# Regulatory Impact Analysis

Control of Hazardous Air Pollutants from Mobile Sources

Chapter 3 Air Quality and Resulting Health and

W elfare Effects of Air Pollution from Mobile Sources

Assessment and Standards Division Office of T ransportation and Air Quality U.S. Environmental Protection Agency

EP A420-R-07 -0 02 F ebr uar y 20 07Fin al Regu latory Im p act An al ysis



### Chapter 3 : Table of Contents

Chapter 3 : Air Quality and Resulting Health an d W e lfare Effects of Air P ollution from Mobile
Sources

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

.. .................................................................... 3
3.1.1 Am bient Monitoring

.. ................................................................................................ 3
3.1.2 Population-Based (Representative) Exposure Measurem ents

.. ................................. 9
3.1.3 Elevated Concentrations and Exposur es 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 Hom e s and Schools

... ....................................................................... 19
3.1.3.2.3 Pedestrians and Bicyclists

... ................................................................. 21
3.1.3.3 Concentrations and Exposure in Hom e s 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 Measurem ents

... .......................................................... 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 Ai r Toxics: Reference Case

... ......................... 35
3.2.1.1.3 Distributions of Air Toxic Concentr ations across the U. S.: Reference
Case 38
3.2.1.1.4 Im pacts of Controls on Am bient 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 Im pacts of Controls on Averag e Inhalation Cancer Risks and Noncancer Hazards

........................................................................................ 67
3.2.1.3 Strengths and Lim itations

................................................................................... 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-H our 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 Metam odel Methodo lo gy

.......................................... 93

3-1Fin al Regu latory Im p act An al ysis



### 3.3.4.3 Ozone Response Surface Metam odel Results

.................................................... 96
3.3.5 Environm ental Effects of Ozone Pollution

............................................................. 97
3.3.5.1 Im pacts on Vegetation

........................................................................................ 97
3.4 Particulate Matter

......................................................................................................... ...... 99
3.4.1 Science of PM Form ation

........................................................................................ 99
3.4.2 Health Effects of Particulate Matter

...................................................................... 100
3.4.2.1 Short-Term Exposure M ortality and Morbidity S t udies

.................................. 100
3.4.2.2 Long-Term Exposure M ortality and Morbidity S t udies

................................... 101
3.4.2.3 Roadway-Related Pollution Exposure

.............................................................. 102
3.4.3 Current and Projected P M Levels

......................................................................... 102
3.4.3.1 Current PM 2. 5 

### Levels

....................................................................................... 102
3.4.3.2 Current PM 10 

### Levels

........................................................................................ 103
3.4.3.3 Projected P M 2. 5 

### Levels

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

............................................................... 104
3.4.3.3.2 Areas at Risk of Future PM 2. 5 

### Violations

........................................... 104
3.4.4 Environm ental Effects of PM Pollution

................................................................ 107
3.4.4.1 Visibility Impairm e nt

....................................................................................... 107
3.4.4.1.1 Current Visibility Im pairm e nt

............................................................ 108
3.4.4.1.2 Current Visibility Im pairm e nt at Mandatory Class I Federal Areas

.. 108
3.4.4.1.3 Future Visibility Im pairm e nt

............................................................. 109
3.4.4.1.4 Future Visibility Im pairm e nt at Mandatory Class I Federal Areas

.... 109
3.4.4.2 At m ospheric Deposition

................................................................................... 110
3.4.4.2.1 Heavy Metals

..................................................................................... 111
3.4.4.2.2 Polycyclic Organic Matter

................................................................. 112
3.4.4.3 Materials Dam a ge and Soiling

......................................................................... 112
3.5 Health and Welfare Im pacts of Near-Roadway Exposure

......................................... 113
3.5.1 Mortality

................................................................................................................ 114
3.5.2 Non-Allergic Respiratory Sym ptom s

.................................................................... 116
3.5.3 Developm ent of Allergic Disease and Asthm a

..................................................... 117
3.5.4 Cardiovascular Effects

.......................................................................................... 118
3.5.5 Birth Outcom es

..................................................................................................... 119
3.5.6 Childhood Cancer

.................................................................................................. 120
3.5.7 Summ ary of Near-Roadway Health Studies

......................................................... 121

Appendix 3A: Influence of Em issions in Attached Garages on Indoor Air Benzene Concentrations and Hum a n Exposure

......................................................................................... 124 Appendix 3B: 8-Hour Ozone Nonattainm ent

............................................................................. 144 Appendix 3C: PM Nonattainm e nt

.............................................................................................. 159 Appendix 3D: Visibility Tables

................................................................................................. . 162

3-2Fin al Regu latory Im p act An al ysis



# Chapter 3: Ai r Quality an d Resulti n g Healt h and W e lfar e Ef fects of
Air Pollution fr om Mobile Sour ces

3.1 Air Quality and Exposure Measurements

3.1.1 Ambient Monitoring

Am bient air toxics data are useful for iden tifying pollutants of greatest concern, areas of unhealthy ambient air toxics c oncentrations, and air toxics trends; evaluating and im proving models; and assess ing th e ef f ectiven ess of air toxics redu ction strategies. Am bient air toxics data though have lim itations f o r use in risk assessm ents

. W hile EPA, states, tribes, and local air regulatory agencies collect m onitoring data for a num ber of toxic air pollutants, both the chem icals monitored and the geographic coverage of the m oni tors vary from state to state. 1 

### In

recen t years, the US EPA and states have initia ted m o re extensive m onitoring of air toxics to assis t in air pollution m a nagem e nt through m easurem ent and m itigation. 2 

### EPA is working with its regulatory partners to build upon the existing monitoring sites to creat e a national monitoring network for a num ber of toxic air pollutants. Th e goal is to ensure that those com pounds that pose the greatest risk are m easured. 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, cons isting of 23 sites in 22 urban areas nationally. 4



### The available m onitoring data help air pollution contro l agen cies track trends in tox i c air pollutants in various locations around the country. EPA conducted a pilot city m onitoring project in 2001 that included sam pling in four urb an areas and six sm all city/rural a r eas (see Figure 3.1-1). This program helped answer seve ral im portant national network design questions (e.g., sam pling and analysis precision, sources of va riability, and m i nim a l dete ction levels).

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

3-3Fin al Regu latory Im p act An al ysis



### Building on the pilot pro g ram , the US EPA a nd s t ates established a national air toxics monitoring program beginning with a 10-city pilo t program , which now consists of the NATTS, and num erous community-scale m onitoring studies. 5 

### To guide developm ent of the m o nitoring

program , a qualitative data analysis project wa s begun in 2001 and the first phase was com pleted in 2004. The analysis showed that typical urban concentration ra nges for most VOCs are approxim ately an order of m agnitude (or m o re ) higher than the backgr ound concentrations. Because air toxics concentrations vary spatia lly, other m onitoring ne tworks are n eed ed to provide additional, especially rural, concentratio ns. Extrapolation for m os t air toxics beyond the urban scale is not recom m e nded without a netw or k of rural m easure m ents capable of capturing gradients between urban and rural areas. For the late st inf o r

m ation on nationa l air to xics monitoring, see www.epa.gov/ttn/am tic/airtxfil.htm l

.

Figure 3.1-2 shows m easurem ents of ben zene taken from 95 urban m onitoring sites around the country. These urban ar eas generally have higher levels of benzene than other areas of the country. Measurem ents taken at these sites show, on average, a 47% drop in benzene levels from 1994 to 2000. During this period, E P A phased in new (so-called “tier 1”) car

em ission sta ndards; requ ired m any cities to begin using clean er-burn i ng g asolin e; and set standard s th at requ ired s i gnif i can t re ductions in benzene and other po llutants em itted f r om oil refineries an d chem ical processes.

Figure 3.1-2. Ambient Benz ene, Annual Average Urban Concentrations, Nationw i de, 1994­ 2000

Following is a summ ary of analyses recentl y perf orm ed on ambient m easurem ents of air toxics to ide n tif y pollu ta nts and geographic areas of c oncern and to evaluate trends. Use of monitoring data to evaluate and im pr ove m odels is discussed in Section 3.2.

EPA recently com pleted a study of the spatial an d tem poral trends in am bient air toxics data within the NATTS a nd other networks from 1990-2003. 6 

### Most data cam e from urban monitors. Nationally, citywide average annual concentrations of benzene, for m aldehyde, and

3-4Fin al Regu latory Im p act An al ysis



### acetaldehyd e, varied by about a factor of five , and 1,3-butad iene by m ore than 10 tim es. The coefficient o f variation a 

### of annual average concentrations be tw een different m onitors within the

sam e city averaged 0.37 for benzene, 0.45 for 1,3-but adiene. Between cities, the coefficient of variation could vary substantiall y. Different pollutants showed different seasonal trends, with average concentrations of ben zene and 1,3-butadiene being highest in colder seasons, while average concentrations of for m aldehyde and acetaldehyde were highe r during warm seasons, reflecting the high photochem i cal production of al dehydes. The concentrations of benzene, butadien e , and acetaldeh yde fell substantiall y over different tim e periods. From 1990- 2003, benzene concentrations fell by 57%. Insufficien t data existed in earlie r years to analyze 1,3­ butadien e an d acetaldehy de. Form aldehyde increased by 134% over th is period, altho ugh changes in sam p ling m e thodology at som e sites around 1995 m a ke this quantification suspect.
From 1995- 2003, the average annual changes in benzene, 1,3-butadiene, form aldehyde, and acetaldehyd e were -47%, -54%, +11 % , and -12 %

. From 1998-2003, the changes were -21%, ­ 46%, +17%, and -4%, respectively. For benzene, th ese trend s were sta tis tica lly s i gnif i cant, bu t for m aldehyde and acetaldehyde trends after 1995 were not.

One recent p ublication evaluated the trends in ambient con cen trations of b enzene and 1,3-butadiene in the Hous ton, TX m e tropolitan area. 7 

### Using data from two air m onitoring networks, a state-based network and the Phot och e m i cal Assessm ent Monitoring System s, the study constructed a s t atistical m odel, contro lli ng for m e teorology and seas onality, to evaluate trends in ambient toxics over the 1997-2004 tim e period. Averaged over state m onitoring sites with data across the tim e period, the model estim ated 1.7% and 3.7% average annual decrease in

am bient benzene and 1,3,-butadiene, respectivel y. Mobile source a nd point source em ission reductions contributed roughly equally to this change. Examining long-term average concentration differences across m onitoring site s, benzene varied by roughly two-fold across monitors while 1,3-butadiene vari ed roughly six-f old across monitors

. This m a y be attributable to the substantial contribution of industrial sources to the loca l 1,3-butadiene inventory, while the benzene inventory is dom i nated by mobile sources

. The study also evaluated differences in weekday and weekend concentration, with the m odel predicting significant m e teorologicallyadjusted con centr ation w eekday inc r e ases re la tiv e to weekend only during the 6-9 A.M. m orning rush hour period.

A recent s t u dy from San Francisco, CA eval uated trends in ambient benz ene em issions and air quality throughout the 1990’s. 8 

### The study noted substantial decreases in benzene

em issions and am bient concentrations. Unique to the study was the attri bution of components of these reduction to specific regulatory program s relate d to vehicles and fuels. In particular, the study attributed a 1-year drop of 54% in ben zene em ission rates to a com b ination of the introduction of California phase 2 RFG (attributed a 50% decrease) and f l eet turnover (attributed a 4% decrease). During the sam e year (1995-1996), a 42% reduction in the am bient concentration of benzene was also observed. Fleet turnover ef fects were shown to be cum ulative over tim e

. The study indicates that in San Franci sco both fuel and vehicle effects are important

a A “co e fficien t of v a riatio n” i s a m easu r e o f v a riab ility, an d fo r a set o f d a t a is d e fin e d as th e stand a rd d e v i atio n

ove r t h e m ean. 

### 3-5Fin al Regu latory Im p act An al ysis



### contributors to changes in em issions a nd am bient concentrations of benzene.

New York State h as a sy stem atic pro g ram in place that h as been m easuring air toxics since the 1990s. 9 

### The network of m o nitors is located throughout urban, indu strial, residential and rural locations. The New York State Depart m ent of Environm ental Conservatio n recen tly

exam ined the spatial and tem poral characteristic s of benzene by analyzi ng five of the 32 total network sites across the state (see Table 3.1-1). Spatial tr ends show a wide range of annual average b e n zene concen trations, with the lowes t value at a rural site and the highest at an industrial site. The recent 3-year period of 2001-2003 was also com pared with the longer 1990­ 2003 period. The 3-year period exhi bits a decrease in m ean concen tration com p ared to the entire period, indicating that benzene concentrations are decreasing over New York State throughout this period. The m ean a nnual rate of change in the period 1990 to 2003 was determ ined using linea r reg r es sion and m o ving aver ag e (KZ f ilter ) on the conc entra tion da ta. The analysis indicated th at site-specif i c am bient concentratio n levels of benzene decreased by 50% or m ore during 1990 to 2003. These decreases occurred in ozone nonattainm e nt areas that had reform ulated gasoline (RFG) requirem e nts as well as in the rest of the s t ate. The do wnward trend can be attributed to regul atory m easures aim e d at reducing toxic em issions from industrial sources, replacem ent of older h i gher em itting veh i cle s with ve hicle s m eeting m ore stringent EPA standards for hydrocarbon em issions, as well as the adoption of RFG in 1995 and 1999 for the 1­ hour ozone nonattainm e nt areas in N e w York State. Since trends were ob served for sites that were not part of the RFG program , decreases m a y also be attributed to the im prove m e nt in vehicle em issions technology and the state-wi de adoption of the California Low Em ission Vehicle program

.

A sim ilar downward trend was observed in Calif ornia. In California, the Air Resources Board (ARB) m aintains an Alm anac of Em issions and Air Quality. 10 

### The Al m a nac summarizes statewide em issions, statewide annu al average con centration s (calculated as a m ean of m onthly

m eans), and statewide av erage health risks for se lected air tox i cs. Currently there are d a ta available for ten air toxics in California, includi ng benzene. T he ARB network consists of 18 air quality m oni toring stations. The data collected, analyzed, and repor ted ref l ect a spatial average ; therefore, ambient concentrations for individual locations m a y be higher or lower. Estim ates show that ap proxim ately 84% of the benzene em itted in Calif ornia com es f r om m o tor vehicles, including evaporative leakage a nd unburned fuel exhaust. T he predom inant sources of total benzene em issions in the atm o sphere are gaso line f ugitive em issions and g asolin e m o tor vehic l e exhaust. Approxim a tely 49% of the s t atewid e ben zene em issions can be attributed to on-road motor vehic l es, with an a ddition al 35 % attribu t ed to othe r m o bile sou r ces such as re cr eation al boats, off-road recreational vehicles, and lawn and garden equipm ent. Currently, the benzene content of g asolin e is les s than 1%. Som e of th e benzene in the f u el is e m itted f r om vehicles as unburned fuel. Benzene is also form ed as a part ial com bustion product of larger arom atic fuel

com ponents. Industry-related stationary s ources contribute 15% and area-wide sources contribute 1% of the statewide benzene em issions . The prim ary stationary sources of reported benzene em issions are crude petroleum and natu ral gas m i ning, petroleum refining, and electric generation. The prim ary area-wide sources include residential com bustion of various types such as cooking and water heating. The prim ary natural sources are petroleum s eeps that form where

3-6Fin al Regu latory Im p act An al ysis



### oil or n atural gas em erge from subsurface sources to the g r oun d or water s u rface. The statewide benzene levels have shown generally steady im provem ent since 1990. To exam ine the trend in benzene while m i ni m i zing the influences of w eather on the trend, the st atewide average benzene concentration for 1990-1992 was compared to that for 2001-2003. The result was a 72% decrease in benzene concentrati on. These downward trends for be nzene and other air toxics are a result of many control m easures implem ented to reduce em issions.

Another recent evaluation of hazard ous ai r po llu tant (HAP) trends was conducted for selected m e tropolitan areas. 11 

### Researchers retrieved historical concentration and em issions data from the US EPA for Boston, New York City, Ph iladelphia, T a m pa Bay, Detroit, Dallas, St. Louis, Denver, Los Angeles, and Seattle, chos en for each of EPA’s ten regions. Annual and seasonal trends were generated to evaluate reductions in HAP e m issions and am bient concentrations during the tim e period 1990-2003. Several air toxics we re targeted, including benzene. T o evaluate the trends, average concentrations from 1990-1994 were com pa r ed to 2002-2003 (these tim e periods were chos en due to availability of data ). The results showed that over 85% of the m e tropolitan area-H AP com b ina tions decreased in thei r 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 m e tropolitan areas (range 19 to 79%).

Each of thes e analys es consis ten tly illustra tes th e signif i can t r eduction s in nationa l ann u al

average con centration s of benzen e and other air toxics. The air pollution m a nagem e nt efforts of the US EPA and states h a ve been effectiv e in re d u cing am bient concen trati ons of air toxics over tim e. Additional redu ctions are exp e cted with th e im plem entation of additional regulatory

m easures such as th is on e. It should be noted that due to the lim ited spatial and tem p oral coverage of air toxics m onitoring networks, usin g am bient monitors to r e present exposure adds substantial uncertainty in exposure assessm ent.

3-7Final Regulatory Impact AnalysisTable 3.1-1.Site Descriptions of the Monitoring StationsAlong with Mean Benzene Concentration from 1990-2003 and 2001­2003, for Monitoring Stations in NewYork State.LackawannaEastern DistrictHigh SchoolTroyNiagara FallsWhitefaceMountain BaseLodgeSite CharacterIndustrialUrbanSmall UrbanUrban IndustrialRuralLocation AreaBuffaloBrooklynHudsonValleyNiagaraEssex2000 Population(thousands)950246515322039Annual VehicleMiles Traveled(million miles)8250424614131546577Period 1990-2003Mean Concentration(μg/m3)5.092.852.311.800.86Period 2001-2003Mean Concentration(μg/m3)2.262.051.681.080.543-8Fin al Regu latory Im p act An al ysis



### 3.1.2 Population-Based (Representative) Exposure Measurements

In addition to m easurements of outdoor concentrations, an im portant component of understanding hum a n exposure to ai r toxics is the body of studies that employ survey techniques

to assess m i croenvironmental and representa tive populations’ exposur es. Typically, these studies are designed to represent a discre te geographic area. T he personal exposure concentration summaries from thes e studies are shown in Table 3.1-3.

The National Hum a n EXposure Assessm ent 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 m i d-1990. 12 

### Nearly 400 personal and indoor air sam p les were obtained from both sm okers and non-sm okers, along with a sm aller num ber of outdoor air

sam p les in residen tial areas. Measu r em ents took place over 6 days per su bject. Overall, av erage personal exposure to benzene was 7.52 μ g/m 3 

### , with indoor air concen trations averaging 7.21

μ g/m 3. Outdoor air concen trations averaged 3.61 μ g/m 3



### . Pe rsonal air concentrations were significantly associated with i ndoor air concentrations, as well as blood concentrations. The prelim inary results of the NHEXAS pilot study in Arizona, another study area, indicate that

am ong the 179 statistically-sam p led hom es, m e dian indoor concentrations were 1.3 μ g/m 3 

### during the m i d-1990’s, while outdoor concentrations w e re 1.0 μ g/m 3



### . 13 

### Furtherm ore, reported results from the Arizona study indicate that fuel-relate d VOCs are elevated in hom e s with attached

garages.

In another study based on a random populati on-based sam ple of an urban population, 37

non-sm oking residents of South Baltimore, MD were equipped with passive m onitors to assess 3-day average personal exposure to VOCs, in addition to ind oor and outd oor air. 14 

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

em ployed in EPA’s National Air Toxics Assessm e nt for 1996, were also obtained f o r the study

area. Overall, m e dian outdoor m odeled concentr ations of benzene and other fuel-related VOCs corresponded well with m easured data in the area (correlation coefficient of m e dian VOC concentrations = 0.97). Average pe rsonal exposure to benzene was 4.06 μ g/m 3 

### , whil e 95 th 

### percentile values were 7.30 μ g/m 3



### . For indoors, the respective values w e re 3.70 and 8.34 μ g/m 3



### , while for outdoors the values were 1.84 and 3.14 μ g/m 3



### . Ove r all, the study provides evidence that m odeling outdoor benzene concentrations usi ng 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 descri bed in the section on attached garages.

While not a population-b a sed study, the recen tly -com pleted Relationship s of Indoor, Outdoor and Personal Air (RIOPA) study provide s a depiction of indoor, outdoor, and personal concentrations of benzene and other toxics in three regions with differing source m i xtures. 15 

### 100

non-sm oking hom e s in each of Los Angeles, CA, Houston, TX, and Elizabeth, NJ were selected for sam pling in areas representing locations dom inated by em issions from m obile sources, stationary sources, and a m i xture of sources, respectively. In th e adult sample, average personal exposures to benzene were 3.64 μ g/m 3 

### , with a 95 th 

### percentile of 10.7 μ g/m 3



### . Respective statistics

3-9Fin al Regu latory Im p act An al ysis



### for indoor air were 3.50 μ g/m 3 

### and 10.0 μ g/m 3 

### , while outdoor sta tistics were 2.15 and 5.16

μ g/m 3



### .

Few studies have system atically addresse d exposures am ong repres entative sam ples of children. Several have been done in Minnesota, with others in New York, Los Angeles, and Baltim ore ar eas.

For the Minnesota Children’s Pesticide Exposure Study (MNCPES), conducted in urban and rura l ar e as in th e vic i nity of Minneapolis-St. P a ul, MN, 16 

### all m onitoring used the sam e 6-day monitoring duration as used in the Region 5 NHEXAS study. In the first phase of the study, a statistically representa tive sam ple of 284 hom e s with children underwent air m onitoring for
VOCs. Low-incom e and m i nority hom e s were ove r sam pled to ensure representation. Indoor benzene concentrations averaged 4.6 μ g/m 3 

### , with the data skewed toward higher concentrations. The 95th percentile concentration was 12.7 μ g/m 3



### . Hom e s 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 intensiv e m onitoring of personal, indoor, and outdoor air as well as activity tracking via diar y. Overall personal exposures were 4.8 μ g/m 3 

### , with a 95th percentile of 9.1 μ g/m 3



### . Indoor concentrations in th e intensive period averaged 3.9 μ g/ m 3 

### and outdoor averaged 3.3 μ g/m 3



### . Regression analysis indicated th at personal exposures generally were higher than the tim e-weight ed average of indoor and outdoor air. Furtherm ore, personal exposures to benzene and toluene were elevated f or children livi ng in a hom e with an attached garage, but only the relationshi p f o r toluene was signif i can t a t the 95 % co nf idence lev el.

3-10Final Regulatory Impact AnalysisTable 3.1-2.Benzene Emission (Tons Per Year) and Concentration (μg/m3) ComparisonMetropolitanArea1990Emissions2002Emissions% ChangeinEmissions1990-1994AverageConcentration2002-2003AverageConcentration% ChangeinConcentrationBoston62622229-64.43.930.81-79.5New York City166537512-54.93.241.35-58.5Philadelphia59612577-56.83.601.26-64.9Tampa Bay31032408-22.4NANANADetroit64804388-32.34.193.40-18.7Dallas79332832-64.31.210.78-35.8St. Louis43582304-47.15.161.43-72.3Denver28001913-31.7NA2.75NALos Angeles197624168-78.98.972.34-73.9Seattle58444315-26.2NA1.39NA3-11Fin al Regu latory Im p act An al ysis



### In another study, students recruited from an inner-city school in Minneapolis, MN participated in an e xposure study called SHIELD. 17 

### Students w e re recruited using stratified random sa mpling, with a total of 153 children pa rticipating between two seasons. Home and personal samples were collected and averaged over two continuous days of sa m pling using passive m e thods. School m easurem ents took pl ace du ring school hours only, over the course o f 5 days, and outdoor m easurem ents were set up to run continuously out side the school through each week sam p led (Monday throug h Friday). T h e study rep o rted m e dian, 10th, and 90th percentile concentrations. In personal sam ples, m e dian benzene concentrations were 1.5 μ g/m 3 

### in spring and 2.1 μ g/m 3 

### in winter. 18



### The TEACH exposure study tracked inner-city high school students’ exposures in N e w York, NY a nd Los Angeles, CA. In the New Yo rk City study, 42 stude nts underwent personal, indoor hom e, and outdoor hom e air quali ty m onitoring during two seasons. 19 

### Average winter benzene personal concentrations were 4.70 μ g/m 3 

### , while indoor and out door concentrations averaged 5.97 and 2.55 μ g/m 3



### . Average indoor co ncentration s exceeding average perso n al concentrations is unique to the TEACH winter results. Summer values were 3.09, 1.75, and 1.31

μ g/m 3 

### , respectively. The authors noted that VOC con centrations within the city tra c k e d traf f i c patterns. There was no s ubstantial evidence for in door 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 μ g/m 3 

### , while Los Angeles average exposure concentrations were 4.64 μ g/m 3



### . 21



### Overall, thes e studies sho w that perso n al a nd 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 sign ificant. So m e of the factors lead ing to th ese elevated concentratio ns are likely a resu lt of motor vehicle im pacts such as exhaust and evaporative

em issions in attached garages, exposures duri ng on-road com m utes and exposures during vehicle re-fueling. These and other fact ors are discussed in m ore deta il in Section 3.1.3. This suggests that risk red u ctions f r om the controls in this proposal will be greater th an can currently be estim ated using national-scale m odeling tools.

3.1.3 Elevated Concentrations and Exposu res in Mobile Source-Impacted Areas

Air quality m easure m ents near roads often id entify elevated concentration s of air tox i c pollutants at these locations. The concentrations of air toxic pollutants n ear heav ily trafficked roads, as well as the pollutant com position and char acteristics, differ from those m easured distant from heavily trafficked roads. Thus, exposur es f or populations residi ng, working, or going to school near m a jor roads are likely different than f or 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 im p acts of mobile source em issions.

3-12Fin al Regu latory Im p act An al ysis



### Table 3.1-3. Personal Exposure to Benz ene f r om Population-Based Studies a



### Location Year(s) Includes Smokers Personal Average ( μ g/m 3 

### ) “Upper Bound” ( μ g/m 3 

### ) Indoor Average ( μ g/m 3 

### ) Outdoor Average ( μ g/m 3 

### ) Reference EPA Region
5 1995­
1996 Yes 7.52 13.71 b 

### 7.21 3.61 Clayton et al. (1999)

Baltim ore, MD 2000­
2001 No 4.06 7.30 c 

### 3.70 1.84 PayneSturges et al. (2004) Elizabeth, NJ, Houston, TX, Los Angeles 1999­
2001 No 3.64 10.7 c



### , 27.4 g 

### 3.50 2.15 W e i s el et al. (2005)

CA Elizabeth, NJ, Houston, TX,
Los Angeles
CA 1999

- 2001 No 4.16 12.0 c



### , 43.6 g 

### N/R h 

### N/R h 

### W e i s el et al. (2005)

Minneapolis

- St. Paul, MN 1997 Yes e 

### 4.8 9.1 3.9 3.3 Adgate et al. (2004a)

Minneapolis,
MN 2000 Yes e 

### 2.1
Winter

1.5 Spring 6.5
Winter b



### 4.2 Spring b 

### 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
Winter d



### 7.0
Summer d



### 12.3 Total f 

### 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 ita lics

b 

### 90 th 

### percen tile

c 

### 95 th 

### percen tile

d 

### Mean +2 standard deviations

e 

### Sm oking in hom es f 

### Maxim u m m easured value

g 

### 99 th 

### percen tile

h 

### Not reported

3-13Fin al Regu latory Im p act An al ysis



### 3.1.3.1 Concentrations Near Major Roadways

3.1.3.1.1 Particulate Matter

Mobile sources influence tem poral and spatia l pa tte rns of criteria po llu tan t s, air tox i cs, and PM concentrations within urban areas. Mo tor vehicle em issions m a y lead to elevated concentrations of pollutants near m a jor roads. Since m o tor vehicle em issions generally occur within the breathing zone, near-roa d populations m a y be exposed to “fresh” prim ary em issions as well as com bustion po llutants “aged ” in the atmo sphere. For particulate m a tter, these fresh versus aged em issions can result in the pres ence of varying particle s i zes n ear ro adways, including ultrafine, fine, and coarse particle m odes.

The range of particle sizes of concern is quite broad and is divided into smaller categories. Defining different size categories is useful since partic les of different sizes behave differently in the atm osphere and in the hum an respiratory system

. Table 3.1-4 lists the four

term s for categorizing particles of dif f erent sizes as defined b y the US EPA. 22



### Table 3.1-4. Descriptions and Particle S i z e s of Each Category of Particles

Description Particle Size, d p 

### ( μ m) Supercoarse d p 

### > 10 Coarse (or T horacic Coarse Mode) 2.5 < d p 

### ≤ 10 Fine (or Accum u lation Mode) 0.1 < d p 

### ≤ 2.5 Ultrafine (or Nuclei Mod e ) a 

### d p 

### ≤ 0.1

a 

### Nuclei Mode has also been defined as d p 

### ≤ 0.05 μ m elsewhere.

Other particle classifications of intere st inc l ude to tal susp ended particulate m a tter (TSP). TSP includes a broad range of par ticle sizes including fine, coarse , and supercoarse particles.
PM 10 

### is defined as particulate m a tter with an aerodynam i c diam eter of less than or equal to 10

μ m. P M 10 

### is regulated as a specific type of "pollu tant" because this size range is considered respirable and can penetrate into the lower resp ir atory trac t. PM 2. 5 

### is pa rticula t e m a tter with an aerodynam i c diam eter less than or equal to 2.5 μ m. PM 2. 5 

### settles quite slowly in the atm o sphere rela tive to c o arse and su pe rcoarse particles. Norm al weather patterns can keep PM 2. 5 

### airborne for several hours to several days a nd enable these particles to tr ansport hundreds of m iles. PM 2. 5 

### can cause health problem s due to widespread exposur es and efficiency at reaching deep into the lungs.

The size distribution of partic les can be defined as a func tion of num b er, surface area, volum e, and m ass. 23, 24 

### Typically, on a num b er basis, em issi ons from m obile sources are heavily dom i nated by ultrafine mode par ticles, which tend to be compri sed of volatile carbon. On a surface area basis, th e av erage d i am e t er of particles em itted b y m obile so urces is 0.1 μ m

. On a volum e and m a ss basis, the size d i stribution of pa rticles em itted f r om m o bile sou r ces has an average particle diam eter of approxim a tely 0.2 μ m.

Evidence of the large nu mber of ultrafine m ode particles emitted by m ot or vehicles can be found in the near-road environm ent. Roadside and am bient on-road m easurem ents show that

3-14Fin al Regu latory Im p act An al ysis



### ultrafine m o de particles dom i na te the num ber concentration in close proxim ity to the roadway, while fine mode dom inates farther from the road. Particle size distributions, m a ss and elem ental carbon concentrations have been exam ined near roads in Los Angeles. 25, 26 

### Researchers observed a four-fold increase in particle num ber concen trations, when com p aring m e asure m ents 300 m and 20 m fr om LA highways. Othe r studies have sim ilarly shown that ultr af ine m ode particle s show a sharp decrease in particle number concen trations as the distance from m a jor roadways increases. 27, 28 

### Evidence w a s recently f ound of increased exposures to ultraf ine particles near roads when it was discov ered th at ch ildren liv ing near m a jor roads had elevated levels of particle-containing alveolar m acrophages. 29 

### Additionally, roadside m onitoring has shown that particle num ber varies w ith vehicle type and vehi cle operating conditions. For exam ple, elevated ultrafine m o de particle concentrations have b een identified when operatin g 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 form ation. 32, 33, 34, 35 

### Engine deterioration is generally a function of vehicle age and m a intenance c ondition. Brake wear em issions are highly dependent on brake pad m aterials. 36 

### Secondary aerosol form ation is dependent on fuel

com position, em ission rates, atm o spheric chem istr y, and m e teorology. Re-entrained road dust, as well as b r ake and tire wear will als o contr i bute to inc r eased concentratio ns of coarse PM.

Meteorological factors can affect exposures to m otor vehicle em issions near the road. Researchers have noted that par tic le num b er concentr ation s c h a nged significantly with changing wind conditions, such as wind speed, near a road. 37 

### Studies sug g est th at am bient tem p erature variation can also affect particle numb er gradients near roads substantially. 38 

### W i nd direction also affects traffic-related air po llution m a ss con centrations inside and outside of schools near motorways. 39, 40 

### Diurnal variations in m i xing layer hei ght will also influence both near-road and regional air pollutant co ncentration s

. Decreases in the he ight of the m i xi ng layer (due to morning inversions, stable atm os phere, etc.) will lead to increa sed pollutant concentrations at both local and regional scales.

3.1.3.1.2 Gaseous Air Toxics

Concentrations of m obile source air toxics have been estim a ted by a number of different

m e thods such as the NATA National-Scale Asse ssm e nt, local-scale m odeling assessments, and
f r om air qua lity m onitoring in loca tio ns in imm ediate p r oxim ity to busy ro adways. Each approach offers a different level of representati on of the concentrations of air toxics near roadways.

Air quality monitoring is one way of evaluating pollutant conc entr ation s at loc ation s near sources such as roadways. Am bient VOC conce n trations were m easured around residences in Elizabeth, NJ, as part of the Relationship am ong Indoor, Outdoor, and Personal Air (RIOPA) study. Data from that study was analyzed to as sess the influence of proxim ity of known a m bient

em ission sources on residences. 41 

### The am bient concentration s of benzene, toluene, ethylbenzene, and xylene isom ers (BTEX) were found to be inversel y associated with: distances
f r om the sampler to in te rsta te highw ays and m ajor urban ro a d s; dis t ance f r om the sampler to gasolin e sta t ions; a t m o spheric s t ab ility; tem p eratu r e; and wind speed. The data indicate that

3-15Fin al Regu latory Im p act An al ysis



### BTEX conc entrations around hom e s within 200 m of roadwa ys a nd gas stations are 1.5 to 4 tim es higher than urban background levels. In a subsequent study, proxim ity to m a jor roadways,

m e teorology, and photochem i stry were all found to be significant dete rm inants of ambient concentration of a range of aldehyde species, including form a l dehyde, acetaldehyde, acrolein, and others. For m ost aldehydes, spring and su mm er concentrations were significantly higher than thos e from colder seasons. 42 

### However, form aldehyde concentrations w e re significantly lower in summertim e, s uggesting greater photoche m i cal destruction than production. On colder days, when photochem i cal activity was lower, concentrations of for m aldehyde, acetaldehyde, acrolein, and other aldehydes were significantly higher with increa sing proxim ity to high-traffic roads.

Several other studies have found that concentr ations of benzene and other mobile source air toxics are significantly elevated near busy roads com p ared to “urban background” concentratio ns m easured at a fixed s i te. 43, 44, 45, 46, 47, 48 

### For exam ple, m easurem ents near a tollboo th in Baltim ore observed m ean benzene c on centr ation s to vary by tim e of day f r om 3 to
22.3 μ g/m 3 

### depending on traffic volum e, vehicle type, and meteorology. 49 

### In com p arison with

am bient levels, Maryland’s Departm e nt of Envi ronm ent reported the ra nge of benzene annual

averages m e asured at seven different m onitoring sites in 2000 between 0.27-0.71 μ g/m 3



### . 50



### Another stu dy m easured the average benzene con cen tr ation in a relativ ely high traffic density (~ 16000 automobiles/day) sam pling area at 9.6 μ g/m 3 

### and in rural areas with hardly any traffic (<
50 autom obiles/day) at 1.3 μ g/m 3



### . 51 

### The concentration of benzene, along w ith several other air toxics (toluene and the isom eric xylenes), in the urban area far exce eded those in the rural area.

According to Gaussian dispers i on theory, pollutants em itted along roadw ays will sho w highest concentrations n eares t a ro ad , and concen trations exp onentially d ecrease with increasing distan ce do wnwind. These near-ro ad pollutant g r adien t s hav e been confirm e d by m e asurem ents of both criteria pollutants and air toxics. 52, 53, 54, 55, 56 

### Researchers have dem onstrated exponential reductions in concentrations of CO, as well as P M num ber, and black carbon (as m easured by an aethalom e ter), with increasing downwind di stan ce from a freeway in Los Angeles. 57, 58 

### These pollutants reached background levels appr oxim a tely 300 m downwind of the freeway.

3.1.3.2 Exposures Near Major Roadw ays

The m odeling assessm ents and air quality m onitoring studies discussed above have increased our understanding of am bi e n t concentrations of m o bile source air toxics and potential population exposures. R e sults from the followi ng exposure studies re veal that populations spending tim e near m a jor roadways likely expe rience elevated persona l exposures to m o tor vehicle related pollutants. In addition, these populations m a y e xperience exposures to differing physical and chem ical compositions of certain air toxic po llutants depend ing on the amount of tim e spent in close proxim ity to m o t o r vehicle em issions. Following is a detailed discussion on exposed populations near m a jor roadways.

3.1.3.2.1 In Vehicles

Several studies suggest that people m a y expe rience significant exposur es while driving in vehicles. A recen t in-v ehicle m onito ring st udy w a s conducted by EPA and consisted of in3-16Fin al Regu latory Im p act An al ysis



### vehicle air sam p ling throughout work shifts with in ten police patrol cars used by the North Carolina S t a t e Highway Patrol (sm o king not pe r m itted insid e the veh i cle s). 59 

### Troopers operated their vehicles in typical pattern s, including highway and city driv ing and refueling. In-vehicle benzene con centration s averaged 12. 8 μ g/m 3 

### , while con centrations m easured at an “a mbient” site located outside a nearby state e nvironm ental office averaged 0.32 μ g/m 3



### . The study also found that the benzene concen trations were closely asso ciated with other fuel-re lated VOCs m easured.

The Am erican Petro l eu m Institu te funded a screening study of “high-end ” exposure

m i croenvironm ents as required by se ction 211(b) of the Clean Air Act. 60 

### The study included vehicle chas e m easurements and m easurem ents in several vehicle-relate d m i croenvironm ents in several cities for benzen e and other air toxics. In-vehicle m i croenvironments (average concentrations in parentheses) included the vehicle cabin tested on congested freeways (17.5

μ g/m 3 

### ), in parking garages above-ground (155 μ g/m 3 

### ) and below-ground (61.7 μ g/m 3 

### ), in urban street canyons (7.54 μ g/m 3 

### ), and during refueling (46.0 μ g/m 3 

### ). It should be noted that sam p le sizes in this screening study were small, usually with only one to two sam ples per

m i croenvironm ent. The final re port of this study is expe cted to be released in 2007.

In 1998, the California A i r Resources Board published an extensive study of concentrations of in-vehicle air toxi cs in Los Angeles and Sacram ento, CA. 61 

### The data set is large and in cluded a va r i ety of sam p ling cond itio ns. On urban f r eeways, in-veh icle b enzene concentrations ranged from 3 to 15 μ g/m 3 

### in Sacram ento and 10 to 22 μ g/m 3 

### in Los Angeles. In

com p arison, am bient benzene concentrations ranged from 1 to 3 μ g/m 3 

### in Sacram ento and 3 to 7

μ g/m 3 

### in Los Angeles.

Studies hav e also been conducted in diesel buses, such as the one recen tly conducted o f LA school buses. 62, 63 

### In the study, five conventional diesel buses, one diesel bus equipped with a catalytic particle filter, and one natural ga s bus were m onitored for benzene, am ong other pollutants. These buses were driven on a series of real school bus r outes in and around Los Angeles, CA. Average benzene concentrations in the buses were 9.5 μ g/m 3 

### , compared with 1.6

μ g/m 3 

### at a background urban fixed site in west Lo s Angeles. Type of bus, traffic congestion levels, and encounters w ith other diesel vehicles cont ributed to high exposur e variability between

runs.

The sam e research ers ad dition ally d e term in ed the relative importance of school busrelated m i croenvironm ents to children’s pollutant exposure. 64 

### Real-tim e concentratio ns of black carbon (BC), particle-bound P AH, ni trogen dioxide (NO 2 

### ), particle counts (0.3-0.5 μ m size range), and PM 2. 5 

### m a ss were m easured inside school bus es during long comm ut es, at bus stops along the routes, at bus loading and unloading zones, and at n earby urban background sites. Across all th e pollu tants, m ean conce n tra tions during bus commutes were higher than in any other m i croenvironm ent. Mean exposures in bus comm utes were 50 to 200 tim es m o re than for loading and unloading zones at th e school, and 20 to 40 tim es m ore than for bus stops along the route, depending on the pollutant. T he in-cabin exposures w e re dom inated by the effect of surrounding traffic when windows were open a nd by the bus’ own exhaust when the windows were closed. The m ean pollutant concentra tions in the three school bus comm ute-related environm ents and background air ar e presented in the Table 3.1-5.

3-17Fin al Regu latory Im p act An al ysis



### Table 3.1-5. Mean Concentrations of Black Carb on (BC), Particle Bound PAH, NO 2 

### , Particle Count (PC), and PM 2. 5 

### in Three School Bus Commute Micro environmen ts and Background Air

Mean Concentrations

Background (Un)Loading
Zone Bus Stops Bus Co mmutes a



### BC ( μ g/m 3 

### ) 2 ± 0.1 2 ± 0.3 4 ± 0.4 3-19 (8)

Partic le Bound -PAH ( μ g/m 3 

### ) 0.027 ± 0.0015 0.015 ± 0.0003 0.044 ± 0.0045 0.064-0.400 (0.134)

NO 2 

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

PC (count/cm 3 

### ) 83 ± 3.1 Not collected 62 ± 1.8 77-236 (130)

PM 2. 5 

### ( μ g/m 3 

### ) 20 ± 2.4 Not collected 25 b 

### 21-62 (43)

a 

### Ranges are associated with different bus types and window position s. Values in parenth esis are th e m ean for all runs.

b 

### Not enough data to establish a confidence interval.

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

were m easured in buses on several routes in Detroit, MI. 65 

### The averag e in -bus concen tration of benzene was 4.5 μ g/m 3 

### , while the av erage con c entrations at three fixed sites taken during the study period ranged from 0.9-2.0 μ g/m 3



### . In this study, daily bus/am bient 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 m o st influenced by local traffic sources

. A num ber of other studies sim ilarly observe that passenger car commuters are exposed to elevated pollutant concentrations while driving on busy roads. 66, 67, 68, 69, 70, 71



### Older stud ies that exam ine in-veh icle concen trations in older m odel year vehicles are difficult to apply for regulatory analyses, due to the relatively rapid chan ges in vehicle em ission controls over the last 15 years. In general, these studies indicate th at conc entra ti ons in vehicles are sign ificantly high er than am bient concentrations. 72, 73, 74 

### The average ben z ene m easurem ents of these older in-vehicle studies (Raleigh, NC and CA South Coast Air Basin) are in Table 3.1-6 along with the m o re recent stud ies f o r com p arison.

Overall, thes e studies sho w that concentr ations experienced by commuters and other roadway users are substantially higher than am bi ent air m easured in typical urban air. As a result, the tim e a person spends in a vehicle will signif i can tly af f ect their overall expo sure.

3-18Fin al Regu latory Im p act An al ysis



### Table 3.1-6. Benz ene Concentrations ( μ g/m 3 

### ) Measured in Vehicles a nd in Ambient Air

Study In-Vehicle Am bient 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 -- Sacram ento, 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 m easurem e n t was taken for each ex perim ental trip.

b 

### The estim ated sam p ling tim e period was 1.5 hours/round-trip. n=191.

c 

### In-vehicle m easure m ent includes both interstate and urban driving, n=40.

d 

### Measurem ents taken from interiors of urban buses.

3.1.3.2.2 In Hom e s and Schools

The proxim ity of schools to m a jor roads m a y result in elevated exposures for children due to potentially increased c oncentrations indoors and incr eased exposures during outdoor activ ities. H ere we dis cu ss inte rna tio nal stud ies in addition to the lim ited num b er of US studies, because while fleets and fuels outsid e the U.S. can be m uch different, the spatial distribution of concentrations is relevant.

There are many sources of indoor air pollu tion in any hom e or school. These include indoor sources and outdoor sources, such as vehi cle exhaust. Outdoor ai r enters and leaves a house by infiltration, natural ven t ilation, and m e c h anical ventilation. In in filtration, ou tdoor air flows into the house through openings, joints, and cracks in walls, floors, and ceilings, and around windows and doors. In natural ventilat ion, air m oves through opened windows and doors.
Air m ove m e nt associated with infiltration and natural ventilation is caused by air temperature differences between indoors and outdoors a nd by wind. Finally, there are a num ber of

m echanical ventilation d evices, f r om outdoor-ve n ted f ans tha t interm itten tly r em ove air f r om a single room , such as bathroom s and kitchen, to ai r handling system s that use fans and duct work to continuou sly rem ove indoor air an d distribute f iltered and condition e d o u tdoor air to strategic points throughout the house. Th e concentrations of outdoor pollu tants can therefore influence indoor concentrations. A review of the literature determ ined that approxim a tely 100% of gaseous compounds, such as benzene, and 80% of diesel PM can penetrate indoors. 75, 76



### In the Fresno Asthm a tic Children’s E nvi ronm ent Study (FACES), traffic-related pollutants w e re m easured on selected days from July 2002 to February 2003 at a central site, and inside and outside of hom e s and outdoor s at schools of asthm a tic children. 77 

### Prelim inary data indicate that PAH concentrations are higher at elem entary schools located near prim a r y roads than at elem entary s choo ls dis t ant fro m prim ary roads (or located near prim ary roads with

3-19Fin al Regu latory Im p act An al ysis



### lim ited access). PAH concentration s also appear to increase with incr ease in annual average daily traf f i c on neares t major co lle cto r

.

The East Ba y Children ’ s Respira t ory Health Study studied traffic -rela ted air po llution outside of schools near busy roads in the San Francisco Bay Area in 2001. 78 

### Concentrations of the traffic pollutants P M 10 

### , PM 2. 5 

### , black carbon, total NO
x 

### , and NO 2 

### were m e asured at ten school sites in neighborhoods that spanned a busy traffi c corridor during the spring and fall seasons. The school sites were selected to represent a range of locations upwi nd and downwind of m a jor roads. Differences were observed in concentr ations between schools nearby (< 300 m ) versus those m ore distant (or upwind) from m a jor roads. Investigat ors found spatial variability in exposure to black carbon, NOx, NO, and (to a le sser extent) NO 2 

### associa t ed with roa d s with heavy traffic within a relatively sm all geograph i c area.

A study to assess children’s exposure to tra ffic-related air pollution while attending schools near roadways was perform e d in the Netherlands. 79 

### Investigators m easured PM 2. 5 

### , NO 2 

### and benzene inside and outside of 24 schools lo cated within 4 00 m of roa dways. The indoor average benzene concentration was 3.2 μ g/m 3 

### , with a range of 0.6-8.1 μ g/m 3



### . The outdoor average benzene concentration was 2.2 μ g/m 3 

### , with a range of 0.3-5.0 μ g/m 3



### . Overall results indicate that indoor pollutant co ncentrations are signi ficantly correlated with traffic density and

com position, percentage of tim e downwi nd, and distance from m a jor roadways.

In another study performed in the Neth erlands, investigator s m easured indoor concentrations of black smoke, PM 10 

### , and NO 2 

### in twelve schools betw een the periods of May and August 1995. 80 

### The schools were located at varyi ng distances from the m otorway (35-645

m )

. Results indicate that black sm oke and NO 2 

### concentrations inside the schools were signif i can tly corre lated with tru c k and/or car traffic intensity as well as percentage of tim e downwind from the m ot orway and distance of the school from the m otor w ay. PM 10 

### concentrations m easured in classroom s duri ng school hours were highl y variable and m uch higher than those m easured outdoors, but they did not correlate with any of the distance or traffic

param eters.

In another Dutch study, researchers monitored children’s personal exposure concentrations, and home indoor and hom e outdoor levels of “soot” (par ticle blackness), NO, and NO 2



### . 81 

### Four-m onth average con c entrations w ere calculated for each p ollutant. Personal exposure to “soot” was 35-38% hi gher in students living w ithin 75 m e ters of roads with 10,000

average ann u al daily traffic, a statis tically si gnificant result. Nonsigni ficant elevations in personal exposure to NO, NO 2, 

### and NOx were also found.

The TEACH study (Toxic Exposure Assessm e n t – Colum b ia/Harvard) m easured the concentrations of VOCs, PM 2. 5 

### , black carbon, and m e tals outsi de 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 ho m es. Average wint er (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 m oto r vehicle pollution, were c onsistent w ith traffic patte rns.

3-20Fin al Regu latory Im p act An al ysis



### Average benzene concentrations were determ ined in a recent evaluation of the exposure of urban inhabitants to atmos pheric b e nzene in Athens, Greece. 83 

### Hom e and personal levels of
50 non-sm okers in six monitoring cam pa igns varied between 6.0-13.4 and 13.1-24.6 μ g/m 3



### , respectively. Urban levels varied between 15.4 and 27.9 μ g/m 3 

### with an annual m e an of 20.4

μ g/m 3



### . The highest values were observed during the first two sam p ling periods in fall and winter, when wind speed was low. The low summer values were attrib uted to decreased veh i cle traffic. Among hom e fa ctors, only proxim ity to busy roads was determ ined to be an im portant influence on indoor benzene levels.

Children are exposed to elevated levels of ai r toxics not only in their homes, classroom s , and outside on school grounds, but al so during their comm ute to sc hool. See above discussion of in-vehicle (school bus and pa ssenger car) concentrations of air toxics for one m e thod of commuting. The discussion below also presents potential exposures to children from another commuting m e thod.

3.1.3.2.3 Pedestrians and Bicyclists

Researchers have noted th at pedestrians and cyclists along m a jor roads experience elevated exposures to m otor vehicle related pollutants. Although commuting near roadways leads to higher levels of expos ure to traffic pollutants, the ge neral consensus is that exposure levels of those comm uti ng by walking or biking is lower than for those who travel by car or bus, (see discussion on in-vehicle expos ure in previous section above). For exam ple, investigators found that personal m easurem ents of exposure to PM 10 

### concentrations w e re 16% higher inside the car than for the walk er on the s a m e route, but noted that a walker m a y have a larger overall exposure due to an increase in journey tim e. 84 

### Similarly, researchers found that traffic-related pollutant exposure concentrations of car drivers were higher than f o r cyclists. 85 

### Cyclists are typically on the border of the road or on dedicated bike paths and therefore further aw ay from the vehicle em issions and are less delayed by traffic jam s

. Howe ver, after accounting for cyclists’ higher ventilation, the uptake of CO, benzene, toluene, and xylenes by cyclists som e tim es approached that of car d r ivers, and for NO 2 

### it was significantly higher.

In the early 1990’s, researchers studied the in-vehicle concentr ations of a large num ber of

com pounds associated w ith m o tor vehicle use an d the exposure to VOCs of a pedestrian on an urban sidew a lk (50 m fr om roadways) in Raleigh, NC. 86 

### The m ean concentration of benzene in the six pedestrian sidewalk sam ples was 6.8 μ g/m 3



### . This concentr ation was lower th an the in

vehicle m ea surem ent (11.6 μ g/m 3 

### ), but higher than the fixed-site m e asurem ent (1.9 μ g/m 3 

### ) on urban roadw ays 100-300 m from streets.

The sam e research ers s t udied the ex posure of commuters in Boston to VOCs during car driving, subway travel, walking, and biking. 87 

### For pedestrians, m ean tim e-weighted concentrations of benzene, tolu ene, and xylenes of 10.6, 19.8, and 16.7 μ g/m 3 

### , respectively, were reported. For cyclists, the tim e-weighted concentra tions we re sim ilar to t hose of pedestrians, at 9.2, 16.3, and 13.0 μ g/m 3 

### , respectively. In-vehicle exposure concentrations were higher as discussed above.

Num e rous other studies which were conducted in Europe and As ia yield sim ilar results.

3-21Fin al Regu latory Im p act An al ysis



### A survey of CO concentration was conducted fo r various transport m odes along heavy traffic routes in Athens, Greece. 88 

### Results showed that m ean CO levels for trips of 30 m i n were 21.4

ppm for private car, 10.4 ppm for bus , and 11.5 ppm for pedestrians. In Northam pton, UK during the winter 1999, personal m easurem ents of exposure to PM 10 

### , PM 2. 5 

### , and PM 1 

### were m a de during walking and in-car journeys on two suburban routes. 89 

### In-car m easurem ents were highest (43.16, 15.54, and 7.03 μ g/m 3 

### for PM 10 

### , PM 2. 5 

### , and PM1, respectively) followed by walking (38.18, 15.06, and 7.14 μ g/m 3 

### , respectively). Background levels were only available for PM 10 

### (26.55 μ g/m 3 

### ), but were significantly lower than the walking exposure levels. Researchers found sim ilar results for CO exposure levels of schoolchildren commuters. 90 

### So although personal exposures are greater for in-vehic le comm utes, pedestrians and bicy clists in proxim ity to heavy traffic are exposed to elevated pol lutant levels relative to background.

3.1.3.3 Concentrations and Exposure in Homes w i th Attached Garages

Residential indoor air quality is a m a jor de term inant of personal exposure, with m ost people spending the m a jority of their tim e indoors at hom e. According to the National Hum a n Activity Pattern Survey, nationally, people spe nd an average of 16.68 hours per day indoors in a residen ce. 91 

### The large f r action of time spent in th is m i croenvironm ent i m plie s that so urces th at im pact indoor air are likely to have a substantial effect on personal exposure.

Indoor air quality is in large part determ in ed 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 ris e s in a h o m e , causin g a pressure drop in the lower part of the hom e, which then c r eates airf lo w into the h o m e f r om hi gher-pressure locations outside the hom e

. Natural ventilation can also be influenced by opening of windows and doors. Mechanical ventilation employs fans and som e tim es ductw ork to m a nage ventilation within a hom e

.

Air can be drawn into a hom e from e ither out doors, or in a hom e 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, hom es with a greater fraction of airflow from the garage are m o re susceptible to air quality decrem ents from in-garage em issions.

Several studies have exam ined hom e s with at tached garages to dete rm ine the fraction of residen t ial air in take fro m the garag e. A recent study from Fairbanks, Alaska used perfluorocarbon tracer (PFT) gases to estim at e that 12.2% of air en tering a m echanically ventilated energy efficient hom e and 47.4% of th e air entering the livi ng spaces of an older passively ventilated hom e originated in the hom es’ attached garages. 92 

### In an Ann Arbor, Michig an ho m e, research ers used PF T gases to estim ate that 16% of the air en tering the hom e entered through the garage. 93 

### A recent study of a represen tativ e sam p le of hom e s in Anchorage, Alaska em ploying PFT estim ated that in hom es with a forced air furnace in an attach ed garage, 36.7% of indoor air originated in the garage. 94 

### In hom e s that h a d forced air furnaces in doors or hytronic heat, 17.0% and 18.4% of indoor air orig inated in the garage, respectively. A study from Minnesota exam ined hom es constructed in 1994, 1998, and 2000. 95 

### Hom e s built in 1994 had 17.4% of airflow originating in the gara ge. Hom e s 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

3-22Fin al Regu latory Im p act An al ysis



### average of 1 3 % of hom e air in take cam e from the garage. 96 

### That study also found that the house-garag e interface area was as leaky as the rest of the building envelo pe. In anoth e r study from W ashi ngton, D.C., the house-g arage interf ace was found to be 2.5 tim es as per m eable as the rest of the house. 97 

### This disc repan cy m ay indicate that ho m es built in colder clim ates ar e built m o re tightly th an h o m es in warm er regions as a resu lt of weather-sealing. However, there is no evidence that in regions w ith cold weather, colder tem p er atures lead to elevated indoor concentrations of VOCs. 98



### Several studies have exam ined the influe nce of attached garages on indoor air and personal exposure. In the 1980’s researchers identified attached garages as a m a jor source of benzene and other VOCs in residences. The Total Exposure Assessm ent Methodology (TEAM) Study was com p leted in 1985. 99 

### The goal of this study was to develop m e thods to m easure individu al to tal expo sure (through air, food a nd water) and res u lting body burden to to xic and carcinogenic chem icals, and then to apply thes e m e thods with a proba bility-based sampling fra m ework to estim ate the exposures and body bur dens of urban populations in several U.S. cities. The study m easured personal exposures of 600 people to a num ber of air toxics. The subjects were selected to repr esent residents of cities in Ne w J e rsey, North Carolina, N o rth Dakota, and California. In the study, a larg e fraction of an average nonsm oker’s benzene exposure originated from sources in attached garages. 100 

### W ork done as part of the TEAM study also id entified stored g a soline as an im portant so urce of elev ated be nzene levels indoors. 101 

### This stored gasoline can be found prim arily in gas cans as well as the fuel tanks of lawn and garden equipm ent, such as lawn mowers and string tr immers. Lawn and garden equipm ent fuel tank

em issions, however, are significan tly lower than evaporative em issions from gas cans, because the fuel tanks are m u ch sm aller than gas cans, typically 0.3 to 0.4 gallons. Em issions are also higher from gas cans because ve nts and spouts are left open.

These early studies have highlighted the role of evaporative em issi ons within the garage as contributors to indoor air pollution. Since th en, m a jor changes have af fected em issions from vehicles, including additional controls on evapora tive em issions, on- board diagnostics, and state inspection and m a intenance progr ams addressing evaporative em ission controls. Several researchers have subsequently conducted air m eas urem ents in hom e s and in attached garages to evaluate the effects on indoor air.

Garage concentrations of benzene and other VOCs are generally m uch higher than either indoor or outdoor air, and cons titute one of the highest-conce ntration m i croenvironm ents to which a person m i ght typically be exposed outside the occupational setting. The garage also supplies con t am inated air to th e home to which it is attached. One recent study from Michig an found average garage benzene concentrations of 36.6 μ g/m 3 

### , with a standard deviation of 38.5

μ g/m 3 

### , compared to m ean and standard deviation concentrations of 0.4 μ g/m 3 

### and 0.12 μ g/m 3 

### in

am bient air. 102 

### In Alaska, where fuel b e nzene levels tend to be very high and hom e s m a y be built v e ry airtigh t , garag e concentrations have be en m easured at even h i g h er lev els. One study from Anchorage m easured average garage benzene concentrations of 103 μ g/m 3 

### , with a standard deviation of 135 μ g/m 3



### . 103 

### More recently, a two-hom e study in Fairbanks found garage benzene average concentrations of 119 μ g/ m 3 

### during sum m er and 189 μ g/m 3 

### during winter in one wellventilated h o m e with an air-to-air h e at exchang e r. 104 

### In an older hom e with passive ventilation summer and winter garage benzen e concentrations were 421 and 103 μ g/m 3 

### , respectively.

3-23Fin al Regu latory Im p act An al ysis



### Other studies have studied the effect of garages or the sour ces within them on indoor air quality. Most prom inently, a group of Canadian investigators conducte d source apportionm ent of indoor non-m e thane hydrocarbons (NMHC) in 16 Ontario hom e s in the late 1990’s. 105 

### They also assem bled source profiles from hot soak an d cold start e m issions, which they u s ed to conduct source apportionm ent of total indoor air NMHC. All em issions sam ples and house testing were conducted using the same 1993 m odel ye ar vehicle. Overall, while the vehicle was hot-soaking in the garage over a four hour sa m pling period, between 9 and 71% of the NMHC inside the house could be attri butable to that vehicle’s em issi ons. Sim ilarly, in the two hours following a cold start event, be tween 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

μ g/m 3 

### , whil e during the hot soak, co ncentration s averaged 13 .4 μ g/m 3. I n the garage, concentrations averaged 121 μ g/m 3 

### during the cold start. Prior to a cold start, indoor benzene concentrations averaged 6.98 μ g/m 3 

### , while for the two hours following cold start, concentrations averaged 25. 9 μ g/m 3



### . In the garage, concen tration s averag ed 4 22 μ g/m 3 over the two hours
f o llowing co ld sta r t.

The study also conducted real -tim e monitoring of CO a nd t otal hydrocarbons (THC) within the house and garage. Ov erall, concentrations of CO a nd THC wer e 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 attributab le to em ission events occurring in the garage.

Other studies have exam ined the influence of attached garages by com par ing hom es with and without attached garages. In another study from Alaska, 137 Anchorage hom es underwent indoor air quality m onitoring for benzene and other VOCs. 106 

### Hom e s with attached garages had significantly higher concentrations of indoor be nzene com pared to hom es without attached garages (70.8 μ g/m 3 vs. 8.6 μ g/m 3 ). In addition, elevated ben zene indoors was also associated with the p r esence of a v e hicle in th e garage, fu el being opened in the garage, and the use of forced-air h eaters.

In another Alaska study, concentrations of be nzene and toluene in indoor air were found to be not significantly associated with their ur inary biom arkers, but indoor concentrations were associated w ith the num ber of gasoline- powered engines stored in the garage. 107 

### In a recent follow-up to the study, ventilati on patterns in two hom e s were evaluated using perfluorocarbon tracers and a m u lti-zon e indoor air qu ality m odel. 108 

### In the study, average garage concentrations were consistently elevated relative to the hom e. Furtherm ore, the study calculated the “virtual”

source s t ren g ths for ben zene and to luene within the garage, and the garage was the only m a jor source of benzene within the hom e. Median garage source strengths for benzene ranged from
14-126 m g/ h.

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

garages. The National Hum a n Exposure Assessm ent Survey (NHEXAS) Phase I pilot study in Arizona was a representative exposure survey of the population. It f ound that in non-smoking hom e s with attached garages, distribution of toluene concentrations indoors was shifted significantly higher in hom e s with attached garag e s. 109 

### Hom e s with attached garages had

3-24Fin al Regu latory Im p act An al ysis



### m e dian toluene levels of 24 μ g/m 3 

### , while hom es without garages ha d m e dian concentrations of 5

μ g/m 3



### . The NHEXAS study in EPA Region 5 states was of sim ilar design, but covering the states of the upper Midw est. Using multivaria te statistics, in vestig ators found that V O Cs includ ing benzene were associated w ith th e st orage of gasoline-powered equipm ent in an attached garage. 110



### In one study from New J e rsey, investigators evaluated the indoor air effects of a vehicle fueled with “M85” – an 85% m e thanol, 15% gaso line blend – parking in the garage of a single hom e

. 111 

### Testing was undertaken with bot h norm a lly-functioning and m a lfunctioning evaporative em issions controls, as well as with the HVAC s ystem on and off. Garage benzene concentratio ns exceeded indoor concentration by a pproxim ately 10-fold. Furtherm ore, the room adjacen t to the garage h a d substantially higher co n centration s than a room on the oppo site side of the house. This study provides evidence that the ga rage is a m a jor source of benzene inside the house.

Appendix 3A presents an EPA analysis of th e effect of attached garages on indoor air under various scenarios. This study was undertak en to evaluate the m a gnitude of exposure underestim ation using the national-scale exposure m odeling techniques discussed above. Using

a m ass balance m odel, steady-state concentration s of benzene were calculated as a function of the concentration of air in the garage, the con centration of outdoor air, and the fraction of house air intake from a garage. Data were obtained from studies di scussed above. Because it is unclear how well the hom e s studied to date repr esent the housing nationall y, it is not currently
f easible to p r ovide a h i g h ly prec ise e stim ate of the effect of attached garages on benzene exposure nationally. Depending on how the availa ble data are summ arized, overall modeled exposure concentrations would be e xpected to increase between 1.2 and 6.6 μ g/m 3 

### above average inhalation exposure concentrations to benzene from a m bient sources (1.4 μ g/m 3 

### , as discussed in Section 3.2). It should be noted that there is consid erable uncertainty a ssociated with this estim ated range, as discussed in Appendix 3A. Proposed reductions in fuel benzene content, new standards for cold tem pe r ature exhaust

em issions during vehicle starts, and reduced em issions from gas cans are all exp ected to significantly reduce this m a jor source of exposure.

3.1.3.4 Concentrations and Exposure in Parking Garages

Relatively lim ited air quality data for parking garages is available in the literature. Th e f o llowing ar e results of a i r qua lity stu d ies perf or m ed in park in g garages, all of which in dicate that air toxics and criteria po llu tants m easured in these env i ro nm ents are substantially higher than found in outdoor air. Because of the lim ited am ount of data, we include results from som e non-U.S. studies, although differences in fuels and control technology lim ite d their app licability to the U.S.

In Novem be r 1990, a study of m i croenvironm ents, partially funded by the US EPA, evaluated the potential range in conc entrations of selected air toxics. 112 

### Ten parking garages, along with gasoline stations and office buildings, were random l y chosen for sam p ling since they

were am ong the le ast s t u d ied of the p o tentiall y im portant exposure m i croenvironm ents. The principal air contam inants m onitored were benzen e, for m aldehyde, and CO. Additional

3-25Fin al Regu latory Im p act An al ysis



### com pounds included toluene, xylenes, 1,2-dichlo roethane, chloroform , carbon tetrachloride, perchloroethylene, 1,1,1-trichloroe thane, 1,3-butadiene, and trichloroethylene. The majority of the com pounds m easured were significantly higher inside the garage com pared to the am bient

sam ple. For exam ple, the m e dian 5-m i nute concentration of benzene was 67.1 μ g/m 3 

### in the parking garage and 12.8 μ g/m 3 

### in a m bient air. C O was 11000 ppb in the parking garage and 2000 ppb in am bient air. The researchers identified elevated levels of selected air toxics in parking garages and pointed out the potential contri bution from cold starts at the end o f the work day.

A m ore recent 2002 study was funded by The Am erican Petroleum Institute to screen “high-end” exposure m i croenvironments as re quired by section 211(b) of the Clean Air Act. 113



### An interim r e port is available. The study included m easurements at underground parking garages and surface park ing lots in several citi es. Air toxics q u antified included hydro carbons (HCs), carbonyl com pounds, BTEX, total VOC, a nd CO. When sam pling at parking lot exits, spikes in po llutan t concentrations were observ e d when vehicles accelera ted out of the parking lot, while presum ably prior to full catalyst warm -up. In underground garages, the levels of BTEX and other com pounds of interest varied with traffic level and reached concentrations that were significantly higher than am bient levels outside the garage. The final report of the 211(b) is expected in 2007.

A com parative study of indoor air quality in Hong Kong showed that the levels of CO, NOx, and nonm ethane hydrocarbons (NMHC) detected in a local park gara ge were the highest

am ong 13 ot her indoor sam p ling locations. 114 

### The study did not specify the type or size of the chosen parking garage, but indicated that it wa s located in an urban commercial area. High indoor/outdoor ratios indicated that the air quality was m a inly affected by indoor sources,

nam e ly the vehicle exhaust. They also conclude d that the pollution generated m i ght cause health hazards to th e users and workers using such an environm ent.

Another assessm ent of the air quality in i ndoor park garages was perform e d in Hong Kong in August through Decem ber 2000. 115 

### Air samples were collected in two different garages (an enclosed and sem i -enclosed parking garage ) as well as outdoors (within 10 m of ea ch parking garage) and analyzed for one hundred different C3-C12 VOCs. Other com p ounds

m easured included CO, CO 2 

### , PM 10 

### , and PM 2. 5



### . The CO levels in the enclosed garage were m ore than in the sem i -enclosed garage, and doubl e the levels of the outdoor air. The PM 10 

### and PM 2. 5 

### concentrations were also found to be higher in the parking garage environm ents than outdoors. High m a ss fractions of aliphatic and arom atic compounds detected in the enclosed garage showed that fuel evaporation and m otor vehicu lar exhaust were the m ajor contributors to the
VOCs. The tota l concen tra tions of NMHC in the enclosed and sem i -enclosed garages ranged from 580 to 4610 μ g/m 3 

### and 43.1 to 175 μ g/m 3 

### , r e spectively. The m ean c oncentration of NMHC

m easured in the enclosed garage (1910 μ g/m 3 

### ) wa s about 17 tim es higher than in the sem i

enclosed garage (94.6 μ g/ m 3 

### ), and 3 tim es higher than m easured at the outdoor sites. Not only was the level of VOCs higher in the enclosed garage, but also the abundance of species identified. The m ost abundant species in sim ilar ranking order for both garages was toluene, 2­

m ethylbutane, m/p -xylenes, n -pentane, 2-m e thylpentane, n -hexane, and n -butane. Other m a jor gasoline components such as ben zene, xylenes, and C4-C7 saturate d HCs were also very high in the enclosed garage. The difference between the two sites could be associated with th e

3-26Fin al Regu latory Im p act An al ysis



### ventilation and location, since th e occupancy rates and fleet m i xe s were sim ilar. The authors also noted th at the absen ce of sunlight in the enclosed garage would result in a slower or negligible photochem i cal deple tion rate of unsaturated hydro carbons, and consequently an increased abundance of the species observed.

In another study of m u lti- level park in g garages in an Athens urban area, C O levels were characterized in autum n 1999. 116 

### Sam p les were collected at th e exit s ite s (r a m p where the f l ow of vehicles was concentrated), th e indoor site (first unde rground level where the m a jority of cars parked), and imm e diately outside of each garage

. Results ind i cate that CO levels varied significantly over site, tim e, and da y of m easurem ent. The peak 1-hour value at the indoor sites ranged from 22.9 to 109.3 ppm

. At the indoor site, levels showed little va riation and rem a ined high over tim e

. The pe ak 1-hour va lue at the exit sites ranged from 8.9 to 57.3 ppm

. At the exit sites, 15-m inute m a xi mum concentrations were 5-15 tim es higher than th e m axi m u m record ed CO level imm e diately outside the garage. CO levels on Saturday were m u ch lower th an a typical weekday due to the reduced traffic, and wee kday values were highe st during the afternoon

sam p ling tim es (12:00-16:00 hour) corres ponding with peak traffic volum es.

In Mum b ai, India, am bient levels of benzene were determ ined during different seasons at several different locations, including two parking areas. 117 

### Param e ters of the parking areas were not specified, but 24-hour geom etric m eans of benzene m easured 117.4 and 74.2 μ g/m 3 

### during the summ er, 94.5 and 75.4 μ g/m 3 

### dur ing the m onsoon, and 148.0 and 703.0 μ g/m 3 

### during the winter seaso n s, respectiv ely. These v a lues we re considerably higher in co mparison to less heavily traf f i cked r eside ntia l loca tio ns. The m e an benzene concentrations of four different residential locations ranged from 4.7 to 32.9 μ g/m 3 

### , 1.9 to 33.5 μ g/m 3 

### , a nd 4.7 to 18.8 μ g/m 3



### , respectively, for the summ er, m onsoon, and winter seasons. The high con centrations in parking

areas wer e a ttr ibuted to c o ld start-up em issions of engines.

A study in the UK of twelve underground parking garages identified highpollutant levels of NOx, CO, CO2, BTEX, and PM. 118 

### The parking garages selected covered a cross-section of sizes (1 to 8 decks), ven t ilati on system (natural and m echanical ), designs (50 to 690 spaces), and usages (business, shopping, and/or residential). Monitoring sites were locat ed inside and at the exit of the parking garage. The highest 15-m i nute averag e CO levels were m easured at the exit of parking garages, but a num ber of the parking garages had CO levels consistently higher inside than at their exit. The N O 2 

### m easurem ents showed sim ilar trends. W eekday benzene concentratio n m easurements averaged over one h our inside the parking garage and at the exit ranged from 60 to 870 μ g/m 3 

### and 10 to 350 μ g/m 3 

### , respectively.

In Madrid, Spain, atm ospheric pollution produ ced by vehicles in parking garages was studied. 119 

### Two parking garages of different desi gn were chosen for m e asurem ents of PM 10 

### , lead, 12 PAHs, and CO. In both garages, CO, NO, TSP, and lead conc en tra tions d i re ctly 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

exam ple, in one garage, the average daily TSP concentrations were 78-122 μ g/m 3 

### on the weekdays versus 39 μ g/m 3 

### on the weekend, which was sim ila r to outdoor city average

m easurem ent (50 μ g/m 3 

### ). The researchers con c lu de that m a ximum concentrations for NO were observed during m a xi mum parking ga rage exits and therefore due to veh i cle cold-sta rts. They

3-27Fin al Regu latory Im p act An al ysis



### also conclud e that the mechanical ventilation used in both garages was not sufficient to disperse the pollutan t s em itted by the vehicles

.

3.1.3.5 Concentrations and Exposure at Service S t ations

Although th ere is relativ ely lim ited air quality d a ta for service stations availab l e curren tly in the litera t ure, the g en eral consens u s is tha t exposures to air toxics at se rvice s t a tion s significan tly exceed am bient backgro und levels. The studies below m eas ure person al exposures and concentrations during refueling either insi de or outside of vehi cles throughout the United
States. Several studies conducted outside of the United States chronicle sim ilar results but are not presented here due to differences in fuels and control technologies.

The TEAM study from t he 1980’s, described ab ove, pum ping gas and exposure to auto exhaust were significantly associated with elev ated benzen e exposure. P e ople who filled their tanks with gasoline had twice as m u ch benzene in their breath as people w ho did not. Estim ated concentrations at the brea thing zone could exceed 1000 μ g/m 3 

### (100 tim es the am bient level), based on the m e dian breath benzene value m easured (n=67) for those who had worked at or been in a service station durin g the past 24 hours. Since this st udy, im ple m entation of fuel controls, onboard vapor recovery, and Stage II vapor recovery have cha nged em ission and concentration levels as discussed in Section 3.1.1.

In March 19 90, another study rando m l y sa m p le d 100 self-service filling stations throughout Southern California al ong with sam p les at 10 parking garages and 10 offices nearby those garages. 120 

### The study took five-m inute sa mples of 13 motor vehicle air pollutants (CO, for m aldehyde, and VOCs) in each microenvironm e nt and in the am bient environm ent. The

m e dian benzene concentration m easur ed at the service stations was 28.8 μ g/m 3 

### with th e

m a xi m u m reported value of 323 μ g/m 3



### . The m e dian benzen e concentration in am bient air was significantly lower at 12.8 μ g/m 3



### .

A 1993 National Institute for Occupational Safety and Health (NIOSH) study assessed benzene and MTBE concentrations and service st ation attendant exposures at service stations with and without Stage II vapor recovery in Cincinnati, Phoenix, and Los Angeles. 121 

### The m ean (and m a xi mum ) benzene exposure m easure m en ts were 96 (927), 160 (1662), and 192 (607)

μ g/m 3 

### , respectively. The study found that Stage II vapor recovery did no t significantly reduce exposure to benzene during refueling. However, th e efficiency of Stage II vapor recovery has
im proved over the years. Northeast S t ates fo r Coordinated Air Use Managem e nt (NESCAUM) has suggested that Stage II vapor recovery syst em s are greater than 90% effective at capturing MTBE and benzene vapors during refueling. 122 

### These system s would theref ore be expected to reduce exposure beyond that shown in the NIOSH exposure assessm ent.

In March 1996 to July 1997, concentrations of MTBE, benzene, and toluene were determ ined inside automobile cabins during fueling. 123 

### Air sam p les were co llected at service stations in N e w Jersey, and the m ean benzene in-cabin concentration was 54.3 μ g/m 3 

### (n=46). The background concentration at the pum p island m easured 9.6 μ g/m 3 

### (n= 36). The highest incabin conc e n tra tions f o r

all three po llutan ts o ccu rred in a car that ha d a malfunctioning vapor recovery system and in a series of cars sam ple d on an unusually warm , calm winter day when the

3-28Fin al Regu latory Im p act An al ysis



### fuel volatility was high, the evaporation m a xim a l, and the wind dispersion m i ni m a l. The incabin concentrations were also typically hi gher when the car window wa s opened during the entire fueling process.

In a study conducted between summer 1998 a nd spring 1999, self-service gas station

custom ers took part in a study to m easure personal and breath concentrations of benzene at gas stations in N e w Jersey. 124 

### Benzene exposure concentrations during refueling (with a m e dian duration of three m i nutes) averaged 2.9 m g/ m 3 

### (SD = 5.8 mg/m 3 

### ). Breath concentrations averaged 16 0 μ g/m 3 

### (SD = 260 μ g/m 3 

### ). Breath b e nzene concentr a tions w ere sign if ica n tly correlated with refueling exposure concentrations , which was itself significantly associated with refueling duration, tim e of year , and fuel octane grade.

Most recently, as discussed in the section on in-vehicle and parki ng garage exposure and concentrations, a screening study of “high-end” exposure m i croenvironments was perfor m ed by the Am erican Petroleum Institute. 60 

### The study included several vehicle-related

m i croenvironm ents in Houston and Atla nta during summer 2002. Am ong the various

m i croenvironm ents exam ined, the highest short-te rm concentrations occurred during refueling. The in-vehicle average concentration of benzene m easured during refueling was 46.0 μ g/m 3



### .

3.1.3.6 Occupation al Exposure

Occupational settings can be considered a m i croenvironm ent in which exposure to benzene and other air toxics can occur. Occupa tional exposures to benzene from m o bile sources or fuels can be several orders of m a gnitude greater than typical exposures in the nonoccupationally exposed population. Several ke y occupational groups are discussed below.

Occupations that involve fuel distribution, storage, and tank rem e diation lead to elevated exposure to mobile-source relate d air toxics. Resear chers published a revi ew of benzene and total hydrocarbon exposures in the downstream petroleum industry, including exposure data from the past two decades am ong workers in the f ollowing categories: refinery, pipelin e, m a rine, rail, bulk term inals, tank truck drivers, servi ce stations, underground storage tanks, tank cleaning, and site rem e diation. 125 

### The studies reviewed indicate that benzene exposure can range from <1

to m o re than 10 m g / m 3 

### , which is approxim a tely three orders of m a gnitude higher than typical non-occupational exposures (althoug h there are occurrences of high benzene exposures in nonoccupational settings as well). Th is review is relevant because of the potential for fuel benzene reductions to reduce their exposures as well. This statem ent is echoed by researchers in the occupational literature. 126 

### Occupational exposures in this range have been associated with increased risk of certain leukem i as in o ccupational epidem iology studies (Section 1.3.1).

Handheld and non-handheld equipm ent operato rs m a y also be exposed to elevated concentrations of air toxics. As discussed be low, several studies were conducted in w ork categories employing sm all engine equipm ent, such as lawn and garden workers, workers in construction / dem o lition, and others. Many of th ese occupatio ns require th e use of pers onal protective equipm ent to prevent hi gh exposures to carbon m o noxide or other species. At present, there are no representative sa m ples of exposures am ong these categories. Non-occupational exposures from these equipm ent types m a y also be im portant contributors to overall exposure.

3-29Fin al Regu latory Im p act An al ysis



### EPA recently conducted a study of occupational exposures among lawn and garden workers using riding tractors, walk-behind lawn m o wers, string trimm ers, and chainsaws. 127 

### Results dem onstrated that equipm ent operators can expe rience highly variable exposures, with shortterm personal concentrations of CO and PM 2. 5 

### ranging over two orders of m a gnitude. The study also repo rted operato r b r eath i ng-zon e concentr ations of for m aldehyde an d acetaldehy de that were higher than background levels in all tests. This study illustrated the role of operator’s activ ity in af f ecting expo sure lev els to f u el-re late d air toxics.

Another study provides som e insight into th e 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 μ g/m 3 

### to 2.5 m g / m 3 

### , a range of at least tw o orders of m a gnitude. Exposures m easured on riders on the back of the vehicle ranged from 0.7-0.8 m g / m 3



### . These

m easure m ents illustrate the potential for rela tively high exposures when operating 2-stroke equipm ent, as used in this study. Yellowstone N a tional Park comm issi oned a study in 2002 to

exam ine occupational exposures of park em ployees to benzene, other VOCs, PM 10 

### , and CO. 129



### Work shift benzene concentrations at a snowm obile entry gate 176.7 μ g/m 3 

### , while sno w m obilebound m obile patrol officers’ expos ure concentrations averaged 137.20 μ g/m 3



### . The highest observed work shift concen tration in the study was 514.1 μ g/m 3



### . At m ajor sites of tou r ist interes t where snowmobiles parked, such as the Old Fa ithful geyser, concentrations averaged 41.3 to
48.8 μ g/m 3



### . 15-m i nute “peak” sam p les of worker s’ personal air ranged from 46.8 μ g/m 3 

### to 842.8

μ g/m 3



### . This study provides an indication of the variability of occupa tional benzene exposure concentrations with tim e, and highlights the potentia l for elevated work shift exposures over several hours.

A prelim inary report published by the Nort heast States for C oordinated Air Use Managem e nt further illustrate s the occupational im pact of nonroad heavy-duty diesel equipm ent. 130 

### In-cab in and work site p e rim e ter m e asurem ents were collected for diesel equipm ent em issions from the agricultural, c onstruction (building and roadway), and lum ber industries in the Northeast. In itial results indicate that PM 2. 5 

### concentrations were 1-16 tim es greater th an the average am bient concentration s in each m onitoring area. In-cab in exp o sures to PM 2. 5 

### for operators ranged from 2 μ g/m 3 

### to over 660 μ g/m 3



### . Additionally, m easured concentratio ns of acetald e hyde, benzene, and form aldehyde were found to be significantly elevated, although concentrations were not presented.

In one recently-published study of diesel exhaust exposures in a representative sam ple of trucking term inals nationally, i nvestigators applied structural equation m odeling to data on personal exposure to diesel exhaust (as elem ental carbon). 131 

### The study found that worker exposure to elem ental carbon depended on work ar ea concentrations and worker tobacco use. Work area concentrations depended on the size a nd type of the trucking term inal, whether the work site was a m echanical shop, work site ve ntilation, and term inal yard concentrations.
Term inal yard concentrations in turn were related to local m e teor ology, the proxim ity of interstate highways, surrounding in dustrial land uses, and region of the country. This study is valuable in showing how personal occupational exposures are a com plicated function of m a ny factors. Sophisticated statistic al m e thods are needed to prope rly estim ate models with highly

com p lex covarian ce structures.

3-30Fin al Regu latory Im p act An al ysis



### In addition, som e occupations require that wo rkers spend considerable tim e in vehicles, which incr e ases the tim e they spend in a highe r- concentration m i croenvir onm ent. In-vehic le concentrations are discus sed in Section 3.1.3.2.1 above.

3.1.4 Uncertainties in Air Toxics Measu rements

A num b er of uncertaintie s lim it our a b ility to fully describe the im pacts of m o tor vehicle

em issions. As described above, m o st people in the U.S. experience some level of exposure to

em issions from m o tor vehicles. Thus, proper char acte r iza tion of the level of these exp o sures is critical. However, the exposure assessm ent tec hniques used m a y not adequately represent the populations’ true exposures to m o tor vehicle em issions.

Air quality and exposure m easure m ents ar e expensive and theref ore are limited. The high costs of m easurem ent techn i ques affect the quantity of sam ples that can be collected and quantity of com pounds that can be identified. As a result, m easurem ents m a y only oc cur at

centr al m onitoring s i tes, rathe r than in m i croe nvironm ents impacted by m o tor vehicle em issions or in personal breathing zone s. Air quality m onitoring at these central sites often do not represent actual exposures, especially for populat ions living near roads or with substantial occupational exposure.

Monitoring sam p les are often integrated and th er ef ore lack tim e resolutio n. This can result in d i f f iculty in de term ining source con t rib u tions. Add ition ally, so m e compounds are ha rd

to m easure accurately. F o r exam ple, 1,3-butad ien e is very reactive in the am bient atmosphere and has a short atm o spheric lifetim e, estim ated to be only two hours. 132 

### Thus, this com pound can easily break down before sam ples are analyz ed. Also, a vapor pressure of 3.3 atm at 25 o 

### C

m a kes it a very volatile com pound. Secondary react ions are a confounding f actor in air quality

m easure m ents and can add additional uncertain ty to m easured am bient concentrations.

Personal exposure m onitoring provides greate r realism in describing a person’s actual exposure to air toxics. However, given the lim itations on size of equipment, detection lim its in personal exposure m onitoring studies are som e ti m e s greater than those found in studies using other techniques.



## 3.2 Modeled Air Quality, Exposu res, and Ris k s for Air Toxics

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

EPA assesses hum an hea lth im pacts from out door, inhalation, chronic exposures to air toxics in the National-Scale Air Tox i cs Assessm ent (NATA). It assesses lifetim e risks assum i ng continuous exposure to levels of air toxics estim ated for a partic ular point in tim e. The m o st

recen t NATA was done for the year 1999. 133 

### It had four steps:

1) Com piled a national em issions inventory of ai r toxics em issions from outdoor sources. The 1999 National Em issions Inventory is the underlying basis for the em issions inform ation in the 1999 assessm ent.

3-31Fin al Regu latory Im p act An al ysis



### 2) Estim ated am bient concentrations based on em issions as input to an air dispersion model (the Assessm ent System for Popul ation Exposure Nationwide, or ASPEN model). 134 

### 3) Estim ated population exposures based on a screening-level inhalation exposure model (Hazardous Air Pollutan t Exposure Model, ve rsion 5, or HAPEM5) and the estim a ted

am bient concentrations (from the ASPEN m odel) as input to the exposure model. 135 

### 4) Characterized 1999 potential public health ri sks due to inhalation of air toxics. This included cancer and noncancer effects, using av ailable inform ation on air toxics health effects, current EPA risk assessm ent and risk ch aracterization guide lines, and estim a ted population exposures. 136



### For this final rule, we have conducted ai r quality, exposure and risk m odeling for the years 1999, 2015, 2020, and 2030, using the sam e gene ral approach as the 1999 NATA. W e modeled all the pollutan t s in Ta ble 2.2-1 for both the reference case, which includes all control

program s currently planned by EP A in regulations, and the co ntrol case, w h ich includes the

cum u lative impacts of the standards propos ed in this rule. These pollutants

• Are on EPA’s list of hazardous air polluta nts in Section 112 of the Clean Air Act

• Are em itted by m obile s ources

• Are included in the National Em issions Inventory

• Are included in the 1999 NATA Note that the m odeling did not include diesel P M and diesel exhaust organic gases. EPA has previously done future-year pr o j ec tio ns of the m o bile sou r ce contribution to air toxics concentrations, exposure, and ri sk for selected air toxics, 137, 138, 139, 140 

### but prior to the proposal for this rule, had never done a com prehensive as sessm ent that includes projections for all m obile source air toxics, as well as the st ationary source contribution for those pollutants. It should be noted that the reference case assessment results developed for the proposal have been published in a pee r rev i ewed journ al ar tic le. 141



### As discussed in Chapter 2, a num ber of m a jor revisions to inventory m e thodology have been m a de relative to w hat was done for both the 1999 NATA, and air quality exposure and risk modeling for the proposal. These include revi sions to cold start em issions, use of NMIM2005 for nonroad equipm ent, addition of portable fuel container em issions, and changes to gasoline distr i bution invento r ies. Also, this f i nal ru le m o deling f o r 19 99 does not include d ata subm itted by States for the 1999 NEI. In addition, the m o deling for the final rule relied on an updated version of the HAPEM model, HAPEM6. 142 

### HAPEM6 im proves 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 n ear m ajor roadways. Other im provem ents to HAPEM are discussed in section 3.2. 1.2.1. This modeling work is discussed in m ore detail in an EPA technical report, “N ational Scale Modeling of Air Toxics for the Final Mobile S ource Air Toxics Rule; Technical Support Docum e nt,” Report Num ber EPA-454/R-07-002. It should be noted that the control case m odeling accounted only for the 0.62 percent st andard, but not the 1.3 vol% m a ximum average. Thus, the em ission re ductions from highway vehicles and other sources attributable to the fuel benzene sta ndard are underestim ated in many areas of the country, particularly in areas wher e fuel benzene levels were highest without control, such as the Northwest.

3-32Fin al Regu latory Im p act An al ysis



### The NATA modeling fram e w ork has a num ber of lim itation s which prev ent its use a s the sole basis for setting regulatory standards. Ev en so, this m odeling fram ework is very useful in identifying air toxic pollutants and sources of gr eatest concern, setting re gulatory priorities, and inform ing the decision making process.

Am ong the significant lim itations of the fram ew ork is that it cannot be used to identify

am bient “hot spots,” as mobile sour ces ar e not r e presen ted e xplic itly as r o ads or othe r loca tions of m obile source ac tivity. In addition, this kind of m odeling assessm ent cannot address the kinds of questions an epidem iology study m i ght allow, such as the relationship between asthm a or cancer risk, and proxim ity of residences to po int sources, roadways and other sources of air toxics em issions. The fram e w ork also does not account for risk from potentially significant sources of air toxics originati ng indoors, such as stoves or out-g assing from building m a terials or evaporative benzene em issions fr om cars in attached garages. The ASPEN m odel perfor m s well for som e pollutants, but has also been show n to system atically underestim ate pollutant concentratio ns relativ e to m easured levels for ce rtain pollutants such as m e tals and som e reactive

com pounds. The cancer unit risk estim a tes for mo st pollutants are “upper bound,” m e a ning they probably lead to overestim ates of risk. It should be noted, however, that the unit risk estim ate for benzene is a m a xim u m likelihood estim a te, which is a best scientific estim ate. The above
lim itations are discussed in detail in Section 3.2.1.4.

Although we do not use it in this m odeling, another tool that EPA uses to assess distributions of concentrations of air toxics at th e na tiona l sca l e is th e Community Multisca le Air Quality (CMAQ) m o deling sys t em

. 143 

### CMAQ can account for photochem i cal destruction and production, deposition and regional tr ansport of toxic air pollutants, and thus can be used to predict the concentrations of HAPs with significant atm ospheric production. In general, predicted co ncentration s of air t oxics from CMAQ were within a f actor of 2 of measured values, with a tendency to underpredict m easured am bient concentrations. 144 

### CMAQ underpredicts monitored benzene levels m ore than ASPEN, be c a use ASPEN values contain a large, added-on concentration based on monitored values of be nzene. CMAQ has sophisticated photochem i stry, but does not yet have the spatial resolution of dispersion m odels such as A SPEN, and thus accounts for less of the total variability in levels of air toxics with localized concentration gradien t s, su ch as benzen e. 145 

### Finally, CMAQ is requires m o re com putational resources, which

m a kes it m o re difficult to use for evaluating tren ds in a large num ber of air toxics over m a ny years or im pacts of control scenarios.

Details of the m e thods used and presentation of key results are discussed in the following sections. Results do not account fo r other potentially significant s ources of inhalation exposure, such as benzene em issions from sources in atta ched garages (such as vehicles, snowblowers, lawnm o wers and gas cans).

3-33Fin al Regu latory Im p act An al ysis



### 3.2.1.1 Air Quality Modeling

3.2.1.1.1 Methods

Prior to perform i ng air quality m odeling of the projected em issions, the em issions from the stationary and m obile inventories (discussed in Chapter 2 ) are p r ocess ed in the Em issions Modeling S y stem for Hazardous Air Pollutants (E MS-HAP) Version 3 to create the em issions input files used by ASPEN to calcu late air quality concentrations. 146 

### In addition to projecting stationary and area sou r ce em issions to future years for som e source categories, EMS

- HAP spatially allocates em issions i nventoried at the county level to the census tract level, and

tem porally allocates them to eight three-hour time periods throughout the day. Once the

em issions are process e d, they are inp u t into ASPE N to calculate air quality concentrations. In addition to the em issions, ASPEN uses m e teorol ogical param e ters and census tract centroid locations for concentration calcu lations. ASPE N estim ates do not accou n t for day-of-week or seasonal variations in em issions. The ASPEN m odel tak es in to accoun t important de term inants of pollutant concentrations, such as : rate of re lea s e, lo cation of release, the height from which the pollutants are released, wind speeds and directions from the m e teorological stations nearest to the release, breakdown of the po llutants in the atmosphere afte r being released (i.e., reactive decay), s e ttling of polluta nts out of th e atm o s phere (i.e., d e pos ition ) , and transform a tion of one pollutant into another. T h e m odel first estim a te s concentratio ns at recep tors arranged in rings around em ission sources up to 50 kilom e ters away. The m odel then interpol ates concentrations

to census tract cen troid s. For 1999, m e teorological cond itio ns in 1999 and 2000 census tract data were used.

In using ASPEN to estim a te projected concentrations in 2015, 2020, and 2030 for this final rule, the sam e m e teorology and census trac t locations were used as for the 1999 N A TA. Details of how ASPEN processe d em issions data are provided in the technical docum e nt, “National-S cale Modeling of Mobile Source Ai r Toxic Em is sions, Air Q uality, Exposure and Risk for the Mobile Source Air Toxics Final Rule .” ASPEN only accounts for sources within a 50-kilom e ter radiu s of each source when cal culating am bient concen trations. Thus, the contribution to am bient levels of air toxics from s ources further away than 50-kilom e ters, as well as the con t ribution of uninvento r ied sources, is addressed through the ad dition of a “background ”

term

. 147 

### Mobile source pollutants which incl ude a background compone nt are 1,3-butadiene, acetaldehyd e, benzene, f o rm aldehyde, and xylen es. Each of the three projection y ears used the

sam e 1999- based background. However, background leve ls are likely to change with em issions. Thus, for the proposal, a sensitivity analysis wa s done to evaluate the potential im pact of not changing the background concen tration (see Section 3.2.1.4).

It should be noted that in the control case scenarios, we have modeled the cum ulative
im pacts on air quality, exposure, a nd risk for all of the program s finalized today, not the im pacts of individual program s. Were we to model each program individually, w e anticipa te that changes in air quality, exposure, and risk would track the pattern s of em ission changes closely.

Also, for the final rule, we estim a ted the c ontribution of secondary form ation to am bient concentrations of MSATs by applying ratios of secondary to prim ary concentrations from 1999 NATA to the m odeled prim ary concentrations for this rule. This is differe nt from the approach

3-34Fin al Regu latory Im p act An al ysis



### used in the proposal where we projected prec ursor em issions and then m o deled secondary for m ation. When we applied the ratio approach to the proposal’s prim ary concentrations, the results were very sim ilar to the full m odeling approach (see Section 3.2.1.3). The com p arisons are dis cussed in the tech nical do cu ment cited above.

We estim ated the contrib u tions to ambien t con cen tra tions f o r the following source

sectors: m a jor, area and othe r, onroad, nonroad, and background. b



### 3.2.1.1.2 Air Quality Trends for Ai r Toxics: Reference Case

Table 3.2-1 summarizes nationwide m ean cen sus tract am bient concentrations, without the controls being finalized in th is rule, of m obile source air to xics in 1999 and projection years for the following source sectors: m a jor source s, area and other sources, highway vehicles, nonroad sources, and background. The behavior of benzene is typical of the projected trends. Over 90% of the m obile source contribution to ambi ent benzene levels is at tributable to gasoline vehicles and engines. Figure 3.2-1 depicts th e trend in nationwide average census tract concentrations of benzene over this tim e peri od. The m obile source contribution to ambient benzene concentrations is projected to decrea se over 40% by 2015, with a decrease in am bient benzene concentration from all sources of about 25%. Subsequently, increases in vehicle m iles traveled (V MT) are p r o j ected to pro duce increas ing concentrations. Sum m a ry tables providing data by State, and for refor m ulated and non-reform ulated (i.e., conventional) gasoline areas, can be found in the docket for this rule. Due to greater population and vehi cle activity, the average

am bient benzene concentration in 1999 is m u ch higher for counties in refor m ulated gasolin e areas than non-reform ulated gasoline areas – about 1.9 μg/m 3 

### versus 1.2 μg/m 3



### . However the percent reduction in average 2015 am bient concentration is sim ilar regardless of fuel type – 22% for non-reform ulated gasoline counties versus 29% for reformulated gasoline counties.

b M a j o r a n d “a r ea an d ot he r” a r e st at i ona ry s o urce em i ssi on sect ors

. M a jo r so urce s, as de f i ned by t h e C l ean Ai r Act

, are tho s e statio n a ry facilities t h at em it o r h a ve th e p o t en tial to em i t 1 0 t o n s of an y on e t o xic air po llu tan t o r 25 t o n s of m o re than one toxic air pol l utant pe r yea r

. Area an d ot h e r s o u r ces i n cl ude s o u r ces t h at gene ral l y ha ve sm al l e r

em i ssi ons on a n i n di vi dual ba si s t h an "m ajor so urce s" a n d a r e oft e n t o o sm al l or u b i q ui t o us i n nat u re t o be
in v e n t oried as in d i v i d u al sou r ces. "Area sou r ces" in clud e fa cilities th at h ave air tox i cs em i ssio n s b el o w t h e m aj o r

sou r ce t h res h ol d as de fi ne d i n t h e ai r t oxi cs s ect i ons of t h e C l ean Ai r Act and t h us em i t less t h a n 10 t o n s o f a si n g l e
to x i c air po llu t a n t or less th an 25 ton s of m u ltip le to x i c air po llu tan t s i n any o n e year. Area sou r ces in cl u d e sm aller facilities, su ch as dry clean ers. "Ot h er sou r ces" in clud e sou r ces su ch as wild fires an d prescrib ed b u rn i n gs th at m a y be m o re a p p r o p ri at el y ad dres sed by ot her p r og ram s rat h er t h an t h ro u g h re gul at i o ns de vel ope d u nde r ce r t ai n ai r
t oxi cs pr o v i s i o ns (sect i o n 11 2 o r 12 9) i n t h e C l ean Ai r Ac t. Fo r ex am p le, wild fires an d prescri b ed burni n g are bei n g add r esse d t h r o ug h t h e bu r n i n g pol i c y ag ree d t o by t h e Int e ri m Federal W i l d l a nd P o l i c y

. “B ackg r o u nd” i n cl ude s

em i ssi ons f r o m t rans p ort a n d uni nve nt o ri e d sou rces

.



### 3-35Final Regulatory Impact AnalysisTable 3.2-1.Mean Ambient Concentrations ofMobile Source Air Toxics in 1999, 2015, 2020, and 2030, Without Controls inthis Rule.1999 average concentrations(μg m-3)2015 annual average concentrations(μg m-3)totaltotalPollutantbackground(μg m-3)majorarea &otheronroadnonroad(includingbackground)majorarea &otheronroadnonroad(includingbackground)1,3-Butadiene5.10E-021.97E-032.05E-025.20E-021.81E-021.44E-012.17E-032.05E-022.28E-021.08E-021.07E-012,2,4-Trimethylpentane0.00E+002.16E-022.32E-027.29E-011.96E-019.70E-011.09E-022.69E-023.66E-011.15E-015.19E-01Acetaldehyde5.17E-012.94E-025.49E-026.78E-011.47E-011.43E+002.97E-025.71E-023.86E-011.10E-011.10E+00Acrolein0.00E+003.21E-032.93E-025.63E-022.27E-021.11E-013.53E-032.62E-022.42E-021.81E-027.20E-02Benzene3.94E-012.20E-021.40E-016.89E-011.77E-011.42E+001.55E-021.63E-013.79E-011.14E-011.07E+00ChromiumIII0.00E+008.22E-044.53E-043.22E-055.53E-051.36E-031.04E-036.16E-044.40E-055.85E-051.76E-03ChromiumVI0.00E+001.07E-041.98E-042.15E-051.25E-053.39E-041.36E-042.72E-042.94E-051.32E-054.50E-04EthylBenzene0.00E+001.84E-029.00E-022.73E-019.73E-024.79E-011.24E-021.19E-011.35E-015.66E-023.24E-01Formaldehyde7.62E-013.99E-028.77E-024.65E-012.21E-011.58E+004.98E-029.82E-021.92E-011.63E-011.27E+00Hexane0.00E+006.68E-024.30E-012.34E-018.56E-028.17E-015.94E-025.21E-011.16E-015.93E-027.56E-01MTBE0.00E+001.30E-026.04E-024.00E-014.04E-018.77E-011.38E-026.52E-021.05E-011.08E-012.93E-01Manganese0.00E+002.71E-032.22E-031.73E-055.46E-064.95E-033.23E-032.92E-032.36E-056.46E-066.17E-03Naphthalene0.00E+004.56E-034.11E-021.46E-024.36E-036.46E-023.97E-035.01E-027.90E-034.49E-036.65E-02Nickel0.00E+007.76E-041.42E-033.96E-059.98E-052.33E-038.87E-041.62E-035.43E-051.15E-042.67E-03POM0.00E+004.93E-031.61E-021.73E-038.60E-042.37E-023.79E-031.86E-029.13E-047.66E-042.40E-02Propionaldehyde0.00E+001.01E-022.33E-021.68E-014.27E-022.45E-019.31E-032.39E-028.24E-022.83E-021.44E-01Styrene0.00E+002.52E-021.40E-022.98E-023.65E-037.27E-023.00E-021.89E-021.50E-022.18E-036.61E-02Toluene0.00E+002.03E-018.05E-011.81E+004.18E-013.24E+001.43E-011.06E+009.00E-012.50E-012.35E+00Xylenes1.70E-019.98E-025.59E-011.01E+003.99E-012.23E+008.22E-027.60E-014.98E-012.18E-011.73E+003-36Final Regulatory Impact AnalysisTable 3.2-1 (cont’d).Mean Ambient Concentrations of MobileSource Air Toxics in 1999, 2015, 2020, and 2030, WithoutControls in this Rule.2020 annual average concentrations(μg m-3)2030 annual average concentrations(μg m-3)totaltotalPollutantbackground(μg m-3)majorarea &otheronroadnonroad(includingbackground)majorarea &otheronroadnonroad(includingbackground)1,3-Butadiene5.10E-022.34E-032.05E-022.37E-021.14E-021.09E-012.34E-032.05E-022.78E-021.30E-021.15E-012,2,4-Trimethylpentane0.00E+001.17E-022.84E-023.66E-011.14E-015.20E-011.17E-022.84E-024.24E-011.24E-015.88E-01Acetaldehyde5.17E-013.10E-025.83E-023.98E-011.09E-011.11E+003.10E-025.83E-024.69E-011.18E-011.19E+00Acrolein0.00E+003.96E-032.54E-022.50E-021.91E-027.34E-023.96E-032.54E-022.94E-022.18E-028.05E-02Benzene3.94E-011.70E-021.69E-013.88E-011.18E-011.09E+001.70E-021.69E-014.54E-011.32E-011.17E+00ChromiumIII0.00E+001.17E-036.96E-044.84E-055.90E-051.97E-031.17E-036.96E-045.94E-056.04E-051.98E-03ChromiumVI0.00E+001.54E-043.07E-043.23E-051.34E-055.07E-041.54E-043.07E-043.96E-051.37E-055.15E-04EthylBenzene0.00E+001.39E-021.31E-011.35E-015.78E-023.38E-011.39E-021.31E-011.57E-016.45E-023.66E-01Formaldehyde7.62E-015.65E-021.03E-011.97E-011.64E-011.28E+005.65E-021.03E-012.31E-011.80E-011.33E+00Hexane0.00E+006.53E-025.62E-011.07E-016.13E-027.96E-016.53E-025.62E-011.18E-016.87E-028.14E-01MTBE0.00E+001.55E-026.67E-028.48E-021.12E-012.79E-011.55E-026.67E-028.42E-021.25E-012.92E-01Manganese0.00E+003.59E-033.21E-032.60E-056.83E-066.83E-033.59E-033.21E-033.19E-057.59E-066.84E-03Naphthalene0.00E+004.46E-035.32E-027.86E-034.80E-037.03E-024.46E-035.32E-029.11E-035.51E-037.23E-02Nickel0.00E+009.61E-041.78E-035.97E-051.20E-042.92E-039.61E-041.78E-037.34E-051.31E-042.95E-03POM0.00E+004.21E-031.90E-029.47E-047.71E-042.49E-024.21E-031.90E-021.12E-038.57E-042.52E-02Propionaldehyde0.00E+009.35E-032.45E-028.45E-022.78E-021.46E-019.35E-032.45E-029.84E-022.99E-021.62E-01Styrene0.00E+003.44E-022.09E-021.57E-022.21E-037.32E-023.44E-022.09E-021.85E-022.47E-037.63E-02Toluene0.00E+001.60E-011.16E+009.11E-012.50E-012.48E+001.60E-011.16E+001.06E+002.75E-012.65E+00Xylenes1.70E-019.29E-028.38E-015.04E-012.18E-011.82E+009.29E-028.38E-015.86E-012.40E-011.93E+003-37Fin al Regu latory Im p act An al ysis



### Figure 3.2-1. Nationwide Average Benz ene Concentr ation, 1999-2030, Without Controls in this Rule.

0. 00 0. 20 0. 40 0. 60 0. 80 1. 00 1. 20 1. 40 1. 60ug/m3 b a ckg ro u n d

nonroad highw ay
area and ot her

ma j o r

1999 2015 2020 2030

Ye ar



### 3.2.1.1.3 Distributions of Air Toxic Concentra tions across the U. S.: Reference Case

Table 3.2-2 gives the dist ribution of census tract con centrations, summed across all source sectors and background, for mobile source ai r toxics across the nation in 2020, absent the controls being finalized in this rule. Distributions for other year s are sim i lar. Summ a r y tables providing distributions for other years, as well as distributions by State an d for reformulated and non-reform ulated gasoline areas, can be found in the docket for this rule. From this table, it can be seen that 95 th 

### percen tiles of averag e census tract con centrations for m ob ile-source dom i nated pollutants such as benzene and 1,3-butadiene are typical ly two to five tim es higher than the

m e dian of c e nsus tract concentrations, even though m obile source em issions are widely dispersed. For pollutants with large m a jor source contributions (e.g., m a nganese), the 95 th 

### percentile of census tract averages can be m uch higher than the m e dian. In addition, average census tract concentratio ns can span one to se veral orders of m a gnitude. Thus, there is considerable variation in averag e con centration s across th e U.S.

Figure 3.2-2 depicts the geogr aphic distribution of county median concentrations of benzene in 2020. Relatively high le vels 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 equipm ent activ ity. Relatively high levels ar e also seen in the Pacific Northwest, parts of Alaska, and the upper Great Lakes region. An alysis of fuel survey data

3-38Fin al Regu latory Im p act An al ysis



### Table 3.2-2. National Distributio n of Census Tract Concentrations for Mobile S o urce Air Toxics in 2020, Without Controls in this Rule.

2020 concent r at ion ( μ g m -3 ) dist rib utio n

5t h 10t h 25t h 75t h 90t h 95t h
Pollutant percentil e percentil e percentil e Median percentil e percentil e percentil e
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- T r i m ethy lpentane 3. 83E

- 02 7. 00E

- 02 1. 74E

- 01 3. 79E

- 01 6. 80E

- 01 1. 12E +00 1. 50E +00
Acetaldehy de 5. 45E

- 01 5. 82E

- 01 6. 99E

- 01 9. 41E

- 01 1. 29E +00 1. 84E +00 2. 49E +00

Acr olein 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

Ch ro m i u m III 5 .7 3 E-0 6 1 .5 2 E-0 5 6 .4 0 E-0 5 2 .4 1 E-0 4 7 .3 1 E-0 4 2 .3 4 E-0 3 4 .8 9 E-0 3

Chr o m i u m VI 3. 52E

- 06 8. 79E

- 06 3. 56E

- 05 1. 22E

- 04 3. 32E

- 04 9. 08E

- 04 1. 55E

- 03

E t hy l Benzene 2. 04E

- 02 3. 79E

- 02 1. 01E

- 01 2. 30E

- 01 4. 06E

- 01 6. 70E

- 01 9. 60E

- 01

Form aldehy de 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

M T BE 3. 34E

- 03 7. 88E

- 03 2. 39E

- 02 7. 22E

- 02 2. 44E

- 01 8. 80E

- 01 1. 30E +00

M a nganese 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

Pr opionaldehy de 1. 24E

- 02 2. 13E

- 02 4. 81E

- 02 1. 07E

- 01 1. 93E

- 01 3. 26E

- 01 4. 33E

- 01
Sty r ene 2. 52E

- 03 4. 88E

- 03 1. 23E

- 02 2. 70E

- 02 5. 39E

- 02 1. 06E

- 01 1. 75E

- 01

T oluene 1. 54E

- 01 2. 83E

- 01 7. 34E

- 01 1. 64E +00 2. 96E +00 5. 31E +00 7. 43E +00

Xy lenes 2. 66E

- 01 3. 43E

- 01 6. 35E

- 01 1. 22E +00 2. 06E +00 3. 61E +00 5. 38E +00



### indicate hig h er than average fuel benzene lev e ls in thes e areas. These areas also h a v e higher benzene em issions in winter due to cold starts. H i gher benzene levels in Idaho are not due to fuel benzene levels, but are prim arily due to w ildf i re em ission estim ates, which were de term ined to be an error in the 1999 National E m issions Inventory a nd the subsequent projections.

Sim ilar benzene m e dian county concentration m a ps for 1999, 2015, and 2030 can be found in the docket for this rule, al ong with m a ps for other mobile source air toxics and tables of concentration distributions.

3.2.1.1.4 Im pacts of Controls on Am bient Concentrations

The standa r d s being f i n aliz ed in th is rule will substantially reduce am bient concentrations of air toxics across the United St ates. As noted above, these resu lts reflect the cum ulative effects of all of the program s fi nalized in today’s rule, not the indivi dual program s. Table 3.2-3 shows the redu ctio n in nationwide average census tract concentrations of MSAT s f rom all so urces in 2015, 2020 and 2030. Table 3.2-4 shows the reduction in the highway vehicle contribution to nationwide average census tract co ncentrations of MSATs. Ta ble 3.2-5 shows that in 2030, the highway vehicle po rtion of am bient benzene con centr ation s will be redu ced alm o st 45% acros s the U.S., the nonroad equipm ent contribu tion will be reduced about 10%, and

3-39Fin al Regu latory Im p act An al ysis



### Figure 3.2-2. Geograp hic Distribution of County Median Concentrations (μg/m 3 

### ) of
Benz ene in 2020 Without Co ntrols in this Rule.

Co n ce n t r a t i o n s

0. 0 6 3

- 0. 33 8

0. 3 3 9

- 0. 56 7

0. 5 6 8

- 0. 88 0

0. 8 8 1

- 1. 31 6

1. 3 1 7

- 2. 11 4

2. 1 1 5

- 4. 92 9Final Regulatory Impact AnalysisTable 3.2-3.Nationwide Average Census Tract Concentrations ofMSATs, With and Without Controls in this Rule, 2015,2020, and 2030.Reference2015Control% ReductionReference2020Control% ReductionReference2030Control% Reduction1,3-Butadiene1.07E-011.03E-013.61.09E-011.03E-015.71.15E-011.04E-019.02,2,4-Trimethylpentane5.19E-014.53E-0112.75.20E-014.19E-0119.55.88E-014.26E-0127.6Acetaldehyde1.10E+001.04E+005.81.11E+001.01E+009.11.19E+001.03E+0013.7Acrolein7.20E-026.79E-025.77.34E-026.69E-028.98.05E-026.97E-0213.4Benzene1.07E+009.56E-0110.31.09E+009.38E-0113.61.17E+009.50E-0118.5ChromiumIII1.76E-031.76E-030.01.97E-031.97E-030.01.98E-031.98E-030.0ChromiumVI4.50E-044.50E-040.05.07E-045.07E-040.05.15E-045.15E-040.0EthylBenzene3.24E-012.99E-017.53.38E-013.01E-0111.13.66E-013.07E-0116.3Formaldehyde1.27E+001.24E+002.31.28E+001.24E+003.61.33E+001.26E+005.6Hexane7.56E-017.37E-012.57.96E-017.70E-013.28.14E-017.76E-014.7MTBE2.93E-012.82E-013.52.79E-012.66E-014.62.92E-012.74E-016.0Manganese6.17E-036.17E-030.06.83E-036.83E-030.06.84E-036.84E-030.0Naphthalene6.65E-026.65E-020.07.03E-027.03E-020.07.23E-027.23E-020.0Nickel2.67E-032.67E-030.02.92E-032.92E-030.02.95E-032.95E-030.0POM2.40E-022.40E-020.02.49E-022.49E-020.02.52E-022.52E-020.0Propionaldehyde1.44E-011.33E-017.81.46E-011.28E-0112.21.62E-011.33E-0118.0Styrene6.61E-026.33E-024.37.32E-026.87E-026.27.63E-026.89E-029.7Toluene2.35E+002.18E+007.12.48E+002.22E+0010.42.65E+002.24E+0015.7Xylenes1.73E+001.64E+005.31.82E+001.68E+007.81.93E+001.70E+0011.83-41Final Regulatory Impact AnalysisTable 3.2-4.Nationwide Highway Vehicle Contribution to Average Census Tract Concentrations of MSATs, With andWithout Controls in this Rule, 2015, 2020, and 2030.Reference2015Control%ReductionReference2020Control% ReductionReference2030Control%Reduction1,3-Butadiene2.28E-021.89E-0217.02.37E-021.74E-0226.32.78E-021.75E-0237.02,2,4-Trimethylpentane3.66E-013.06E-0116.33.66E-012.71E-0125.94.24E-012.70E-0136.4Acetaldehyde3.86E-013.22E-0116.53.98E-012.97E-0125.44.69E-013.06E-0134.8Acrolein2.42E-022.01E-0217.02.50E-021.85E-0226.22.94E-021.87E-0236.6Benzene3.79E-012.83E-0125.33.88E-012.55E-0134.24.54E-012.54E-0144.0ChromiumIII4.40E-054.40E-050.04.84E-054.84E-050.05.94E-055.94E-050.0ChromiumVI2.94E-052.94E-050.03.23E-053.23E-050.03.96E-053.96E-050.0Ethyl Benzene1.35E-011.14E-0116.01.35E-011.01E-0125.61.57E-011.00E-0136.1Formaldehyde1.92E-011.62E-0115.31.97E-011.50E-0123.62.31E-011.56E-0132.4Hexane1.16E-011.05E-019.81.07E-018.89E-0216.91.18E-018.84E-0225.0MTBE1.05E-011.01E-014.38.48E-027.77E-028.38.42E-027.30E-0213.4Manganese2.36E-052.36E-050.02.60E-052.60E-050.03.19E-053.19E-050.0Naphthalene7.90E-037.90E-030.07.86E-037.86E-030.09.11E-039.11E-030.0Nickel5.43E-055.43E-050.05.97E-055.97E-050.07.34E-057.34E-050.0POM9.13E-049.13E-040.09.47E-049.47E-040.01.12E-031.12E-030.0Propionaldehyde8.24E-027.12E-0213.68.45E-026.66E-0221.19.84E-026.92E-0229.6Styrene1.50E-021.22E-0218.81.57E-021.12E-0228.81.85E-021.11E-0239.8Toluene9.00E-017.47E-0117.19.11E-016.66E-0126.91.06E+006.62E-0137.7Xylenes4.98E-014.14E-0116.95.04E-013.69E-0126.75.86E-013.67E-0137.53-42Final Regulatory Impact AnalysisTable 3.2-5.Contributions of Source Sectors to Nationwide AverageCensus Tract Concentrations of Benzene, With andWithout Controls in this Rule, 2015, 2020, and 2030.2015 annual averageconcentrations(μg m-3)2020 annual average concentrations (μg m-3)2030 annual average concentrations (μg m-3)majorarea &otherhighwayvehiclesnonroadtotal(includingbackground)majorarea &otherhighwayvehiclesnonroadtotal(includingbackground)majorarea&otherhighwayvehiclesnonroadtotal(includingbackground)Reference1.55E-021.63E-013.79E-011.14E-011.07E+001.70E-021.69E-013.88E-011.18E-011.09E+001.70E-021.69E-014.54E-011.32E-011.17E+00Control1.54E-021.61E-012.83E-011.02E-019.56E-011.69E-021.67E-012.55E-011.05E-019.38E-011.69E-021.67E-012.54E-011.18E-019.50E-01% Difference0-1-25-10-100-1-34-10-140-1-44-10-19AverageNationwideDifference in Ambient Benzene Concentration-- NonRFGAreasReference1.08E-021.43E-012.96E-018.15E-028.93E-011.20E-021.48E-013.06E-018.34E-029.11E-011.20E-021.48E-013.57E-019.29E-029.71E-01Control1.08E-021.41E-012.17E-016.82E-027.99E-011.20E-021.46E-012.00E-016.95E-027.89E-011.20E-021.46E-011.97E-017.72E-027.94E-01% Difference0-2-27-16-110-2-35-17-130-2-45-17-18AverageNationwideDifference in Ambient Benzene Concentration-- RFGAreasReference2.39E-021.99E-015.29E-011.72E-011.38E+002.58E-022.08E-015.34E-011.79E-011.40E+002.58E-022.08E-016.29E-012.03E-011.52E+00Control2.38E-021.97E-014.02E-011.63E-011.24E+002.58E-022.05E-013.54E-011.70E-011.21E+002.58E-022.05E-013.57E-011.92E-011.23E+00% Difference0-1-24-5-100-1-34-5-140-1-43-5-193-43Fin al Regu latory Im p act An al ysis



### the area source contribution will be reduced about 1 to 2%. Th e reduction for area sources is due to the im pac t s of fuel benzene control on ga soline distribution em issions, and reductions in portable fuel container (P FC) e m issions from PFC and fuel benzene controls. Reductions in non-reform ulated gasoline areas are even larger. It should be noted that the estim a ted total reductions in am bient concentrations from all sources are probably sign if icantly und erestim a ted, since we could not account for the impacts of controls on background levels, which includes transport of em issions from these sources. Fi gure 3.2-3 presents the distribution of percent reductions in m e dian ambient benz ene concen tr ations f o r U. S. counties with the con t rols b eing finalized in 2030. Again, since the 1.3% m a xi mum average fuel benzene standard is not included in the m odeling, reductions in som e parts of the country, including the Pacific Northwest, are underestim ated. Summ ary tables providi ng data by State, as well as m a ps of MSAT concentrations w ith controls and percen t reductions with controls, can be found in the docket f o r th e rule.

Figure 3.2-3. Distribu tion of Percent Reducti ons in Median Ambient Benz ene Concentrations, 2030, for U. S. Counties w i th the Controls in this Rule.

Pe r cen t di ffe r e n c e

- 1 3. 547 % - - 6. 4 07%

- 6 .4 0 6 % - -4

. 2 4 1 %

- 4 .2 4 0 % - -2

. 8 6 3 %

- 2 .8 6 2 % - -1

. 8 2 1 %

- 1 .8 2 0 % - -0

. 9 5 6 %

- 0 .9 5 5 % - -0

. 0 0 8 %



### 3.2.1.2 Exposure and Risk Modeling

3.2.1.2.1 Methods

The HAPEM6 exposure m odel used in this as sessm ent is the m o st recent version in a series of m o dels that the EPA has used to m odel population exposur es and risks at the urban and national scale in a num ber of assessm e nts. 148, 149, 150 

### HAPEM6 is designed to assess average

3-44Fin al Regu latory Im p act An al ysis



### long-term inhalation exposures of the genera l population, or a speci fic 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 m ove a m ong indoor and outdoor

m i croenvironm ents and am ong geographic locati ons (a total of 14, HAPEM5 had 37). The estim ated po llutant concentrations in each m i cr oenvironm ent visited are com b ined into a tim eweighted average concentration, which is assi gned to m e m ber s of the demographic group. HAPEM calculates 30 replicates with different ex posures for each dem o graphic g r oup

. These data can be used to develop a distribution of exposures for the entire U. S. population.

HAPEM6 uses five prim ary sources of info r m a tion: year 2000 population data from the
U.S. Census, population activ ity data, ai r quality data, roadway locations, and

m i croenvironm ental data. The population data used are obtained from the U.S. Census. Two kinds of activity data are used: activity patter n data and commuting pattern data. The activity pattern data quantify the am ount of tim e individuals spend in a variety of m i croenvironm ents and

com e from EPA’s Consolidated Hum a n 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 num b er of residents of each tract th at work in that tract and every other U.S. Census

trac t, as well as data on c o mm uting tim es and distances. The air qua lity d a ta com e f r om ASPEN (after background has been added). The road locations are determ ined from geographic inform ation system files from the U.S. Census. The m i croenvironm ental data consist of factors that es tim ate air tox i c co ncentrations in speci fic m i croenvironm ents, based on penetration of outdoor air into the m i croenvironm ent, proxim ity of the m i croenvironm ent to the em i ssion source, and em ission sources within the m i cr oenvironm ent. These factors vary am ong pollutan t s. 152



### New to HAPEM6 are algorithm s which accoun t for the gradient in concentrations of prim ary (dir ectly em itte d) m obile source air tox i cs within 20 0 m eters of m ajor roadways. 153 

### HAPEM6 adjusts am bient concentrations gene rated by ASPEN for each census tract using concentration gradients developed w ith the CALPUFF dispersion m odel. 154 

### For location s within
75 m eters and from 75 to 200 m eters from m ajor roads, am bient concentrations are adjusted upward, while locations further from m a jor road ways are adjusted downward. These adjustm e nts are consistent with results from prio r m odeling studies that ex plicitly a cco unted f o r concentration gradients around m a jor roads within census tracts. 155 

### These adjusted concentrations are then employed in m i cr oenvironm ental concentration calculations.

HAPEM6 has a num ber of other technical im provem ents over the previous version of
HAPEM. These im provem e nts, along with other de tails of the m odel, are described in the HAPEM6 User’s Guide. 156 

### In short, H A PEM6 reduces the number of de mographic groups to 6 age-based groups from 10 age-gender groups in HAPEM5, a nd reduces the num ber of

m i croenvironm ents m o deled, from 37 to 14. This reduces m odeling run tim e significantly with little im pact on results. HAPEM6 also account s for comm uting tim e better, basing commute tim es and travel m odes for each cen sus tract on d i stributions reported in th e 2000 Census. The HAPEM runs used year 2000 census data. Averag e lifetim e exposure for an individual in a census tract was calculated from data for indivi dual dem ographic groups using a post-processing routine. W e estim ated the contributions to am bient concentrations for the following so urce
sectors: m ajor, area and othe r, onroad, nonroad, and background.

3-45Fin al Regu latory Im p act An al ysis



### Once HAPEM runs were com pleted, cancer risk and noncancer risk were calculated for each of the mobile source air toxi c pollutants, b a sed on pop ulation e xpo sure dis t ribu tions. In the HAPEM6 output, for each source category, there ar e 30 replicate exposure concentrations for each of the six dem ographic groups (180 concen trations per census tract f o r each sou r ce catego r y). F o r each sou r ce category and each of the 30 rep licates, a lifetim e exposure concentratio n was calcu lated. A risk estim ate was then calculated for each of the 30 replicates. The resulting data were used to develop dist ributions of population risks at various summary levels (census tract, county, state, national). More detail is provided in the technical support docum ent. Table 3.2-6 lists the pollutants with their respective unit risk estim ates (U REs) for cancer calcu lations and reference con centration s (RfCs) for noncancer calculations. Th ese are the sam e values used in the 1999 NATA, and m o re detailed infor m ation on how dose-response values were selected is provide d a t th e website f o r tha t asses s m ent. Also listed are the cancer weight of evidence class i fications an d target organ system (s) for noncancer calculations.

Table 3.2-6. Dose-Response Values Use in Risk Modeling (Concentrations in μ g/m 3 

### )

HAP Carcinogen Class URE (per μ g/ m 3) Source Organ Sys t ems Rf C (mg/

m 3) Source

1, 3-B u t a di en e A 3. 0x 1 0 -5 IR IS R e pr od uct i v e 2. 0x 1 0 -3
2, 2, 4­ Trim e t h y lp en t an e N/A N/A N/A N/A

Acet al dehy de B 2 2. 2x 1 0 -6 I RI S Resp ir ator y 9

. 0 x10 -3 IRIS

A c ro lein 0 Resp ir ator y 2

. 0 x10 -5 IRIS
Benzene A 7.8x10 -6 * IR IS Im m une 3. 0x 1 0 -2 IRIS
Chr o m i u m III N/A N/A N/A N/A
Chr o m i u m VI A 1. 2x 1 0 -2 I RI S Resp ir ator y 1

. 0 x10 -4 IRIS
Et hy l B e nze n e 0 Devel opm ent a l 1. 0 IR IS
Form al dehy de B 5. 5x 1 0 -9 CI I T Resp ir ator y 9

. 8 x10 -3 ATS D R
Hexa ne N/ A Resp ir ator y, N e uro log ical 2. 0x 1 0 -1 IRIS

M a nga nese N/ A N e uro log ical 5

. 0 x10 -5 IRIS

MTBE N / A Li ver, Ki dney

, 3. 0 IR IS Ocula r N a ph th alen e C 3

. 4 x10 -5 CA L Resp ir ator y 3

. 0 x10 -3 IRIS
Nickel A 1.6x10 -4 EPA/
OA QP S Resp ir ator y, Im m une 6. 5x 1 0 -5 CAL

POM 1 B 2 5. 5x 1 0 -5 OA QP S N/A
POM 2 B 2 5. 5x 1 0 -5 OA QP S N/A
POM3 B2 1. 0x 1 0 -1 OA QP S N/A
POM4 B2 1. 0x 1 0 -2 OA QP S N/A
POM5 B2 1. 0x 1 0 -3 OA QP S N/A
POM6 B2 1. 0x 1 0 -4 OA QP S N/A
POM7 B2 1. 0x 1 0 -5 OA QP S N/A
POM8 B2 2. 0x 1 0 -4 OA QP S N/A Styrene N/A Neu r ol o g i cal 1. 0 IR IS

Tolue n e N/A Resp ir ator y, N e uro log ical 4. 0x 1 0 -1 IRIS

Xy lenes N/A N e uro log ical 1

. 0 x10 -1 IRIS



### *represents upper end of a range of MLE values

3-46Fin al Regu latory Im p act An al ysis



### The weight of evidence classifi cations provided in this tabl e were developed under E P A’s 1986 risk as sessm ent guidelin es where: A = Known hum a n carcinogen B1 = Probable hum an carcinogen, based on incomplete human data B2 = Probable hum an carcinogen, based on adequate anim al data C = Possible hum a n carcinogen

Dose-response values were select ed using the following hierarchy:

1) EPA IRIS assessm ents. 2) Agency for Toxic Substances and Dis ease Re gistry (ATSDR) m i ni m u m risk leve ls (MRLs) for noncancer effects – used as RfC. 3) California Office of Environm ental He alth H azard Asses sm e nt (OEHHA) values.

There are a num b er of excep tion s to this hiera r ch y:

1) Form aldehyde -- EP A no longer consider s the for m aldehyde URE reported in IR IS, which is based on a 1987 study, to represent th e best available sc ience in the peerreviewed literature. Accordi ngly, the 1999 risk estim ates for form aldehyde are based on a dose-response value developed by the CIIT Ce nters for Health Research (for m erly the Che m ical 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 i nhalation shown in Tabl e 3.2.-6 w a s derived from evidence of the carcinogenic effects of insoluble nickel com p ounds in crystalline form

. Soluble nickel species, and insoluble sp ecies in amorphous form , do not appear to produce genotoxic effects by the sam e toxic m ode of action as insoluble crystalline nickel. Nick el spec iation inf o rm ation f o r som e of the la rges t n i ckel-em ittin g sources (including oil com bustion, coal com bustion, and others) suggests that at least 35% of total nickel em issions may be soluble co m pounds. The rem a ining insoluble nickel

em issions are not well-charact erized, however. Consistent with this lim ited inform ation, this an alys is has conserv ative l y assu m ed that 65% of em itte d nickel is insoluble, an d that all insolub l e nickel is c r ystalline. On th is basis, the nickel URE (based on nickel subsulfide, and representative of pure insoluble crystalline n i ckel) was ad justed to reflect

an assum ption that 65% of the total m ass of nickel m a y be carcinogenic. The ATSDR MRL in Table 3.2.-6 was not adjusted , however, because the n oncancer effects of nickel are not thought to be limited to the c r ystalline, ins o luble f o rm

. 3) POM -- POM was divided into eight toxi city catego r ies to c over the range of unit risks of the individual POM species and POM groups contained in the 1999 NEI. The unit risks for those eight categor ies w e re based on the m i dpoint of the range of unit risks defining the toxicity category. More details on the developm en t 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 th e Science Ad visory Boar d on the 1996 National-S cale Assessm ent. 157



### Individual cancer risk estim at es (the product of unit risk estim a tes and exposure levels) for various pollutants were assum e d to be addi tive, since there was no evidence of non-additive

3-47Fin al Regu latory Im p act An al ysis



### interactions for any of the pollu tants. Most of the estim ates are based on the statistical upper conf idence lim it (UCL) of the f itted dose-re spon se curve, bu t the es tim ates f o r hexava lent chrom i um , nickel, and benzene are based on the statistical be s t f

it (“m aximum likelihoo d estim ate,” or MLE). Except for benzene and chro m i um, where risks are b ased on m axim u m likelihood dose-response values, risk s from m o bile source air toxics should all be considered upper-bound values. True risks coul d 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 quo tient (HQ), which is the ratio between the concen tration to which a person is exposed and the RfC. A value of the HQ less than one indi cates that the exposure is lower than the RfC and that no adverse health effects would be expected. A value of th e HQ greater th an one indicates that the expo su re is h i gher than th e RfC. However, because m a ny RfCs incorporate protective as sum p tions in the f ace of uncertainty, an HQ greater than one does not necessarily suggest a lik elihood of adverse effects. Furthermor e, the HQ cannot be translated to a probability that adv erse effects will occur a nd is not lik ely to be proportio n al to risk. A HQ greater than one can best be described as indicati ng that a potential exists for a dverse h ealth effects. However one should evaluate the weight of evidence supp orting the RfC value for a particular chem ical before determ ining potential risks. Following th e approach used in the 1999 NATA, com bined noncancer h azards were calcu lated u sing the h a za rd index (HI), defined as the sum of hazard quotients for individual air toxics compounds that affect the sam e organ or organ system . The HI is only an ap proxim a tion of the com b ined effect, because som e of the subs tances m a y affect the target organ s in different (i.e., non-ad ditiv e) ways . As with the HQ, a value of the HI below 1.0 will likely n o t resu lt in a dverse ef f ec t s over a lif e tim e of exposure. However, a va lue o f the HI greater th an 1.0 does not necessarily suggest a likelihood of ad verse effects. Furtherm ore, the HI cannot be translated to a probability that adv e rse effects will occur a nd is not lik ely to be proportional to risk. An HI greater than one can be best describe d as indicating that a potential

m ay exist for advers e health effects.

3.2.1.2.2 Exposure and Risk Trends for Air Toxics: Reference Case

Tables 3.2-7 and 3.2-8 summ arize nati onwide averages of m e dian and 90 th 

### percen tile census tract exposure concentr ations of m obile source air toxics in 1999, 2015, 2020, and 2030, without the controls being finali zed in this rule. It should be noted that all the other noninventoried sources, as well as th e contribution f r om transport, co ntribute to background levels. Overall, exp o sure to am bient con cen trations tend s to be less than am bient concentrations because penetration rates to indoor m i croenviro nm ents are typically less than one. c 

### However, highway vehicles m a ke a larger contri bution to overall average populati on exposures than they do to

am bient levels. This is largely b ecau se of elev ated exposures experienced inside v e hicles.

c In t h e e x pos u r e m oni t o ri n g st udi es di sc usse d i n sect i o n 3. 1 .2, ave r a g e m e asure d pe rso n a l exp o s u re co n cent r at i o ns

are greater tha n those in both in doo r and ou td oor air

. Th ese d i ffere n ces may be attribut able to sev e ral facto r s. First, HAPEM6 do es no t in clud e pollu tio n sou r ces with in i n doo r micr o e nv ir onmen ts, su ch a s attached gara ges, envi ro nm ent a l t obacc o sm oke , an d sol v ent st ora g e. Sec o nd , m easured pe r s on al b r eat hi ng zo ne c once n t r at i ons a r e integrate d m e a surem ents th at account for time-wei ghte d a v e r age (T WA) c o ncent r ations that incorporate every

so urce, activ ity, and l o cation with wh ich a m o n ito r co m es in to con t act. Micro e nv ironmen tal m o d e ls lik e HAPEM 6 sim p l i fy i ndi vi dual t i m e bu dg et s so t h ey fi t wi t h i n t h e m i croen v i r o n m e nt s m odel e d o r m oni t o re d.



### 3-48Fin al Regu latory Im p act An al ysis



### Table 3.2-9 summarizes national average populati on cancer risk across census tracts f or these years by pollutant, as well as total cancer risk across pollutants. T he total cancer risk from mobile source air toxics (including the stationary source co n t ribu tion ) was about 25 in a m illion in 1999.

In all projection years, b e nzene em issions are by f ar the larg es t contri butor to cancer risk from m obile sources (see Figure 3.2-4). Other significant contributors to cancer risk from mobile source air toxics incl ude 1,3-butadiene, acetaldehyde, naphthalene, and hexavalent chrom i um

. It should be noted, however, that we have no actual m easurements of hexavalent chrom i um e m issions from m obile sources, and that the risk es tim ate f o r this pollu tan t is based on

an assum ption that forty percent of the chrom i um from highway vehicles and eighteen percent of the chrom i um from nonr oad sources was assum e d to be the highly toxic hexavalent form

. The estim ate f o r highway vehicle s is ba s ed on data f r o m utility b o ile rs, 158 

### and the estim ate for nonroad equipm ent is, based on combustion data from stationary com bustion turbines that burn diesel fuel. 159 

### Thus there is a great deal of uncer ta inty in es tim ates f o r th is pollutant.

Despite s i gn if icant reduc tions in risk f r om m obile source air toxics, average inhalation cancer risk s for these pollutan t s in 20 30, accoun ting for both mobile and stationary so urce contributions, rem a in well above 20 in 1,000,000 (Figur e 3.2-5). In addition, average risk from exposure to benzene remains above 9 in 1,000,000.

3-49Final Regulatory Impact AnalysisTable 3.2-7.National Meansof Census Tract Median Population Exposure Concentrations of Mobile Source Air Toxics in1999, 2015, 2020, and 2030, Without Controls in this Rule.1999 annual average concentrations(μg m-3)2015 annual average concentrations(μg m-3)totaltotalPollutantbackground(μg m-3)majorarea &otheronroadnonroad(includingbackground)majorarea &otheronroadnonroad(includingbackground)1,3-Butadiene3.96E-021.54E-031.66E-026.39E-021.64E-021.38E-011.72E-031.69E-022.88E-021.01E-029.71E-022,2,4-Trimethylpentane0.00E+001.70E-021.86E-028.23E-011.57E-011.02E+008.68E-032.18E-024.16E-019.26E-025.39E-01Acetaldehyde4.00E-012.34E-024.33E-028.08E-011.18E-011.39E+002.41E-024.60E-024.70E-019.07E-021.03E+00Acrolein0.00E+002.56E-032.35E-026.62E-021.83E-021.10E-012.91E-032.14E-022.90E-021.49E-026.83E-02Benzene3.05E-011.76E-021.16E-018.08E-011.51E-011.40E+001.25E-021.37E-014.53E-019.87E-021.01E+00ChromiumIII0.00E+003.23E-041.79E-041.93E-052.21E-055.43E-044.11E-042.43E-042.64E-052.34E-057.03E-04ChromiumVI0.00E+004.25E-057.94E-051.30E-055.06E-061.40E-045.40E-051.09E-041.78E-055.38E-061.86E-04Ethyl Benzene0.00E+001.45E-027.49E-023.22E-018.02E-024.91E-019.92E-031.00E-011.62E-014.69E-023.19E-01Formaldehyde6.12E-013.29E-027.20E-025.78E-011.88E-011.48E+004.15E-028.26E-022.46E-011.38E-011.12E+00Hexane0.00E+005.50E-023.60E-012.85E-017.13E-027.71E-014.94E-024.41E-011.44E-014.98E-026.85E-01MTBE0.00E+001.05E-024.84E-024.61E-013.40E-018.59E-011.26E-031.17E-031.48E-052.84E-062.45E-03Manganese0.00E+001.05E-038.93E-041.08E-052.40E-061.96E-031.13E-025.35E-021.24E-018.90E-022.78E-01Naphthalene0.00E+003.82E-033.37E-021.79E-023.85E-035.92E-023.37E-034.18E-029.89E-034.02E-035.91E-02Nickel0.00E+003.02E-045.78E-042.38E-054.17E-059.46E-043.47E-046.50E-043.29E-054.80E-051.08E-03POM0.00E+002.87E-031.00E-021.56E-035.48E-041.50E-022.26E-031.16E-028.33E-044.97E-041.52E-02Propionaldehyde0.00E+007.73E-031.80E-021.93E-013.35E-022.52E-017.24E-031.89E-029.56E-022.28E-021.45E-01Styrene0.00E+002.04E-021.14E-023.40E-023.03E-036.88E-022.40E-021.56E-021.73E-021.83E-035.86E-02Toluene0.00E+001.61E-016.57E-012.14E+003.42E-013.30E+001.16E-018.80E-011.09E+002.06E-012.29E+00Xylenes1.28E-018.08E-024.66E-011.21E+003.33E-012.22E+006.79E-026.43E-016.11E-011.85E-011.63E+003-50Final Regulatory Impact AnalysisTable 3.2-7 (cont’d).National Meansof Census Tract Median Population Exposure Concentrations of Mobile Source AirToxics in 1999, 2015, 2020, and 2030, Without Controls in this Rule.2020 annual average concentrations(μg m-3)2030 annual average concentrations(μg m-3)totaltotalPollutantbackground(μg m-3)majorarea &otheronroadnonroad(includingbackground)majorarea &otheronroadnonroad(includingbackground)1,3-Butadiene3.96E-021.86E-031.69E-022.98E-021.07E-029.88E-021.86E-031.69E-023.49E-021.21E-021.05E-012,2,4-Trimethylpentane0.00E+009.37E-032.31E-024.16E-019.21E-025.41E-019.37E-032.31E-024.81E-011.00E-016.14E-01Acetaldehyde4.00E-012.52E-024.70E-024.85E-019.01E-021.05E+002.52E-024.70E-025.68E-019.78E-021.14E+00Acrolein0.00E+003.27E-032.07E-022.99E-021.58E-026.97E-023.27E-032.07E-023.51E-021.79E-027.70E-02Benzene3.05E-011.37E-021.42E-014.64E-011.02E-011.03E+001.37E-021.42E-015.40E-011.15E-011.12E+00ChromiumIII0.00E+004.59E-042.74E-042.90E-052.37E-057.86E-044.59E-042.74E-043.56E-052.43E-057.93E-04ChromiumVI0.00E+006.14E-051.23E-041.96E-055.45E-062.09E-046.14E-051.23E-042.40E-055.62E-062.14E-04Ethyl Benzene0.00E+001.11E-021.10E-011.62E-014.83E-023.32E-011.11E-021.10E-011.87E-015.41E-023.62E-01Formaldehyde6.12E-014.71E-028.68E-022.52E-011.38E-011.14E+004.71E-028.68E-022.94E-011.51E-011.19E+00Hexane0.00E+005.44E-024.77E-011.33E-015.19E-027.17E-015.44E-024.77E-011.46E-015.83E-027.36E-01MTBE0.00E+001.27E-025.48E-021.01E-019.25E-022.61E-011.27E-025.48E-021.00E-011.04E-012.72E-01Manganese0.00E+001.40E-031.29E-031.62E-053.00E-062.71E-031.40E-031.29E-031.99E-053.35E-062.71E-03Naphthalene0.00E+003.78E-034.44E-029.84E-034.31E-036.23E-023.78E-034.44E-021.14E-024.94E-036.45E-02Nickel0.00E+003.77E-047.15E-043.62E-055.02E-051.18E-033.77E-047.15E-044.45E-055.47E-051.19E-03POM0.00E+002.51E-031.18E-028.63E-045.01E-041.57E-022.51E-031.18E-021.02E-035.58E-041.59E-02Propionaldehyde0.00E+007.27E-031.94E-029.81E-022.25E-021.47E-017.27E-031.94E-021.14E-012.42E-021.65E-01Styrene0.00E+002.74E-021.72E-021.80E-021.87E-036.45E-022.74E-021.72E-022.13E-022.10E-036.80E-02Toluene0.00E+001.30E-019.68E-011.10E+002.09E-012.41E+001.30E-019.68E-011.28E+002.30E-012.61E+00Xylenes1.28E-017.68E-027.10E-016.18E-011.87E-011.72E+007.68E-027.10E-017.17E-012.06E-011.84E+003-51Final Regulatory Impact AnalysisTable 3.2-8.National Meansof Census Tract 90thPercentile Population Exposure Concentrations ofMobile Source AirToxics in 1999, 2015, 2020, and 2030, Without Controls in this Rule.1999 annual average concentrations(μg m-3)2015 annual average concentrations(μg m-3)totaltotalPollutantbackground(μg m-3)majorarea &otheronroadnonroad(includingbackground)majorarea &otheronroadnonroad(includingbackground)1,3-Butadiene5.88E-022.03E-032.23E-021.00E-012.49E-022.08E-012.15E-032.16E-024.11E-021.39E-021.38E-012,2,4-Trimethylpentane0.00E+002.65E-023.12E-021.42E+002.65E-011.75E+001.30E-023.56E-027.08E-011.54E-019.10E-01Acetaldehyde5.82E-013.48E-026.34E-021.27E+001.80E-012.13E+003.32E-026.27E-026.89E-011.28E-011.49E+00Acrolein0.00E+003.68E-033.36E-021.07E-012.82E-021.72E-013.72E-032.79E-024.36E-022.11E-029.64E-02Benzene4.50E-012.57E-021.71E-011.24E+002.28E-012.12E+001.70E-021.91E-016.52E-011.39E-011.45E+00ChromiumIII0.00E+004.55E-042.59E-042.88E-053.15E-057.74E-045.81E-043.51E-043.97E-053.35E-051.01E-03ChromiumVI0.00E+006.16E-051.15E-041.92E-057.20E-062.03E-047.88E-051.57E-042.63E-057.67E-062.70E-04EthylBenzene0.00E+002.33E-021.19E-015.49E-011.35E-018.27E-011.51E-021.51E-012.63E-017.53E-025.04E-01Formaldehyde8.03E-014.21E-029.22E-027.89E-012.52E-011.98E+004.93E-029.74E-023.03E-011.67E-011.42E+00Hexane0.00E+007.54E-025.11E-014.32E-011.07E-011.13E+006.51E-025.95E-012.04E-017.06E-029.34E-01MTBE0.00E+001.46E-027.13E-027.22E-015.16E-011.32E+001.45E-027.24E-021.92E-011.34E-014.13E-01Manganese0.00E+001.44E-031.18E-031.47E-053.25E-062.64E-031.72E-031.55E-032.01E-053.85E-063.30E-03Naphthalene0.00E+004.81E-034.39E-022.44E-025.09E-037.83E-024.07E-035.13E-021.25E-024.99E-037.29E-02Nickel0.00E+004.25E-048.25E-043.52E-056.04E-051.35E-034.94E-049.09E-044.77E-056.89E-051.52E-03POM0.00E+003.68E-031.21E-022.04E-037.05E-041.85E-022.89E-031.38E-021.04E-036.14E-041.84E-02Propionaldehyde0.00E+001.30E-022.79E-023.36E-015.58E-024.33E-011.15E-022.72E-021.60E-013.57E-022.34E-01Styrene0.00E+002.87E-021.78E-025.90E-025.23E-031.11E-013.31E-022.31E-022.87E-023.01E-038.79E-02Toluene0.00E+002.52E-011.05E+003.61E+005.66E-015.48E+001.70E-011.32E+001.71E+003.21E-013.52E+00Xylenes2.04E-011.23E-017.05E-011.95E+005.25E-013.50E+009.59E-029.14E-019.13E-012.72E-012.40E+003-52Final Regulatory Impact AnalysisTable 3.2-8 (cont’d).National Meansof Census Tract 90thPercentile Population Exposure Concentrations of Mobile SourceAir Toxics in 1999, 2015, 2020, and 2030, Without Controls in this Rule.2020 annual average concentrations(μg m-3)2030 annual average concentrations(μg m-3)totaltotalPollutantbackground(μg m-3)majorarea &otheronroadnonroad(includingbackground)majorarea &otheronroadnonroad(includingbackground)1,3-Butadiene5.88E-022.32E-032.16E-024.28E-021.48E-021.40E-012.32E-032.16E-025.11E-021.72E-021.51E-012,2,4-Trimethylpentane0.00E+001.40E-023.75E-027.09E-011.54E-019.14E-011.40E-023.75E-028.25E-011.69E-011.05E+00Acetaldehyde5.82E-013.47E-026.42E-027.14E-011.28E-011.52E+003.47E-026.42E-028.55E-011.41E-011.68E+00Acrolein0.00E+004.15E-032.70E-024.53E-022.23E-029.87E-024.15E-032.70E-025.37E-022.56E-021.10E-01Benzene4.50E-011.86E-021.99E-016.68E-011.44E-011.48E+001.86E-021.99E-017.93E-011.65E-011.63E+00ChromiumIII0.00E+006.51E-043.97E-044.37E-053.39E-051.13E-036.51E-043.97E-045.40E-053.48E-051.14E-03ChromiumVI0.00E+008.98E-051.77E-042.90E-057.78E-063.04E-048.98E-051.77E-043.58E-058.04E-063.11E-04EthylBenzene0.00E+001.68E-021.65E-012.62E-017.71E-025.21E-011.68E-021.65E-013.06E-018.71E-025.75E-01Formaldehyde8.03E-015.60E-021.02E-013.11E-011.67E-011.44E+005.60E-021.02E-013.70E-011.86E-011.52E+00Hexane0.00E+007.12E-026.39E-011.86E-017.27E-029.69E-017.12E-026.39E-012.06E-018.21E-029.98E-01MTBE0.00E+001.61E-027.34E-021.52E-011.37E-013.78E-011.61E-027.34E-021.50E-011.53E-013.93E-01Manganese0.00E+001.92E-031.71E-032.21E-054.07E-063.65E-031.92E-031.71E-032.72E-054.54E-063.66E-03Naphthalene0.00E+004.55E-035.44E-021.24E-025.33E-037.66E-024.55E-035.44E-021.45E-026.16E-037.96E-02Nickel0.00E+005.39E-041.00E-035.25E-057.19E-051.66E-035.39E-041.00E-036.45E-057.86E-051.68E-03POM0.00E+003.21E-031.42E-021.08E-036.21E-041.91E-023.21E-031.42E-021.29E-036.97E-041.94E-02Propionaldehyde0.00E+001.16E-022.78E-021.65E-013.52E-022.39E-011.16E-022.78E-021.94E-013.83E-022.72E-01Styrene0.00E+003.78E-022.55E-022.99E-023.07E-039.64E-023.78E-022.55E-023.57E-023.47E-031.03E-01Toluene0.00E+001.88E-011.44E+001.73E+003.23E-013.68E+001.88E-011.44E+002.04E+003.61E-014.03E+00Xylenes2.04E-011.08E-011.01E+009.22E-012.74E-012.51E+001.08E-011.01E+001.09E+003.07E-012.71E+003-53Final Regulatory Impact AnalysisTable 3.2-9.National Average Cancer Risk Across Census Tracts for 1999, 2015, 2020, and 2030 by Pollutant, WithoutControls in this Rule.1999 average individual risk2015 annual average individual riskPollutantmajorarea &otheronroadnonroadtotal(includingbackground)majorarea &otheronroadnonroadtotal(includingbackground)1,3-Butadiene4.36E-084.85E-072.06E-065.39E-074.43E-064.62E-084.50E-078.69E-073.20E-072.97E-06Acetaldehyde5.65E-081.10E-071.96E-062.89E-073.39E-065.59E-081.16E-071.08E-062.12E-072.43E-06Benzene1.49E-079.82E-076.79E-061.30E-061.18E-051.00E-071.13E-063.66E-068.25E-078.33E-06ChromiumVI5.32E-079.43E-071.69E-077.18E-081.72E-066.67E-071.25E-062.29E-078.11E-082.23E-06Formaldehyde1.81E-104.51E-103.36E-091.11E-098.69E-092.10E-105.18E-101.35E-097.69E-106.43E-09Naphthalene1.21E-071.22E-066.38E-071.37E-072.11E-061.01E-071.46E-063.43E-071.39E-072.04E-06Nickel4.81E-089.79E-084.17E-096.20E-091.56E-075.53E-081.07E-075.65E-096.87E-091.75E-07POM1.77E-071.06E-061.05E-073.62E-081.38E-061.46E-071.25E-065.39E-083.25E-081.48E-062020 annual average individual risk2030 annual average individual riskPollutantmajorarea &otheronroadnonroadtotal(includingbackground)majorarea &otheronroadnonroadtotal(includingbackground)1,3-Butadiene4.95E-084.38E-078.92E-073.39E-073.00E-064.82E-084.19E-071.03E-063.86E-073.16E-06Acetaldehyde5.80E-081.19E-071.10E-062.08E-072.46E-065.75E-081.19E-071.28E-062.23E-072.65E-06Benzene1.09E-071.17E-063.71E-068.54E-078.45E-061.08E-071.16E-064.29E-069.59E-079.13E-06ChromiumVI7.53E-071.40E-062.50E-078.34E-082.49E-067.48E-071.38E-063.05E-078.78E-082.52E-06Formaldehyde2.34E-105.47E-101.38E-097.63E-106.49E-092.28E-105.54E-101.59E-098.22E-106.76E-09Naphthalene1.12E-071.54E-063.39E-071.48E-072.14E-061.09E-071.52E-063.91E-071.69E-072.19E-06Nickel6.02E-081.16E-076.19E-097.10E-091.90E-076.01E-081.15E-077.55E-097.60E-091.90E-07POM1.61E-071.30E-065.54E-083.27E-081.55E-061.61E-071.31E-066.52E-083.59E-081.57E-063-54Fin al Regu latory Im p act An al ysis



### Figure 3.2-4. Contributions to A verage Inhalation Cancer Risk from Air Toxics Emitted

by Mobile S ources, 202 0 (Not Inclu d ing Dies el PM and Diesel Exhaust Organic Ga ses), Without Controls in this Rule.

1

, 3

- B ut a di en e 15 %

A c e t al de hy de
16 %

B e nz en e

58 % Ni c k e l
0% Na p h t h a l e n e 6%

Ch r o m i u m V I
4% PO M
1% F o r m a l de hyde
0%



### Figure 3.2-5. Average Nationw i de Cancer Risk fro m Emissions of Mobile Source Air Toxics fro m both Mo bile and Sta t ionary Sources acro ss Census Tracts, 1999 to 2030 (Not Including Diesel PM and Diesel Exhaust Orga nic Gases), Without Controls in this Rule.

3. 0 E

- 0 5

2. 5 E

- 0 5

2. 0 E

- 0 5

Al l M SA T s 1. 5 E

- 0 5 B e nz en e
1. 0 E

- 0 5

5. 0 E

- 0 6

0. 0 E + 0 0

19 99 2 01 5 20 20 20 30

Y

earAverageNationwide CancerRisk



### 3-55Fin al Regu latory Im p act An al ysis



### It should also be noted that becau se of popul atio n growth projected to occur in the United States, the n u m b er of Am ericans above cancer risk benchm arks will increase. Figure 3.2-6 depicts the U. S. population at various risk be nchm arks for mobile s ource air toxics in 1999, 2015, 2020, and 2030, using population projections from EPA’s BenMAP m odel, a tool the EPA uses to estim a te benefits of ai r pollution control strate gies, and average cens us tract exposures. (BenMAP was rec ently u sed f o r EPA’s Clean Air Inter sta te A i r Quality R u le (CAIR), 160 

### and is also discussed in Chapter 12 of the RIA). Thes e statistics do not include populations in Alaska and Hawaii; thus populations in th es e States were assum e d to rem a in at year 2000 levels. More details on the m e thodology used to project the U. S. population above various cancer risk benchm arks are provided in the technical suppor t docum ent “National-Scal e Modeling of Mobile Source Air Toxic Em issions, Air Quality, Exposur e and Risk for the Final Mobile Source Air Toxics Rule.” From Figure 3.2-6 it can be seen that, based on aver age census tract risks, the vast

m a jority of the populatio n experiences risks b etw een one in a m illion (1x1 0 -6 

### ) and one in ten thousand (1x10 -4 

### ). However, the number of people e xperiencing risks above one in a hundred thousand (1x10 -5 

### ) increases from 223 m illion in 1999 to 272 m illion in 2 030.

Figure 3.2-6. U. S. Population at Various Cancer Ri sk B e nchmarks due to Exposure to Mobile Sou rce Air Tox i cs, 1999 – 2 030, Witho u t Controls in this Rule.



### 0 50 100 150 200 250 300 350 400

1999 201 5 2020 2030Millions



### Ye a rPopulation 

### Ris k < 1 E

- 0 6

1E

- 06 < = R i s k < 1E

- 05

1E

- 05 < = R i s k < 1E

- 04

R i s k >= 1 E

- 0 4

Tables 3.2-10 and 3.2-11 summarize nationa l average population hazard quotients for chronic non -cancer effects acro ss cen sus tracts for these years by pollutant, as well as the respirato r y h azard index across po llu tants. The respiratory sys t em is the only target o r gan system where the hazard index exceeds one. A lthough the average respiratory hazard index for mobile source air toxics decrea ses by alm ost 33% between 19 99 and 2030 (Figure 3.2-7), it is still over 4 in 2030, indicating a pot ential for adv e rse health eff ects. The reduction in hazard index occurs despite la rge increases in activity for highway and nonroad sources. In addition,

3-56Fin al Regu latory Im p act An al ysis



### about 90% of this non-cancer risk is attributable to acr olein in all projection years. It should be noted that the confidence in the RfC for acrol ein is m edium

. About 25% of prim ary acrolein

em issions are from m obi le sources, and about 70% of a m bient concentr ations of acrolein (and about 75% of exposure) are attri butable to m obile sources. T h e m obile source contribution to concentrations and exposure is largely attributable to the cont ribution from m obile source 1,3­ butadiene, which is transfor m e d to acrolein in th e atm osphere. Moreover, projected growth in the U. S. population and increasing vehicle m iles tr aveled will increase th e num b er of Am ericans with a resp iratory hazard index for mobile so u r ce air toxics ab ove one, from 258 m illion in 1999
to 307 m illion in 2030 (Figure 3.2-8 )

.

Detailed su mmary tables presen ting cancer risk, hazard quo tients and hazard ind i ces b y State, and for reform ulated and non-refor m ulated (i.e., conventional) ga soline areas, can be found in the docket for this rule , along with statistics on num be r of individuals above various cancer and non-cancer benchm arks, 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 nati onwide individual cancer risks f o r m obile source air toxics in 2020, absent the controls being finalized in this rule. Summ ary tables providing distributions for other years, as well as distributions by State an d for reformulated and non-reform ulated gasoline areas, can be found in th e docket for this rule. Risk distributions are broader than the distributions of am bi ent concen trations in Table 3.2-2. F or instance, while the
95 th 

### percentile benzene concentration is about twice the m e dian value, the 95 th 

### percen tile cancer risk is rough ly three tim e s the m e dian risk. A key reason for this is th e variability in activity patterns, concentrations among m i croenvironm en ts, and commuting patterns. Figures 3.2-9 through 3.2-12 depict the geographi c distributions of m e dian county cancer risks in 2020 for all mobile source air toxics, and separately for be nzene, acetaldehyde and 1,3-butadiene. These geographic distributions closely tr ack distributions of a m bient c oncentrations, with the highest risks in m a jor population centers of the country where m obile s ource activity is the greatest. Relativ ely h i gh benzene risks are als o seen in area s of the cou n try where f u el benzen e 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 ben zene em issions and higher exposures. Higher risks are also seen in States with colder winter s, due to elevated cold start em issions.

Previously discussed changes to the HAPEM exposure m o del, to a ccoun t for near ro ad
im pacts, can im pact distributions of risk. In ord er to evalu ate the effect of switching to HAPEM6 from HAPEM 5 on individual risks nationally, we conducte d model runs using identical input data. F i gure 3.2-13 depicts the national distribution of individual cancer risks from benzene, com paring HAPEM6 and HAPEM5. Note that the graph is on a logarithm i c 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 sh if ts s lightly higher when comparing HAPEM6 to HAPEM5, but the largest ef fects are observed in the populations with the highest and lowest risk le vels, which are generally sm all fractions of the total population.

3-57Fin al Regu latory Im p act An al ysis



### Table 3.2-13 gives th e distribu tion of nati onwide individu al h azard quo tients for acro l ein, and hazard indices for th e respirator y target system in 2020. Patterns for other years are sim ilar. The average respirato r y hazard index at the 95 th 

### percentile is over 20 tim e s that at th e 5 th 

### percentile, and about 4 tim es the m e dian. Thus , som e populations are ex periencing much higher hazard indices than o t hers. Fi gure 3. 2-14 depicts the geograp hic distribution of m e dian county respirato r y h azard indices in 2020. The high haza rd indices in Idaho are th e result of high invento r y es tim ates for wildfires an d reflect a k now n error in the Idaho inventory for this source. This error was discovered at too late a date to produce and upda te em issions inventories for use in the analyses undertaken for this rule. The errors are not expected to affect the analyses of the
im pacts of controls undertaken for this rule.

3-58Table 3.2-10.National Average Population Hazard Quotient forChronic Noncancer Effects Across Census Tracts, 1999 –2030, Without Controls in this Rule.1999 average Hazard Quotient2015 average Hazard QuotientPollutantTarget Systemmajorarea &otheronroadnonroadtotal(includingbackground)majorarea &otheronroadnonroadtotal (includingbackground)1,3-ButadieneReproductive7.27E-048.08E-033.43E-028.98E-037.39E-027.69E-047.49E-031.45E-025.34E-034.96E-02AcetaldehydeRespiratory2.86E-035.54E-039.92E-021.46E-021.71E-012.82E-035.84E-035.46E-021.07E-021.23E-01AcroleinRespiratory1.44E-011.28E+003.70E+001.03E+006.16E+001.58E-011.13E+001.54E+008.10E-013.63E+00BenzeneImmunological6.35E-044.20E-032.90E-025.55E-035.06E-024.29E-044.83E-031.56E-023.53E-033.56E-02Chromium VIRespiratory4.43E-047.86E-041.41E-045.98E-051.43E-035.56E-041.04E-031.90E-046.76E-051.86E-03Ethyl BenzeneDevelopmental1.60E-058.09E-053.60E-049.17E-055.48E-041.05E-051.05E-041.74E-045.30E-053.42E-04FormaldehydeRespiratory3.36E-038.37E-036.23E-022.05E-021.61E-013.90E-039.62E-032.51E-021.43E-021.19E-01HexaneNeurological,Respiratory2.76E-041.89E-031.55E-033.95E-044.11E-032.43E-042.21E-037.58E-042.71E-043.48E-03MTBELiver,Kidney,Ocular3.86E-061.76E-051.72E-041.28E-043.21E-043.94E-061.88E-054.43E-053.20E-059.90E-05ManganeseNeurological2.04E-021.93E-022.27E-044.59E-053.99E-022.65E-022.56E-023.07E-045.32E-055.24E-02NaphthaleneRespiratory1.19E-031.19E-026.25E-031.35E-032.07E-029.88E-041.43E-023.36E-031.36E-032.00E-02NickelRespiratory, Immunological4.62E-039.42E-034.01E-045.96E-041.50E-025.32E-031.03E-025.43E-046.61E-041.68E-02StyreneNeurological2.38E-051.28E-053.77E-053.46E-067.78E-052.85E-051.76E-051.84E-052.05E-066.66E-05TolueneRespiratory,Neurological4.55E-041.82E-035.96E-039.69E-049.20E-033.12E-042.39E-032.88E-035.72E-046.16E-03XylenesNeurological8.47E-045.00E-031.32E-023.72E-032.43E-026.85E-046.69E-036.38E-032.02E-031.72E-023-59Final Regulatory Impact AnalysisTable 3.2-10 (cont’d).National Average Population Hazard Quotient for Chronic Noncancer Effects Across Census Tracts,Without Controls in this Rule.2020 average Hazard Quotient2030 average Hazard QuotientPollutantTarget Systemmajorarea &otheronroadnonroadtotal(includingbackground)majorarea &otheronroadnonroadtotal (includingbackground)1,3-ButadieneReproductive8.25E-047.30E-031.49E-025.64E-035.00E-028.03E-046.98E-031.72E-026.43E-035.26E-02AcetaldehydeRespiratory2.93E-035.99E-035.58E-021.05E-021.24E-012.90E-036.02E-036.47E-021.13E-021.34E-01AcroleinRespiratory1.78E-011.09E+001.57E+008.52E-013.69E+001.78E-011.08E+001.82E+009.62E-014.04E+00BenzeneImmunological4.67E-044.99E-031.58E-023.65E-033.61E-024.63E-044.96E-031.83E-024.10E-033.90E-02Chromium VIRespiratory6.28E-041.17E-032.09E-046.95E-052.07E-036.23E-041.15E-032.54E-047.32E-052.10E-03Ethyl BenzeneDevelopmental1.17E-051.14E-041.72E-045.44E-053.52E-041.15E-051.12E-041.96E-046.09E-053.81E-04FormaldehydeRespiratory4.34E-031.02E-022.55E-021.42E-021.20E-014.23E-031.03E-022.95E-021.53E-021.25E-01HexaneNeurological,Respiratory2.66E-042.37E-036.92E-042.82E-043.61E-032.65E-042.32E-037.53E-043.17E-043.66E-03MTBELiver,Kidney,Ocular4.36E-061.91E-053.53E-053.28E-059.16E-054.26E-061.90E-053.44E-053.62E-059.38E-05ManganeseNeurological2.99E-022.80E-023.37E-045.59E-055.83E-023.08E-022.81E-024.11E-046.15E-055.94E-02NaphthaleneRespiratory1.09E-031.51E-023.33E-031.45E-032.10E-021.07E-031.49E-023.83E-031.65E-032.14E-02NickelRespiratory, Immunological5.78E-031.12E-025.95E-046.83E-041.83E-025.78E-031.10E-027.26E-047.30E-041.83E-02StyreneNeurological3.29E-051.96E-051.90E-052.09E-067.36E-053.32E-051.97E-052.22E-052.32E-067.74E-05TolueneRespiratory,Neurological3.47E-042.63E-032.89E-035.78E-046.45E-033.44E-042.63E-033.32E-036.37E-046.93E-03XylenesNeurological7.69E-047.35E-036.39E-032.04E-031.80E-027.59E-047.26E-037.33E-032.25E-031.90E-023-60Fin al Regu latory Im p act An al ysis



### Table 3.2-11. National Respiratory Haz ard Index for Chronic Noncancer Effec t s across Census Tracts, Without Controls in this Rule.

Resp irat o r y Sy st em A v erag e Haz ard In d ex

Y ear bac k ground maj o r a rea & ot h e r onroad nonroad t o t a l ( i ncl udi ng 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 Haz ard Index for U.S. Population (Aggregate of Haz ard Quotient s f o r Individual Pollutan t s), Without Controls in this Rule.



### 0 1 2 3 4 5 6 7

1 99 9 20 15 20 20 2 03 0

Y

earHazardIndex


### mo b i le

st a t i o n a r y

ba c k gr o un d

3-61Fin al Regu latory Im p act An al ysis



### Figure 3.2-8. U. S. Population at Various No n-Cancer Haz ard Benchmarks due to Exposure to Mobile So urce Air Toxics, 1999 – 2030, With out Controls in this Rule.

0 50 10 0 15 0 20 0 25 0 30 0 35 0 40 0

19 99 20 15 2 02 0 2 03 0Millions

Y earPopulation HQ < 0

. 1

0

. 1 <= H Q < 1

1 <= H Q < 1 0

H Q >= 1 0



### Table 3.2-12. Distribu tion of Individual Cancer Ris k s fo r Mobile So urce Air Toxics in 2020, With out Controls in this Rule.

2020 risk dist ribut ion

Pollutant 5t h percentil e 10 th percentil e 25t h percentil e Median 75t h percentil e 95t h percentil e

Total Risk: All H A Ps 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
Acetaldehy de 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

Chr o m i u m VI 3. 85E

- 08 7. 93E

- 08 2. 38E

- 07 7. 01E

- 07 1. 81E

- 06 4. 54E

- 06 7. 29E

- 06

Form aldehy de 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



### 3-62Fin al Regu latory Im p act An al ysis



### Figure 3.2-9. 2020 County Media n Cancer Risk for All Mobile Sou rce Air Tox i cs, Without Controls in this Rule.

R i s k ( N in a m i ll i o n)

1. 4

- 6. 4

6. 5

- 12. 0

12. 1

- 21

. 2

21. 3

- 41

. 9

42. 0

- 10 1. 8



### Figure 3.2-10. 2020 County Median Cancer Risk fo r Benz ene, Without Controls in this
Rule.

Ris k ( N i n a m il l ion )

0. 5

- 2. 8

2. 9

- 5. 0

5. 1

- 8. 2

8. 3

- 15. 4

15

. 5

- 51

. 6



### 3-63Fin al Regu latory Im p act An al ysis



### Figure 3.2-11. 2020 County Median Cancer Risk fo r Acet aldehyde, Without Controls in this Rule.

R i s k ( N in a m i ll i o n)

0. 8

- 1

. 4

1. 5

- 1

. 9

2. 0

- 2

. 8

2. 9

- 4

. 6

4. 7

- 1 0. 2



### Figure 3.2-12. 2020 County Median Cancer Risk fo r 1,3 -Butadiene, Without Controls in this Rule.

Ris k ( N i n a m il l ion )

0. 0

- 0

. 9

1. 0

- 2

. 1

2. 2

- 4

. 1

4. 2

- 9

. 8

9. 9

- 2 1. 4



### 3-64Population Fin al Regu latory Im p act An al ysis



### Figure 3.2-13. 1999 Comparison Betw een H A PEM6 and HAPEM5 Nationw i de Individual
Benz ene Cancer Risk, Without Controls in this Rule.

1 00,000 ,000

10,000 ,000

1,000 ,000

100 ,000

10 ,000

1

, 000

100

10

1

01.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

Populat ion w i th R i s k > Risk bin

H APEM6 H APEM5



### 3-65Fin al Regu latory Im p act An al ysis



### Table 3.2-13. Distribu tion of Individual Ha zard Quotients/Haz a rd Indices for Mobile Source Air Toxics (fro m both Mo bile and Sta t ionary Sources) in 2020, Without Controls in this Rule.

2020 a v erage H a zard Q u ot ient or H a z a rd Index

5t h 10t h 25t h 75t h 90t h 95t h
Pollutant percentil e percentil e percentil e Median percentil e percentil e percentil e Ac rolein 0. 41 0. 61 1. 18 2. 31 4. 47 8. 05 11. 3 Re spiratory System 0. 53 0. 75 1. 36 2. 57 4. 83 8. 54 11. 9



### Figure 3.2-14. 2020 County Median Non-Cancer Haz ar d I ndex Respiratory Mobile Source Air Toxics, Without Controls in this Rule.

Ha z a r d I n de x

0. 05 0

- 1. 2 68

1. 26 9

- 3. 1 13

3. 11 4

- 6. 5 43

6. 54 4

- 13

. 225

13. 2 26

- 3 1. 13 1



### 3-66Fin al Regu latory Im p act An al ysis



### 3.2.1.2.4 Im pacts of Controls on Average Inhala tion Cancer Risks and Noncancer Hazards

The standa r d s being f i n aliz ed in th is rule will substantially redu ce inhalation cancer and noncancer risk from exposure to air toxics em itte d by m obile sources acro ss the United States. Table 3.2-14 shows that in 2030, the highway vehi cle contrib u tion to MSAT cancer risk will be reduced on average 36% across the U.S., and the nonroad equipm ent contribution will be reduced abo u t 6%. In 2 030, the hig h way vehicl e contributio n to benzene cancer risk will be reduced on average by 4 3 % acros s th e U.S., and the nonroad contribution will be redu ced by
11%. Table 3.2-15 summ arizes the change in m e dian and 95 th 

### percentile inhalation cancer risks from benzene and all MS ATs attributable to all outdoor sources in 2015, 2020, and 2030, with the controls being finalized in this rule. Re ductions are significantly larger for individuals in the 95 th 

### pe rc entile than in the 50 th 

### percentile. Thus, this rule is providing bigger benefits to individuals experiencing the highest levels of risk. In states w ith high fuel benzene levels and high cold start em issions, the cancer risk reduc tion from total MSATs is about 40% or higher (Table 3.2-16). d 

### Figure 3.2-15 depicts the im pact on the m obile source contribution to nationwide average population can cer risk from all MSATs and benzene in 2030. Nationwide, the canc er r i sk attribu t ab le to tota l M S ATs woul d be reduced by 30%, and the risk from m obile source benzene would be reduced by 37%. Figur es 3.2-16 and 3.2.-17 present the distribution of percent reductions in average MSAT and benzene cancer risk, respectively, from all sou r ces in 2030 with the controls being finalized in 2030. Table 3.2-17 shows reductions in hazard quotien t s an d hazard ind i ces for acro lein and resp iratory effects, resp ectiv ely. Nation w ide, the mobile source contribution to the acrolein ha zard quotien t an d respirat ory hazard index would both be redu ced about 23 %, and the h i ghway vehicle con t ribu tion will b e reduced abou t 35%.
Summ ary tables providing exposure and risk data by State, as well as m a ps of cancer risks and noncancer h azards with contro ls and percen t red u c tions with contro ls, can be found in the docket for the rule.

It should be noted that the estim a te d total relative reducti ons are significant underestim ates, since we could not account for fu rther reductions in em issions from transport, i.e., background sources. In Section 3.2.1.4, we provi de a quantitative estim a te of the expected reductions in background concentrations in future years. Again, as noted previously, since this modeling did not include the 1.3 vol% m a xi m u m aver age fuel benzene level, reductions in risk for som e parts of the country, such as the Pacific Northwest, are underestim ated.

d R e d u ct i ons ar e l i k el y t o be h i ghe r t h a n est i m a t e d by t h i s m odel i ng, du e t o t h e 1. 3% m a xi m u m averag e f u el benze n e l e vel

.



### 3-67Final Regulatory Impact AnalysisTable 3.2-14.Contributions of Source Sectors to Nationwide Average Cumulative MSAT Cancer Risk, With and Without Controls,2015, 2020, and 20302015 AverageRisks2020AverageRisks2030Average Riskstotaltotal (includingtotaltotal (includingtotaltotal(includingmajorarea& othertotalonroadnonroadbackground)majorarea&othertotalonroadnonroadbackground)majorarea & othertotalonroadnonroadbackground)TotalMSATsReference1.17E-065.76E-066.24E-061.62E-061.97E-051.30E-066.08E-066.35E-061.67E-062.03E-051.29E-066.02E-067.37E-061.87E-062.14E-05Control1.17E-065.74E-064.98E-061.53E-061.83E-051.30E-066.06E-064.58E-061.58E-061.84E-051.29E-066.01E-064.69E-061.77E-061.86E-05% Difference0.00.320.25.36.90.00.327.95.59.30.00.336.35.613.1BenzeneReference1.00E-071.13E-063.66E-068.25E-078.33E-061.09E-071.17E-063.71E-068.54E-078.45E-061.08E-071.16E-064.29E-069.59E-079.13E-06Control1.00E-071.12E-062.73E-067.38E-077.30E-061.09E-071.15E-062.45E-067.62E-077.09E-061.08E-071.15E-062.43E-068.54E-077.15E-06% Difference0.31.325.410.512.30.31.334.010.816.20.31.343.410.921.73-68

### Table 3.2-15. Change in Median and 95 th 

### Percentile Inhala tion Cancer Risk from Benz ene and all MSATs Attributable to Outdoor Sources in 2015 , 2020, and 2030 w i th the Controls Being Finaliz e d in this Rule.

2015 2020 2030

m e dian 95th m e dian 95th m e dian 95 th



### All
MSATs

W ithout 1.50x10 -5 

### 4.75x10 -5 

### 1.53x10 -5 

### 4.93x10 -5 

### 1.61x10 -5 

### 5.28x10 -5



### Controls
W ith 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

W ithout 6.86x10 -6 

### 1.82x10 -5 

### 6.93x10 -6 

### 1.86x10 -5 

### 7.37x10 -6 

### 2.06x10 -5



### Controls
W ith 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 w i th Highest Reductions in Average Benz ene Cancer Risk R esulting from Mobile Source Emiss i ons, 20 30.

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 Wyom ing 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

3-69

### Figure 3.2-15. Contribution to Nationw i de Average Po pulation Cancer Risk from Mobile Source MSATs and Be nz ene 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 W i t hout C ont r ol s

W i t h C ont rol s
4. E

- 06

3. E

- 06Average CancerRisk

2. E

- 06

1. E

- 06

0. E + 00

T ot al M S A T s B enz ene



### Figure 3.2-16. Distrib u tion of Percent Reductions in Med ian MSAT Cancer Risk, 2030, for U.S. Cou n ties with Con t rols in this Rule.

D i ffe r e n c es

- 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. Distrib u tion of Percent Reduction s in Med ian Benz ene Cancer Risk, 2030, for U.S. Counties With Controls in this Rule.

D i f f er en ce s

- 46. 51 9% - - 2 1. 86 1 %

- 21. 86 0% - - 1 5. 84 3 %

- 15. 84 2% - - 1 0. 89 3 %

- 10. 89 2% - - 5

. 9 32 %

- 5. 931 % - 0

. 7 47%



### As a result of the controls being finalized in this rule, the num ber of people above the 1 in 100,000 cancer risk level due to exposure to all m obile source air toxics from all sources will decrease by over 11 m illion in 2020 and by about 17 m illion in 2030. The num ber of people above the 1 in 100,000 increased cancer risk level from exposure to benzene from all sources decreases by about 30 m illion in 2020 and 46 m illion in 2030 (Table 3.2-18 ).

3-71Table 3.2.-17.Reductions in HazardQuotients and Hazard Indices for Acrolein and Respiratory Effects Due to MSAT Controls.2015 AverageHazardIndex/Quotient2020 AverageHazardIndex/Quotient2030 AverageHazardIndex/Quotientarea &othertotalonroadtotalnonroadtotal (includingbackground)majorarea &othertotalonroadtotalnonroadtotal(includingbackground)majorarea &othertotalonroadtotalnonroadtotal (includingbackground)1.171.630.843.920.191.141.660.883.990.191.131.920.994.351.171.350.843.650.191.141.240.883.560.191.131.240.993.670.016.70.06.90.00.025.60.010.70.00.035.40.015.61.131.540.813.630.181.091.570.853.690.181.081.820.964.043-72

### Table 3.2-18. Decrease in Number of People w i th Inhalation Exposure above th e 1 in 100,000 Cancer Risk L e vel due to Inhalation Exposure from Ambien t Sources, With Controls in this Rule.

Year Benz ene 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 fin a lized will also im pact on the num be r of people above variou s respirato r y hazard index levels (Table 3.2-19).

Table 3.2-19. Decrease in Number of People w i th I nhalation Exposure above a Respiratory
Haz a rd Index of One d u e to Inhalation Exposu re from Ambient Sources, With Controls in this Rule.

Year Decrease in Population

w i th Respiratory HI > 1
2015 5,639,000
2020 10,227,000
2030 16,919,000

3-73

### 3.2.1.3 Strengths and Limitations

Air quality, exposure, and risk were assesse d using the best available suite of tools for national-scale analysis of air toxics. The sam e ge neral su ite of tools was used in 1996 and 1999
NATA. The 1996 NATA was reviewed by EPA’s Sc ience Advisory Board, and the analyses done for 1999 incorporate several changes in respons e to comments m a de in this peer review.
Am ong the improvem ent s were:

• Im proved em ission inventory with detailed ch aracterization of source categories within the onroad and nonroad source sectors and m ore speciated data for som e pollutant groups (POM) with in par ticu l ar source c ateg o ries.

• Speciation of chrom i um to hexavalent form based on em ission sources rather than a single num ber applied across all sources

• Im proved surrogates for spatial allocation in EMS-HAP.

• Im proved estim ation of “background” concentr ations for m a ny pollutants. These background levels were previously unif orm across the country. Now, for m a ny pollutants, b ackground levels are based on recen t m onitor data and spatially vary depending on county population density. 161



### • Im proved version of HAPEM, which includes m o re recent census data, commuting algorithm s and better characte rization of exposure distribu tions through improvem e nt s in modeling long-term activity patterns and va riability in conce n tra tion leve ls in

m i croenvironm ents.

In addition to the im provem e nts for the 1999 NATA, i m provem e nts were m a de in analyses for this rule, including inve ntory im provem ents and updates to HAPEM discussed earlier.

The SAB expressed their belief that due to th e lim itations inherent in the analysis, the 1996 NATA should not be used to support regulator y action. However, the use of the im proved analyses in this rule does provide useful insight on the nature of the m obil e source air toxics problem and the possible public health im prove ments associated with this rule.

In addition to the strengths listed above, ther e are lim itations due to unce r tain ty. The inventory uncertainties are discu ssed in Chapter 2

. There are a num ber of additional significant uncertainties associated with the air quality, exposure and risk m odeling. These uncertainties result from a num b er of param eters including : developm ent of county-level estim ates from broader geographic data (i.e., state, regional or national), surrogates used to allocate em issions to

census tracts , param e ters used to characterize photochem ical processes, long range transport, terrain effects, deposition rates, hum an act ivity pattern parameters, assumptions about relationships between ambient levels in di fferent m i croenvironm ents, and dose-response

param eters. Uncertainties in dose-response param e ters are discussed in Chapter 1 of the RIA. The m odeling also has certain key limitations : res u lts are m o st accurate for large g e ographic

areas, exposure m odeling does not fully reflect variation am ong individuals, non-inhalation exposure pathways and indoor sources are not ac counted for; and for some pollutants, the ASPEN dispersion m odel m a y underestim ate con centrations. Also, the 1999 NATA does not

3-74

### include default adjustm e nts for early life exposur es recently recomm ende d in the Supplem e ntal Guidance for Assessing Suscep tibility from Early-Life Exposure to Carcinogens. 162 

### If warranted, incorporation of such adjustm e nts wo uld lead to higher estim ates of lifetime risk. EPA will determ ine as p art of the IRIS assessm ent proces s which substan ces m eet the criter ia f o r

m aking adjustm ents, and f u ture asses sm ents will ref l ect them

.

As part of the 1999 NATA, EPA compar ed ASPEN- m odeled concentrations with available, but geographically lim ited, am bient air quality m oni toring data f or 1999. For each monitor-pollutant com bination, EPA com pared the annual average concentration estim a ted by the ASPEN model at the exact ge ographical coordinates of the m onitor location with the annual

average m onitor ed valu e to get a po int- to-po i nt com p arison between the model and monitor concentrations. The agreem ent between m odel and m onitor values for benzene was very good, with a m e dian m odel to monitor ratio of 0.95, and 74% of sites within a f actor of 2. Agreem ent for acetaldehyde was almost as good as benzene, but data su ggest th at A SPEN could be underpredicting for other m obile s ource air toxics (see Table 3.2-20).

More detailed discussion of m odeling lim ita tions and uncertainties can be found on the 1999 NATA website.

Table 3.2-20. Agreement of 1999 Model and Monitors b y Pollutant on a Point-to-Point Basis Pollutants lis ted w ere Monitored in at least 30 Sites and in a Broad Geogra phical 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%
Form aldehyde 68 0.64 60% 28% 76%
Chrom i um 42 0.29 26% 5% 95% Manganese 34 0.4 44% 15% 91%
Nickel 40 0.53 48% 18% 75%

In addition to the lim itations and uncerta inties associated with m odeling the 1999 base year, there are additional ones in the projection year m odeling. Fo r instance, the m odeling is not accounting for im pacts of de m ographic shifts that ar e likely to occur in the future. Assum ptions about future-year m e teorology introduce additiona l uncertainty in am bient concentrations and resulting exposures. Another lim itation is the use of 1999 “bac kground” levels to account for

m i d-range to long-range transport. However, si nce background is related to em issions far away from receptors, these levels shoul d decrease as those em issions d ecrease. For the pro posed rule we perform e d a sensitiv ity analysis for ben zene, f o rm aldehyde, acetaldeh yde and 1,3-butadien e to evaluate the potential bias introduced by this assum ption. We used background estim ates scaled by the change in the proposed rule inve ntory for a future year relative to 1999. The scaling factors applied to the background level fo r an individual county were based on em issions for counties within 300 kilom e ters of that county’s centroid. Our analysis indicated that using a scaled background reduced benzene concentrati ons about 15% on averag e across the U. S in 2015, 2020, and 2030. Table 3.2-21 com pares national average total concentrations from the

3-75

### proposed rule using 1999 versus scaled backgrounds

. More details are provided in the technical support docum ent for the proposed rule. 163



### Table 3.2-21. National Average Total Concentr ations (All Sources and Background) for 2015, 2020, and 2030 using both the 1999 Background and the Scaled Backgrounds (Data from Proposed Rule).

HAP To ta l Co ncentra t io ns ( μ g m -3 ) usi n g 19 9 9
Back ground To ta l Co ncentra t io ns ( μ g m -3 ) usin g Sc ale d Concentrations
2 015 2 020 2 030 2 015 2 020 2 030 1, 3-B u t a di en e 9. 81× 1 0 -2 9. 77× 1 0 -2 1. 00× 1 0 -1 7. 57× 1 0 -2 7. 50× 1 0 -2 7. 86× 1 0 -2

Acet al dehy de 9. 66× 1 0 -1 9. 36× 1 0 -1 9. 56× 1 0 -1 7. 77× 1 0 -1 7. 47× 1 0 -1 7. 78× 1 0 -1

B e nzene 9. 13× 1 0 -1 9. 02× 1 0 -1 9. 24× 1 0 -1 7. 57× 1 0 -1 7. 40× 1 0 -1 7. 71× 1 0 -1

Form al dehy de 1. 22 1. 22 1. 25 9. 56× 1 0 -1 9. 68× 1 0 -1 1. 01 Xy l e nes 1. 55 1. 61 1. 65 1. 50 1. 56 1. 60



### The largest impacts were in the Midwest as can be seen in F i gure 3.2-19, which depicts ratios of the ASPEN- modeled am bient benzene concentrations with an adjusted background versus the 1999 background in 2020. Data tables w ith results of the sensitivity com parison by U.
S. County, along with maps of pollutant concentr ations with and without an adjusted background can be found in the docket for the rule.

While accou nting for im pacts of em ission redu ctions on back ground levels would red uce estim ated po pulation risk s, it would increas e estim ated reductions in risk of control s t rateg i es in a given year, since background levels would be reduced. Also, if the m odeling accounted for equipm ent and fuels in attached garages and increased risks from early lifetim e exposures, estim ated risks and risk reductions from fuel benzene control would be larger.

3-76

### Figure 3.2-19. Ratios of Benz ene Concentr ations w i th and w i thout an Adjusted Background, 2020 (from modeling done to support proposed rule).

Leg e nd

0.51 6 - 0. 63 8

0.63 9 - 0. 71 6

0.71 7 - 0. 78 6

0.78 7 - 0. 89 3

0.89 4 - 1. 06 8



### 3.2.1.4. Perspective on Cancer Cases

We have not quantified the can cer-related h ealth benefits of expected MS AT reductions in term s of a voided cancer cases or dollars . The EPA Science Advisory Board (SAB) specifically commented in their review of the 1996 National Air Toxics Assessm ent (NATA) that these to ols were not yet ready for use in a national-scale benefits analysis, because they did not consider the full distributi on of exposure and risk, or addr ess sub-chronic health ef fects. 164



### W h ile EPA has since improved m any of these to ols, the r e r e m ain critic al lim itations f o r estim ating cancer incidence. Fo r the MSATs of greatest con c ern, for example, we are currently unable to estim a te cessation lag, which is the tim e between reduction in e xposure and decline in risk to “steady state level.” 165 

### W e have also not resolved th e an alytical challenges as soc i ated with quantifying partial lifetim e probabilities of cancer for dif f erent age groups or estim ating changes in survival rates over tim e . Indeed, so me of these issues are likely to rem a in highly uncertain for the fores e eable future.

We can, however, present som e perspective on how average individual risks could translate into cum u lative exce ss cancer cases across the U.S. population over a lifetim e,

assum i ng continuous exposure at a given level fo r 70 years. Cancer cases were estimated by summing the distribution of individual cancer ri sks from the national-scale m odeling done to support this rule.

To estim ate annual incidence, this would be divided by 70. However, without knowing when within a lifetim e cancer is m ore likely to occur, a nd without accounting for time-varying

3-77

### exposure, any estim ate of incidence f or a given ca lendar year is highly un certain. W e also note that a proper calculation would enta il the use of a life tab l e of inci den ce ra tes with in discrete age ranges and a dose-response form ula tion expressing rate ratios as a function of benzene inhalation exposure concentration.

In 2030, the cum u lative excess average individua l cancer risk from outdoor em issions of mobile source air toxics is estim a ted at 2.1x10 -5



### . If the entire U. S. population (projected to be about 364 million) 166 

### were exposed to this level of risk ove r a 70-year lifetim e, it would result in about 7700 cancer cases, which translates into 110 annual ca ncer cases.

In its review of the 1996 NATA, SAB recomm e nded that if cancer cases were calculated for benefits assessm ent, a “best estim ate” of risk (rather than an upper bound), should be used. We believe that the m a ximum likelihood unit risk ra nge for benzene represents a best estim ate. In our analyses, we have used the upper end of th is range, as did the 1999 NATA. I f we used the lower end of this range, incidence estim a tes woul d be lower by a factor of about 3.5. Following is a discussion related to benzene specific ally, including a discussion of the potential
im plications of the lim itations of our nationa l-scale m odeling, which were noted in Section
3.2.1.4.

In 2030, the national average inhalation indivi dual cancer risk from outdoor m obile and stationary so urces of ben zene, in the absence of the standards being finalized in this rule, is estim ated at approxim ately 9.1x10 -6 

### , based on the m odeling done for this rule. If the entire U. S. population were exposed to that level of risk over a 70-year lifetim e, it would result in approxim ately 47 excess cancer cas es per year (E quation 1).

( 1 ) Excess Cancer Cases at 2030 Exposure Level = 

# ( Average Individual Cancer Risk 

# ) ( 2030 Population 

# ) ×

= 9

. 1 × 10

− 6 

### × 3

. 64 × 10 8 

### ≈ 3300

Annual Cancer Cases = 3300 / 70 = 47

As discussed in Section 3.1.3.3, EPA’s estim ate 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, tim e-weighted average individual exposures to benzene co uld increase by roughly 1.2 to 6.6 μg/m 3 

### (Appendix 3A). There is a great deal of uncertain ty associated with these estim a te s. This could result in about another 3400 to 18700 excess cancer cases (equation 3). The num erical ra nges expressed here

m a y not full y address all sources of uncerta inty involved in m a king these projections.

( 3 ) Attached Garage Excess Cancer Cases = 

# ( Average Exposure 

# ) x ( Benzene URE ) × 

# ( Population 

# )

3 3 8



### = 

# ( 1

. 2

− 6

. 6 μ g / m 

# ) ( x / μ g / m 

# ) x 

# ( 3

. 64 x 10 

# )

= 3400

− 18700 x 7

. 8 10

− 6



### Annual Cancer Cases = 49

− 268

3-78

### Thus, including attached garages would increas e the num ber of benzene-related excess cancer cases to somewhere betw een 96 and 315 annually. This estimate would still not include higher exposure levels from occupational exposures, va por em issions from leaking underground storage tanks, or other accidental releases into the e nvironm ent. Any populati on risk characterization that does not account for these factors underestim ates the excess cancer related to benzene.

W ith the controls b eing f i nalized in this rule, average ind i vid u al risk, not including attached garage exposures, is reduced to 7.3x10 -6 

### , which results in approxim ately 37 cancer

cases per year. Thus, excess le ukemia cases would be reduce d by 10 annually. A roughly 40% reduction in overall benzene em i ssions could reduce attached garage exposures by approxim a tely 0.5-2.6 μg/m 3 

### as well, thus reducing excess annual cance r cases from this source of exposure by another estim ated 20 to 100 excess cancer cases. Thus, this rule would prevent roughly 30 to 110 benzene-related excess cancer cases annuall y, assum i ng continuous lifetim e exposure to 2030 levels, given the assum ptions of populati on size and lifetim e above, and not including excess leukem i a from occupational exposure or f r om leaking underground storage tanks.
Em ission reductions in 2030 would reduce cancer cas es not just in 2030, but also well beyond this period. There would also be further unquantifie d reductions in incidence due to the other air toxics reductions.

Such estim ates should be interpreted with extr eme caution since they co uld im ply an artificial sense of precision. Seri ous lim itations include:

• As discussed in Chapter 1, the current unit risk estim ate for benzene m ay underestim ate risk from leukem i a, because som e recent ep idem iology data, includ ing key studies published after the m o st recen t IRIS assessm ent, suggest a su pralinear rather than linear dose-response at low doses. However, the st udies published after the m ost recent IRIS

assessm ent have not yet been form al ly eval uated by EPA as part of the IRIS review process, and it is no t clear whether th ese data pro v ide sufficient eviden ce to reject a lin ear dose-response curve. A better understanding of the biological m echanism of benzeneinduced leukem ia is needed.

• Geographically heterogeneous percentage em i ssions reductions do not translate directly into changes in am bient levels, exposure, and risk.

• The U.S. population would have experienced hi gher average exposures in previous years, but this is n o t accoun ted f

o r.

• The extent to which available studies of indoor air hom es in with attached garage are representative of the nati onal housing stock is unknown.

• Cessation lag between reduction in exposure and reduction in risk is not accounted for.

• Differences in risk am ong various age groups are not known, and the age structure of the
U.S. population is expected to change over tim e

.

3.2.2 Local-Scale Modeling

Modeling at the national or regional scale, such th e m odeling done for the NATA National-Scale Assessm ent described in Section 3.2.1, is designed to identify and prioritize air toxics, em ission source types and locations which are of greatest potential concern in term s of contributing to population risk. Such assessm ents also help elucidate patterns of exposure and risk across broad geographic areas, and can help charac terize trends in air toxics risk and

3-79

### potential impacts of controls at a broad geographic scale, as dem onstrated above. However, more localized assessm ents are n eed ed to char acterize and co mpare risks at local levels, and identify potential “hotspots.”

National or regional-scale assessm ents typi cally rely on a “top down” approach to estim ate em issions. Under a “top down” approach, em issions are es tim ated at the cou n ty level, typically starting from more aggregated inform ation (e.g., state or national level) on activity. Spatial surrogates are then used to allocate em issi ons to grid cells or cen sus tracts for modeling. Use of m ore local data can greatly improve the characterization of the ma gnitude and distribution of

air toxic e m issions. Air qua lity m odeling c an al so be cond ucted with b ette r spa tia l resolu tion than is com p utationally feasib le in a regional or national-scale a ssessm ent. As a result, spatial gradien t s of air toxic con centratio ns and locations where the highest risks are likely to occur can be m o re accurately iden tified.

Local-s cale modeling is typically done using steady-state pl um e dispersion m odels, such as the Integrated Source Com plex (ISC) Mode l, the newly prom ulga ted AERMOD (AMS/EPA Regulatory Model), or non-steady- state puff m odels such as CALPUFF. These m ode ls have a
lim ited ability to sim u late chem ical reactions in the atm osphere. As discu ssed in Section 3.2.1, grid-based models, such as CMAQ, which better sim u late chem ical processes, do not yet have the spatial resolution of dispersion models. Si gn ificant advan ces are being m a de, however, in

com bining features of grid-based m odels and pl um e/puff models. These advances are described in a recent p aper. 167 

### A case study of diesel exhaust part iculate m atter in W i lm ington, CA was

recen tly con ducting em ploying som e of these adv ances. 168 

### The researchers com b ined Gaussian and regional photochem i cal grid m odels. They found that local data, when m odeled, provided a much m o re refined picture of the m a gnitude and distribution of possible community “hot spots” than m o re tr aditional, re gional da ta, which rely on m ore default assum ptions. An evaluation of the appro ac h determ ined that sp atial alloc ation a nd em ission rates contribute m o st to uncertainty
in m odel results, and this uncertainty could be substantially reduced through the collection and integration of site specific inform ation about the location of em ission sources, and the activity and em ission rates of key sources affecting m ode l concentrations. They conclude that for neighborhood assessm ents, incorpora ting site-specific data can lead to improvem e nt in m odeled estim ates of concentratio ns, especially where site -specific data are lacking in regu lato ry databases.

The W ilm ington study discussed above also allocated m o tor vehicle em issions to individual road “links,” rather than using sp atia l surroga tes to alloc ate co unty leve l v ehicle

em issions to grid cells. In using spatia l surrogates to allocate em issions, high local concentratio ns m a y not be captured for environm ents near m a jor roadways, which are often clustered in urban centers. One local-scale assess m e nt done in the Minneapolis-St. Paul area of Minnesota, using such an inventory with the ISC m odel, found that the model tended to overpredict at low m onitored benzene concen trations and underpred ict at high m onitored concentratio ns. 169 

### Local-scale m odeling using activity data for individual road links can better characterize distributions of con centrations, and differentiate be tween locations n ear roadways and those further away, as obser ved in the follow i ng studies.

As discussed in Section 3.1.3.2, local-scale m odeling in Houston assigned em issions to

3-80

### individual road links. 170 

### Researchers at US EPA deve loped a m e thodology which utilized a Geographic Inform ation System (GI S ) to allocate benzene em issions in Houston to m a jor road

segm ents in an urban area and m odel the segm ents as elongated area sources. The Industrial Source Complex Short T e rm (ISCST) dispersi on model used both gr idded and link-based

em issions to evaluate the effect of i m proved sp atial allocation of e m issions on am bient m odeled benzene con centration s. Allocat ing onroad m obile em issions to road segm ents i m proved the

agreem ent between m o deled concentrations when com p ared with m onitor observations, and also resulted in h i gher es tim ated concen trations in the urban center where the density of neighborhood streets is greater and the largest am ount of tra ffic found. The calculated annual average benzene m odel concentrations at m on itor sites are compared to the observed annual average concentrations in Figure 3.2-20. Most of the gridded m odel em is sions show lower benzene con centration s than both the link-b a sed and observed monito r con centration s. Allocating the onroad mobile em issions to road se gm ents resulted in an increas e in th e averag e benzene concentration, resulting in values that m ore closely m a tch concentrations reported by monitors.

Recent air q uality m odeling in Portland, OR using the CALPUFF dispersion m odel assigned em issions to specific roadway links. 171 

### The resulting data were used to develop a regression model to approxim a te th e CALPUFF predic ted c oncentr ation s, determ ine the im pacts of roadway proxim ity on am bient concentration of three hazardous air pollutants (1,3-butadiene, benzene, and diesel PM), and to estim a te the z one of influence around roadways. Concentrations

were m odeled at several distances from m a jor roadways (0-50, 5-200, 200-400, and > 400

m e ters). For benzene, the resulting av erage concentrations were 1.29, 0.64, 0.40, and 0.12

μ g/m 3 

### , respectively, illustrating the steep concentration gradient along roadways. There was a zone of influence between 200 and 400 m e ters, w ith concentrations falling to urban background levels beyond this distance. The overall m ean motor veh i cle benzene con centration m odeled in Portland was about 0.21 μ g/m 3 

### , with concentrations increasing to 1.29 μ g/m 3 

### at m odel receptor sites within 50 m e ters of a road. The results ind i c a te th at in o r der to c a pture localized impacts of hazardous air pollu tants in a dispersio n m odel, there is a n eed to include in dividual roadway links.

3-81

### Figure 3.2-20. Model to Monitor Compariso n s of Houston Benz ene Concentrations

A recent rev i ew of local-scale m odeling studies concluded th at: 172 

### 1) Significant variations in air toxic concentratio ns occu rred across the cities, with highest conc entra tions o ccurr ing nea r the h i ghest em itting so urces, illu str ating the nee d for m odeling on a local scale. 2) Increas in g the receptor de nsity near high em i ssion sour ces changes th e location of

m axim u m concentr ation s, illu str atin g the concen tra tion gr adients th at can occur nea r high

em ission sources and th e im portance of recepto r placem ent and density f o r m odel perform a nce. 3) Allocating on-road mobile em issions to road segm ents i m proved the agreem ent between m o deled concentrations when com pared with the observations, and also resulted in higher estim a ted concentra tions in the urban center. 4) It is im portant to refine the national em i ssions inventory for input into local air quality model applications.

In another US EPA study, researchers provide a com parison of “top down” and “bottom up” approaches to developing a m otor vehicl e em issions inventory for one urban area, Philadelphia, in calendar year 1999. 173 

### Under the “top down” approach, em issions were estim ated at the county level, typically starting from m o re a ggregated inform ation. Data on vehicle m iles traveled (VMT) in the m e tropolitan statistical area were allo cated to counties using population infor m ation. Default national m odel input s (e.g. fleet characteristics, vehicle speeds) rather than local data were also u sed. The “bo ttom up” approach utilizes vehicle activity data from a travel dem a nd model (TDM), and this “bottom up” approach estim a tes e m ission rates using m o re local inpu t d ata to b etter estim ate levels and spatial distribution of onroad motor vehicle em issions. TDM data can include info rmation on the spatial di stribution of vehicle activity, speeds along those road s (which can have a large im pact on em issions), and the distribution of the VMT am ong vehi cle classes for different speed ranges. These data can be

3-82

### used to m o re accurately estim ate the m agnitude of toxic em issions at the local scale an d where they occur. Both the spatial distribution of em iss i ons and th e total county em issions in the Philadelphia area differed signi ficantly between the top-down and the bottom

- up m e thodologies as shown in Table 3.2-22.

Table 3.2-22. Comparison of Annual 1999 Benz ene Emissions from Tw o Approaches in Philadelphia Area Counties

Coun t y Local (T DM) Bas ed Natio nal (NEI) Percen t Differen ce
Camd en 165 210 -27%
Dela wa re 162 160 1%
Glou ce ster 110 104 6% Montgom ery 333 209 59% Philadelp hia 255 467 -45%
Total 1,025 1,150 -12%



### In the case of Philadelphia County, using loca l registration distributi on data resulted in significan tly lower air to xics em issio n factor s and resultant em issions, while Montgom e ry County showed higher em issions. In the 1999 Na tional Air Toxics Assessm ent, higher countylevel em issions were generally a ssociated with higher county-level average concen trations, so it is anticip ate d that coun ty-leve l conc entra tions w ill f o llow sim ilar trends. However, in

m i croscale s e ttings near specific road links, these results m a y not apply.

Local-scale modeling could also be improve d by using local data on nonroad equipm ent activity for lawn and garden, recreational, cons truction and other sector s. EPA’s county-level inventories used in NATA and other m odeling ar e developed using activity a lloca ted f

rom the national or state leve l using surrogates.

The use of more spatially refined em ission i nventories, in conjunction with other refined air quality modeling tech niques, im prove the perform a nce of air quality models. They also enable better characterization of the m a gnitude and distribution of air toxic em issions, exposure and risk in urban areas, includi ng ris ks associated with locati ons heavily impacted by mobile

sources.

In conclusion, local scale m odeling studies i ndicated higher concentrations of air toxics than predicted by National scale analysis, particul arly in near-source m i croenvironm ents such as near roads. Thus, National scale analyses such as 1999 NATA are lik ely underestim ating high end exposures and risks.



## 3.3 Oz one

In this sec tio n we review th e health and welfare effects of ozone. W e also describe the air quality monitoring and m ode ling data which indicate that people in m a ny areas across the country con t inue to be exposed to high levels of am bient ozone and will continue to be into the

3-83

### future. Em i ssions of volatile organic com pounds ( VOCs) from the gas cans subject to this final rule have been shown to contribute to these ozon e concentrations. Information on air quality was gather ed f r om a variety of sources, including m onitor e d ozon e concentrations, air qua lity modeling forecasts cond ucted for th is rulem a king, and other state and local air quality inform ation.

3.3.1 Science of Oz one Formation

Ground-level ozone pollution is form ed by the reaction of VOCs and nitrogen oxides (NO
x 

### ) in the atm o sphere in the p r esen ce of heat an d sunlight. These pollutants, often referred to

as ozone pr e cursors, are em itted by m any types of pollution sources su ch as highway and nonroad m otor vehicles, gas cans, power plants, chem ical plants, refineries, m a kers of consum er and comm er cial products, industrial fac ilities, and sm aller area sources.

The science of ozone form ation, tran sport, and accum ulation is com plex. 174 

### Groundlevel ozone is produced and destroyed in a cyclic al set of chem ical reactions, m a ny of which are sensitive to tem p erature and sunlight. W h en a m bient tem p eratures and su nlight leve ls rem ain high for several days and the air is relatively st agnant, ozone and its precursors can build up and result in m o re ozone than typically w ould occu r on a single high-tem perature day. Ozone also can be transported into an area from polluti on sources found hundreds of m iles upwind, resulting in elev ated o zone levels even in a r ea s with low VOC or NO
x 

### em issions.

The highest levels of ozone are produced when both VOC and NO
x 

### e m issions are present in significant quantities on clear summ er days. Relatively small am ounts of NO
x 

### enable ozone to for m rapidly when VOC levels are relatively high, but ozone producti on is quickly lim ited by

rem oval of the NO
x 

### . Under these conditions NO
x 

### reductions are highly ef f ective in re ducing ozone while VOC reductions have little e ffect. S uch conditions are called “NO
x 

### -lim ited”. Because the contribution of VOC emissions from biogenic (n atural) sou r ces to lo cal ambient ozone concentrations can be significant, even som e areas where m a n- m a de VOC e m i ssions are rela tive l y lo w can be NO
x 

### -lim ited.

When NO
x 

### l e vels are relatively high and VOC levels re la tive l y low, NO
x 

### for m s inorganic nitrates (i.e., particles) but relatively little ozone. Such c onditions are called “VOC-lim ited.” Under these conditions, VOC reductions ar e effective in reducing ozone, but NO
x 

### reductions can actually increase local oz one under certain circumstances. Even in VOC-lim ited urban areas,
NO
x 

### reductions are not expected to increas e ozo ne levels if the NO
x 

### reductions are su fficiently large.

Rural areas are usually NO
x 

### -lim ited, due to the relatively large am ounts of biogenic VOC

em issions in m any rural areas. Urba n areas can be eith er V O C- or NO
x 

### -lim ited, or a m i xture of both, in which ozone lev e ls exh i bit moderate se nsitiv ity to c h anges in e i ther pollutan t

.

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide with ozone, form i ng nitrogen dioxide (NO 2 

### ); as the air moves downwind and the cycle continues, the
NO 2 

### f o rm s addition al oz one. The importanc e of th is rea c tion depends, in part, on the r e lative concentrations of NO
x 

### , VOC, and ozone, all of which chan g e with tim e and loca tion

.

3-84

### The Clean Air Act (CAA) requires EPA to set Nationa l Am bient Air Quality Stand ard s (NAAQS) for wide-spread pollutants fro m diverse sources considered harm ful to public health and the environm ent. The CAA established two types of NAAQS: prim a r y standards to protect public health, secondary standard s to protect public welfare. The prim ary and secondary ozone NAAQS are identical. T he 8-hour ozone standard is m e t when the 3-year average of the annual
4 th 

### highest daily m a xim u m 8-hour ozone concentrati on is less than or equal to 0.08 ppm. (62 FR 38855, July 18, 1997)

3.3.2 Health Effects of Oz one

Exposure to am bient ozone contributes to a wide range of adve rse health effects. e 

### Thes e health effects are well do cum e nted and are crit ically assessed in the EPA ozone Air Quality Crite ria Doc u m ent (ozon e AQCD) an d EPA staff paper. 175, 176 

### W e are relying on the data and conclusions in the ozone AQCD a nd staff paper, regarding the h ealth effects associated with

ozone exposure.

Ozone-related health effects include lung function decrem ents, respiratory sym p tom s, aggravation of asthm a , i n creas ed hospital and em ergency room visits, in creas ed asth m a

m e dication usage, inflammation of the lungs, an d a variety of other re spiratory effects and cardiov ascular effects. P e ople who are m o re suscep tib le to effects as sociated with exp o sure to ozone include children, asthm a tics and the elderly. There is also suggestive evidence that certain people m a y have greater genetic susceptibility. T hose with greater exposures to ozone, for instance due to tim e spent outdoors (e.g., outdoor workers), are also of concern.

Based on a large num ber of scientific studi es, EP A has identified several key health effects associated with exposure to levels of ozone found today in m a ny areas of the country. Short-term (1 to 3 hours) and prolonged expos ures (6 to 8 hours) to higher am bient ozone concentrations have been linked to lung functio n decrem ents, respiratory sym ptom s, i ncreased hospital adm i ssions and em ergency room visits for respiratory problem s. 177, 178, 179, 180, 181, 182 

### Repeated ex posure to ozone can increase suscep tibility to res p ira t ory infection and lung inflamm a tion and can ag gravate preexisting resp iratory d i seas es, such as asthm a

. 183, 184, 185, 186, 187 

### Repeated exposure to sufficient c oncentrations of ozone can also cause inflamm a tion of the lung,
im pair m ent of lung defense m echanism s, and possi bly irreversible changes in lung structure, which over tim e could lead to prem ature aging of the lungs and/or chronic respiratory illnesses, such as em physem a and chronic b r on chitis. 188, 189, 190, 191



### Children and adults who are outdoors and active during th e s u mm er m o nths, such as construction workers and other outd oor workers, are am ong those m o st at risk of elevated ozone

exposures. 192 

### Children and outdoor workers tend to ha ve higher ozone exposures because they typically are active outside, working, playing and exercising, during tim e s of day and seasons (e.g., the summer) when ozone levels are highest. 193 

### For exam ple, summ er cam p studies in the Eastern United States and Southeastern Canada have reported signifi cant reductions in lung

e H u m a n exp o s u re t o oz one va ri es o v e r t i m e due t o c h a nge s i n am bi ent oz one co nce n t r at i o n an d beca use pe opl e

m ove bet w ee n l o cat i ons w h i c h have n o t a bl e di ffe re nt oz o n e co ncent r at i o n s

. Al so, t h e am ou nt of oz o n e del i v ere d t o
t h e l u ng i s not onl y i n fl ue nce d by t h e am bi ent co nce n t r at i o n but al s o by t h e i n di vi du al s breat hi n g r o ut e an d rat e

. 

### 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 system s are still developing. These individuals (as well as people with respiratory
illn esses su ch as asthm a, especially asthm atic children) can experien ce reduced lung function and increased respiratory sym ptom s , such as ch est pain and cough, when exposed to relatively low ozone levels during prolonged periods of m o derate exertion. 202, 203, 204, 205



### 3.3.3 Current 8-Hour Oz one Levels

The gas can em ission reductions will assist 8-hour ozone nonattainm ent areas in reaching the standard by each area’s resp ect ive attainm e nt date and assist 8-hour o z one m a intenance areas
in m a intaining the 8-hour ozone stan dard in the f u ture. In this section and the next section we present inform ation on current and model- projected future 8-hour ozone levels.

A nonattainm e nt area is defined in the CAA as an area that is viol ating a NAAQS or is contributing to a nearby area that is violating the NAAQS. EP A designated nonattainm ent 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 (6 9 FR 23858, April 30, 2004) lays out the factors that EPA considered in m a king the 8-hour ozone nonattainm ent designations, including 2001­
2003 m easured data, air quality in ad jacen t areas, and other factors. f



### As of October 26, 2006, approxim a tely 157 m illi on people live in the 11 6 areas that are

curren tly designated as nonattainm e nt for eith er failing to m eet the 8

- hour ozone NAAQS or for contributing to poor air quality in a nearby area. There are 461 fu ll or partial counties that m a ke up the 116 8-hour ozone nonattainm e nt areas. Figur e 3.3-1 illustrates the widespread nature of these problem s

. Shown in this figure are counties designa ted as nonattainm ent for the 8-hour ozone NAAQS, also depicted are PM 2. 5 

### nonattainm ent areas and the m andatory class I federal

areas. The 8-hour ozone nonattainm e nt areas, non attainm e nt counties and populations are listed in Appendix 3B to this R I A.

f A n ozo n e desi gn val u e i s t h e conce n t r at i o n t h at det e rm i n es whet her t h e oz one l e vel s rec o rde d at a

m oni t o ri ng si t e

m eet t h e NA A Q S fo r oz one

. The l e vel o f a desi g n val u e i s det e rm i n ed ba sed o n t h ree c o nsec ut i v e-y ear m oni t o ri ng peri ods

. Fo r e x am pl e, an 8- h o u r desi g n val u e i s t h e f o urt h hi g h est dai l y m a xim u m 8-h o u r ave r age oz o n e

conce n tration measured ove r a three

- y ear pe ri o d at a gi ve n m oni t o r. Great er detail on how the se values are determ ined (i ncluding how to account fo r m i ssing val u es a n d othe r c o m p le xities) is give n in Appendices H a n d I of
40 C F R Pa rt 5 0

. D u e t o t h e preci si o n wi t h whi c h t h e st an dar d s a r e e x pre ssed (0 .0 8 p p m f

o r t h e 8- h o u r NA A Q S val u e ) , a vi ol at i on o f t h e 8

- h o u r st an dar d i s d e fi ne d as a n y d e si gn val u e gre a t e r t h a n or eq ual t o 0. 0 8 5 p p m , or 8 5

pp b. F o r a n y p a rt i c ul ar c o unt y , t h e desi g n v a l u e i s t h e hi gh est desi g n val u e fr om am ongs t al l t h e m oni t o rs havi ng

v a lid d e sign v a lu es with in t h at co un ty. If th ere are no o z o n e m o n ito rs lo cated in a p a rticu l ar co un ty, th at co un ty is

n o t assign ed a d e sign v a lu e. Howev e r, readers sh ou l d n o te th at o z on e d e sig n v a lu es represen t air qu ality o v e r a br oa d area and the absence of a desi gn val u e for a specific c o unty do es no t i m p l y th at th at county is in com p liance

w ith th e NAAQ S f or o z o n e
. There f ore, o u r anal y s i s m a y unde rest i m at e t h e n u m b er o f c o unt i e s wi t h oz o n e l e vel s , i.e., design val u es, which are abov e t h e lev e l of th e o z on e NAAQS.



### 3-86

### Figure 3.3.-1. 8-Hour Oz one and PM 2. 5 

### Nonattainment Areas and Mandatory Class I Federal Areas

Counties designated as 8-hour ozone nonattai nm ent 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 furthe r classified, on the basis of their 8-hour ozone design value, as m a rginal, m oderate, serious, seve re or extrem e. The m a xi mum attainm e nt date assigned to an ozone nonattainm e nt area is based on the area’s classification.

Table 3B-1 presents the 8-hour ozone nona ttainm ent areas, their 8-hour design values, and their category or classificati on. States with 8-hour ozone nonatt ainm ent areas are required to take action to bring those areas in to com p liance prior to the ozone s eason in the attainm e nt year. Based on the final rule designating and classi fying 8-hour ozone nonattainm ent areas, most 8­ hour ozone nonattainm e nt areas will be required to attain the 8-hour ozone NAAQS in the 2007 to 2013 tim e fra m e and then be required to m a intain the 8-hour ozone NAAQS thereafter. g 

### The

gas can em ission standards being finalized in th is action will becom e ef fe ctive in 2009. Thus,

g Th e Los A n g e les Sou t h Co ast A i r Basi n 8

- ho ur o z o n e nonattain m e n t area w ill h a v e t o at tain b e fore June 15

, 20 21

. 

### 3-87

### the expected ozone precursor em ission inventory reductions from the standards finalized in this action will be useful to S t ates in attaining and/or m a intaining the 8-hour ozone NAAQS.

EPA’s review of the ozone NAAQS is curr ently underway and a proposed decision in this review is scheduled for June 2007 with a fi nal rule scheduled for March 2008. If the ozone NAAQS is revised then new nonattainm e nt areas c ould be designated. While EPA is not relying on it for purposes of justifying this rule, the em ission reductions fr om this rulem aking would also be helpful to states if ther e is an ozone NAAQS revision.

3.3.4 Projected 8-Hour Oz one Levels

Recent air q u ality m odeling pred icts that without additional lo cal , regional or national

contro ls the r e will con tin ue to be a ne ed f o r reductions in 8-ho ur ozone co ncentr ation s in som e areas in the future. In th e following sections we describe recent ozone air quality m o deling from the CAIR analysis as well as results o f th e ozone response surf ace m eta m o del (RSM) analysis

we com pleted to assess the potential ozone im p acts resulting from the VOC e m issions controls for gas cans.

3.3.4.1 CAIR O z one Air Quality Modeling

Recently ozone air qu ality analy ses were pe rform e d for the Clean Air In ters tate Rule (CAIR), which was promulgated by E P A in 2005. The Com prehensive Air Quality Mo del with Extension (CAMx) was used as the tool for sim u lating base and future year concentrations of ozone in sup port of the C A IR ozone air qua lity ass essm ent. The CAIR analysis inc l ude d all f i na l federal rules up to and including CAIR contro ls. Details on th e ai r quality modeling are provided in the Air Quality Modeling Techni cal Support Docum e nt for the Final Clean Air
Inter sta te R u le, inc l ude d in the doc ket f o r this f i nal rule. 206



### Air quality modeling perform e d for CAIR indicates that in the absence of additional controls, counties with pr ojected 8-hour ozone concentrations greater than or equal to 85 ppb are like l y to pe r sist in the f u ture. The C A IR anal ysis provided estim ates of future ozone levels across the country. For exam ple, in 2010, in the absence of controls beyond those relied on for the CAIR modeling, we project that 24 m illion p eople 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 cou n ties, wher e 61 m illion p eople a r e pr ojected to live, with 201 0 projec ted design values that do not violate th e 8-hour ozone NAAQS but are with in ten percent of it, in the absence of em ission reductions beyond those co nsidered in the CAIR m o deling. These are counties that are not projected to violate the standard, but to be cl ose to it. The rule m a y help ensure that these counties con tinue to m a intain th eir attainm e nt status and the em ission reductions from this final rule wi ll be included by the states in their baseline inventory modeling for their o zo n e m aintenance plans.

h Cou n ties forecast to rem a in in n o n a ttain m e n t m a y n eed to ado p t add itio nal lo cal or reg i o n a l con t ro ls t o attain t h e
st anda rd s by d a t e s set p u rs ua nt t o t h e C l ean Ai r Act . T h e e m i ssi ons re d u c t i ons as soci at e d with th is prop o s ed ru le would hel p the se areas attain the ozon e st an d a rd by t h ei r st a t ut ory dat e

. 

### 3-88

### Table 3.3-1. Eastern Counties w i th 2010 projected 8-hou r Oz one Concentration s Above and w i thin 10% of the 8-hour Oz one Standard

2010 Projec ted 8-hou r Ozo n e State Coun t y Conc entrati on (ppb ) a 2000 po p b 2010 po p c

Ark a n s as Critten d e n Co 8 0 .8 5 0 ,866 5 2 ,889

Co nn ecticu t Fairfield C o 92 .2 88 2, 5 67 89 1, 6 94
C o n n ect i c ut Hart f o rd C o 80 .1 85 7, 1 83 85 9, 0 80
Connecticut Middlesex Co 90 .6 15 5, 0 71 16 4, 2 02
C o n n ect i c ut New Ha ve n C o 91 .3 82 4, 0 08 82 9, 1 81
C on nect i c ut New L on d on C o 83 .4 25 9, 0 88 26 7, 1 99
C on nect i c ut Tol l a n d C o 82 .7 13 6, 3 64 14 2, 9 88
D.C. Was h in gto n C o 85 .0 57 2, 0 58 55 4, 4 74
Del a wa re Kent C o 78 .7 12 6, 6 97 13 9, 3 76
Del a wa re New C a st l e C o 84 .7 50 0, 2 64 53 4, 6 31
Del a wa re Susse x C o 80 .3 15 6, 6 38 18 1, 9 62
Geo r gi a B i bb C o 80 .0 15 3, 8 87 15 8, 2 91
Geo r gi a C o b b C o 79 .4 60 7, 7 50 74 4, 4 88
Geo r gi a C o wet a C o 76 .6 89 ,2 1 5 11 1, 5 22
Geo r gi a De Kal b C o 81 .9 66 5, 8 64 69 8, 3 35
Geo r gi a Do u g l a s C o 78 .7 92 ,1 7 4 11 4, 3 80
Geo r gi a Fay e t t e C o 76 .7 91 ,2 6 3 11 7, 5 80
Geo r gi a Ful t o n C o 85 .1 81 6, 0 05 85 5, 8 26
Geo r gi a Hen ry C o 80 .3 11 9, 3 41 15 3, 9 57
Geo r gi a R o ck dal e C o 80 .4 70 ,1 1 1 87 ,9 7 7
Il l i noi s C oo k C o 81 .8 5, 37 6, 7 39 5, 36 3, 4 64
Il l i noi s Jersey C o 77 .0 21 ,6 6 8 22 ,9 0 5
Il l i noi s Lake C o 76 .8 64 4, 3 56 73 1, 6 90
Il l i noi s M c Hen ry C o 76 .6 26 0, 0 77 30 7, 4 00

In di ana B o o n e C o 78 .1 46 ,1 0 7 54 ,0 3 5

In di ana C l ark C o 78 .4 96 ,4 7 2 10 7, 0 96

In di ana Ham i l t on C o 81 .7 18 2, 7 40 23 0, 5 65

In di ana Hanc oc k C o 80 .4 55 ,3 9 1 65 ,2 8 2

In di ana La P o rt e C o 81 .8 11 0, 1 06 11 1, 5 66

In di ana Lake C o 82 .8 48 4, 5 63 48 9, 2 20

In di ana M a di so n C o 78 .6 13 3, 3 58 13 7, 7 10

In di ana M a ri on C o 79 .6 86 0, 4 53 87 9, 9 32

In di ana Port e r C o 81 .1 14 6, 7 98 16 5, 3 50

In di ana Shel by C o 81 .6 43 ,4 4 5 46 ,5 6 5

In di ana St Jose p h C o 77 .8 26 5, 5 59 27 5, 0 31

Kent ucky C a m pbel l C o 81 .5 88 ,6 1 6 92 ,1 0 9

Lo ui si ana B o ssi er Pari s h 77 .0 98 ,3 1 0 11 0, 8 38

Lo ui si ana East B a t o n R o uge Pa ri sh 80 .6 41 2, 8 52 46 5, 4 11

Lou i sian a Ib erv ille Parish 7 9 .4 3 3 ,320 3 3 ,089

Lo ui si ana Jeffe rs on Pari s h 78 .6 45 5, 4 66 49 3, 3 59

Lo ui si ana Li vi n g st o n Pa r i sh 77 .8 91 ,8 1 4 12 4, 8 95

Lo ui si ana West B a t o n R o uge Pa ri sh 78 .8 21 ,6 0 1 22 ,6 7 2

M a i n e Hanc oc k C o 80 .5 51 ,7 9 1 53 ,8 8 6



### 3-892010 Projec ted 8-hou r Ozo n e State Coun t y Conc entrati on (ppb ) a 2000 po p b 2010 po p c

M a i ne Yo rk C o 80 .2 18 6, 7 42 20 1, 0 82

M a ry l a nd An ne Ar u n d el C o 88 .6 48 9, 6 56 54 3, 7 85

M a ry l a nd B a l t i m ore C o 83 .7 75 4, 2 92 79 2, 2 84

M a ry l a nd C a rr ol l C o 80 .0 15 0, 8 97 17 9, 9 18 Marylan d Cecil Co 89 .5 8 5 ,951 9 6 ,574

M a ry l a nd C harl e s C o 78 .7 12 0, 5 46 14 5, 7 63

M a ry l a nd Fre deri c k C o 78 .1 19 5, 2 77 23 4, 3 04

M a ry l a nd Har fo r d C o 92 .8 21 8, 5 90 26 8, 2 07

M a ry l a nd Kent C o 85 .8 1 9 ,197 2 0 ,233

M a ry l a nd M ont gom ery C o 79 .3 87 3, 3 41 94 0, 1 26

M a ry l a nd Pri n ce Ge or ges C o 84 .2 80 1, 5 15 84 2, 2 21

M a ssach uset t s B a rnst a b l e C o 83 .6 22 2, 2 30 24 9, 4 95

M a ssach uset t s B ri s t o l C o 83 .0 53 4, 6 78 55 8, 4 60

M a ssach uset t s Essex C o 81 .7 72 3, 4 19 74 7, 5 56

M a ssach uset t s Ham pden C o 80 .2 45 6, 2 28 45 2, 7 18

M a ssach uset t s Ham p shi re C o 78 .0 15 2, 2 51 15 8, 1 30

M a ssach uset t s M i ddl esex C o 79 .1 1, 46 5, 3 96 1, 48 6, 4 28

M a ssach uset t s Suf f o l k C o 78 .1 68 9, 8 07 67 4, 1 79

M i chi gan Al l e gan C o 82 .1 10 5, 6 65 12 1, 4 15

M i chi g an B e nzi e C o 77 .9 15 ,9 9 8 17 ,8 4 9

M i chi g an B e rri en C o 78 .1 16 2, 4 53 16 4, 7 27

M i chi g an C a ss C o 78 .2 51 ,1 0 4 53 ,5 4 4

M i chi g an Gene see C o 76 .7 43 6, 1 41 44 1, 1 96 Michigan Macom b Co 85 .4 78 8, 1 49 83 8, 3 53
Mich ig an Maso n Co 7 8 .9 2 8 ,274 3 0 ,667

M i chi gan M uske g on C o 82 .0 17 0, 2 00 17 5, 9 01

M i chi gan Oakl a nd C o 80 .7 1, 19 4, 1 55 1, 29 9, 5 92

M i chi g an Ot t a wa C o 76 .6 23 8, 3 14 27 7, 4 00

M i chi g an St C l ai r C o 80 .6 16 4, 2 35 17 8, 3 91

M i chi gan Was ht e na w C o 81 .0 32 2, 8 95 34 4, 3 98

M i chi g an Way n e C o 84 .7 2, 06 1, 1 61 1, 96 4, 2 09

M i ssou ri C l ay C o 76 .5 18 4, 0 06 21 3, 6 43

M i ssou ri Jeffe rs on C o 76 .7 19 8, 0 99 23 0, 5 39

M i ssou ri St C h arl e s C o 80 .5 28 3, 8 83 34 1, 6 86

M i ssou ri St Lo ui s C i t y 79 .4 34 8, 1 88 32 4, 1 56

M i ssou ri St Lo ui s C o 80 .5 1, 01 6, 3 15 1, 02 4, 9 64

New Ham p sh ire Hillsb o roug h Co 7 6 .6 3 8 0

, 84 1 4 1 2

, 07 1

New Jersey At l a nt i c C o 80 .4 25 2, 5 52 26 9, 7 54

New Jersey Berge n C o 86 .0 88 4, 1 18 89 8, 4 50 New Jersey Ca m d en Co 91 .6 50 8, 9 32 50 9, 9 12

New Jersey C u m b erl a nd C o 84 .4 14 6, 4 38 14 9, 5 95 New Jersey Gloucester C o 91 .3 25 4, 6 73 27 8, 6 12

New Jersey Hu ds on C o 84 .3 60 8, 9 75 60 7, 2 56

New Jersey Hu nter d o n Co 88 .6 12 1, 9 89 13 9, 6 41 New Jersey Mercer C o 95 .2 35 0, 7 61 35 9, 9 12 New Jersey Middlesex Co 92 .1 75 0, 1 62 80 5, 5 37



### 3-902010 Projec ted 8-hou r Ozo n e State Coun t y Conc entrati on (ppb ) a 2000 po p b 2010 po p c

New Jersey M onm out h C o 86 .4 61 5, 3 01 67 0, 9 71

New Jersey M o rris Co 85 .5 47 0, 2 12 50 0, 0 33 New Jersey Ocean Co 10 0. 3 51 0, 9 16 57 2, 3 64

New Jersey Passai c C o 79 .7 48 9, 0 49 49 5, 6 10

New Y or k B r o nx C o 79 .7 1, 33 2, 6 49 1, 29 8, 2 06

New Y or k C haut a uq ua C o 81 .8 13 9, 7 50 13 9, 9 09

New Y o r k Dut c hess C o 81 .0 28 0, 1 50 29 1, 0 98

N e w Yo rk Er ie Co 86 .9 95 0, 2 65 95 3, 0 85

N e w Yo rk Essex Co 7 7 .6 3 8 ,851 3 9 ,545

New Y o r k Jeffe rs on C o 80 .5 11 1, 7 38 11 3, 0 75

New Y or k M on roe C o 76 .9 73 5, 3 43 74 5, 3 50

New Y o r k Ni aga ra C o 82 .3 21 9, 8 46 22 0, 4 07

New Y or k Ora n ge C o 77 .1 34 1, 3 67 37 1, 4 34

New Y o r k Put n am C o 82 .3 95 ,7 4 5 10 7, 9 67

New Y or k Quee ns C o 78 .3 2, 22 9, 3 79 2, 23 9, 0 26

New Y o r k R i chm ond C o 87 .1 44 3, 7 28 48 8, 7 28

N e w Yo rk Su ffo lk Co 90 .8 1, 41 9, 3 69 1, 47 2, 1 27

New Y o r k West chest e r C o 84 .7 92 3, 4 59 94 4, 5 35

No rt h C a r ol i na M eckl e n bu rg C o 81 .4 69 5, 4 53 81 4, 0 88

No rt h C a r ol i na R owa n C o 80 .1 13 0, 3 40 14 3, 7 29

No rt h C a r ol i na Wake C o 77 .2 62 7, 8 46 78 7, 7 07
Ohi o Al l e n C o 76 .8 10 8, 4 73 10 6, 9 00
Ohi o Asht a b ul a C o 83 .5 10 2, 7 28 10 4, 8 50
Ohi o B ut l e r C o 78 .0 33 2, 8 06 38 4, 4 10
Ohi o C l er m ont C o 78 .0 17 7, 9 77 20 5, 3 65
Ohi o C l i nt on C o 81 .4 40 ,5 4 3 47 ,1 3 7
Ohi o C uy a h oga C o 77 .3 1, 39 3, 9 77 1, 34 8, 3 13
Ohi o Del a wa re C o 77 .3 10 9, 9 89 13 6, 1 25
Ohi o Fra nkl i n C o 81 .9 1, 06 8, 9 77 1, 14 2, 8 94
Ohi o Geau ga C o 86 .6 90 ,8 9 5 10 2, 0 83
Ohi o Ham i l t on C o 78 .6 84 5, 3 02 84 3, 2 26
O h i o K nox Co 7 6 .5 5 4 ,500 5 9 ,435
Ohi o Lake C o 82 .2 22 7, 5 11 23 7, 1 61
Ohi o Lorai n C o 78 .5 28 4, 6 64 29 2, 0 40
Ohi o Lucas C o 80 .0 45 5, 0 53 44 7, 3 02
Ohi o M e di na C o 76 .5 15 1, 0 95 17 3, 9 85
Ohi o Port a ge C o 79 .8 15 2, 0 61 16 2, 6 85
Ohi o Sum m i t C o 82 .4 54 2, 8 98 55 2, 5 67
Ohi o Trum bul l C o 79 .7 22 5, 1 16 22 6, 1 57
Ohi o War ren C o 80 .0 15 8, 3 83 18 6, 2 19
Ohi o Wo o d C o 77 .4 12 1, 0 65 12 9, 1 24
Okl a hom a Tul s a C o 79 .2 56 3, 2 99 61 0, 5 36

Pen nsy l va ni a Al l e ghe ny C o 81 .9 1, 28 1, 6 65 1, 25 9, 0 40

Pen n sy l v a n i a Arm s t ron g C o 79 .7 72 ,3 9 2 72 ,8 2 9

Pen n sy l v a n i a B eaver C o 79 .6 18 1, 4 12 18 3, 6 93

Pen n sy l v a n i a B e rks C o 81 .7 37 3, 6 37 38 8, 1 94



### 3-912010 Projec ted 8-hou r Ozo n e State Coun t y Conc entrati on (ppb ) a 2000 po p b 2010 po p c

Penn sylv an ia Bu ck s Co 94 .3 59 7, 6 35 64 8, 7 96

Pen n sy l v a n i a C a m b ri a C o 76 .9 15 2, 5 98 14 6, 8 11

Penn sylv an ia Ch ester C o 85 .4 43 3, 5 01 47 8, 4 60

Pen n sy l v a n i a Dau p h i n C o 80 .8 25 1, 7 98 26 5, 0 19

Pen n sy l v a n i a Del a wa re C o 84 .0 55 0, 8 63 54 3, 1 69

Pen n sy l v a n i a Eri e C o 79 .1 28 0, 8 43 28 4, 8 35

Pen n sy l v a n i a Fra nkl i n C o 80 .2 12 9, 3 13 13 5, 0 88

Pen n sy l v a n i a Lancast e r C o 83 .6 47 0, 6 57 51 3, 6 84

Pen n sy l v a n i a Lehi g h C o 82 .1 31 2, 0 90 32 3, 2 15

Pen n sy l v a n i a M e rcer C o 78 .1 12 0, 2 93 12 2, 5 46

Pen n sy l v a n i a M ont gom ery C o 87 .6 75 0, 0 97 77 2, 8 49

Pen n sy l v a n i a No rt ham p t o n C o 81 .8 26 7, 0 66 27 9, 7 97

Penn sylv an ia Ph ilad e lph ia C o 89 .9 1, 51 7, 5 49 1, 42 0, 8 03

Pen n sy l v a n i a Was h i n gt o n C o 77 .3 20 2, 8 97 20 5, 1 53

Pen n sy l v a n i a West m o rel a nd C o 76 .7 36 9, 9 93 37 2, 9 41

Pen n sy l v a n i a Yo rk C o 79 .4 38 1, 7 50 40 4, 8 07
Rh od e I s land K e n t C o 86 .2 16 7, 0 90 17 4, 1 26

R

h o d e Isl a n d Pro v i d ence C o 81 .2 62 1, 6 02 62 1, 3 55

R

h o d e Isl a n d Was h i n gt o n C o 84 .2 12 3, 5 46 13 7, 7 56
So ut h C a rol i na R i chl a nd C o 76 .9 32 0, 6 77 34 9, 8 26

Ten n essee Sevi er C o 76 .5 71 ,1 7 0 96 ,0 9 7

Ten n essee Shel by C o 76 .7 89 7, 4 71 95 8, 5 01

Texas B razo ri a C o 84 .1 24 1, 7 67 28 1, 9 60

Tex as Co llin Co 8 2 .5 4 9 1

, 67 5 6 7 7

, 86 8

Texas Dal l a s C o 82 .2 2, 21 8, 8 99 2, 38 2, 6 57

Texas Dent on C o 86 .8 43 2, 9 76 55 4, 0 33

Texas Gal v est o n C o 84 .6 25 0, 1 58 28 3, 9 63

Texas Gre g g C o 79 .1 11 1, 3 79 12 1, 2 41

Texas Harris Co 97 .4 3, 40 0, 5 77 3, 77 0, 1 29

Texas Jeffe rs on Co 85 .0 25 2, 0 51 26 0, 8 47

Texas Joh n s o n C o 78 .2 12 6, 8 11 15 7, 5 45

Texas M ont gom ery C o 81 .2 29 3, 7 68 41 3, 0 48

Texas Tarra nt Co 87 .2 1, 44 6, 2 19 1, 71 0, 9 20
Vi r gi ni a Al exa nd ri a C i t y 80 .9 12 8, 2 83 13 0, 4 22 Virg in ia Arling t on C o 86 .0 18 9, 4 53 19 3, 3 70

Vi r g i n i a C h arl e s C i t y C o 77 .7 6, 92 6 7, 38 2 Virg in ia Fairfax C o 85 .4 96 9, 7 49 1, 08 5, 4 83
Vi r gi ni a Ham pt on C i t y 78 .7 14 6, 4 37 15 3, 2 46

V i rg in ia H a nov er Co 8 0 .9 8 6 ,320 9 8 ,586
Vi r gi ni a Hen ri c o C o 78 .2 26 2, 3 00 29 4, 1 74
Vi r gi ni a Lo ud o un C o 78 .6 16 9, 5 99 21 4, 4 69 Virg in ia Su ffo lk City 7 7 .5 6 3 ,677 6 9 ,003

W i sco n si n Do or C o 82 .1 27 ,9 6 1 30 ,5 0 8

W i sconsin Kenos h a C o 91 .0 14 9, 5 77 16 6, 3 59



### 3-92State Coun t y 2010 Projec ted 8-hou r Ozo n e Conc entrati on (ppb ) a 2000 po p b 2010 po p c

W i scon sin

W i sco n si n W i sco n si n W i sconsin

W i sco n si n W i sco n si n K e w a un ee C o M a ni t o w o c C o M i l waukee C o

Ozaukee C o R aci ne C o She boy gan C o 7 9 .9
80 .0
82 .1
85 .8
83 .9
87 .7 2 0 ,187
82 ,8 8 7
94 0, 1 64
8 2 ,317
18 8, 8 31

11 2, 6 46 2 0 ,538
83 ,5 1 6
92 2, 9 43
9 5 ,549
19 9, 1 78

11 8, 8 66

Numb er of Violating Counti e s Population of Violating C o u n ties
Numb er of Countie s within 10% Population of Cou n ties with in 10% 37

148 22,724,0 10

58,453,9 62 24,264,5 74

61,409,0 62 a) Bol d ed conc entrations i ndi cat e l e vel s ab o v e t h e 8

- ho ur o z one sta n dard. b) Po p u l a t i ons are based o n 2 0 0 0 ce ns us dat a

. c) Pop u lation s ar e b a sed on 20 00 cen s u s pr oj ectio ns.



### 3.3.4.2 Oz one Response Surface Metamodel Methodology

We perfor med ozone air quality m odeling sim u lations for the Eastern United States using the ozone R S M. The ozone RSM is a scre ening- level air qu a lity m odelin g tool th at a l lows users to quick ly as sess the estim ated air quality ch anges over the m odeling dom ain. The ozone RSM is a m odel of a full-scale air quality model and is based on st atis tic al r ela tionship s between model inputs and outputs obtained from the full-scal e air quality m odel. In other words, the ozone RSM uses statistical technique s to relate a response variable to a set of factors that are of interest, e.g., em issions of precursor pollutant s from particular source s and locations. The following section describes the m odeling m e t hodology, including the developm ent of the multidim ensional experim ental design for control stra tegies and implem entation and verification of the RSM tec hnique. Addition al de ta il is avai lable in the Air Quality Mo deling Tech nical Support Docum e nt (AQMTSD) for this rule. 207



### The foundation for the o z one respon se surface m e tam odeling analyses was the CAMx modeling done in support of the final Clean Air In terstate Rule (CAIR). The CAIR m o deling is fully described in the CA IR Air Quality Mode ling Technical Support Docum e nt, but a brief description is provided below. 208 

### The modeling procedures used in the CA IR analysis (e.g., dom a in, episodes, m e teorology) have been used for several EP A rulem a king analyses over the past five years and are well-e stablish ed at th is po int.

The ozone RSM uses the 2015 controlled CAIR em issions inventor y as its baseline. 209 

### This inventory does not include th e g a s can em issions that are bei ng controlled in this rule. The uncontrolled and controlled gas can em issions have been inco rporated in to the base and control runs of the ozone RSM (see Section 2.1 for m ore de tail about the gas can em issions inventory). The inventory also does not include the higher estim ates of cold tem p eratu r e em issions for gasoline vehicles developed for this rule ; however, thes e em issions a r e n o t lik ely to h ave a significant impact on ozone for m ati on. Finally, the inventory include s an error in mobile source NOx for 13 Northeastern states. The im pact of this error is m i ni m i zed as the m odel is used in a relative way

. Because th e base years of our air qu ality m odeling analysis are 2020 an d 2030, we
3-93

### extrapo l ate the m odel from 2015 to 2020 and 2030. Addition a l detail on how the m o del was extrapo l ated to reflect gas can em issions a nd various projection years is included in the AQMTSD for this f i n a l rule. 210



### The m odeling sim u lations that com p r i sed the m e tam odeling were conducted using CAMx version 3.10. It s hould be noted that b ecause the ozon e RSM is built from CAMx air quality m o del runs, it therefore has the sam e strengths and lim itations of the underlying m odel and its inputs. CAMx is a non-proprietary com put er m odel that sim u lates the form ation and fate of photochem i cal oxidants includin g ozone for give n inpu t sets of m e teorological conditions an d

em issions. The gridded m e teorological data for three historical episode s were developed using the Regional At m ospheric Modeli ng System (RAMS), version 3b. 211 

### In all, 30 episode days

were m odeled using frequently-occurring, ozone- conducive, m e teorological conditions from the summer of 1995. Em issions estim ates were devel oped for the evaluation y ear (1995) as well as a future year (2015).

The CAMx model applications were perform e d for a dom ain covering all, or portions of, 37 States (and the District of Columbia) in the E a stern U.S., as shown in Figure 3.3-2. The dom a in has nested horizontal grids of 36 km a nd 12 km

. However, the output data from the

m e ta m odeling 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 dom a i n of the ozone RSM is the 37 Eastern states, the gas can controls are a na tionwide 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 th at have their own gas can control program s and how the controls finalized h e re im pact states which al ready have gas can control program s.

3-94

### Figure 3.3-2. Map of the CAMx Domain used for MSAT Oz one Metamodeling

The ozone RSM used for assessing the im p acts of gas can em ission reductions was developed broadly to look at vari ous control strategies with respect to attaining the 8-hour ozone NAAQS. The experim e ntal design for the ozone RS M covered three key areas: type of precursor

em ission (NOx or VOC) , em ission source type (i.e., onroad vehicles, nonroad vehicles, area sources, electrical generating utility (EGU) sour ces, and non-utility point sources), and location in or out of a 2015 m odel-projecte d residual ozone nonattainm ent area. This resulted in a set of
14 em issions factors. Since som e of the spillage em issions associated wi th gas cans are currently included in the NONR OAD e m issions m odel, fo r the purposes of the ozone RSM we have included gas can em issions as part of the nonr oad factor in our ai r quality modeling.

The 14 em is sion factors were random l y vari ed and used as inputs to CAMx. The experim e ntal design for these 14 factors was developed using a Maxim i n Latin Hypercube

m e thod. Ba sed on a rule of thum b of 10 runs pe r factor, we developed an overall design with 154 runs (a base case plus 139 control runs pl us 10 evaluation runs plus 4 boundary condition runs). The range of em is sions reductions cons idered within the m e ta m ode l ranged from 0 to 120 percent of the 2015 CAIR e m issions. This experi m e ntal design resulted in a set of CAMx sim u lations that serve as the inpu ts to the ozone response su rface m e ta model. Becau se the

m e ta m odeling was going to be used to assess the im pacts of the gas can standards, the

3-95

### experim e ntal design also include d oversam pling 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 s u rface appro x im ation to CAMx, we used a m u ltidim ensional kriging approach, im plem ented through the MI XED procedure in SAS. W e m odeled the predicted ch anges in ozo n e in each C A Mx grid ce ll as a function of the weighted av erage of the modeled responses in th e experim e ntal design. A response-surface was then fit for the ozone design value m e tric. Validati on was perform e d a nd is summ a r ized in the AQMTSD. The validation exercises indicated that the ozone RSM replicates CAMx response to em issions changes very well for most em issions co m binations and in most locations.

The assessm ent of gas can controls conducted for this analysis involved adjusting the nonroad m o bile source VOC e m issions both in and out of ozone nonattainm ent areas and looking at the im pact on the 8-hour ozone design value m e tr ic. W e created an input or adjustm e nt factor for the nonroad m obile source VOC em ission factor by adding future year gas can em ission estim ates to the projected CAIR e m i ssion inventor y and then relating the future year em issions estim ate to 2015. For this assessm ent the future years m odeled are 2020 and 2030.

3.3.4.3 Oz one Response Surface Metamodel Results

This sec tion summarizes the resu lts o f our m odeling of ozone air quality impacts in the future with and without the reductions in gas can em issions. Based upon our previous CAIR air quality m o deling, we anticipate that without em ission reducti ons beyond those already required under promulgated regu lations and approved SIP s, ozone non attainm ent will likely p ersis t in to the f u ture.

The inventories that underlie the ozone m odeling conducted f or this rulemaking included

em ission red u ctions f r om all cu rren t o r comm itted f ederal, s t ate

, and loca l c ontrols, inc l uding the

recen t CAIR. There was no attem p t to exam ine th e prospects of areas attaining or m a intain ing the 8-hour ozone standard with possible additional future controls (i.e., controls beyond current

or comm itted f e deral, State, and loca l controls ).

According to the ozone response su rface m e ta model (RSM), the gas can contro ls are projected to result in a very sm all population-we ighted net improvem e nt in future ozone. The net im provem e nt is generally so sm all as to be rendered insignificant when presenting design values. The model chan ges are sm aller than th e precision with which the ozone standard is expressed (0 .08 parts per m illion (pp m )) and to which 8-hou r ozone data is repo rted. i



### Nonetheless

, there are so m e areas where th e oz one im prove ment is m ore significant. These areas include Chicago, Milwaukee, Detroit and New York City. It is also im portant to note that the ozone R S M results indicate that the counties which are projected to experience the greatest
im prove m e nt in ozone design values are generally also thos e that are projected to have the highest ozone design values. Thos e counties that are pro j ected to experience an extrem ely sm all increase in ozone design values generally have design values that are lower, below 70 ppb. The results from the m e ta m o deling projections indi cate a net overall im provem ent in future 8-hour

i A ppe n d i x I of 40 C F R Pa rt 5 0

. 

### 3-96

### ozone design values due to the gas can cont rols, when weighted by population. The AQMTSD, contain ed in the docke t f o r this f i n al rule, incl udes additional detail on the ozone RSM results.

3.3.5 Environmental Effects of Oz one Pollution

There are a num b er of public welfare effects ass ociated with the presence of ozone in the

am bient air. 212 

### In this section we discuss the im pact of ozone on plants, including trees,

agronom ic crops and urban ornam e ntals.

3.3.5.1 Impacts on Vegetation

The ozone AQCD notes that “ozone affects vegetation throughout the U nited States,
im pairing crops, native vegetation, and ecosyst em s m o re than any other air pollutant.” 213 

### Like carbon dioxide (CO 2 

### ) and other gaseous substances, ozone enters plant tissues prim arily through apertu res (stom ata) in le aves in a p r o cess called “uptake.” To a lesser extent, ozone can also diffuse directly th rough surface layers to the p l an t' 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 dam a ge essential cellular com ponent s and functions, includ ing enzym e activities, lipids, and c ellu lar m e mbranes, disru p ting th e plant' s os m o tic (i.e., water) balance and energy utiliza tion p atte rns. 215, 216 

### This dam a ge is commonly m a nifested as visible foliar injury such as chloro tic o r necrotic spo t s, increas ed leaf sene scence (accelerated leaf aging) and/or reduced photosynthesis. All these effect s reduce a plant’s capacity to for m carbohydrates, which are the prim ary form of energy used by plants. 217 

### W ith f ewer resour c es available, the plan t re alloc ates existing resources away from root growth a nd storage, above ground growth or yield, and reproductive processes, toward leaf repair and maintenance. St udies have shown that plants stressed in these ways may exhibit a general lo ss of vigor, which can lead to secondary im pacts that m odify plants' responses to other environm ental facto r s. Specifically, plants m a y becom e more sensitive to other air pollutants, m ore susc eptible to disease, insect attack, harsh weather (e.g., drought, frost) and other envi ronm ental stresses. Furtherm ore, there is som e evidence that ozone can interfere with the form ation of m y corrhiza, essential sy m biotic fungi associated with the roots of most terrestrial plants, by reduci ng the am ount of carbon availa ble for transfer from the host to the sym b iont. 218



### Ozone can produce both acute and chronic in jury in sensitive species depending on the concentration level and the durati on of the exposure. Ozone effects also tend to accum u late over the growing season of the plant, so th at even lower concen trations experien ced for a lon g er duration have the potential to create chronic stress on sensitive vegetation. Not all plants, however, ar e equally se nsitiv e to oz one. Much of the varia t ion in sensitivity be twee n individu al plants or whole species is relate d to the plant’s ab ility to regulat e the extent of gas exchange via leaf stom ata (e.g., avoidance of O 3 

### uptake through closure of stom ata). 219, 220, 221 

### Other resistance

m echanis m s m a y involve the intercellular pr oduction of detoxifyi ng substances. Several biochem i cal substances capable of detoxifying ozone have been reported to occur in plants includ ing th e antiox i dan t s asco rbat e and glutathione. After injuries have occurred, plants m a y be capable of repairing the dam a ge to a lim ited extent. 222 

### Because of the differing sensitiv ities

am ong plants to ozone, ozone pollution can also exer t a selective pressure that leads to changes in plan t community composition. G i ven the rang e of plant sensitiv ities an d the fact th at

3-97

### num e rous other environm ental factors m odify pl ant uptake and response to ozone, it is not possible to identify threshold valu es above which ozone is consiste ntly toxic for al l plants. The next few paragraphs present additional inform ation on ozone dam a ge to trees, ecosystem s,
agronom ic crops and urban ornam e ntals.

Ozone also has been shown conclusively to cause discernible inju ry t o forest trees. 223, 224



### In term s of forest productivity and ecosystem di versity, ozone m a y be the pollutant with the greatest po te ntia l f o r regional-sc ale f o rest im pacts. 225 

### Studies have dem onstrated repeatedly that ozone concentrations commonly observed in pollut ed areas can have substantial im pa cts on plant function. 226, 227



### Because plants are at the cen ter of the food web in m a ny ecosy stem s, changes to th e p l ant community can affect as sociated org a nism s and eco system s (including the su itability of habitats that support threatened or endangered species and below ground organism s living in the root zone). Ozone im pacts at the comm unity and ecosystem level vary widely depending upon

num e rous factors, including con centration and temporal variation of tropospheric ozone, species

com position, soil properties and clim atic factors. 228 

### In m ost instances, responses to chronic or

recurrent ex posure in forested ecosys tem s are s ubtle and not observable for m a ny years. These injur i es c an cause stand- level f o re st declin e in se nsitiv e ecos y stem s. 229, 230, 231 

### It is not yet possible to predict ecosystem responses to ozone with m uch certainty; however, considerable knowledge of potential ecosystem responses has been acquired through long-term observations in highly dam a ged forests in the United States.

Laboratory and field experim e nts have also shown reductions in yields for agronomic crops exposed to ozone, includi ng vegetables (e.g., lettuce) an d field crops (e.g., cotton and wheat). The m ost extensive field experi m e nts, conducted under the National Crop Loss
Assessm ent Network (NCLAN) exa m ined 15 speci es and num e rous cultivars. The NCLAN results show that “several econom ically im porta nt crop species are sens itive to ozone levels typical of those found in the Unites States.” 232 

### In addition, econom i c studies have shown reduced econom ic benefits as a result of pred icted reductions in crop yields associated with observed ozone levels. 233, 234, 235



### Urban orna m entals repr esent an add ition al vege tation c atego ry lik ely to e xperien ce so m e degree of negative effects associat ed with exposure to am bient ozone levels and likely to im pact large econom i c sectors. It is estim a ted that m ore than $20 billion (1990 dollars) are spent annually on landscaping using ornam e ntals, bot h by private property ow ners/tenants and by

governm e ntal units responsible for public areas. 236 

### This is th erefore a potentially costly environm ental effect. However, in the absen ce of adequate exposure-response functions and

econom ic dam age functions for the potential ra nge 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 p e rcen tage of these exp en d itur es tha t m ay be related to
im pacts associated w ith ozone exposure.

3-98

## 3.4 Particulate Matter

In this sec tio n we review th e health and welfare effects of particulate m atter (PM). W e also describe air quality mon itoring and m odeling data that indicate m any areas acro ss the country continue to be exposed to levels of am bi ent PM above the NAAQS. E m issions of PM and VOC from the vehicles subj ec t to this rule c ontribu te to these PM co ncentr ation s

. Inform ation on air quality was gathered from a variety of sources, including m onitored PM concentrations, air quality m odeling done for rece nt EPA rulem a kings and other state and local air quality infor m ation.

3.4.1 Science of P M Formation

Particulate m atter (PM) repr esents a broad class of che m ically and physically diverse substances. It can be principally characterized as discrete partic le s tha t exist in th e con d ensed (liquid or solid) phase spanning se veral orders of m a gnitude in size. PM is further described by breaking it d o wn into siz e f r action s. PM 10 

### refers to particle s g enera lly less than or equal to 10

m i crom eters (μm ) in diam eter. PM 2. 5 

### refers to fine particles, thos e par t icles gener ally less th an or equal to 2.5 μm in diam eter. Inhalable (or “t ho racic”) co ars e particles re f er to those particle s generally greater than 2.5 μm but less than or equa l to 10 μm in diam eter. Ultraf ine P M ref ers to particles with diam eters generally less than 100 nanom eters (0.1 μm ). Larger particles (>10 μm ) tend to be removed by the respiratory clearan ce m echanism s, whereas smalle r par tic le s are deposited deeper in the lungs.

Fine particles are produced prim arily by com bust i on processes and by transfor m a tions of

gaseous em issions (e.g., SOx, NOx and VOCs) in the atm o sphere. The chem ical and physical properties of PM 2. 5 

### m a y vary greatly with tim e, region, m e teorology and source category. Thus,
PM 2. 5 

### , m a y i nclude a complex m i xture of different po llutants including sulfates, nitrates, organic

com pounds, elem ental carbon and m e tal com pounds

. These particles can rem a in in the

atm o sphere for days to weeks and travel th rough the atm osphere hundr eds to thousands of kilom eters.

The vehicles that will be covered by the standards contribute to am bient PM levels through prim ary (direct) and second ary (ind irect) PM

. Primary PM is d i rectly em itted into th e air, and s eco ndary PM f o rm s in the atmosphere f r om gases em itted by f u el com bustion and other

sources. Along with prim ary PM, the vehicles cont rolled in this action em it VOC, which react in the atm o sphere to form secondary P M 2. 5 

### , nam e ly organic carbonaceous PM 2. 5



### . The gas cans that will be cov e red by the s t andards a l so em it VOC which contr i bute to s eco ndary PM 2. 5



### . Bot h types of directly and indirectly form e d particles from vehicles and gas cans are found principally in the fine fraction.

EPA has recently am ended the PM NAAQS (71 FR 61144, Oc tober 17, 2006). The final rule, signed on Septem ber 21, 2006 and published on October 17, 2006, addressed revisions to the prim ary and secondary NAAQS for PM to provi de increased protection of public health and welfare, res p ectiv ely. T h e prim ary PM 2. 5 

### NAAQS include a short-term(24-hour) and a longterm (annual) standard. The level of the 24-hour PM 2. 5 

### NAAQS has been revised from 65 μ g/ m 3



### to 35 μ g/m 3 

### to provide increased protection against he alth effects as sociat ed with short-term

3-99

### exposures to fine particles. The current form of the 24-hour P M 2. 5 

### standard was retained (e.g., based on the 98 th 

### percentile concentration averaged over three years). The level of the annual
PM 2. 5 

### NAAQS was retained at 15 μ g/m 3, 

### continuing protection against health effects associated with long-term exposures. The current form of the annual PM 2. 5 

### standard was retained as an annual arithm etic m ean averaged over three years, however, the following two aspects of the spatial averaging criteria were narrow e d: (1) the a nnual m ean concentration at each site shall be within 10 percent of the spatia lly averaged annual m ean, and (2) the daily values for each monitoring site pair sh all yield a correlation coefficien t of at l east 0.9 for each calend ar quarter. W ith regard to the p r im ary PM 10 

### standards, the 24-hour PM 10 

### NAAQS was reta ined a t a leve l of
150 μ g/m 3 

### not to be exceeded m ore than once per year on average over a three-year period. Given that the available evidence does not suggest an associati on between long-term exposure to coarse particles at current am bient levels a nd health effects, E P A has revoked the annual PM 10 

### standard.

W ith regard to the secondary PM standard s, EPA has revised these standards to be identical in all respec ts to the rev i sed prim ar y standards. Specifically, EPA has revised the current 24-hour PM 2. 5 

### secondary standard by m a king it id entical to the revised 24-hour PM 2. 5 

### prim ary standard, retained the annual PM 2. 5 

### and 24-hour PM 10 

### secondary standards, and revoked the annual PM 10 

### secondary standards. Th is suite of secondary PM standards is intended to provide protection against PM-rel ated public welfare effects, in cluding visibility im pa irm e nt, effects on vegetation and ecosystem s, and m a terial dam a ge and soiling.

3.4.2 Health Effects of Particulate Matter

As stated in the EPA Particu l ate Matte r Air Quality Criteria Docum e nt (PMAQC D), available scientific findings “dem onstrate well that hum an health ou tco m es are associated with

am bient PM.” j 

### W e are relying p r im arily on the d ata and conclusions in the PM AQCD and PM staff paper, which reflects EP A’s analysis of policy-relevant science from the PM AQCD, regard ing th e health effects asso cia t e d with par tic ulate m atte r. 237, 238 

### W e also present add i tio n al

recen t stud ies k 

### published af ter the cut- of f date f o r the PM AQCD. 239 

### Taken together this inform ation supports the conclusion that PM-related em issions such as those controlled in this action are as sociated with a dverse h e alth effects.

3.4.2.1 Short-Term Exposure Mortality and Morbidity Studies

As discussed in the PM AQCD, short-term exposure to PM 2. 5 

### is assoc i a t ed with

prem ature mortality from cardiopu lmonary dise as es (PM AQCD, p. 8-305), hospitalization and

j Per s on al exposu r e in clud es co n t r i bu tio n s fro m m a n y d i f f e r e n t typ e s of par ticles, f r o m man y sou r ces, an d in m a n y diffe re nt environm ents. T o ta l pers onal exposure to PM incl ude s bot h am bient and n onam b ient c o m pone nts; and bot h c o m pone nt s m a y cont ri but e t o a dve rse heal t h e ff ect s. k These additional studies a r e include d i n t h e 2006 Provisional Assessm ent of Recent St udies on Health Effects of
Part i c ul at e M a t t e r Ex p o s u re. The pr o v i s i o na l assessm ent di d not a n d c oul d not ( g i v e n a very s h ort t i m efram e )

un de rg o t h e e x t e nsi v e c r i t i cal revi e w by EP A , C A S A C

, an d t h e p u b l i c , as d i d t h e PM AQ C D

. The p r ovi si onal

assessm en t foun d th at th e “n ew” st u d i es expan d th e scien tific in fo rm atio n an d pro v i d e imp o rtan t i n sigh t s on t h e rel a t i ons hi p be t w een PM e x p o su re a n d heal t h e ffect s of P M

. T h e provisional asse ssm e n t also found t h at the “ n e w ”

st

udi es ge neral l y st rengt hen t h e e v i d e n ce t h at acut e a n d ch ro ni c e x p o s u re t o fi ne part i c l e s an d ac ut e e x p o su re t o thoracic coa r se pa rticles are a sso ciated with h ealth e ffects. 

### 3-100

### em ergency departm e nt visits for cardiopulm ona ry diseases (PMAQCD, p. 9-93), increased respiratory sym ptom s (PM AQC D, p. 9-46), decreased lung f unction (PM AQCD Table 8-34) and physiological changes or biom arkers for cardiac changes (PM AQCD, Section 8.3.1.3.4). In addition, the PM AQCD describes a lim ited body of new evidence from e pidem iologic studies for potential relationships between short-term exposure to PM a nd health endpoints such as low birth weight, preterm birth, and neonatal and infant m o rtality (PM AQCD, Section 8.3. 4).

Am ong the studies of effects from short-term exposure to PM 2. 5 

### , several studies specifically address the contribution of m obile sources to short-term PM 2. 5 

### effects on daily mortality. T h ese stud ies indicate th at there ar e s t a tistica lly s i g n if icant a sso ciations be tween mortality and PM related to m obile source em issions (PM A Q CD, p.8-85). The analyses incorporate source apportionm ent tools into daily m o rtality studies and are briefly m e ntioned here. Analyses incorporating source apportionm en t by factor analysis with daily tim e-series studies of daily death indicated a relationship between m obile source PM 2. 5 

### and m o rtality. 240, 241 

### Another recent study in 14 U.S. c ities exam ined the effect of PM 10 

### exposures on daily hospital adm i ssions for cardiovascular disease. They found that the effect of PM 10 

### was significantly greater in areas with a larger proportion of PM 10 

### com i ng from m o tor vehicles, indicating that
PM 10 

### from t hese sources m a y have a greater effect on the toxicity of am bient PM 10 

### when

com p ared with oth er sou r ces. 242 

### These studies provide evid ence that PM-related em issions, specifically from m obile sources, are associ ated with advers e health effects.

3.4.2.2 Long-Term Exposure Mortality and Morbidity Studies

Long-term exposure to elevated am bient PM 2. 5 

### is associated with m o rtality from cardiopulm onary diseases and lung cancer (PM AQCD, p. 8-307), and effects on the respiratory system such as decreased lung function or the developm ent of chronic respiratory disease (PM AQCD, pp. 8-313, 8-314). Of specific im portance to this rule, the PM AQC D also notes that the
PM com ponents of gasoline and diesel engine exhaust represent one class of hypothesized likely
im portant contributors to obser ved ambient PM-related increase s in lung cancer incidence and mortality (P M AQCD, p. 8-318).

The PM AQCD and PM Staff Paper em phasize the results of two long-term studies, the Six Cities an d Am erican Cancer Society (ACS) prospective c ohort stud ies, based on s e veral factors – the inclusion of m easur ed PM data, the fact that the study populations were sim ilar to the general population, and the fact that these studies have unde rgone extensive reanalysis (PM AQCD, p. 8-306, Staff Paper, p.3-18). 243, 244, 245 

### These studies ind i cate that ther e are sign if ica n t associations for all-cause, car diopulmonary, and lung cancer mort ality with long-term exposure to PM 2. 5 

### . A variety of studies have been published si nce the com pletion of the AQCD. One such study, which was summa rized in EPA’ s provisional assessment, was an analysis of a subset of the ACS cohort data, which was published after th e PM AQCD was finalized but in tim e for the 2006 Provisional Assessm e nt, found a larger association than had previously been reported between lon g -term PM 2. 5 

### exposure and m ortality in the Los Angeles area using a new exposure estim ation m ethod that accounted f o r variati ons in concen tration within the city. 246 

### EPA is assessing the significance of this stu dy within th e contex t of the broad er lite ratu re.

As discusse d in the PM AQCD, the morbidity s t udies th at co mbine the features of cro ss3-101

### sectional and cohort studies provide the best ev idence for chronic exposure effects. Long-term studies evaluating the effect of a m bient PM on children’s developm ent have shown som e evidence ind i cating effects of PM 2. 5 

### and/or PM 10 

### on reduced lung function growth (PM AQCD, Section 8.3.3.2.3). A variety of studies have been published since the com pletion of the AQCD. One such study, which was summarized in EPA ’ s pr ovisional assessm e nt, re ported the results of a cross-sectional study of outdoor PM 2. 5 

### and m e asures of atherosclerosis in the Los A ngeles basin. 247 

### The study found significant associati ons between am bient residential PM 2. 5 

### and carotid intim a-m e di a thickness (CIMT), an indicator of s ubclinical atherosclerosi s, an underlying factor in cardiovascular disease. EPA is assessing the significance of th is study within the context of the broad er lite ratu re.

3.4.2.3 Roadw ay-Related Pollu t ion Exposure

A recent bo dy of studies reinforces the fi ndings of these PM morbidity and m ortality effects by looking at traf fic-rela ted exposures, P M m easured al ong roadways, or tim e spent in traffic and adverse health effects. While m a ny of these studies did not m easure PM specifically, they include potential exhaust exposures which include m obile source PM because they em ploy indices such as roadway proxim ity or traffic volum es. One study with specific relevance to
PM 2. 5 

### health effects is a study that was done in N orth Carolina looking at concentrations of PM 2. 5 

### inside police cars and correspondi ng physiological changes in th e police personnel driving the

cars. The authors report signifi can t elevations in m a rkers of cardiac risk associated with concentrations of PM 2. 5 

### inside police cars on Nort h Carolina state highway s. 248 

### A num be r of studies of traffic-related pollution have shown a ssociations between fine particles and adverse respiratory outcom es in children who live near m a jor roadways. 249, 250, 251 

### Additio nal inf o rm ation on near-roadway health effects is incl uded in Section 3.5 of this RIA.

3.4.3 Current and Projected PM Levels

The em ission reductions from this rule will assist PM nonattainm e nt areas in reaching the standard by each area’s respective attainm e nt date and assist PM m a inten a nce areas in

m aintaining the PM stan dards in the f u ture. In this section w e present inf o rm ation on current and future attainm e nt of the PM standards.

3.4.3.1 Current PM 2. 5 

### Levels

A nonattainm e nt area is defined in the Clean Ai r Act (CAA) as an area that is violating

an am bient standard or is contributing to a near by area that is violati ng the standard. In 2005, EPA designated 39 nonattainm e nt areas for the 1997 PM 2. 5 

### NAAQS based on air quality design values (using 2001-2003 or 2002-2004 m easurem ents) and a num b er 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 coun ties with a to tal popu latio n exceeding 88 m illion. The 1997 PM 2. 5 

### nonattainm ent areas and populations, as of Oct ober 2006, are listed in Appendix 3C to this RIA. As m e ntioned in Section 3.4.1, the 1997 PM 2. 5 

### NAAQS was recently re vised and the 2006 PM 2. 5 

### NAAQS becam e effective on Decem b er 18, 2006. Nonattainm ent areas will b e designated with respect to the 2006 PM 2. 5 

### NAAQS in early 2010. Table 3.4-1 pres ents the num ber of counties in areas

l Th e fu ll d e tails inv o l v e d in calcu latin g a PM 2.5 desi g n val u e a r e gi ve n i n A ppe n d i x N o f 40 C F R Pa rt 50

. 

### 3-102

### currently designated as nona ttainm e nt for the 1997 PM 2. 5 

### NAAQS as well as the number of additional counties which have m oni tore d data that is violating the 2006 PM 2. 5 

### NAAQS.

Table 3.4-1. PM 2. 5 

### Standards: Current Nonattainment Ar eas and Oth er Violating Counties

Num ber of Counties Population 1



### 1997 PM 2. 5 

### Standards: 39 areas currently designated 208 88,394,000

2006 PM 2. 5 

### Standards: Counties wi th violating monitors 2 

### 49 18,198,676

Total 257 106,592,676

1) Po p u l a t i o n num bers a re fr om 200 0 ce ns u s dat a

. 2) Thi s t a bl e pr ovi des a n est i m a t e of t h e co unt i e s vi ol at i n g t h e 2 0 0 6 PM 2.5 NAAQS b a sed o n 200 3-0 5 air qu ality dat a

. The area s desi gnat e d as n onat t a i n m e nt f

o r t h e 20 0 6 P M 2.5 NAAQS will b e b a sed on 3 years of air qu ality d a ta fro m later years. Also , t h e co un ty nu m b ers in th e su mmary tab l e in clud es on ly th e cou n ties with m o n ito rs v i o l atin g th e 200 6 PM 2.5 N A A Q S

. T h e m oni t o red co u n t y vi ol at i o ns m a y be an u n d e rest im at e of t h e num ber of c o u n t i e s a n d

p opu latio n s th at will ev en t u ally b e in cl u d e d in areas with mu ltip le coun ties d e si g n a ted n o n a ttain m e n t

.



### States with PM 2. 5 

### nonattainm ent areas will b e required to tak e action to b r ing thos e areas into com p lia nce in th e f u ture. Mos t PM 2. 5 

### nonattainm ent areas will b e required to attain the 1997
PM 2. 5 

### NAAQS in the 2010 to 2015 tim e fra m e and th en be required to m a intain the 1997 PM 2. 5 

### NAAQS thereafter. m 

### The attainm e nt dates as sociated w ith th e potential n onatta inm ent are as based on the 2006 PM 2. 5 

### NAAQS would likely be in the 2015 to 2020 tim efra m e

. The em ission standard s being finalized in this actio n w ill beco m e e ffective between 20 09 and 2015. The expected P M 2. 5 

### and PM 2. 5 

### precursor inventory re ductions from the standards finalized in this action will be useful to states in attain ing or m aintaining the P M 2. 5 

### NAAQS.

3.4.3.2 Current PM 10 

### Levels

EPA designated PM 10 

### nonattainm e nt areas in 1990. n 

### As of October 2006, approxim a tely
28 m illion p eople live in the 46 ar eas that a r e des i gnated as P M 10 

### nonattainm e nt, for either
f ailing to m eet the PM 10 

### NAAQS or for contributing to poor ai r quality in a nearby area. There are 46 f u ll o r partia l cou n ties that m ake up the P M 10 

### nonattainm e nt areas. The PM 10 

### nonattainm e nt areas and populations are li sted in Appendix 3C to this RIA.

As m e ntioned in Section 3.4.1, the 19 97 PM NAAQS was recently revised and the 200 6 PM NAAQS becam e effective on D ecem ber 18, 2006. The annual PM 10 

### NAAQS wa s revoked and the 24 hour PM 10 

### N AAQS was not changed. The projec ted reductions in em issions from the

contro ls f i na lized in th is action will b e usef ul to s t ates to m aintain the PM 10 

### NAAQS.

m

The EP A final i zed PM 2.5 attain m e n t an d non attain m e n t areas in Ap ril 200 5. Th e EPA p r op o s ed t h e PM
Im pl em ent a ti on rul e i n No ve m ber 20 0 5 ( 7 0 FR 6 59 8 4)

.
n A PM 10 d e sign v a lu e is th e co n c en tr ation that d e ter m in es w h eth e r a m o nito r ing site m e ets th e N AAQS fo r PM 10
. Th e fu ll d e tails in vo lv ed i n calcu l atin g a PM 10 desi g n val u e are gi ve n i n A ppe n d i ces H a n d I o f 40 C F R Part 5 0

. 

### 3-103

### 3.4.3.3 Projected PM 2. 5 

### Levels

Recent air q u ality m odeling pred icts that wi thout additional controls there will con tinu e to be a need for reductions in PM concentrations in the future. In the following sections we describ e the recen t PM air quality m o deling and results of th e modeling.

3.4.3.3.1 PM Modeling Methodology

Recently P M air quality analyses w e re pe rformed for the PM NAAQS final rule, which

was prom ul gated by EP A in 2006. The Comm unity Multiscale Air Quality (CMAQ) m odel was used as the tool for sim u lating base and future year concentrations of PM, visibility and deposition in support of the PM NAAQS air qua lity assessm ent. The P M 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 qual ity modeling are provided in the Regulatory Im pact Analysis (RIA) for the Final PM NAAQS Rule, included in the docket
f o r this f i nal rule. 252



### 3.4.3.3.2 Areas at Risk of Future PM 2. 5 

### Violations

Air quality modeling perform e d for the final PM NAAQS indicates that in the absen c e of additional local, regional or national controls, there will likely continue to be counties that will not attain som e combin ation of the annual 2006 PM 2. 5 

### standard (15 μg/m 3 

### ) and the daily 2006
PM 2. 5 

### standard (35 μg/m 3 

### ). The PM NAAQS analysis p r ovides estim a tes of future PM 2. 5 

### leve ls across the country. For exam ple, in 2015 base d on em ission controls currently adopted or expected to be in place o 

### , we project that 53 m illio n people will liv e in 52 counties with projected
PM 2. 5 

### design values at and above the 2006 standard, see Table 3.4-2. p 

### The rule will a ssist these countie s in a tta ining th e PM 2. 5 

### NAAQS. Table 3.4-2 also lis ts the 54 cou n ties, where 27 m illion people are projected to live, with 2015 projec ted design values that do not violate the PM 2. 5 

### NAAQS but are within ten percent of it. The rule m a y help ensure that these counties continue

to m aintain their a tta inment status.

Table 3.4-2. Counties w i th 2015 Projected Annual and Daily PM 2. 5 

### D e sign Values Above and w i thin 10% of the 2006 PM 2. 5 

### Standard a

2015 2015 Projecte d Projecte d Annu al Daily PM 2.5 PM 2.5 Design Design Value Value 2015 State Coun t y (μg/m 3 ) (μg/m 3 ) Population b

o Cou n ties forecast to rem a in in n o n a ttain m e n t m a y n eed to ado p t add itio nal lo cal or reg i o n a l con t ro ls t o attain t h e
stan d a rds b y dates set pu rsu a n t to t h e Clean Air Act. Th e emissio n s redu ctio n s asso ciated with th is ru le will h e lp these area s attain the PM stan dar d s by t h ei r s t at ut ory dat e

. p No te th at t h is an alysis id en tifies on ly cou n t i e s proj ected to h a v e a v i o l ating m o n ito r; t h e n u m b e r o f coun ties to b e designated and the ass o ciated populati on would likely e x ce ed the se estim a t es. 

### 3-104Alabama Jefferson Co
California Alameda Co
California Butte Co
California Colu sa Co
California Contra Co sta Co
California Fre s no Co
California Imperial Co
California Inyo Co
California Kern Co
California Kings Co
California Los Ang ele s Co
California Merced Co
California Oran ge Co
California Place r Co
California Riverside Co
California Sacramento Co San Berna r di no California Co
California San Dieg o Co
California San Fran ci sco Co
California San Joa quin Co San Luis O bi s po California Co
California San Mateo Co
California Santa Cla r a Co
California Solano Co
California Sonoma Co
California Stanislau s Co
California Sutter Co
California Tulare Co
California Ventura Co
California Yolo Co
Con ne c ticut Fairfield Co Georgia Bibb Co Georgia Clayton Co Georgia DeKalb Co Georgia Floyd Co Georgia Fulton Co Georgia Muscog ee Co Georgia Wilki n son Co
Idaho Ada Co
Idaho Banno ck Co
Idaho Canyo n Co
Idaho Powe r Co
Idaho Shosh one 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 36.9 669,85 0
59.4 1,628,69 8
50.7 242,16 6
33.5 23,066
61.3 1,155,32 3
73.0 960,93 4
45.7 173,48 2
38.1 19,349
81.4 804,94 0
70.6 161,60 7
62.2 9,910,80 5
54.4 250,15 2
41.1 3,467,12 0
38.1 403,62 4
73.5 2,015,95 5
49.8 1,488,45 6

65.7 2,157,92 6
40.7 3,489,36 8
52.5 765,84 6
51.1 675,36 2

35.8 304,07 9
41.9 785,94 9
48.5 1,899,72 7
57.7 529,78 4
38.9 569,48 6
61.9 547,04 1
39.3 99,716
77.2 441,18 5
38.8 923,20 5
33.0 206,38 8
31.6 893,62 9
27.0 160,46 8
28.7 280,47 6
31.5 715,94 7
30.9 97,674
32.2 877,36 5
34.2 197,63 4
29.3 11,259
32.2 397,45 6
40.2 88,033
32.6 154,13 7
36.6 8,932
36.2 15,646
37.1 5,362,93 1
35.5 271,85 4
30.4 251,61 2
32.0 634,06 8
31.1 112,52 3
40.8 490,79 5 

### 3-105Indiana Mario n Co Kentucky Jefferson Co Marylan d Anne Arun del Co Marylan d Baltimore city Marylan d Baltimore Co
Massa c hu set t s Hamp den Co Michig an Kalama zoo Co Michig an Kent Co Michig an Oakl and Co Michig an St. Clair Co Michig an Wayne Co
Montana Lincoln Co
Montana Missoula Co
Ne w Je rsey Camd en Co
Ne w Je rsey Hud s o n Co
Ne w Je rsey Unio n Co
Ne w York Bronx Co
Ne w York Ne w York Co
Ohio Cuyah oga Co
Ohio Fran klin Co
Ohio Hamilton Co
Ohio Jefferson Co
Ohio Lucas Co
Ohio Scioto Co
Ohio Trumb ull Co Oreg on Ja ckso n Co Oreg on Klamath Co Oreg on Lane Co Oreg on Wa shin gton Co Pennsylvani a Allegheny Co Pennsylvani a Beaver Co Pennsylvani a Berks Co Pennsylvani a Dau phin Co Pennsylvani a Lan ca ster Co Pennsylvani a Lehig h Co Pennsylvani a Mercer Co Pennsylvani a North a mpto n Co Pennsylvani a Philadelp h ia Co Pennsylvani a York Co
Tenne ssee Knox Co
Utah Box Elder Co
Utah Ca che Co
Utah Salt Lake Co
Utah Utah Co
Utah Web er C o Wa shin gton Clark Co
Wa shin gton King Co
Wa shin gton Pierce Co
Wa shin gton Snohomi s h Co
Wa shin gton 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 33.1 889,64 5
33.4 710,23 1
33.2 574,32 2
35.5 596,07 6
32.6 810,17 2
32.9 452,05 5
32.7 257,81 7
31.9 654,44 9
33.2 1,355,67 0
32.5 185,97 0
39.0 1,921,25 3
42.4 19,875
32.1 118,30 3
32.1 512,13 5
32.8 604,03 6
32.8 525,09 6
33.2 1,283,31 6
33.2 1,551,64 1
40.0 1,325,50 7
33.5 1,181,57 8
34.2 841,85 8
34.2 68,909
32.2 443,23 0
34.3 81,013
34.2 227,54 6
37.6 250,16 9
39.1 69,423
53.6 387,23 7
32.0 639,83 9
53.4 1,245,91 7
33.2 184,64 8
35.5 396,41 0
33.3 272,74 8
33.7 535,62 2
34.7 328,52 3
31.6 123,57 7
35.0 286,83 8
35.2 1,372,03 7
35.9 417,40 8
29.6 448,93 1
39.0 49,878
51.9 114,72 9
49.3 1,133,41 0
36.7 508,10 6
36.2 229,80 7
34.3 479,00 2
34.0 2,013,80 8
43.0 879,36 3
40.1 782,31 9
34.9 264,36 4



### 3-106Wa shin gton Yakima Co 9.6 34.9 261,45 2
We st Virginia Berkeley Co 12.0 32.7 99,349
We st Virginia Han c o c k Co 13.4 32.7 30,857
We st Virginia Kanawha Co 13.9 28.9 196,49 8
Wisco nsi n Milwau ke e Co 12.1 32.1 908,33 6
Wisco nsi n Wau k e s h a Co 11.8 32.4 441,48 2 Wyoming Sherid an Co 10.5 31.8 28,623

Numb er of Violating Counti e s 

### 52
Population of Violating Co u n ties 53,468,5 15

Numb er of Countie s within 10% 

### 54

Population of Cou n ties with in 10% 26,896,9 26

a) Bol d ed conc entrations i ndi cate levels above the PM 2.5 st anda r d

. b ) Popu latio n s ar e b a sed on 20 00 cen s u s pr oj ectio ns.



### 3.4.4 Environmental Effects of PM Pollu tion

In this section we discuss public welf ar e effects of PM and its precursors including visibility im pairm e nt, atmospheric d e pos ition, an d m a terials dam a ge and soiling.

3.4.4.1 Visibility Impairmen t

Visibility can be defined as the degree to which the atm osphe re is transp a r ent to vis i ble light. 253 

### Visibility im pairment m anif ests in two prin cipal ways : as loca l vis i bility im pair m ent and as reg i o n al haze. q 

### Local visibility im pair m e nt m a y take the for m of a localized plum e, a band or layer of discoloration appearing well ab o v e the terrain as a resu lt from com p l ex local

m eteorological cond itio ns. Alterna t ively, local visi bility im pairm e nt m a y m a nif e st a s an urban haze, som etim es referred to as a “b ro wn cloud.” This urban h aze is larg ely caused by em issions
f r om m u ltiple sources in the urban ar eas and is n ot typically attributable to only one n earby source or to long-rang e transport. The second ty pe of visibility im pairm e nt, reg i onal haze, usually resu lts from multiple po llutio n sources sp read over a large geog raphic reg i on. Regional haze can impair v i sib ility over la rge regions and across s t ates

.

Visibility is im portant because it has direct significance to p eo p le’s en joy m ent 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 th ey enjoy recreational opportunities. Visibility is also highly valued in significant natura l 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 PM AQCD as wel l as the 2005 PM Staff Paper. 254, 255



### Fine particles are th e m a jor cause of reduced visibility in parts of the United States. To address the welfare effects of PM on visibility, EPA set secondary PM 2. 5 

### standards which would

q See d i scu s sion in U.S. EPA , Nation a l Am b i en t Air Qu ality Stan d a rd s for Particu l ate Matter; Propo sed Ru le;
Janu ar y 1 7 , 2 0 0 6 , V o l 7 1 p 26 76

. Th is in form at io n is av ai lab l e electronically at h ttp ://ep a.go v / fedrgstr/EPAA I R/200 6 /Januar y /D ay- 1 7 /a17 7 .pdf

. 

### 3-107

### act in conjunction with the establishm ent of a re gional haze program

. In setting this secondary standard, EP A concluded that PM 2. 5 

### causes adverse effects on visi bility in various locations, depending on PM concentrations and factors such as chem ical com position and average relativ e hum i dity. T he secondary (welfare-based) PM 2. 5 

### NAAQS was estab lish e d as equal to the suite of prim ary (he alth -based ) NAAQS. Furtherm ore, Section 169 A of the Act provides ad dition al authority to address existing visibi lity im pairm ent and prev en t f u ture visib ility im pair m ent in the 156 national parks, forests and wilderness areas ca tegorized as m a ndatory class I federal areas (62 FR 38680-81, July 18, 1997). r 

### In July 1999 the region al haze ru le (6 4 FR 35714) was put in place to protect th e vis i bility in m andatory c l ass I federal a r eas

. Visibility can be sa id to be
im paired in both PM 2. 5 

### nonattainm e nt areas and m a nda tory class I federal areas.

Data showing PM 2. 5 

### nonattainm e nt areas and visibility levels above background at the Mandatory Class I Fede ral Are as de monstrate th at vis i bility impairm ent is experience d throughout the U.S., in multi-s t ate regions, urban areas, and rem o te m andatory Federal class I

areas. The PM and PM precursor em issions from the vehicles and gas cans subject to this proposed rule contribute to th ese visibility effects.

3.4.4.1.1 Current Visibility Im pairm e nt

The need for reductions in the levels of PM 2. 5 

### is widespread. Currently, high am bient
PM 2. 5 

### levels are m easured throughout the country. Fine particles m a y remain suspended for days or weeks an d trave l hun dreds to tho u sands of kilom eters, and thus f i ne pa rticles em itted or created in one county may contribute to am bi ent concentrations in a neighboring region. 256



### As m e ntioned above the secondary P M 2. 5 

### standards were set as equal to the suite of prim ary PM 2. 5 

### standards. Recently designated PM 2. 5 

### nonattainm ent areas indicate that alm ost 90

m illion people live in 20 8 counties th at are in non attainm e nt for the 1997 PM 2. 5 

### NAAQS, see Appendix 3C. Thus, at least th ese populations (plus others w ho travel to these areas) would likely be experiencing visibility im pairm e nt.

3.4.4.1.2 Current Visibility Im pairm e nt at Mandatory Class I Federal Areas

Detailed inform ation about current and histor ical visibility co nditions in m a ndatory class I federal areas is summ a r ized in the EPA Report to Congress and the 2002 EPA Trends Report. 257, 258 

### The conclusions draw upon the Intera gency Monitoring of Protected Visual Environm ents (IMPROVE) network data. One of the objectives of the I MPROVE monitoring network pro g ram is to provide reg i on al haze m onitoring repres enting all m andatory class I federal areas where practical. The N a tional Park Service repo rt a l so describes the state of national park visibility conditions an d discusses the need for improvem e nt

. 259



### The regional haze ru le requires s t ates to esta blish goals for each affected mandatory class I federal area to im prove visibility on the hazi est days (20% most im paired days) and ensure no degradation occurs on the cleanest days (20% least im paired days). Although there have been

r These a r ea s are de fine d in Section 162 of the Act as t h o s e nat i o nal par k s e x cee di n g 6,000 acres , wilderness

areas a n d m e m o ri al par k s e x c eedi n g 5, 00 0 a c res, a n d al l i n t e rn ation a l p a rks wh ich were i n ex isten ce on Augu st 7,
1 977

. 

### 3-108

### general trends toward improved visibility, pr ogress is still needed on the haziest days. Specifically, as discussed in the 2002 EPA Tre nds Report, without the ef fects of pollution a natural visual range in the Unite d States is approxim ately 75 to 150 km in the East and 200 to 300 km in the W e st. In 2001, the m e an visual range for the worst days was 29 km in the East and 98 km i n the W e st. 260



### 3.4.4.1.3 Future Visibility Im pairm e nt

Recent m od e ling for the final PM NAAQS rule was used to project PM 2. 5 

### levels in th e U.S. in 2015. The results suggest that PM 2. 5 

### levels above the 2006 NAAQS will pers ist in the future. W e predicted th at in 2015, there will be 52 counties with a population of 53 m illion where annual PM 2. 5 

### lev e ls will ex ceed the 2006 PM 2. 5 

### NAAQS, see Table 3.4-1. Thus, in the future, a percentage of the populat ion m a y continue to experience vi sibility im pairment in areas where they live, work an d recreate.

The PM and PM precursor em issions from the vehicles and g as cans sub j ect to the proposed controls contribute to vi sibility im pairm e nt. Thes e em issions occur in and around areas with PM 2. 5 

### levels above the annual 1997 PM 2. 5 

### NAAQS. Thu s , the em issions fro m these sources

contr i bute to the cur r ent and anticipa ted vis i bility im pairm ent and the em ission reduc tions
f i nalized h e r e m a y help improve f u ture vis i bility impairm ent.

3.4.4.1.4 Future Visibility Im pairm e nt at Mandatory Class I Federal Areas

Achieving the PM 2. 5 

### NAAQS will help im prove visibil ity across the co untry, but it will not be sufficient to m eet the statutory goal of no m anmade impairm ent in the m andatory 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 NA AQS in combination with the reg i on al haze p r og ram , is deem ed to improve visibility consis tent with the goals of the Act. In the East, there are and will continue to be areas with PM 2. 5 

### concentrations above the PM 2. 5 

### NAAQS and where light extinction is significantl y above natural background. Thus, large areas of the Eastern United States h ave air po llution that is caus i ng and will c ontinue to cause vis i bility im pairment. In the West, scenic vistas are es pecially im portant to public welfare. Although the PM 2. 5 

### NAAQS is

m et in m o st areas ou tsid e of Calif or nia, virtually the entire W e st is in close proxim ity to a scenic

m a ndatory class I federal area protected by 169A and 169B of the CAA.

Recent m o deling for CAIR was also used to pro j ect visibility conditions in m a ndatory class I federal areas across the country in 2015. The results for the m andatory class I federal areas suggest that these areas ar e predicted to continue to have visibility im pairm ent above background on the 20% worst days in the future.

The overall goal of the r egional h aze program is to prevent f u ture vis i bility im pairm ent and rem edy existing visibility im pairm ent in m and atory class I federal areas. As shown by the
f u ture vis i bility estim a tes in Appendix 3D it is pr ojected tha t there will co ntinue to be m andatory class I federal areas with visibi lity levels above background in 2015. 261 

### Addition al em ission reductions will be ne ed ed f r om the broad set of sources that contr i bute, including the vehicles and gas cans subject to this rule. The reductions being finalized in this action are a part of the

3-109

### overall strategy to ach ie ve the visibility go als of the Act and the reg i onal haze prog ra m.

3.4.4.2 Atmospheric Deposition

Wet and dry deposition of a m bient particul ate m a tter delivers a com p lex m i xture of

m e tals (e.g., m e rcury, zinc, lead, nickel, al um inum, cadm i um), organic compounds (e.g., POM, dioxins, furans) and inorganic com pounds (e.g., nitr ate, sulfate) to terrestrial and aquatic ecosystem s. The chem ical form of the com pounds deposited is im pacted by a variety of factors including ambient conditions (e.g., tem p erature, hum i dity, oxidant levels) and the sources of the

m a terial. Chem ical and physical transfor m a tions of the particulate com pounds occur in the

atm o sphere as well as the m e dia onto which th ey deposit. T hese transform a tions in turn influence the fate, bioavailability and potenti al toxicity of these com pounds. Atm o spheric deposition has been identified as a key com ponent of the environm ental and hum a n health hazard posed by several pollutants incl uding m e rcury, dioxin and P CBs. 262



### Adverse impacts on water quality can occur when atm o spheric contam inants deposit to the water su rface or when m a terial d e posited on the land en ters a waterbo dy through runoff. Potential im pacts of atm o spheric deposition to w a terbod ies include tho se rela ted to b o th nutr i ent and toxic inputs. Adverse effect s to hum a n health and welfare can occur from the addition of excess particulate nitrate nutrient en richm e nt wh ich contributes to toxic algae bloom s and zones of depleted oxygen, which can lead to fish kill s, frequently in coas tal waters. Particles

contam inated with heavy m e tals or other toxins m a y lead to the ingestion of contam i nated fish, ingestion of contam inated water, dam a ge to the m a rine ecology, and lim ited recreational uses. Several studies have been conducted in U.S. co astal waters and in the Great Lakes Region in which the role of am bient PM de position and runoff is investigated. 263, 264, 265, 266, 267



### Adverse impacts on so il chem istry and plant life have been o bserved for areas h e avily
im pacted by atm o spheric deposition of nutrients, m etals and acid spec ies, resulting in species shifts, loss of biodiversity, forest decline and da m a ge to fore st productivity. Potential im pacts also in clude adverse effects to hum an health th rough ingestion of contam inated vegetation or lives tock ( as in the c ase f o r dioxin d e position ) , r e duction in cr op yield, an d lim ited us e of land due to contam ination.

In the following subsections, atm os pheric deposition of heavy m e tals and particulate organic m a terial is discussed.

3-110

### 3.4.4.2.1 Heavy Metals

Heavy m e tals, including cadm i um , c opper, lead, chrom i um , m e rcury, nickel and zinc, have the greatest potential for influe ncing forest growth (PM AQCD, p. 4-87). 268 

### Investigation of trace m e tals near road ways and in dustrial facil ities ind i cate that a subs tantial burden of heavy

m e tals can accum u late on vegetative surfaces. C opper, zin c , and nickel h a ve been do cum e nted to cause d i rect tox i city to vegetation under fiel d conditions (P M AQCD, p. 4-75). Little res e arch has been conducted on the effects associated with m i xtures of contam ina n ts found in am bient
PM. W h ile m e tals typic a lly exh i bit low solubi lity, lim iting their bioavailability and direct toxicity, chem ical transform a tions of m e tal com p ounds occur in the environm ent, particularly in the pres ence of acidic or other ox idiz ing species . These chem ical chang es inf l uence th e m obility and toxicity of m e tals in the environm ent. On ce taken up into plant ti ssue, a m e tal compound can undergo chem ical changes, accum u late and be p a ssed along to herb ivores or can re-enter the soil and further cycle in the environm ent.

Although there has been no dire ct evidence of a physiologica l association between tree injury and heavy m e tal exposures, heavy m e tals have been im plicated because of sim ilarities between m etal depos itio n patte rns an d f o rest dec l ine (PM AQCD, p. 4-76). 269 

### Contam ination of plant leaves by heavy m e tals can lead to elev ated soil levels. Som e trace m etals absor b ed into the plan t an d can bind to the leaf tis sue (PM AQCD, p. 4-75). W hen these leaves fall and decom pose, the heavy m etals are tran sferred in to the soil. 270, 271



### The environm ental sources and cycling of m e rc ury are currently of pa rticular concern due to the bioaccum ulation and biom agnification of this m etal in aquatic ecos y stem s and the potent toxic nature of m e rcury in the form s in whic h is it ingested by people and other anim als. Mercury is unusual com p ared with other m e tals in that it larg ely par t ition s into th e ga s phase ( i n elem ental form ), and therefore has a longer reside nce tim e in the atm o sphere than a metal found predom inantly in th e par t icle ph ase. This prope r t y enables a portion of e m itted m ercury to trave l far from the prim ary source before b eing depos it ed and accu mulating in the aquatic ecosystem

. Localiz ed or regiona l im pacts a r e a l s o observed f o r m ercury em itted f r om com bustion sources. The m a jor source of m e r c ury in the Great Lakes is from at m o spheric deposition, accounting for approxim a tely eighty percent of the m e rcury in Lake Michigan. 272, 273 

### Over fifty percent of the

m e rcury in the Chesapeake Bay has been attributed to atm ospheric deposition. 274 

### Overall, the National Science and Technology Council (NSTC, 1999) identifies atm o spheric d epo sition as th e prim ary source of m ercury to aquatic system s. Fort y-four states have issued health ad visories for the consum ption of fish contam inated by m e rcury; however, most of these advisories are issued in areas without a m e rcury point source.

Elevated levels of zinc and lead have been identified in streambed sediments, and these elevated levels have been correlated w ith population density and m otor vehicle use. 275, 276 

### Zinc and nickel have also been identified in urban water and soils. In a ddition, platinum , pa lladium

, and rhodium , m e tals found in the catalysts of m o dern m o tor vehicles, have been m easured at elevated levels along roadsides. 277 

### Plant uptake of platinum has been observed at these locations.

3-111

### 3.4.4.2.2 Polycyclic Organic Matter

Polycyclic organic m a tter (POM) is a byproduc t of incom plete com bustion and consists of organic com pounds with m ore than one benzen e ring and a boiling point greater than or equal to 100 degrees centigrade. 278 

### Polycyclic arom atic hydrocarbons (PAHs) are a class of P O M that contains compounds which are known or suspected carcinogens.

Major sources of PAHs i nclude m obile s ources. PAHs in the environm ent m a y be present as a gas or adso rbed ont o airborne particulate m atte r. Since the m ajority of PAHs are adsorbed onto particles less than 1.0 μm in diam e t er, long range tr ansport is possible. However, studies have shown that PAH co m pounds adsorbed onto diesel e xhaust particulate and exposed to ozone have half lives of 0.5 to 1.0 hours. 279



### Since PAHs are inso luble, the com pounds gene rally are particle reactive and accum u late in sedim e nts. At m ospheric deposit ion of particles is be lieved to be the m a jor source of PAHs to the sedim e nts of Lake Michigan. 280, 281 

### Analyses of PAH deposition to Chesapeake and Galveston B a y indicate that dry depo sition and gas exchange f r om the atmosphere to the surface water predo m inate. 282, 283 

### Sedim e nt concentrations of PAHs are high enough in som e segm ents of Ta m pa Bay to pose an environm ental health th reat. EPA funded a study to better characterize the sources and loading rates for PA Hs into Tampa Bay. 284 

### PAHs that enter a waterbody through gas exchange likely parti tion into organic rich particle s and be biologically recycled, while dry deposition of aerosols containing PAHs tends to be m ore resistant to biological recycling. 285 

### Thus, dry deposition is likely the m a in pathway f or PAH concentr ation s in sedim e nts while gas/water exchange at the surf ace m a y lead to PAH distribution into the food web, leading to increased health risk concerns.

Trends in P AH deposition levels are difficu lt to discern b ecause of highly variable

am bient air concentrations, lack of consistency in m onitoring m e thods, and the significant influence of local sources on deposition levels. 286 

### Van Metre et al. (2000) noted PAH concentrations in urban reservoir sedim e nts have increased by 200-300% over the last forty years and correlates with in creases in au to mobile use. 287



### Cousins et al. (1999) estim ates that greater than ninety percent of sem i -volatile organic

com pound ( S VOC) e m i ssions in the United Kingdom deposit on soil. 288 

### An analysis of polycyclic arom atic hydrocarbon (PAH) concentr ations near a Czechoslo vakian ro ad way indicated that concentrations were thirty tim es greater than background. 289



### 3.4.4.3 Materials D amage and Soiling

The deposition of airborne particles can also red u ce the aesthetic a ppeal of buildings and culturally important articles thr ough soiling, and can cont ribute directly (or in conjunction with other pollutants) to stru ctural dam a ge by m eans of corrosion or erosion. 290 

### Particles affect

m a terials principally by prom oti ng and accelerating the corro sion of m e tals, by degrading pain ts, and by deteriorating building m a terials such as co ncrete and lim estone. Particles contribute to these effects because of their e l ec trolytic, hyg ros c opic, and a c idic p r opertie s, and their ability to

3-112

### sorb corrosive gases (principally sulf ur dioxi de). The rate of m e tal corrosion depends on a

num ber of factors, including the deposition rate and nature of th e polluta nt; th e inf l ue nce of the

m e tal protective corrosion film ; the amount of moisture present; variability in the electrochem i cal reaction s; the p r esen ce and concen tration of other surfac e elec tro l yte s ; and th e orien t ation o f the m etal surface.



## 3.5 Health and Welfare Impacts of Near-Roadw ay Exposure

Over the years there have been a large num ber of studies that have exam ined associations between living near m a jor roads and different ad verse health endpoints. T h ese studies generally

exam ine people living near heavily-trafficked road ways, typically within several hundred m e ters, where fresh em issions from m o tor vehicles ar e not yet fully diluted with background air.

As discussed in Chapter 3.1.3, m a ny studies have m easured elevated concentrations of pollutants e m itted direc t ly by m o tor vehicles nea r larg e road ways, as co mpared to o v erall urban background levels. These elevated concentratio ns generally occur w ithin approxim a tely 200

m e ters of the road, although the distance m a y vary depending on traffic and environm ental conditions. Pollutants measured with elevated concentrations include benzene, polycyclic

arom atic hydrocarbons, carbon m o noxide, nitrogen dioxide, black carbon, and coarse, fine, and ultrafine particles. In addition, resuspended road dust, and w ear particles from tire and brake use also show concentration increases in proxim ity of m a jor roadways.

As noted in section 3.2, HAPEM6 es tim ates the changes in tim e -weighted exposures associated w ith proxim ity to roadways for indivi dual pollutants. The studies discussed in this section address exposures and health effects that are at least partially captured by our modeling, but there m a y be additional exposur es and health effects associated with pollutants, singly or in

com bination, that are not explicitly quantified. H owever, because the studies discussed in this section often em ploy exposure es tim ation m etrics associated with m u ltiple pollu tants, exposureresponse inform ation from these studies m a y not be suitable f o r risk assessm e nt geared around one or several chem icals.

At this point, there exists no exposure m e tr ic specific to “traffic,” although as noted above, a wide variety of gaseous, particulate, and sem i -volatile specie s are elevated near roadways. As a result, the exposure m e trics em ployed generally indicate the presence and/or intensity of a m i xture of air pollutants for exposure assessm e nt. Many of the health studies discussed below em ploy non-specific exposure metr ics, including traffic on roads nearest hom e or school, distance to the nearest road, m easured outdoor nitrogen dioxi de concentrations, air quality dispersion m odeling of specific traffic- generated chem icals, and exposure assignm ent based on land use. These exposure m e trics repres ent the m i xture of traffic-generated pollutants, rather than individual pollutants. Accordingly, such results are not directly com parable with community epidem iology studies that em ploy a m bi ent m easurem ents of particulate m atter or ozone over a fixed tim e period, or to toxicologi cal studies em ploying a single pollutant to evaluate responses in hum a ns or anim als.

A wide range of health effects are reported in the litera t ure re lated to near roadway and in-vehicle exposures. T his is not unexpected, given the chem ical and physical complexity of the

3-113

### m i xture to which people are exposed in this e nvironm ent. These effects overlap with those identified in our discussion of the effects of PM and ozone. Th e discussion below addresses the studies in detail. However, in general term s, the near-roadway health studies provide stronger evidence for som e health endpoints than others. Ep idem iologic evidence of adverse responses to traffic-related pollution is strongest for non-alle rgic respiratory symptoms, and several wellconducted epidem iologic studies have shown associa tions w ith cardiovascul ar effects, prem ature adult m ortality, and adverse birth outcom es, includi ng low birth weight and size. Traffic-related pollutants h a ve been rep eated ly asso ciated with increas ed prevalence of asthm a -related respiratory sym ptom s in children, although epidem iologic evidence rem a ins inconclusive for a hypothesized link between traffic and the developm ent of alle rgies and new onset asthm a

.

For childhood cancer, in particular childhood leukem i a, epidem iologic studies have shown less a b ility to de te ct the risk s p r edic ted from toxicological studies . Several sm all stud ies report positive associations, though such effects have not been obser ved in two larger studies. As described above in Chapter 1.3, benzene and 1,3-butadiene are both known hum a n leukem ogens in adults from occupational exposures. As prev iously m e ntioned, epidem iologic studies have shown an increased risk of leukem i a am ong child ren whose parents have been occupationally exposed to b enzene. W h ile epid em iologic stud ie s of near-road way exposures have not always shown a statistically sign ificant association with childhood leuk em ias, the results are consistent with the risks predicted f r om the studies at hi gher exposure levels. As a whole the toxicology and epidem iology are consistent with a potenti ally serious children' s health concern and additional research is needed.

Signif i cant s cientif ic unc erta inties r e m ain in research on health effects near roads, including the exposures of great est concern, the importance of ch ronic versus acute exposures, the role of fuel type (e.g. diesel or gasoline) and com pos ition (e.g., percent arom atics), and relevant traffic patterns. Furtherm ore, in these studies, it is often difficu lt to understand the role of co-stresso rs including noise and so cioeconom ic status (e.g., access to he alth care, nu tritional status), and the role of dif f erential su sceptibility.

3.5.1 Mortality

The quantifiable effects of this rule on prem ature mortality associated with exposure to
PM 2. 5 

### are as sessed as p a rt of the ben e fits es tim ates fo r this rule. In addition to studies that have docum ented the relationship between am bient PM and premature m o rtality, a few recent s t udies have investigated the relationship between pr em ature m ortality and broader indicators of transportation em issions, such as residence near tr affic. The extent to which these stu d ies are detecting any additional effect s not accounted for in the am bient PM-prem a ture m o rtality relationship is unclear.

Living near m a jor roads has been investigated in both long-term and short-term m o rtality studies. Lo ng-term stud ies track sub j ects over tim e and investigate th e mortality rates am ong groups with different levels of exposure to am bi ent pollutants. Short te rm studies em ploy daily variation in am bient concentrations to estim ate the daily deaths attrib utable to air pollution.

3-114

### A total of three cohort studies have exam ined prem ature m o rt ality in relation to residence near traffic, another exam ined county-level tra ffic density, while one other has exam i ned stroke mortality. In addition, one study accounted for the effect of residence along a m a jor road on association s with daily d eaths in a tim e-series study. These studies cons titu te all of the studies

exam ining mortality with refere n ce to proxim ity to tr af f i c.

Prem ature mortality in adults in ass o ciati on with living near high-traffic roadways has been stud ied in three recent cohort studies for all-cause and cardiopulm onary m o rtality from the Netherlands, Ontario, Canada, and most recently, Germ any. 291, 292, 293 

### Canadian vehicles and

em ission standards largely m i rror the U.S. vehicle fleet. Both studies defined living near a m a jor road as hav i ng a resid en ce within 100 m e ters of a highway or w ithin 50 m eters of a m ajor urban roadway. In the first study, involving appr oxim a tely 5,000 people over 55 years old living throughout the Netherlands, residence near m a jor ro adways was associated with a 41% increase in the m o rtality r ate f r om all c auses a nd a 95% in creas e in th e cardiopu lmonary m o rtality rate. 294



### The second study involved over 5,200 subjects aged 40 years or m ore, all living in the
Ha m ilton, Ontario area. This study exam ined to tal m o rtality, finding a statis tically significant 18% increase associated with liv ing near a m a jor roadway. No difference in response was found

am ong those with pre-existing respiratory illness. The study also calculated “rate advancem ent periods,” which describe the effect of an exposure in term s of t h e tim e period by which exposed persons reach prem aturely the sam e disease risk as unexposed persons reach later on. The rate advancem ent period for total m o rtality was 2.5 year s. The rate advancem ent periods were also calcu lated f o r other risk factors for mortalit y, including chronic pulm onary disease excluding asthm a (3.4 years), chro nic is chem ic heart d i seas e (3.1 years), and diabetes m e llitus (4 .4 years). A subsequent follow-up study found elevated m o rt ality rates from circulatory causes in the Canadian study population.

Most recently, Germ an i nvestig ators followed up a series of cross-sectional studies on

wom e n age 50-59 living in the North Rhine-W e stphalia region during the late 1980’s and 1990’s, tracking vital status a nd m i gration to the years 2002-2003. 295 

### In total, the cohort consisted of approxim a tely 4800 wom e n. Expos ures were categorized using am bient NO 2 

### and
PM 10 

### (estim ated from TSP), and an indicator of re sidence within 50 m of a “m ajor road”, defined

at ≥ 10,000 cars/day. Overall, living within 50 meters of a m a jor road wa s associated with a significan t 7 0 % increase in the rate of car diopulmonary m ortality. Nearest-m onitor NO 2 

### and
PM 10 

### were also associated with a 57% and 34% increase in the rate of cardiopulm onary mortality. E xposure to NO 2 

### was also associated with a 17% increas e in all-caus e m o rtality.

Despite d i fferences in th e vehicle fleets of Europe and Canada, whose emission standards largely m i rror those of the U.S., the re sults of th ese stud ies are sim ilar.

In another study evaluating a c ohort of older, hypertensive m a le U.S. veterans, countylevel traffic index and pollution estim a tes were em ployed in estim ating exposure to traffic activity and other air pollutants. 296 

### Area-based traffic density w a s significan tly asso ciated with incre ased morta lity rates

, as were co nstitu ents of m o tor vehic l e exhaus t, s u ch as elemental carbon.

3-115

### One cohort study conducted in the United Kingd om exa m ined cardiocerebral (stroke) mortality in rela tion to living near traffic. 297 

### Those living in census areas near roadways had significan tly higher s t rok e m o rtality rates. In a study involvin g nearly 190 ,000 stroke deaths in 1990-1992, Maheswaran and Ellio tt (2002) exam ined st roke mortality rates in censu s districts throughout England and W a les. Ce nsus districts closest to m a jor roads showed significant incre ases in stroke m o rta lity rates f o r m en a nd wom en. Compared to tho se liv ing in census districts whose center w a s greater than 1000 m from a m ain road, m en and wom en living in census regions with centers less than 200 m awa y had stroke m ort ality rates 7% and 4% higher,

respec tive l y.

One study from the Netherlands used tim e-series analysis to evaluate the change in the

m a gnitude of the association betw een daily concentrations of black sm oke, an air m e tric related to black carbon, and daily deaths, for populations liv ing along roads with at least 10,000 vehicles per day. 298 

### Compared with the population living else where, the traffic-exposed population had significantly higher associat ions between black sm oke and daily m ortality.

Although the studies of mortality have em pl oyed different study desi gns and m e trics of exposure, th ey provide evidence for increas ed m o rta lity rates in proxim ity of heavy traffic. In evaluating the generalizability of these study resu lts, questions rem a in regarding differences in housing stock, residential ventila tion, vehicle type and fuel differences, personal activity patterns, and the appropriate exposure m e tric. Furtherm ore, in the cohort studies, although controls for incom e level were incorporated ba sed on postal code or cen su s area, it is p o ssible that other unm easured covariates expl ain the associations with traffic.

3.5.2 Non-Allergic Respiratory Symptoms

Our analysis of the benefits asso ciated with reduced exposure to PM 2. 5 

### includes chronic bronchitis, hospital adm i ssions for respiratory cause s, em ergency room visits for asthm a , acute bronchitis, u pper and lo wer respiratory sym p to m s and exacerbation of asthm a

. In addition, studies in E urope, Asia and No rth Am erica have found increased risk of respiratory sym p tom s such as wheeze, cough, chronic ph legm production, and dyspnea (shortness of breath) in children and adults with increased proxim ity to roadways and/or as sociated with local traffic density. Most of these studies were cros s-sectional and relied solely on questionnaire assessm e nts of health outcom es, in combination with sim ple e xposure indicators. There are a large num b er of studies available, but for the sa ke of brevity, only studies cond ucted in the United Sta t e s are discussed h e re. European studies reach sim ilar c onclusions, as summ arized in a recen t rev i ew of the European literature. 299 

### The discussion below covers all studies conducted in the United
States. EPA has not form ally evaluated the ex tent to which these studies m a y be docum enting health effects that are alrea dy included in the benefits analysis associated with PM.

Most recently, a study from Cincinnati, OH ex am ined 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 enrollm e nt. The study com pared infants living near stop-and-go truck tra ffic with others living

near sm oothly-flowing truck traffi c, and others further from traffic. Infants with wheeze were signif i can tly m o re like l y to liv e near stop-and

- go traffic than e ither those living near smoothlyflowing traffic or those living aw ay from traffic. Truck volu m e was not associated w ith wheeze.

3-116

### A respiratory health study in the east Sa n Francisco Bay area l ooked at a series of community schools upw ind and downwind of m a jor ro ads along a m a jor tran sportation corridor, where am bient air quality was m onitored. 301 

### Over 1,100 children in gr ades three through five attending the schools were asse ssed for respiratory sym p tom s and physician’s diagnosis of asthm a

. Overall, concen trations of traffic-rela ted air po llutants m easured at each scho ol were associated w ith increased prevalence of bronc hitis sym ptom s a nd physician confirm e d a s thm a , both within the last 12 months.

A case-control study in Erie County, NY co m p ar ed hom e proxim ity to traffic a m ong child ren ad m itted into local hosp itals for as thm a with those adm itted for non-resp iratory conditions. 302 

### Overall, children hospitalized for asth m a were more likely to live within 2 00

m e ters of roads above the 90 th 

### percentile of daily vehicle m iles traveled, and to have trucks and

tra ile rs pass ing within 20 0 m ete rs of their residences. However, hospitalization for asthm a was not associated with residential dis t ance from major state routes.

A study in San Diego County, CA compared the residential location of asthm a tic children with children having a non-respiratory dia gnosis within the stat e Medicaid system

. 303 

### Traffi c volum es on streets nearby the hom e were not as s o ciated with the prev alence of asthma. However, among asthmatic children, high street vol um es on the nearest street were associated with an increased annual frequency of m e dical visits for asthm a

.

In the only U.S. study exam ining adult resp iratory sym ptoms, Massachusetts veterans were evaluated for traffic-health relationships. 304 

### In the study, living w ithin 50 m of a major roadway was associated with increas ed reporting of persisten t wheeze. This trend held only for roads with at least 10,000 vehicles per day. Patients experiencing chronic phlegm we re also more likely to live within 50 m e ters of roads w ith at least 10,000 vehicl es per day. However, chronic cough was not associated with living near traffic.

The studies described above em ploy different exposure m e tr ics and health endpoints,

m a king evaluation difficult. However, num erous other studies from around the world also provide evidence for increased prevalence of respiratory sym p tom s a m ong people living near

m a jor roads. For a detailed listi ng, refer to the docket of this ru le. Taken together, these studies provide evidence that respiratory s y mp t o ms ma y b e a s s o c i a t ed w ith liv ing near m ajor roadways, particularly in children, upon whom the preponderance of studies have focused.

3.5.3 Development of Allergic Disease and Asthma

A significant num ber of studies have exam ined evidence of a role of traffic-generated pollution in the developm ent (e.g. new onset) of atopic illnesses (i.e ., hypersensitivity to alle rgens ) , s u ch as asth m a, allergic rhinitis, and derm atitis. A critical re view of evidence, prim arily generated in E u ropean stu d ies, was recently pub lished. 305 

### Overall, th e rev i e w concluded that there is som e lim ited evidence of an association betw een traffic-generated pollutants and asthm a incidence. More recent studies have also found significant associations between prevalent asthm a and liv ing near m ajor roads. 306 

### Toxicological evidence provides som e evidence that particles from diesel en gine exha ust m a y serve as adjuvants to IgE-m e diated

3-117

### immune responses. EPA’s Health Assessm ent Do cum e nt for Diesel Engine Exhaust addresses

m a ny of the toxicological studies on diesel exhaust. Howeve r, in comm unity epidem iology studies, the evidence rem ains tentat ive. The potential for these e ffects is not taken into account in the ben e fits analysis f o r PM becau se EPA’s va rious scien t ific advisors have argued that the literature is not strong enough to support a causal association.

3.5.4 Cardiovascular Effects

Cardiovascu l ar effects are currently s een as a po tentia lly im portan t set of m echanism s whereby PM 2. 5 

### m ay be leading to prem ature m o rtal ity. In Chapter 12, we estim ate the quantifiable benefits of PM-rela ted non-fatal acute m yocardial infarction and cardiovascular hospital adm i ssions. The studies described in Section 3.5.1 found highe r relative risks for cardiopulm onary causes of death.

In addition to cardiopulmonary m or t ality, so m e studies have looked at m orbidity. A

recen t study from Ger m a ny also found significan t increased o dds of coronary heart disease (CHD) in a cohort of approxim a tely 3400 participants. 307 

### Residents living within 150 m e ters of

m a jor roads were com p a r ed to those living furthe r 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 m e n, in participants under 60 year of age, and in never-smokers.

Several additional stud ies have provided suggestive evidence that exposure to fresh

em issions from traffic predispose people to advers e cardiovascular events. Studies have focused on both short-term variations in exposure, as well as long-term residential history. As discussed in the summary sec tion b e low, ther e are stresso rs in the roadway environm ent in addition to

am bient air pollutants (e.g., noise, anxiety) that also have an impact on cardiovascular activity. The potential role of thes e co-stresso rs has not been adequately investigated.

A study from Augsburg, Germ any i nterviewed survivors of myocardial infarction (MI) shortly after they had recove red to ex am ine am bient pollution and activities tha t m i ght predispose som e one to having a heart attack. 308 

### Survivors of MI were nearly three tim es 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 tim e s. Ambient air pollutants m easur ed in the hour prior to MI at a central site in the city were not associat ed with the risk of MI.

A study of healthy young North Carolina stat e patrolm e n conducted by EPA’s Office of Research and Development m onitored in-veh icle concentratio ns of PM 2. 5 

### , VOCs, and m e tals. 309



### In-vehicle P M 2. 5 

### concen trations were associated with alte red heart rate variabili ty, an indicator of cardiac stres s. In-veh icle concentrations were al so associated with increased concentrations of factors in the blood asso ciated with long-term cardiac risk, such as C-reactive protein, an indicator of inflamm a tion. Th is study provides infor m ation on possible m echanis m s by which cardiac stress could be induced by exposur es to traffic-gene rated air pollution.

Heart rate variability has also been m easured in a study of elderly residents of the Boston

area. 310 

### In the study, am bient PM 2. 5 

### was associated with change s consistent with reduced

3-118

### autonom ic control of the heart. Black carbon, of ten a m o re reliable index of traffic-related pollution, was also associated with these change s. In a related study, ST-segm e nt depression, a cardiographic indicator of cardiac ischem ia or inflamm a tion, was associated with black carbon levels as well. 311 

### These studies further docum ent a hypot hesized m echanism associated with motor vehicle em issions, but do not necessarily suggest effects inde pendent of those identified in our discussion of PM health effects.

3.5.5 Birth Outcomes

A few studies exam ining birth outcom es in populations living near m a jor traffic sources have found evidence of low birth weight, preter m birth, reduced head circumference and heart defects am ong children of m o thers living in close proxim ity to heavy traffic. Our discussion of PM health effects also quantitatively accounts for prem ature mortality effects in infants and qualitatively accounts fo r low birth weight.

One m easure of exposure to traffic-genera ted pollution is “distance-weighted traffic density,” where traffic volum e is treated as a m e asure th at “d ispers es” alo ng a Gaussian bellshaped curve evenly on both sides of a roadway. This approach captures so m e of the patterns of dispersion from line sources, but does not accoun t for m i crometeorology. One study from Los Angeles County, California em ployed this m e tric in a study of birth outcom e s for births from
1994 to 1996. The study showed associations be tween distance-weighted traffic volum e near

wom e n’s residences during pregnancy and prem ature birth and low bi rth weight in their babies. 312 

### The elevated ris ks occurred prim arily fo r m o thers whose third trim esters fell during fall or winter m onths.

The sam e researchers had conducted an ear lier study of births o ccurring between 1989 and 1993. In that study, consisting of ove r 125,000 births, exposures to ambient carbon monoxide (CO), an indicator of traffic polluti on, during the third trim ester were significantly associated w ith increased risk of low birth weight. 313 

### In another study, preterm birth was associated w ith am bient PM 10 

### 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 m onth of pregnancy. 315 

### The role of socioeco nom i c statu s and factors associated w ith it should be investigated in future study design.

Although the exposure m etrics em ployed in these studies are based on surrogate approaches to exposure estim ation, other resear chers have shown associations between New York m o thers’ m easured personal exposure to polycyclic arom atic hydrocarbons (P AHs) during pregnancy and an increased risk of low birth weight and size. 316 

### Subsequent follow-up of the

sam e birth cohort to age three found evidence of neurode velopm ental deficits associated with

m a ternal exposure to P AHs during pregnanc y, particularly in cognitive developm ent. 317



### Overall, although the number of studies exam in ing perinatal exposures is sm all, there is

som e evidence that expo sure to traffic-related pollutants m a y be associated with adverse birth

outcom e s, including low birth wei ght and preterm birth. However, given the variety of exposure

m e trics em ployed and the relatively lim ited geogra phic extent of studies, the generalization of

3-119

### the conclusions requires a better understanding of relevant sources, pollutants, susceptibility, and local factors.

3.5.6 Childhood Cancer

Several MS ATs are associated with cancer in adult populations. Ho wever, children have physical and biochem i cal differences that m a y af fect their susceptibility to and m e tabolism of
MSATs. Particularly in the first year or two after birth, infants’ liver enzym e profiles undergo rapid change. As such, children m a y respond to MSATs in different ways from adults. Som e evidence exists that children m a y fac e different can cer risks f r om adults as a result of exposure to certain MS ATs and other com ponents of m o tor vehicle exh a ust. EPA recently reco mmended default adjustm e nts to cancer risk estim ates fo r com pounds with a m u tagenic m ode of action to account for early life exposures in the Supplem e ntal Guidance for A ssessing Susceptibility from Early-L i fe Exposure to C a rcinogens. 318



### Evidence from hum an a nd anim al studies sugge sts that increases in childhood leukem i a

m ay be associated with in utero exposures to benzene and m a tern al and paternal exposure prior to conception. Furtherm ore, there is som e eviden ce that key changes related to the developm ent of childhood leukem i a occur in the developing fetus. 319



### In the last 15 years, several studies have evalu ated the asso ciati on between m a ternal or childhood residence near busy roads a nd the risk of cancer in children. Most studies to date have been ecological in nature, with several em ployi ng individual-level exposure estim ates within cohort designs. The studies em ployed widely varying exposure m e trics, including m odeled air quality, prox im ity to sour ces, and dis t ance-weigh t ed traf f i c vo lum es. Positive s t udie s tend to have used sm all population sizes, although one recent positiv e study used a larg e pop ulation. Due to differences in ages studied, study de sign, exposure metrics, and study location (e.g. Europe vs. U.S.), a system atic comparison between studies is difficult. A description of several key studies f r om this lite ratur e f o llo ws.

One early study from Colorado show ed signifi cant elevated risk of childhood leukem i a in children under age 15 associated with living n ear roads with higher traffic volum es. The strongest associations were with road s with at least 10,000 vehicles per day. 320 

### The study was reanalyzed u sing an approach to com b ine tra ffic volum e with residential distance from m a jor roads to assess “distance- weighted traffic volum e.” 321 

### The study found that the significant, monotonically increasing risks a ssociated with increased dist ance-weighted traffic volum e

.

NO 2 

### has been used as an indicator of traffi c em issions in som e studies; however, it is
im portant to note that NO 2 

### is not im plicated as causing can cer. For instance, a study used a dispersion model of NO 2 

### from traffic to conduct a case -control study of childhood cancer in Sweden. 322 

### The study found that in the highest-e xposed group, risk of any cancer was significantly elevated. Risks in the most-expos ed group were also elev ated for leukemia and central nervous system tum ors, but we re not statistically sign if icant.

These earlier studies were based on relatively small populations of ch ildren with cancer. In response, subsequent studies focused on either replicating the earlier studies or studying larger

3-120

### groups of children. A study in Los Angeles, Ca lifornia applied the sa m e distance-weighted traffic volum e 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 m o deled con centrations of benzene and NO 2 

### at home and the risk of leukem i a, central nervous system tumors, or total cancers. 324 

### However, the study did find a dose-dependent relationship betw een Hodgkin’s disease and modele d air pollution from traffic.

Several large studies were c onducted in California using a st atewide registry of cancer. These studies em ployed study sizes of several th ousand subjects. In one cross-sectional study, the potency-weighted sum of concentrations of 25 air toxics mode led using EPA’s ASPEN model was not associated with m obile sour ce em i ssions, but increas ed rates of childhood leukem i a were found when accountin g for all sou r ces of air to xics tog e ther, and for point sources

separa tely. 325 

### Another study from the sam e researcher s found that roadway density and traffic density within 500 m e ters of children’s hom es wa s not associated with risk of cancer. 326



### Most recently, a novel appro ach to assessing childhood leukem i a in relation to early life

exposures w a s em ployed in the United Kingdom

. The study exam ined all children dying of cancer betw een 1955 and 1980, consisting of over 22,000 cases. Birth and death addresses of children with cancer who m oved before death were com pared with regard to proxim ity to nearby sources and em issions of specific ch em icals. 327 

### An excess of births ne ar sources, relative to deaths, was used to indicate sour ces in early life associated with greatest cancer. Greater risks were associated with birth addresses within 300 m e ters of high em issions of benzene, 1,3­ butadiene, NO
x 

### , PM 10 

### , dioxins, and benzo[a]pyrene. In a ddition, births within 1.0 km of bus stations, hos pita ls, f r eigh t te rm inals, r ailways, and oil ins t a lla tions were as s o ciated with elevated risk. Overall, locations with the highest em issions of 1,3-butadiene and carbon m onoxide showed the greatest risk.

In summ ary, the la ck of consistency in re su lts b etween large s t udies and th e m u ltiplic ity of study designs m a kes it difficult to draw fi rm c onclusions. Epidem iologic m e thods for detection of childhood cancer risks m a y lack sufficient power to detect risks with precision. However, given the well-establ ished carcinogenicity of benz ene and 1,3-butadiene in the toxicological and occupational ep idem iologic literature, and data suggesting exposure to benzene prior to conception and in utero can lead to increased risk of childhood leukem ia, the potential for public health con cern is pres ent. The st andards proposed in this rule will reduce s uch

exposures.

3.5.7 Summary of Near-Roadw ay Health Studies

Taken together, the available studies of health effects in residents near m a jor roadways suggest a possible public health concern. These studies’ exposure m e trics are reflective of a

com p lex m i xture f r om traf f i c, and the standa rds will r educe a broad r ang e of pollutan t s pres ent in higher concentrations near roadways. It is unc lear to what extent these h ealth effects are attributable to PM versus other com ponents of the com plex m i xture. Note that the benefits associated w ith the direct PM reductions from the cold tem p erature v ehicle standards are presented in Chapter 12 of this RIA.

3-121

### 3.5.8 Si z e and Characteristics of Popul ations Living near Major Roads

In asses sing the public h ealth im plicati ons of near-roadway h ealth concerns, som e understanding of the population liv ing near m a jor roads is requi red. Those living near m a jor roadways are a subpopulation of th e total population included in quant itative analysis, and to the extent that there m a y be additi onal exposures and health effects not captured in analyses for the total population, we enum erate the size and charac teristics of the subpopu lation. A study of the populations nationally using geographi c information system s indicated that m ore than half of the population lives within 200 m e ters of a m a jor road (see file USbytract.txt in the docket for this rule). s 

### It should be noted that th is analysis relied on the Cens us Bureau definition of a m a jor road, which is not based on traffic volum e

. Thus, som e of the roads des i g n ated as
"m ajor" m a y carry a low volum e of traffic. Deta iled analyses of data were conducted in three states, Colorado, Georgia, and New York. In Colorado, 22% live within 75 m e ters of a m a jor road, while an additional 33% live between 75 and 200 m e ters of m a jor roads. In Georgia, the respective percentages are 17% living within 75 m e ters and an additional 24% living between 75 and 200 m e t e rs. In New York, the percentages are 31% and 36%. 328



### To date, the only source of national data on populations living in close proxim ity to m a jor transpo r ta tio n sources is the Am erican Hous ing Survey, conducted by the U.S. Census
Bureau. 329 

### This study characterizes the propertie s and neighborhood charac teristics of housing units throughout the U.S. According to the Census Bureau’s summaries of the 2003 survey,

am ong appr oxim a tely 120,777,000 housing units in the nation, 15,182,000 were within 300 feet of a “4-or-m ore-lane hig h way, railro ad, or airpor t.” This con stitu tes 12.6 % of total U

. S. housing units. A sim ple assum ption that the U.S. popula tion is uniform ly distributed am ong all types of housing lead s to the conclusion th at approxim a tely 37.4 m illion people live in what m i ght be considered a “m obile source hot spot.”

According to the Am erican Housing Survey’s summary tables, occupied housing units in central cities are 35% m ore like ly to be close to major transp ortation sources than housing units in suburban areas. 330 

### Furtherm ore, nationally, housing un its that are renter-occupied are 2.3 tim es m o re likely to be close to m ajor transporta tion sources, com pared to housing units that are owner-occupied. In the 2003 Am erican Housing Survey, m e dian household incom e for owneroccupied units was $52,803, while only $26,983 for re nter-occupied units. These statistics im ply that those houses sited near m a jo r transportation sources are likely to be lower in inco m e than houses not located near m a jor transportation sources.

A few popul ation-based epidem iology studies have also exam ined whet her discrete groups of people live close to m a jor roadways. In one study of veterans living in southeastern Massachusetts, 23% lived within 50 m eters of a “m ajor road,” 33% lived within 100 m e ters, and 51% within 200 m e ters. 331 

### In exam ining traffic volum e s, 13% lived within 50 m e ters of a road with annual average daily traffic of 10,000 vehicl es or m ore, while other distances were not analyzed.

In another study using 150 m e ters as a definition of “nea r” a road, 2.3% of California

s M a j o r r o ads ar e de fi ne d as t h ose roa d s de fi n e d by t h e U.S. Census as one of the following: “lim ited access hi ghwa y,” “highway,” “m ajor roa d (prim a ry , secondary and c o nnec ting roa d s ),” or “ram p.” 

### 3-122

### public schools were found to be near a road w ith 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 enrollm e nt, respectively. In th at study, traf fic exposure increased, the fractions of school populations comprised of black and Hi spanic students also increased, as did the fraction of children in governm e nt-subsidized m e al program s.

Another study in California defined the i ssue differently, exam ining the child population living in census block groups and traffic density. 333 

### The study found that approxim a tely 3% of the state child population resided in the highest tr affic density census tracts. Furtherm ore, block groups with lower incom e were m ore likely to ha ve high traffic density. Children of color were more likely than white children to live in high traffic density areas.

In summ ary, a substantial fraction of the U.S. population lives within approxim a tely 200

m eters of major ro ads.

3-123

# Appendix 3A: Influence of Emissi ons in Attached Garages on
Indoor Air Benzene Concentrations and Human Exposur e

Introductio n

Measurem ent studies provide strong evidence that VOC sources in attached garages can significan tly increase V O C c oncentrations inside hom e s. 334 

### Prelim inary analyses of data from a pilot study for the National Hum a n Exposure A ssessm ent Survey (NHEXAS) in Arizona also found indoor concentrations of m obile source -related VOC com pounds si gnificantly higher in hom e s with attached garages than in hom e s without them

. 335 

### This population-based exposure study included m easurem ents from 187 hom e s. A study in 50 Alaska residences found that in
hom e s with attached garages, indoor benzene levels averaged 70.8 μ g/m 3 

### , while in ho m es without attached garages, concentrations averaged 8.6 μ g/m 3



### . 336 

### Multip le f a ctors, includ ing house architecture, ventilation design, garage co nfiguration, and clim ate can all play roles as well. National-scale air toxics mode ling efforts, such as those discussed in RIA Section 3.2.1.2,

em ploy Gaussian dispersion m odels in com b inati on with human exposure m odels to calculate the concentrations of air toxics in various m i croenvironm ents. Exposure m o dels calculate an average exposure resulting from the move m e nt of a sim u lated population through a tim e -activity patte rn tha t brings th em into con t act w ith air in the various m i croenvironm ents. At this po int, the NATA and the ana l yses perform e d for this rulem a king have only included exposures from outdoor sources. Although the HAPEM6 exposure m odel is capable of addressing indoor sources, m ore thorough analyses of the prevalence and use of e m ission sources within attached garages are required to develop quantitative estim ates of model param e ters to address attached garage contri butions across the U.S. population. This appendix addresses the poten tial im pact of all benzene so urces with in an attached garage on residential indoor air quality.

Methods

Calculation of Within-g arage Source Emission Rate

Em ission rates for indoor sources of VOCs can be derived by several m e thods. Most accurately, the actual emission rate s of an indoor VOC source can be m easured th rou gh the use of

a Sealed Housing f o r Evaporative Determ inati on (SHED). However, test cond itio ns m u st be representative of real w orld ap plications. Short of SHED-based m easurem ent, several surrog ate approaches m a y be e m ployed. For evaporative lo sses from a sealed container, the change in weight of a container over tim e m a y be used to calculate a total m ass loss rate, which can be

assum e d to be in the form of VOC. Alterna tive l y, if the air c oncentr ation s and ventila tion conditions o f a defined indoor space are known, m ass balance equations can be em ployed to derive a “virtual” em ission rate fo r a ll sour ces within th e spac e. This appendix em ploys the latter approach in calculating source em ission factors. The general approach of a mass balan ce equation is to calcu late the ch ange in m a ss over a given tim e , accounting for the m a ss of a pollutant transported into a space, the m a ss of pollutant transported out of a space, the em ission rate of a source w ithin the space, and the decay of any pollutants

3-124

### within th e s pace, which can be treated as a first- o r der decay. A sim p le space like a garage can b e treated as a single zone. The di f f erentia l equatio n repres enting this m ass balance is a s f o llows:
dM

t
, i 

### dV dM i 

### dV (1) = C k +

− C dt o 

### dt dt i 

### dt Here, dM t,i



### /d t represents the rate of change of total indoor m a ss, C i 

### is the indoor concentration,
C

o 

### is the outdoor concentration, dV/d t is the volum etric air flow through the space, k is the penetration f r action indicating the proportion of mass that passes through the wall of the

com p art m ent, and dM i 

### /d t repres ents the m ass em ission ra te in side th e spac e. Note tha t all a i r entering the garage is assum e d to enter from outdoors.
Assum i ng steady-state conditions, dM t,i 

### /dt assu m es the value of zero, meaning th at the concentration in the garage does not change over tim e. Algebrai c ally, this allows th e e quation above to be represented as:

(2) 

### ⎜ ⎛ dV ⎟ ⎞ 

# ( C i 

### − C

o 

### k 

# )

= dM i 

### ⎝ dt ⎠ dt In other words, the indoor source term s can be calculated if the volum etric flow through the space and co ncentration s indoor and outdoor are know n. Any gradien t in concentration between indoor and outdoor concentrations is explained by i ndoor sources and the fraction of m a ss that does not penetrate from indoors to outdoors. The volum etric f l ow can be calcu late d by m u ltiplying the vo lum e of the space by the

num b er of tim es per hour that the air with in th e s p ace is turned over. As s u ch:

(3) 

### dV

= α V dt Here, 

### α is the “air exchange rate,” expressed in air changes per hour (ACH). Com bining equation s (2 ) and (3), th e m ass em ission rate is represented as:

(4) α V 

# ( C i 

### − C

o 

### k 

# )

= dM i 

### dt


### A recent s t u dy in Ann Arbor, MI m easured th e air exchange rates and the in-garage an d outdoor concentrations of VOCs need ed to perform these calculations. 337 

### The hom es i n the study were based on a convenience sam ple, and so m a y 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 thre e walls adjoin in g a resid e nce. The distributions of garage benzene concentratio n and ACH are show n in Figure 3A-1. The distri butions of each were not significantly different from l ognorm a l, judging by the Kolm ogorov-Sm irnov Z statistic. Values of k, the penetration factor, ar e dependent on the physical pathways through which air p a sses in to a g arage, a s well as the pres ence and ch em i cal composition of any insula ting m ater i al throu gh which air passes. In the case of garages, the infrequency of insulated garages and the low reactivity of ben zene jus tif ie s the assum p tion th at k= 1. 338



### These data f r om the Ann Arbor, MI study were used to solve equa tion (2) to derive a distribution of benzene m a ss e m ission rates in e ach garag e in the study, based on variability in

m easure m ents of outdoor concentrations. Equa tion 4 was implem ented using a Microsoft Excel spreadshe e t with the @R isk probab i lis tic sim ulation add-in (version 4.5). 339 

### Monte Carlo

sam p ling was used for all term s in deriving the em ission rates. As described below, this distribution can be us ed to evaluate the effect of various fuel control m e a sures on indoor ben zene concentrations. A single l ognorm a l distribution was used to

3-125

### repres ent C o 

### in equation 4, based on othe r studies of a m bient air, which have found that m a ny pollutants’ concentrations are lognorm ally distributed.



### Calculation of Garage C ontributions to Indoor A i r

In the sam e way that a m ass balance calcu lati on can be used to cal cula te em ission rates for sources within garages, a m a ss balance e quation can be used to estim ate the additional concentratio n in a hom e that will occur as a resu lt of elevated concentratio ns in the g a rage. However, unlike the garage case, it is not valid to assum e that all air en tering the ho m e com es directly from outdoors. Recent stud ies have prov ided ind i cations that ov e r m u ltiple se quentia l day s, variab ility in within-hom e benzene co ncentration is relativ el y sm all. A recent study fro m Ann Arbor, MI found a coefficient of variati on (CO V ) of 4.6% for benzene. 340 

### Furtherm ore, recent data obtained by EPA through the Environm ental and Occupational Health Sciences Institute (EOHSI) on hom es in the Elizabe t h, NJ area ind i cates no s i g n if icant d i f f erence s in w ithin -hom e concentratio ns at a 95% confidence level. t, 341 

### These data are prelim inary, an d analyses are still in progress. Given the fraction of air entering the ho m e throug h the hom e-garage interface, the appropriate m a ss balance equation for a single -com partm e nt (e.g. well-mixed) hom e can be

repres ented as such:

(5) 

### dM

t
, i



### = kC

o 

### ( 1



### − f
g 

### ) 

### dV + kC
g 

### f
g 

### dV

− C i 

### dV

dt dt dt dt Here, 

### C i 

### is the in-house concentration, 

### C
o 

### is the outdoor concentration, 

### C g 

### is the con centr ation in the garage, 

### dV/dt is the volum etric air flow through the house, and 

### f g 

### is the f r action of air en ter i ng the hom e from the garage. One assumption m a de he re is tha t the pene tra t ion factor for the air moving thro ugh the house-garage in terface is th e sam e as air moving thro ugh the house-outdoors interface. Reactive decay is assum e d to be ze ro. Such m ass balance equations are stan dard approaches in environm ental science and engi neering, and are frequently f ound in textbooks on these sub j ects. 342 

### Again assum i ng steady-state conditions, dM t,i 

### /d t = 0, the eq uation abov e sim p lif ies to:

(6) 

### C i 

### = kC

o 

### ( 1

− f
g 

### ) + kC
g 

### f
g 

### Or m o re simply, the indo or concen tration unde r steady state conditions is proportional to the fraction of air entering the house through the garage. Figure 3A-2 is a contour plot illustrating th e range of average indoor air concentrations that could plausibly arise gi ven a range of values of C g 

### and f g 

### , with a background concentration of zero. However, Figure 3A-2 does not answer th e question of what the li kely indoor air values are in a sample of real h o m es. The text below describes procedures and results of a s m all-scale m odeling study.

Modeling Approach

t In that st udy, one air sam p le was obtai ned in the room ad jacent to an attached ga ra ge in each hom e and anothe r was obt ai ne d i n a n ot he r l o cat i o n. The Ne w Je rs ey Depa rt m e nt o f E nvi ro nm ent a l Pr ot ect i o n and EP A pr o v i d ed j o i n t f und ing for th e stud y. A t w o-sid e d p a ir ed t

- test w a s app lied to d a ta ob tain ed f r o m 3 6 h o m es ov er appr oxi m a tel y 2 4 h our s. 

### 3-126

### All m odeling analyses employed Equation 6 in a Microsoft E x cel spreads h eet with th e @Risk probabilistic m o deling add-in was the so ftware em pl oyed in all modeling analyses.
W h ere appropriate, each of the term s in Equation 6 was treated as a rando m variable represen ted as either a p aram etric distribu tion o r as an em pirical distribution ba sed on m easured data. Often, in employing data obtai ned from m ore than one study, com bining data into a single d i stribution was n o t justif ied o n a priori grounds. In ventilation studies, am bient conditions such as tem p erature and geography can substantially affect air flow patterns and building constructions. For inst an ce, residen t ial air ex change rates d i ffer significan tly between regions with substantia lly different clim ates. 343 

### Furtherm ore, based on the lim ited num b er of studies available, com b ining data f r o m m u ltiple s t udies into a single d ata set had the p o tential to apply de facto weights to data, pote n tia lly sh if ting the f itted m odel para m eters away f r om truly “representative” dis t ribu tions. Another consideration is the pot en tial for indepen dence of the f g 

### and C g 

### variab les. There is no a priori reason why the “leakiness” of the ho use-ga rage envelope should be related to the concentratio n of benzene in the garag e

. Because of these consid erations, data on f g 

### or C g 

### f r om studies in different areas were n o t for m ally co mbined. Rather, d i stribu tions fit se parately to data from each study were u sed to develop several m odel “scenarios.” As described be low, four different studies provided data for
C

g 

### and three different studies provided data for f g 

### . As such, a m i ni m u m of 12 (3 x 4) scenarios were needed to represen t the to tality of available data. For each scenario m odeled, @Risk sam p led from each distribution 20,0 00 tim es using a proprietary Latin Hypercube sam pling fra m ewor k. The large num ber of sam ples and Latin Hypercube strategy were em ployed to ensure that m odeled concentration distributions achieved stability. Lastly, for com parison to the current appro aches for exposure m odeling, the following equation was used, paralleling the approach taken by HAPEM5 with no garage em issions: (7) C i 

### = kC

o



### Data for Populating Model Parameters

Fraction of Air Entering Hom e through the Garage (f g 

### )

Several studies have exam ined the fraction of air entering the hom e from t h e garage. Except for o n e, all of these studies to ok place in northern states and Canada, where h o m e s are built with m o re insu latio n

. A recent study of a set of hom e s in Ontario, C a nada found that approxim ately 13% of the air enteri n g the hom e cam e from the garage. 344 

### One study from Minnesota found that in newer hom es, houses bu ilt in the y e ar 1994 had an average of 17.4% of total air leakage com i ng 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

recen t stud ies have em ployed perfluo r ocarbon tr acer (PFT) gases to estim ate air transp ort between different “zones” of houses with attached garages. A r ecent stu dy by Isbell et al. (2005) based in Fairbanks, Alaska found that in a m odern air-tight Alaskan hom e ventilated with an airto-air heat exchanger, 12.2% of the air ente ring a hom e entered through the garage, while 47.4% of the air entering an older hom e ventilated passively by structural defects cam e through the attached garage. 346 

### Another study of a hom e in Ann Arbor, Michigan built in 1962 found that

3-127

### 16% of the air entering the hom e originated in th e garage. 347 

### In a m o re recent study fro m Ann Arbor, inves tigators dep l oyed PFT tracers in 15 h o m e s and calculated th e fraction of air en tering each hom e through an attached garag e , with an av erage of 6.5 ±5.3% of th e air enterin g through the garage. 348 

### From these studies, it is apparent that across hom es, the fractio n of air entering through th e garage is hig h ly variable, m a king it n ecessary to acknowledge significan t uncertainties in char acte r izing “ t ypic al” inf i ltr ation patte rns.

Benzene Concentration s in Garage Air (C g 

### )

Four sources of in-garage concentration data are available in the form at relevan t for steady-state modeling over extended periods of tim e. First, th ere is the s t u dy by Batterm an et al. (2005), in w hich average garage concentrations of benzene were m easured over a period of four days in each of 15 hom e s using passive sam pli ng badges. The average garage concentration reported was 36.6 μ g/m 3 

### , with a standard deviation of 38.5 μ g/m 3



### . Second, a study in Alaska by George et al. ( 2002) m easured benzene concentrations in 28 Alaska homes and 48 garages with passive diffusion badges. 349 

### One disadvantage of this study is the relatively high detecti on lim it for benzene, 7 ppb (22 μ g/m 3 

### ). As a result, m any of the data available are based on a reported value of 50% of the detection lim it. In the Alaska study, ingarage benzene concentrations averaged 103 μ g/m 3 

### , and the standard deviation was 135 μ g/m 3



### . The study included concurrent in-hom e measurem ent of benzene in hom es with attached garages, allowing evaluation of th e modeled indoor concen trations. However, it is not apparent that this study underwent scientific peer review. A third study in one New Jersey hom e also ev aluated garage and indoor benzene, as part of an investigation into in-garag e emissions of vehicles fueled with m e thanol blends. 350 

### Only one hom e was sam p led, but it was s a m p led m u ltiple tim es inside the g ara ge and at m u ltip le locations inside the residence. A fourth st udy from Fairbanks, Alaska conducted m easurem ents in 12-hour periods on four separate days in two houses in two seasons, summer and winter. 351 

### The study o b tained two daily m easurem ents of be nzene concentration within each garage over a 12-hour sam pling period. One hom e was a m oder n, well-insulated hom e with an air-to-air heat exchanger f o r ventila tio n. The other was an olde r hom e ventilated p assiv ely by structural def ec t s in the bu ilding envelope. Because of the large dif f erences in concentration s between h o m e s and seasons, data from each hom e-seaso n com b ination was treated as a sep a rate distribution within the indoor air m odel (Equation 6 in Excel/@Risk). Treating these da ta as separate distributions increased the num ber of mode led “scenarios” to 21 (3 f g 

### x 7 C g 

### ).

3-128

### Penetration Factor (k)

The values of k in this case were obtaine d from the HAPEM5 user’s m a nual, using the PEN-1 factor, representing the fr action of benzene from outdoor ai r penetrating indoors. The values in HAPEM5 are presented as a distribu tio n th at assigns a 2/3 weight to the value 0.8 and a 1/3 weight to the value 1. These estim a tes ar e based on a comprehensive review of indoor and outdoor air quality studies.

Outdoor Ambient Concentration (C o 

### )

The 1999 National Air Toxics Assessm ent (NATA) provided am bient concentration estim ates for every census tract in the U.S. Fo r this m odeling exercise, a lognorm a l distribution was f it to th ese data.

Results



### With in-Gara g e Emission Rates

Equation 4 was used with Monte Carlo sam p ling to calcula te a distribu tio n of em ission factors for each hom e, based on the variability in outdoor concen trations reported in Batterm an et al. (2005). As shown in Figure 3-A3, the with in-garage variation was a very sm all com ponent of

overall va riab ility co m p ared to b etween-ga r a g e variation. This f i ndin g im plies th at the f ac t or s in individual garages, such as storage of vehi cles, nonroad equipm ent, and fuels, have a m a jor effect on in-garage con c entrations. In aggregate, the m ean em ission rate for all garages sam p led fell along a lognorm al distribution (p > 0.05). Th e m ean e m ission rate was 3049 μ g/hr (73 m g /day), with a standard
deviation of 4220 μ g/hr (101 m g/day). To evaluate the plausibility of these steady-state em ission f actors, known em ission factors for o t her em issio n sources w ere evaluate d. The California Air Resources Board (CARB) conducted a study of em i ssions from portable fu el containers, finding that volum e-specific

em issions ra tes f o r to tal VOC due to evapora ti on and perm eation was 0.37 g/gal-day. Assum i ng
an averag e fuel container volum e of two gallons, the average em ission factor per can would be
0.74 g VOC/day. To evaluate the derived em i ssion rates relative to CARB’s measurem ents , a benzene f u el vapor pressure fraction of 0.5-1% was assu m e d, based on MOBILE6.2 e vaporative em ission factors. Giv en that assu mption, the average b en zene em ission rate from CARB’s study is 3.7-7.4

m g /day. This valu e is in the lower ra nge of e m ission rates shown in Figure 3A-3. This

com p arison suggests that em issions due to pe rm eation and evaporation from portable fuel containers m ay be a relatively sm all fraction of overall garage benzene. Subsequently, one additional study used perfluorocarbon tracers (PFT) and VOC

m easure m ent in two Fairbanks, Alaska hom es to estim ate two garages ’ “s ource streng ths” for benzene. 352 

### For a new, en ergy efficien t “tight” ho m e w ith an air-to-air h e at exchang er, m edian

garage em ission estim a tes for benzene were 21 mg/h in summ er and 14 mg/h in winter. In an older hom e with passive ventilation due to struct ural defects, m e dian ben zene source streng ths were calculated at 40 and 22 m g/h in summer a nd winter, res p ectiv ely. T h ese valu es are substantially higher than those calculated based on Batterm an et al. (2005). However, the

3-129

### difference may be attributable to higher fuel benzene in Fair banks than in Michigan. Study design m a y also play a key role. In the Fair banks study, the m easure m ent periods were 12 hours each in du ration. In the Mich ig an study, m easurem ent periods lasted four days each. The Michigan study’s longer duration m a y have allowe d for a broader range of em issions activities than the Fairbanks study.

Garage Contributions to Benzene in Indoor Air

Figures 3A-4 to 3A-8 display the results of @Risk sim ulations of indoor air. Each figure

repres ents th e m odeled outputs as cu mulative p r o b ability distributions. In the leg end o f each figure, the label of each distribution describ e s its f g 

### and C g 

### so urces. For instance, “George et al. (2002) / Fugler FG Ci” indicates a distribution using garage concen tration data from George et al. (2002) and f g 

### data from Fugler et al. Figure 3A-4 presents the output of Equa tion 6, a daily aver age indoor benzene concentration including contributio ns from outdoor air and from att ached garages. As noted in the “Methods” section of this appendix, it was ne c essary to run a larg e num ber of scena r ios to account for different combinations of f g 

### and C g 

### data sources. The figure depicts results using studies that contain C g 

### d ata f r om m u ltip le hom es as bold solid lines, while the m odel sim u lations based on studies that em ploy C g 

### data from only one hom e are shown in dashed lines. As indica ted in the f i gure, there is no m ajor d i f f erence in th e C i 

### distributions predicted by using C g 

### data f r om m u ltip le hom es or by usin g C g 

### m easured from a single hom e. The average m odeled
indoor benzene concentrations ranged from 2.9 to 16.4 μ g/m 3



### . For com p arison, Figure 3A-5 presen ts cum u la tiv e distribu tio ns of the observed results from several studies that m easur ed indoor air concentrations in hom e s with attached garages. Schlapia and Morris (1998) m easured integrat ed 24-hour benzene con centrations inside 91 hom e s with attached garages in An chorage, Alaska between 1994 and 1996. 353 

### George et al. (2002) reported average benzene concentrations in 36 hom e s in Anchorage, Alaska, but no distributional data. Mentioned above, Isbell et al. (2005) also m easured integrated 12-hour benzene in two seasons in one m oder n air-tight h o m e (“Hom e V” in Figu res 3A-4 to 3A-8) and one older passively-ventilated hom e (“Ho m e NV” in Figures 3 A -4 to 3A-8). 354 

### Both homes were located in Fairbanks, Alaska. Batterm an et al. (2006) m easured indoor air benzene concentrations in 15 hom e s in southeastern Mi chigan over f our-day sam pling periods throughout spring and summ er of 2005. 355 

### Lastly, W e isel (2006) conduc ted a study of indoor air in 21 hom e s in Union County, NJ between April 2005 a nd January 2006. One monitor in each hom e was sited in the room adjacent to the garage, wh ile another was located in another p art of the house. 356



### Com paring Figures 3A-4 and 3A-5 , it is apparent that the di stributions of m odeled indoor air con centrations of benzene ar e very sim ilar to those observed in m onitoring stud ie s. Both
f i gures ind i c ate th at th er e is subs tantial va riab ility in concentrations betw een hom es and between studies. Figure 3A-6 presents the m ean concentra tions from m odeling scenarios and from monitoring studies. In general, th e range of m ean concentrations is close to the values m onitored in the indoor air studies. Notabl e exceptions are the indoor air values by G e orge et al. (2002), the winter data from the passively-ventilated “N V” hom e from Isbell et al. (2005), and by Schlapia and Morris (1998)

. All of these studie s took place in Alaska, which m a y have uniquely high benzene fuel levels or housing architectures that crea te higher garage air infiltration indoors.

3-130

### Of particular note, all of these studies included substantial num b ers of hom e s with “tuck-under” garages where one or m o re room s of a house are situated above a garage. Schlapia and Morris (1998) reported a very high average value that was not m a tched by the “average” conditions of any other run. It is notable tha t th is high value is the ave r age across 91 ho m e s with attached

garages. Another consistent trend shown in Fi gure 3A-6 is that scenarios em ploying f g 

### data from
Batterm an et al. (2006) produced consistently lower average benzene concentrations than scenarios employing other sources. This tr end is attribu t able to the lower average f g 

### reported in
Batterm an et al. (2006), 6.5%, as compared to va lues found in Sheltersource (11.7%) and Fugler et al. (13.6%). It is un cle ar whethe r the studies m easuring C g 

### , f g 

### , and C i 

### constitute a rep r e senta tive

sam p le of hom es. In general Alaskan studies repo rt high er co ncentr ation s , but not con sisten tly. The relatively greater prevalence of hom e s with “tuck under” garages in som e Alaskan studies

m a y explain this discrepancy.
In com parison to the values reported in Figur es 3A-4 and 3A-5, indoor air concentrations calcu lated w ith th e def ault C i 

### = kC
o 

### approach, sim ilar to that em ployed in the national-scale modeling for this rule, averaged 1.2 μ g/m 3



### . Overall, m odeled concentrations presented he re appear to provide a c r ed ible e s tim ate of indoor benzene concentration in hom es with attached garages. However, it is unclear whether the hom es included in the studies employed he rein m a y be considered “representative. ”

Implica t ion s



### Effect on Exposures Nationwide

In calculating the hypothetical eff ect of attached garage on na tional estimates of chronic, tim e-weighted average (TW A ) hum a n exposure, pr ecise estim a tes are not possible. As noted previously, the extent to which av ailable studies of indoor air of hom e s wi th attached garages is representative of the enti re population of such hom es is unclear

. Furthermore, the distribution of housing stock by clim ate and m eteorology is no t well understood. Howe ver, despite these
lim itations, a bounding exercise is still feas ible. One sim ple approach for such a bounding exercise is determ ined by the following equation: (8) E g 

### = C i,g 

### * P g 

### * T g 

### Here, E g 

### represents the national average exposur e to benzene in ai r attributable to attached garages. C i,g 

### represents the average ind oor concen tr ation a t tribu t able to an a ttach ed

garage, P g 

### represents the fraction of the population livi ng in a hom e with an attached garage, and
T

g 

### represents the tim e spent in a hom e with an attached garag e

. C i,g 

### is derived from Equa tion 6, and can be derived by setting the outdoor concentration

term ( C
o 

### ) to zero. An estim a te of the attached gara ge contribution to indoo r air can be m ade for studies with only indoor m easur e m ents as well. T his can be accom plished by substituting ASPEN concentration estim ates for the county in which each study took p l ace. For Eq uation 6,
C

o 

### estim ates from NATA for each census tract in the relevant county w ere assem b led into a lognorm a l distribution. W ith this data and the ot her assum ptions of Equation 6, an estim a te of
C i,g 

### could be derived from the m easurem ent studies.

3-131

### To estim ate P g 

### , an estimate of the national fraction of hom e s with attached garages is required. T he Residential Energy C onsum ption Survey (RECS), run by the U.S. Energy Inform ation Adm i nistration, provides an estim a te of the fraction of hom e s with attached

garages. 357 

### RECS estim ates a to tal of 107.0 m illio n housing u n its nationally, 37.1 m illion (34.7%) of which are hom e s with attached garage s. Assum i ng that the population is uniform ly distributed across housing units allows this figure to se rve as an estim a te of P g 

### . Inform ation on the fraction of tim e s p ent in a residence ( T g 

### ) is required to determ ine how the m i croenvironm ental concentration in hom es with attached garage s affects overall tim eweighted exposure concentrations. As cited in EPA’s Exposure Factors Handbook, the average person studied by the National Hum a n Activity Pa ttern Survey (NHAPS) spent 1001.39 m i nutes (16.68 hours) per day indoors with in any room of a residence. 358



### Results of model sim u lations using E quation 7 are shown in Figure 3A-7. As before, the results of each com bination of C g 

### and f g 

### data sou r ce are shown. Fo r each study, the legend lists the source for both C g 

### and f g 

### data. As described above, the estim ates C i,g 

### derived from indoor air

m easurem ents are also p r esented in Figure 3A-7. In the legen d of Figure 3A-6, these studies are denoted by the term “Direct Ci Meas ure.” As shown, there tends to be a greater deg r ee of

agreem ent between m o deling s cenarios for lower concentratio n estim ates, but less agreem ent for higher concentration estim a tes. As described above, it is unclear to what the extent to which the hom es studied for benzene related to attach ed garages are rep r es entative of homes nationally. As such, in summarizin g the scen arios, several d i fferent a pproaches to “averaging ” across scen arios are presented here. Figure 3A-8 shows the results of th ese d i fferent averagin g scenarios. In the “All Data” distribution show n in the figure, all scenar ios are averaged togeth er. In the “W eighted Average” distribu tion, w eights are eq ual to the nu mber of hom e s included in each s t u dy. In the “Model Only” distribution, only scenarios involving m odeling C i 

### are sh own. In the “Measure Only” distribution, only t hose studies in which C i 

### was m easured directly are shown. In the “AK Only” distribution, only scenarios employing Alaskan C g 

### or f g 

### studies are shown. In the “NonAK Only” distribution, only scenario s exclud ing Alaskan C g 

### or f g 

### data are shown. These scenarios are intend ed to span a ran g e of estim ates f o r the n ationa l es tim ate. The average concentrations from these “summ a r y scenarios” are shown in Table 3A-1. As shown in Table 3A-1 and in Figure 3A-8, scenarios em ploying only measured indoor data resulted in higher predicted benzene TW A expos ure concentrations than the studies employing only m odeling. Scenarios em ploying Alaskan data result in higher benzene concentrations than scenarios excluding Alaskan data. Also weightin g scenarios by the num ber of hom es resulted in higher benzene concentrations. Accordingly, the national average T W A exposur e concentration attributable to attached garages is estim ated to be 1.2 – 6.6 μ g/m 3



### . This range is inten d ed to span possible v alues of average TWA exposure from attached garages, give n currently available infor m ation. The actual average TWA exposure concentratio n due to attached garages could be outside of this range. Because of lim ited inform ation on the representativeness of th e hom es studied, a m o re precis e “central estim a te” is not appropria te at th is tim e. The width o f the range, with the upp er end being 5.5 tim es the lower end, is an indicator of the m agnitude of uncertainty in the estim ate. It is not a conf idence inte r v al in the tra d ition al se n s e. As m o re data becom e available, m o re precis e es tim ates will hopefully emerge.
In com p arison, the national average expos ure concentration of census tract m e dian exposure concentrations in this rule is estim ated at 1.4 μ g/m 3 

### for calendar year 1999.

3-132

### Accordingly, if the attached garage exposure cont ribution is considered, th e estim ate of national

average exp o sure to ben zene rises to 2.6 – 8.0 μ g/m 3 

### , corresponding to an increase of 85-471%.

Effects of E m ission Standards

Several lim itations prev ent pre cise e stim ation of the ef f ect of the standard s in this ru le on garage-related exposures. First, cold tem p erat ure vehicle ign ition and ev aporative vehicle, engine, and fuel contain er em issions can occur eith er in a garage or outdo ors. Second, detailed tracking of the tim e during which people are insi de a house during cold vehicle starting or hot vehicle soak ing, when a m a jority of benzene em issions are likely to occu r, is lim ited. However, a bounding exercise can provide som e esti m a tes as to the effect of the standards in this rule. First, assum i ng full m i xing and steady-state c onditions, concentrations within a garage is estim able 359 

### as : (9) C g 

### = ( dM i 

### /d t ) / 

### α V Here, the term s are sim i l a r to Equations 1-7. Given a change in the m ass benzen e em ission rates from vehi cle co ld tem p eratu r e ignition, fuel evaporation from vehicles, engines, and fuel co ntainers, an estim ate of a change in
C

g 

### is feasible. Table 2.2

- 52 of the RIA displa ys the em ission reductions attributab le to each

program

. By splitting the em ission reductions into evaporative and exhaust em issions and applying several sim ple assum ptions about where em issions occur (in garage vs. outdoors), the fraction of em ission reductions occurring within attached garages can be estim ated. This estim ate is calculated by assum i ng ranges of valu es for the fraction of evaporative and exhaust

em issions from each pro g ram that occur with in an attached g arage. u 

### As such, while the tota l benzene m o bile source and PFC e m i ssion reductions occurring as a result of the rule in 2030 are 37% less than the projected em issions without co ntrols (Table 2.2-52 of the RIA), emissions inside attach ed garages are redu ced b y an estim a ted 43-44%. Applying this fraction to E quation 8 and Equation 7, for th e “average” scenarios m odeled presented in Table 1, this am ounts to a national average exposure reductio n of approxim a tely 0.5

– 2.6 μ g/m 3



### .

Limita tions



### As apparent in the wide range of “scena rio” averages, there rem a ins considerable uncertainty in ascertaining the true magnitude of attached garage exposure contributions nationally. There are a num b er of lim itations in the approaches undertaken here. First, although

com p arison with m easured indoor data shows re asonable perform a nce for the m odeling approach

em ployed here, the selection of sim p le one-com partm e nt m a ss balance models for both garage and hom e modeling m a y substantially understate th e variation in concentrations within these

m i croenvironm ents. All estim ates here assum e d steady-state conditions, and this m a y not be

u The ass u m e d fraction of e v a p orative and e x haust em i ssion re ductions from each source occurri ng withi n a n attached gara ge are as follows. Ra nge s are represe n te d as [min , m a x ]

. For LD GV , abou t 90 % o f em issi o n redu ction s are ex h a u s t-related, of wh ich P g *[ 25 %, 75%] occ u r wi t h i n at t ached ga ra ges; the fraction of e v aporative redu ction s o c cu rring with in attach ed g a rag e s are P g * [ 25% ,5 0%]

. Fo r sm al l no nr oa d gas o l i n e e qui pm ent

, a b o u t 72 % of em i ssions are f rom exha ust

, o f whi c h P g *[0% ,2%] occ u r in attached gara ge s; 24% are e v a p orat ive, of which
P g *[90 %,10 0%] o ccur i n attach ed g a rag e s, 4 % are refilling

- related

, of wh ich P g *[25% ,75%] occ u r in attached

gara ges. F o r p o rt a b l e f u el c o nt ai ners , P g * [ 25 %, 75%] of e m i ssi ons are as sum e d t o occu r i n at t ache d gar a ges. 

### 3-133

### appropriate for a source like a g arage, where door opening, car entry and ignition, and other

m a jor sources of benzene are likely to produce sh ort-term spikes in e xposure not accounted for with steady-state assum p tions. Second, the preponderance of these data were collected in locations with cold clim ates, so the results m a y not be applicable to warm er locations where houses are not built with the

sam e degree of weather-tightness. Furtherm ore, studies suggest that indoor concentrations arising from attached garages va ry considerably in response to em ission-related activities in a

garage su ch as cold veh i cle ig nition and parki ng a hot vehicle. 360 

### Am bient tem p eratures m ay affect the magnitude of em i ssions f r om these activities. Lastly, the extent to which the houses st udied in the publications cited here are “representative” of the nati onal housing stock is unknown.

Conclusions



### Modeled indoor benzene concentrations indica te that indoor air con centrations in hom e s with attached garages m a y be substantially high er than in hom e s without attached garages. Garage concentrations of benzene appear to be a m a jor source of indoor benzene in hom e s with attached garages. According to the modeling conducted here , this source could explain the m ajority of exposures experienced by typica l residents of such hom es. Given this finding, interventions that resu lt in a reduction in em issions w ithin th e g arage would be a relatively efficient m eans of reducing overall personal exposure, particularly in areas geographically sim ilar to the areas of the studies upon which this analysis relies. Given the proxim ity of this source to hom es, one m ajor set of beneficiaries of the ru le’s em ission controls is lik ely to b e people with hom es with atta ched ga r a ge s , pa r tic ul a rl y i n are a s wi t h hi gh fue l benzene levels. Em issions from vehicles and fu el containers also m a y have greater relative
im pacts on those with attached garag e s. An elem entary calcu lation of the intake f r a ction (iF) of

em issions occurring within attached garages wi th very basic assum ptions indicates that for benzene em itted in a gar age, approx im ately 3-18 pa rts pe r tho u sand are in haled by a p erson in an attached garage. This es tim ate is far in excess of estim ated iF from am bient sources, and sim ilar to estim a ted iF estim ates for indoor sources. 361



### 3-134

### Table 3A-1. Summ ary of National Average Expo sure Estim ates Attr ibuta b le to Attac h ed Garages. Different “averaging” assumptions shown.

" A v e r a ging" Ben z en e T W A
Scen ario (ug/m3 )
All D a ta 4.3
W e ighted Aver age 6.6
Meas ur e O nly 6.1 Model O n ly 3.4 AK O n ly 5.5
Non- AK O n ly 1.2



### 3-135

### Figure 3A-1a. Density of Garage Benzene C onc entra tions f r om Batte rman et al. (20 05)

Figure 3A-1b. Density of Air Exchange Rates (ACH)

3-136Cg=10 

### Figure 3A-2. Additional Indoor Air Concentrations from Ga rage as a Function of C g 

### and f g

In door Conc ent rat i on as a Func tion of Garag e Conce ntr ation ( C g) and %I nt ak e Air
f r om Garage ( fg)

Cg=110

Cg=100

Cg=90

Cg=80 40 -45

35 -40 Cg=70 30 -35 Cg=60 25 -30

20 -25 Cg=50 15 -20 Cg=40 10 -15

5- 10 Cg=30 0- 5 Cg=20Fg=0.02Fg=0.06Fg=0.1Fg=0.14Fg=0.18Fg=0.22Fg=0.26Fg=0.3Fg=0.34Fg=0.38Fg=0.42Fg=0.46Fg=0.5



### 3-137

### Figure 3A-3. Distributio ns of Individual Ga rage Em ission Factors

E mi s s i on R a t e ( u g/ hr )18000160001400012000100008000600040002000000.10.20.30.40.50.60.70.80.91PercentileGarage 1Garage 2Garage 3Garage 4Garage 5Garage 6Garage 7Garage 8Garage 9Garage 10Garage 11Garage 12Garage 13Garage 14Garage 15



### 3-138

### Figure 3A-4. Cum ulative Di stribution of Modeled Indoor Benzene Concentrations

Be nze ne Conc e nt r a t i on ( ug/ m 3 )



### 3-139Modeled Indoor ConcentrationDistributionsbased onCg/ FgCombinationsSolidLines:Multiple Home Studies withSingle MeasurementPer Home,DashedLines:Single Homeswith100.090.080.070.060.050.040.030.020.010.00.00%10%RepeatedMeasuresBatterman etal.(2005)/Batterman FG CiBatterman etal.(2005)/FuglerFGCiBattermanetal.(2005) /SheltersourceFGCiGeorgeetal. (2002) /BattermanFGCiGeorgeetal. (2002) /Fugler FG CiGeorgeetal. (2002) /Sheltersource FG CiIsbelletal.(2005),HomeV,Summer/SheltersourceFGCiIsbelletal. (2005),HomeNV,Summer / SheltersourceFGCiIsbelletal. (2005),HomeV,Winter / Sheltersource FGCiIsbelletal. (2005),HomeNV,Winter /Sheltersource FGCiIsbelletal.(2005),Home V, Summer/FuglerFGCiIsbelletal. (2005),HomeNV,Summer / Fugler FGCiIsbelletal. (2005),HomeV,Winter / Fugler FG CiIsbelletal. (2005),HomeNV,Winter /Fugler FG CiIsbelletal.(2005), HomeV,Summer/ BattermanFGCiIsbelletal. (2005),HomeNV,Summer / Batterman FG CiIsbelletal. (2005),HomeV,Winter / Batterman FG CiIsbelletal. (2005),HomeNV,Winter /BattermanFGCiTsai&Weisel(2000)/BattermanFGCiTsai&Weisel(2000) /Fugler FG CiTsai&Weisel(2000) /Sheltersource FGCi20%30%40%50%60%70%80%90%100%Percentile ofModelOutputper Scenario(Variability)

### Garages Figure 3A-5. Cum ulative Distribut io ns of Observed Benzene Levels in H o m e s with Attach ed

Be nz e n e C onc e n t r a t i o n ( ug/ m 3 )100.090.080.070.060.050.040.030.020.010.00.0Isbelletal.(2005),HomeV,Summer/MeasuredCiIsbelletal.(2005),HomeNV,Summer /MeasuredCiIsbelletal.(2005),HomeV,Winter/MeasuredCiIsbelletal.(2005),HomeNV,Winter /MeasuredCiBattermanetal.(2006) /MeasuredCiSchlapia &Morris(1998) /MeasuredCiWeisel -NJDEP/EPA(2006) /MeasuredCiNote:Asshown,Schlapia and Morrisdistributionare basedon alognormal distribution fittoartithmeticmeanandgeometricmean asreported instudy.Maximumindoor valuemeasuredin the study:1161ug/m3.Maxmimumindoorvalue forIsbelletal.,home0%10%20%30%40%50%60%70%80%90%100%NV summer:125.1 ug/m3.Percentileof ReportedStudyData



### 3-140Percentile DistributionofIndoor Benzene Concentrations in Studies ofHomeswithAttachedGaragesSolidLines:Multiple Homes,1Measure/Home;DashedLine:Single Home withMultipleMeasures/Home

### Figure 3A-6. Com parison of Modeled and Observed Indoor Benzene Concentrations

Be nzene Con c e ntr ation (ug/m3 )Cg Data Source Batte r m an e t al. ( 2 0 05/6)

G eor g e et a l . ( 200 2)

Is bell e t al. ( 2 005)

, Home NV, S u mmer

Is bel l et a l . ( 200 5)

, Home NV , Winter

Is bel l et a l . ( 200 5)

, H o m e V, Summ er

Is bel l et a l . ( 200 5)

,

Ho m e V

, W i nt e r

S c hl a p i a & M o rri s ( 1 998)

T s ai & W e is el ( 2 000)

Weis el - NJ DEP / E P A ( 20 06)01020304050607080Batterman et al. (2006)Fugleretal.SheltersourceMeasured



### 3-141

### Figure 3A-7

. Multip le S cenar io Output of Predicted Nationa l Average B e nzene Exp o sure Attribu t able to Attach ed Garages

B en z en e T W A C o n cen trati o n (u g / m 3)



### 3-142706050403020100-100%10%20%30%40%50%60%70%80%90%100%CumulativePercentileBatterman et al. (2005) /SheltersourceBatterman et al. (2005) /FuglerBatterman et al. (2005) /BattermanBatterman et al. (2005) /DirectGeorge etal.(2002) /SheltersourceGeorge etal.(2002) /FuglerGeorge etal.(2002) /BattermanTsai&Weisel(2000)/ SheltersourceTsai&Weisel(2000)/ FuglerTsai&Weisel(2000)/ BattermanIsbellet al. (2005),Home V,Summer/ SheltersourceIsbellet al. (2005),Home V,Summer/ FuglerIsbellet al. (2005),Home V,Summer/ BattermanIsbellet al. (2005),Home V,Summer/ DirectIsbellet al. (2006),HomeNV, Summer / SheltersourceIsbellet al. (2006),HomeNV, Summer / FuglerIsbellet al. (2006),HomeNV, Summer / BattermanIsbellet al. (2006),HomeNV, Summer / DirectIsbellet al. (2006),Home V,Winter/ SheltersourceIsbellet al. (2006),Home V,Winter/ FuglerIsbellet al. (2006),Home V,Winter/ BattermanIsbellet al. (2006),Home V,Winter/ DirectIsbellet al. (2006),HomeNV, Winter / SheltersourceIsbellet al. (2006),HomeNV, Winter / FuglerIsbellet al. (2006),HomeNV, Winter / BattermanIsbellet al. (2006),HomeNV, Winter / DirectSchlapia &Morris (1998)/ DirectWeisel-NJDEP/EPA(2006) / Direct

### 3-143 Figure 3A-8. Average “Summ a rized ” Benzene E xposure Distributions051015202530 Be nze n e TWA Con c e n tr a t i on (ug/ m3 )AllDataWeightedAverageMeasure OnlyModelOnlyAKOnlyNon-AKOnlyVariation inDistributionsof National PopulationTWAExposuresAttributableto AttachedGarages, BasedonDifferent "Averaging" Assumptions00.10.20.30.40.50.60.70.80.91CumulativePercentile

# Appendix 3B: 8- Hour Ozone Non a tt ainment

Table 3B-1. 8-Hour Oz one Nonattainment Areas, Counties and Populations (D ata is Current through October 2006 and Population Numbers are fro m 200 0 Census Data)

8-hou r Ozo n e Nona ttain m ent Area State C lassific atio n a,b Coun t y Name Whole /Part 2000 Ct y Pop Albany-Sche necta dy-Troy Area NY Subpa rt 1 Albany Co W 294,56 5 Albany-Sche necta dy-Troy Area NY Subpa rt 1 Gree ne Co W 48,195 Albany-Sche necta dy-Troy Area NY Subpa rt 1 Montgom ery Co W 49,708 Albany-Sche necta dy-Troy Area NY Subpa rt 1 Ren s sela er Co W 152,53 8 Albany-Sche necta dy-Troy Area NY Subpa rt 1 Saratog a Co W 200,63 5 Albany-Sche necta dy-Troy Area NY S ubpa rt 1 Schen ecta dy Co W 146,55 5 Albany-Sche necta dy-Troy Area NY Subpa rt 1 Schoh ari e Co W 31,582 Allegan Coun ty Area MI Subpa rt 1 Allegan Co W 105,66 5
Allentown

- Be thlehem -East on Area PA Subpa rt 1 Carbon Co W 58,802
Allentown

- Be thlehem -East on Area PA Subpa rt 1 Lehig h Co W 312,09 0
Allentown

- Be thlehem -East on Area PA Subpa rt 1 North a mpto n Co W 267,06 6
Altoona Area PA Subpa rt 1 Blair Co W 129,14 4 Amador a nd Calave ra s Co unties (Ce ntral Mo u ntain Co untie s) Area CA Subpa rt 1 Amador Co W 35,100 Amador a nd Calave ra s Co unties (Ce n tral Mo u n tain Co untie s) Area CA Subpa rt 1 Calave ra s Co W 40,554

Atlanta Area GA Subpa rt 2/Margi nal Barro w Co W 46,144

Atlanta Area GA Subpa rt 2/Margi nal Bartow Co W 76,019

Atlanta Area GA Subpa rt 2/Margi nal Carroll Co W 87,268

Atlanta Area GA Subpa rt 2/Margi nal Che r o k ee Co W 141,90 3

Atlanta Area GA Subpa rt 2/Margi nal Clayton Co W 236,51 7

Atlanta Area GA Subpa rt 2/Margi nal Cob b Co W 607,75 1

Atlanta Area GA Subpa rt 2/Margi nal Co weta Co W 89,215

Atlanta Area GA Subpa rt 2/Margi nal De Kalb Co W 665,86 5

Atlanta Area GA Subpa rt 2/Margi nal Dou gla s Co W 92,174

Atlanta Area GA Subpa rt 2/Margi nal Fayette Co W 91,263

Atlanta Area GA Subpa rt 2/Margi nal Forsyth Co W 98,407

Atlanta Area GA Subpa rt 2/Margi nal Fulton Co W 816,00 6

Atlanta Area GA Subpa rt 2/Margi nal Gwin nett Co W 588,44 8

Atlanta Area GA Subpa rt 2/Margi nal Hall Co W 139,27 7



### 3-144Atlanta Area GA Subpa rt 2/Margi nal Hen r y Co W 119,34 1

Atlanta Area GA Subpa rt 2/Margi nal Ne wton Co W 62,001

Atlanta Area GA Subpa rt 2/Margi nal Paulding Co W 81,678

Atlanta Area GA Subpa rt 2/Margi nal Ro ckdale Co W 70,111

Atlanta Area GA Subpa rt 2/Margi nal Spalding Co W 58,417

Atlanta Area GA Subpa rt 2/Margi nal Walton Co W 60,687

Baltimore Area MD Subpa rt 2/Mode rate Anne Arun del Co W 489,65 6

Baltimore Area MD Subpa rt 2/Mode rate Baltimore (Cit y) W 651,15 4

Baltimore Area MD Subpa rt 2/Mode rate Baltimore Co W 754,29 2

Baltimore Area MD Subpa rt 2/Mode rate Carroll Co W 150,89 7

Baltimore Area MD Subpa rt 2/Mode rate Harfo r d Co W 218,59 0

Baltimore Area MD Subpa rt 2/Mode rate Ho ward Co W 247,84 2

Baton Rou ge Area LA Subpa rt 2/Margi nal Asce nsi on Pa r W 76,627

Baton Rou ge Area LA Subpa rt 2/Margi nal Eas t Baton Rou ge Par W 412,85 2

Baton Rou ge Area LA Subpa rt 2/Margi nal Iberville Par W 33,320

Baton Rou ge Area LA Subpa rt 2/Margi nal Livingsto n Par W 91,814

Baton Rou ge Area LA Subpa rt 2/Margi nal We st Baton Rou ge Par W 21,601

Beaumo n t-Po rt Arthur Are a TX Subpa rt 2/Margi nal Hardin Co W 48,073

Beaumo n t-Po rt Arthur Are a TX Subpa rt 2/Margi nal Jefferson Co W 252,05 1

Beaumo n t-Po rt Arthur Are a TX Subpa rt 2/Margi nal Oran ge Co W 84,966
Benton Ha rb or Area MI Subpa rt 1 Berrie n Co W 162,45 3
Benzie Cou nty Area MI Subpa rt 1 Benzie Co W 15,998 Berkeley and Jefferson Co unties Area WV Subpa rt 1 - EAC Berkeley Co W 75,905 Berkeley and Jefferson Co unties Area WV Subpa rt 1 - EAC Jefferson Co W 42,190 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Barn stable Co W 222,23 0 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Bristol Co W 534,67 8 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Du ke s Co W 14,987 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Essex Co W 723,41 9



### 3-145Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Middle s ex Co W 1,465,39 6 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Nantu c ket Co W 9,520 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Norfol k Co W 650,30 8 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Plymouth Co W 472,82 2 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Suffolk Co W 689,80 7 Boston -La w r enc e- Wo rc es ter (E. Mass) Area MA Subpa rt 2/Mode rate Wo rce s te r Co W 750,96 3 Boston -Man cheste r-Po r tsmouth (SE) Area NH Subpa rt 2/Mode rate Hillsboroug h Co P 336,51 8 Boston -Man cheste r-Po r tsmouth (SE) Area NH Subpa rt 2/Mode rate Merri ma ck Co P 11,721 Boston -Man cheste r-Po r tsmouth (SE) Area NH Subpa rt 2/Mode rate Ro cki ngh am Co P 266,34 0 Boston -Man cheste r-Po r tsmouth (SE) Area NH Subpa rt 2/Mode rate Strafford Co P 82,134
Buffalo-Ni aga ra Fall s Area NY Subpa rt 1 Erie Co W 950,26 5
Buffalo-Ni aga ra Fall s Area NY Subpa rt 1 Niag ara Co W 219,84 6
Canto n-M assillon Area OH Subpa rt 1 Stark Co W 378,09 8

Ca ss Cou nty Area MI Subpa rt 2/Margi nal Ca ss Co W 51,104

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Cab arru s Co W 131,06 3

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Gasto n Co W 190,36 5

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Iredell Co P 39,885

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Lincoln Co W 63,780

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Mecklen bu r g Co W 695,45 4

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Ro wan Co W 130,34 0

Charlotte-Gastonia- Rock Hill Area NC Subpa rt 2/Mode rate Unio n Co W 123,67 7

Charlotte-Gastonia- Rock Hill Area SC Subpa rt 2/Mode rate York Co P 102,00 0
Chattan oog a Area GA Subpa rt 1 - EAC Catoo s a Co W 53,282
Chattan oog a Area TN Subpa rt 1 - EAC Hamilton Co W 307,89 6
Chattan oog a Area TN Subpa rt 1 - EAC Meigs Co W 11,086

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Coo k Co W 5,376,74 1

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Du Page Co W 904,16 1

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Grun dy Co P 6,309

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Kane Co W 404,11 9

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Kendall Co P 28,417



### 3-146Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Lake Co W 644,35 6

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Mc He nry Co W 260,07 7

Chi c ag o-G ary-Lake Co unt y Area IL Subpa rt 2/Mode rate Will Co W 502,26 6

Chi c ag o-G ary-Lake Co unt y Area IN Subpa rt 2/Mode rate Lake Co W 484,56 4

Chi c ag o-G ary-Lake Co unt y Area IN Subpa rt 2/Mode rate Porter Co W 146,79 8
Chi c o Are a CA Subpa rt 1 Butte Co W 203,17 1
Cin c inn a ti-Ha m ilton Area IN Subpa rt 1 Dea r bo rn Co P 10,434
Cin c inn a ti-Ha m ilton Area KY Subpa rt 1 Boone Co W 85,991
Cin c inn a ti-Ha m ilton Area KY Subpa rt 1 Camp bell Co W 88,616
Cin c inn a ti-Ha m ilton Area KY Subpa rt 1 Kenton Co W 151,46 4
Cin c inn a ti-Ha m ilton Area OH Subpa rt 1 Butler Co W 332,80 7
Cin c inn a ti-Ha m ilton Area OH Subpa rt 1 Clermont Co W 177,97 7
Cin c inn ati-Ha m ilton Area OH Subpa rt 1 Clinton Co W 40,543
Cin c inn ati-Ha m ilton Area OH Subpa rt 1 Hamilton Co W 845,30 3
Cin c inn ati-Ha m ilton Area OH Subpa rt 1 Wa rre n Co W 158,38 3 Clea rfield an d Indiana Co unties Area PA Subpa rt 1 Clea rfield Co W 83,382 Clea rfield an d Indiana Co unties Area PA Subpa rt 1 Indiana Co W 89,605

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Ashtabul a Co W 102,72 8

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Cuyah oga Co W 1,393,97 8

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Geau ga Co W 90,895

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Lake Co W 227,51 1

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Lorai n Co W 284,66 4

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Medina Co W 151,09 5

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Portage Co W 152,06 1

Clevela nd-A k ron

- Lo rain Area OH Subpa rt 2/Mode rate Summit Co W 542,89 9
Colum b ia Are a SC Subpa rt 1 - EAC Lexington Co P 181,26 5
Colum b ia Are a SC Subpa rt 1 - EAC Richlan d Co P 313,25 3
Colum b u s Area OH Subpa rt 1 Dela wa re Co W 109,98 9
Colum bu s Area OH Subpa rt 1 Fairfield Co W 122,75 9
Colum bu s Area OH Subpa rt 1 Fran klin Co W 1,068,97 8
Colum b u s Area OH Subpa rt 1 Knox Co W 54,500
Colum bu s Area OH Subpa rt 1 Licking Co W 145,49 1
Colum b u s Area OH Subpa rt 1 Madison Co W 40,213

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Collin Co W 491,67 5

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Dalla s Co W 2,218,89 9



### 3-147Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Dento n Co W 432,97 6

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Ellis Co W 111,36 0

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Joh nson Co W 126,81 1

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Kaufman Co W 71,313

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Parker Co W 88,495

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Ro ck wall Co W 43,080

Dalla s-Fort Worth Area TX Subpa rt 2/Mode rate Tarrant Co W 1,446,21 9 Dayton-S prin gfield Area OH Subpa rt 1 Clark Co W 144,74 2 Dayton-S prin gfield Area OH Subpa rt 1 Gree ne Co W 147,88 6 Dayton-S prin gfield Area OH Subpa rt 1 Miami Co W 98,868 Dayton-S prin gfield Area OH Subpa rt 1 Montgom ery Co W 559,06 2 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Adams Co W 348,61 8 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Arapa hoe Co W 487,96 7 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Boulde r Co W 269,81 4 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Broomfield Co W 38,272 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Denve r Co W 554,63 6 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Dou gla s Co W 175,76 6 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Jefferson Co W 525,50 7 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Larim er Co P 239,00 0 Denver-Boulder-Greeley-Ft

. CollinsLove. Area CO Subpa rt 1 - EAC Weld Co P 172,00 0

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Lena we e Co W 98,890

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Livingsto n Co W 156,95 1

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Maco mb Co W 788,14 9

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Monroe Co W 145,94 5

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Oakl and Co W 1,194,15 6

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal St Clair Co W 164,23 5

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Wa shten aw Co W 322,89 5

Detroit

- Ann A r bo r Area MI Subpa rt 2/Margi nal Wayne Co W 2,061,16 2
Doo r Co unty Area WI Subpa rt 1 Doo r Co W 27,961
Erie Area PA Subpa rt 1 Erie Co W 280,84 3



### 3-148Essex Count y (Whitefa c e Mtn.) Area NY Subpa rt 1 Essex Co P 1,000 Fayetteville Area NC Subpa rt 1 - EAC Cumb erl and Co W 302,96 3
Flint Area MI Subpa rt 1 Gene se e Co W 436,14 1
Flint Area MI Subpa rt 1 Lape er Co W 87,904 Fort Wayn e Area IN Subpa rt 1 Allen Co W 331,84 9
Fran klin Cou nty Area PA Subpa rt 1 Fran klin Co W 129,31 3
Fred eri ck Co unty Area VA Subpa rt 1 - EAC Fred eri ck Co W 59,209
Fred eri ck Co unty Area VA Subpa rt 1 - EAC Win c he ster W 23,585
Gran d Ra pid s Area MI Subpa rt 1 Kent Co W 574,33 5
Gran d Ra pid s Area MI Subpa rt 1 Ottawa Co W 238,31 4

Greate r Conn ecticut Area CT Subpa rt 2/Mode rate Hartfo rd Co W 857,18 3

Greate r Conn ecticut Area CT Subpa rt 2/Mode rate Litchfield Co W 182,19 3

Greate r Conn ecticut Area CT Subpa rt 2/Mode rate Ne w Lon don Co W 259,08 8

Greate r Conn ecticut Area CT Subpa rt 2/Mode rate Tolland Co W 136,36 4

Greate r Conn ecticut Area CT Subpa rt 2/Mode rate Wind ham Co W 109,09 1
Gree ne Coun ty Area PA Subpa rt 1 Gree ne Co W 40,672

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Alaman ce Co W 130,80 0

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Ca swell Co W 23,501

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC David s on Co W 147,24 6

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Davie Co W 34,835

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Forsyth Co W 306,06 7

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Guilford Co W 421,04 8

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Ran dolph Co W 130,45 4

Gree nsbo ro-Win ston-S ale m-Hi gh Point Area NC Subpa rt 2/Margi nal - EAC Ro cki ngh am Co W 91,928 Greenville-Spart anburg-Anderson Area SC Subpa rt 1 - EAC Anderso n Co W 165,74 0 Greenville-Spart anburg-Anderson Area SC Subpa rt 1 - EAC Gree nville Co W 379,61 6 Greenville-Spart anburg-Anderson Area SC Subpa rt 1 - EAC Spartan burg Co W 253,79 1
Han c o c k, Knox, Lincoln a nd Wal do ME Subpa rt 1 Han c o c k Co P 29,805



### 3-149Cou n ties (Ce n tral Main e Coast) Area Han c o c k, Knox, Lincoln a nd Wal do Cou n ties (Ce n tral Main e Coast) Area ME Subpa rt 1 Knox Co P 33,563 Han c o c k, Knox, Lincoln a nd Wal do Cou n ties (Ce n tral Main e Coast) Area ME Subpa rt 1 Lincoln Co P 28,504 Han c o c k, Knox, Lincoln a nd Wal do Cou n ties (Ce n tral Main e Coast) Area ME Subpa rt 1 Wald o Co P 604
Harri s bu rg -Le bano n-Ca rlisl e Area PA Subpa rt 1 Cumb erl and Co W 213,67 4
Harri s bu rg -Le bano n-Ca rlisl e Area PA Subpa rt 1 Dau phin Co W 251,79 8
Harri s bu rg -Le bano n-Ca rlisl e Area PA Subpa rt 1 Leba non Co W 120,32 7
Harri s bu rg -Le bano n-Ca rlisl e Area PA Subpa rt 1 Perry Co W 43,602 Haywood a nd Swain Co unt ies (Great Smoky NP) Area NC Subpa rt 1 Haywood Co P 28 Haywood a nd Swain Co unt ies (Great Smoky NP) Area NC Subpa rt 1 Swain Co P 260
Hicko r y-Mo rg anton-Len oir Area NC Subpa rt 1 - EAC Alexande r Co W 33,603
Hicko r y-Mo rg anton-Len oir Area NC Subpa rt 1 - EAC Burke Co P 69,970
Hicko r y-Mo rg anton-Len oir Area NC Subpa rt 1 - EAC Cald well Co P 64,254
Hicko r y-Mo rg anton-Len oir Area NC Subpa rt 1 - EAC Cata wba Co W 141,68 5

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Brazoria Co W 241,76 7

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Cham be rs Co W 26,031

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Fort Bend Co W 354,45 2

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Galvesto n Co W 250,15 8

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Harri s Co W 3,400,57 8

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Liberty Co W 70,154

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Montgom ery Co W 293,76 8

H o us to n- G a lve s to n- Br az or ia Ar e a T X Subpa rt 2/Mode rate Walle r Co W 32,663
Huntin gton-A s hla nd Are a KY Subpa rt 1 Boyd Co W 49,752
Huron Count y Area MI Subpa rt 1 Huron Co W 36,079

Imperial Cou n ty Area CA Subpa rt 2/Margi nal Imperial Co W 142,36 1
Indiana polis Area IN Subpa rt 1 Boone Co W 46,107
Indiana polis Area IN Subpa rt 1 Hamilton Co W 182,74 0
Indiana polis Area IN Subpa rt 1 Han c o c k Co W 55,391
Indiana polis Area IN Subpa rt 1 Hen dri cks Co W 104,09 3
Indiana polis Area IN Subpa rt 1 Joh nson Co W 115,20 9
Indiana polis Area IN Subpa rt 1 Madison Co W 133,35 8
Indiana polis Area IN Subpa rt 1 Mario n Co W 860,45 4
Indiana polis Area IN Subpa rt 1 Morg an Co W 66,689
Indiana polis Area IN Subpa rt 1 Shelby Co W 43,445



### 3-150Jame sto w n A r ea NY Subpa rt 1 Cha utauq ua Co W 139,75 0

Jefferson Co unty Area NY Subpa rt 2/Mode rate Jefferson Co W 111,73 8
Joh nson City-Kingsp ort-B ri stol Area TN Subpa rt 1 - EAC Ha wki ns Co W 53,563
Joh nson City-Kingsp ort-B ri stol Area TN Subpa rt 1 - EAC Sullivan Co W 153,04 8
Joh nsto w n Area PA Subpa rt 1 Camb ria Co W 152,59 8
Kalama zoo

- B attle Cree k Area MI Subpa rt 1 Calh oun Co W 137,98 5
Kalama zoo

- B a ttle Cree k Area MI Subpa rt 1 Kalama zoo Co W 238,60 3
Kalama zoo

- B a ttle Cree k Area MI Subpa rt 1 Van Buren Co W 76,263 Kent and Qu een Anne 's Countie s Area MD Subpa rt 2/Margi nal Kent Co W 19,197 Kent and Qu een Anne 's Countie s Area MD Subpa rt 2/Margi nal Quee n Anne s Co W 40,563 Kern County (Easte rn Kern) Area CA Subpa rt 1 Kern Co P 99,251
Kewau nee County Area WI Subpa rt 1 Kewau nee Co W 20,187
Knoxville Area TN Subpa rt 1 Anderso n Co W 71,330
Knoxville Area TN Subpa rt 1 Blount Co W 105,82 3
Knoxville Area TN Subpa rt 1 Co cke Co P 20
Knoxville Area TN Subpa rt 1 Jefferson Co W 44,294
Knoxville Area TN Subpa rt 1 Knox Co W 382,03 2
Knoxville Area TN Subpa rt 1 Loud on Co W 39,086
Knoxville Area TN Subpa rt 1 Sevier Co W 71,170

La Porte Cou n ty Area IN Subpa rt 2/Margi nal La Porte Co W 110,10 6

Lan ca ster Area PA Subpa rt 2/Margi nal Lan ca ster Co W 470,65 8
Lan sing -Ea s t Lan sing Area MI Subpa rt 1 Clinton Co W 64,753
Lan sing -Ea s t Lan sing Area MI Subpa rt 1 Eaton Co W 103,65 5
Lan sing -Ea s t Lan sing Area MI Subpa rt 1 Ingham Co W 279,32 0 Las Veg a s Area NV Subpa rt 1 Clark Co P 1,348,86 4
Lima Area OH Subpa rt 1 Allen Co W 108,47 3 Los Ang e le s and San Bern ardin o Cou nties ( W Mojav e De se rt) Area CA Subpa rt 2/Mode rate Los Ang ele s Co P 297,05 8 Los Ang e le s and San Bern ardin o Cou nties ( W Mojav e De se rt) Area CA Subpa rt 2/Mode rate San Berna r di no Co P 359,35 0 Los Ang e le s-South Coa s t Air Basin Area CA Subpa rt 2/Severe 17 Los Ang e le s Co P 9,222,28 0 Los Ang e le s-South Coa s t Air Basin Area CA Subpa rt 2/Severe 17 Oran ge Co W 2,846,28 9 Los Ang e le s-South Coa s t Air Basin Area CA Subpa rt 2/Severe 17 Riverside Co P 1,194,85 9 Los Ang e le s-South Coa s t Air Basin Area CA Subpa rt 2/Severe 17 San Berna r di no Co P 1,330,15 9
Loui sville Are a IN Subpa rt 1 Clark Co W 96,472
Loui sville Are a IN Subpa rt 1 Floyd Co W 70,823
Loui sville Are a KY Subpa rt 1 Bullitt Co W 61,236
Loui sville Are a KY Subpa rt 1 Jefferson Co W 693,60 4
Loui sville Are a KY Subpa rt 1 Oldham Co W 46,178
Maco n Area GA Subpa rt 1 Bibb Co W 153,88 7
Maco n Area GA Subpa rt 1 Monroe Co P 50
Manitowoc County Area WI Subpa rt 1 Manitowoc Co W 82,887



### 3-151Marip o sa and Tuolumn e Countie s (Southe rn Mo untain Counti es) A r ea CA Subpa rt 1 Marip osa Co W 17,130 Marip o sa and Tuolumn e Countie s (Southe rn Mo untain Counti e s) A r ea CA Subpa rt 1 Tuolum ne Co W 54,501
Maso n Co unt y Area MI Subpa rt 1 Maso n Co W 28,274

Memphis Area AR Subpa rt 2/Margi nal Crittend en Co W 50,866

Memphi s Area TN Subpa rt 2/Margi nal Shelby Co W 897,47 2

Milwau ke e-Racin e Area WI Subpa rt 2/Mode rate Kenosha Co W 149,57 7

Milwau ke e-Racin e Area WI Subpa rt 2/Mode rate Milwau ke e Co W 940,16 4

Milwau ke e-Racin e Area WI Subpa rt 2/Mode rate Oza ukee Co W 82,317

Milwau ke e-Racin e Area WI Subpa rt 2/Mode rate Ra cine Co W 188,83 1

Milwau ke e-Racin e Area WI Subpa rt 2/Mode rate Wa shin gton Co W 117,49 3

Milwau ke e-Racin e Area WI Subpa rt 2/Mode rate Wau k e s h a Co W 360,76 7 Murray Cou n ty (Chattaho o c he e Nat Fore st) Ar ea GA Subpa rt 1 Murray Co P 1,000

Muskeg on Area MI Subpa rt 2/Margi nal Muskeg on Co W 170,20 0
Na shville Are a TN Subpa rt 1 - EAC David s on Co W 569,89 1
Na shville Are a TN Subpa rt 1 - EAC Ruthe r ford Co W 182,02 3
Na shville Are a TN Subpa rt 1 - EAC Sumner Co W 130,44 9
Na shville Are a TN Subpa rt 1 - EAC William s on Co W 126,63 8
Na shville Are a TN Subpa rt 1 - EAC Wilson Co W 88,809
Nevad a Co un ty (Weste r n p a rt) Are a CA Subpa rt 1 Nevad a Co P 77,735 Ne w York-N. Ne w Je rsey-L ong Island Area CT Subpa rt 2/Mode rate Fairfield Co W 882,56 7 Ne w York-N. Ne w Je rsey-L ong Island Area CT Subpa rt 2/Mode rate Middle s ex Co W 155,07 1 Ne w York-N. Ne w Je rsey-L ong Island Area CT Subpa rt 2/Mode rate Ne w Have n Co W 824,00 8 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Berge n Co W 884,11 8 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Essex Co W 793,63 3 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Hud s o n Co W 608,97 5 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Hunte r do n Co W 121,98 9 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Middle s ex Co W 750,16 2 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Monmo u th Co W 615,30 1 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Morri s Co W 470,21 2 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Passaic Co W 489,04 9



### 3-152Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Somerset Co W 297,49 0 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Sussex Co W 144,16 6 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Unio n Co W 522,54 1 Ne w York-N. Ne w Je rsey-L ong Island Area NJ Subpa rt 2/Mode rate Wa rre n Co W 102,43 7 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Bronx Co W 1,332,65 0 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Kings Co W 2,465,32 6 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Na ssau Co W 1,334,54 4 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Ne w York Co W 1,537,19 5 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Quee ns Co W 2,229,37 9 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Richmon d Co W 443,72 8 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Ro ckl and Co W 286,75 3 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate Suffolk Co W 1,419,36 9 Ne w York-N. Ne w Je rsey-L ong Island Area NY Subpa rt 2/Mode rate We stch este r Co W 923,45 9 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Che s a pea ke W 199,18 4 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Glou ce ster Co W 34,780 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Hampto n W 146,43 7 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Isle Of Wight Co W 29,728 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Jame s City Co W 48,102 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Ne wpo r t Ne ws W 180,15 0 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Norfol k W 234,40 3 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Poquo son W 11,566 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Portsm outh W 100,56 5 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Suffolk W 63,677 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal Virginia Bea c h W 425,25 7 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal William s burg W 11,998 Norfol k

- Virgi n ia Beach

- Ne wpo r t Ne ws (Ha m pt on Ro ad s) Area VA Subpa rt 2/Margi nal York Co W 56,297
Parkersbu r g

- Marietta Are a OH Subpa rt 1 Wa shin gton Co W 63,251
Parkersbu r g

- Marietta Are a WV Subpa rt 1 Woo d Co W 87,986



### 3-153Philadelp h ia-Wilmingto n -A tlantic City Area DE Subpa rt 2/Mode rate Kent Co W 126,69 7 Philadelp h ia-Wilmingto n -A tlantic City Area DE Subpa rt 2/Mode rate Ne w Ca stle Co W 500,26 5 Philadelp h ia-Wilmingto n -A tlantic City Area DE Subpa rt 2/Mode rate Sussex Co W 156,63 8 Philadelp h ia-Wilmingto n -A tlantic City Area MD Subpa rt 2/Mode rate Ce cil Co W 85,951 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Atlantic Co W 252,55 2 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Burlingto n Co W 423,39 4 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Camd en Co W 508,93 2 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Cap e May Co W 102,32 6 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Cumb erl and Co W 146,43 8 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Glou ce ster Co W 254,67 3 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Mercer Co W 350,76 1 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Oce an Co W 510,91 6 Philadelp h ia-Wilmingto n -A tlantic City Area NJ Subpa rt 2/Mode rate Salem Co W 64,285 Philadelp h ia-Wilmingto n -A tlantic City Area PA Subpa rt 2/Mode rate Bucks Co W 597,63 5 Philadelp h ia-Wilmingto n -A tlantic City Area PA Subpa rt 2/Mode rate Che s ter Co W 433,50 1 Philadelp h ia-Wilmingto n -A tlantic City Area PA Subpa rt 2/Mode rate Dela wa re Co W 550,86 4 Philadelp h ia-Wilmingto n -A tlantic City Area PA Subpa rt 2/Mode rate Montgom ery Co W 750,09 7 Philadelp h ia-Wilmingto n -A tlantic City Area PA Subpa rt 2/Mode rate Philadelp hia Co W 1,517,55 0
Phoenix-M e sa Area AZ Subpa rt 1 Mari cop a Co P 3,054,50 4
Phoenix-M e sa Area AZ Subpa rt 1 Pinal Co P 31,541 Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Allegheny Co W 1,281,66 6 Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Armstrong Co W 72,392 Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Beaver Co W 181,41 2 Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Butler Co W 174,08 3 Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Fayette Co W 148,64 4 Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Wa shin gton Co W 202,89 7 We stmor elan d Pittsburg h -Be a ver Valley Area PA Subpa rt 1 Co W 369,99 3

Portland Area ME Subpa rt 2/Margi nal An d r osc o gg in Co P 3,390

Portland Area ME Subpa rt 2/Margi nal Cumb erl and Co P 252,90 7

Portland Area ME Subpa rt 2/Margi nal Sagada ho c Co W 35,214
Portland Area ME Subpa rt York Co P 164,99 7



### 3-1542/Margi nal

Pough kee psi e Area NY Subpa rt 2/Mode rate Dutche ss Co W 280,15 0

Pough kee psi e Area NY Subpa rt 2/Mode rate Oran ge Co W 341,36 7

Pough kee psi e Area NY Subpa rt 2/Mode rate Putnam Co W 95,745

Providen ce (all of RI) Area RI Subpa rt 2/Mode rate Bristol Co W 50,648

Providen ce (all of RI) Area RI Subpa rt 2/Mode rate Kent Co W 167,09 0

Providen ce (all of RI) Area RI Subpa rt 2/Mode rate Ne wpo r t Co W 85,433

Providen ce (all of RI) Area RI Subpa rt 2/Mode rate Providen ce Co W 621,60 2

Providen ce (all of RI) Area RI Subpa rt 2/Mode rate Wa shin gton Co W 123,54 6
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Chath am Co P 21,320
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Durham Co W 223,31 4
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Fran klin Co W 47,260
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Granville Co W 48,498
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Joh nsto n Co W 121,96 5
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Oran ge Co W 118,22 7
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Person Co W 35,623
Ralei gh-Du rh am-Cha pel Hi ll Area NC Subpa rt 1 Wa ke Co W 627,84 6
Rea d ing Area PA Subpa rt 1 Berks Co W 373,63 8

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Cha r le s City Co W 6,926

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Che s terfiel d Co W 259,90 3

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Colo nial Hei g hts W 16,897

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Han over Co W 86,320

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Hen r ico Co W 262,30 0

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Hop ewell W 22,354

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Petersburg W 33,740

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Prince George Co W 33,047

Richmon d -Pe t ersb urg Area VA Subpa rt 2/Margi nal Richmon d W 197,79 0 Riverside Co unty (Coa ch e lla Valley) Area CA Subpa rt 2/Seriou s Riverside Co P 324,75 0
Roa n o k e Are a VA Subpa rt 1 - EAC Botetourt Co W 30,496
Roa no k e Are a VA Subpa rt 1 - EAC Roa no k e W 94,911
Roa no k e Are a VA Subpa rt 1 - EAC Roa no k e Co W 85,778
Roa n o k e Are a VA Subpa rt 1 - EAC Salem W 24,747
Ro che s ter Area NY Subpa rt 1 Gene se e Co W 60,370
Ro che s ter Area NY Subpa rt 1 Livingsto n Co W 64,328



### 3-155Ro che s ter Area NY Subpa rt 1 Monroe Co W 735,34 3
Ro che s ter Area NY Subpa rt 1 Ontario Co W 100,22 4
Ro che s ter Area NY Subpa rt 1 Orlea ns Co W 44,171
Ro che s ter Area NY Subpa rt 1 Wayne Co W 93,765
Ro cky Mou nt Area NC Subpa rt 1 Edgecomb e Co W 55,606
Ro cky Mou nt Area NC Subpa rt 1 Na sh Co W 87,420

Sacramento Metro Are a CA Subpa rt 2/Seriou s El Dora do Co P 124,16 4

Sacramento Metro Are a CA Subpa rt 2/Seriou s Place r Co P 239,97 8

Sacramento Metro Are a CA Subpa rt 2/Seriou s Sacramento Co W 1,223,49 9

Sacramento Metro Are a CA Subpa rt 2/Seriou s Solano Co P 197,03 4

Sacramento Metro Are a CA Subpa rt 2/Seriou s Sutter Co P 25,013

Sacramento Metro Are a CA Subpa rt 2/Seriou s Yolo Co W 168,66 0
San Antonio Area TX Subpa rt 1 - EAC Bexar Co W 1,392,93 1
San Antonio Area TX Subpa rt 1 - EAC Comal Co W 78,021
San Antonio Area TX Subpa rt 1 - EAC Guad alup e Co W 89,023 San Dieg o Area CA Subpa rt 1 San Dieg o Co P 2,813,43 1

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Alameda Co W 1,443,74 1

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Contra Co sta Co W 948,81 6

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Marin Co W 247,28 9

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Nap a Co W 124,27 9

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal San Fran ci sco Co W 776,73 3

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal San Mateo Co W 707,16 1

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Santa Cla r a Co W 1,682,58 5

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Solano Co P 197,50 8

San Fran ci sco Bay Area CA Subpa rt 2/Margi nal Sonoma Co P 413,71 6

San Joa quin Valley Area CA Subpa rt 2/Seriou s Fre s no Co W 799,40 7

San Joa quin Valley Area CA Subpa rt 2/Seriou s Kern Co P 550,22 0

San Joa quin Valley Area CA Subpa rt 2/Seriou s Kings Co W 129,46 1

San Joa quin Valley Area CA Subpa rt 2/Seriou s Made ra Co W 123,10 9

San Joa quin Valley Area CA Subpa rt 2/Seriou s Merced Co W 210,55 4

San Joa quin Valley Area CA Subpa rt 2/Seriou s San Joa quin Co W 563,59 8
San Joa quin Valley Area CA Subpa rt Stanislau s Co W 446,99 7



### 3-1562/Seriou s

San Joa quin Valley Area CA Subpa rt 2/Seriou s Tulare Co W 368,02 1
Scra nton-Wil k e s -Ba r re Area PA S ubpa rt 1 Lacka wa nna Co W 213,29 5
Scra nton-Wil k e s -Ba r re Area PA S ubpa rt 1 Luzern e Co W 319,25 0
Scra nton-Wil k e s -Ba r re Area PA Subpa rt 1 Monroe Co W 138,68 7
Scra nton-Wil k e s -Ba r re Area PA Subpa rt 1 Wyoming Co W 28,080

Sheboyga n Area WI Subpa rt 2/Mode rate Sheboyga n Co W 112,64 6
South Bend-Elkha r t Area IN Subpa rt 1 Elkha r t Co W 182,79 1
South Bend-Elkha r t Area IN Subpa rt 1 St Jose ph Co W 265,55 9

Springfiel d ( W . Mas s ) Ar e a MA Subpa rt 2/Mode rate Berkshi r e Co W 134,95 3

Springfiel d ( W . Mas s ) Ar e a MA Subpa rt 2/Mode rate Fran klin Co W 71,535

Springfiel d ( W . Mas s ) Ar e a MA Subpa rt 2/Mode rate Hamp den Co W 456,22 8

Springfiel d ( W . Mas s ) Ar e a MA Subpa rt 2/Mode rate Hamp shi r e Co W 152,25 1

St. Louis Are a IL Subpa rt 2/Mode rate Jersey Co W 21,668

St. Louis Are a IL Subpa rt 2/Mode rate Madison Co W 258,94 1

St. Louis Are a IL Subpa rt 2/Mode rate Monroe Co W 27,619

St. Louis Are a IL Subpa rt 2/Mode rate St Clair Co W 256,08 2

St. Louis Are a MO Subpa rt 2/Mode rate Fran klin Co W 93,807

St. Louis Are a MO Subpa rt 2/Mode rate Jefferson Co W 198,09 9

St. Louis Are a MO Subpa rt 2/Mode rate St Charle s Co W 283,88 3

St. Louis Are a MO Subpa rt 2/Mode rate St Louis W 348,18 9

St. Louis Are a MO Subpa rt 2/Mode rate St Louis Co W 1,016,31 5 State College Area PA Subpa rt 1 Centre Co W 135,75 8
Steubenville-Weirto n Area OH Subpa rt 1 Jefferson Co W 73,894
Steubenville-Weirto n Area WV Subpa rt 1 Broo ke Co W 25,447
Steubenville-Weirto n Area WV Subpa rt 1 Han c o c k Co W 32,667 Sutter County (part ) (Sutter Buttes ) Area CA Subpa rt 1 Sutter Co P 1 Tioga County Area PA Subpa rt 1 Tioga Co W 41,373
Toledo Area OH Subpa rt 1 Lucas Co W 455,05 4
Toledo Area OH Subpa rt 1 Woo d Co W 121,06 5

Ventura Cou n ty (part) Are a CA Subpa rt 2/Mode rate Ventura Co P 753,19 7

Wa shin gton Area DC Subpa rt 2/Mode rate Entire Di strict W 572,05 9

Wa shin gton Area MD Subpa rt 2/Mode rate Calvert Co W 74,563
Wa shin gton Area MD Subpa rt Cha r le s Co W 120,54 6


### 3-1572/Mode rate

Wa shin gton Area MD Subpa rt 2/Mode rate Fred eri ck Co W 195,27 7

Wa shin gton Area MD Subpa rt 2/Mode rate Montgom ery Co W 873,34 1

Wa shin gton Area MD Subpa rt 2/Mode rate Prince Geo r g e's Co W 801,51 5

Wa shin gton Area VA Subpa rt 2/Mode rate Alexandri a W 128,28 3

Wa shin gton Area VA Subpa rt 2/Mode rate Arlington Co W 189,45 3

Wa shin gton Area VA Subpa rt 2/Mode rate Fairfax W 21,498

Wa shin gton Area VA Subpa rt 2/Mode rate Fairfax Co W 969,74 9

Wa shin gton Area VA Subpa rt 2/Mode rate Falls Chu r ch W 10,377

Wa shin gton Area VA Subpa rt 2/Mode rate Loud oun Co W 169,59 9

Wa shin gton Area VA Subpa rt 2/Mode rate Mana ssas W 35,135

Wa shin gton Area VA Subpa rt 2/Mode rate Mana ssas Pa rk W 10,290

Wa shin gton Area VA Subpa rt 2/Mode rate Prince William Co W 280,81 3 Wa shin gton Cou nty (Hag ersto w n ) Area MD Subpa rt 1 - EAC Wa shin gton Co W 131,92 3
Whe e ling Are a OH Subpa rt 1 Belmont Co W 70,226
Whe e ling Are a WV Subpa rt 1 Marshall Co W 35,519
Whe e ling Are a WV Subpa rt 1 Ohio Co W 47,427
York Area PA Subpa rt 1 Adams Co W 91,292
York Area PA Subpa rt 1 York Co W 381,75 1 Young stown-Wa rre n-Sh aron Area OH S ubpa rt 1 Colum bian a Co W 112,07 5 Young stown-Wa rre n-Sh aron Area OH Subpa rt 1 Mahoni ng Co W 257,55 5 Young stown-Wa rre n-Sh aron Area OH Subpa rt 1 Trumb ull Co W 225,11 6 Young stown-Wa rre n-Sh aron Area PA S ubpa rt 1 Mercer Co W 120,29 3

a) Under the C A A these nona ttain m ent areas are furthe r cla ssif ied as su bpar t 1 or sub p a rt 2 (s ub pa rt 2 is f u rthe r cl assi fi ed as m a rgi n al , m oder a t e , seri o u s , se vere o r e x t r em e) based o n t h e i r desi gn val u e s

. An Ea rl y A c t i on Com p act (EAC) area is one t h at ha s e n tere d into a com p act with t h e E P A and ha s a g r e ed to r e du ce g r oun d lev e l

o z on e po llu tion earlier th an t h e C AA wo u l d req u i re in ex ch ang e t h e EPA will d e fer t h e effectiv e d a te o f th e

no nat t a i n m e nt desi g n at i o n. T h e se vere de si g n at i o n i s den o t e d as seve re- 1 5 or seve re- 1 7 base d on t h e m a xi m u m attain m ent date associated wit h the classifica tion.

b) B o st on -M an chest e r

- P o rt sm out h (SE ) , N H has t h e sam e classification a s Bosto n -La w re nce- Worce ster (E. M A ),
MA.



### 3-158

# Appendix 3C: PM Nonattainment

Table 3C-1. PM 2. 5 

### Nonattainment Areas and Populations (data is current throug h October 2006 and the population numbers are from 2000 census data)

PM 2.5 Nonattainment Are a Population
Atlanta, GA 4,231,75 0 Baltimore, M D 2,512,43 1 Birmingh am, AL 807,61 2
Canto n -M assillon, OH 378,09 8
Cha r le ston, WV 251,66 2
Chattan oog a, AL-TN-GA 423,80 9
Chi c ag o-G ary-Lake Co unt y, IL-IN 8,757,80 8
Cin c inn a ti-Ha m ilton, OH-K Y-IN 1,850,97 5
Clevela nd-A k ron

- Lo rain, O H 2,775,44 7
Colum b u s , OH 1,448,50 3 Dayton-S p rin g field, OH 851,69 0
Detroit

- Ann A r bo r, MI 4,833,49 3 Evansville, IN 277,40 2
Gree nsbo ro-Win ston Sale m-Hi gh Point, NC 568,29 4
Harri s bu rg -Le bano n-Ca rlisl e, PA 585,79 9
Hicko r y, NC 141,68 5
Huntin gton-A s hla nd, WV-K Y-OH 340,77 6
Indiana polis, IN 1,329,18 5
Joh n sto w n, PA 164,43 1 Knoxville, TN 599,00 8
Lan ca ster, PA 470,65 8 Libby, MT 2,626 Liberty-Clai r ton, PA 21,600 Los Ang e le s-South Coa s t Air Basin, CA 14,593,5 8 7
Loui sville, KY-IN 938,90 5
Maco n, GA 154,83 7
Martin sbu r g, WV-Hag ersto w n, MD 207,82 8
Ne w York-N. Ne w Je rsey-L ong Isla nd,NY-NJ-CT 19,802,5 8 7
Parkersbu r g

- Marietta, WV-OH 152,91 2 Philadelp h ia-Wilmingto n , PA-NJ-DE 5,536,91 1 Pittsburg h -Be a ver Valley, PA 2,195,05 4
Rea d ing, PA 373,63 8 Rome, GA 90,565
San Joa quin Valley, CA 3,191,36 7 St. Louis, MO-IL 2,486,56 2
Steubenville-Weirto n , OH-WV 132,00 8
Wa shin gton, DC-M D-VA 4,377,93 5
Whe e ling, WV-OH 153,17 2 York, PA 381,75 1

Total 88,394,3 6 1



### 3-159

### Table 3C-2. PM 10 

### Nonattainment Areas and Populations (data is current throug h March 2006 and the population numbers are from 2000 census data)

PM10 No nattainment Are a s Liste d Alph abetically Cla ssifi cation Numb er 2000 EPA State of Population Regi on Cou nties (thou san d s)

NAA

Ajo (Pima Co unty), AZ Mode rate 1 8 9 AZ Anthony, NM Mode rate 1 3 6 NM
Bonne r Co (S andp oint), ID Mode rate 1 37 10 ID Butte, MT Moderate 1 35 8 MT Clark Co, NV Seriou s 1 1,376 9 N V
Coa c h e lla Va lley, CA Seriou s 1 182 9 C A
Colum b ia Fall s, MT Moderate 1 4 8 MT
Cos o Junc tion, CA Moderate 1 7 9 CA
Dou gla s (Cochise Cou nty), AZ Mode rate 1 16 9 A Z Eagle Rive r, AK Mode rate 1 195 10 AK El Paso Co, TX Mode rate 1 564 6 TX Eugene -Sp r in gfield, OR Mode rate 1 179 10 OR
Flathead Cou n ty; Whitefish and vicinity, MT Mode rate 1 5 8 MT
Fort Hall Re servation, ID Mode rate 2 1 1 0 I D Hayden/Miami, AZ Moderate 2 4 9 AZ Imperial Valle y, CA Seriou s 1 120 9 C A
Jun eau, AK Mode rate 1 14 10 AK Kalispell, MT Mode rate 1 15 8 M T
LaGrand e, OR Mode rate 1 12 10 OR Lake Co, O R Mode rate 1 3 1 0 OR
Lame Dee r , MT Mode rate 1 1 8 MT Lane Co, OR Mode rate 1 3 1 0 OR Libby, MT Mode rate 1 3 8 MT Los Ang ele s South Coa s t Air Basin, CA Seriou s 4 14,594 9 C A
Medford-A s hl and, OR Mode rate 1 78 10 OR Missoula, MT Mode rate 1 52 8 M T
Mono Ba sin, CA Mode rate 1 0 9 CA Mun. of Guay nabo, PR Mode rate 1 92 2 P R
Ne w York Co , NY Mode rate 1 1,537 2 N Y Nog a le s, AZ Mode rate 1 25 9 A Z Ogde n, UT Mode rate 1 77 8 U T
Owens Valley

, CA Serious 1 7 9 CA Paul Spur, AZ Mode rate 1 1 9 AZ Phoenix, AZ Seriou s 2 3,112 9 A Z
Pinehu rst, ID Mode rate 1 2 1 0 I D Polson, MT Mode rate 1 4 8 MT
Portneuf Vall ey, ID Mode rate 2 66 10 ID Rillito, AZ Moderate 1 1 9 AZ
Ron an, MT Mode rate 1 3 8 MT Sacramento Co, CA Mode rate 1 1,223 9 C A Salt Lake Co, UT Mode rate 1 898 8 U T
San Berna r di no Co, CA Mode rate 1 199 9 C A
San Joa quin Valley, CA Seriou s 7 3,080 9 C A



### 3-160Sande rs Cou n ty (part);Th o m pso n Fall s and vicinity, Mode rate 1 1 8 MT MT Sherid an, WY Mode rate 1 16 8 W Y
Shosh one Co

, ID Mode rate 1 10 10 ID
Tron a, CA Mode rate 1 4 9 CA Utah Co, UT Mode rate 1 369 8 U T
Wa sho e Co, NV Seriou s 1 339 9 N V
Weirto n, WV Mode rate 2 15 3 W V Yuma, AZ Mode rate 1 82 9 A Z

51 Total Area s 51 28,674



### 3-161

# Appendix 3D: Vi sibility T a bles

Table 3D-1. List of 156 Mandatory Class I Fe deral Areas Where Visib ility is an I m portant Value (As Listed in 40 CFR 81) *

Federal Land State Area Nam e Acre age Manag e r
Alabama Sipsey Wild erness Are a 12,646 USDA-FS
Alaska Bering Sea Wilde r ne ss Area 41,113 USDI-FWS
Den a li NP (fo r merly Mt. McKinley NP) 1,949,49 3 USDI-NPS
Simeonof Wil dern e ss Are a 25,141 USDI-FWS
Tuxedni Wil d erne ss Area 6,402 USDI-FWS

Arizo na Chiri c a hua National Mon u m ent Wilde r n e ss Area 9,440 USDI-NPS

Chiri c a hua Wildern e ss Are a 18,000 USDA-FS
Galiuro Wild e r ne ss A r ea 52,717 USDA-FS
Gran d Ca nyo n NP 1,176,91 3 USDI-NPS
Mazat z al Wildern e ss Are a 205,13 7 USDA-FS Mount Baldy Wilde r ne ss Area 6,975 USDA-FS
Petrified Fore st NP 93,493 USDI-NPS
Pine Mountai n Wilde r ne ss Area 20,061 USDA-FS Sagua ro Wil d erne ss Area 71,400 USDI-FS
Sierra An ch a Wilde r ne ss Area 20,850 USDA-FS Superstition Wilde r ne ss Area 124,11 7 USDA-FS Sycamore Ca nyon Wild ern e ss Area 47,757 USDA-FS
Arka nsas Can ey Cre ek Wilde r ne ss Area 4,344 USDA-FS Upp e r Buffalo Wilderne ss Area 9,912 USDA-FS
California Agua Tibia Wildern e ss Are a 15,934 USDA-FS
Cari bou Wild erne ss Area 19,080 USDA-FS
Cu camo nga Wilde r ne ss Area 9,022 USDA-FS
De solatio n Wildern e ss Are a 63,469 USDA-FS
Dome L and Wilde r ne ss Area 62,206 USDA-FS Emigrant Wildern e ss Are a 104,31 1 USDA-FS
Hoove r Wild e r ne ss A r ea 47,916 USDA-FS
Joh n Muir Wil dern e ss Are a 484,67 3 USDA-FS
Jo shu a Tre e Wilde r ne ss Area 429,69 0 USDI-NPS 36,300 USDI-BL M
Kaise r Wild erness Are a 22,500 USDA-FS Kings Canyo n NP 459,99 4 USDI-NPS
Lasse n Volca n ic NP 105,80 0 USDI-NPS
Lava Beds Wildern e ss Are a 28,640 USDI-NPS
Marbl e Mount ain Wild ern e ss Area 213,74 3 USDA-FS
Minarets Wil d erne ss Area 109,48 4 USDA-FS
Mokel u mme Wilde r ne ss Area 50,400 USDA-FS
Pinnacl e s Wil dern e ss Are a 12,952 USDI-NPS Point Reyes Wilde r ne ss Area 25,370 USDI-NPS
Red w o od NP 27,792 USDI-NPS



### 3-162Federal Land State Area Nam e Acre age Manag e r
San Gab r iel Wilde r ne ss Area 36,137 USDA-FS San Gorgoni o Wilde r ne ss Area 56,722 USDA-FS 37,980 USDI-BL M
San Ja cinto Wilde r ne ss Area 20,564 USDA-FS
San Rafael Wilde r ne ss Area 142,72 2 USDA-FS Sequoia NP 386,64 2 USDI-NS
South Wa rne r Wilde r n e ss Area 68,507 USDA-FS
Thou san d La ke s Wild ern e ss Area 15,695 USDA-FS
Ventana Wil d erne ss Area 95,152 USDA-FS Yolla Bolly-Mi ddle Eel Wild erne ss Area 111,84 1 USDA-FS 42,000 USDI-BL M
Yosemite NP 759,17 2 USDI-NPS

Colo rad o Black Canyo n of the Gunn ison Wilde r ne ss Area 11,180 USDI-NPS

Eagles Ne st Wilde r ne ss Area 133,91 0 USDA-FS Flat Tops Wil dern e ss Are a 235,23 0 USDA-FS
Great San d Dune s Wild ern e ss Area 33,450 USDI-NPS
La Garita Wil dern e ss Are a 48,486 USDA-FS
Maro on Bells-Sno wma ss Wilde r ne ss Area 71,060 USDA-FS
Mesa Ve rde NP 51,488 USDI-NPS
Mount Zirkel Wilde r ne ss Area 72,472 USDA-FS
Ra wah Wilde r ne ss A r ea 26,674 USDA-FS
Ro cky Mou n tain NP 263,13 8 USDI-NPS
Wemin u che Wilde r ne ss Area 400,90 7 USDA-FS
We st Elk Wil dern e ss Are a 61,412 USDA-FS
Florid a Cha s sah owit z ka Wild ern e ss Area 23,360 USDI-FWS Everglad es NP 1,397,42 9 USDI-NPS
St. Marks Wil dern e ss Are a 17,745 USDI-FWS Georgia Coh o tta Wild erne ss Area 33,776 USDA-FS
Okefe no k ee Wilde r ne ss Area 343,85 0 USDI-FWS
Wolf Islan d Wilde r ne ss Area 5,126 USDI-FWS
Ha waii Hale akala NP 27,208 USDI-NPS
Ha waii Volca noe s NP 217,02 9 USDI-NPS
Idaho Crate r s of the Moon Wild erness Are a a 43,243 USDI-NPS
Hell s Ca nyon Wilderne ss Area 83,800 USDA-FS
Sawtooth Wildern e ss Are a 216,38 3 USDA-FS Selway-Bitterroot Wild erne ss Area b 988,77 0 USDA-FS
Yellowstone NP c 31,488 USDI-NPS Kentucky Mammoth Ca ve NP 51,303 USDI-NPS
Loui siana Breton Wil derness Are a 5,000 + USDI-FWS
Maine Acadia Natio nal Park 37,503 USDI-NPS
Mooseho rn Wilde r ne ss Area 7,501 USDI-FWS
Edmund s Uni t 2,706 USDI-FWS Baring Unit 4,680 USDI-FWS Michig an Isle Royal e NP 542,42 8 USDI-NPS Seney Wilde r ness Are a 25,150 USDI-FWS


### 3-163Federal Land State Area Nam e Acre age Manag e r

Minne sota Bounda ry Wa ters Cano e Area Wilde r ne ss Area 747,84 0 USDA-FS

Voyageu rs NP 114,96 4 USDI-NPS
Missou ri Hercul es-Gl a des Wilde r ne ss Area 12,315 USDA-FS Mingo Wil de r ness Are a 8,000 USDI-FWS
Montana Anaco nda -Pi n tlar Wild ern e ss Area 157,80 3 USDA-FS
Bob Marshall Wilde r ne ss Area 950,00 0 USDA-FS
Cabi net Mou n tains Wilde r ness Are a 94,272 USDA-FS
Gates of the Mtn Wilde r ne ss Area 28,562 USDA-FS
Glaci e r NP 1,012,59 9 USDI-NPS
Medici ne La ke Wilde r ne ss Area 11,366 USDI-FWS
Missi on Mo u n tain Wild ern e ss Area 73,877 USDA-FS
Red Ro ck L a k e s Wild ern e ss Area 32,350 USDI-FWS Scape goat Wildern e ss Are a 239,29 5 USDA-FS Selway-Bitterroot Wild erne ss Area d 251,93 0 USDA-FS
U. L. Bend Wildern e ss Are a 20,890 USDI-FWS
Yellowstone NP e 167,62 4 USDI-NPS
Nevad a Jarbidg e Wild erne ss Area 64,667 USDA-FS
Ne w Ham p sh ire Great G u lf Wi ldern e ss Are a 5,552 USDA-FS
Presi dential Ran ge-Dry Ri ver Wilde r n e ss Area 20,000 USDA-FS

Ne w Je rsey Brigantin e Wi lder n e ss Are a 6,603 USDI-FWS
Ne w Mexico Bandelie r Wil dern ess Are a 23,267 USDI-NPS Bosqu e del A pache Wil der ness Are a 80,850 USDI-FWS
Carl sb ad Cavern s NP 46,435 USDI-NPS
Gila Wild ern e ss Area 433,69 0 USDA-FS
Peco s Wild erness Are a 167,41 6 USDA-FS
Salt Cree k Wi ldern ess Are a 8,500 USDI-FWS
San Pedro P arks Wilde r ne ss Area 41,132 USDA-FS
Whe e ler Pe a k Wild ern e ss Area 6,027 USDA-FS
White Mou n tain Wild ern e ss Area 31,171 USDA-FS
North Ca rolin a Great Smo k y Mountain s NP f 273,55 1 USDI-NPS
J o yce Kilmer-Slic kr oc k W i lder ness Ar ea g 10,201 USDA-FS Linville Gorge Wilderne ss Area 7,575 USDA-FS Shining Rock Wilderne ss Area 13,350 USDA-FS Swanq ua rter Wilde r ne ss Area 9,000 USDI-FWS
North Da kota Lost w oo d Wil dern ess 5,557 USDI-FWS
Theod ore Ro osevelt NP 69,675 USDI-NPS
Okla homa Wichita Moun tains Wil dern e ss 8,900 USDI-FWS Oreg on Crate r La ke NP 160,29 0 USDA-NPS
Diamo nd Pea k Wild ern e ss 36,637 USDA-FS Eagle Ca p Wi ldern ess 293,47 6 USDA-FS
Gearha rt Mountain Wi l derness 18,709 USDA-FS
Hell s Ca nyon Wilderne ss a 108,90 0 USDA-FS 22,700 USDI-BL M Kalmiop s is Wildern e ss 76,900 USDA-FS



### 3-164Federal Land State Area Nam e Acre age Manag e r

Mountain L a kes Wil derne ss 23,071 USDA-FS
Mount Ho od Wilde r ne ss 14,160 USDA-FS
Mount Jeffe rson Wild ern e ss 100,20 8 USDA-FS
Mount Washi ngton Wil derness 46,116 USDA-FS
Strawb erry Mountain Wilde r ne ss 33,003 USDA-FS
Thre e Sisters Wilderne ss 199,90 2 USDA-FS
South Ca rolin a Cap e Rom ain Wilderne ss 28,000 USDI-FWS
South Da kota Badland s Wil dern ess 64,250 USDI-NPS
Wind Cave NP 28,060 USDI-NPS
Tenne ssee Great Smo k y Mountain s NP f 241,20 7 USDI-NPS
J o yce Kilmer-Slic kr oc k W i lder ness g 3,832 USDA-FS
Texas Big Bend NP 708,11 8 USDI-NPS
Guad alup e Mountain s NP 76,292 USDI-NPS
Utah Arch es NP 65,098 USDI-NPS Bryce Canyo n NP 35,832 USDI-NPS Canyo n land s NP 337,57 0 USDI-NPS Capitol Reef NP 221,89 6 USDI-NPS
Zion NP 142,46 2 USDI-NPS
Vermont Lye Broo k Wi ldern ess 12,430 USDA-FS Virgin Isla nd s Virgin Isla nd s NP 12,295 USDI-NPS Virginia Jame s Rive r Face Wilde r n ess 8,703 USDA-FS
Shenan doa h NP 190,53 5 USDI-NPS
Wa shin gton Alpine La ke s Wilde r ne ss 303,50 8 USDA-FS
Glaci e r Pea k Wilde r ne ss 464,25 8 USDA-FS
Goat Ro cks Wilde r ne ss 82,680 USDA-FS
Mount Adam s Wild ern e ss 32,356 USDA-FS
Mount Rai n e r NP 235,23 9 USDI-NPS
North Ca scad es NP 503,27 7 USDI-NPS Olympic NP 892,57 8 USDI-NPS Pasayten Wildern e ss 505,52 4 USDA-FS
We st Virginia Dolly Sods Wildern e ss 10,215 USDA-FS
Otter Cree k Wilde r ne ss 20,000 USDA-FS Wyoming Bridge r Wild e r ne ss 392,16 0 USDA-FS Fitzpatri ck Wi ldern e ss 191,10 3 USDA-FS
Gran d Teton NP 305,50 4 USDI-NPS
North Ab sa ro ka Wil derne ss 351,10 4 USDA-FS
Teton Wild erness 557,31 1 USDA-FS
Wa sha k ie Wil dern ess 686,58 4 USDA-FS
Yellowstone NP h 2,020,62 5 USDI-NPS
Ne w Brun s w i ck, Can ada Roo s evelt Ca mpobell o Internation a l Park 2,721 i

* U.S. EPA (2 001) Vi sibility in Mandatory Federal Clas s I Areas (1994-1998): A Report to Congress. EPA-452/R-01-008. This docu m ent is available in Do cket EPA-HQ-OAR-2005-0036.



### 3-165a) Hell s Cany on Wild ern ess Area, 19 2,7 00 acre s ov erall, of which 1 08,900 a c res are in O r eg on and 83,800 a c res are in Idah o.

b) Selway Bitterroot Wild erness Ar e a, 1,240,70 0 acre s overall, of which 9 88,70 0 acres a r e in I daho a nd 251,93 0 acre s are in M ont ana.

c) Yello wston e Nation al Park, 2,21 9,737 acres ove r all, of which 2,0 20,625 a c res are in Wyomi ng, 167,62 4 acre s are in M ont ana, and 3 1,4 88 acre s are in Idaho

d) Selway-Bitterro ot Wilde r ness Ar e a, 1,240,70 0 acre s overall, of which 9 88,77 0 acres a r e in I daho a nd 251,93 0 acre s are in M ont ana.

e) Yello wsto n e Nation al Park, 2,21 9,737 acres ove r all, of which 2,0 20,625 a c res are in Wyomi ng, 167,62 4 acre s are in M ont ana, and 3 1 ,4 88 acre s are in Idaho.

f) Great Smo k y Mountain s National Pa rk, 514,75 8 acre s ove r all, of which 273, 5 51 acre s are in North Carolina, an d 241,207 a c re s are in T enn essee.

g) Joy c e Kilm er-Sli ckro ck Wilde r ne ss Area, 14,03 3 a c re s ove r all, of which 10,2 01 acre s are in North Carolina, an d 3,832 acre s are in Te nne ssee.

h) Yello wsto n e Nation al Park, 2,21 9,737 acres ove r all, of which 2,0 20,625 a c res are in Wyomi ng, 167,62 4 acre s are in M ont ana, and 3 1 ,4 88 acre s are in Idaho.

i) Chai rma n, RCIP Co mmi ssi on.

Abbreviatio n s Use d in Tabl e: USDA-FS: U.S. Departme n t of Agri cultu r e, U.S. Fore st Service USDI-BL M: U.S. Departme n t of Interior, Burea u of La nd Mana gem ent USDI-FWS: U.S. Depa rtm ent of In terior, Fish and Wil d life Service USDI-NPS: U.S. Departme n t of In terior, Nation al Park Service



### 3-166

### Table 3D-2. Current (1 998-2002) Visibility, P r ojected (20 15) Visibility, and Natural Background Levels for the 20 % W orst Days at 116 IMPROVE Sites

Class I Area Nam e a State 19 9 8

- 2 00 2 B a s e l i n e Visibility (deciviews) b 20 1 5 CAI R C o nt rol Ca se Visibility c

(deciviews) Na tur a l
Back ground (deciviews)

Acadi a M E 22 .7 21 .0 11 .5 Ag ua Ti bi a C A 23 .2 23 .2 7. 2
Al pi ne La kes WA 18 .0 17 .4 7. 9
Anac o nda
- Pi nt l e r M T 12 .3 12 .2 7. 3
Arc h es UT 12 .0 12 .1 7. 0

B a dl an ds SD 17 .3 16 .8 7. 3

B a ndel i e r NM 13 .2 13 .2 7. 0

B i g B e n d TX 18 .4 18 .3 6. 9

B l ack C a ny o n of t h e G u n n i s o n C O 11 .6 11 .4 7. 1

B ob M a rs hal l M T 14 .2 14 .0 7. 4

B o u n d ary Wat e rs C a noe A re a M N 20 .0 19 .0 11 .2

B ri dge r WY 11 .5 11 .3 7. 1

B ri g ant i n e NJ 27 .6 25 .4 11 .3

Br yce Can yon U T 1 2 .0 1 1 .9 7

. 0
C a bi net M o u n t a i n s M T 13 .8 13 .4 7. 4
C a ney C ree k AR 25 .9 24 .1 11 .3
C a ny o n l a n d s UT 12 .0 12 .0 7. 0
C a pe R o m a i n SC 25 .9 23 .9 11 .4
C a ri bo u C A 14 .8 14 .6 7. 3
C a rl sba d C a ve rns NM 17 .6 17 .9 7. 0
C h assa ho wi t z k a FL 25 .7 23 .0 11 .5
C h i ri cah ua N M AZ 13 .9 13 .9 6. 9
C h i ri cah ua W AZ 13 .9 13 .9 6. 9
C rat ers of t h e M o o n ID 14 .7 14 .7 7. 1
Desol a t i o n C A 12 .9 12 .8 7. 1 Dolly S o ds WV 27.6 23.9 11.3
Dom e Lan d C A 20 .3 19 .9 7. 1 Eagl e C a p OR 19 .6 19 .0 7. 3 Eagl es Nest C O 11 .3 11 .4 7. 1

Em i g rant C A 17 .6 17 .4 7. 1
Ever gl ade s FL 20 .3 19 .2 11 .2
Fi t z pat ri c k WY 11 .5 11 .3 7. 1
Fl at To ps C O 11 .3 11 .4 7. 1
Gal i u r o AZ 13 .9 14 .1 6. 9
Gat e s of t h e M o u n t a i n s M T 11 .2 10 .8 7. 2
Gi l a NM 13 .5 13 .5 7. 0
Gl aci er M T 19 .5 19 .1 7. 6
Gl aci er Pea k WA 14 .0 13 .8 7. 8
Gra n d Tet o n WY 12 .1 12 .0 7. 1
Great G u l f NH 23 .2 21 .2 11 .3
Great San d D u nes C O 13 .1 13 .0 7. 1
Great Sm oky M o u n t a i n s TN 29 .5 26 .1 11 .4
Gua d al u p e M o unt ai n s TX 17 .6 17 .5 7. 0
Hel l s C a ny o n OR 18 .1 18 .0 7. 3 Isle Royale MI 21.1 20.1 11.2



### 3-167Class I Area Nam e a State 19 9 8

- 2 00 2 B a s e l i n e Visibility (deciviews) b 20 1 5 CAI R C o nt rol Ca se Visibility c

(deciviews) Na tur a l
Back ground (deciviews)

Jam e s River Face VA 28.5 25.1 11.2
Jarbi dge NV 12 .6 12 .8 7. 1
Jos hua Tree C A 19 .5 20 .3 7. 1 Joy ce Ki lm er - Sl i c kr oc k NC 29 .5 26 .1 11 .5
Kal m i opsi s OR 14 .8 14 .4 7. 7
K i ng s Can yon CA 2 3 .5 2 4 .1 7

. 1
La Ga ri t a C O 11 .6 11 .5 7. 1
Lassen V o l can i c C A 14 .8 14 .6 7. 3
Lava B e ds C A 16 .6 16 .5 7. 5
Lin v ille Gorg e NC 2 7 .9 2 4 .6 1 1 .4
Lost w o od ND 19 .6 18 .7 7. 3 Ly e B ro o k VT 23 .9 21 .1 11 .3

M a m m ot h C a ve KY 30 .2 27 .0 11 .5
M a rbl e M ou nt a i n C A 17 .1 16 .8 7. 7
M a ro on B e l l s - Sn o wm a ss C O 11 .3 11 .3 7. 1
Mazatzal AZ 13.1 13.5 6.9

M e di ci ne La ke M T 17 .7 17 .1 7. 3

M e sa Ve rde C O 12 .8 12 .8 7. 1

M i ng o M O 27 .5 25 .9 11 .3

M i ssi on M o u n t a i n s M T 14 .2 14 .0 7. 4

M okel u m n e C A 12 .9 12 .8 7. 1

M oose h or n M E 21 .4 20 .3 11 .4

M ou nt H o o d OR 14 .0 13 .7 7. 8

M o u n t Je ffe rso n OR 15 .7 15 .2 7. 8

M ou nt R a i ni e r WA 18 .9 19 .4 7. 9

M o u n t Washi n gt o n OR 15 .7 15 .2 7. 9

M o u n t Zi r k el C O 11 .7 11 .8 7. 1
N o rt h C a scade s WA 14 .0 14 .0 7. 8
Oke fe n o k ee GA 26 .4 24 .7 11 .5
Ot t e r C ree k WV 27 .6 24 .0 11 .3 Pasay t en WA 14 .7 14 .5 7. 8
Pet ri fi e d Fo res t AZ 13 .5 13 .8 7. 0
Pi ne M o u n t a i n AZ 13 .1 13 .4 6. 9
Presi d e n t i a l R a nge - D ry NH 23 .2 20 .9 11 .3

R a wah C O 11 .7 11 .7 7. 1

R e d R o c k Lak e s WY 12 .1 12 .1 7. 1

R e dw o od C A 16 .5 16 .5 7. 8

R ocky M ou nt a i n C O 14 .1 14 .1 7. 1

R oose v el t C a m p o b el l o M E 21 .4 20 .1 11 .4
Sal t C reek NM 17 .7 17 .3 7. 0
San G o r g o n i o C A 21 .5 22 .1 7. 1
San J aci nt o C A 21 .5 21 .4 7. 1
San Ped r o Pa r k s NM 11 .4 11 .4 7. 0
Sawt o o t h ID 13 .6 13 .5 7. 2 Scape goat M T 14 .2 14 .1 7. 3 Selway - Bitterroo t MT 1 2 .3 1 2 .1 7

. 3 Seney M I 23 .8 22 .6 11 .4 Seq uoi a C A 23 .5 24 .1 7. 1
She n an d o ah VA 27 .6 23 .4 11 .3



### 3-168Class I Area Nam e a State 19 9 8

- 2 00 2 B a s e l i n e Visibility (deciviews) b 20 1 5 CAI R C o nt rol Ca se Visibility c

(deciviews) Na tur a l
Back ground (deciviews)

Si erra A n cha AZ 13 .4 13 .7 6. 9
Si psey AL 28 .7 26 .1 11 .4
So ut h Wa rne r C A 16 .6 16 .5 7. 3
St raw b er ry M o unt ai n OR 19 .6 19 .2 7. 5 Sup e rstitio n AZ 1 4 .7 1 5 .0 6

. 9
Swa n q u a rt e r NC 24 .6 21 .9 11 .2 Sy cam o re C a n y on AZ 16 .1 16 .6 7. 0
Tet on WY 12 .1 12 .1 7. 1
The o d o re R o os evel t ND 17 .6 16 .8 7. 3
Th ousa n d La k e s C A 14 .8 14 .6 7. 3
Three Si st ers OR 15 .7 15 .2 7. 9
UL B e nd M T 14 .7 14 .1 7. 2 Up pe r B u ffal o AR 25 .5 24 .3 11 .3 Voy a geu rs M N 18 .4 17 .6 11 .1
Wem i nuche C O 11 .6 11 .4 7. 1
West El k C O 11 .3 11 .3 7. 1
W i n d C a ve SD 16 .0 15 .4 7. 2
Wol f I s land GA 26.4 24.9 11.4
Yel l o w s t o ne WY 12 .1 12 .1 7. 1
Yol l a B o l l y - M i ddl e Eel C A 17 .1 16 .9 7. 4
Yosem i t e C A 17 .6 17 .4 7. 1
Zi on UT 13 .5 13 .3 7. 0

a) 11 6 IM PR O V E si t e s rep r es ent 1 5 5 of t h e 15 6 M a n d at ory Class I Fede ra l Areas

. One i solated Ma ndat o ry Class I Fede ral Area (Bering Sea, an uni nha bited and infre quently vi sited island 20 0 m i les fro m t h e co ast o f Alask a ), was considere d t o be so rem o te from e l ect ri cal po wer an d pe opl e t h at i t

w oul d b e im pract i cal t o c o l l ect ro ut i n e aer osol

sam p les. U.S. EPA (2003) guidance for Trac king Progress Unde r the Regi onal Haze R u l e

. E P A-454/B-03-004.
Th is d o c u m en t is av ailab l e i n Do ck et EPA-HQ-OAR -200 5-0 036

.

b) The deci vi e w m e t r i c descr i bes percei v e d vi sual c h a nges i n a l i n ea r fash i on o v er i t s ent i re ra nge , a n al og o u s t o t h e d ecib e l scale fo r soun d. A d e civ i ew o f 0 represen ts pris tin e con d ition s . The h igh er th e d e civ iew v a lu e, t h e worse t h e

v i sib ility, an d an im p r ov em e n t in v i sib ility is a d ecrease in d eciv i ew v a l u e.

c) T h e 20 1 5 m odel i n g p r o j ect i ons are base d on t h e Clear Ai r Interstate R u le analyses (EPA, 2005).



### 3-169

### References for Chapter 3

1 

### U. S. EPA ( 2003) National Air Quality and Trends Report, 2003 Sp ecial Studies Edition. Office of Air Quality Planning and Standards, Re s earch Tr iang le Park, NC. Publica tion No. EPA 454/R-03-005. http://www.epa.gov/air/airtrends/aqtrnd03/

. T h is docum ent is av ailable in Docket EPA-HQ-OAR-2005-0036.

2 

### U. S. EPA ( 2004) Air T oxics W e bsite. http://www.epa.gov/ttn/atw/stprogs.htm l

3 

### U. S. EPA ( 2004) Natio nal Air Toxics Monito ring Strategy, Draft. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January 2004. http://www.epa.gov/ttn/am tic/file s/ambient/airto x/atstrat104.pdf

.
4 

### Environm e ntal Protection Agency (2006) Nationa l Air Toxics Trends Stations. [Online at http://www.epa.gov/ttn/am tic/natts.htm l ]

5 

### Kenski, D; Koerber, M.; Hafner, H. et al. ( 2005) Lessons learned from air toxics data. A national perspective. Environ Manage. June 2005: 19-22.

6 

### Toum a, J.S. ; Cox, W .M. ; Tikvart, J.A. (2006) Sp atial and temporal vari ability of ambient air toxics d ata. J Air & W aste Manag e Assoc (in press).

7 

### Reiss, R. (2006) Tem poral trend and week end-weekday differences for benzene and 1,3­ butadiene in Houston, Texas. Atm o s Environ 40: 4711-4724.

8 

### Harley, R.A.; Hooper, D.S.; Kean, A.J.; Kirc hstetter, T.W .; Hesson, J.M.; Balberan, N.T.; Stevenson, E.D.; Kendall, G.R. (2006) Effects of reform ulated gasoline and m otor vehicle fleet

turnover on em issions and am bient concentrations of benzene. Environ Sci Technol 40: 5084­
5088.

9 

### Aleksic, N.; Boynton, G.; Sistla, G.; Perry, J. (2 005) Concentrations and trends of benzene in

am bient air over New York State duri ng 1990-2003. At m os Environ 39: 7894-7905.

10 

### California Air Resources Board (2005) The Cali fornia Alm a nac of Em issions and Air Quality

- 2005 Edition. http://w ww.arb.ca.gov/aqd/alm a nac/alm a nac05/alm a nac2005all.pdf

.
11 

### Oomm en, R.; Hauser, J.; Dayt on, D; Brooks, G. (2005) Eval uating HAP trends: A look at

em issions, concentrations, and regulation analys es for selected m e tropolitan statistical areas. Presentation at the 14 th 

### International Em issions Inventor y Conference: Transform i ng Em ission Inventories Meeting Future Chal lenges Today. April 12-14, 2005.

12 

### Clayton, C.A.; Pellizzari, E.D.; W hi t m ore, R.W.; et al. (1999 ) Na tional Hum a n Exposure Assessm ent Survey (NHEXAS): di stributions and associ ations of lead, arsenic, and volatile organic compounds in EPA Region 5. J Exposur e Analysis Environ Epidem iol 9: 381-392.

13 

### Gordon, S.M.; Callahan, P.J.; Nishioka, M. G.; et al. (1999) Residential environm ental

m easure m ents in the National Hum a n Exposur e Assessm ent Survey (NHEXAS) pilot study in Arizona: p r elim inary re sults f o r pes t icide s and VOCs. J Exposure Anal Environ Epidem iol 9:
456-470.

3-17014 

### Payne-Sturges, D.C.; Burke, T.A.; Beysse, P. ; et al. (2004) Personal exposure m eets risk

assessm ent: a com p arison of m easured and m ode le d exposures and risk in an urban community. Environ Health Perspect 112: 589-598.

15 

### W e isel, C.P.; Zhang, J.; Turpin, B.J.; et al. (2005) Relationships of Indoor, Outdoor, and Personal Air (RIOPA). Part I. Collection m e t hods and descriptive analyses. Res Rep Health Effects Inst 130.

16 

### Adgate, J.L.; Eberly, L.E.; Stroebel, C.; et al. (2004) Personal, indoor, and outdoor VOC exposures in a probability sam ple of children. J Exposure Analysis Environ Epidem iol 14: S4­
S13.

17 

### Adgate, J.L.; Church, T.R.; Ryan, A. D.; et al . (2004) Outdoor, indoor, and personal exposures to VOCs in children. E nviron H ealth Perspect 112: 1386-1392.

18 

### Sexton, K.; Adgate, J.L.; Church, T.R.; et al . (2005) Children’s exposure to volatile organic

com pounds as determ ined by longitudinal m easurem en ts in blood. Environ Health P e rsepct 113:
342-349.

19 

### Kinney, P.L.; Chillrud, S.N.; Ram strom , S.; et al. (2002) Ex posures to m u ltip le air to xics in New York City. Environ Health Perspect 110 (suppl 4): 539-546.

20 

### Sax, S.N.; Bennett, D. H.; Chillrud, S.N.; et al. (2004) Differences in sou r ce em ission rates of volatile organic com pounds in innter-city reside nces of New York City and Los Angeles. J Exposure Analysis Environ Epidem iol 14: S95-S109.

21 

### Sax, S.N.; Bennett, D.J .; Chillrud, S.N.; Ross, J.; Kinney, P.L.; Spengler, J.D. (2006) A cancer risk as sessm ent of inner

- city teenage r s liv ing in New York City and Los Angeles. Environ Health Perspect (in press) do i: 10.1289/ehp.8507. [Online at http://dx.doi.org/ ]

22 

### http://www.epa.gov/eogapti1/m odul e3/category/category.htm . This docum ent is ava ilab l e in Docket EPA-HQ-OAR-2005-0036.

23 

### Kittleson, D

. ; W atts, W

. ; Johnson, J. (2002) Dies el aerosol s a mpling m ethodology. Coordinating Research Council report E-43. http://www.crcao.com . Thi s docum ent is available in Docket E P A-HQ-OAR-2005-0036.

24 

### Zhang, K.M.; W e xler, A.S.; Zhu, Y.F.; et al. (2 004) Evolution of partic le num b er distribution near roadways. Part II: the ‘Road-to-Am bient’ process. Atm os Environ 38: 6655-6665.

25 

### Zhu, Y.; Hinds, W .C.; Ki m , S.; et al. (2002) St udy of ultrafine particle s near a m a jor highway with heavy-duty diesel traffi c. Atm o s Environ 36: 4323-4335.

26 

### Zhu, Y.; Hinds, W .C.; Ki m , S.; Sioutas, C. ( 2002) Concentration and size distribution of ultrafine particles near a m ajor highway. J Air & Wast e Manage Assoc 52: 1032-1042.

27 

### Kittelson, D.B.; W a tts, W .F.; Johnson, J.P. (2004) Nanoparticle em ission s on Minnes o ta highways. Atm os Environ 38: 9-19.

28 

### Reponen, T.; Grinshpun, S.A.; Trakum as, S.; et al. (2003) C oncentration gradient patterns of aerosol p ar t icles ne ar in tersta te h i gh ways in th e Greater Cin c innati airs hed. J Environ Monit 5:
557-562.

3-17129 

### Bunn, H.J.; Dinsdale, D.; Sm ith, T.; Grigg, J. (2001) Ultrafine pa rticles in alveolar

m acrophages from nor mal chil dren. Thorax 56: 932-934.

30 

### Kittelson, D

. B.; W atts, W

. F.; Johnson, J.P. (2004) Nanoparticle em ission s on Minnes o ta highways. Atm os Environ 38: 9-19.

31 

### W e sterdahl, D.; Fruin, S.; Sax, T.; Fine, P.M.; Sioutas, C. (2005) Mobile platform

m easurem ents of ultrafine part icles and associated pollutant concentrations on freeways and residential streets in Los Angele s. Atmos Environ 39: 3597-3610.

32 

### Kittelson, D

. B.; W atts, W

. F.; Johnson, J.P. (2004) Nanoparticle em ission s on Minnes o ta highways. Atm os Environ 38: 9-19.

33 

### Sanders, P.G.; Xu, N; Dalka, T.M.; Maricq, M. M. (2003) A i rborne brake wear debris: size distributions, com position, and a comparison of dynam o meter and vehicle tests. Environ Sci Technol 37: 4060-4069.

34 

### Ka m e ns, R. M.; Jang, M.; Lee, S.; et al. (2003) S econdary organic aerosol form ation: som e new and exciting insights. Am er ican Geophysical Union 5: 02915.

35 

### Kupiainen, K.J.; Tervah attu, H; Rais anen, M.; et al. (2005) S i ze and com p osition of airborne particles form pave m e nt wear, tires, and traction sanding. En viron Sci Technol 39: 699-706.

36 

### Sanders, P.G.; Xu, N; Dalka, T.M.; Maricq, M. M. (2003) A i rborne brake wear debris: size distributions, com position, and a comparison of dynam o meter and vehicle tests. Environ Sci Technol 37: 4060-4069.

37 

### Hitchins, J.; Morawska, L.; W ol ff, R.; Gilber t, D. (2000) Concentra tions of subm icrom etre particles from vehicle em issions near a m a jor road. Atm os Environ 34: 51-59.

38 

### Zhang, K.M.; W e xler, A.S. (2004) Evolution of pa rticle number distribution near roadways -
Part I: analysis of aerosol dynam i cs and its im plications for engine em ission m easurem ent. At m os Environ 38: 6643-6653.

39 

### Janssen, N.A.H.; van Vliet, P.H.N.; Aarts, F. ; et al. (2001) A ssessm ent of exposure to traffic related air pollution of children attending sc hools near m otorways. At m o s Environ 35: 3875­
3884.

40 

### Roorda-Knape, M.C.; Janssen, N.A.H.; De Hartog, J.J.; et al. (1998) Ai r pollution from traffic in city districts near m ajor m o to rwa ys. Atm os Environ 32: 1921-1930.

41 

### Kwon, J. (2005) Developm ent of a RIOPA databa se and evaluation of the effect of proxim ity on the potential residential exposure to VOCs fr om a m bient sources. Rutgers, the S t ate University o f New Jersey and University of Medicine and Dentistry of New Jersey. PhD dissertation. This document is avai lable in Docket EPA-HQ-OAR-2005-0036.

42 

### Liu, W .; Zhang, J.; Kwon, J.; W e isel, C.; Turpin, B.; Zhang, L.; Korn, L.; Morandi, M .; Stock, T.; Colom e , S. (2006) Concentrations and source characteristics of ai rborne carbonyl com pounds

m easured outside urban residences. J Air & W a ste Manage A ssoc 56: 1196-1204.

43 

### Skov, H.; Hansen, A.B.; Lorenzen, G.; et al. (2 001) Benzene exposure and the effect of traffic pollution in Copenhagen, Denm ark. At m os Environ 35: 2463-2471.

3-17244 

### Jo, W .; Kim, K.; Park, K.; et al. (2003) Com p arison of outdoor and indoor m obile sourcerelated volatile organic com pounds between low- and high-floor apartm e nts. Environ Res 92:
166-171.

45 

### Fischer, P.H.; Joek, G.; van Reeuwijk, H.; et al. (2000) Traffic-relate d differences in outdoor and indoor concentrations of particle and vol atile organic compounds in Am sterda m

. At m os Environ 34: 3713-3722.

46 

### Ilgen, E.; K a rfich, N.; L e vsen, K.; et al. (2001) A r om atic hydrocarbons in the atm o spheric environm ent: part I. Indoor versus outdoor source s, the influence of tra ffic. Atm o s Environ 35:
1235-1252.

47 

### Rodes, C.; Sheldon, L.; W hitaker, D.; et al. (1 998) Measuring concentrations of selected air pollutants inside California vehicl es. Final r epor t to Calif o rnia Air Resou r ces Board. Contract
No. 95-339.

48 

### Sapkota, A.; Buckley, T .J. (2003) T he m obile source effect on curbside 1,3-butadiene, benzene, and particle-bound polyc yclic arom atic hydrocarbons asse ssed at a tollbooth. J Air Waste Manage Assoc 53: 740-748.

49 

### Sapkota, A.; Buckley, T .J. (2003) T he m obile source effect on curbside 1,3-butadiene, benzene, and particle-bound polyc yclic arom atic hydrocarbons asse ssed at a tollbooth. J Air Waste Manage Assoc 53: 740-748.

50 

### http://www.mde.state.m d.us/Pr ogram s/AirProgram s/airData/ . This docu m ent is available in Docket EPA-HQ-OAR-2005-0036.

51 

### Ilgen, E.; K a rfich, N.; L e vsen, K.; et al. (2001) A r om atic hydrocarbons in the atm o spheric environm ent: part I. Indoor versus outdoor source s, the influence of tra ffic. Atm o s Environ 35:
1235-1252.

52 

### Hoek G.; Meliefste K.; Cyrys J.; et al. (2002) S p a tial variability of fine particle concentrations in three European areas. At m os. Environ. 36: 4077-4088.

53 

### Etyem ezian V.; Kuhns H.; Gillies J. ; et al. (2003 ) Vehicle-based road dus t em ission

m easurem ent (III): effect of speed, traffic volum e , location, and season on PM10 road dust

em issions in the Treasure Valley, ID. At m os. Environ. 37: 4583-4593.

54 

### Harrison R. M.; Tilling R.; Rom e ro M.S.C. ; et al. (2003) A study of trace m e tals and polycyclic arom atic hydrocarbons in the roadside environm ent. Atm o s. Environ. 37: 2391-2402.

55 

### Zhang K.M.; W e xler A.S.; Zhu Y.F. ; et al. ( 2004) Evolution of partic le num b er distribution near roadways. Part II: the ‘Road-to-Am bie nt’ process. Atm o s. Environ. 38: 6655-6665.

56 

### Zhu Y.F.; Hinds W .C.; Shen S.; Sioutas C. (2 004) Seasonal trends of concentration and size distribution of ultrafine particle s near m a jor highways in Los Angeles. Aerosol Sci. T e chnol. 38:
5-13.

57 

### Zhu, Y.; Hinds, W .C.; Ki m , S.; et al. (2002) St udy of ultrafine particle s near a m a jor highway with heavy-duty diesel traffi c. Atm o s Environ 36: 4323-4335.

58 

### Zhu, Y.; Hinds, W .C.; Ki m , S.; Sioutas, C. ( 2002) Concentration and size distribution of ultrafine particles near a m ajor highway. J Air & Wast e Manage Assoc 52: 1032-1042.

3-17359 

### Riediker, M

. ; W illiam s, R.; Devlin, R.; et al. (2 0 03) Exposure to particu l ate m atter, v o latile organic compounds, and other air po llutants inside patrol cars

. Environ Sci Technol 37: 2084­
2093.

60 

### Zielin ska, B.; Fujita, E

. M.; Sagebiel, J.C.; et al. (2002) In terim data report for Section 211(B) Tier 2 high end exposure screen ing study of baseline and oxygena ted gasoline. Prepared for
Am erican Petro l eum Institu te. November 19, 2002. This docum ent is availab l e in Do cket EPAHQ-OAR-2005-0036.

61 

### Rodes, C.; Sheldon, L.; W hitaker, D.; et al. (1 998) Measuring concentrations of selected air pollutants inside California vehicl es. Final r epor t to Calif o rnia Air Resou r ces Board. Contract
No. 95-339.

62 

### Fitz, D. R.; W i ner, A. M.; Colom e, S.; et a l . (200 3) Charac ter i zing the Ra nge of Children’s Pollutant Exposure During School Bus Comm ute s

. Final Report Prepared for the California
Resources B o ard. This docum e nt is available in Docket EPA-HQ-OAR-2005-0036.

63 

### Sabin, L.D.; Behrentz, E.; W i ner, A.M.; et al. (2005) Characterizing the ra nge of children’s air pollutant exposure during school bus commutes. J Expos Anal Environ Epidem iol 15: 377-387.

64 

### Behrentz, E .; Sabin, L.D.; W i ner, A.M.; et al. (2005) Relative importance of school busrelated m i croenvironm ents to children’s polluta nt exposure. J Air & W a ste Manage Assoc 55:
1418-1430.

65 

### Batterm an, S.A.; Peng, C.Y.; and Braun, J. ( 2002) Levels and com pos ition of volatile organic

com pounds on commuting routes in Detroit, Michigan. Atmos Environ 36: 6015-6030.

66 

### Fruin, S.A.; W i ner, A.M.; Rodes, C.E. ( 2004) Black carbon concentrations in California vehicles and estim ation of in-veh icle diesel exhaust particulat e m a tter exposures. Atmos Environ
38: 4123-4133.

67 

### Ada m s, H.S.; Nieuwenhuijsen, M.J.; Colvile, R. N. (2001) Determ inants of fine particle (PM2.5) personal exposure levels in transport m i croenvironments, London, UK. At mos Environ
35: 4557-4566.

68 

### Leung, P.-L.; Harrison, R.M. (1999) Roadside a nd in-vehicle concentrations of m o noarom atic hydrocarbons. Atm os Environ 33: 191-204.

69 

### W e inhold, B. (2001) Pollutants lurk inside vehicles. Environ Health Perspec 109 (9): A422­
A427.

70 

### Riediker, M

. ; W illiam s, R.; Devlin, R.; et al. (2 0 03) Exposure to particu l ate m atter, v o latile organic compounds, and other air po llutants inside patrol cars

. Environ Sci Technol 37: 2084­
2093.

71 

### Van W ijnen J.H.; Verhoeff A. P.; Jans H. W .A.; Van Bruggen M. (1995) T h e exposure of cyclists, car drivers and pedestri ans to traf f i c-r ela ted air pollu tants. Int Arch Occup Environ Health 67: 187-193.

72 

### Chan C.-C.; Ozkaynak H.; Spengler J.D.; Shel don L. (1991) Driver Exposure to Volatile Organic Compounds, CO, Ozone, and NO2 under Di fferent Driving Conditions. Env i ron. Sci. Technol. 25: 964-972.

3-17473 

### Shikiya, D.C., C.S. Liu, M.I. Kahn, et al. (1989) In-vehicle air t oxics characte rization study in the sou t h coast a i r ba s i n. South Coast Air Qu a lity M a nage m e nt Distric t , El Monte, CA. May, 1989. This docum ent is availabl e in Docket EPA-HQ-OAR-2005-0036.

74 

### Chan C.-C., Spengler J. D., Ozkaynak H., a nd Lefkopoulou M. (1991) C o mm uter Exposures to VOCs in Boston, Massachusetts. J. Ai r W a ste Manage. Assoc. 41, 1594-1600.

75 

### U.S. EPA ( 2000) Developm ent of microenviron m e ntal factors for the H A PEM4 in support of the National Air Toxics Assessm ent (NATA). E x ternal Review Draft Report Prepared by ICF Consulting and TRJ Environm ental, Inc. for the U.S. EPA, Office of Air Quality Planning and Standards, R e search Triangle Park, N C . This docum ent is availab l e in Do cket EPA-HQ-OAR2005-0036.

76 

### U.S. EPA ( 2000) Determ ination of m i croenviro nm ental factors for diesel PM. An addendum to: Develop m ent of m i croenvironm ental f acto r s f o r the HAPEM4 in sup port of the National Air Toxics Assessm ent (NATA). Extern al Review Draft Report Prepared by ICF Consulting and TRJ Environm ental, Inc

. f o r the U.S. EPA, Of f i ce of Air Quality P l annin g and Stand ards, Research Triangle Park, NC. This docum ent is available in D ocket EPA-HQ-OAR-2005-0036.

77 

### Personal communication with FACES Investigators Fred Lurm ann, Paul Roberts, and Katharine Hammond. Data is currently being prepared for publication.

78 

### Ki m , J.J.; Smorodinsky, S.; Lipsett, M.; et al. (2004) Traffic-relate d 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) A ssessm ent of exposure to traffic related air pollution of children attending sc hools near m otorways. At m o s Environ 35: 3875­
3884.

80 

### Roorda-Knape, M.C.; Janssen, N.A.H.; De Hartog, J.J.; et al. (1998) Ai r pollution from traffic in city districts near m ajor m o to rwa ys. Atm os Environ 32: 1921-1930.

81 

### Van Roosbroeck, S.; W i chm a nn, J.; Janssen, N.A.H.; Hoek, G.; van W ijnen, J.H; Lebret, E.; Brunekreef, B. (2006) Long-term personal exposure to traffic-related ai r pollution am ong school children, a validation study. Sci Total Environ 368: 565-573.

82 

### Kinney, P.L.; Chillrud, S.N.; Ram strom , S.; et al. (2002) Ex posures to m u ltip le air to xics in New York City. Environ Health Perspect 110 (Suppl 4): 539-546.

83 

### Chatzis, C.; Alexopoulos, E.C.; and Linos, A. (2005) Indoor and outdoor personal exposure to benzene in Athens, Greece. Sci Total Environ 3 49: 72-80.

84 

### Gulliver, J.; Briggs, D.J. (2004) Pers onal expos u r e to particulate air pollution in transp ort

m i croenvironm ents. Atmos Environ 38: 1-8.

85 

### Van W ijnen, J.H.; Verhoeff, A. P.; Jans, H. W

. A. ; Van Bruggen, M. (1995) The exposure of cyclists, car drivers and pedestri ans to traf f i c-r ela ted air pollu tants. Int Arch Occup Environ Health 67: 187-193.

86 

### Chan, C.-C. ; Ozkaynak, H.; Spengler, J.D.; Shel don, L. (1991) Driver Exposure to Volatile Organic Compounds, CO, Ozone, and NO2 under Di fferent Driving Conditions. Env i ron. Sci. Technol. 25: 964-972.

3-17587 

### Chan C.-C., Spengler J. D., Ozkaynak H ., and Lefkopoulou M. (1991) C o mm uter Exposures to VOCs in Boston, Massachusetts. J. Air W a ste Manage. Assoc. 41, 1594-1600.

88 

### Duci, A.; Chaloulakou, A.; Spyr ellis N. (2003) Exposure to ca rbon m onoxide in the Athens urban area during commuting. Sci T otal Environ 309: 47-58.

89 

### Gulliver, J.; Briggs, D.J. (2004) Pers onal expos u r e to particulate air pollution in transp ort

m i croenvironm ents. Atmos Environ 38: 1-8.

90 

### Ashm ore, M.R.; Batty, K.; Machin, F.; et a l . (2000) Effects of traffic m a nagem e nt and transport m ode on the exposure of schoolchildr en to carbon m onoxide. Environ Monitoring and
Assessm ent 65: 49-57.

91 

### U.S. EPA ( 1997) Exposure factors handbook. This docum ent 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: sim u ltaneous m easure m ent of ventilation, benzene, and toluene in reside ntial indoor air of two hom e s. Sci Total Environ 345: 31-40.

93 

### Batterm an, S.; Jia, C.; H atzivasilis, G.; G odwin, C. (2006) Sim u ltaneous m easurem e n t of ventilation using tracer gas tech niques and VOC concentrations in hom es, garages and vehicles. J Environ Monit 8: 249-256.

94 

### Morris, S.S. “Influence of attached garage s on indoor VOC concentrations in Anchorage hom es.” Presented at the annual m eeting of th e Air & W aste Managem ent Association’s Pacific Northwest S ection, November 4, 2004. This docum ent is available in Docket EPA-HQ-OAR2005-0036.

95 

### Sheltersource, Inc. (2002) Eval uating Minnesota hom e s. Final re po rt. Prepared for Minnesota Departm e nt of Commerce. U.S. Departm e nt of Energy grant DE-FG45-96R530335. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

96 

### Fugler, D.; Grande, C.; Graham

, L. (2002) Attached garages are likely path for pollutants.
ASHR AE I AQ Applications 3(3): 1-4.

97 

### Emm e rich, S.J.; Gorfain, J.E.; Howard-Reed, C. (2003) Air and pollutant transport from attached garages to resid e ntial living spaces – literatu re rev i ew and field tests. In J Ventilation 2:
265-276.

98 

### Isbell, M.; Gordian, M.E.; Duffy, L. (2002) W i nter indoor air pollution in Alaska: identifying

a m yth. Environ Pollution 117: 69-75.

99 

### U.S. EPA ( 1987) The Total Exposure Asse ssm e nt Methodology (TEAM) Study: Summary and Analysis: Volum e I. Office of Research and Development, W a shington, D.C. June 1987. EPA Report No. EPA/600/6-87/002a.

100 

### W allace, L. (1996) Environm ental exposure to b e nzene: an update. Env i ron Health Perspect 104 (Suppl 6): 1129-1136.

101 

### Thom as, K. W

. ; Pellizz ari, E. D.; Clayton, C. A.; Perr it, R.; Dietz, R. N.; Goorich, R. W .; Nelson, W .; Wallace, L. 1993. Temporal Vari ab ility of Benzene Exposures in several New Jersey hom es with attached garages or tobacco smoke. J. Expos. Anal. Environ. Epi. 3: 49-73.

3-176102 

### Batterm an, S.; Hatzivasilis, G.; Jia, C. (2005) Concentrations and em issions of gasoline and other vapors from residential vehicle garages. Atmos Environ (in press).

103 

### George, M.; Kaluza, P.; Maxwell, B.; et al. ( 2002) Indoor air quality & ventilation strategies in new homes in Alaska. Alaska Bu ilding Sc ien ce Network. [ O nline at http://www.absn.com / ] This document is available in Docket EPA-HQ-OAR-2005-0036.

104 

### Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: sim u ltaneous m easure m ent of ventilation, benzene and toluene in residen tial indoor air of two hom e s. Sci Total Environ 345: 31-40.

105 

### Graham , L. A.; Noseworthy, L.; Fugler, D.; et al. (2004) Contribution of vehicle em issions from an attached garage to residential indoor air pollution levels. J Ai r & Waste Manage Assoc
54: 563-584.

106 

### Schlapia, A .; Morris, S.S. (1998) Architect ural, behavioral, and environm ental factors associated w ith VOCs in Anchorage hom e s. Proceedings of the Air & W a ste Man a gem e nt Association’s 94th Annual Conferen ce & Exhibition. Paper 98-A504.

107 

### Isbell, M.; Ricker, J.; Gordian, M.E.; Duffy, L. K. (1999) Use of biom arkers in an indoor air study: lack of correlatio n between arom atic VOCs with respective urinary biom arkers. Sci Total Environ 241: 151-159.

108 

### Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: sim u ltaneous m easure m ent of ventilation, benzene and toluene in residen tial indoor air of two hom e s. Sci Total Environ 345: 31-40.

109 

### Gordon, S.M.; Callahan, P.J.; Nishioka, M. G.; et al. (1999) Residential environm ental

m easure m ents in the National Hum a n Exposur e Assessm ent Survey (NHEXAS) pilot study in Arizona: p r elim inary re sults f o r pes t icide s and VOCs. J Exposure Analy sis Environ Epidem iol
9: 456-470.

110 

### Bonanno, L.J.; Freem an, N.C.G.; Gr eenberg, M.; Lioy, P.J. (2001) Multivariate analysis on levels of selected m e tals, particulate m a tter, VOC, and household characteristics and activities from the Midwestern states NHEXAS. Appl Occup Environ Hygiene 16: 859-874.

111 

### Tsai, P.; Weisel, C.P. (2000) Penetration of ev aporative em issions into a hom e from an M85­ fueled vehicle parked in an attached gara ge. J Air & W a ste Manage Assoc 50: 371-377.

112 

### W ilson, A.L.; Colom e , S.D.; and Tian, Y. ( 1991) Air toxics m i croenvironm ent exposure and monitoring s t udy. Final Report. Prep ared for Sout h Coast Air Quality M a nagem e nt District and
U.S. Environm ental Pro t ection Agency. This do cum e nt is availab l e in D o cket EPA-HQ-OAR2005-0036.

113 

### Zielin ska, B.; Fujita, E

. M.; Sagebiel, J.C.; et al. (2002) In terim data report for Section 211(B) Tier 2 high end exposure screen ing study of baseline and oxygena ted gasoline. Prepared for
Am erican Petro l eum Institu te. November 19, 2002. This docum ent is availab l e in Do cket EPAHQ-OAR-2005-0036.

114 

### Lee, S.C.; Chan, L.Y.; and Chiu, M.Y. (1999) Indoor and outdoor air qu ality investigation at 14 public p l aces in Hon g Kong. Environ In t 25: 443-450.

3-177115 

### W ong, Y.-c .; Sin, D. W .-m.; and Yeung L.L. (2002) Assessm ent of the air quality in indoor car parks. Indoor & Built Environm e nt 11: 134-145.

116 

### Chaloulakou, A.; Duci, A.; Spyrellis, N. ( 2002) Exposure to carbon m onoxide in enclosed multi-level parking garages in th e central Athens urban area. Indoor & Bu ilt Env i ronment 11:
191-201.

117 

### Srivastava, A.; Joseph, A.E.; and Nair, S. (2004 ) Am bient levels of benzene in Mum b ai city. Int J Environ Health Res 14 (3): 215-222.

118 

### Schwar, M.; Booker, J.; Tait, L. (1997) Car Park Air Pollution Exposur e of Operatives and the General Public. Clean Air & Environ Protection 27 (5): 129-137

119 

### Morillo, P.; Dos Santos, S.G.; Santam aria, J.; et al. (1998) A st udy of the atm ospheric pollution produced by vehicles in car parks in Madrid, Spain. Indoor Bu ilt Environ 7: 156-164.

120 

### W ilson, A.L.; Colom e , S.D.; and Tian, Y. ( 1991) Air toxics m i croenvironm ent exposure and monitoring s t udy, Final Report. Prep ared for Sout h Coast Air Quality M a nagem e nt District and
U.S. Environm ental Pro t ection Agency. This do cum e nt is availab l e in D o cket EPA-HQ-OAR2005-0036.

121 

### Hartle, R. (1993) Exposure to m e thyl tert-b utyl ether and benzene am ong service station attendants and operators. Envi ron Health Perspect Supplem ents: 101 (Suppl. 6): 23-26.

122 

### Northeast S t ates for Coordinated Air Use Managem e nt (1999) RFG/MTBE Findings and Recommendations. August 1999. This docum en t is available in Docket EPA-HQ-OAR-2005­
0036.

123 

### Vayghani, S.A.; W e isel, C. (1999) The MTBE air concentrations in the cabin of autom obiles while fueling. J Expos Analysis Environ Epidem 9: 261-267.

124 

### Egeghy, P.P.; Tornero-Velez, R.T.; Rappaport, S.M. (2000) Environm ental and biological monitoring of benzene during self-s ervice automobile refueling. Environ Health Perspect 108:
1195-1202.

125 

### Verm a, D. K.; Johnson, D.M.; Shaw, M.L.; et al. (2001) Benzene and total hydrocarbon exposures in the downstream pe troleum industries. Am Indust Hygiene Assoc J 62: 176-194.

126 

### Verm a, D.K. and des Tom b e, K. (2002) Benzen e in gaso line and crude o il: o ccupatio n al and environm ental im plications. Am Indust Hygiene Assoc J 63: 225-230.

127 

### Baldauf, R.; Fortune,C.; Weinstein. J.; et al. (2005) Air contam inant exposures during the operation of lawn and garden equipm ent. J Expos Anal Environ Epidem iol 16: 362-370

128 

### Eriksson, K .; Tjarner, D .; Marqvardsen, I.; et al. (2003) Exposure to benzene, toluene, xylenes, and total hydrocarbons am ong snowm o b ile drivers in Sweden. Chem osphere 50: 1343­
1347.

129 

### Kado, N.Y. ; Kuzm icky, P.A.; and Okam oto, R. A. (2001) Environm ental and occupational exposure to toxic air pollutants from winter snow mobile use in Yellowstone National Park. Final report to the Yellowstone Park Foundation, Pew Charitable Trusts, and National Park Service. This docum ent is availa ble in Docket EPA-HQ-OAR-2005-0036.

3-178130 

### NESCAUM (2003) E valuating the occupational and environm ental im pact of nonroad diesel equipm ent in the Northeast. Interim Report June 9, 2003. This docum ent is available in Docket EPA-HQ-OAR-2005-0036. http://w ww.nescaum.org/focus-areas/m obile-sources/m obilesources-docum ents

.
131 

### Davis, M.E.; Sm ith, T.J.; Laden, F.; Hart, J.E.; R yan, L.M.; Garshick, E. (2006) Modeling particle exposure in U.S . trucking term in als. Environ Sci Technol 40: 4226-4232.

132 

### Atkinson, R.; Arey, J.; Hoover, S.; Preston, K. (2005) Atm o spheric Chem istry of Ga solineRelated Em issions: Form ation of Pollutants of Potential Co ncern. Draft Report Prep ared for California E nvironm ental Protection Agency. Se ptem ber 2005. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

133 

### U. S. EPA ( 2006) National-Scale Air Toxics Assessm ent 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. Offi ce of Air Quality Planning and Standards, Research Triangle Park, NC, Report No. EPA-45 4/R-00-017. This document is available in Docket EPA-HQ-OAR-2005-0036. http://www.epa.gov/scram 001/userg/other/aspenug.pdf

135 

### Rosenbaum, A. (2005) The HAPEM5 User’s Gu ide: Hazardous Air P ollutant Exposure Model, Version 5. Prepared by ICF, Inc. for U. S. EPA. This docum ent is available in Docket EPA-HQ-OAR-2005-0036. http://w ww.epa.gov/ttn/fera/hapem5/hapem 5_guide.pdf

.
136 

### U. S. EPA ( 2005) Risk - Ai r Toxics Risk Assessm e nt. http://www.epa.gov/ttn/f e ra/risk_atoxic.htm l

.
137 

### U. S. EPA ( 1993) Motor Vehicle-Related Ai r Toxics Study. R e port No. EPA420-R-93-005. http://www.epa.gov/otaq/reg s/toxics/tox_archive.htm #2

.
138 

### U. S. EPA ( 2000) Techn i cal Suppo rt Docum e nt: Control of Hazardous Air Pollutants from Motor Vehicles and Mo tor Vehicle Fuels. Office of Transportation and Ai r Quality. Report No. EPA-420-R-00-023. http://www.epa.gov/otaq/toxics.htm and at http://www.epa.gov/otaq/regs/toxics/r00023.pdf

.
139 

### Cook, R., J ones, B., Cleland, J. (2004) A Cohort Based Approach for Characterizing Lifetim e Inhalation C a ncer Risk f r om Ti m e -Varying Expo sure to Air Toxics from Am bient Sources. Environm ental Prog ress 23(2): 120-125.

140 

### Cook, R., Strum

, M., Toum a, J., et al. 2002. Trends in Mobile Source-R e lated Am bi ent Concentrations of Hazardous Air Pollutan ts, 199 6 – 2007. SAE Paper No. 2002-01-1274. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

141 

### Cook, R., Strum

, M., Toum a, J., Palm a, T ., Thurm a n, J., Ensley, D., Smith, R. 2006. Inhalation E xposure and Risk from Mobile Source Air Toxics in Future Years. Journal of Exposure Science and E nvironmental Epidemiology, in pres s

. [Advance online publication, 27 Septem ber 2006; doi:10.1038/sj.jes. 7500529, http://www.nature.com /jes/jour nal/vaop/ncurrent/index.htm l .]

142 

### U.S. EPA. 2007. The HAPEM6 User’s Guide. Prepared for Ted Palm a, Office of Air Quality Plan ning and Sta ndards, Rese arch Tr iangle Park, NC, by Arlene Rosenbaum and

3-179

### Michael Huang, ICF International, January 2007. http://www.epa.gov/ttn/f e ra/hum an_hapem .ht m l

. This docu m ent is available in Dock et EPAHQ-OAR-2005-0036.

143 

### Byun, D. W., Ching, J. K. S. 1999. Scie nce Algorithm s of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System

. U. S. EPA, Office of Research and Developm ent, W a shington, DC. Report No. EP A/600/R-99/030. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

144 

### Luecken, D. J., Hutzell, W. T., Gips on, G. J. 2005. Developm ent and Analysis of air quality modeling simulations for hazar dous air pollutants. Atm ospheric Environm ent. 40: 5087-5096.

145 

### Seigneur, C ., Pun, B., Lohm an, K., Wu, S.-Y. 2003. Regional m odeling of the atmospheric fate and transport of benzene and diesel part icles. Environ. Sci. Technol. 37: 5236-5246.

146 

### U. S. EPA. 2004. User’s Guide for the Em issions Modeling System for Hazardous Air Pollutants (EMS-HAP, Version 3.0), Office of Ai r Quality Planning and Standards, R e search Triangle Park, NC, Report No. EPA-454/B-00-007. This docum ent is available in Docket EPAHQ-OAR-2005-0036. http://www.epa.gov/scram001/userg/other/em shapv3ug.pdf

.
147 

### Battelle. 2003. Estim ated background concen trations for th e National-S cale Air Toxics
Assessm ent. Prepared f or U. S. EPA, Office of Air Quality Planning an d Standards. Contract
No. 68-D-02-061. W ork Assignm ent 1-03. This do cum e nt is available in Docket EPA-HQ-OAR2005-0036.

148 

### U. S. EPA. 1993. Motor Vehicl e-Related Air Toxics Study. Of fice of Mobile Sources, Ann Arbor, MI. Report No. EPA 420- R-93-005. This docum ent is available in Docket EPA-HQOAR-2005-0036. http://www.epa.go v/otaq/r egs/toxics/tox_archive.htm

149 

### U. S. EPA. 1999. Analysis of the Im pacts of Control Program s on Motor Vehicle Toxics
Em issions and Exposure in Urban Areas and Nationw ide. Prepared for U. S. EPA, Office of Transportation and Air Quality, by Sierra Re s e a r ch, Inc., an d Radian International Corporation/Eastern Research Group. Report No. EPA 420 –R-99-029/030. This docum ent is available in Docket EPA-HQ-OAR-2005-0036. http://www.epa.gov/otaq/reg s/toxics/tox_archive.htm

150 

### U. S. EPA. 2002. 1996 National-Scale Air Toxics Assessm ent. http://www.epa.gov/ttn/atw/nata/

151 

### Glen, G., Lakkadi, Y., Tippett, J. A., de l Valle-Torres M. 1997. Developm ent of NERL/CHAD: The National Exposure Research Laboratory Consolid ated Hum a n Ac tivity Database. Prepared by ManTech Environm ental Technology, Inc. EPA Contract No. 68-D5­ 0049. This docum ent is availabl e in Docket EPA-HQ-OAR-2005-0036.

152 

### Long, T,; Johnson, T. ; Laurensen, J.; Rosenbaum, A. 2004. Developm e nt of Penetration and Proxim ity Microenvironm ent Factor Distributions for the HAPEM5 in Support of the 1999 National-Sc ale Air Tox i cs Assessm ent (NATA). Mem o randum f

r om TRJ Consulting and ICF Consulting, Inc. to Ted Palm a, U. S. EPA, Of f i ce of Air Quality P l annin g and Stand a rds, RTP, NC., April 5, 2004. This docum ent is av ailable in Docket EPA-HQ-OAR-2005-0036. http://www.epa.gov/ttn/f e ra/hum an_hapem .ht m l

3-180153 

### U.S. EPA. 2007. The HAPEM6 User’s Guide. Prepared for Ted Palm a, Office of Air Quality Plan ning and Sta ndards, Rese arch Tr iangle Park, NC, by Arlene Rosenbaum and Michael Huang, ICF International, January 2007. http://www.epa.gov/ttn/f e ra/hum an_hapem .ht m l

. This docu m ent is available in Dock et EPAHQ-OAR-2005-0036.

154 

### Cohen, J.; Cook, R.; Bailey, C.R.; Carr, E. (2005) Relationship between m o tor vehicle

em issions of hazardous p o llutants, ro adway proxim ity, and ambient concentr ations in Portland, Oregon. Environ Modelling & Software 20: 7-12.

155 

### Pratt, G. C.; W u, C. Y.;Bock, D.; et al. (2004) C o m p aring air disp ersion model predictions with m easured concentrations of VOCs in urba n communities. Environ. Sci. Technol. 38: 1949­
1959.

156 

### U.S. EPA. 2007. The HAPEM6 User’s Guide. Prepared for Ted Palm a, Office of Air Quality Plan ning and Sta ndards, Rese arch Tr iangle Park, NC, by Arlene Rosenbaum and Michael Huang, ICF International, January 2007. http://www.epa.gov/ttn/f e ra/hum an_hapem .ht m l

. This docu m ent is available in Dock et EPAHQ-OAR-2005-0036.

157 

### U. S. EPA. 2001. National-Scale Air Toxi cs Assessm ent for 1996: Draft for EPA Science Advisory Board Review

. Report No. EPA-453/R- 01-003 This docum ent is available in Docket EPA-HQ-OAR-2005-0036. http://w ww.epa.gov/ttn/atw/sab /natar eport.p d f

158 

### U. S. EPA. 2001. National-Scale Air Toxi cs Assessm ent for 1996: Draft for EPA Science Advisory Board Review

. Report No. EPA-453/R- 01-003 This docum ent is available in Docket EPA-HQ-OAR-2005-0036. http://w ww.epa.gov/ttn/atw/sab /natar eport.p d f

159 

### Taylor, M. Mem o randum : Revised HAP Em issi on Factor s f o r Stationary Com bustion Turbines, Prepared by A l pha-Gamma Technologies, Inc for S i m s Roy, EPA OAQPS ESD
Com bustion Group. August, 2003. Docket ID: OAR-2003-0189. http://docket.epa.gov/edkpub/do/EDKS taffItemDetailView ? objectId=090007d480271237

160 

### U. S. EPA. 2004. Benefits of the Proposed Inter-State Air Qualit y Rule. Office of Air Quality Plan ning and Standards, Research Tria ngle Park, North Carolina. Report No. EPA 452/­ 03-001. This docum ent is availabl e in Docket EPA-HQ-OAR-2005-0036. http://www.epa.gov/CAIR/technical.htm l or http://www.epa.gov/CAIR/pdfs/tsd0175.pdf

161 

### U. S. EPA ( 2003) Estim ated Background Concen trations for the National-Scale Air Toxics
Assessm ent. Em issions, Monitoring, and Analysis Division, Office of Ai r Quality Planning and Standards, R e search Triangle Park, N C

. This docum ent is availab l e in Do cket EPA-HQ-OAR2005-0036. http://www.epa.gov/ttn/atw/ nata1999/background.htm l

.
162 

### U. S. EPA ( 2005) Supplem ental Guidance fo r Assessing Susceptibility from Early-Life Exposure to Carcinogens. Report No. EPA/630/ R-03/003F. This docum ent is available in Docket EPA-HQ-OAR-2005-0036. http://cfpub.epa.gov/ncea/cfm /recordisplay.cfm ?deid=160003

3-181163 

### U. S. EPA. 2006. National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule (Proposed); Technical Support Docum e nt,” Repor t Number EPA-454/R-06-002.

164 

### Science Advisory Board. 2001. NATA – Eval uating the National-Scale Air Toxics
Assessm ent 1996 Data - An SAB Advisory

. Report No. EPA-SAB-EC-ADV-02-001

165 

### Chen, C. W.; Gibb, H. (2003). Procedures for Calculating C e ssation Lag. Reg. Toxicol. Pharm acol. 38: 157-165.

166 

### U. S. EPA. (2004) Ben e fits of the P r oposed In ter-State Air Quality Rule. Office of Air Quality Plan ning and Standards, Research Tria ngle Park, North Carolina. Report No. EPA 452/­ 03-001. This docum ent is availabl e in Docket EPA-HQ-OAR-2005-0036. http://www.epa.gov/CAIR/technical.htm l and at http://www.epa.gov/CAIR/pdfs/tsd0175.pdf

167 

### Toum a, J. S.; Isakov, V. ; Ching, J.; Seigneur , C. (2006). Air Quality M odeling of Hazardous Air Pollutants: Current S t atus and Future Dir ections. J. Air and W a st e Manage. Assoc., 56:547­
558..

168 

### Isakov, V.; Venkatram , A. (2005) Resolving neighborhood scale in air toxics m odeling: a case study in W ilm ington, Californi a. J Air & Waste Manage. Assoc. 56:559-568.

169 

### Pratt, G. C.; W u, C. Y.;Bock, D.; et al. (2004) C o m p aring air disp ersion model predictions with m easured concentrations of VOCs in urba n communities. Environ. Sci. Technol. 38: 1949­
1959.

170 

### Kinnee, E.J.; Toum a, J. S.; Mason, R.; Thurm a n, J.; Beidler, A.; Bailey, C.; Cook, R. (2004) Allocation o f Onroad Mobile Em issions to Road Segm ents for Air Toxics Modeling in Harris County, Texas. Transport Res. Part D: Transport and Environ. 9:139-150.

171 

### Cohen, J.; Cook, R.; Bailey, C.R.; Carr, E. (2005) Relationship between m o tor vehicle

em issions of hazardous p o llutants, ro adway proxim ity, and ambient concentr ations in Portland, Oregon. Environ Modelling & Software 20: 7-12.

172 

### Toum a, J. S.; Isakov, V. ; Ching, J.; Seigneur , C. (2006). Air Quality M odeling of Hazardous Air Pollutants: Current S t atus and Future Dir ections. J. Air and W a st e Manage. Assoc., 56: 547­
558.

173 

### Cook, R.; Beidler, A.; T o um a, J.S.; Stru m

M. (2006) Preparing Highway Em issions Inventories f or Urban Scale Modeling: A Case St udy in Philadelphia. T r ansportation Res. Part
D: Transport and Environ. 11: 396-407.

174 

### U.S. EPA. 1996. Air Quality Criteria fo r Ozone and Related Photochem i cal Oxidants,
EPA600-P-93-004aF. This docum ent is av ailable in Docket E P A-HQ-OAR-2005-0036.

175 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

176 

### U.S. EPA ( 2007) Review of National Am bien t Air Quality Standards for Ozone, Assessm ent of Scientific and Technical Inform ation, OAQPS Staff Paper, EPA-452/R-07-003. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

177 

### Bates, D.V.; Baker-Anderson, M.; S i zto, R. (199 0) Asthm a atta ck pe riodicity: a study of

3-182

### hospital em ergency visits in Vancouver. Environ. Res . 51: 51-70.

178 

### Thurston, G.D.; Ito, K.; Kinney, P.L.; Lippm ann, M. (1992) A m u lti-year study of air pollution and respiratory hospital adm i ssions in three New York State m e tropolitan areas: results for 1988 and 1989 summers. J. Exposure Anal. Environ. Epidemiol . 2:429-450.

179 

### Thurston, G.D.; Ito, K.; Hayes, C.G. ; Bates, D.V.; Lippm ann, M. (1994) Respiratory hospital adm i ssions and summ er tim e haze air pollu tion in Tor onto, Ontario : con sideration of the ro le of acid aerosols. Environ. Res . 65: 271-290.

180 

### Lipfert, F.W.; Hammerstrom , T. (1992) Tem poral patterns in air pollution and hospital adm i ssions. Environ. Res . 59: 374-399.

181 

### Burnett, R.T .; Dales, R.E .; Raizenne, M.E.; Krewski, D.; Sum m ers, P.W .; Roberts, G.R.; Raad-Young, M.; Dann,T.; Brook, J. (1994) Effects of low ambient levels of ozone and sulfates on the frequency of respiratory adm i ssions to Ontario hospitals. Environ. Res . 65: 172-194.

182 

### U.S. EPA. Air Quality Criteria for Ozone and Related Photochem i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

183 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

184 

### Devlin, R. B.; McDonnell, W . F.; Mann, R.; Beck er, S.; House, D. E.; Schreinem achers, D.; Koren, H. S. (1991) Exposure of hum ans to a m bien t levels of ozone for 6.6 hours causes cellullar and biochem i cal changes in the lung. Am. J. Respir. Cell M o l. Biol

. 4: 72- 81.

185 

### Koren, H. S.; Devlin, R. B.; Becker, S.; Perez, R.; McDonnell, W

. F. (1991) Tim e-dependent changes of m a rkers associated with inflamm a ti on in the lungs of hum ans exposed to am bient levels of ozone. Toxicol. Pathol . 19: 406-411.

186 

### Koren, H. S.; Devlin, R. B.; Graham

, D. E.; Mann, R.; McGee, M. P.; Horstm an, D. H.;
Kozum bo, W. J.; Becker, S.; House, D. E.; Mc Donnell, W . F.; Bromberg, P. A. (1989a) Ozoneinduced inf l am m ation in the lowe r airways of hum an subjects. Am. Rev. Respir. Dis . 139: 407­
415.

187 

### Schelegle, E.S.; Siefkin, A.D.; McDonald, R.J. (1991) Tim e c ourse of ozone-induced neutrophilia in norm a l hum ans. Am. Rev. Respir. Dis . 143:1353-1358.

188 

### U.S. EPA. Air Quality Criteria for Ozone and Related Photochem i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

189 

### Hodgkin, J.E.; Abbey, D.E.; Euler, G.L.; Magie, A.R. (1984) COPD prevalence in

nonsm okers in high and low photoc hem i cal air pollution areas. Chest 86: 830-838.

190 

### Euler, G.L.; Abbey, D.E. ; Hodgkin, J.E.; Magi e, A.R. (1988) Chroni c obstructive pulmonary disease symptom effects of long- term cum u lative exposure to am bi ent levels of total oxidants

3-183

### and nitrogen dioxide in California S e venth-day Adventist residents. Arch. Environ. Health 43:
279-285.

191 

### Abbey, D.E. ; Petersen, F .; Mills, P.K.; Beeson, W.L. (1993) Long-term am bient concentrations of total suspended particulat es, ozone, and sulfur dioxide and respiratory

sym p tom s i n a nonsm o king population. Arch. Environ. Health 48: 33-46.

192 

### U.S. EPA. Review of the Nationa l Am bient Air Quality Sta ndards f o r Ozone: Policy
Assessm ent of Scientific and Technical Info rm a tion, OAQPS Staff Pape r – Second Draft. EPA452/D-05-002.

193 

### U.S. EP A. Air Quality Criteria for Ozone and Related Photochem i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

194 

### U.S. EPA. Air Quality Criteria for Ozone and Related Photochem i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

195 

### Avol, E. L.; Trim , S. C.; Little, D. E.; Spier, C. E.; Sm ith, M. N.; Peng, R.-C.; Linn, W . S.; Hackney, J. D.; Gross, K. B.; D'Arcy, J. B.; Gibbons, D.; Higgins, I. T. T. (1990) Ozone exposure and lung function in children attendi ng a southern C a lifornia summer ca m p. Presented at: 83rd annual m eeting and exhibition of th e Air & W aste Managem ent Association; June; Pittsbu rgh, PA. Pittsburgh, PA: Air & W a ste Ma nagem e nt Association ; p a per no. 90-150.3.

196 

### Higgins, I. T . T.; D'Arcy, J. B.; Gibbons, D. I.; Avol, E. L.; Gross, K. B. (1990) Effect of exposures to am bient ozone on vent ilatory lung function in children. Am. Rev. Respir. Dis. 141:
1136-1146.

197 

### Raizenne, M. E.; Burnett, R. T.; Stern, B.; Fr anklin, C. A.; Spengler, J. D. (1989) Acute lung function responses to ambient acid aerosol exposures in children. Enviro n. Health Perspect . 79 : 179-185.

198 

### Raizenne, M.; Stern, B .; Burnett, R.; Spengl er, J. (1987) Acute re spiratory function and transported air pollutants: obser vational studies. Presented at : 80th annual m eeting of the Air Pollution Control Assoc i ation ; June; New Yo rk, NY. Pittsbur gh, PA: Air Pollution Control Association; paper no. 87-32.6.

199 

### Spektor, D. M.; Lippm ann, M. (1991) Health eff ects of am bient ozone on healthy children at a

summer camp. In: Berglund, R. L.; Lawson, D. R.; McKee, D. J., eds. Tropospheric ozone and the environm ent: papers from an internati onal conference; March 1990; Los Angeles, CA. Pittsbu rgh, PA: Air & W aste Mana gem ent Associat ion; pp. 83-89. (A&W MA transaction ser i es no. TR-19).

200 

### Spektor, D. M.; Thurston, G. D.; Mao, J.; He, D.; Hayes, C.; Lippm ann, M. (1991) E ffects of single- and multiday ozone exposures on resp ir atory functio n in active n o rm al children. Environ.
Res . 55: 107-122.

201 

### Spektor, D. M.; Lippm an, M.; Lioy, P. J.; Thurston, G. D.;s Citak, K.; Ja m e s, D. J.; Bock, N.;

3-184

### Speizer, F. E.; Hayes, C. (1988a) Effects of am bient ozone on respiratory function in active,

norm al child ren. Am. Rev. Respir. Dis. 137: 313-320.

202 

### U.S. EPA. Air Quality Criteria for Ozone and Related Photochem i cal Oxidants (Final). U.S. Environm ental Protection Agency, Wash ington, DC, EPA/600/R-05/004aF-cF, 2006.

203 

### Hazucha, M. J.; Folinsb ee, L. J.; Seal, E., Jr. ( 1992) Effects of steady-st ate and variable ozone concentration profiles on pulm onary function. Am. Rev. Respir. Dis . 146: 1487-1493.

204 

### Horstm an, D.H.; Ball, B.A.; Folinsbee, L.J.; Br own, J.; Gerrity, T. (199 5) Com p aris on of pulm onary responses of asthm a tic and nonasthm a tic subjects perform i ng light exercise while exposed to a low level of ozone. Toxicol. Ind. Health

.

205 

### Horstm an, D.H.; Folinsbee, L.J.; Ives, P.J.; Abdul-Salaam , S.; McDonnell, W

. F. (1990) Ozone concentration and pulm o nary response relationshi ps for 6.6-hour exposures with five hours of moderate exercise to 0.08, 0.10, and 0.12 ppm

. Am. Rev. Respir. Dis. 142: 1158-1163.

206 

### U.S. EPA. 2005. Technical Suppo rt Docum ent for the Final Clean Air Interstate Rule - Air Quality Mo deling. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

207 

### U.S. EPA. 2005. Technical Support Docum e nt for the Proposed Mobile Source Air Toxics Rule – Air Quality Mo deling. This docum ent is available in Dock et EPA-HQ-OAR-2005-0036.

208 

### U.S. EPA. 2005. Technical Suppo rt Docum ent for the Final Clean Air Interstate Rule - Air Quality Mo deling. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

209 

### U.S. EPA. 2005. Clean Air Interstate Ru le Em i ssions Inventory Technical Support
Docum ent. Available from : http://w ww.epa.gov/cai r/technical.htm l . This docum ent is available in Docket E P A-HQ-OAR-2005-0036.

210 

### U.S. EPA. 2005. Technical Support Docum e nt for the Proposed Mobile Source Air Toxics Rule – Ozone Air Quality Modeling. This doc ument is available in Docket EPA-HQ-OAR-2005­
0036.

211 

### Pielke, R.A., W .R. Cotton, R.L. W a lko, et al. 1992. “A Compre hensive Meteorological Modeling S y stem – RAMS.” Meteo r. Atmos. Phys., Vol. 49, pp. 69-91.

212 

### U.S. EPA. 1999. The Benefits and Costs of th e Clean Air Act, 1990-2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radi ation, Office of Policy Analysis and Review, Washington, DC, Nove mber; EPA report no. EPA 410-R-99-001. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

213 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

214 

### W i nner, W

. E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and consequences for growth.” Trends in Ecology and Evolution 1:15-18.

215 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washi ngton, DC, EPA/600/R-05/004aF-cF, 2006.This docum ent is available in Docket EPA-HQ-OAR-2005-0036.
3-185216 

### Tingey, D.T., and Taylor, G.E. 1982. “Variatio n in plant response to ozone: a conceptual model of physiological events.” In: Effects of Gaseous Ai r P ollution in A griculture and Horticulture (Unsworth, M.H., Om rod, D.P., eds.) London, UK: Butterworth Scientific, pp. 113­
138.

217 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washi ngton, DC, EPA/600/R-05/004aF-cF, 2006.This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

218 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

219 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

220 

### Ollinger, S.V., J.D. Abe r and P.B. Reich. 1997. “Sim ulating ozone effects on forest productivity: interactions between le af canopy and stand level processes.” Ecological Applications 7:1237-1251.

221 

### W i nner, W

. E., 1994. “Mechanistic analysis of plant responses to air pollution.” Ecological Applications, 4(4):651-661.

222 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

223 

### U U.S. EP A. Air Quality Criteria for Ozone and Related Photochem i cal Oxidants (F inal).
U.S. Environm ental Protection Agency, W a sh ington, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

224 

### Fox, S., and R. A. Mickler, eds. 1996. Im p act of Air Pollutants on Southern Pine Forests. Springer-Verlag, NY, Ecol. Studies, Vol. 118, 513 pp.

225 

### National Acid Precipitati on Assessment Program (NAPAP), 1991. National Acid Precipitation Assessm ent Program . 1990 Integr ated Assessment Report. National Acid Precip itation Program . Office of the Directo r , W ashington D C

.
226 

### De Steiguer, J., J. Pye, C. Love. 1990. “Air Pollution Dam a ge to U.S. Forests.” Journal of Forestry , V ol 88(8) pp. 17-22.

227 

### Pye, J.M. 1988. “Im pact of ozone on the growth and yield of trees: A review.” Journal of Environmental Quality 17 pp.347-360.

228 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

229 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

3-186230 

### McBride, J. R., P.R. Miller, and R.D. Laven. 1985. “Effects of oxidant air p o llutants on forest

success i on in the m i xed conifer fores t type of sou t hern California.” In: A i r Pollu tan t s Effects On Forest Ecosystem s, Sym posium Pr oceedings, St. P, 1985, p. 157-167.

231 

### Miller, P.R., O.C. Taylor, R.G. W ilhour. 1982. Oxidant air pollution effects on a western conif erous f o rest e cosys tem

. Corvallis, OR: U.S. Environm ental Pro t ec tio n Agency, Environm ental Research Laborat ory (EPA600-D-82-276). This docum en t is available in Docket EPA-HQ-OAR-2005-0036.

232 

### U.S. EPA. Air Quality Criteri a for Ozone and Related Photoche m i cal Oxidants (Final). U.S. Environm ental Protection Agency, Washingt on, DC, EPA/600/R-05/004aF-cF, 2006. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

233 

### Kopp, R. J.; Vaughn, W

. J.; Hazilla, M.; Carson, R. 1985. “Implications of environmental policy f o r U.S. agricu ltu re: the cas e of a m bient ozone standards.” J. Environ. Manage

. 20:321­
331.

234 

### Ada m s, R. M.; Ham ilton, S. A.; McCarl, B. A. 1986. “The benefits of pollution control: the case of ozon e and U.S. agricu lture.” Am. J. Agric. Econ

. 34: 3-19.

235 

### Ada m s, R. M.; Glyer, J. D.; Johnson, S. L.; McCarl, B. A. 1989. “A reassessm ent of the

econom ic effects of ozone on U.S. agriculture.” JAPCA 39:960-968.

236 

### Abt Associates, Inc. 1995. Urban ornam e nt al plants: sensitivity to ozone and potential

econom ic losses. U.S. EPA, Office of Air Qua lity Planning and Standard s, Research Triangle Park. Under contract to RADIAN Corporation, contract no. 68-D 3-0033, WA no. 6. pp. 9-10.

237 

### U.S. EPA. 2004. Air Quality Criteria fo r Particulate Matter. EPA/600/P-99/002aF and EPA/600/P-99/002bF. This document is avai lable in Docket EPA-HQ-OAR-2005-0036.

238 

### U.S. EPA. 2005. Review of the National Am bien t Air Quality Standard for Particulate Matter: Policy Assessm ent of Sc ientific and Technical Infor m ation, OAQPS Staff Paper. EPA-452/R05-005. This docum ent is availabl e in Docket EPA-HQ-OAR-2005-0036.

239 

### U.S. EPA 2006. Provisional Assessm e nt of R ecent Studies on Health E ffects of Particulate Matter Exposure. EPA/600/R-06/063. This doc um ent is available in Docket EPA-HQ-OAR2005-0036.

240 

### Laden F; Neas LM; Dockery DW ; e t al. 2000. “Association of fine particulate m a tter from dif f erent sou r ces with d aily m o rtality in six U.S. c ities.” Envir on. Health P e rspect. 108(10):941­
947.

241 

### Schwartz J; Laden F; Zanobetti A. 2002. “The concentration-response relation betw een PM(2.5) and daily deaths.” Environ. Health Perspect. 110(10): 1025-1029.

242 

### Janssen NA; Schwartz J ; Zanobetti A.; et al

. 20 02. “Air con d ition i ng an d source-specific particles as modifiers of the e ffect of PM10 on hospital adm i ssions for heart and lung disease.” Environ Health Persp ec t. 110(1):43-49.

243 

### Dockery, DW ; Pope, CA, III; Xu, X; et al. 199 3. “An assoc i ation betwe e n air po llu tion and mortality in six U.S. cities.” N. Engl. J. Med. 329:1753-1759.

3-187244 

### Pope, CA, III; Burnett, RT; Thun, MJ; Calle, E E ; et al. 200 2. “Lung cancer, cardio pulm onary mortality, and long-term exposure to fine particulate air pollution.” J. Am. Med. Assoc. 287:
1132-1141.

245 

### Krewski, D; Burnett, RT; Goldberg , M S; et al. 2000. “Reanalysis of th e Harvard Six Cities study and the Am erican Cancer So ciety study of particulate air pol lution and m o rtality. A special report of the Institute's Partic le Epidem iology Re analysis Proj ect.” Cam b ridge, MA: Health Effects Institute. This docum e nt is available in Dock et EPA-HQ-OAR-2005-0036.

246 

### Jerrett, M; Burnett, RT; Ma, R; et al. 2005. “S patial analysis of air po llution and m o rtality in Los Angeles. Epidemiolo gy 16(6):727-736.

247 

### Künzli, N.; Jerrett, M. ; Mack, W

. J.; et al. 2005. Am bient ai r pollution and atherosclerosis in
Los Angeles. Environ Health Per spe ct. 113:201-206.

248 

### Riediker, M.; Cascio, W.E.; Griggs, T.R.; et al . 2004. “Particulate m a tter exposure in cars is associated w ith cardiovascular effects in healthy young m e n.” Am J Respir Crit Car e Med 169:
934–940.

249 

### Van Vliet, P.; Knape, M.; de Hartog, J.; Janssen, N.; Harssem a , H.; Br unekreef, B. (1997). Motor vehicle exhaust and chroni c r espira t ory s y m p tom s in ch ildren living near freeways. Env. Research 74: 122-132.

250 

### Brunekreef, B., Janssen, N.A.H.; de Hartog, J.; Harssem a , H.; Knape, M.; van Vliet, P. (1997). Air pollution from truck traffic and l ung f unction in children liv ing near roadways. Epidemiolog y 8:298-303.

251 

### Ki m , J.J.; Smorodinsky, S.; Lipsett, M.; Si nger, B.C.; Hodgs on, A.T.; Ostro, B (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.

252 

### U.S. EPA. 2006. Regulatory Im pact Analysis for the final PM NAAQS. This document is available in Docket EPA-HQ-OAR-2005-0036.

253 

### National Research Council, 1993. P r otecting Visibility in Nationa l Parks and W ilderness
Areas. National Academy of Sciences Comm itt ee on Haze in National P a rks and W ilderness
Areas. National Academy Press, Washington, D C

. This book can be viewed on the National Academ y Press W ebsite at http://www.nap.edu/books/0309048443/htm l / and is available in Docket EPA-HQ-OAR-2005-0036.

254 

### U.S. EPA ( 2004) Air Quality Criteria for Part iculate Matter (Oct 2004) , Volum e I Docum ent No. EPA600/P-99/002aF and Volume II Docum e nt No. EPA600/P-99/002bF. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

255 

### U.S. EPA ( 2005) Review of the National Am bient Air Quality Standard for Particulate
Matter: Po licy Assessm ent of Scientif ic and Te chnical Inform ation, OA QPS Staff Paper. EPA452/R-05-005. This docum ent is avai lable in Docket EPA-HQ-OAR-2005-0036.

256 

### U.S. EPA. 2005. Review of the National Am bien t Air Quality Standard for Particulate Matter: Policy Assessm ent of Sc ientific and Technical Infor m ation, OAQPS Staff Paper. EPA-452/R05-005. This docum ent is availa ble in Docket EPA-HQ-OAR-2005-0036.

3-188257 

### U.S. EPA. 1993. Effects of the 1990 Clean Ai r Act Am endm ents on Vi sibility in Class I Areas: An EPA Report to Congress. EPA452-R-93-014.

258 

### U.S. EPA ( 2002) Latest Findings on National Air Quality – 2002 Stat us and Trends. EPA 454/K-03-001. This docum ent is ava ilable in D ocket EPA-HQ-OAR-2005-0036.

259 

### National Park Service. Air Quality in th e National Parks, Second edition. NPS, Air
Resources D i vision. 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 Stat us and Trends. EPA 454/K-03-001. This docum ent is ava ilable in D ocket EPA-HQ-OAR-2005-0036.

261 

### U.S. EPA ( 2005). Technical Support Docum e nt fo r the Final Clean Air Interstate Rule - Air Quality Mo deling. This docum ent is available in Docket EPA-HQ-OAR-2005-0036.

262 

### U.S. EPA ( 2000) Deposition of Air Pollutan t s to the Great W a ters: Third Report to Congress. Office of Air Quality Planning and Standards. EP A-453/R-00-0005.

263 

### U.S. EPA ( 2004) Natio nal Coastal Condition R e port II. Office of Research and Dev e lopm ent/ Office of W ater. EPA-620/R-03/002.

264 

### Gao, Y., E. D. Nelson, M.P. Field, et al. 2002. Characterization of atm o spheric trace elem ents on PM2.5 particulate m atter over the New York-New Jersey harbor estuary. Atmos.
Environ. 36: 1077-1086.

265 

### Ki m , G., N. Hussain, J.R. Scudlark, and T. M. Church. 2000. Factors influencing the

atm ospheric depositional fluxes of stable Pb, 210 Pb, and 7Be into Chesap eake Bay. J. At mos. Chem. 36: 65-79.

266 

### Lu, R., R.P. Turco, K. Stolzenbach, et al. 2003. Dry deposition of airborne trace m e tals on the Los Angeles Bas i n and adjacen t coastal waters. J. Geoph ys. Res. 108(D2, 4074): AAC 11-1
to 11-24.

267 

### Marvin, C.H., M.N. Charlton, E. J. Reiner, et al. 2002. Surfic ial sedim e nt contam ination in Lakes Erie and Ontario: A com p arative analys is. J. Great Lakes Res. 28(3): 437-450.

268 

### Sm ith, W

. H. 1991. Air pollution and forest dam age.” Chemical Engineering News , 69(45):
30-43.

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 m easurem ents. Natu re , 381: 64-65.

270 

### Cotrufo, M.F.; DeSanto, A.V.; Alfani, A.; et al. 1995. Effects of ur ban heavy m e tal pollution on organic m atter decomposition in Quercus ilix L. woods. Environmental Pollution , 89: 81-87.

271 

### Niklinska, M.; Laskowski, R.; Maryanski, M. 1998. Effect of heavy m e tals and storage tim e on two types of f

o rest litter: bas al res p ira tion r ate and exchang eable m etals

. Ecotoxicological Environmental Safety , 41: 8-18.

272 

### Mason, R.P. and Sulliv a n, K.A. 1997. Mercury in Lake Mich igan. Enviro nmental Science & Technology , 31: 942-947. (from Delta Report “Atmosphe ric deposition of toxics to the Great Lakes”)

3-189273 

### Landis, M.S. and Keeler, G.J. 2002. At m os pheric m e rcury deposition to Lake Michigan during the Lake Michigan Mass Balance Study. E n vironmental Science & Technology , 21: 4518­
24.

274 

### U.S. EPA. 2000. EPA453/R-00-005 , “Deposition of Air Pollutant s to the Great W a ters: Third Report to C ongress,” Office of Air Quality Plan ning and Standards, Research Triangle Park, North Carolina. This docum e nt is available in D ocket EPA-HQ-OAR-2005-0036.

275 

### Callender, E. and Rice, K.C. 2000. The ur ban environm ental gradient: Anthropogenic
inf l uences o n the spa tia l and tem poral dist ributions of lead and zinc in sedim e nts. Environmental
Science & T echnology , 34: 232-238.

276 

### Rice, K.C. 1999. Trace elem ent concentration s in stream bed sedim e nt across the

conterm i nous United States. Environ m ental Science & Techn ology , 33: 2499-2504.

277 

### Ely, JC; Neal, CR; Kulpa, CF; et al. 2001. Im plications of platinum -group elem ent

accum u latio n along U.S. roads from catalytic-co nverter attrition. Environ . Sci. Techn o l . 35:
3816-3822.

278 

### U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estim ating Air Em is sions from Sources of

Polycyclic Organic M atte r,” Of f i ce of Air Quality P l annin g and Stand ards, Resea r ch Triang le Park, North Carolina. T h is docum ent is available in Docket E P A-HQ-OAR-2005-0036.

279 

### U.S. EPA. 1998. EPA454/R-98-014, “Locating and Estim ating Air Em is sions from Sources of

Polycyclic Organic M atte r,” Of f i ce of Air Quality P l annin g and Stand ards, Resea r ch Triang le Park, North Carolina. T h is docum ent is available in Docket E P A-HQ-OAR-2005-0036.

280 

### Sim c ik, M.F.; Eisenreich, S.J.; Golden, K.A.; et al. 1996. Atmospheric loading of polycyclic

arom atic hydrocarbons to Lake Michigan as recorded in the sedim e nts.” Environmenta l Scien ce and Technology , 30: 3039-3046.

281 

### Sim c ik, M.F.; Eisenreich, S.J.; and Lioy, P.J. 1999. Source apportionm ent and source/sink rela tionsh i p of PAHs in the coastal a t m o sphere of Chicago and Lake Michigan. Atmospheric
Environment , 33: 5071-5079.

282 

### Arzayus, K.M.; Dickhut, R.M.; and Canuel, E. A. 2001. Fate of atm osphe rically deposited polycyclic arom atic hydrocarbons (P AHs) in Chesapeake Bay.” Environmental Science & Technology , 35, 2178-2183.

283 

### Park, J.S.; Wade, T.L.; and Sweet, S. 2001. At mospheric distribution of polycyclic arom atic hydrocarbons and deposition to Galveston Bay, T e xas, USA. Atmospheric Environment , 35:
3241-3249.

284 

### Poor, N.; Trem blay, R.; Kay, H.; et al. 2002. Atmospheric concentration s and dry deposition rates of polycyclic arom atic hydrocarbons (PAHs) for Tam p a Bay, Florida, USA. At mospheric
Environment 38: 6005-6015.

285 

### Arzayus, K.M.; Dickhut, R.M.; and Canuel, E. A. 2001. Fate of atm osphe rically deposited polycyclic arom atic hydrocarbons (P AHs) in Chesapeake Bay. Environmental Science & Technology , 35, 2178-2183.

3-190286 

### U.S. EPA. 2000. EPA453/R-00-005 , “Deposition of Air Pollutant s to the Great W a ters: Third Report to C ongress,” Office of Air Quality Plan ning and Standards, Research Triangle Park, North Carolina. This docum e nt is available in D ocket 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 sem i -volatile organic co mpounds across the air-soil in terface. The Science of the Total Enviro nment , 228: 5-24.

289 

### Tuhackova, J., Cajtham l, T.; Novak, K.; et al. 2001. Hydrocarbon deposition and soil

m i croflora as affected by highway traffic. Environmental Pollution , 113: 255-262.

290 

### U.S. EPA ( 2005) Review of the National Am bient Air Quality Standard for Particulate
Matter: Po licy Assessm ent of Scientif ic and Te chnical Inform ation, OA QPS Staff Paper. EPA452/R-05-005. This docum ent is ava ilable in Docket EPA-HQ-OAR-2005-0036.

291 

### Hoek, G.; Brunekreef, B.; Goldbohm, S.; et al. (2002) Association between m ortality and indicators of traffic-related air pollu tion in the Netherla nds: a cohort study. Lancet 360: 1203­
1209.

292 

### Finkelstein, M.M.; Jerrett, M.; Sears, M.R. (2004) Traffic air polluti on and m ortality rate advancem ent periods. A m . J. Epidemiol. 160: 173-177.

293 

### Finkelstein, M.M.; Jerrett, M.; Sears , M.R. (2005) Environm ental ine quality and circulato r y disease m o rtality grad ien t s

. J. Epide m iol. Community Health 59: 481-486.

294 

### Hoek, G.; Brunekreef, B.; Goldbohm, S.; et al. (2002) Association between m ortality and indicators of traffic-related air pollu tion in the Netherla nds: a cohort study. Lancet 360: 1203­
1209.

295 

### Gehring, U.; Heinrich, J.; Kram er, U.; Grot e, V.; Hochadel, M.; Dugiri, D.; Kraft, M.; Rauchfuss, K.; Eberwein, H.G.; W i chm a nn, H- E. (2006) Long-term exposure to ambient air pollution and cardiopulmonary m o rtality in wo m en. Epidemiology 17: 545-551.

296 

### Lipf ert, F. W

. ; Baty, J.D.; Miller, J

. P.; W y zga, R.E. PM2.5 constituten t s and related air quality var i a b les as p r edictors of surv ival in a coh o rt of U.S. m ilitary vete rans. Inhalation
Toxicol . 18: 645-657.

297 

### Maheswaran, M.; Elliott, P. ( 2003) Stroke m o rtality a ssociated with living near m a in roads in England and W a les. A geographical study. Stroke 34: 2776-2780.

298 

### Roe m er, W

. H.; van W ijnen, J.H. (2001) Daily mortality and air polluti on along busy streets in
Am sterdam

, 1987-1998. Epidemiolo gy 12: 649-653.

299 

### Heinrich, J.; W i chm ann, H-E. (2004) Traffic re lated pollutants in Eur ope and their effect on alle rgic d i se ase. Curren t Opin. Clinical Epidemiol . 4: 341-348.

300 

### Ryan, P.H.; LeMasters, G.; Biagni ni, J.; et al. (2005) Is it tra ffic type, volum e, or distance?
W h eezing in infants livin g near truck and bus traffic. J. Allergy Clin. Immunol. 116: 279-284.

3-191301 

### Ki m , J.J.; Smorodinsky, S.; Lipsett, M.; et al. (2004) Traffic-relate d air pollution near busy roads: the E a st Bay Children 's Respiratory Health Study. Am. J. Respir. Crit. Care Med. 170:
520-526.

302 

### Lin, S.; Munsie, J.P.; H w ang, S-A.; et al. (2002) Childhood asthm a hospitalization and residential exposure to state route traffic. Environ. Res. 88: 73-81.

303 

### English, P.; Neutra, R.; Scalf, R.; et al. (1999) E xam ining associations between childhood asthm a and traffic flow using a geographic inform ation system. Environ. Health Perspect. 107:
761-768.

304 

### Garshick, E

. ; Laden, F.; Hart, J.E.; C aron, A. (20 03) Residen ce near a m a jor road and respirato r y s y m p tom s in U.S. veteran s. Epidemio logy 14: 728-736.

305 

### Heinrich, J.; W i chm ann, H-E. (2004) Traffic re lated pollutant in Europe and their effect on alle rgic d i se ase. Curren t Opin. Clinical Epidemiol . 4: 341-348.

306 

### McConnell, R.; Berhane, K.; Yao. L., Jerrett, M.; Lurm ann, F.; Gill iland, F.; Kunzli, N.; Gauderm an, J.; Avol, E.; Thom as, D.; Peters, J. (2006) Traffic, susceptibility, and childhood asthm a. Environ. Health Perspect. 114: 766-772.

307 

### Hoffm ann, B.; Moebus, S.; Stang, A.; B eck, E.M .; Dragano, N.; Mohlenk a m p , S.; Scherm und, A.; Memm e s heim er, M.; Mann, K.; Erbe l, R.; Jockel, K.H. (2006) Residence close to high traffic and prevalence of coro nary heart disease. Eur. Heart J. 27: 2696-2702.

308 

### Peters, A.; von Klot, S.; Heier, M.; et al. (2004) Exposure to tra ffic and the onset of myocardial infarction. N . Engl. J. Med. 351: 1721-1730.

309 

### Riediker, M.; Cascio, W.E.; Gri ggs, T.R..; et al. (2003) Particul ate m a tter exposures in cars is associated w ith cardiovascular effects in healthy young m e n. Am. J. Respir. Crit. Care Med.
169:934-940.

310 

### Schwartz, J.; Litonjua, L .; Suh, H.; et al. (2005) T r affic related po llution 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-segm e nt depression in elderly subjects. Environ. Health Perspect. 113: 883-887.

312 

### W ilhelm , M.; Ritz, B. (2002) Residential proxim ity to traffi c and adverse birth outcom e s in Los Angeles County, California, 1994-1996

. Environ. Health Prospect. 111:207-216.

313 

### Ritz B; Yu F. (1999) The effect of am bient carbon m onoxide on low birth weight among children born in southern Ca lifornia between 1989 and 1993. Environ. Health Persp ec t. 107:17­
25.

314 

### Ritz B; Yu F; Capa G; Fruin S. (2000) Effect of air pollution on prem ature birth am ong children born in southern Ca lifornia between 1989 and 1993. Epidemiolog y 11:502-511.

315 

### Ritz B; Yu F; Fruin S; et al . (2002) Am bient 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 transplacen tal exposure to environm ental pollutan t s on birth ou tcom es in a multiethn i c population. Environ. Health Perspect. 111: 201-205.

3-192317 

### Perera, F.P. ; Rauh, V.; W hyatt, R.M.; Tsai, W .Y.; Tang, D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Cam a nn, D.; Kinney, P. (2006) Effect of prenatal exposure to airborne polycyclic

arom atic hydrocarbons on neurodevelopm ent in the first 3 years of life among inner-city child ren. E nviron. Health Perspect. 114: 1287-1292.

318 

### U. S. EPA ( 2005) Supplem ental Guidance fo r Assessing Susceptibility from Early-Life Exposure to Carcinogens. Report No. EPA/630/ R-03/003F. This docum ent 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) Associati on of childhood cancer with residential traffic density. Scand. J. Work Environ. Health 15: 360-363.

321 

### Pearson, R.L.; W achtel, H.; Ebi, K.L. (2000) Distance-weighted traffic density in proxim ity to a hom e is a risk factor for leukem i a and other childhood cancers

. J. A i r Waste Man age. Assoc.
50: 175-180.

322 

### Feychting, M.; Svensson, D.; Ahlbom, A. (1998) Exposure to m o tor vehicle exhaust and childhood cancer. Scan. J. Work Environ. Health 24(1): 8-11.

323 

### Langholz, B.; Ebi, K.L.; Thom as, D.C.; et al. (2002) Traffic density and the risk of childhood leukem i a in a Los Angeles case-control study. A nn. Epidemiol. 12: 482-487.

324 

### Raaschou-Nielsen, O.; Hertel, O.; T h om sen, B. L.; Olsen, J.H. (2001) Air pollution from traffic at the residen ce of child ren with cancer. A m . J. Epidemiol. 153: 433-443.

325 

### Reynolds, P.; Von Behren, J.; Gunier, R.B.; et al. (2003) Childhood cancer incidence rates and hazardo us air po llu tants in California: an ex pl oratory an alysis. Env i ron Health P e rspect 111:
663-668.

326 

### Reynolds, P.; Von Behren, J.; Gunier, R.B.; et al. (2004) Residential exposure to traf fic in California and childhood cancer. Ep idemiology 15: 6-12.

327 

### Knox, E.G. (2005) Oil com bustion and childhood cancers. J . Epidemiol. Community Health
59: 755-760.

328 

### U.S. EPA. 2007. The HAPEM6 User’s Guide. Appendix B. Prepared f or Ted Palma, Office of

Air Quality Planning and Standar d s, Resear ch Triangle Pa rk, NC, by Arlene Rose nbaum and Michael Huang, ICF International, January 2007. http://www.epa.gov/ttn/f e ra/hum an_hapem .ht m l

. This docu m ent is available in Dock et EPAHQ-OAR-2005-0036.

329 

### United States Census Bureau. (2004) Am erican Housing Survey web page. [Online at http://www.census.gov/hhes/www/hous ing/ahs/ahs03/ahs03.htm l ] Table IA-6.

330 

### This statistic is based on the odds ratio of being near m a jor transportation sources, compared to not being near transportation sources for housing units located in different geographic regions.

3-193331 

### Garshick, E.; Laden, F.; Hart, J.E.; C a ron, A. (20 03) Residen ce near a m a jor road and respirato r y s y m p tom s in U.S. veteran s. Epidemio logy 14: 728-736.

332 

### Green, R.S.; Sm orodinsky, S.; Kim , J .J.; McLaughlin, R.; Ostro, B. (2004) Proxim ity of California public schools to busy roads. Environ. Health Perspect. 112: 61-66.

333 

### Gunier, R.B.; Hertz, A.; Von Behren, J.; Reynolds , P. (2003) Traffic dens ity in California: socioeconom i c and ethnic differences am ong potentially exposed children. J. Expos. Anal. Environ. Epidemiol. 13: 240-246.

334 

### Emm e rich, S.; Gorfain, J.C.; Huang, M.: Howa rd-Reed, C. (2003) Air and pollutant transport from attache d garages to residen t ial living spaces. National In stitu te of Sta ndards and Technology report num ber NISTIR 7072.

335 

### Gordon, S.M.; Callahan, P.J.; Nishioka, M. G.; Brinkm an, M.C.; O’Rourke, M.K.; Lebowitz, M.D.; Moschandreas, D.J. (1999) Residential environm ental m easure m ents in the National
Hum a n Exposure Assesm ent Survey (NHEXAS) p ilot study in Arizona: prelim inary results for pesticides and VOCs. J . Expos. Analysis. Environ. Epidemiol. 9: 456-470.

336 

### Schlapia, A .; Morris, S.S. (1998) Architect ural, behavioral and environm ental factors associated w ith VOCs in Anchorage hom e s. Proceedings of the Air & W a ste Man a gem e nt Association’s 91 st 

### Annual Meeting & Exhibition. Paper no. 98-A504.

337 

### Batterm an, S.; Hatzivasilis, G.; Jia, C. (2006) Concentrations and em issions of gasoline and other vapors from residential vehicle garages. A t mos. Environ. 40: 1828-1844.

338 

### Mem o randum from Tom Long, Te d Johnson, Ji m Laurenson, and Arlene Rosenbaum to Ted Palm a. Subj ect: Developm ent of penetra tion and proxim ity m i cr oenvironm ent factor distributions for the HAPEM5 in support of the 1999 National-scale Ai r Toxics Assessm ent (NATA). EPA contr act no. GS-10F-0124J. http://www.epa.gov/ttn/f e ra/hapem 5/hapem 5_ m e _factor_m e m o.pdf

339 

### Palisade Corporation. Guide to Using @Risk. Risk Analysis and Sim ulation Add-In for Microsoft E xcel. Version 4.5

. Newfield: Palisade Corporation. Pages i-iv.

340 

### Batterm an, S.; Jia, C.; H atzivasilis, G.; G odwin, C. (2006) Sim u ltaneous m easurem e n t of ventilation using tracer gas tech niques and VOC concentrations in hom es, garages and vehicles.
J Environ. Monit . 8: 249-256.

341 

### Data obtained from Dr. Cliffo rd W e isel, EOHSI. E-m ail co rrespondence dated November 3, 2006 from Dr. Clifford Weisel, EOHSI to Rich Cook and Chad Bailey, U.S. EPA. Docket

num b er Docket EP A-HQ-OAR-2005-0036.

342 

### J.L. Schoor, “Chapter 1: Che m ical Fate and Transport in the Environm e nt,” in Fate of
Pestic ides a nd Chemicals in the Env i ronment (J.L. Schoor, Ed.). John Wiley & Sons, 1992, pp.
1-24.

343 

### Murray, D.M.; Burm aster, D.E. (1995) Resident ial air exchange rates in the United S t ates:

em pirical an d estim ated param etric d i stri butions by season and clim atic region. Risk A nalysis
15: 459-465.

344 

### Fugler, D.; Grande, C.; Graham

, L. (2002) Attached garages are likely path for pollutants.
ASHR AE I AQ Applica tions 3(3): 1-4.

3-194345 

### Sheltersource, Inc. (2002) Evaluating Minnesota hom e s. Final repo rt. Prepared for Minnesota Departm e nt of Commerce. U.S. Departm e nt of Energy grant DE-FG45-96R530335.

346 

### Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: sim u ltaneous m easure m ent of ventilation, benzene a nd tolu e n e in res i den tia l air of two hom es. Sci. Total E n viron. 345: 31-40.

347 

### Batterm an, S.; Jia, C.; H atzivasilis, G.; G odwin, C. (2006) Sim u ltaneous m easurem e n t of ventilation using tracer gas tech niques and VOC concentrations in hom es, garages and vehicles.
J. Environ. Monit. 8: 249-256.

348 

### Batterm an, S.; Jia, C.; H a tzivasilis, G. (2006) Migration of volatile organic com pounds from attached garages to residences: a m a jor exposure source. Environ Res (In Press).

349 

### George, M.; Kaluza, P.; Maxwell, B.; Moore, G.; W i sdom , S. (2002) Indoor air quality & ventilation strategies in new hom e s in Alaska. Final Report

. Alaska Building Science Network.

350 

### Tsai, P.; Weisel, C.P. (2000) Penetration of ev aporative em issions into a hom e from an M85­ fueled vehicle park ed in an attached garage. J. A i r & Waste Manage. Assoc. 50: 371-377.

351 

### Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: sim u ltaneous m easure m ent of ventilation, benzene and toluene in residen tial indoor air of two hom es. Sci. Total Enviro n. 345: 31-40.

352 

### Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: sim u ltaneous m easure m ent of ventilation, benzene and toluene in residen tial indoor air of two hom es. Sci. Total Enviro n. 345: 31-40.

353 

### Schlapia, A .; Morris, S.S. (1998) Architect ural, behavioral, and environm ental factors associated w ith VOCs in Anchorage hom e s. Presented at the Air & W aste Managem ent Association’s 91 st 

### Annual Meeting & Exhibition. June 14-18, 1998, San Diego, CA.

354 

### Isbell, M.A.; Stolzberg, R.J.; Duffy, L.K. (2005) Indoor climate in interior Alaska: sim u ltaneous m easure m ent of ventilation, benzene and toluene in residen tial indoor air of two hom es. Sci. Total Enviro n. 345: 31-40.

355 

### Batterm an, S.; Jia, C.; H a tzivasilis, G. (2006) Migration of volatile organic com pounds from attached garages to residences: a m a jor exposure source. Environ Res. (In Press).

356 

### Data obtained from Dr. Cliffo rd W e isel, EOHSI. E-m ail co rrespondence dated November 3, 2006 from Dr. Clifford Weisel, EOHSI to Rich Cook and Chad Bailey, U.S. EPA. Docket

num b er Docket EP A-HQ-OAR-2005-0036.

357 

### U.S. Energy Inform ation Adm i nistration. Re sidential Energy Consum ption Survey, 2001. Table HC1-4a. Housing Unit Characteristics by Type of Housing Unit, Million U.S. Households, 2001 [Online at http://w ww.eia.doe.gov/em eu/recs/recs2001/hc_pdf/housunits/hc1­ 4a_housingunits2001.pdf ]

358 

### Environm e ntal Protection Agency. (1997) E xposure factors handbook. National Center for Environm ental Assessment report E P A/600/P-95/002Fa-c [Online at http://www.epa.gov/ncea/pdfs/efh/front.pdf ]

3-195359 

### Batterm an, S.; Jia, C.; H atzivasilis, G.; G odwin, C. (2006) Sim u ltaneous m easurem e n t of ventilation using tracer gas tech niques and VOC concentrations in hom es, garages and vehicles.
J. Environ. Monit. 8: 249-256.

360 

### Graham , L. A.; Noseworthy, L.; Fugler, D.; O’Leary, K.; Karm an, D.; Grande, C. (2004) Contribution of vehicle em ission from an attach ed garage to residen tial indoor air pollution levels. J. Air & Waste Manage. Assoc. 54: 563-584.

361 

### Evans, J.S.; W olff, S.K. ; Phonboon, K.; Levy, J.I.; Sm ith, K. R. (2002) Exposure efficiency: an idea who se tim e has com e? Chemosphere 49: 1075-1091.

3-196