Assuming steady-state conditions, dMt,i/dt assumes the value of zero, meaning that the 

concentration in the garage does not change over time. Algebraically, this allows the equation 

above to be represented as: 

(2) ⎜

⎛ dV ⎟⎞

(Ci − Co k ) = dM i 

⎝ dt ⎠ dt 

In other words, the indoor source terms can be calculated if the volumetric flow through the 

space and concentrations indoor and outdoor are known. Any gradient in concentration between 

indoor and outdoor concentrations is explained by indoor sources and the fraction of mass that 

does not penetrate from indoors to outdoors. 

The volumetric flow can be calculated by multiplying the volume of the space by the 

number of times per hour that the air within the space is turned over. As such: 

(3) dV =αV 

dt 

Here, α is the “air exchange rate,” expressed in air changes per hour (ACH). Combining 

equations (2) and (3), the mass emission rate is represented as: 

(4) αV (Ci − Co k ) = dM i 

dt 

A recent study in Ann Arbor, MI measured the air exchange rates and the in-garage and 

outdoor concentrations of VOCs needed to perform these calculations.337 The homes in the 

study were based on a convenience sample, and so may not be generally representative of the 

local or national housing stock. All garages but one adjoined a house. All attached garages had 

between one and three walls adjoining a residence. The distributions of garage benzene 

concentration and ACH are shown in Figure 3A-1. The distributions of each were not 

significantly different from lognormal, judging by the Kolmogorov-Smirnov Z statistic. 

Values of k, the penetration factor, are dependent on the physical pathways through 

which air passes into a garage, as well as the presence and chemical composition of any 

insulating material through which air passes. In the case of garages, the infrequency of insulated 

garages and the low reactivity of benzene justifies the assumption that k=1.338 

These data from the Ann Arbor, MI study were used to solve equation (2) to derive a 

distribution of benzene mass emission rates in each garage in the study, based on variability in 

measurements of outdoor concentrations. Equation 4 was implemented using a Microsoft Excel 

spreadsheet with the @Risk probabilistic simulation add-in (version 4.5).339 Monte Carlo 

sampling was used for all terms in deriving the emission rates. 

As described below, this distribution can be used to evaluate the effect of various fuel 

control measures on indoor benzene concentrations. A single lognormal distribution was used to 

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represent Co in equation 4, based on other studies of ambient air, which have found that many 

pollutants’ concentrations are lognormally distributed. 

Calculation of Garage Contributions to Indoor Air 

In the same way that a mass balance calculation can be used to calculate emission rates 

for sources within garages, a mass balance equation can be used to estimate the additional 

concentration in a home that will occur as a result of elevated concentrations in the garage. 

However, unlike the garage case, it is not valid to assume that all air entering the home comes 

directly from outdoors. 

Recent studies have provided indications that over multiple sequential days, variability in 

within-home benzene concentration is relatively small. A recent study from Ann Arbor, MI 

found a coefficient of variation (COV) of 4.6% for benzene.340 Furthermore, recent data 

obtained by EPA through the Environmental and Occupational Health Sciences Institute 

(EOHSI) on homes in the Elizabeth, NJ area indicates no significant differences in within-home 

concentrations at a 95% confidence level.t,341 These data are preliminary, and analyses are still 

in progress. 

Given the fraction of air entering the home through the home-garage interface, the 

appropriate mass balance equation for a single-compartment (e.g. well-mixed) home can be 

represented as such: 

(5) 

dM t,i = kCo (1− f g ) dV 

+ kCg f g 

dV − Ci dV 

dt dt dt dt 

Here, Ci is the in-house concentration, Co is the outdoor concentration, Cg is the concentration in 

the garage, dV/dt is the volumetric air flow through the house, and fg is the fraction of air entering 

the home from the garage. One assumption made here is that the penetration factor for the air 

moving through the house-garage interface is the same as air moving through the house-outdoors 

interface. Reactive decay is assumed to be zero. Such mass balance equations are standard 

approaches in environmental science and engineering, and are frequently found in textbooks on 

these subjects.342 

Again assuming steady-state conditions, dMt,i/dt = 0, the equation above simplifies to: 

(6) Ci = kCo (1− f g ) + kCg f g 

Or more simply, the indoor concentration under steady state conditions is proportional to the 

fraction of air entering the house through the garage. 

Figure 3A-2 is a contour plot illustrating the range of average indoor air concentrations 

that could plausibly arise given a range of values of Cg and fg, with a background concentration 

of zero. However, Figure 3A-2 does not answer the question of what the likely indoor air values 

are in a sample of real homes. 

The text below describes procedures and results of a small-scale modeling study. 

Modeling Approach 

t

 In that study, one air sample was obtained in the room adjacent to an attached garage in each home and another was 

obtained in another location. The New Jersey Department of Environmental Protection and EPA provided joint 

funding for the study. A two-sided paired t-test was applied to data obtained from 36 homes over approximately 24 

hours. 

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All modeling analyses employed Equation 6 in a Microsoft Excel spreadsheet with the 

@Risk probabilistic modeling add-in was the software employed in all modeling analyses. 

Where appropriate, each of the terms in Equation 6 was treated as a random variable represented 

as either a parametric distribution or as an empirical distribution based on measured data. 

Often, in employing data obtained from more than one study, combining data into a 

single distribution was not justified on a priori grounds. In ventilation studies, ambient 

conditions such as temperature and geography can substantially affect air flow patterns and 

building constructions. For instance, residential air exchange rates differ significantly between 

regions with substantially different climates.343 Furthermore, based on the limited number of 

studies available, combining data from multiple studies into a single data set had the potential to 

apply de facto weights to data, potentially shifting the fitted model parameters away from truly 

“representative” distributions. 

Another consideration is the potential for independence of the fg and Cg variables. There 

is no a priori reason why the “leakiness” of the house-garage envelope should be related to the 

concentration of benzene in the garage. 

Because of these considerations, data on fg or Cg from studies in different areas were not 

formally combined. Rather, distributions fit separately to data from each study were used to 

develop several model “scenarios.” As described below, four different studies provided data for 

Cg and three different studies provided data for fg. As such, a minimum of 12 (3 x 4) scenarios 

were needed to represent the totality of available data. 

For each scenario modeled, @Risk sampled from each distribution 20,000 times using a 

proprietary Latin Hypercube sampling framework. The large number of samples and Latin 

Hypercube strategy were employed to ensure that modeled concentration distributions achieved 

stability. 

Lastly, for comparison to the current approaches for exposure modeling, the following 

equation was used, paralleling the approach taken by HAPEM5 with no garage emissions: 

(7) Ci = kCo 

Data for Populating Model Parameters 

Fraction of Air Entering Home through the Garage (fg) 

Several studies have examined the fraction of air entering the home from the garage. 

Except for one, all of these studies took place in northern states and Canada, where homes are 

built with more insulation. A recent study of a set of homes in Ontario, Canada found that 

approximately 13% of the air entering the home came from the garage.344 One study from 

Minnesota found that in newer homes, houses built in the year 1994 had an average of 17.4% of 

total air leakage coming through their garages, houses build in 1998 had an average leakage 

fraction of 10.5%, and houses built in 2000 had an average leakage fraction of 9.4%.345 Two 

recent studies have employed perfluorocarbon tracer (PFT) gases to estimate air transport 

between different “zones” of houses with attached garages. A recent study by Isbell et al. (2005) 

based in Fairbanks, Alaska found that in a modern air-tight Alaskan home ventilated with an airto-air heat exchanger, 12.2% of the air entering a home entered through the garage, while 47.4% 

of the air entering an older home ventilated passively by structural defects came through the 

attached garage.346 Another study of a home in Ann Arbor, Michigan built in 1962 found that 

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16% of the air entering the home originated in the garage.347 In a more recent study from Ann 

Arbor, investigators deployed PFT tracers in 15 homes and calculated the fraction of air entering 

each home through an attached garage, with an average of 6.5±5.3% of the air entering through 

the garage.348 From these studies, it is apparent that across homes, the fraction of air entering 

through the garage is highly variable, making it necessary to acknowledge significant 

uncertainties in characterizing “typical” infiltration patterns. 

Benzene Concentrations in Garage Air (Cg) 

Four sources of in-garage concentration data are available in the format relevant for 

steady-state modeling over extended periods of time. First, there is the study by Batterman et al. 

(2005), in which average garage concentrations of benzene were measured over a period of four 

days in each of 15 homes using passive sampling badges. The average garage concentration 

reported was 36.6 μg/m3

, with a standard deviation of 38.5 μg/m3. 

Second, a study in Alaska by George et al. (2002) measured benzene concentrations in 28 

Alaska homes and 48 garages with passive diffusion badges.349 One disadvantage of this study 

is the relatively high detection limit for benzene, 7 ppb (22 μg/m3

). As a result, many of the data 

available are based on a reported value of 50% of the detection limit. In the Alaska study, ingarage benzene concentrations averaged 103 μg/m3

, and the standard deviation was 135 μg/m3. 

The study included concurrent in-home measurement of benzene in homes with attached 

garages, allowing evaluation of the modeled indoor concentrations. However, it is not apparent 

that this study underwent scientific peer review. 

A third study in one New Jersey home also evaluated garage and indoor benzene, as part 

of an investigation into in-garage emissions of vehicles fueled with methanol blends.350 Only 

one home was sampled, but it was sampled multiple times inside the garage and at multiple 

locations inside the residence. A fourth study from Fairbanks, Alaska conducted measurements 

in 12-hour periods on four separate days in two houses in two seasons, summer and winter.351 

The study obtained two daily measurements of benzene concentration within each garage over a 

12-hour sampling period. One home was a modern, well-insulated home with an air-to-air heat 

exchanger for ventilation. The other was an older home ventilated passively by structural defects 

in the building envelope. Because of the large differences in concentrations between homes and 

seasons, data from each home-season combination was treated as a separate distribution within 

the indoor air model (Equation 6 in Excel/@Risk). Treating these data as separate distributions 

increased the number of modeled “scenarios” to 21 (3 fg x 7 Cg). 

3-128
 
Penetration Factor (k) 

The values of k in this case were obtained from the HAPEM5 user’s manual, using the 

PEN-1 factor, representing the fraction of benzene from outdoor air penetrating indoors. The 

values in HAPEM5 are presented as a distribution that assigns a 2/3 weight to the value 0.8 and a 

1/3 weight to the value 1. These estimates are based on a comprehensive review of indoor and 

outdoor air quality studies. 

Outdoor Ambient Concentration (Co) 

The 1999 National Air Toxics Assessment (NATA) provided ambient concentration 

estimates for every census tract in the U.S. For this modeling exercise, a lognormal distribution 

was fit to these data. 

Results 

Within-Garage Emission Rates 

Equation 4 was used with Monte Carlo sampling to calculate a distribution of emission 

factors for each home, based on the variability in outdoor concentrations reported in Batterman 

et al. (2005). As shown in Figure 3-A3, the within-garage variation was a very small component 

of overall variability compared to between-garage variation. This finding implies that the factors 

in individual garages, such as storage of vehicles, nonroad equipment, and fuels, have a major 

effect on in-garage concentrations. 

In aggregate, the mean emission rate for all garages sampled fell along a lognormal 

distribution (p > 0.05). The mean emission rate was 3049 μg/hr (73 mg/day), with a standard 

deviation of 4220 μg/hr (101 mg/day). 

To evaluate the plausibility of these steady-state emission factors, known emission 

factors for other emission sources were evaluated. The California Air Resources Board (CARB) 

conducted a study of emissions from portable fuel containers, finding that volume-specific 

emissions rates for total VOC due to evaporation and permeation was 0.37 g/gal-day. Assuming 

an average fuel container volume of two gallons, the average emission factor per can would be 

0.74 g VOC/day. 

To evaluate the derived emission rates relative to CARB’s measurements, a benzene fuel 

vapor pressure fraction of 0.5-1% was assumed, based on MOBILE6.2 evaporative emission 

factors. Given that assumption, the average benzene emission rate from CARB’s study is 3.7-7.4 

mg/day. This value is in the lower range of emission rates shown in Figure 3A-3. This 

comparison suggests that emissions due to permeation and evaporation from portable fuel 

containers may be a relatively small fraction of overall garage benzene. 

Subsequently, one additional study used perfluorocarbon tracers (PFT) and VOC 

measurement in two Fairbanks, Alaska homes to estimate two garages’ “source strengths” for 

benzene.352 For a new, energy efficient “tight” home with an air-to-air heat exchanger, median 

garage emission estimates for benzene were 21 mg/h in summer and 14 mg/h in winter. In an 

older home with passive ventilation due to structural defects, median benzene source strengths 

were calculated at 40 and 22 mg/h in summer and winter, respectively. These values are 

substantially higher than those calculated based on Batterman et al. (2005). However, the 

3-129
 
difference may be attributable to higher fuel benzene in Fairbanks than in Michigan. Study 

design may also play a key role. In the Fairbanks study, the measurement periods were 12 hours 

each in duration. In the Michigan study, measurement periods lasted four days each. The 

Michigan study’s longer duration may have allowed for a broader range of emissions activities 

than the Fairbanks study. 

Garage Contributions to Benzene in Indoor Air 

Figures 3A-4 to 3A-8 display the results of @Risk simulations of indoor air. Each figure 

represents the modeled outputs as cumulative probability distributions. In the legend of each 

figure, the label of each distribution describes its fg and Cg sources. For instance, “George et al. 

(2002) / Fugler FG Ci” indicates a distribution using garage concentration data from George et 

al. (2002) and fg data from Fugler et al. 

Figure 3A-4 presents the output of Equation 6, a daily average indoor benzene 

concentration including contributions from outdoor air and from attached garages. As noted in 

the “Methods” section of this appendix, it was necessary to run a large number of scenarios to 

account for different combinations of fg and Cg data sources. The figure depicts results using 

studies that contain Cg data from multiple homes as bold solid lines, while the model simulations 

based on studies that employ Cg data from only one home are shown in dashed lines. As 

indicated in the figure, there is no major difference in the Ci distributions predicted by using Cg 

data from multiple homes or by using Cg measured from a single home. The average modeled 

indoor benzene concentrations ranged from 2.9 to 16.4 μg/m3

. 

For comparison, Figure 3A-5 presents cumulative distributions of the observed results 

from several studies that measured indoor air concentrations in homes with attached garages. 

Schlapia and Morris (1998) measured integrated 24-hour benzene concentrations inside 91 

homes with attached garages in Anchorage, Alaska between 1994 and 1996.353 George et al. 

(2002) reported average benzene concentrations in 36 homes in Anchorage, Alaska, but no 

distributional data. Mentioned above, Isbell et al. (2005) also measured integrated 12-hour 

benzene in two seasons in one modern air-tight home (“Home V” in Figures 3A-4 to 3A-8) and 

one older passively-ventilated home (“Home NV” in Figures 3A-4 to 3A-8).354 Both homes 

were located in Fairbanks, Alaska. Batterman et al. (2006) measured indoor air benzene 

concentrations in 15 homes in southeastern Michigan over four-day sampling periods throughout 

spring and summer of 2005.355 Lastly, Weisel (2006) conducted a study of indoor air in 21 

homes in Union County, NJ between April 2005 and January 2006. One monitor in each home 

was sited in the room adjacent to the garage, while another was located in another part of the 

house.356 

Comparing Figures 3A-4 and 3A-5, it is apparent that the distributions of modeled indoor 

air concentrations of benzene are very similar to those observed in monitoring studies. Both 

figures indicate that there is substantial variability in concentrations between homes and between 

studies. 

Figure 3A-6 presents the mean concentrations from modeling scenarios and from 

monitoring studies. In general, the range of mean concentrations is close to the values monitored 

in the indoor air studies. Notable exceptions are the indoor air values by George et al. (2002), 

the winter data from the passively-ventilated “NV” home from Isbell et al. (2005), and by 

Schlapia and Morris (1998). All of these studies took place in Alaska, which may have uniquely 

high benzene fuel levels or housing architectures that create higher garage air infiltration indoors. 

3-130
 
Of particular note, all of these studies included substantial numbers of homes with “tuck-under” 

garages where one or more rooms of a house are situated above a garage. Schlapia and Morris 

(1998) reported a very high average value that was not matched by the “average” conditions of 

any other run. It is notable that this high value is the average across 91 homes with attached 

garages. 

Another consistent trend shown in Figure 3A-6 is that scenarios employing fg data from 

Batterman et al. (2006) produced consistently lower average benzene concentrations than 

scenarios employing other sources. This trend is attributable to the lower average fg reported in 

Batterman et al. (2006), 6.5%, as compared to values found in Sheltersource (11.7%) and Fugler 

et al. (13.6%). 

It is unclear whether the studies measuring Cg, fg, and Ci constitute a representative 

sample of homes. In general Alaskan studies report higher concentrations, but not consistently. 

The relatively greater prevalence of homes with “tuck under” garages in some Alaskan studies 

may explain this discrepancy. 

In comparison to the values reported in Figures 3A-4 and 3A-5, indoor air concentrations 

calculated with the default Ci = kCo approach, similar to that employed in the national-scale 

modeling for this rule, averaged 1.2 μg/m3

. 

Overall, modeled concentrations presented here appear to provide a credible estimate of 

indoor benzene concentration in homes with attached garages. However, it is unclear whether 

the homes included in the studies employed herein may be considered “representative.” 

Implications 

Effect on Exposures Nationwide 

In calculating the hypothetical effect of attached garage on national estimates of chronic, 

time-weighted average (TWA) human exposure, precise estimates are not possible. As noted 

previously, the extent to which available studies of indoor air of homes with attached garages is 

representative of the entire population of such homes is unclear. Furthermore, the distribution of 

housing stock by climate and meteorology is not well understood. However, despite these 

limitations, a bounding exercise is still feasible. 

One simple approach for such a bounding exercise is determined by the following 

equation: 

(8) Eg = Ci,g*Pg

*Tg 

Here, Eg represents the national average exposure to benzene in air attributable to 

attached garages. Ci,g represents the average indoor concentration attributable to an attached 

garage, Pg represents the fraction of the population living in a home with an attached garage, and 

Tg represents the time spent in a home with an attached garage. 

Ci,g is derived from Equation 6, and can be derived by setting the outdoor concentration 

term (Co) to zero. An estimate of the attached garage contribution to indoor air can be made for 

studies with only indoor measurements as well. This can be accomplished by substituting 

ASPEN concentration estimates for the county in which each study took place. For Equation 6, 

Co estimates from NATA for each census tract in the relevant county were assembled into a 

lognormal distribution. With this data and the other assumptions of Equation 6, an estimate of 

Ci,g could be derived from the measurement studies. 

3-131
 
To estimate Pg, an estimate of the national fraction of homes with attached garages is 

required. The Residential Energy Consumption Survey (RECS), run by the U.S. Energy 

Information Administration, provides an estimate of the fraction of homes with attached 

garages.357 RECS estimates a total of 107.0 million housing units nationally, 37.1 million 

(34.7%) of which are homes with attached garages. Assuming that the population is uniformly 

distributed across housing units allows this figure to serve as an estimate of Pg. 

Information on the fraction of time spent in a residence (Tg) is required to determine how 

the microenvironmental concentration in homes with attached garages affects overall timeweighted exposure concentrations. As cited in EPA’s Exposure Factors Handbook, the average 

person studied by the National Human Activity Pattern Survey (NHAPS) spent 1001.39 minutes 

(16.68 hours) per day indoors within any room of a residence.358 

Results of model simulations using Equation 7 are shown in Figure 3A-7. As before, the 

results of each combination of Cg and fg data source are shown. For each study, the legend lists 

the source for both Cg and fg data. As described above, the estimates Ci,g derived from indoor air 

measurements are also presented in Figure 3A-7. In the legend of Figure 3A-6, these studies are 

denoted by the term “Direct Ci Measure.” As shown, there tends to be a greater degree of 

agreement between modeling scenarios for lower concentration estimates, but less agreement for 

higher concentration estimates. 

As described above, it is unclear to what the extent to which the homes studied for 

benzene related to attached garages are representative of homes nationally. As such, in 

summarizing the scenarios, several different approaches to “averaging” across scenarios are 

presented here. Figure 3A-8 shows the results of these different averaging scenarios. In the “All 

Data” distribution shown in the figure, all scenarios are averaged together. In the “Weighted 

Average” distribution, weights are equal to the number of homes included in each study. In the 

“Model Only” distribution, only scenarios involving modeling Ci are shown. In the “Measure 

Only” distribution, only those studies in which Ci was measured directly are shown. In the “AK 

Only” distribution, only scenarios employing Alaskan Cg or fg studies are shown. In the “NonAK Only” distribution, only scenarios excluding Alaskan Cg or fg data are shown. These 

scenarios are intended to span a range of estimates for the national estimate. 

The average concentrations from these “summary scenarios” are shown in Table 3A-1. 

As shown in Table 3A-1 and in Figure 3A-8, scenarios employing only measured indoor data 

resulted in higher predicted benzene TWA exposure concentrations than the studies employing 

only modeling. Scenarios employing Alaskan data result in higher benzene concentrations than 

scenarios excluding Alaskan data. Also weighting scenarios by the number of homes resulted in 

higher benzene concentrations. 

Accordingly, the national average TWA exposure concentration attributable to attached 

garages is estimated to be 1.2 – 6.6 μg/m3

. This range is intended to span possible values of 

average TWA exposure from attached garages, given currently available information. The actual 

average TWA exposure concentration due to attached garages could be outside of this range. 

Because of limited information on the representativeness of the homes studied, a more precise 

“central estimate” is not appropriate at this time. The width of the range, with the upper end 

being 5.5 times the lower end, is an indicator of the magnitude of uncertainty in the estimate. It 

is not a confidence interval in the traditional sense. As more data become available, more 

precise estimates will hopefully emerge. 

In comparison, the national average exposure concentration of census tract median 

exposure concentrations in this rule is estimated at 1.4 μg/m3 for calendar year 1999. 

3-132
 
Accordingly, if the attached garage exposure contribution is considered, the estimate of national 

average exposure to benzene rises to 2.6 – 8.0 μg/m3

, corresponding to an increase of 85-471%. 

Effects of Emission Standards 

Several limitations prevent precise estimation of the effect of the standards in this rule on 

garage-related exposures. First, cold temperature vehicle ignition and evaporative vehicle, 

engine, and fuel container emissions can occur either in a garage or outdoors. Second, detailed 

tracking of the time during which people are inside a house during cold vehicle starting or hot 

vehicle soaking, when a majority of benzene emissions are likely to occur, is limited. However, 

a bounding exercise can provide some estimates as to the effect of the standards in this rule. 

First, assuming full mixing and steady-state conditions, concentrations within a garage is 

estimable359 as: 

(9) Cg = (dMi/dt) / αV 

Here, the terms are similar to Equations 1-7. 

Given a change in the mass benzene emission rates from vehicle cold temperature 

ignition, fuel evaporation from vehicles, engines, and fuel containers, an estimate of a change in 

Cg is feasible. Table 2.2-52 of the RIA displays the emission reductions attributable to each 

program. By splitting the emission reductions into evaporative and exhaust emissions and 

applying several simple assumptions about where emissions occur (in garage vs. outdoors), the 

fraction of emission reductions occurring within attached garages can be estimated. This 

estimate is calculated by assuming ranges of values for the fraction of evaporative and exhaust 

emissions from each program that occur within an attached garage.u

 As such, while the total 

benzene mobile source and PFC emission reductions occurring as a result of the rule in 2030 are 

37% less than the projected emissions without controls (Table 2.2-52 of the RIA), emissions 

inside attached garages are reduced by an estimated 43-44%. 

Applying this fraction to Equation 8 and Equation 7, for the “average” scenarios modeled 

presented in Table 1, this amounts to a national average exposure reduction of approximately 0.5 

– 2.6 μg/m3

. 

Limitations 

As apparent in the wide range of “scenario” averages, there remains considerable 

uncertainty in ascertaining the true magnitude of attached garage exposure contributions 

nationally. There are a number of limitations in the approaches undertaken here. First, although 

comparison with measured indoor data shows reasonable performance for the modeling approach 

employed here, the selection of simple one-compartment mass balance models for both garage 

and home modeling may substantially understate the variation in concentrations within these 

microenvironments. All estimates here assumed steady-state conditions, and this may not be 

u

 The assumed fraction of evaporative and exhaust emission reductions from each source occurring within an 

attached garage are as follows. Ranges are represented as [min, max]. For LDGV, about 90% of emission 

reductions are exhaust-related, of which Pg*[25%,75%] occur within attached garages; the fraction of evaporative 

reductions occurring within attached garages are Pg *[25%,50%]. For small nonroad gasoline equipment, about 

72% of emissions are from exhaust, of which Pg *[0%,2%] occur in attached garages; 24% are evaporative, of which 

Pg *[90%,100%] occur in attached garages, 4% are refilling-related, of which Pg *[25%,75%] occur in attached 

garages. For portable fuel containers, Pg *[25%,75%] of emissions are assumed to occur in attached garages. 

3-133
 
appropriate for a source like a garage, where door opening, car entry and ignition, and other 

major sources of benzene are likely to produce short-term spikes in exposure not accounted for 

with steady-state assumptions. 

Second, the preponderance of these data were collected in locations with cold climates, 

so the results may not be applicable to warmer locations where houses are not built with the 

same degree of weather-tightness. Furthermore, studies suggest that indoor concentrations 

arising from attached garages vary considerably in response to emission-related activities in a 

garage such as cold vehicle ignition and parking a hot vehicle.360 Ambient temperatures may 

affect the magnitude of emissions from these activities. 

Lastly, the extent to which the houses studied in the publications cited here are 

“representative” of the national housing stock is unknown. 

Conclusions 

Modeled indoor benzene concentrations indicate that indoor air concentrations in homes 

with attached garages may be substantially higher than in homes without attached garages. 

Garage concentrations of benzene appear to be a major source of indoor benzene in 

homes with attached garages. According to the modeling conducted here, this source could 

explain the majority of exposures experienced by typical residents of such homes. Given this 

finding, interventions that result in a reduction in emissions within the garage would be a 

relatively efficient means of reducing overall personal exposure, particularly in areas 

geographically similar to the areas of the studies upon which this analysis relies. Given the 

proximity of this source to homes, one major set of beneficiaries of the rule’s emission controls 

is likely to be people with homes with attached garages, particularly in areas with high fuel 

benzene levels. Emissions from vehicles and fuel containers also may have greater relative 

impacts on those with attached garages. An elementary calculation of the intake fraction (iF) of 

emissions occurring within attached garages with very basic assumptions indicates that for 

benzene emitted in a garage, approximately 3-18 parts per thousand are inhaled by a person in an 

attached garage. This estimate is far in excess of estimated iF from ambient sources, and similar 

to estimated iF estimates for indoor sources.361 

3-134
 
Table 3A-1. Summary of National Average Exposure Estimates Attributable to Attached 

Garages. Different “averaging” assumptions shown. 

"Averaging" Benzene TWA 

Scenario (ug/m3) 

All Data 4.3 

Weighted Average 6.6 

Measure Only 6.1 

Model Only 3.4 

AK Only 5.5 

Non-AK Only 1.2 

3-135
 
Figure 3A-1a. Density of Garage Benzene Concentrations from Batterman et al. (2005) 

Figure 3A-1b. Density of Air Exchange Rates (ACH) 

3-136
 
Cg=10 

Figure 3A-2. Additional Indoor Air Concentrations from Garage as a Function of Cg and fg 

Indoor Concentration as a Function of Garage Concentration (Cg) and %Intake Air 

from Garage (fg) 

Cg=110 

Cg=100 

Cg=90 

Cg=80 40-45
 

Cg=70 35-40

30-35
 Cg=60 25-30
 

Cg=50 20-25

15-20
 Cg=40 10-15
 

Cg=30 5-10

0-5
 Cg=20 

Fg=0.02Fg=0.06

Fg=0.1

Fg=0.14Fg=0.18Fg=0.22Fg=0.26

Fg=0.3

Fg=0.34Fg=0.38Fg=0.42Fg=0.46

Fg=0.5 

3-137
 
Figure 3A-3. Distributions of Individual Garage Emission Factors 

Emission Rate (ug/hr) 

18000 16000 14000 12000 10000 

8000 6000 4000 2000 

0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

Percentile 

Garage 1 Garage 2 Garage 3 Garage 4 Garage 5 Garage 6 Garage 7 Garage 8 Garage 9 Garage 10 Garage 11 Garage 12 Garage 13 Garage 14 Garage 15 

3-138
 
Figure 3A-4. Cumulative Distribution of Modeled Indoor Benzene Concentrations 

Benzene Concentration (ug/m3) 

3-139
 

Model

ed Indoor Concentration Distributions bas

ed o

n Cg / Fg Com

bination

s

Solid Lines: Multiple Home Studies with Single M

easurement Per H

o

me, Dash

ed 

Lines: Single H

o

mes with

100.0 

90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 

0.0 

0% 10% 

Re

p

eat

e

d M

easures

Batterman et al. (2005) / Batterman FG Ci Batterman et al. (2005) / Fugler FG Ci Batterman et al. (2005) / Sheltersource FG Ci George et al. (2002) / Batterman FG Ci George et al. (2002) / Fugler FG Ci George et al. (2002) / Sheltersource FG Ci Isbell et al. (2005), Home V, Summer / Sheltersource FG IsCi

bell et al. (2005), Home NV, Summer / Sheltersource 

FG Ci Isbell et al. (2005), Home V, Winter / Sheltersource FG Ci Isbell et al. (2005), Home NV, Winter / Sheltersource FG IsCi

bell et al. (2005), Home V, Summer / Fugler FG Ci 

Isbell et al. (2005), Home NV, Summer / Fugler FG Ci Isbell et al. (2005), Home V, Winter / Fugler FG Ci Isbell et al. (2005), Home NV, Winter / Fugler FG Ci Isbell et al. (2005), Home V, Summer / Batterman FG Ci Isbell et al. (2005), Home NV, Summer / Batterman FG Ci Isbell et al. (2005), Home V, Winter / Batterman FG Ci Isbell et al. (2005), Home NV, Winter / Batterman FG Ci Tsai & 

Weisel (2000) / Batterman FG Ci 

Tsai & Weisel (2000) / Fugler FG Ci Tsai & Weisel (2000) / Sheltersource FG Ci 

20% 30% 40% 50% 60% 70% 80% 90% 100% 

Percentile of M

o

d

el Output per Scenario (Variability) 
Garages 

Figure 3A-5. Cumulative Distributions of Observed Benzene Levels in Homes with Attached 

Benzene Concentration (ug/m3) 

100.0

10.020.030.040.050.060.070.080.090.0

0.0 

Isbell et al. (2005), Home V, Sum

mer / Measured Ci 

Isbell et al. (2005), Home NV, Sum

mer / Measured Ci 

Isbell et al. (2005), Home V, Winter / Measured Ci Isbell et al. (2005), Home NV, Winter / Measured 

Ci 

Batterman et al. (2006) / Measured 

Ci

Schlapia & Morris (1998) / Measured Ci Weisel - NJDEP/EPA (2006) / Measured Ci 

Note: As shown, Schlapia and Morris distribution are based on a lognor

mal distribution fit to 

artithmetic mean and geo

metric mean as 

reported in study. Maximum indoor value measured in the study: 1161 ug/m3. Max

mimum indoor value for Isbell et al., home 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NV sum

mer: 125.1 ug/m3. 

Percentile of Reported 

Study Data 

3-140
 

Perce

ntile Distrib

ution 

of Indoor B

e

n

zene C

o

ncentrations in Stu

dies of H

omes 

with Attache

d

Garages

Solid Lines: Multiple Homes, 1 Measure/

H

ome; Dashed Line: Single Home with Multiple Measures/Ho

me
Figure 3A-6. Comparison of Modeled and Observed Indoor Benzene Concentrations 

Benzene Concentration (ug/m3) 

Cg Data Source 

Batterman et al. 

(2005/6) 

George et al. (2002)
 

Isbell et al. (2005),
 

Home NV, Summer
 

Isbell et al. (2005), 

Home NV, Winter 

Isbell et al. (2005), 

Home V, Summer 

Isbell et al. (2005), 

Home V, Winter 

Schlapia & Morris 

(1998) 

Tsai & Weisel (2000) 

Weisel - NJDEP/EPA 

(2006) 

0

1020304050607080 

Batter

man et al. (2006) 

Fugler et al. Sheltersource Measured 

3-141
 
Figure 3A-7. Multiple Scenario Output of Predicted National Average Benzene Exposure 

Attributable to Attached Garages 

Benzene TWA Concentration (ug/m3) 

3-142
 

10203040506070

0

-10 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Cumulative Percentile 

Batterman et al. (2005) / Sheltersource Batterman et al. (2005) / Fugler Batterman et al. (2005) / Batterman Batterman et al. (2005) / Direct George et al. (2002) / Sheltersource George et al. (2002) / Fugler George et al. (2002) / Batterman Tsai & Weisel (2000) / Sheltersource Tsai & Weisel (2000) / Fugler Tsai & Weisel (2000) / Batterman Isbell et al. (2005), Home V, Summer / Sheltersource Isbell et al. (2005), Home V, Summer / Fugler Isbell et al. (2005), Home V, Summer / Batterman Isbell et al. (2005), Home V, Summer / Direct Isbell et al. (2006), Home NV, Summer / Sheltersource Isbell et al. (2006), Home NV, Summer / Fugler Isbell et al. (2006), Home NV, Summer / Batterman Isbell et al. (2006), Home NV, Summer / Direct Isbell et al. (2006), Home V, Winter / Sheltersource Isbell et al. (2006), Home V, Winter / Fugler Isbell et al. (2006), Home V, Winter / Batterman Isbell et al. (2006), Home V, Winter / Direct Isbell et al. (2006), Home NV, Winter / Sheltersource Isbell et al. (2006), Home NV, Winter / Fugler Isbell et al. (2006), Home NV, Winter / Batterman Isbell et al. (2006), Home NV, Winter / Direct Schlapia & Morris (1998) / Direct Weisel - NJDEP/EPA (2006) / Direct 
3-143 

Figure 3A-8. Average “Summarized” Benzene Exposure Distributions 

05

1015202530 

Benzene TWA Concentration (ug/m3) All Data 

Weighted Average Measure Only Model Only AK Only Non-AK Only 

Variation i

n Distributions of National P

opulation TW

A E

xposures 

Attributa

ble to Attac

hed 

Garages, Based on Different "A

v

eraging" Assumptions 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

Cumulati

v

e P

erc

entile 
Appendix 3B: 8-Hour Ozone Nonattainment 

Table 3B-1. 8-Hour Ozone Nonattainment Areas, Counties and Populations (Data is 
 

Current through October 2006 and Population Numbers are from 2000 Census Data) 
 

8-hour Ozone Nonattainment 

Area State Classificationa,b County Name 

Whole 

/Part 

2000 Cty 

Pop 

Albany-Schenectady-Troy Area NY Subpart 1 Albany Co W 294,565 

Albany-Schenectady-Troy Area NY Subpart 1 Greene Co W 48,195 

Albany-Schenectady-Troy Area NY Subpart 1 Montgomery Co W 49,708 

Albany-Schenectady-Troy Area NY Subpart 1 Rensselaer Co W 152,538 

Albany-Schenectady-Troy Area NY Subpart 1 Saratoga Co W 200,635 

Albany-Schenectady-Troy Area NY Subpart 1 Schenectady Co W 146,555 

Albany-Schenectady-Troy Area NY Subpart 1 Schoharie Co W 31,582 

Allegan County Area MI Subpart 1 Allegan Co W 105,665 

Allentown-Bethlehem-Easton Area PA Subpart 1 Carbon Co W 58,802 

Allentown-Bethlehem-Easton Area PA Subpart 1 Lehigh Co W 312,090 

Allentown-Bethlehem-Easton Area PA Subpart 1 Northampton Co W 267,066 

Altoona Area PA Subpart 1 Blair Co W 129,144 

Amador and Calaveras Counties 

(Central Mountain Counties) Area CA Subpart 1 Amador Co W 35,100 

Amador and Calaveras Counties 

(Central Mountain Counties) Area CA Subpart 1 Calaveras Co W 40,554 

Atlanta Area GA 

Subpart 

2/Marginal Barrow Co W 46,144 

Atlanta Area GA 

Subpart 

2/Marginal Bartow Co W 76,019 

Atlanta Area GA 

Subpart 

2/Marginal Carroll Co W 87,268 

Atlanta Area GA 

Subpart 

2/Marginal Cherokee Co W 141,903 

Atlanta Area GA 

Subpart 

2/Marginal Clayton Co W 236,517 

Atlanta Area GA 

Subpart 

2/Marginal Cobb Co W 607,751 

Atlanta Area GA 

Subpart 

2/Marginal Coweta Co W 89,215 

Atlanta Area GA 

Subpart 

2/Marginal De Kalb Co W 665,865 

Atlanta Area GA 

Subpart 

2/Marginal Douglas Co W 92,174 

Atlanta Area GA 

Subpart 

2/Marginal Fayette Co W 91,263 

Atlanta Area GA 

Subpart 

2/Marginal Forsyth Co W 98,407 

Atlanta Area GA 

Subpart 

2/Marginal Fulton Co W 816,006 

Atlanta Area GA 

Subpart 

2/Marginal Gwinnett Co W 588,448 

Atlanta Area GA 

Subpart 

2/Marginal Hall Co W 139,277 

3-144
 
Atlanta Area GA 

Subpart 

2/Marginal Henry Co W 119,341 

Atlanta Area GA 

Subpart 

2/Marginal Newton Co W 62,001 

Atlanta Area GA 

Subpart 

2/Marginal Paulding Co W 81,678 

Atlanta Area GA 

Subpart 

2/Marginal Rockdale Co W 70,111 

Atlanta Area GA 

Subpart 

2/Marginal Spalding Co W 58,417 

Atlanta Area GA 

Subpart 

2/Marginal Walton Co W 60,687 

Baltimore Area MD 

Subpart 

2/Moderate 

Anne Arundel 

Co W 489,656 

Baltimore Area MD 

Subpart 

2/Moderate Baltimore (City) W 651,154 

Baltimore Area MD 

Subpart 

2/Moderate Baltimore Co W 754,292 

Baltimore Area MD 

Subpart 

2/Moderate Carroll Co W 150,897 

Baltimore Area MD 

Subpart 

2/Moderate Harford Co W 218,590 

Baltimore Area MD 

Subpart 

2/Moderate Howard Co W 247,842 

Baton Rouge Area LA 

Subpart 

2/Marginal Ascension Par W 76,627 

Baton Rouge Area LA 

Subpart 

2/Marginal 

East Baton 

Rouge Par W 412,852 

Baton Rouge Area LA 

Subpart 

2/Marginal Iberville Par W 33,320 

Baton Rouge Area LA 

Subpart 

2/Marginal Livingston Par W 91,814 

Baton Rouge Area LA 

Subpart 

2/Marginal 

West Baton 

Rouge Par W 21,601 

Beaumont-Port Arthur Area TX 

Subpart 

2/Marginal Hardin Co W 48,073 

Beaumont-Port Arthur Area TX 

Subpart 

2/Marginal Jefferson Co W 252,051 

Beaumont-Port Arthur Area TX 

Subpart 

2/Marginal Orange Co W 84,966 

Benton Harbor Area MI Subpart 1 Berrien Co W 162,453 

Benzie County Area MI Subpart 1 Benzie Co W 15,998 

Berkeley and Jefferson Counties 

Area WV Subpart 1 - EAC Berkeley Co W 75,905 

Berkeley and Jefferson Counties 

Area WV Subpart 1 - EAC Jefferson Co W 42,190 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Barnstable Co W 222,230 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Bristol Co W 534,678 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Dukes Co W 14,987 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Essex Co W 723,419 

3-145
 
Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Middlesex Co W 1,465,396 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Nantucket Co W 9,520 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Norfolk Co W 650,308 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Plymouth Co W 472,822 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Suffolk Co W 689,807 

Boston-Lawrence-Worcester (E. 

Mass) Area MA 

Subpart 

2/Moderate Worcester Co W 750,963 

Boston-Manchester-Portsmouth 

(SE) Area NH 

Subpart 

2/Moderate Hillsborough Co P 336,518 

Boston-Manchester-Portsmouth 

(SE) Area NH 

Subpart 

2/Moderate Merrimack Co P 11,721 

Boston-Manchester-Portsmouth 

(SE) Area NH 

Subpart 

2/Moderate Rockingham Co P 266,340 

Boston-Manchester-Portsmouth 

(SE) Area NH 

Subpart 

2/Moderate Strafford Co P 82,134 

Buffalo-Niagara Falls Area NY Subpart 1 Erie Co W 950,265 

Buffalo-Niagara Falls Area NY Subpart 1 Niagara Co W 219,846 

Canton-Massillon Area OH Subpart 1 Stark Co W 378,098 

Cass County Area MI 

Subpart 

2/Marginal Cass Co W 51,104 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Cabarrus Co W 131,063 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Gaston Co W 190,365 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Iredell Co P 39,885 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Lincoln Co W 63,780 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Mecklenburg Co W 695,454 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Rowan Co W 130,340 

Charlotte-Gastonia-Rock Hill Area NC 

Subpart 

2/Moderate Union Co W 123,677 

Charlotte-Gastonia-Rock Hill Area SC 

Subpart 

2/Moderate York Co P 102,000 

Chattanooga Area GA Subpart 1 - EAC Catoosa Co W 53,282 

Chattanooga Area TN Subpart 1 - EAC Hamilton Co W 307,896 

Chattanooga Area TN Subpart 1 - EAC Meigs Co W 11,086 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Cook Co W 5,376,741 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Du Page Co W 904,161 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Grundy Co P 6,309 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Kane Co W 404,119 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Kendall Co P 28,417 

3-146
 
Chi

cago-Gary-Lake County Area IL 

Subpart 

2/Moderate Lake Co W 644,356 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Mc Henry Co W 260,077 

Chicago-Gary-Lake County Area IL 

Subpart 

2/Moderate Will Co W 502,266 

Chicago-Gary-Lake County Area IN 

Subpart 

2/Moderate Lake Co W 484,564 

Chicago-Gary-Lake County Area IN 

Subpart 

2/Moderate Porter Co W 146,798 

Chico Area CA Subpart 1 Butte Co W 203,171 

Cincinnati-Hamilton Area IN Subpart 1 Dearborn Co P 10,434 

Cincinnati-Hamilton Area KY Subpart 1 Boone Co W 85,991 

Cincinnati-Hamilton Area KY Subpart 1 Campbell Co W 88,616 

Cincinnati-Hamilton Area KY Subpart 1 Kenton Co W 151,464 

Cincinnati-Hamilton Area OH Subpart 1 Butler Co W 332,807 

Cincinnati-Hamilton Area OH Subpart 1 Clermont Co W 177,977 

Cincinnati-Hamilton Area OH Subpart 1 Clinton Co W 40,543 

Cincinnati-Hamilton Area OH Subpart 1 Hamilton Co W 845,303 

Cincinnati-Hamilton Area OH Subpart 1 Warren Co W 158,383 

Clearfield and Indiana Counties 

Area PA Subpart 1 Clearfield Co W 83,382 

Clearfield and Indiana Counties 

Area PA Subpart 1 Indiana Co W 89,605 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Ashtabula Co W 102,728 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Cuyahoga Co W 1,393,978 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Geauga Co W 90,895 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Lake Co W 227,511 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Lorain Co W 284,664 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Medina Co W 151,095 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Portage Co W 152,061 

Cleveland-Akron-Lorain Area OH 

Subpart 

2/Moderate Summit Co W 542,899 

Columbia Area SC Subpart 1 - EAC Lexington Co P 181,265 

Columbia Area SC Subpart 1 - EAC Richland Co P 313,253 

Columbus Area OH Subpart 1 Delaware Co W 109,989 

Columbus Area OH Subpart 1 Fairfield Co W 122,759 

Columbus Area OH Subpart 1 Franklin Co W 1,068,978 

Columbus Area OH Subpart 1 Knox Co W 54,500 

Columbus Area OH Subpart 1 Licking Co W 145,491 

Columbus Area OH Subpart 1 Madison Co W 40,213 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Collin Co W 491,675 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Dallas Co W 2,218,899 

3-147
 
Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Denton Co W 432,976 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Ellis Co W 111,360 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Johnson Co W 126,811 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Kaufman Co W 71,313 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Parker Co W 88,495 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Rockwall Co W 43,080 

Dallas-Fort Worth Area TX 

Subpart 

2/Moderate Tarrant Co W 1,446,219 

Dayton-Springfield Area OH Subpart 1 Clark Co W 144,742 

Dayton-Springfield Area OH Subpart 1 Greene Co W 147,886 

Dayton-Springfield Area OH Subpart 1 Miami Co W 98,868 

Dayton-Springfield Area OH Subpart 1 Montgomery Co W 559,062 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Adams Co W 348,618 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Arapahoe Co W 487,967 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Boulder Co W 269,814 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Broomfield Co W 38,272 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Denver Co W 554,636 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Douglas Co W 175,766 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Jefferson Co W 525,507 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Larimer Co P 239,000 

Denver-Boulder-Greeley-Ft. CollinsLove. Area CO Subpart 1 - EAC Weld Co P 172,000 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Lenawee Co W 98,890 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Livingston Co W 156,951 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Macomb Co W 788,149 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Monroe Co W 145,945 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Oakland Co W 1,194,156 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal St Clair Co W 164,235 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Washtenaw Co W 322,895 

Detroit-Ann Arbor Area MI 

Subpart 

2/Marginal Wayne Co W 2,061,162 

Door County Area WI Subpart 1 Door Co W 27,961 

Erie Area PA Subpart 1 Erie Co W 280,843 

3-148
 
Essex County (Whiteface Mtn.) 

Area NY Subpart 1 Essex Co P 1,000 

Fayetteville Area NC Subpart 1 - EAC Cumberland Co W 302,963 

Flint Area MI Subpart 1 Genesee Co W 436,141 

Flint Area MI Subpart 1 Lapeer Co W 87,904 

Fort Wayne Area IN Subpart 1 Allen Co W 331,849 

Franklin County Area PA Subpart 1 Franklin Co W 129,313 

Frederick County Area VA Subpart 1 - EAC Frederick Co W 59,209 

Frederick County Area VA Subpart 1 - EAC Winchester W 23,585 

Grand Rapids Area MI Subpart 1 Kent Co W 574,335 

Grand Rapids Area MI Subpart 1 Ottawa Co W 238,314 

Greater Connecticut Area CT 

Subpart 

2/Moderate Hartford Co W 857,183 

Greater Connecticut Area CT 

Subpart 

2/Moderate Litchfield Co W 182,193 

Greater Connecticut Area CT 

Subpart 

2/Moderate New London Co W 259,088 

Greater Connecticut Area CT 

Subpart 

2/Moderate Tolland Co W 136,364 

Greater Connecticut Area CT 

Subpart 

2/Moderate Windham Co W 109,091 

Greene County Area PA Subpart 1 Greene Co W 40,672 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Alamance Co W 130,800 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Caswell Co W 23,501 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Davidson Co W 147,246 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Davie Co W 34,835 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Forsyth Co W 306,067 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Guilford Co W 421,048 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Randolph Co W 130,454 

Greensboro-Winston-Salem-High 

Point Area NC 

Subpart 

2/Marginal - 

EAC Rockingham Co W 91,928 

Greenville-Spartanburg-Anderson 

Area SC Subpart 1 - EAC Anderson Co W 165,740 

Greenville-Spartanburg-Anderson 

Area SC Subpart 1 - EAC Greenville Co W 379,616 

Greenville-Spartanburg-Anderson 

Area SC Subpart 1 - EAC Spartanburg Co W 253,791 

Hancock, Knox, Lincoln and Waldo ME Subpart 1 Hancock Co P 29,805 

3-149
 
Counties (Central Maine Coast) 

Area 

Hancock, Knox, Lincoln and Waldo 

Counties (Central Maine Coast) 

Area ME Subpart 1 Knox Co P 33,563 

Hancock, Knox, Lincoln and Waldo 

Counties (Central Maine Coast) 

Area ME Subpart 1 Lincoln Co P 28,504 

Hancock, Knox, Lincoln and Waldo 

Counties (Central Maine Coast) 

Area ME Subpart 1 Waldo Co P 604 

Harrisburg-Lebanon-Carlisle Area PA Subpart 1 Cumberland Co W 213,674 

Harrisburg-Lebanon-Carlisle Area PA Subpart 1 Dauphin Co W 251,798 

Harrisburg-Lebanon-Carlisle Area PA Subpart 1 Lebanon Co W 120,327 

Harrisburg-Lebanon-Carlisle Area PA Subpart 1 Perry Co W 43,602 

Haywood and Swain Counties 

(Great Smoky NP) Area NC Subpart 1 Haywood Co P 28 

Haywood and Swain Counties 

(Great Smoky NP) Area NC Subpart 1 Swain Co P 260 

Hickory-Morganton-Lenoir Area NC Subpart 1 - EAC Alexander Co W 33,603 

Hickory-Morganton-Lenoir Area NC Subpart 1 - EAC Burke Co P 69,970 

Hickory-Morganton-Lenoir Area NC Subpart 1 - EAC Caldwell Co P 64,254 

Hickory-Morganton-Lenoir Area NC Subpart 1 - EAC Catawba Co W 141,685 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Brazoria Co W 241,767 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Chambers Co W 26,031 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Fort Bend Co W 354,452 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Galveston Co W 250,158 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Harris Co W 3,400,578 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Liberty Co W 70,154 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Montgomery Co W 293,768 

Houston-Galveston-Brazoria Area TX 

Subpart 

2/Moderate Waller Co W 32,663 

Huntington-Ashland Area KY Subpart 1 Boyd Co W 49,752 

Huron County Area MI Subpart 1 Huron Co W 36,079 

Imperial County Area CA 

Subpart 

2/Marginal Imperial Co W 142,361 

Indianapolis Area IN Subpart 1 Boone Co W 46,107 

Indianapolis Area IN Subpart 1 Hamilton Co W 182,740 

Indianapolis Area IN Subpart 1 Hancock Co W 55,391 

Indianapolis Area IN Subpart 1 Hendricks Co W 104,093 

Indianapolis Area IN Subpart 1 Johnson Co W 115,209 

Indianapolis Area IN Subpart 1 Madison Co W 133,358 

Indianapolis Area IN Subpart 1 Marion Co W 860,454 

Indianapolis Area IN Subpart 1 Morgan Co W 66,689 

Indianapolis Area IN Subpart 1 Shelby Co W 43,445 

3-150
 
Jamestown Area NY Subpart 1 Chautauqua Co W 139,750 

Jefferson County Area NY 

Subpart 

2/Moderate Jefferson Co W 111,738 

Johnson City-Kingsport-Bristol Area TN Subpart 1 - EAC Hawkins Co W 53,563 

Johnson City-Kingsport-Bristol Area TN Subpart 1 - EAC Sullivan Co W 153,048 

Johnstown Area PA Subpart 1 Cambria Co W 152,598 

Kalamazoo-Battle Creek Area MI Subpart 1 Calhoun Co W 137,985 

Kalamazoo-Battle Creek Area MI Subpart 1 Kalamazoo Co W 238,603 

Kalamazoo-Battle Creek Area MI Subpart 1 Van Buren Co W 76,263 

Kent and Queen Anne's Counties 

Area MD 

Subpart 

2/Marginal Kent Co W 19,197 

Kent and Queen Anne's Counties 

Area MD 

Subpart 

2/Marginal 

Queen Annes 

Co W 40,563 

Kern County (Eastern Kern) Area CA Subpart 1 Kern Co P 99,251 

Kewaunee County Area WI Subpart 1 Kewaunee Co W 20,187 

Knoxville Area TN Subpart 1 Anderson Co W 71,330 

Knoxville Area TN Subpart 1 Blount Co W 105,823 

Knoxville Area TN Subpart 1 Cocke Co P 20 

Knoxville Area TN Subpart 1 Jefferson Co W 44,294 

Knoxville Area TN Subpart 1 Knox Co W 382,032 

Knoxville Area TN Subpart 1 Loudon Co W 39,086 

Knoxville Area TN Subpart 1 Sevier Co W 71,170 

La Porte County Area IN 

Subpart 

2/Marginal La Porte Co W 110,106 

Lancaster Area PA 

Subpart 

2/Marginal Lancaster Co W 470,658 

Lansing-East Lansing Area MI Subpart 1 Clinton Co W 64,753 

Lansing-East Lansing Area MI Subpart 1 Eaton Co W 103,655 

Lansing-East Lansing Area MI Subpart 1 Ingham Co W 279,320 

Las Vegas Area NV Subpart 1 Clark Co P 1,348,864 

Lima Area OH Subpart 1 Allen Co W 108,473 

Los Angeles and San Bernardino 

Counties (W Mojave Desert) Area CA 

Subpart 

2/Moderate Los Angeles Co P 297,058 

Los Angeles and San Bernardino 

Counties (W Mojave Desert) Area CA 

Subpart 

2/Moderate 

San Bernardino 

Co P 359,350 

Los Angeles-South Coast Air Basin 

Area CA 

Subpart 

2/Severe 17 Los Angeles Co P 9,222,280 

Los Angeles-South Coast Air Basin 

Area CA 

Subpart 

2/Severe 17 Orange Co W 2,846,289 

Los Angeles-South Coast Air Basin 

Area CA 

Subpart 

2/Severe 17 Riverside Co P 1,194,859 

Los Angeles-South Coast Air Basin 

Area CA 

Subpart 

2/Severe 17 

San Bernardino 

Co P 1,330,159 

Louisville Area IN Subpart 1 Clark Co W 96,472 

Louisville Area IN Subpart 1 Floyd Co W 70,823 

Louisville Area KY Subpart 1 Bullitt Co W 61,236 

Louisville Area KY Subpart 1 Jefferson Co W 693,604 

Louisville Area KY Subpart 1 Oldham Co W 46,178 

Macon Area GA Subpart 1 Bibb Co W 153,887 

Macon Area GA Subpart 1 Monroe Co P 50 

Manitowoc County Area WI Subpart 1 Manitowoc Co W 82,887 

3-151
 
Mariposa and Tuolumne Counties 

(Southern Mountain Counties) Area CA Subpart 1 Mariposa Co W 17,130 

Mariposa and Tuolumne Counties 

(Southern Mountain Counties) Area CA Subpart 1 Tuolumne Co W 54,501 

Mason County Area MI Subpart 1 Mason Co W 28,274 

Memphis Area AR 

Subpart 

2/Marginal Crittenden Co W 50,866 

Memphis Area TN 

Subpart 

2/Marginal Shelby Co W 897,472 

Milwaukee-Racine Area WI 

Subpart 

2/Moderate Kenosha Co W 149,577 

Milwaukee-Racine Area WI 

Subpart 

2/Moderate Milwaukee Co W 940,164 

Milwaukee-Racine Area WI 

Subpart 

2/Moderate Ozaukee Co W 82,317 

Milwaukee-Racine Area WI 

Subpart 

2/Moderate Racine Co W 188,831 

Milwaukee-Racine Area WI 

Subpart 

2/Moderate Washington Co W 117,493 

Milwaukee-Racine Area WI 

Subpart 

2/Moderate Waukesha Co W 360,767 

Murray County (Chattahoochee Nat 

Forest) Area GA Subpart 1 Murray Co P 1,000 

Muskegon Area MI 

Subpart 

2/Marginal Muskegon Co W 170,200 

Nashville Area TN Subpart 1 - EAC Davidson Co W 569,891 

Nashville Area TN Subpart 1 - EAC Rutherford Co W 182,023 

Nashville Area TN Subpart 1 - EAC Sumner Co W 130,449 

Nashville Area TN Subpart 1 - EAC Williamson Co W 126,638 

Nashville Area TN Subpart 1 - EAC Wilson Co W 88,809 

Nevada County (Western part) Area CA Subpart 1 Nevada Co P 77,735 

New York-N. New Jersey-Long 

Island Area CT 

Subpart 

2/Moderate Fairfield Co W 882,567 

New York-N. New Jersey-Long 

Island Area CT 

Subpart 

2/Moderate Middlesex Co W 155,071 

New York-N. New Jersey-Long 

Island Area CT 

Subpart 

2/Moderate New Haven Co W 824,008 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Bergen Co W 884,118 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Essex Co W 793,633 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Hudson Co W 608,975 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Hunterdon Co W 121,989 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Middlesex Co W 750,162 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Monmouth Co W 615,301 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Morris Co W 470,212 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Passaic Co W 489,049 

3-152
 
New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Somerset Co W 297,490 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Sussex Co W 144,166 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Union Co W 522,541 

New York-N. New Jersey-Long 

Island Area NJ 

Subpart 

2/Moderate Warren Co W 102,437 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Bronx Co W 1,332,650 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Kings Co W 2,465,326 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Nassau Co W 1,334,544 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate New York Co W 1,537,195 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Queens Co W 2,229,379 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Richmond Co W 443,728 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Rockland Co W 286,753 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Suffolk Co W 1,419,369 

New York-N. New Jersey-Long 

Island Area NY 

Subpart 

2/Moderate Westchester Co W 923,459 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Chesapeake W 199,184 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Gloucester Co W 34,780 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Hampton W 146,437 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Isle Of Wight Co W 29,728 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal James City Co W 48,102 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Newport News W 180,150 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Norfolk W 234,403 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Poquoson W 11,566 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Portsmouth W 100,565 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Suffolk W 63,677 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Virginia Beach W 425,257 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal Williamsburg W 11,998 

Norfolk-Virginia Beach-Newport 

News (Hampton Roads) Area VA 

Subpart 

2/Marginal York Co W 56,297 

Parkersburg-Marietta Area OH Subpart 1 Washington Co W 63,251 

Parkersburg-Marietta Area WV Subpart 1 Wood Co W 87,986 

3-153
 
Philadelp

hia-Wilmington-Atlantic 

City Area DE 

Subpart 

2/Moderate Kent Co W 126,697 

Philadelphia-Wilmington-Atlantic 

City Area DE 

Subpart 

2/Moderate New Castle Co W 500,265 

Philadelphia-Wilmington-Atlantic 

City Area DE 

Subpart 

2/Moderate Sussex Co W 156,638 

Philadelphia-Wilmington-Atlantic 

City Area MD 

Subpart 

2/Moderate Cecil Co W 85,951 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Atlantic Co W 252,552 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Burlington Co W 423,394 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Camden Co W 508,932 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Cape May Co W 102,326 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Cumberland Co W 146,438 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Gloucester Co W 254,673 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Mercer Co W 350,761 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Ocean Co W 510,916 

Philadelphia-Wilmington-Atlantic 

City Area NJ 

Subpart 

2/Moderate Salem Co W 64,285 

Philadelphia-Wilmington-Atlantic 

City Area PA 

Subpart 

2/Moderate Bucks Co W 597,635 

Philadelphia-Wilmington-Atlantic 

City Area PA 

Subpart 

2/Moderate Che

ster Co W 433,501 

Philadelphia-Wilmington-Atlantic 

City Area PA 

Subpart 

2/Moderate Delaware Co W 550,864 

Philadelphia-Wilmington-Atlantic 

City Area PA 

Subpart 

2/Moderate Montgomery Co W 750,097 

Philadelphia-Wilmington-Atlantic 

City Area PA 

Subpart 

2/Moderate Philadelphia Co W 1,517,550 

Phoenix-Mesa Area AZ Subpart 1 Maricopa Co P 3,054,504 

Phoenix-Mesa Area AZ Subpart 1 Pinal Co P 31,541 

Pittsburgh-Beaver Valley Area PA Subpart 1 Allegheny Co W 1,281,666 

Pittsburgh-Beaver Valley Area PA Subpart 1 Armstrong Co W 72,392 

Pittsburgh-Beaver Valley Area PA Subpart 1 Beaver Co W 181,412 

Pittsburgh-Beaver Valley Area PA Subpart 1 Butler Co W 174,083 

Pittsburgh-Beaver Valley Area PA Subpart 1 Fayette Co W 148,644 

Pittsburgh-Beaver Valley Area PA Subpart 1 Washington Co W 202,897 

Westmoreland 

Pittsburgh-Beaver Valley Area PA Subpart 1 Co W 369,993 

Portland Area ME 

Subpart 

2/Marginal 

Androscoggin 

Co P 3,390 

Portland Area ME 

Subpart 

2/Marginal Cumberland Co P 252,907 

Portland Area ME 

Subpart 

2/Marginal Sagadahoc Co W 35,214 

Portland Area ME Subpart York Co P 164,997 

3-154
 
2/Marginal 

Poughkeepsie Area NY 

Subpart 

2/Moderate Dutchess Co W 280,150 

Poughkeepsie Area NY 

Subpart 

2/Moderate Orange Co W 341,367 

Poughkeepsie Area NY 

Subpart 

2/Moderate Putnam Co W 95,745 

Providence (all of RI) Area RI 

Subpart 

2/Moderate Bristol Co W 50,648 

Providence (all of RI) Area RI 

Subpart 

2/Moderate Kent Co W 167,090 

Providence (all of RI) Area RI 

Subpart 

2/Moderate Newpo

rt Co W 85,433 

Providence (all of RI) Area RI 

Subpart 

2/Moderate Providence Co W 621,602 

Providence (all of RI) Area RI 

Subpart 

2/Moderate Washington Co W 123,546 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Chatham Co P 21,320 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Durham Co W 223,314 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Franklin Co W 47,260 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Granville Co W 48,498 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Johnston Co W 121,965 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Orange Co W 118,227 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Person Co W 35,623 

Raleigh-Durham-Chapel Hill Area NC Subpart 1 Wake Co W 627,846 

Reading Area PA Subpart 1 Berks Co W 373,638 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Charles City Co W 6,926 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Che

sterfield Co W 259,903 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Colonial Heights W 16,897 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Hanover Co W 86,320 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Hen

rico Co W 262,300 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Hopewell W 22,354 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Petersburg W 33,740 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal 

Prince George 

Co W 33,047 

Richmond-Petersburg Area VA 

Subpart 

2/Marginal Richmond W 197,790 

Riverside County (Coachella 

Valley) Area CA 

Subpart 

2/Serious Riverside Co P 324,750 

Roanoke Area VA Subpart 1 - EAC Botetourt Co W 30,496 

Roanoke Area VA Subpart 1 - EAC Roanoke W 94,911 

Roanoke Area VA Subpart 1 - EAC Roanoke Co W 85,778 

Roanoke Area VA Subpart 1 - EAC Salem W 24,747 

Rochester Area NY Subpart 1 Genesee Co W 60,370 

Rochester Area NY Subpart 1 Livingston Co W 64,328 

3-155
 
Roche

ster Area NY Subpart 1 Monroe Co W 735,343 

Rochester Area NY Subpart 1 Ontario Co W 100,224 

Rochester Area NY Subpart 1 Orleans Co W 44,171 

Rochester Area NY Subpart 1 Wayne Co W 93,765 

Rocky Mount Area NC Subpart 1 Edgecombe Co W 55,606 

Rocky Mount Area NC Subpart 1 Nash Co W 87,420 

Sacramento Metro Area CA 

Subpart 

2/Serious El Dorado Co P 124,164 

Sacramento Metro Area CA 

Subpart 

2/Serious Placer Co P 239,978 

Sacramento Metro Area CA 

Subpart 

2/Serious Sacramento Co W 1,223,499 

Sacramento Metro Area CA 

Subpart 

2/Serious Solano Co P 197,034 

Sacramento Metro Area CA 

Subpart 

2/Serious Sutter Co P 25,013 

Sacramento Metro Area CA 

Subpart 

2/Serious Yolo Co W 168,660 

San Antonio Area TX Subpart 1 - EAC Bexar Co W 1,392,931 

San Antonio Area TX Subpart 1 - EAC Comal Co W 78,021 

San Antonio Area TX Subpart 1 - EAC Guadalupe Co W 89,023 

San Diego Area CA Subpart 1 San Diego Co P 2,813,431 

San Francisco Bay Area CA 

Subpart 

2/Marginal Alameda Co W 1,443,741 

San Francisco Bay Area CA 

Subpart 

2/Marginal Contra Costa Co W 948,816 

San Francisco Bay Area CA 

Subpart 

2/Marginal Marin Co W 247,289 

San Francisco Bay Area CA 

Subpart 

2/Marginal Napa Co W 124,279 

San Francisco Bay Area CA 

Subpart 

2/Marginal 

San Francisco 

Co W 776,733 

San Francisco Bay Area CA 

Subpart 

2/Marginal San Mateo Co W 707,161 

San Francisco Bay Area CA 

Subpart 

2/Marginal Santa Cla

ra Co W 1,682,585 

San Francisco Bay Area CA 

Subpart 

2/Marginal Solano Co P 197,508 

San Francisco Bay Area CA 

Subpart 

2/Marginal Sonoma Co P 413,716 

San Joaquin Valley Area CA 

Subpart 

2/Serious Fre

sno Co W 799,407 

San Joaquin Valley Area CA 

Subpart 

2/Serious Kern Co P 550,220 

San Joaquin Valley Area CA 

Subpart 

2/Serious Kings Co W 129,461 

San Joaquin Valley Area CA 

Subpart 

2/Serious Madera Co W 123,109 

San Joaquin Valley Area CA 

Subpart 

2/Serious Merced Co W 210,554 

San Joaquin Valley Area CA 

Subpart 

2/Serious San Joaquin Co W 563,598 

San Joaquin Valley Area CA Subpart Stanislaus Co W 446,997 

3-156
 
2/Serious 

San Joaquin Valley Area CA 

Subpart 

2/Serious Tulare Co W 368,021 

Scranton-Wilkes-Barre Area PA Subpart 1 Lackawanna Co W 213,295 

Scranton-Wilkes-Barre Area PA Subpart 1 Luzerne Co W 319,250 

Scranton-Wilkes-Barre Area PA Subpart 1 Monroe Co W 138,687 

Scranton-Wilkes-Barre Area PA Subpart 1 Wyoming Co W 28,080 

Sheboygan Area WI 

Subpart 

2/Moderate Sheboygan Co W 112,646 

South Bend-Elkhart Area IN Subpart 1 Elkhart Co W 182,791 

South Bend-Elkhart Area IN Subpart 1 St Joseph Co W 265,559 

Springfield (W. Mass) Area MA 

Subpart 

2/Moderate Berkshi

re Co W 134,953 

Springfield (W. Mass) Area MA 

Subpart 

2/Moderate Franklin Co W 71,535 

Springfield (W. Mass) Area MA 

Subpart 

2/Moderate Hampden Co W 456,228 

Springfield (W. Mass) Area MA 

Subpart 

2/Moderate Hampshi

re Co W 152,251 

St. Louis Area IL 

Subpart 

2/Moderate Jersey Co W 21,668 

St. Louis Area IL 

Subpart 

2/Moderate Madison Co W 258,941 

St. Louis Area IL 

Subpart 

2/Moderate Monroe Co W 27,619 

St. Louis Area IL 

Subpart 

2/Moderate St Clair Co W 256,082 

St. Louis Area MO 

Subpart 

2/Moderate Franklin Co W 93,807 

St. Louis Area MO 

Subpart 

2/Moderate Jefferson Co W 198,099 

St. Louis Area MO 

Subpart 

2/Moderate St Charles Co W 283,883 

St. Louis Area MO 

Subpart 

2/Moderate St Louis W 348,189 

St. Louis Area MO 

Subpart 

2/Moderate St Louis Co W 1,016,315 

State College Area PA Subpart 1 Centre Co W 135,758 

Steubenville-Weirton Area OH Subpart 1 Jefferson Co W 73,894 

Steubenville-Weirton Area WV Subpart 1 Brooke Co W 25,447 

Steubenville-Weirton Area WV Subpart 1 Hancock Co W 32,667 

Sutter County (part) (Sutter Buttes) 

Area CA Subpart 1 Sutter Co P 1 

Tioga County Area PA Subpart 1 Tioga Co W 41,373 

Toledo Area OH Subpart 1 Lucas Co W 455,054 

Toledo Area OH Subpart 1 Wood Co W 121,065 

Ventura County (part) Area CA 

Subpart 

2/Moderate Ventura Co P 753,197 

Washington Area DC 

Subpart 

2/Moderate Entire District W 572,059 

Washington Area MD 

Subpart 

2/Moderate Calvert Co W 74,563 

Washington Area MD Subpart Charles Co W 120,546 

3-157
 
2/Moderate 

Washington Area MD 

Subpart 

2/Moderate Frederick Co W 195,277 

Washington Area MD 

Subpart 

2/Moderate Montgomery Co W 873,341 

Washington Area MD 

Subpart 

2/Moderate 

Prince George's 

Co W 801,515 

Washington Area VA 

Subpart 

2/Moderate Alexandria W 128,283 

Washington Area VA 

Subpart 

2/Moderate Arlington Co W 189,453 

Washington Area VA 

Subpart 

2/Moderate Fairfax W 21,498 

Washington Area VA 

Subpart 

2/Moderate Fairfax Co W 969,749 

Washington Area VA 

Subpart 

2/Moderate Falls Church W 10,377 

Washington Area VA 

Subpart 

2/Moderate Loudoun Co W 169,599 

Washington Area VA 

Subpart 

2/Moderate Manassas W 35,135 

Washington Area VA 

Subpart 

2/Moderate Manassas Park W 10,290 

Washington Area VA 

Subpart 

2/Moderate 

Prince William 

Co W 280,813 

Washington County (Hagerstown) 

Area MD Subpart 1 - EAC Washington Co W 131,923 

Wheeling Area OH Subpart 1 Belmont Co W 70,226 

Wheeling Area WV Subpart 1 Marshall Co W 35,519 

Wheeling Area WV Subpart 1 Ohio Co W 47,427 

York Area PA Subpart 1 Adams Co W 91,292 

York Area PA Subpart 1 York Co W 381,751 

Youngstown-Warren-Sharon Area OH Subpart 1 Columbiana Co W 112,075 

Youngstown-Warren-Sharon Area OH Subpart 1 Mahoning Co W 257,555 

Youngstown-Warren-Sharon Area OH Subpart 1 Trumbull Co W 225,116 

Youngstown-Warren-Sharon Area PA Subpart 1 Mercer Co W 120,293 

a) Under the CAA these nonattainment areas are further classified as subpart 1 or subpart 2 (subpart 2 is further 

classified as marginal, moderate, serious, severe or extreme) based on their design values. An Early Action 

Compact (EAC) area is one that has entered into a compact with the EPA and has agreed to reduce ground level 

ozone pollution earlier than the CAA would require in exchange the EPA will defer the effective date of the 

nonattainment designation. The severe designation is denoted as severe-15 or severe-17 based on the maximum 

attainment date associated with the classification. 

b) Boston-Manchester-Portsmouth (SE), NH has the same classification as Boston-Lawrence- Worcester (E. MA), 

MA. 

3-158
 
Appendix 3C: PM Nonattainment 

Table 3C-1. PM2.5 Nonattainment Areas and Populations (data is current through October 

2006 and the population numbers are from 2000 census data) 

PM2.5 Nonattainment Area Population 

Atlanta, GA 4,231,750 

Baltimore, MD 2,512,431 

Birmingham, AL 807,612 

Canton-Massillon, OH 378,098 

Charleston, WV 251,662 

Chattanooga, AL-TN-GA 423,809 

Chicago-Gary-Lake County, IL-IN 8,757,808 

Cincinnati-Hamilton, OH-KY-IN 1,850,975 

Cleveland-Akron-Lorain, OH 2,775,447 

Columbus, OH 1,448,503 

Dayton-Springfield, OH 851,690 

Detroit-Ann Arbor, MI 4,833,493 

Evansville, IN 277,402 

Greensboro-Winston Salem-High Point, NC 568,294 

Harrisburg-Lebanon-Carlisle, PA 585,799 

Hickory, NC 141,685 

Huntington-Ashland, WV-KY-OH 340,776 

Indianapolis, IN 1,329,185 

Johnstown, PA 164,431 

Knoxville, TN 599,008 

Lancaster, PA 470,658 

Libby, MT 2,626 

Liberty-Clairton, PA 21,600 

Los Angeles-South Coast Air Basin, CA 14,593,587 

Louisville, KY-IN 938,905 

Macon, GA 154,837 

Martinsburg, WV-Hagerstown, MD 207,828 

New York-N. New Jersey-Long Island,NY-NJ-CT 19,802,587 

Parkersburg-Marietta, WV-OH 152,912 

Philadelphia-Wilmington, PA-NJ-DE 5,536,911 

Pittsburgh-Beaver Valley, PA 2,195,054 

Reading, PA 373,638 

Rome, GA 90,565 

San Joaquin Valley, CA 3,191,367 

St. Louis, MO-IL 2,486,562 

Steubenville-Weirton, OH-WV 132,008 

Washington, DC-MD-VA 4,377,935 

Wheeling, WV-OH 153,172 

York, PA 381,751 

Total 88,394,361 

3-159
 
Table 3C-2. PM10 Nonattainment Areas and Populations (data is current through March 

2006 and the population numbers are from 2000 census data) 

PM10 Nonattainment Areas Listed Alphabetically Classification Number 2000 EPA State 

of Population Region 

Counties (thousands) 

NAA 

Ajo (Pima County), AZ Moderate 1 8 9 AZ 

Anthony, NM Moderate 1 3 6 NM 

Bonner Co (Sandpoint), ID Moderate 1 37 10 ID 

Butte, MT Moderate 1 35 8 MT 

Clark Co, NV Serious 1 1,376 9 NV 

Coachella Valley, CA Serious 1 182 9 CA 

Columbia Falls, MT Moderate 1 4 8 MT 

Coso Junction, CA Moderate 1 7 9 CA 

Douglas (Cochise County), AZ Moderate 1 16 9 AZ 

Eagle River, AK Moderate 1 195 10 AK 

El Paso Co, TX Moderate 1 564 6 TX 

Eugene-Springfield, OR Moderate 1 179 10 OR 

Flathead County; Whitefish and vicinity, MT Moderate 1 5 8 MT 

Fort Hall Reservation, ID Moderate 2 1 10 ID 

Hayden/Miami, AZ Moderate 2 4 9 AZ 

Imperial Valley, CA Serious 1 120 9 CA 

Juneau, AK Moderate 1 14 10 AK 

Kalispell, MT Moderate 1 15 8 MT 

LaGrande, OR Moderate 1 12 10 OR 

Lake Co, OR Moderate 1 3 10 OR 

Lame Deer, MT Moderate 1 1 8 MT 

Lane Co, OR Moderate 1 3 10 OR 

Libby, MT Moderate 1 3 8 MT 

Los Angeles South Coast Air Basin, CA Serious 4 14,594 9 CA 

Medford-Ashland, OR Moderate 1 78 10 OR 

Missoula, MT Moderate 1 52 8 MT 

Mono Basin, CA Moderate 1 0 9 CA 

Mun. of Guaynabo, PR Moderate 1 92 2 PR 

New York Co, NY Moderate 1 1,537 2 NY 

Nogales, AZ Moderate 1 25 9 AZ 

Ogden, UT Moderate 1 77 8 UT 

Owens Valley, CA Serious 1 7 9 CA 

Paul Spur, AZ Moderate 1 1 9 AZ 

Phoenix, AZ Serious 2 3,112 9 AZ 

Pinehurst, ID Moderate 1 2 10 ID 

Polson, MT Moderate 1 4 8 MT 

Portneuf Valley, ID Moderate 2 66 10 ID 

Rillito, AZ Moderate 1 1 9 AZ 

Ronan, MT Moderate 1 3 8 MT 

Sacramento Co, CA Moderate 1 1,223 9 CA 

Salt Lake Co, UT Moderate 1 898 8 UT 

San Bernardino Co, CA Moderate 1 199 9 CA 

San Joaquin Valley, CA Serious 7 3,080 9 CA 

3-160
 
Sanders County (part);Thompson Falls and vicinity, Moderate 1 1 8 MT 

MT 

Sheridan, WY Moderate 1 16 8 WY 

Shoshone Co, ID Moderate 1 10 10 ID 

Trona, CA Moderate 1 4 9 CA 

Utah Co, UT Moderate 1 369 8 UT 

Washoe Co, NV Serious 1 339 9 NV 

Weirton, WV Moderate 2 15 3 WV 

Yuma, AZ Moderate 1 82 9 AZ 

51 Total Areas 51 28,674 

3-161
 
Appendix 3D: Visibility Tables 

Table 3D-1. List of 156 Mandatory Class I Federal Areas Where Visibility is an Important 

Value (As Listed in 40 CFR 81)* 

Federal 
 

Land 
 

State Area Name Acreage Manager 
 

Alabama Sipsey Wilderness Area 12,646 USDA-FS 

Alaska Bering Sea Wilderness Area 41,113 USDI-FWS 

Denali NP (formerly Mt. McKinley NP) 1,949,493 USDI-NPS 

Simeonof Wilderness Area 25,141 USDI-FWS 

Tuxedni Wilderness Area 6,402 USDI-FWS 

Arizona 

Chiricahua National Monument Wilderness 

Area 9,440 USDI-NPS 

 Chiricahua Wilderness Area 18,000 USDA-FS 

Galiuro Wilderness Area 52,717 USDA-FS 

Grand Canyon NP 1,176,913 USDI-NPS 

 Mazatzal Wilderness Area 205,137 USDA-FS 

Mount Baldy Wilderness Area 6,975 USDA-FS 

Petrified Forest NP 93,493 USDI-NPS 

Pine Mountain Wilderness Area 20,061 USDA-FS 

Saguaro Wilderness Area 71,400 USDI-FS 

Sierra Ancha Wilderness Area 20,850 USDA-FS 

 Superstition Wilderness Area 124,117 USDA-FS 

Sycamore Canyon Wilderness Area 47,757 USDA-FS 

Arkansas Caney Creek Wilderness Area 4,344 USDA-FS 

Upper Buffalo Wilderness Area 9,912 USDA-FS 

California Agua Tibia Wilderness Area 15,934 USDA-FS 

 Caribou Wilderness Area 19,080 USDA-FS 

 Cucamonga Wilderness Area 9,022 USDA-FS 

Desolation Wilderness Area 63,469 USDA-FS 

Dome Land Wilderness Area 62,206 USDA-FS 

 Emigrant Wilderness Area 104,311 USDA-FS 

Hoover Wilderness Area 47,916 USDA-FS 

John Muir Wilderness Area 484,673 USDA-FS 

Joshua Tree Wilderness Area 429,690 USDI-NPS 

36,300 USDI-BLM 

Kaiser Wilderness Area 22,500 USDA-FS 

Kings Canyon NP 459,994 USDI-NPS 

Lassen Volcanic NP 105,800 USDI-NPS 

Lava Beds Wilderness Area 28,640 USDI-NPS 

Marble Mountain Wilderness Area 213,743 USDA-FS 

Minarets Wilderness Area 109,484 USDA-FS 

 Mokelumme Wilderness Area 50,400 USDA-FS 

 Pinnacles Wilderness Area 12,952 USDI-NPS 

Point Reyes Wilderness Area 25,370 USDI-NPS 

 Redwood NP 27,792 USDI-NPS 

3-162
 
Federal 

Land 

State Area Name Acreage Manager 

San Gabriel Wilderness Area 36,137 USDA-FS 

San Gorgonio Wilderness Area 56,722 USDA-FS 

37,980 USDI-BLM 

San Jacinto Wilderness Area 20,564 USDA-FS 

San Rafael Wilderness Area 142,722 USDA-FS 

 Sequoia NP 386,642 USDI-NS 

South Warner Wilderness Area 68,507 USDA-FS 

Thousand Lakes Wilderness Area 15,695 USDA-FS 

Ventana Wilderness Area 95,152 USDA-FS 

Yolla Bolly-Middle Eel Wilderness Area 111,841 USDA-FS 

42,000 USDI-BLM

 Yosemite NP 759,172 USDI-NPS 

Colorado 

Black Canyon of the Gunnison Wilderness 

Area 11,180 USDI-NPS 

Eagles Nest Wilderness Area 133,910 USDA-FS 

Flat Tops Wilderness Area 235,230 USDA-FS 

Great Sand Dunes Wilderness Area 33,450 USDI-NPS 

La Garita Wilderness Area 48,486 USDA-FS 

Maroon Bells-Snowmass Wilderness Area 71,060 USDA-FS 

 Mesa Verde NP 51,488 USDI-NPS 

Mount Zirkel Wilderness Area 72,472 USDA-FS 

 Rawah Wilderness Area 26,674 USDA-FS 

Rocky Mountain NP 263,138 USDI-NPS 

 Weminuche Wilderness Area 400,907 USDA-FS 

West Elk Wilderness Area 61,412 USDA-FS 

Florida Chassahowitzka Wilderness Area 23,360 USDI-FWS 

 Everglades NP 1,397,429 USDI-NPS 

St. Marks Wilderness Area 17,745 USDI-FWS 

Georgia Cohotta Wilderness Area 33,776 USDA-FS 

 Okefenokee Wilderness Area 343,850 USDI-FWS 

Wolf Island Wilderness Area 5,126 USDI-FWS 

Hawaii Haleakala NP 27,208 USDI-NPS 

Hawaii Volcanoes NP 217,029 USDI-NPS 

Idaho Craters of the Moon Wilderness Areaa 43,243 USDI-NPS 

Hells Canyon Wilderness Area 83,800 USDA-FS 

 Sawtooth Wilderness Area 216,383 USDA-FS 

Selway-Bitterroot Wilderness Areab 988,770 USDA-FS 

 Yellowstone NPc 31,488 USDI-NPS 

Kentucky Mammoth Cave NP 51,303 USDI-NPS 

Louisiana Breton Wilderness Area 5,000+ USDI-FWS 

Maine Acadia National Park 37,503 USDI-NPS 

 Moosehorn Wilderness Area 7,501 USDI-FWS 

 Edmunds Unit 2,706 USDI-FWS 

 Baring Unit 4,680 USDI-FWS 

Michigan Isle Royale NP 542,428 USDI-NPS 

Seney Wilderness Area 25,150 USDI-FWS 

3-163
 
Federal 

Land 

State Area Name Acreage Manager 

Minnesota Boundary Waters Canoe Area Wilderness 

Area 747,840 USDA-FS 

 Voyageurs NP 114,964 USDI-NPS 

Missouri Hercules-Glades Wilderness Area 12,315 USDA-FS 

Mingo Wilderness Area 8,000 USDI-FWS 

Montana Anaconda-Pintlar Wilderness Area 157,803 USDA-FS 

Bob Marshall Wilderness Area 950,000 USDA-FS 

Cabinet Mountains Wilderness Area 94,272 USDA-FS 

Gates of the Mtn Wilderness Area 28,562 USDA-FS 

 Glacier NP 1,012,599 USDI-NPS 

Medicine Lake Wilderness Area 11,366 USDI-FWS 

Mission Mountain Wilderness Area 73,877 USDA-FS 

Red Rock Lakes Wilderness Area 32,350 USDI-FWS 

Scapegoat Wilderness Area 239,295 USDA-FS 

Selway-Bitterroot Wilderness Aread 251,930 USDA-FS 

U. L. Bend Wilderness Area 20,890 USDI-FWS 

 Yellowstone NPe 167,624 USDI-NPS 

Nevada Jarbidge Wilderness Area 64,667 USDA-FS 

New Hampshire Great Gulf Wilderness Area 5,552 USDA-FS 

Presidential Range-Dry River Wilderness 

Area 20,000 USDA-FS 

New Jersey Brigantine Wilderness Area 6,603 USDI-FWS 

New Mexico Bandelier Wilderness Area 23,267 USDI-NPS 

Bosque del Apache Wilderness Area 80,850 USDI-FWS 

Carlsbad Caverns NP 46,435 USDI-NPS 

Gila Wilderness Area 433,690 USDA-FS 

Pecos Wilderness Area 167,416 USDA-FS 

Salt Creek Wilderness Area 8,500 USDI-FWS 

San Pedro Parks Wilderness Area 41,132 USDA-FS 

Wheeler Peak Wilderness Area 6,027 USDA-FS 

White Mountain Wilderness Area 31,171 USDA-FS 

North Carolina Great Smoky Mountains NPf 273,551 USDI-NPS 

Joyce Kilmer-Slickrock Wilderness Areag 10,201 USDA-FS 

Linville Gorge Wilderness Area 7,575 USDA-FS 

Shining Rock Wilderness Area 13,350 USDA-FS 

 Swanquarter Wilderness Area 9,000 USDI-FWS 

North Dakota Lostwood Wilderness 5,557 USDI-FWS 

Theodore Roosevelt NP 69,675 USDI-NPS 

Oklahoma Wichita Mountains Wilderness 8,900 USDI-FWS 

Oregon Crater Lake NP 160,290 USDA-NPS 

Diamond Peak Wilderness 36,637 USDA-FS 

Eagle Cap Wilderness 293,476 USDA-FS 

Gearhart Mountain Wilderness 18,709 USDA-FS 

Hells Canyon Wildernessa 108,900 USDA-FS 

22,700 USDI-BLM

 Kalmiopsis Wilderness 76,900 USDA-FS 

3-164
 
Federal 

Land 

State Area Name Acreage Manager 

Mountain Lakes Wilderness 23,071 USDA-FS 

 Mount Hood Wilderness 14,160 USDA-FS 

Mount Jefferson Wilderness 100,208 USDA-FS 

Mount Washington Wilderness 46,116 USDA-FS 

 Strawberry Mountain Wilderness 33,003 USDA-FS 

Three Sisters Wilderness 199,902 USDA-FS 

South Carolina Cape Romain Wilderness 28,000 USDI-FWS 

South Dakota Badlands Wilderness 64,250 USDI-NPS 

 Wind Cave NP 28,060 USDI-NPS 

Tennessee Great Smoky Mountains NPf 241,207 USDI-NPS 

 Joyce Kilmer-Slickrock Wildernessg 3,832 USDA-FS 

Texas Big Bend NP 708,118 USDI-NPS 

Guadalupe Mountains NP 76,292 USDI-NPS 

Utah Arches NP 65,098 USDI-NPS 

Bryce Canyon NP 35,832 USDI-NPS 

 Canyonlands NP 337,570 USDI-NPS 

Capitol Reef NP 221,896 USDI-NPS 

 Zion NP 142,462 USDI-NPS 

Vermont Lye Brook Wilderness 12,430 USDA-FS 

Virgin Islands Virgin Islands NP 12,295 USDI-NPS 

Virginia James River Face Wilderness 8,703 USDA-FS 

 Shenandoah NP 190,535 USDI-NPS 

Washington Alpine Lakes Wilderness 303,508 USDA-FS 

 Glacier Peak Wilderness 464,258 USDA-FS 

 Goat Rocks Wilderness 82,680 USDA-FS 

Mount Adams Wilderness 32,356 USDA-FS 

Mount Rainer NP 235,239 USDI-NPS 

 North Cascades NP 503,277 USDI-NPS 

 Olympic NP 892,578 USDI-NPS 

 Pasayten Wilderness 505,524 USDA-FS 

West Virginia Dolly Sods Wilderness 10,215 USDA-FS 

Otter Creek Wilderness 20,000 USDA-FS 

Wyoming Bridger Wilderness 392,160 USDA-FS 

 Fitzpatrick Wilderness 191,103 USDA-FS 

 Grand Teton NP 305,504 USDI-NPS 

North Absaroka Wilderness 351,104 USDA-FS 

 Teton Wilderness 557,311 USDA-FS 

 Washakie Wilderness 686,584 USDA-FS 

 Yellowstone NPh 2,020,625 USDI-NPS 

New Brunswick, 

Canada Roosevelt Campobello International Park 2,721 i 

* U.S. EPA (2001) Visibility in Mandatory Federal Class I Areas (1994-1998): A Report to Congress. 

EPA-452/R-01-008. This document is available in Docket EPA-HQ-OAR-2005-0036. 

3-165 
a) Hells Canyon Wilderness Area, 192,700 acres overall, of which 108,900 acres are in Oregon and 

83,800 acres are in Idaho. 

b) Selway Bitterroot Wilderness Area, 1,240,700 acres overall, of which 988,700 acres are in Idaho and 

251,930 acres are in Montana. 

c) Yellowstone National Park, 2,219,737 acres overall, of which 2,020,625 acres are in Wyoming, 

167,624 acres are in Montana, and 31,488 acres are in Idaho 

d) Selway-Bitterroot Wilderness Area, 1,240,700 acres overall, of which 988,770 acres are in Idaho and 

251,930 acres are in Montana. 

e) Yellowstone National Park, 2,219,737 acres overall, of which 2,020,625 acres are in Wyoming, 

167,624 acres are in Montana, and 31,488 acres are in Idaho. 

f) Great Smoky Mountains National Park, 514,758 acres overall, of which 273,551 acres are in North 

Carolina, and 241,207 acres are in Tennessee. 

g) Joyce Kilmer-Slickrock Wilderness Area, 14,033 acres overall, of which 10,201 acres are in North 

Carolina, and 3,832 acres are in Tennessee. 

h) Yellowstone National Park, 2,219,737 acres overall, of which 2,020,625 acres are in Wyoming, 

167,624 acres are in Montana, and 31,488 acres are in Idaho. 

i) Chairman, RCIP Commission. 

Abbreviations Used in Table: 

USDA-FS: U.S. Department of Agriculture, U.S. Forest Service 

USDI-BLM: U.S. Department of Interior, Bureau of Land Management 

USDI-FWS: U.S. Department of Interior, Fish and Wildlife Service 

USDI-NPS: U.S. Department of Interior, National Park Service 

3-166
 
Table 3D-2. Current (1998-2002) Visibility, Projected (2015) Visibility, and Natural 

Background Levels for the 20% Worst Days at 116 IMPROVE Sites 

Class I Area Name

a

 State 

1998-2002 Baseline 

Visibility 

(deciviews)b 

2015 CAIR Control 

Case Visibilityc 

(deciviews) 

Natural 

Background 

(deciviews) 

Acadia ME 22.7 21.0 11.5 

Agua Tibia CA 23.2 23.2 7.2 

Alpine Lakes WA 18.0 17.4 7.9 

Anaconda - Pintler MT 12.3 12.2 7.3 

Arches UT 12.0 12.1 7.0 

Badlands SD 17.3 16.8 7.3 

Bandelier NM 13.2 13.2 7.0 

Big Bend TX 18.4 18.3 6.9 

Black Canyon of the Gunnison CO 11.6 11.4 7.1 

Bob Marshall MT 14.2 14.0 7.4 

Boundary Waters Canoe Area MN 20.0 19.0 11.2 

Bridger WY 11.5 11.3 7.1 

Brigantine NJ 27.6 25.4 11.3 

Bryce Canyon UT 12.0 11.9 7.0 

Cabinet Mountains MT 13.8 13.4 7.4 

Caney Creek AR 25.9 24.1 11.3 

Canyonlands UT 12.0 12.0 7.0 

Cape Romain SC 25.9 23.9 11.4 

Caribou CA 14.8 14.6 7.3 

Carlsbad Caverns NM 17.6 17.9 7.0 

Chassahowitzka FL 25.7 23.0 11.5 

Chiricahua NM AZ 13.9 13.9 6.9 

Chiricahua W AZ 13.9 13.9 6.9 

Craters of the Moon ID 14.7 14.7 7.1 

Desolation CA 12.9 12.8 7.1 

Dolly Sods WV 27.6 23.9 11.3 

Dome Land CA 20.3 19.9 7.1 

Eagle Cap OR 19.6 19.0 7.3 

Eagles Nest CO 11.3 11.4 7.1 

Emigrant CA 17.6 17.4 7.1 

Everglades FL 20.3 19.2 11.2 

Fitzpatrick WY 11.5 11.3 7.1 

Flat Tops CO 11.3 11.4 7.1 

Galiuro AZ 13.9 14.1 6.9 

Gates of the Mountains MT 11.2 10.8 7.2 

Gila NM 13.5 13.5 7.0 

Glacier MT 19.5 19.1 7.6 

Glacier Peak WA 14.0 13.8 7.8 

Grand Teton WY 12.1 12.0 7.1 

Great Gulf NH 23.2 21.2 11.3 

Great Sand Dunes CO 13.1 13.0 7.1 

Great Smoky Mountains TN 29.5 26.1 11.4 

Guadalupe Mountains TX 17.6 17.5 7.0 

Hells Canyon OR 18.1 18.0 7.3 

Isle Royale MI 21.1 20.1 11.2 

3-167
 
Class I Area Name

a

 State 

1998-2002 Baseline 

Visibility 

(deciviews)b 

2015 CAIR Control 

Case Visibilityc 

(deciviews) 

Natural 

Background 

(deciviews) 

James River Face VA 28.5 25.1 11.2 

Jarbidge NV 12.6 12.8 7.1 

Joshua Tree CA 19.5 20.3 7.1 

Joyce Kilmer - Slickrock NC 29.5 26.1 11.5 

Kalmiopsis OR 14.8 14.4 7.7 

Kings Canyon CA 23.5 24.1 7.1 

La Garita CO 11.6 11.5 7.1 

Lassen Volcanic CA 14.8 14.6 7.3 

Lava Beds CA 16.6 16.5 7.5 

Linville Gorge NC 27.9 24.6 11.4 

Lostwood ND 19.6 18.7 7.3 

Lye Brook VT 23.9 21.1 11.3 

Mammoth Cave KY 30.2 27.0 11.5 

Marble Mountain CA 17.1 16.8 7.7 

Maroon Bells - Snowmass CO 11.3 11.3 7.1 

Mazatzal AZ 13.1 13.5 6.9 

Medicine Lake MT 17.7 17.1 7.3 

Mesa Verde CO 12.8 12.8 7.1 

Mingo MO 27.5 25.9 11.3 

Mission Mountains MT 14.2 14.0 7.4 

Mokelumne CA 12.9 12.8 7.1 

Moosehorn ME 21.4 20.3 11.4 

Mount Hood OR 14.0 13.7 7.8 

Mount Jefferson OR 15.7 15.2 7.8 

Mount Rainier WA 18.9 19.4 7.9 

Mount Washington OR 15.7 15.2 7.9 

Mount Zirkel CO 11.7 11.8 7.1 

North Cascades WA 14.0 14.0 7.8 

Okefenokee GA 26.4 24.7 11.5 

Otter Creek WV 27.6 24.0 11.3 

Pasayten WA 14.7 14.5 7.8 

Petrified Forest AZ 13.5 13.8 7.0 

Pine Mountain AZ 13.1 13.4 6.9 

Presidential Range - Dry NH 23.2 20.9 11.3 

Rawah CO 11.7 11.7 7.1 

Red Rock Lakes WY 12.1 12.1 7.1 

Redwood CA 16.5 16.5 7.8 

Rocky Mountain CO 14.1 14.1 7.1 

Roosevelt Campobello ME 21.4 20.1 11.4 

Salt Creek NM 17.7 17.3 7.0 

San Gorgonio CA 21.5 22.1 7.1 

San Jacinto CA 21.5 21.4 7.1 

San Pedro Parks NM 11.4 11.4 7.0 

Sawtooth ID 13.6 13.5 7.2 

Scapegoat MT 14.2 14.1 7.3 

Selway - Bitterroot MT 12.3 12.1 7.3 

Seney MI 23.8 22.6 11.4 

Sequoia CA 23.5 24.1 7.1 

Shenandoah VA 27.6 23.4 11.3 

3-168
 
Class I Area Name

a

 State 

1998-2002 Baseline 

Visibility 

(deciviews)b 

2015 CAIR Control 

Case Visibilityc 

(deciviews) 

Natural 

Background 

(deciviews) 

Sierra Ancha AZ 13.4 13.7 6.9 

Sipsey AL 28.7 26.1 11.4 

South Warner CA 16.6 16.5 7.3 

Strawberry Mountain OR 19.6 19.2 7.5 

Superstition AZ 14.7 15.0 6.9 

Swanquarter NC 24.6 21.9 11.2 

Sycamore Canyon AZ 16.1 16.6 7.0 

Teton WY 12.1 12.1 7.1 

Theodore Roosevelt ND 17.6 16.8 7.3 

Thousand Lakes CA 14.8 14.6 7.3 

Three Sisters OR 15.7 15.2 7.9 

UL Bend MT 14.7 14.1 7.2 

Upper Buffalo AR 25.5 24.3 11.3 

Voyageurs MN 18.4 17.6 11.1 

Weminuche CO 11.6 11.4 7.1 

West Elk CO 11.3 11.3 7.1 

Wind Cave SD 16.0 15.4 7.2 

Wolf Island GA 26.4 24.9 11.4 

Yellowstone WY 12.1 12.1 7.1 

Yolla Bolly - Middle Eel CA 17.1 16.9 7.4 

Yosemite CA 17.6 17.4 7.1 

Zion UT 13.5 13.3 7.0 

a) 116 IMPROVE sites represent 155 of the 156 Mandatory Class I Federal Areas. One isolated Mandatory Class I 

Federal Area (Bering Sea, an uninhabited and infrequently visited island 200 miles from the coast of Alaska), was 

considered to be so remote from electrical power and people that it would be impractical to collect routine aerosol 

samples. U.S. EPA (2003) guidance for Tracking Progress Under the Regional Haze Rule. EPA-454/B-03-004. 

This document is available in Docket EPA-HQ-OAR-2005-0036. 

b) The deciview metric describes perceived visual changes in a linear fashion over its entire range, analogous to the 

decibel scale for sound. A deciview of 0 represents pristine conditions. The higher the deciview value, the worse the 

visibility, and an improvement in visibility is a decrease in deciview value. 

c) The 2015 modeling projections are based on the Clear Air Interstate Rule analyses (EPA, 2005). 

3-169
 
References for Chapter 3 

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25 Zhu, Y.; Hinds, W.C.; Kim, S.; et al. (2002) Study of ultrafine particles near a major highway 

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27 Kittelson, D.B.; Watts, W.F.; Johnson, J.P. (2004) Nanoparticle emissions on Minnesota 

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29 Bunn, H.J.; Dinsdale, D.; Smith, T.; Grigg, J. (2001) Ultrafine particles in alveolar 

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30 Kittelson, D.B.; Watts, W.F.; Johnson, J.P. (2004) Nanoparticle emissions on Minnesota 

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31 Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P.M.; Sioutas, C. (2005) Mobile platform 

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32 Kittelson, D.B.; Watts, W.F.; Johnson, J.P. (2004) Nanoparticle emissions on Minnesota 

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33 Sanders, P.G.; Xu, N; Dalka, T.M.; Maricq, M.M. (2003) Airborne brake wear debris: size 

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34 Kamens, R.M.; Jang, M.; Lee, S.; et al. (2003) Secondary organic aerosol formation: some 

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35 Kupiainen, K.J.; Tervahattu, H; Raisanen, M.; et al. (2005) Size and composition of airborne 

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37 Hitchins, J.; Morawska, L.; Wolff, R.; Gilbert, D. (2000) Concentrations of submicrometre 

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38 Zhang, K.M.; Wexler, A.S. (2004) Evolution of particle number distribution near roadways - 

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39 Janssen, N.A.H.; van Vliet, P.H.N.; Aarts, F.; et al. (2001) Assessment of exposure to traffic 

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40 Roorda-Knape, M.C.; Janssen, N.A.H.; De Hartog, J.J.; et al. (1998) Air pollution from traffic 

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41 Kwon, J. (2005) Development of a RIOPA database and evaluation of the effect of proximity 

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42 Liu, W.; Zhang, J.; Kwon, J.; Weisel, C.; Turpin, B.; Zhang, L.; Korn, L.; Morandi, M.; Stock, 

T.; Colome, S. (2006) Concentrations and source characteristics of airborne carbonyl compounds 

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43 Skov, H.; Hansen, A.B.; Lorenzen, G.; et al. (2001) Benzene exposure and the effect of traffic 

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44 Jo, W.; Kim, K.; Park, K.; et al. (2003) Comparison of outdoor and indoor mobile sourcerelated volatile organic compounds between low- and high-floor apartments. Environ Res 92: 

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45 Fischer, P.H.; Joek, G.; van Reeuwijk, H.; et al. (2000) Traffic-related differences in outdoor 

and indoor concentrations of particle and volatile organic compounds in Amsterdam. Atmos 

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46 Ilgen, E.; Karfich, N.; Levsen, K.; et al. (2001) Aromatic hydrocarbons in the atmospheric 

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47 Rodes, C.; Sheldon, L.; Whitaker, D.; et al. (1998) Measuring concentrations of selected air 

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48 Sapkota, A.; Buckley, T.J. (2003) The mobile source effect on curbside 1,3-butadiene, 

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49 Sapkota, A.; Buckley, T.J. (2003) The mobile source effect on curbside 1,3-butadiene, 

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Waste Manage Assoc 53: 740-748. 

50 http://www.mde.state.md.us/Programs/AirPrograms/airData/. This document is available in 

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51 Ilgen, E.; Karfich, N.; Levsen, K.; et al. (2001) Aromatic hydrocarbons in the atmospheric 

environment: part I. Indoor versus outdoor sources, the influence of traffic. Atmos Environ 35: 

1235-1252. 

52 Hoek G.; Meliefste K.; Cyrys J.; et al. (2002) Spatial variability of fine particle concentrations 

in three European areas. Atmos. Environ. 36: 4077-4088. 

53 Etyemezian V.; Kuhns H.; Gillies J.; et al. (2003) Vehicle-based road dust emission 

measurement (III): effect of speed, traffic volume, location, and season on PM10 road dust 

emissions in the Treasure Valley, ID. Atmos. Environ. 37: 4583-4593. 

54 Harrison R.M.; Tilling R.; Romero M.S.C.; et al. (2003) A study of trace metals and 

polycyclic aromatic hydrocarbons in the roadside environment. Atmos. Environ. 37: 2391-2402. 

55 Zhang K.M.; Wexler A.S.; Zhu Y.F.; et al. (2004) Evolution of particle number distribution 

near roadways. Part II: the ‘Road-to-Ambient’ process. Atmos. Environ. 38: 6655-6665. 

56 Zhu Y.F.; Hinds W.C.; Shen S.; Sioutas C. (2004) Seasonal trends of concentration and size 

distribution of ultrafine particles near major highways in Los Angeles. Aerosol Sci. Technol. 38: 

5-13. 

57 Zhu, Y.; Hinds, W.C.; Kim, S.; et al. (2002) Study of ultrafine particles near a major highway 

with heavy-duty diesel traffic. Atmos Environ 36: 4323-4335. 

58 Zhu, Y.; Hinds, W.C.; Kim, S.; Sioutas, C. (2002) Concentration and size distribution of 

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3-173
 
59 Riediker, M.; Williams, R.; Devlin, R.; et al. (2003) Exposure to particulate matter, volatile 

organic compounds, and other air pollutants inside patrol cars. Environ Sci Technol 37: 2084­

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60 Zielinska, B.; Fujita, E.M.; Sagebiel, J.C.; et al. (2002) Interim data report for Section 211(B) 

Tier 2 high end exposure screening study of baseline and oxygenated gasoline. Prepared for 

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61 Rodes, C.; Sheldon, L.; Whitaker, D.; et al. (1998) Measuring concentrations of selected air 

pollutants inside California vehicles. Final report to California Air Resources Board. Contract 

No. 95-339. 

62 Fitz, D. R.; Winer, A. M.; Colome, S.; et al. (2003) Characterizing the Range of Children’s 

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63 Sabin, L.D.; Behrentz, E.; Winer, A.M.; et al. (2005) Characterizing the range of children’s air 

pollutant exposure during school bus commutes. J Expos Anal Environ Epidemiol 15: 377-387. 

64 Behrentz, E.; Sabin, L.D.; Winer, A.M.; et al. (2005) Relative importance of school busrelated microenvironments to children’s pollutant exposure. J Air & Waste Manage Assoc 55: 

1418-1430. 

65 Batterman, S.A.; Peng, C.Y.; and Braun, J. (2002) Levels and composition of volatile organic 

compounds on commuting routes in Detroit, Michigan. Atmos Environ 36: 6015-6030. 

66 Fruin, S.A.; Winer, A.M.; Rodes, C.E. (2004) Black carbon concentrations in California 

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38: 4123-4133. 

67 Adams, H.S.; Nieuwenhuijsen, M.J.; Colvile, R.N. (2001) Determinants of fine particle 

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35: 4557-4566. 

68 Leung, P.-L.; Harrison, R.M. (1999) Roadside and in-vehicle concentrations of monoaromatic 

hydrocarbons. Atmos Environ 33: 191-204. 

69 Weinhold, B. (2001) Pollutants lurk inside vehicles. Environ Health Perspec 109 (9): A422­

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70 Riediker, M.; Williams, R.; Devlin, R.; et al. (2003) Exposure to particulate matter, volatile 

organic compounds, and other air pollutants inside patrol cars. Environ Sci Technol 37: 2084­

2093. 

71 Van Wijnen J.H.; Verhoeff A.P.; Jans H.W.A.; Van Bruggen M. (1995) The exposure of 

cyclists, car drivers and pedestrians to traffic-related air pollutants. Int Arch Occup Environ 

Health 67: 187-193. 

72 Chan C.-C.; Ozkaynak H.; Spengler J.D.; Sheldon L. (1991) Driver Exposure to Volatile 

Organic Compounds, CO, Ozone, and NO2 under Different Driving Conditions. Environ. Sci. 

Technol. 25: 964-972. 

3-174
 
73 Shikiya, D.C., C.S. Liu, M.I. Kahn, et al. (1989) In-vehicle air toxics characterization study 

in the south coast air basin. South Coast Air Quality Management District, El Monte, CA. May, 

1989. This document is available in Docket EPA-HQ-OAR-2005-0036. 

74 Chan C.-C., Spengler J. D., Ozkaynak H., and Lefkopoulou M. (1991) Commuter Exposures to 

VOCs in Boston, Massachusetts. J. Air Waste Manage. Assoc. 41, 1594-1600. 

75 U.S. EPA (2000) Development of microenvironmental factors for the HAPEM4 in support of 

the National Air Toxics Assessment (NATA). External Review Draft Report Prepared by ICF 

Consulting and TRJ Environmental, Inc. for the U.S. EPA, Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. This document is available in Docket EPA-HQ-OAR2005-0036. 

76 U.S. EPA (2000) Determination of microenvironmental factors for diesel PM. An addendum 

to: Development of microenvironmental factors for the HAPEM4 in support of the National Air 

Toxics Assessment (NATA). External Review Draft Report Prepared by ICF Consulting and 

TRJ Environmental, Inc. for the U.S. EPA, Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. This document is available in Docket EPA-HQ-OAR-2005-0036. 

77 Personal communication with FACES Investigators Fred Lurmann, Paul Roberts, and 

Katharine Hammond. Data is currently being prepared for publication. 

78 Kim, J.J.; Smorodinsky, S.; Lipsett, M.; et al. (2004) Traffic-related air pollution near busy 

roads. Am J Respir Crit Care Med 170: 520-526. 

79 Janssen, N.A.H.; van Vliet, P.H.N.; Aarts, F.; et al. (2001) Assessment of exposure to traffic 

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3884. 

80 Roorda-Knape, M.C.; Janssen, N.A.H.; De Hartog, J.J.; et al. (1998) Air pollution from traffic 

in city districts near major motorways. Atmos Environ 32: 1921-1930. 

81 Van Roosbroeck, S.; Wichmann, J.; Janssen, N.A.H.; Hoek, G.; van Wijnen, J.H; Lebret, E.; 

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children, a validation study. Sci Total Environ 368: 565-573. 

82 Kinney, P.L.; Chillrud, S.N.; Ramstrom, S.; et al. (2002) Exposures to multiple air toxics in 

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83 Chatzis, C.; Alexopoulos, E.C.; and Linos, A. (2005) Indoor and outdoor personal exposure to 

benzene in Athens, Greece. Sci Total Environ 349: 72-80. 

84 Gulliver, J.; Briggs, D.J. (2004) Personal exposure to particulate air pollution in transport 

microenvironments. Atmos Environ 38: 1-8. 

85 Van Wijnen, J.H.; Verhoeff, A.P.; Jans, H.W.A.; Van Bruggen, M. (1995) The exposure of 

cyclists, car drivers and pedestrians to traffic-related air pollutants. Int Arch Occup Environ 

Health 67: 187-193. 

86 Chan, C.-C.; Ozkaynak, H.; Spengler, J.D.; Sheldon, L. (1991) Driver Exposure to Volatile 

Organic Compounds, CO, Ozone, and NO2 under Different Driving Conditions. Environ. Sci. 

Technol. 25: 964-972. 

3-175
 
87 Chan C.-C., Spengler J. D., Ozkaynak H., and Lefkopoulou M. (1991) Commuter Exposures 

to VOCs in Boston, Massachusetts. J. Air Waste Manage. Assoc. 41, 1594-1600. 

88 Duci, A.; Chaloulakou, A.; Spyrellis N. (2003) Exposure to carbon monoxide in the Athens 

urban area during commuting. Sci Total Environ 309: 47-58. 

89 Gulliver, J.; Briggs, D.J. (2004) Personal exposure to particulate air pollution in transport 

microenvironments. Atmos Environ 38: 1-8. 

90 Ashmore, M.R.; Batty, K.; Machin, F.; et al. (2000) Effects of traffic management and 

transport mode on the exposure of schoolchildren to carbon monoxide. Environ Monitoring and 

Assessment 65: 49-57. 

91 U.S. EPA (1997) Exposure factors handbook. This document is available in Docket EPA-HQOAR-2005-0036. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464. 

92 Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: 

simultaneous measurement of ventilation, benzene, and toluene in residential indoor air of two 

homes. Sci Total Environ 345: 31-40. 

93 Batterman, S.; Jia, C.; Hatzivasilis, G.; Godwin, C. (2006) Simultaneous measurement of 

ventilation using tracer gas techniques and VOC concentrations in homes, garages and vehicles. 

J Environ Monit 8: 249-256. 

94 Morris, S.S. “Influence of attached garages on indoor VOC concentrations in Anchorage 

homes.” Presented at the annual meeting of the Air & Waste Management Association’s Pacific 

Northwest Section, November 4, 2004. This document is available in Docket EPA-HQ-OAR2005-0036. 

95 Sheltersource, Inc. (2002) Evaluating Minnesota homes. Final report. Prepared for Minnesota 

Department of Commerce. U.S. Department of Energy grant DE-FG45-96R530335. This 

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96 Fugler, D.; Grande, C.; Graham, L. (2002) Attached garages are likely path for pollutants. 

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97 Emmerich, S.J.; Gorfain, J.E.; Howard-Reed, C. (2003) Air and pollutant transport from 

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98 Isbell, M.; Gordian, M.E.; Duffy, L. (2002) Winter indoor air pollution in Alaska: identifying 

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99 U.S. EPA (1987) The Total Exposure Assessment Methodology (TEAM) Study: Summary 

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100 Wallace, L. (1996) Environmental exposure to benzene: an update. Environ Health Perspect 

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101 Thomas, K. W.; Pellizzari, E. D.; Clayton, C. A.; Perrit, R.; Dietz, R. N.; Goorich, R. W.; 

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102 Batterman, S.; Hatzivasilis, G.; Jia, C. (2005) Concentrations and emissions of gasoline and 

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103 George, M.; Kaluza, P.; Maxwell, B.; et al. (2002) Indoor air quality & ventilation strategies 

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104 Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: 

simultaneous measurement of ventilation, benzene and toluene in residential indoor air of two 

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105 Graham, L.A.; Noseworthy, L.; Fugler, D.; et al. (2004) Contribution of vehicle emissions 

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106 Schlapia, A.; Morris, S.S. (1998) Architectural, behavioral, and environmental factors 

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107 Isbell, M.; Ricker, J.; Gordian, M.E.; Duffy, L.K. (1999) Use of biomarkers in an indoor air 

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108 Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: 

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109 Gordon, S.M.; Callahan, P.J.; Nishioka, M.G.; et al. (1999) Residential environmental 

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110 Bonanno, L.J.; Freeman, N.C.G.; Greenberg, M.; Lioy, P.J. (2001) Multivariate analysis on 

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111 Tsai, P.; Weisel, C.P. (2000) Penetration of evaporative emissions into a home from an M85­

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112 Wilson, A.L.; Colome, S.D.; and Tian, Y. (1991) Air toxics microenvironment exposure and 

monitoring study. Final Report. Prepared for South Coast Air Quality Management District and 

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113 Zielinska, B.; Fujita, E.M.; Sagebiel, J.C.; et al. (2002) Interim data report for Section 211(B) 

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114 Lee, S.C.; Chan, L.Y.; and Chiu, M.Y. (1999) Indoor and outdoor air quality investigation at 

14 public places in Hong Kong. Environ Int 25: 443-450. 

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115 Wong, Y.-c.; Sin, D.W.-m.; and Yeung L.L. (2002) Assessment of the air quality in indoor 

car parks. Indoor & Built Environment 11: 134-145. 

116 Chaloulakou, A.; Duci, A.; Spyrellis, N. (2002) Exposure to carbon monoxide in enclosed 

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117 Srivastava, A.; Joseph, A.E.; and Nair, S. (2004) Ambient levels of benzene in Mumbai city. 

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118 Schwar, M.; Booker, J.; Tait, L. (1997) Car Park Air Pollution Exposure of Operatives and 

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119 Morillo, P.; Dos Santos, S.G.; Santamaria, J.; et al. (1998) A study of the atmospheric 

pollution produced by vehicles in car parks in Madrid, Spain. Indoor Built Environ 7: 156-164. 

120 Wilson, A.L.; Colome, S.D.; and Tian, Y. (1991) Air toxics microenvironment exposure and 

monitoring study, Final Report. Prepared for South Coast Air Quality Management District and 

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121 Hartle, R. (1993) Exposure to methyl tert-butyl ether and benzene among service station 

attendants and operators. Environ Health Perspect Supplements: 101 (Suppl. 6): 23-26. 

122 Northeast States for Coordinated Air Use Management (1999) RFG/MTBE Findings and 

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123 Vayghani, S.A.; Weisel, C. (1999) The MTBE air concentrations in the cabin of automobiles 

while fueling. J Expos Analysis Environ Epidem 9: 261-267. 

124 Egeghy, P.P.; Tornero-Velez, R.T.; Rappaport, S.M. (2000) Environmental and biological 

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125 Verma, D.K.; Johnson, D.M.; Shaw, M.L.; et al. (2001) Benzene and total hydrocarbon 

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126 Verma, D.K. and des Tombe, K. (2002) Benzene in gasoline and crude oil: occupational and 

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127 Baldauf, R.; Fortune,C.; Weinstein. J.; et al. (2005) Air contaminant exposures during the 

operation of lawn and garden equipment. J Expos Anal Environ Epidemiol 16: 362-370 

128 Eriksson, K.; Tjarner, D.; Marqvardsen, I.; et al. (2003) Exposure to benzene, toluene, 

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129 Kado, N.Y.; Kuzmicky, P.A.; and Okamoto, R.A. (2001) Environmental and occupational 

exposure to toxic air pollutants from winter snowmobile use in Yellowstone National Park. 

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130 NESCAUM (2003) Evaluating the occupational and environmental impact of nonroad diesel 

equipment in the Northeast. Interim Report June 9, 2003. This document is available in Docket 

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131 Davis, M.E.; Smith, T.J.; Laden, F.; Hart, J.E.; Ryan, L.M.; Garshick, E. (2006) Modeling 

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132 Atkinson, R.; Arey, J.; Hoover, S.; Preston, K. (2005) Atmospheric Chemistry of GasolineRelated Emissions: Formation of Pollutants of Potential Concern. Draft Report Prepared for 

California Environmental Protection Agency. September 2005. This document is available in 

Docket EPA-HQ-OAR-2005-0036. 

133 U. S. EPA (2006) National-Scale Air Toxics Assessment for 1999. 

http://www.epa.gov/ttn/atw/nata1999. 

134 U.S. EPA (2000) User’s Guide for the Assessment System for Population Exposure 

Nationwide (ASPEN, Version 1.1) Model. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, Report No. EPA-454/R-00-017. This document is available in 

Docket EPA-HQ-OAR-2005-0036. http://www.epa.gov/scram001/userg/other/aspenug.pdf 

135 Rosenbaum, A. (2005) The HAPEM5 User’s Guide: Hazardous Air Pollutant Exposure 

Model, Version 5. Prepared by ICF, Inc. for U. S. EPA. This document is available in Docket 

EPA-HQ-OAR-2005-0036. http://www.epa.gov/ttn/fera/hapem5/hapem5_guide.pdf. 

136 U. S. EPA (2005) Risk - Air Toxics Risk Assessment. 

http://www.epa.gov/ttn/fera/risk_atoxic.html. 

137 U. S. EPA (1993) Motor Vehicle-Related Air Toxics Study. Report No. EPA420-R-93-005. 

http://www.epa.gov/otaq/regs/toxics/tox_archive.htm#2. 

138 U. S. EPA (2000) Technical Support Document: Control of Hazardous Air Pollutants from 

Motor Vehicles and Motor Vehicle Fuels. Office of Transportation and Air Quality. Report No. 

EPA-420-R-00-023. http://www.epa.gov/otaq/toxics.htm and at 

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139 Cook, R., Jones, B., Cleland, J. (2004) A Cohort Based Approach for Characterizing Lifetime 

Inhalation Cancer Risk from Time-Varying Exposure to Air Toxics from Ambient Sources. 

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140 Cook, R., Strum, M., Touma, J., et al. 2002. Trends in Mobile Source-Related Ambient 

Concentrations of Hazardous Air Pollutants, 1996 – 2007. SAE Paper No. 2002-01-1274. This 

document is available in Docket EPA-HQ-OAR-2005-0036. 

141 Cook, R., Strum, M., Touma, J., Palma, T., Thurman, J., Ensley, D., Smith, R. 2006. 

Inhalation Exposure and Risk from Mobile Source Air Toxics in Future Years. Journal of 

Exposure Science and Environmental Epidemiology, in press. [Advance online publication, 27 

September 2006; doi:10.1038/sj.jes. 7500529, 

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142 U.S. EPA. 2007. The HAPEM6 User’s Guide. Prepared for Ted Palma, Office of Air 

Quality Planning and Standards, Research Triangle Park, NC, by Arlene Rosenbaum and 

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Michael Huang, ICF International, January 2007. 

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143 Byun, D. W., Ching, J. K. S. 1999. Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System. U. S. EPA, Office of Research and 

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Docket EPA-HQ-OAR-2005-0036. 

144 Luecken, D. J., Hutzell, W. T., Gipson, G. J. 2005. Development and Analysis of air quality 

modeling simulations for hazardous air pollutants. Atmospheric Environment. 40: 5087-5096. 

145 Seigneur, C., Pun, B., Lohman, K., Wu, S.-Y. 2003. Regional modeling of the atmospheric 

fate and transport of benzene and diesel particles. Environ. Sci. Technol. 37: 5236-5246. 

146 U. S. EPA. 2004. User’s Guide for the Emissions Modeling System for Hazardous Air 

Pollutants (EMS-HAP, Version 3.0), Office of Air Quality Planning and Standards, Research 

Triangle Park, NC, Report No. EPA-454/B-00-007. This document is available in Docket EPAHQ-OAR-2005-0036. http://www.epa.gov/scram001/userg/other/emshapv3ug.pdf. 

147 Battelle. 2003. Estimated background concentrations for the National-Scale Air Toxics 

Assessment. Prepared for U. S. EPA, Office of Air Quality Planning and Standards. Contract 

No. 68-D-02-061. Work Assignment 1-03. This document is available in Docket EPA-HQ-OAR2005-0036. 

148 U. S. EPA. 1993. Motor Vehicle-Related Air Toxics Study. Office of Mobile Sources, Ann 

Arbor, MI. Report No. EPA 420-R-93-005. This document is available in Docket EPA-HQOAR-2005-0036. http://www.epa.gov/otaq/regs/toxics/tox_archive.htm 

149 U. S. EPA. 1999. Analysis of the Impacts of Control Programs on Motor Vehicle Toxics 

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150 U. S. EPA. 2002. 1996 National-Scale Air Toxics Assessment. 

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151 Glen, G., Lakkadi, Y., Tippett, J. A., del Valle-Torres M. 1997. Development of 

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152 Long, T,; Johnson, T. ; Laurensen, J.; Rosenbaum, A. 2004. Development of Penetration and 

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153 U.S. EPA. 2007. The HAPEM6 User’s Guide. Prepared for Ted Palma, Office of Air 

Quality Planning and Standards, Research Triangle Park, NC, by Arlene Rosenbaum and 

Michael Huang, ICF International, January 2007. 

http://www.epa.gov/ttn/fera/human_hapem.html. This document is available in Docket EPAHQ-OAR-2005-0036. 

154 Cohen, J.; Cook, R.; Bailey, C.R.; Carr, E. (2005) Relationship between motor vehicle 

emissions of hazardous pollutants, roadway proximity, and ambient concentrations in Portland, 

Oregon. Environ Modelling & Software 20: 7-12. 

155 Pratt, G. C.; Wu, C. Y.;Bock, D.; et al. (2004) Comparing air dispersion model predictions 

with measured concentrations of VOCs in urban communities. Environ. Sci. Technol. 38: 1949­

1959. 

156 U.S. EPA. 2007. The HAPEM6 User’s Guide. Prepared for Ted Palma, Office of Air 

Quality Planning and Standards, Research Triangle Park, NC, by Arlene Rosenbaum and 

Michael Huang, ICF International, January 2007. 

http://www.epa.gov/ttn/fera/human_hapem.html. This document is available in Docket EPAHQ-OAR-2005-0036. 

157 U. S. EPA. 2001. National-Scale Air Toxics Assessment for 1996: Draft for EPA Science 

Advisory Board Review. Report No. EPA-453/R-01-003 This document is available in Docket 

EPA-HQ-OAR-2005-0036. http://www.epa.gov/ttn/atw/sab/natareport.pdf 

158 U. S. EPA. 2001. National-Scale Air Toxics Assessment for 1996: Draft for EPA Science 

Advisory Board Review. Report No. EPA-453/R-01-003 This document is available in Docket 

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159 Taylor, M. Memorandum: Revised HAP Emission Factors for Stationary Combustion 

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160 U. S. EPA. 2004. Benefits of the Proposed Inter-State Air Quality Rule. Office of Air 

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161 U. S. EPA (2003) Estimated Background Concentrations for the National-Scale Air Toxics 

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162 U. S. EPA (2005) Supplemental Guidance for Assessing Susceptibility from Early-Life 

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163 U. S. EPA. 2006. National Scale Modeling of Air Toxics for the Mobile Source Air Toxics 

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164 Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics 

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166 U. S. EPA. (2004) Benefits of the Proposed Inter-State Air Quality Rule. Office of Air 

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170 Kinnee, E.J.; Touma, J.S.; Mason, R.; Thurman, J.; Beidler, A.; Bailey, C.; Cook, R. (2004) 

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172 Touma, J. S.; Isakov, V.; Ching, J.; Seigneur, C. (2006). Air Quality Modeling of Hazardous 

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207 U.S. EPA. 2005. Technical Support Document for the Proposed Mobile Source Air Toxics 

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208 U.S. EPA. 2005. Technical Support Document for the Final Clean Air Interstate Rule - Air 

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209 U.S. EPA. 2005. Clean Air Interstate Rule Emissions Inventory Technical Support 

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210 U.S. EPA. 2005. Technical Support Document for the Proposed Mobile Source Air Toxics 

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211 Pielke, R.A., W.R. Cotton, R.L. Walko, et al. 1992. “A Comprehensive Meteorological 

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213 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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214 Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and 

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215 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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216 Tingey, D.T., and Taylor, G.E. 1982. “Variation in plant response to ozone: a conceptual 

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217 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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218 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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219 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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220 Ollinger, S.V., J.D. Aber and P.B. Reich. 1997. “Simulating ozone effects on forest 

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221 Winner, W.E., 1994. “Mechanistic analysis of plant responses to air pollution.” Ecological 

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222 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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223 U U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

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224 Fox, S., and R. A. Mickler, eds. 1996. Impact of Air Pollutants on Southern Pine Forests. 

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225 National Acid Precipitation Assessment Program (NAPAP), 1991. National Acid 

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227 Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of 

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228 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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229 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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230 McBride, J.R., P.R. Miller, and R.D. Laven. 1985. “Effects of oxidant air pollutants on forest 

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232 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. 

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233 Kopp, R. J.; Vaughn, W. J.; Hazilla, M.; Carson, R. 1985. “Implications of environmental 

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236 Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential 

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237 U.S. EPA. 2004. Air Quality Criteria for Particulate Matter. EPA/600/P-99/002aF and 

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238 U.S. EPA. 2005. Review of the National Ambient Air Quality Standard for Particulate Matter: 

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239 U.S. EPA 2006. Provisional Assessment of Recent Studies on Health Effects of Particulate 

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240 Laden F; Neas LM; Dockery DW; et al. 2000. “Association of fine particulate matter from 

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242 Janssen NA; Schwartz J; Zanobetti A.; et al. 2002. “Air conditioning and source-specific 

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243 Dockery, DW; Pope, CA, III; Xu, X; et al. 1993. “An association between air pollution and 

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244 Pope, CA, III; Burnett, RT; Thun, MJ; Calle, EE; et al. 2002. “Lung cancer, cardiopulmonary 

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245 Krewski, D; Burnett, RT; Goldberg, M S; et al. 2000. “Reanalysis of the Harvard Six Cities 

study and the American Cancer Society study of particulate air pollution and mortality. A special 

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246 Jerrett, M; Burnett, RT; Ma, R; et al. 2005. “Spatial analysis of air pollution and mortality in 

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247 Künzli, N.; Jerrett, M.; Mack, W.J.; et al. 2005. Ambient air pollution and atherosclerosis in 

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248 Riediker, M.; Cascio, W.E.; Griggs, T.R.; et al. 2004. “Particulate matter exposure in cars is 

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249 Van Vliet, P.; Knape, M.; de Hartog, J.; Janssen, N.; Harssema, H.; Brunekreef, B. (1997). 

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250 Brunekreef, B., Janssen, N.A.H.; de Hartog, J.; Harssema, H.; Knape, M.; van Vliet, P. 

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251 Kim, J.J.; Smorodinsky, S.; Lipsett, M.; Singer, B.C.; Hodgson, A.T.; Ostro, B (2004). 

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252 U.S. EPA. 2006. Regulatory Impact Analysis for the final PM NAAQS. This document is 

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253 National Research Council, 1993. Protecting Visibility in National Parks and Wilderness 

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Docket EPA-HQ-OAR-2005-0036. 

254 U.S. EPA (2004) Air Quality Criteria for Particulate Matter (Oct 2004), Volume I Document 

No. EPA600/P-99/002aF and Volume II Document No. EPA600/P-99/002bF. This document is 

available in Docket EPA-HQ-OAR-2005-0036. 

255 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate 

Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. EPA452/R-05-005. This document is available in Docket EPA-HQ-OAR-2005-0036. 

256 U.S. EPA. 2005. Review of the National Ambient Air Quality Standard for Particulate Matter: 

Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. EPA-452/R05-005. This document is available in Docket EPA-HQ-OAR-2005-0036. 

3-188
 
257 U.S. EPA. 1993. Effects of the 1990 Clean Air Act Amendments on Visibility in Class I 

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258 U.S. EPA (2002) Latest Findings on National Air Quality – 2002 Status and Trends. EPA 

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259 National Park Service. Air Quality in the National Parks, Second edition. NPS, Air 

Resources Division. D 2266. September 2002. This document is available in Docket EPA-HQOAR-2005-0036. 

260 U.S. EPA (2002) Latest Findings on National Air Quality – 2002 Status and Trends. EPA 

454/K-03-001. This document is available in Docket EPA-HQ-OAR-2005-0036. 

261 U.S. EPA (2005). Technical Support Document for the Final Clean Air Interstate Rule - Air 

Quality Modeling. This document is available in Docket EPA-HQ-OAR-2005-0036. 

262 U.S. EPA (2000) Deposition of Air Pollutants to the Great Waters: Third Report to Congress. 

Office of Air Quality Planning and Standards. EPA-453/R-00-0005. 

263 U.S. EPA (2004) National Coastal Condition Report II. Office of Research and Development/ 

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264 Gao, Y., E.D. Nelson, M.P. Field, et al. 2002. Characterization of atmospheric trace 

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265 Kim, G., N. Hussain, J.R. Scudlark, and T.M. Church. 2000. Factors influencing the 

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266 Lu, R., R.P. Turco, K. Stolzenbach, et al. 2003. Dry deposition of airborne trace metals on 

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267 Marvin, C.H., M.N. Charlton, E.J. Reiner, et al. 2002. Surficial sediment contamination in 

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268 Smith, W.H. 1991. Air pollution and forest damage.” Chemical Engineering News, 69(45): 

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269 Gawel, J.E.; Ahner, B.A.; Friedland, A.J.; and Morel, F.M.M. 1996. Role for heavy metals in 

forest decline indicated by phytochelatin measurements. Nature, 381: 64-65. 

270 Cotrufo, M.F.; DeSanto, A.V.; Alfani, A.; et al. 1995. Effects of urban heavy metal pollution 

on organic matter decomposition in Quercus ilix L. woods. Environmental Pollution, 89: 81-87. 

271 Niklinska, M.; Laskowski, R.; Maryanski, M. 1998. Effect of heavy metals and storage time 

on two types of forest litter: basal respiration rate and exchangeable metals. Ecotoxicological 

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272 Mason, R.P. and Sullivan, K.A. 1997. Mercury in Lake Michigan. Environmental Science & 

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3-189
 
273 Landis, M.S. and Keeler, G.J. 2002. Atmospheric mercury deposition to Lake Michigan 

during the Lake Michigan Mass Balance Study. Environmental Science & Technology, 21: 4518­

24. 

274 U.S. EPA. 2000. EPA453/R-00-005, “Deposition of Air Pollutants to the Great Waters: Third 

Report to Congress,” Office of Air Quality Planning and Standards, Research Triangle Park, 

North Carolina. This document is available in Docket EPA-HQ-OAR-2005-0036. 

275 Callender, E. and Rice, K.C. 2000. The urban environmental gradient: Anthropogenic 

influences on the spatial and temporal distributions of lead and zinc in sediments. Environmental 

Science & Technology, 34: 232-238. 

276 Rice, K.C. 1999. Trace element concentrations in streambed sediment across the 

conterminous United States. Environmental Science & Technology, 33: 2499-2504. 

277 Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. Implications of platinum-group element 

accumulation along U.S. roads from catalytic-converter attrition. Environ. Sci. Technol. 35: 

3816-3822. 

278 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources 

of Polycyclic Organic Matter,” Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. This document is available in Docket EPA-HQ-OAR-2005-0036. 

279 U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estimating Air Emissions from Sources 

of Polycyclic Organic Matter,” Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina. This document is available in Docket EPA-HQ-OAR-2005-0036. 

280 Simcik, M.F.; Eisenreich, S.J.; Golden, K.A.; et al. 1996. Atmospheric loading of polycyclic 

aromatic hydrocarbons to Lake Michigan as recorded in the sediments.” Environmental Science 

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281 Simcik, M.F.; Eisenreich, S.J.; and Lioy, P.J. 1999. Source apportionment and source/sink 

relationship of PAHs in the coastal atmosphere of Chicago and Lake Michigan. Atmospheric 

Environment, 33: 5071-5079. 

282 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. Fate of atmospherically deposited 

polycyclic aromatic hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & 

Technology, 35, 2178-2183. 

283 Park, J.S.; Wade, T.L.; and Sweet, S. 2001. Atmospheric distribution of polycyclic aromatic 

hydrocarbons and deposition to Galveston Bay, Texas, USA. Atmospheric Environment, 35: 

3241-3249. 

284 Poor, N.; Tremblay, R.; Kay, H.; et al. 2002. Atmospheric concentrations and dry deposition 

rates of polycyclic aromatic hydrocarbons (PAHs) for Tampa Bay, Florida, USA. Atmospheric 

Environment 38: 6005-6015. 

285 Arzayus, K.M.; Dickhut, R.M.; and Canuel, E.A. 2001. Fate of atmospherically deposited 

polycyclic aromatic hydrocarbons (PAHs) in Chesapeake Bay. Environmental Science & 

Technology, 35, 2178-2183. 

3-190
 
286 U.S. EPA. 2000. EPA453/R-00-005, “Deposition of Air Pollutants to the Great Waters: Third 

Report to Congress,” Office of Air Quality Planning and Standards, Research Triangle Park, 

North Carolina. This document is available in Docket EPA-HQ-OAR-2005-0036. 

287 Van Metre, P.C.; Mahler, B.J.; and Furlong, E.T. 2000. Urban sprawl leaves its PAH 

signature.” Environmental Science & Technology, 34: 4064-4070. 

288 Cousins, I.T.; Beck, A.J.; and Jones, K.C. 1999. A review of the processes involved in the 

exchange of semi-volatile organic compounds across the air-soil interface. The Science of the 

Total Environment, 228: 5-24. 

289 Tuhackova, J., Cajthaml, T.; Novak, K.; et al. 2001. Hydrocarbon deposition and soil 

microflora as affected by highway traffic. Environmental Pollution, 113: 255-262. 

290 U.S. EPA (2005) Review of the National Ambient Air Quality Standard for Particulate 

Matter: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. EPA452/R-05-005. This document is available in Docket EPA-HQ-OAR-2005-0036. 

291 Hoek, G.; Brunekreef, B.; Goldbohm, S.; et al. (2002) Association between mortality and 

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292 Finkelstein, M.M.; Jerrett, M.; Sears, M.R. (2004) Traffic air pollution and mortality rate 

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293 Finkelstein, M.M.; Jerrett, M.; Sears, M.R. (2005) Environmental inequality and circulatory 

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294 Hoek, G.; Brunekreef, B.; Goldbohm, S.; et al. (2002) Association between mortality and 

indicators of traffic-related air pollution in the Netherlands: a cohort study. Lancet 360: 1203­

1209. 

295 Gehring, U.; Heinrich, J.; Kramer, U.; Grote, V.; Hochadel, M.; Dugiri, D.; Kraft, M.; 

Rauchfuss, K.; Eberwein, H.G.; Wichmann, H-E. (2006) Long-term exposure to ambient air 

pollution and cardiopulmonary mortality in women. Epidemiology 17: 545-551. 

296 Lipfert, F.W.; Baty, J.D.; Miller, J.P.; Wyzga, R.E. PM2.5 constitutents and related air 

quality variables as predictors of survival in a cohort of U.S. military veterans. Inhalation 

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297 Maheswaran, M.; Elliott, P. (2003) Stroke mortality associated with living near main roads in 

England and Wales. A geographical study. Stroke 34: 2776-2780. 

298 Roemer, W.H.; van Wijnen, J.H. (2001) Daily mortality and air pollution along busy streets in 

Amsterdam, 1987-1998. Epidemiology 12: 649-653. 

299 Heinrich, J.; Wichmann, H-E. (2004) Traffic related pollutants in Europe and their effect on 

allergic disease. Current Opin. Clinical Epidemiol. 4: 341-348. 

300 Ryan, P.H.; LeMasters, G.; Biagnini, J.; et al. (2005) Is it traffic type, volume, or distance? 

Wheezing in infants living near truck and bus traffic. J. Allergy Clin. Immunol. 116: 279-284. 

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301 Kim, J.J.; Smorodinsky, S.; Lipsett, M.; et al. (2004) Traffic-related air pollution near busy 

roads: the East Bay Children's Respiratory Health Study. Am. J. Respir. Crit. Care Med. 170: 

520-526. 

302 Lin, S.; Munsie, J.P.; Hwang, S-A.; et al. (2002) Childhood asthma hospitalization and 

residential exposure to state route traffic. Environ. Res. 88: 73-81. 

303 English, P.; Neutra, R.; Scalf, R.; et al. (1999) Examining associations between childhood 

asthma and traffic flow using a geographic information system. Environ. Health Perspect. 107: 

761-768. 

304 Garshick, E.; Laden, F.; Hart, J.E.; Caron, A. (2003) Residence near a major road and 

respiratory symptoms in U.S. veterans. Epidemiology 14: 728-736. 

305 Heinrich, J.; Wichmann, H-E. (2004) Traffic related pollutant in Europe and their effect on 

allergic disease. Current Opin. Clinical Epidemiol. 4: 341-348. 

306 McConnell, R.; Berhane, K.; Yao. L., Jerrett, M.; Lurmann, F.; Gilliland, F.; Kunzli, N.; 

Gauderman, J.; Avol, E.; Thomas, D.; Peters, J. (2006) Traffic, susceptibility, and childhood 

asthma. Environ. Health Perspect. 114: 766-772. 

307 Hoffmann, B.; Moebus, S.; Stang, A.; Beck, E.M.; Dragano, N.; Mohlenkamp, S.; 

Schermund, A.; Memmesheimer, M.; Mann, K.; Erbel, R.; Jockel, K.H. (2006) Residence close 

to high traffic and prevalence of coronary heart disease. Eur. Heart J. 27: 2696-2702. 

308 Peters, A.; von Klot, S.; Heier, M.; et al. (2004) Exposure to traffic and the onset of 

myocardial infarction. N. Engl. J. Med. 351: 1721-1730. 

309 Riediker, M.; Cascio, W.E.; Griggs, T.R..; et al. (2003) Particulate matter exposures in cars is 

associated with cardiovascular effects in healthy young men. Am. J. Respir. Crit. Care Med. 

169:934-940. 

310 Schwartz, J.; Litonjua, L.; Suh, H.; et al. (2005) Traffic related pollution and heart rate 

variability in a panel of elderly subjects. Thorax 60: 455-461. 

311 Gold, D.R.; Litonjua, A.A.; Zanobetti, A.; et al. (2005) Air pollution and ST-segment 

depression in elderly subjects. Environ. Health Perspect. 113: 883-887. 

312 Wilhelm, M.; Ritz, B. (2002) Residential proximity to traffic and adverse birth outcomes in 

Los Angeles County, California, 1994-1996. Environ. Health Prospect. 111:207-216. 

313 Ritz B; Yu F. (1999) The effect of ambient carbon monoxide on low birth weight among 

children born in southern California between 1989 and 1993. Environ. Health Perspect. 107:17­

25. 

314 Ritz B; Yu F; Capa G; Fruin S. (2000) Effect of air pollution on premature birth among 

children born in southern California between 1989 and 1993. Epidemiology 11:502-511. 

315 Ritz B; Yu F; Fruin S; et al. (2002) Ambient air pollution and risk of birth defects in Southern 

California. Am. J. Epidemiol. 155:17-25. 

316 Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect of transplacental exposure to 

environmental pollutants on birth outcomes in a multiethnic population. Environ. Health 

Perspect. 111: 201-205. 

3-192
 
317 Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; 

Tu, Y.H.; Camann, D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic 

aromatic hydrocarbons on neurodevelopment in the first 3 years of life among inner-city 

children. Environ. Health Perspect. 114: 1287-1292. 

318 U. S. EPA (2005) Supplemental Guidance for Assessing Susceptibility from Early-Life 

Exposure to Carcinogens. Report No. EPA/630/R-03/003F. This document is available in 

Docket EPA-HQ-OAR-2005-0036. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=160003 

319 U. S. EPA. 2002. Toxicological Review of Benzene (Noncancer effects). Report No. EPA 

635/R-02/001-F. 

320 Savitz, D.A.; Feingold, L. (1989) Association of childhood cancer with residential traffic 

density. Scand. J. Work Environ. Health 15: 360-363. 

321 Pearson, R.L.; Wachtel, H.; Ebi, K.L. (2000) Distance-weighted traffic density in proximity 

to a home is a risk factor for leukemia and other childhood cancers. J. Air Waste Manage. Assoc. 

50: 175-180. 

322 Feychting, M.; Svensson, D.; Ahlbom, A. (1998) Exposure to motor vehicle exhaust and 

childhood cancer. Scan. J. Work Environ. Health 24(1): 8-11. 

323 Langholz, B.; Ebi, K.L.; Thomas, D.C.; et al. (2002) Traffic density and the risk of childhood 

leukemia in a Los Angeles case-control study. Ann. Epidemiol. 12: 482-487. 

324 Raaschou-Nielsen, O.; Hertel, O.; Thomsen, B.L.; Olsen, J.H. (2001) Air pollution from 

traffic at the residence of children with cancer. Am. J. Epidemiol. 153: 433-443. 

325 Reynolds, P.; Von Behren, J.; Gunier, R.B.; et al. (2003) Childhood cancer incidence rates 

and hazardous air pollutants in California: an exploratory analysis. Environ Health Perspect 111: 

663-668. 

326 Reynolds, P.; Von Behren, J.; Gunier, R.B.; et al. (2004) Residential exposure to traffic in 

California and childhood cancer. Epidemiology 15: 6-12. 

327 Knox, E.G. (2005) Oil combustion and childhood cancers. J. Epidemiol. Community Health 

59: 755-760. 

328 U.S. EPA. 2007. The HAPEM6 User’s Guide. Appendix B. Prepared for Ted Palma, Office 

of Air Quality Planning and Standards, Research Triangle Park, NC, by Arlene Rosenbaum and 

Michael Huang, ICF International, January 2007. 

http://www.epa.gov/ttn/fera/human_hapem.html. This document is available in Docket EPAHQ-OAR-2005-0036. 

329United States Census Bureau. (2004) American Housing Survey web page. [Online at 

http://www.census.gov/hhes/www/housing/ahs/ahs03/ahs03.html] Table IA-6. 

330This statistic is based on the odds ratio of being near major transportation sources, compared 

to not being near transportation sources for housing units located in different geographic regions. 

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