EXHIBIT C 
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Draft Report – Near Roadway Emissions:

Measurement, Exposure, and Monitoring

June 21, 2011 

Gregory Gould, Ph.D. 

Transportation and Air Quality Science Fellow 

Natural Resources Defense Council 

1 Introduction

On‐road motor vehicles, referred to as "highway vehicles" herein, are a significant source of air pollutant

emissions, accounting for 50% of carbon monoxide (CO), 32% of nitrous oxides (NOX) and 21% of volatile

organic compounds (VOCs) emissions during 2008 in the United States (EPA 2009b). While highway

vehicles are a minor source of particulate matter in EPA’s national inventory, accounting for 1.2% of

particulate matter < 10 μm in diameter (PM10) and 0.1% of particulate matter < 2.5 μm in diameter

(PM2.5), highway vehicles are a large source of particulate matter in urban areas. For example, highway

vehicles accounted for 8% of PM10 and 17% of PM2.5 emissions in Los Angeles County during 2008

(CARB 2008).  

Despite progress in reducing vehicle emissions over the past 4 decades unhealthy levels of air pollution

persist in many densely populated regions of the United States. The EPA reports that 127 million people

(42% of the population) live in areas that do not meet national ambient air quality standards

(NAAQS)(EPA 2010). Nonconforming regions continue to strive towards cleaner air through programs

and policies established under the Clean Air Act; however, the spatial disparity of improvements within

regions, conforming or otherwise, is a serious concern.  

The NAAQS target regional air quality. EPA monitors each region’s air quality and progress towards

conformity with NAAQS through a network of ambient air quality monitors. EPA requires states with

nonconforming regions to submit a state implementation plan (SIP) that describes the actions the state

will take to eventually meet the NAAQS. Attainment is demonstrated when the ambient air quality

monitoring network no longer detects violations of the NAAQS.  

Meeting the NAAQS, demonstrated by the lack of violations registered at air quality monitors, does not

mean that everyone within a conforming region is breathing cleaner air. That’s because the monitoring

network is very sparse, and as a result many people may breathe air contaminated by local air pollution

sources that are not measured by air quality monitors. For example, during 2010 there were just 782

active PM10 monitors across the entire U.S1 or one PM10 monitor per 4,850 mi2 on average. However,

air quality monitors are not evenly dispersed, they are typically clustered around urban areas or other

                                                           

1 Information about the ambient air quality monitoring network is available from EPA’s Air Explorer website:

http://www.epa.gov/airexplorer/
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areas where air quality is a concern. As Figure 1 shows the monitoring network is still relatively sparse

even within urban areas.  

Figure 1Location of PM10 air quality monitors that were active during 2010 in the contiguous United States.

Areas along busy roads are a particular concern because highway vehicles are a major source of air

pollution, many people live in close proximity to busy roads, and few monitors are located by them. For

example, there are just 11 active PM10 monitors (1% of all active PM10 monitors) located within 100m

of very busy roads (≥ 100,000 vehicle trips per day) and 36 (5% of all active PM10 monitors) located

within 300m2 (see Table 1 for more details). The American Housing Survey (AHS) indicates that a large

number of households are sited very close to busy roadways. The AHS asks respondents if any 4 lane

highways, railroads or air ports are located within 300ft (91m) of their residence. We cannot determine

specifically how many housing units are located along just 4 lane highways; however, we assume that

the majority of the responses refer to proximity to 4 lane highways because the highway network is

much denser than the rail network or location of airports. The AHS indicates that 17% of U.S.

households were located within 300ft (91m) of a 4 lane highway, railroad, or airport in 2009 and as

Figure 2 shows, this percentage has grown over the past 12 years. The housing survey data also indicate

that rental units are more likely to be located near these major transportation facilities raising potential

                                                           

2 We counted the number of air quality monitors along busy roads using a geographic information system. 100m

and 300m buffers were drawn around every road in the U.S. with at least 100,000 vehicle trips per day and the

number of active (during 2010) monitors located within each buffer were counted. The traffic data were obtained

from the Federal Highway Administration’s highway performance monitoring system (HPMS), the roadway

network from the 2010 National Transportation Atlas Database, and the location of ambient air quality monitors

from EPA’s Air Explorer website.
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equity concerns (Figure 2). The growing percentage of new housing units located near major

transportation facilities is also concerning.  

Table 1 Near road air quality monitors

> 50,000 AADTa > 100,000 AADT

   100mb 300m 500m     100m 300m 500m

CO 14 (4%)c 37 (12%) 68 (22%) 8 (3%) 18 (6%) 39 (12%)

NO2 4 (1%) 23 (6%) 45 (12%) 2 (1%) 14 (4%) 31 (8%)

Ozone 5 (< 1%) 34 (3%) 78 (6%) 1 (< 1%) 12 (1%) 36 (3%)

PM10 19 (2%) 57 (7%) 88 (11%) 10 (1%) 35 (4%) 58 (7%)

PM2.5 17 (2%) 63 (7%) 115 (12%)     9 (1%) 38 (4%) 68 (7%) a

Average Annual Daily Traffic counts in 2008 from the Federal Highway Administration Highway Performance Measurement

System.

b

Distance in meters measured from highway centerlines as represented by the U.S. Department of Transportation’s 2010

National Transportation Atlas Database. c

Percent of all monitors that measured this pollutant in 2010.

Figure 2 Percentage of U.S. households located within 300ft (91m) of a 4 lane highway, railroad, or airport3

                                                           

3 Percentages calculated with data from the 2009, 2007, 2005, 2003, 2001, 1999, and 1997 American Housing

Surveys available from the U.S. Census Bureau:

http://www.census.gov/hhes/www/housing/ahs/nationaldata.html

0%

5%

10%

15%

20%

25%

30%

1997 1999 2001 2003 2005 2007 2009

Year

Owner

Renter

New Construction (last 4 years)
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This study considers the adequacy of the current ambient air quality monitoring network. We begin by

reviewing the literature on near roadway air quality which provides strong evidence that concentrations

of many air pollutants are elevated above regional background levels along major roads. Briefly, we

review the main findings of the health literature that describes a number of negative health outcomes

linked with exposure to increased concentrations air pollutants from highway vehicles. We then review

a relatively recent literature that investigates how different population groups may be

disproportionately exposed to air pollutants from busy roads.  Many studies have demonstrated that

lower socioeconomic status or being part of a racial minority groups is associated with exposure to

higher concentrations of air pollutants from motor vehicles.  

In our study we consider the adequacy of the ambient air quality monitoring network at detecting air

pollution from motor vehicles. We define an adequate monitoring network as one designed to monitor

the air quality in any populated region to promote attainment of the NAAQS in the entire geographic

area. We realize that even the best designed monitoring network would not be able to detect every air

pollution hot spot in a region; however, as we demonstrate, the near highway region is not a hot spot, it

represents a significant portion of land and population in urban areas. The ability of the monitoring

network to detect poor air quality equally well across neighborhoods of all socioeconomic and racial

composition is also critical to ensure that environmental justice concerns are considered.   

Our study begins with estimating how many people live along busy highways where the concentration of

traffic related air pollutants is highest and then determine if minority groups are disproportionately

represented.  We carry out this analysis for each county in California. Many previous population

exposure and environmental justice studies have considered specific geographic areas – typically

because the researchers suspected environmental justice issues or the location was convenient. We find

that in some counties close to 35% of the population may live in close proximity (less than 500 m) to a

busy roadway (greater than 50,000 AADT). Unsurprisingly, these counties are the most densely

populated which means that a very large population is potentially being exposed to unhealthy

concentrations of air pollutants. In many counties we also find that the odds of living near a busy

highway are much greater if you belong to a racial minority. We find positive correlations between the

odds of being part of a minority group and distance to roadways and the roadway’s traffic level.  

Finding that many people live in close proximity to busy highways we then consider the layout of the

monitoring network. We evaluate if monitors are located along populated stretches of busy highways in

each region and also if they are located by the busiest highways where we would expect the greatest

exposure to occur. Finally we suggest how a more comprehensive monitoring network may be designed.
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2 Literature Review

The ability of the air quality monitoring network to protect public health requires consideration of

several factors. Where are the highest concentrations of air pollutants? What are the health risks from

exposure? Who is exposed, and are disadvantaged or more vulnerable groups at higher risk for

exposure?  The following review summarizes our current understanding of these questions.

2.1 Air Pollution Near Roads

Highway vehicles contribute to regional and local air pollution. Generally, the concentration of air

pollutants from highway vehicles is elevated along roads though this is not always the case. The gradient

and spatial extent of elevated air pollutant concentrations extending from the edge of a road to a point

where back ground levels are reached depends on many factors including the reactivity and physical

properties of each pollutant, traffic volume and vehicle fleet, atmospheric conditions, and the local

topography including presence of trees and other barriers.  

Emissions from highway vehicles may be considered either reactive or inert. The concentration of

relatively inert pollutants, such as CO and directly emitted particulate matter, will generally decay

exponentially with distance from a road due to diffusion and dilution by mixing with cleaner air. Strong

winds can further dilute pollutant concentrations through increased mixing while calm winds can

transport pollutants downwind from a road, extending spatial extent of elevated concentrations.  When

reactive pollutants are exposed to other chemicals in the atmosphere (including other pollutants), heat

and light, new chemicals or compounds may be formed. Some of these new chemicals or compounds

are considered “secondary” pollutants. The time lag in the formation of secondary air pollutants and a

complex series of reactions that depend on atmospheric conditions and concentrations of other

pollutants, results in a more complex or non‐existent near road gradient. For example, secondary air

pollutants such as ozone and secondary particles (sulfates and nitrates) may take several hours to form,

by the time these pollutants are created they can be dispersed over a wide area or transported far from

their primary source (Seinfeld 1989; Finlayson‐Pitts 1997). Other secondary pollutants such as NO2 are

formed rapidly upon emission leading to elevated concentrations along roads, over a longer period of

time, further from roads, and depending on atmospheric conditions, NO2 can from Ozone and particle

pollution (Palmgren et al. 1996; Finlayson‐Pitts 1997).  

Directly emitted particulate matter, though relatively inert, can exhibit complex concentration gradients

along roads. Unlike gaseous air pollutants, ambient particulate matter concentration is measured using

two different metrics: particle number and particle mass.  Generally, particle number concentrations are

used for very small particles (< 100 nm) and mass concentration is used for larger particles (> 2.5 μm).

The rationale for using particle counts rather than mass stems from studies that suggest a link between

negative health outcomes and particle number concentration (EPA 2002), in addition to exposure to the

specific chemical components of each particle, and differences in metrology equipment for analyzing

very small particles. The smaller the particle size the greater the number of particles for a given mass

concentration. The same mass of smaller particles has a larger surface area and can be breathed more

deeply into the lung which may add to their potency (EPA 2002). The concentration gradient of particles

depends on many factors in addition to diffusion and dilution. Particle size can change over time, for
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example by agglomeration to form larger particles with increasing distance from a road (Shi 1999).

Different size particles will behave differently with larger particles settling faster and closer to roads

than smaller particles that behave more like gases. Particles are also deposited onto the ground, washed

out of the atmosphere by rain, and are absorbed onto plants or other surfaces (EPA 2009a).  

A recent meta‐analysis of near roadway monitoring studies published through June 2008 estimates the

distance decay gradient of several commonly measured air pollutants from highway vehicles (Karner et

al. 2010). The study considers two methods for comparing distance decay gradients across 263 distance

decay data sets obtained from 41 studies. The first method normalizes each air pollutant

concentration/distance data point by dividing by the background concentration reported in each study.

The authors note several weaknesses with this approach: not all studies report background

concentrations and there are no standard methods for measuring background concentrations.

Background concentrations are typically estimated by measuring the air pollutant concentration upwind

from a road or by using air quality data from a nearby monitor that is located away from busy roads.

Considering these limitations, a second method that normalizes each data point by dividing by the

roadway edge concentration is considered. Not every study measured concentrations at the roadway

edge so the authors estimated roadway edge concentrations using the available distance decay data in a

regression analysis to estimate gradients for each pollutant. Roadway edge concentrations could then

be determined from the estimated gradients. They note that errors in concentrations normalized by

roadway edge concentration are likely less severe than concentrations normalized by background

concentrations. The roadway edge method is also more consistent across datasets and is therefore

more appropriate for meta‐analysis.  

Karner et al. (2010) find that the concentration of air pollutants are 2–21 times above background levels

at the edge of a road and that a gradient of elevated concentrations extends to a distance of 100–600 m

from the road. Most pollutants were 2 to 4 times background levels at roads edge, with CO an exception

at 21 times the background level (Table 2). Table 3 provides concentration gradients estimated by

Karner et al. (2010)  and compares them with results from a previous meta‐analysis that (Zhou & Levy

2007). Zhou and Levy (2007) considered studies published through 2005 that measured or modeled the

spatial extent of impacts from highway vehicle emissions. Impacts were considered as either elevated

air pollution concentrations or increased health risks. They identified 21 monitoring, 5 modeling, and 7

epidemiology studies. Studies that estimated elevated cancer risks were later removed because the

definition of elevated cancer risk was extremely variable. They find that elevated concentrations of air

pollutants from highway vehicles extend 100–500 m from a road’s edge (Table 3). The results from both

studies shown in Table 3 largely agree though different methods were used. A comprehensive review of

each air monitoring study considered by Karner et al. (2010) is available in the online appendix for their

paper.
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Table 2 Multipliers indicating the amount that roadway edge concentrations are elevated above background concentrations

adapted from Karner et al. (2010).

Pollutant method 1a (m) method 2b  (m)

NO2

c 2.9 1.7

PM2.5d  ‐  1.3

PM10e 1.3  ‐ 

ECf 1.7 2.3

UFP (<3nm)g 4 4.8

UFP (<15nm)h 4.8 7.1

COi 21 10

Benzene 2.1 1.8

NOj 3.3 2.9

NOx

k 1.8 2.1

VOC2l 2 2.6 a normalized by background concentration b normalized by roadway edge concentration c concentration of nitrogen dioxide d mass concentration of particulate matter less than 2.5 μm in diameter e mass concentration of particulate matter less than 10 μm in diameter

f

concentration of elemental carbon (including black carbon and black smoke) g

ultra fine particle number concentration (particles less than 3 nm in diameter) h

ultra fine particle number concentration (particles less than 15 nm in diameter) i

concentration of carbon monoxide

j

concentration of nitrogen oxide k concentration of nitrous oxides l

concentration of volatile organic compounds that did vary with distance from roads. More details about these compounds

available from the online appendix of (Karner et al. 2010).
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Table 3 Comparison of near road air pollutant gradients from two meta‐analysis. Values indicate the distance that air

pollutant concentrations reach background levels.  

(Karner et al. 2010)   Zhou and Levy (2007)

Pollutant method 1m (m) method 2n

  (m)     (m)

NO2

a 380 550     200–500

PM2.5b  ‐o 986     100–400

PM10c 176  ‐    

ECd 420 130    

UFP (<3nm) 100–300 e 189 210

UFP (<15nm)f 910 570    

Fine Particles (<300nm)g

  ‐  ‐    

n/ap

COh  ‐  170

benzene 280 320

NOi 565 200

NOx

j 570 115

VOC1k 270 180

VOC2l  ‐  ‐     a concentration of nitrogen dioxide b mass concentration of particulate matter less than 2.5 μm in diameter c mass concentration of particulate matter less than 10 μm in diameter d concentration of elemental carbon (including black carbon and black smoke) e

ultra fine particle number concentration (particles less than 3 nm in diameter)

f

ultra fine particle number concentration (particles less than 15 nm in diameter) g fine particle number concentration (particles less than 300 nm in diameter) h concentration of carbon monoxide i

concentration of nitrogen oxide

j

concentration of nitrous oxides k

concentration of volatile organic compounds that did vary with distance from roads. More details about these compounds

available from the online appendix of (Karner et al. 2010). l

concentration of volatile organic compounds that did not vary with distance from roads. More details about these compounds

available from the online appendix of (Karner et al. 2010). m normalized by background concentration n normalized by roadway edge concentration o pollutant showed no distance decay concentration gradient within the measured range p Zhou and Levy (2007) did not provide data on these pollutants

As shown in Table 3, near road air pollution concentrations and gradients are dependent on the specific

pollutant for the reasons discussed earlier. Concentration of NO, a very reactive species that quickly

forms NO2, is very high at the edge of roads and decays quickly to background levels. Elevated

concentrations of NO2, CO, and some types of particles, which are less reactive, extended further from

the edge of roadways.  The background concentration of air pollutants also affects the measurement of

concentrations and gradients. For example, CO is mainly produced by motor vehicles, and as a result

concentrations are elevated many times above background levels near roadways.  On the other hand,

concentrations of relatively large particles such as PM10, PM2.5, and fine particles which are produced
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by many sources and therefore found in high concentrations throughout many areas are elevated less

than the other pollutants measured. Particles are made out of many different materials and some types

could be more toxic than others. For example, particular health risks have been associated with diesel

particulate matter, notably increased risk of cancer (Garshick et al. 2004; Laden et al. 2006), and road

dust particles which contains toxic heavy metals (Lough et al. 2005). Therefore, when considering health

risks from particle exposure, consideration should be given to the concentration of particles from

particularly risky sources in additional to total particle concentrations. In contrast to course particles,

concentrations of ultra fine particles are very high near roadways and show strong gradients. Like CO,

motor vehicles are a major source of ultra fine particles.  

Structures in the near road environment can also influence air pollutant concentrations and gradients.

An evolving literature has shown some evidence that trees can absorb particulate matter, potentially

reducing downwind concentrations (Fujii et al. 2008; Micha et al. 2009). Physical barriers such as sound

walls can also affect near road pollution concentrations, generally reducing downwind concentrations

(Lidman 1985; G. E. Bowker et al. 2007), though walls may increase concentrations under certain wind

conditions (R. Baldauf et al. 2008). In dense urban areas tall buildings can create street canyons with

complex air flow patterns that generally limit dilution and diffusion causing higher near road

concentrations than in more open spaces (Vardoulakis 2003).  

Atmospheric conditions also have a large affect on near road air pollutant concentrations and distance

decay gradients. All of the studies considered by Karner et al. (2010) measured air pollutant

concentrations during the day and in places where winds were generally perpendicular to the road.

Several studies have measured particulate matter concentration gradients when winds were parallel to

the road. Hitchins (2000)  found that concentration gradients during parallel winds were about half the

distance as those observed under perpendicular winds from the road. They also found that elevated

concentrations only extended several meters on the up‐wind side of roads. Reponen et al. (2003) also

observed a shorter distance decay gradient under parallel wind conditions, though background

concentrations were also much higher. Zhu et al. (2009) did not find any consistent distance decay

gradient under parallel wind conditions, noting that small shifts in wind direction constantly changed the

up‐ and down‐wind sides of the road. Time of day also affects near road concentrations and distance

decay gradients. During the day sunlight warms air near the ground causing the air, and air pollutants

with in it, to rise and mix vertically while winds mix and transport pollutants horizontally. During the

evening the atmosphere is more stable with little vertical mixing or wind. Several studies have measured

near road air pollutant concentrations during the evening, finding higher concentrations and longer

gradients than in the day, even when considering the differences in pre‐down traffic levels (Zhu et al.

2006; Hu et al. 2009; Durant et al. 2010). Hu et al. (2009) observed extremely slow decay of ultra fine

particle concentration to back ground levels during pre‐dawn hours in Los Angeles; over 2,000 m were

required. Durant et al. (2010) found that multiple air pollutant concentrations near a busy highway in

Massachusetts fell by about 50% as dawn broke.  

2.2 Exposure to Mobile Source Emissions and Environmental Justice

A large literature has documented inequalities in exposure to toxic pollutants with respect to race and

socioeconomic status beginning with a study by the United Church of Christ that found disproportionally
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high numbers of toxic waste sites in communities with large minority populations (United Church of

Christ 1987) . While most studies have not been able to determine what causes the observed

inequalities, Pastor et al. (2001) find that toxic waste facilities in Los Angeles have been preferentially

sited in low income and minority neighborhoods. This suggests that not only do inequalities exist in

exposure to pollutants, but that discriminatory decisions may cause the observed inequalities. Until

Gunier et al.’s (2003) study of near roadway populations in California, the environmental justice

literature was confined to stationary sources or broad indicators of poor air quality. Since then a number

of studies have found disparities in socioeconomic status and race and exposure to air pollutants from

highway vehicles (Table 1). Each study has generally compared the characteristics of the populations

most exposed to emissions with the least exposed population using either exposure proxies, such traffic

density, or estimates of air pollutant concentrations from dispersion models.  Most, but not all, studies

have found evidence of disparate exposures depending on socioeconomic status and race (Table 4).

None of these studies have determined what has caused the observed inequalities.  

The first studies to investigate environmental justice with respect to highway vehicle emissions exposure

relied on proxies for exposure. Gunier et al. (2003) evaluated several demographic variables including

socioeconomic status (income, poverty, employment type and educational attainment) and ethnicity or

race of children in census blocks with high traffic density in California. Traffic density was calculated by

summing vehicles miles traveled (VMT) within a 200m buffer of each 1990 census block group and

dividing by the area of the census block group. The federal highway administration’s 1992 highway

performance monitoring system (HPMS) annual average daily traffic (AADT) count data and roadway

length were used to estimate VMT for roadways that fell within the 200m buffer around each block

group using a geographic information system. HPMS data is limited to highways and other major

roadways where traffic is monitored and does not include traffic on many local roads. Block groups with

greater than 500,000 VMT/mi2 were defined as “high traffic density”. They present results that show

disproportionate numbers of low income, racial and ethnic minorities, and persons with low

socioeconomic status in high traffic density census blocks groups when compared to the statewide

population. Seven percent of block groups in the lowest income quartile were defined as high traffic

density compared to just 2% of block groups in the highest income quartile. Similar results were

obtained for other socioeconomic status variables. They also found that 5.2% of Hispanic children and

4.4% of African American children lived in high traffic density areas compared to only 1.5% of white

children. They did not provide an analysis of adults by race or ethnicity.
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Table 4 Summary of near road environmental justice studies

Study Geographic Scope Exposure measure or proxy Geographic Analysis Unit Demographic Variable 

Gunier et al. (2003) California, U.S. traffic density within 200m buffer U.S. census block group median family income

median income  poverty level professionals  

college degree  racial or ethnic minori(children under 15)

Houston et al.(2004)

South Coast Region of

California, U.S.

traffic density within 200m buffer U.S. census block group poverty prevalence 

racial or ethnic minori

Apelberg et al.(2005)

Maryland, U.S. Cancer risk from HAPs U.S. census tract household income  

per capita income  

owner occupied  public assistance poverty > 25% with our high sc

racial or ethnic minori

Kingham et al.(2007)

Christchurch, New

Zealand

PM10 concentration New Zealand census area unit (~

U.S. census tract)

household income 

social deprivation inde

racial or ethnic minori

Buzzelli and Jerrett

(2007)

Toronto, Canada NO2 concentration Statistics Canada census tract (~

U.S. census tract)

% low income family 

% low education median income 

% lone parents 

housing unit value  % minority  % high status job 
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Cesaroni et al.(2010)

Rome proximity to roadway (> 10,000

AADT)

Individual Households Social Economic Positi

traffic density with in 150m

buffer

Social Economic Positi

Rome (city center) proximity to roadway (> 10,000

AADT)

Social Economic Positi

Green et al. (2004) California AADT range of busiest road with

in 150m

Schools racial or ethnic minori

U.S. Census tracts, used to

supplement school data

receiving subsidized mEnglish as a second lan

% below poverty % born abroad 

% without high school

diploma

Appatova et al.

(2008)

9 MSA's throughout the

U.S.

proximity to a major road (with in

150m abd 400m)

Schools racial or ethnic minori

MSA

Chakraborty (2009) Tampa Bay MSA Health Risk from HAPs (cancer

and other risks)

U.S census tract racial or ethnic minori

% below poverty % owner occupied  

% no vehicles 

% with disability % over age 65  

Havard et al.

(2009)

Strasbourg, France NO2 concentration French census block (~ U.S.

census tract)

deprivation index 

a no correlation found or insufficient evidence to determine correlation.
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A similar study by Houston et al. (2004) followed the same procedure as Gunier et al. (2003) for

estimating traffic density by block group; however, they used year 2000 census and traffic data and also

limited their study to the Los Angeles area. To evaluate potential inequalities they classified each block

group by prevalence of poverty (very poor, poor, low poverty and not poor) and as either a minority

area or not. They find that poorer and minority block groups are located in areas with higher traffic

density. Traffic density was 93% higher in block groups defined as very poor when compared to those

defined as not poor and traffic density was 140% higher in minority block groups when compared to

non‐minority block groups.  

Cesaroni et al. (2010) performed a more disaggregate adaptation of the studies performed by Gunier et

al. (2003) and Houston et al (2004). Rather than estimating traffic density for buffered geographic

subdivisions they estimated traffic density for 150m buffers centered on the geocoded residences of

1,898,898 individuals in Rome (everyone in Rome whose address could be geocoded). Distance to busy

roads (> 10,000 AADT) was also estimated from each residence. They found that higher socioeconomic

status was correlated with greater traffic density and proximity to busy roadways. This result was

attributed to wealthier individual’s preference to live within the city center and the relatively

inexpensive housing in the city’s outlying areas. When the analysis was restricted to the city center the

opposite result was found; higher socioeconomic status was correlated with less traffic exposure.  

The studies reviewed above have considered the proximity to roadways as a proxy for exposure to air

pollutants from highway vehicles. Generally exposure is higher closer to busy roads where the

concentration of most air pollutants is highest (see Tables 2 and 3); however as discussed previously, the

concentration gradient of air pollutants released along highways is very complex and influenced by

many local factors. The next group of studies use air dispersion models, and in one case a land use

regression model, to estimate the concentration of air pollutants from highway vehicles rather than

relying on distance proxies.  

Apelberg et al. (2005) evaluate racial and socioeconomic disparities in cancer risk from toxic air

pollutants released by highway vehicles using the EPA’s 1996 National Air Toxics Assessment (NATA) and

year 2000 census data. They evaluate each census tract in Maryland and labeled those with greater than

the 90th percentile cancer risk as “high risk”. For each demographic variable (including household

income, prevalence of minorities, and percent impoverished), tracts are divided into quartiles and the

percent of high risk tracts is calculated. They also estimate the relative risk from being in the quartile

that defines the lowest socioeconomic status or largest minority population compared to the quartiles

that define the highest socioeconomic status or smallest minority population. Their results show that

tracts with lower income, more poverty and a greater percentage of minority residents are more likely

to be high risk.  

A similar study using the more recent 1999 NATA evaluated cancer and other health risks from

hazardous air pollutants (HAPs) and diesel particulate matter produced by highway vehicles in the

Tampa Bay Metropolitan Statistical Area (MSA) (Chakraborty 2009).  Chakraborty (2009) uses a spatial

error regression model to evaluate the influence of race, poverty, and several other indicators of

socioeconomic status on cancer risk and other health risks. The results indicate that census tracts with
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larger proportions of minorities have larger cancer and other health risks. The results also indicate that

lower socioeconomic status (lower home ownership rate, lower vehicle ownership rate, greater

prevalence of disabilities) is also an indicator of greater health risks; however, the poverty rate was not a

significant indicator of health risks.  

NATA emissions and health risk estimates have been used by Apelberg et al. (2005) and Chakraborty

(2009) as a refined alternative to earlier studies that used exposure proxies based on proximity to busy

roads. However, NATA emission estimates may not be more reliable than using distance based or traffic

density exposure proxies. Census track estimates of highway vehicle emissions used in the NATA to

estimate heath risks are derived from very aggregate (i.e., county) VMT estimates (ICF International

2011). Emission inventories are estimated for each county using the VMT estimates and EPA’s MOBILE6

emission factor model. The county level emission inventories do not account for specific traffic

conditions along area roadways, such as the proportion of heavy‐duty diesel trucks, different speeds,

and congestion levels, all factors that influence emission rates. The emission inventories for each county

are disaggregated to U.S. census tracts by using spatial surrogates for mobile sources (i.e., length of

roads, traffic volume, etc.). The end results are estimated health risks that depend on proxies for

pollution exposure that are very similar, if not identical, to the distance and traffic density proxies used

in previous studies. Additionally, the earlier studies have used more disaggregate census data and

geography (i.e. block groups rather than tracts) and therefore may be more reliable.  

Studies similar to those using NATA data have also been conducted outside of the U.S. Havard et al.

(2009) evaluated exposure to nitrogen dioxide (NO2), primarily a mobile source pollutant, in Strasbourg,

France and Kingham et al. (2007) evaluated exposure to PM10 from highway vehicles in Christchurch,

New Zealand. These studies also find racial and socioeconomic disparities in exposure to air pollutants

from highway vehicles. Similar to the studies in the U.S. that relied on the NATA, emissions data are

aggregate and the spatial analysis units are large.  

Land use regression (LUR) models are also used for estimating air pollutant concentrations from highway

vehicles by predicting pollutant concentrations from land use observations. Buzzelli and Jerrett (2007)

use an LUR model that estimates NO2 concentration from observed land use in Toronto, Canada .The

LUR model explains about 70% of the variation in NO2 concentration and provides estimates on a 5m

grid. The gridded NO2 concentration estimates are used to estimate average NO2 concentrations by

census tract so that concentrations can be paired with tract level census data. A series of regression

models are used to determine how tract level socioeconomic status and race are related to NO2

exposure. The results are mixed. They find that NO2 exposure increases with decreasing median income,

less academic achievement, and increasing proportions of single parent households. However, they also

find that NO2 exposure decreases with an increase in the proportion of minority residents and increases

with the proportion of residents having high status jobs, and the value of housing units. While Buzzelli

and Jerrett (2007) begin with spatially detailed NO2 concentration estimates, their analysis is based on

tract level statistics. Census tracts can be quite large with significant spatial variation in pollution levels

and demographics.  It is unclear if their methods offer any advantages over previously used methods

considering the aggregate data and the use of an LUR model that provides good though still relatively

uncertain concentration estimates (the LUR model explains 70% of NO2 variation).  
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15

Exposure to air pollution occurs throughout the day, while at home, work, school and traveling between

these and other destinations. When children are not at home they are usually at school. A few studies

have focused on children’s exposure to traffic related air pollutants at school by considering the

proximity of schools to busy highways and disparities in proximity by race, ethnicity and socioeconomic

status. Considering childhood exposure is critical since evidence suggests that children may be more

susceptible to air pollution due to increased exposure and greater sensitivity (Schwartz 2004).  

Green et al. (2004) measured the proximity of schools in California to busy roadways. Each K‐12 school

in the state was placed into one of three traffic exposure categories (low, medium and high) dependant

on the maximum AADT of roads with in 150m of the geocoded address of each school (< 25,000 AADT =

low, 25,000‐49,999 AADT = medium, and ≥ 50,000 AADT = high) . They used a combination of school

based and 2000 census tract based demographic variables to evaluate disparities in school proximity.

Odds ratios were constructed to evaluate if schools with higher proportions of students from families

with low socioeconomic status or minority students had higher odds of medium or high traffic exposure

compared to schools with low traffic exposure. They found that schools with high proportions of

minority students, those receiving subsidized meals, and those learning English were more likely to have

medium or high traffic exposure (odds ratios ranged from 1.39‐1.71 for high traffic exposure and 1.17‐

1.48 for medium traffic exposure). They also found that schools in census tracts with high proportions of

impoverished residents, those without a high school diploma, and those born abroad were more likely

to have medium or high traffic exposure (odds ratios ranged from 1.37‐2.52 for high traffic exposure and

1.32‐2.23 for medium traffic exposure).  

Appatova et al. (2008) also considered proximity of schools to busy roadways. They counted students

enrolled in schools within 150m and 400m of highways in 9 metropolitan statistical areas (MSAs)

representing different regions of the U.S. They find that a greater percentage of white students in the

MSAs evaluated are enrolled in school close to highways than any other race; however, results vary by

each individual MSA. For example, while white students in Boston, Philadelphia, and San Antonio are

more likely to attend schools close to highways than other racial groups, Hispanic students are more

likely to attend schools close to highways in Cincinnati, Denver, and Los Angeles, and Black students in

Memphis. The results also vary with the distance considered, 150m or 400m. A major weakness of this

study was that all highways were considered equal when in reality some highways experience much

more traffic than others. Most other studies have taken traffic levels into consideration. It is unclear

how much this omission influences the findings.

  
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16

3 Research Methods and Data

We consider several metrics to evaluate the adequacy of the ambient air quality monitoring network.

First we estimate the population residing in close proximity to busy roads and then determine if racial

minorities are over represented in these regions. We also plot the air quality monitoring network to

determine if they are positioned to measure air pollution near the busiest roads.

The population residing along busy highways was estimated from block level population estimates from

the 2000 U.S. census4

. Buffers starting at 100m and extending out to 500m in 100m increments were

created along each road segment with greater than 50,000 AADT and the population within the buffer

was summed.  This procedure was repeated for roadway segments with greater than 60,000 AADT up to

100,000 AADT in increments of 10,000 AADT. The road network and AADT data were obtained from the

National Transportation Atlas Database (DOT 2010) and reflect the traffic counts and road network from

the Highway Performance Measurement System. The Highway Performance Measurement System is a

compilation of traffic counts and detector data collected by states for major roadways and submitted to

the Federal Highway Administration. The latest available data, which are used in our study, are for the

year 2008.  

Buffers and census block boundaries do not align, as shown in Figure 3. When a census block partially

intersects a buffer, a portion of the block’s population in proportion to the area covered by the buffer

was added to the total buffer population. This assumes that population is evenly distributed within each

block group which is not strictly true. However, we do not expect this assumption to cause significant

error or bias because census blocks are relatively small with homogenous land uses, particularly in urban

areas.      

Figure 3 Example of a 500m buffer intersecting U.S. census blocks

                                                           

4 The 2010 Census would have been preferable, but data is not yet available at the block level.  
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A range of buffer distances and traffic levels are considered for several reasons. The current literature

provides a range of distances from busy highways where concentrations of air pollutants from highway

vehicles are elevated and the distances vary by pollutant type. Additionally, the pollutant concentrations

that cause negative health outcomes will also vary by pollutant type. Therefore, several distance buffers

are used to reflect this uncertainty. There is no definition of a busy highway, but previous studies have

generally considered busy highways as roads with at least 50,000 to 100,000 AADT. A range of distance

buffers and highway volumes are also used to determine if over representation of minority or low

income groups is correlated with either factor.  

To investigate over representation we first estimate the odds of living within each distance buffer of a

busy highway conditional on belonging to a specific racial or ethnic group in each county (eq. 1).

ܱ݀݀ݏ௠௖ௗ ൌ ௉೎೘೏

௉೎೟ି௉೎೘೏

                     eq. 1

where;

Oddscmd = odds of living within the distance buffer d, conditional on belonging to group m and living in

county c.

Pcmd   = population of group m in county c within the distance buffer d.

Ptc   = total population of group m in county c.

Over representation is then measured by taking the ratio of the odds of living near a busy road in each

county between each minority group and the white population (eq. 2).

ܱܴ௖௠ௗ ൌ ைௗௗ௦೎೘೏

ைௗௗ௦೎ೢ೏

                 eq. 2

where;

ORcmd     = the odds ratio for county c, minority group m, and distance buffer d.

Oddscwd = odds of living within the distance buffer d, conditional on being white (w) and living in county

c.

An odds ratio greater than one in equation 2 indicates that the odds of living within distance d of a busy

road in county c is (insert the value of ORcmd here) times greater if you belong to the minority group m

than if you are white. Values less than one are interpreted similarly and indicate an over representation

of the white population along busy highways. Odds ratios were estimated for each racial and ethnic

minority group where population estimates were provided at the block level in the 2000 U.S. census, for

each distance buffer and each roadway traffic level.
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4 Results

Our analysis indicates that a large portion of California’s population lives in close proximity to busy

roads. We refer to “busy” roads as those carrying greater than 50,000 AADT and “very busy” roads as

those carrying greater than 100,000 AADT. A typical busy road is a multi lane, limited access urban state

or interstate highway, but also includes heavily used multi lane urban arterials. Few roads outside of

major urbanized areas except for major interstate routes are classified as busy. A typical very busy road

is almost always an urban limited access state or interstate highway. Almost 20% of the population (over

6 million people) lives within 500 m of a busy road  in California (Table 5). Near road monitoring studies

have indicated that concentrations of many mobile source air pollutants are elevated above background

levels within this range (see Table 3). Approximately 3% of the population (almost 1 million people) lives

within only 100 m of busy roads. This is an area where air pollutant concentrations may be elevated

significantly above background levels (see Table 2). The near highway population is largest in urban

areas as Figure 4 shows. In most of California’s urbanized counties, more than 20% of the population

lives within 500 m of a busy road and 15% within 500m of a very busy road.  Residents of Santa Clara

County are the most likely to live near a busy road with 35% of the population within 500m (see

Appendix A for a complete list of counties).   

Table 5 Population living near busy roads in California

California Near Road Population

AADTa 100m 300m 500m

>50,000 988,919 2.9%b 3,622,998 10.7% 6,431,172 19.0%

>100,000 628,209 1.9% 2,427,669 7.2% 4,382,578 12.9% a average annual daily trips b percentage of state population

Our analysis also found evidence of significant over representation of minority residents in areas close

to busy and very busy roads in many counties. For example, the plots in Figure 5 show the relationship

between the odds ratio, distance from highway, and traffic volume in San Mateo and Los Angeles

Counties. The plots show odds ratios that describe the relative odds of living next to a busy road if you

are black or Latino compared with being white. Odds ratios greater than 1 indicate that a minority

resident is more likely to live within a specific distance buffer than a white resident. For example, the

odds that a Latino living in San Mateo County lives within 100 m of a road with greater than 100,000

AADT are 2.5 times greater than for a white resident. The plots in Figure 5 indicate that Latino and black

residents are more likely to live closer to busier highways than the white population in San Mateo and

Los Angeles Counties. The plots also indicate that the trends vary by minority group and county. For

example, disparities are greater for Latinos than blacks in Los Angeles County and disparities for Latinos

are greater in San Mateo County than in Los Angeles County.  
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Table 6 Near Roadway Population by State

Figure 4 Percentage of county population living within 500m of a road with greater than 50,000 AADT in New York and New

Jersey.
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20

(San Mateo, Latino) (San Mateo, black)

(Los Angeles, Latino) (Los Angeles, black)

Figure 5 Relationship between odds ratios, distance from highway and traffic level for Latino and black populations in San

Mateo and Los Angeles counties.  

Rather than display separate plots as in Figure 5 for each county, the relationship between odds ratios

and distance to roads can be summarized with correlation coefficients. Pearson’s correlation coefficients

indicate the strength of linear relationships between two data sets, with values greater than 0.5

generally indicating strong correlation. The maps in Figures 6 and 7 show the correlation coefficients for

each county where there are roads with greater than 50,000 AADT. Negative values indicate that the

odds ratio decreases with increasing distance from a busy road similar to the trends shown in Figure 5.

The maps indicate that in most areas of the state, including almost all urbanized counties, there is a
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21

strong negative correlation between the odds ratio (for both the Latino and black populations) and

distance from busy roads (see Appendix B for a complete summary of odds ratios).  

The correlation coefficients indicate the presence and direction of a linear relationship between the

odds ratio and distance; however, it does not indicate the magnitude of the odds ratio (i.e., the amount

of disparity). The odds ratios for the Latino and black population are mapped in Figures 8 and 9 for

residents living within 200 – 300 m of a busy road. The maps indicate that both Latino and black

residents are more likely to live closer to busy roads than white residents in every urban county.  The

disparities are greatest in the San Francisco Bay area where Latino and black residents in several

counties are more than twice as likely to live within 200‐300 m of a road with greater than 50,000 AADT

than the white population (see Appendix B for a detailed summary).
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22

Figure 6 Correlation between distance buffer and odds ratio (Latino/white) for 50K
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Figure 7 Correlation between distance buffer and odds ratio (black/white) for 50K
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Figure 8 Relative odds (i.e., odds ratio) of living within 200 – 300 m of a road with greater than 50,000 AADT by county,

Latino vs. white. Warm colors indicate disproportionately high numbers of Latino residents near busy roads.
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Figure 9 Relative odds (i.e., odds ratio) of living within 200 – 300 m of a road with greater than 50,000 AADT by county, black

vs. white. Warm colors indicate disproportionately high numbers of black residents near busy roads.
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5 Conclusions

Our analysis indicates that a large portion of California’s population lives in close proximity to busy roads

and that in many places minority residents are over represented in these areas. These results confirm

findings from previous studies, but also find that there are fewer disparities in rural areas, a finding that

is new to the literature. Previous research has indicated that concentrations of many mobile source air

pollutants are elevated above back ground levels within 500 m of busy roads and that this exposure is

likely the cause of a number of negative health outcomes ranging from asthma to cancer. The NAAQS

set an upper limit on air pollutant concentrations, but air quality monitors set up to enforce NAAQS are

generally placed too far from busy roads to capture near roadway gradients. As shown in Table 7, 8 and

Figure 10,11 few monitors are placed close to busy or very roads in California5

. Only two monitors (only

one that monitors multiple pollutants) are within 100 m of a busy road, a region where concentrations

of air pollutants may be 2 to 4 times background concentrations and that is home to almost 1 million

Californians.

Table 7 Location and description of near busy roads (> 50,000 AADT) air quality monitors in California

Distance

(m)

Precisiona

(m)

Monitor Site

ID City CO NO2 Ozone PM10 PM2.5

06‐041‐0001 San Rafael 0‐100 ±2 x x x x x

06‐067‐0007 Sacramento 0‐100 ±0 x             

06‐029‐0014 Bakersfield 101‐300b ±151 x x x x

06‐037‐0113 West Los Angeles 101‐300 ±0 x x x

06‐061‐0006 Roseville 101‐300 ±151 x x x x

06‐065‐6001 Perris 101‐300 ±0 x

06‐079‐8001 Atascadero 101‐300 ±0     x x x x

06‐001‐2004 Berkeley 301‐500 ±15 x x x x

06‐037‐4002 Long Beach 301‐500 ±0 x x x x x

06‐059‐0007 Anaheim 301‐500 ±20 x x x x x

06‐065‐0012 Banning 301‐500 ±151 x x

06‐073‐0006 San Diego 301‐500 ±0 x x x

06‐073‐1008

Camp Pendleton

South 301‐500 ±54 x x

06‐073‐1010 San Diego 301‐500 ±10 x x x x x

06‐073‐1016 San Diego 301‐500 ±1 x

06‐075‐0005 San Francisco 301‐500 ±0 x x x x x

06‐077‐1002 Stockton 301‐500 ±0 x x x x x

06‐079‐2006 San Luis Obispo 301‐500 ±5 x x

06‐085‐1001 Los Gatos 301‐500 ±0 x

                                                           

5 Air quality monitor data are from EPA’s Air Explorer website. The data include the species monitored, when the

monitor was active, and the monitor location (street address and latitude and longitude). We have noted in at

least one case, monitor 06‐029‐0014 in Bakersfield, that the street address and latitude and longitude do not

correspond (> 2,000m difference).   
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27

06‐087‐0007 Santa Cruz 301‐500 ±13 x x

06‐111‐2002 Simi Valley 301‐500 ±0     x x x x a Accuracy of reported latitude and longitude coordinates.   b The reported latitude and longitude coordinates for this site appear to be in error. The reported street address is over 2,000m

from the lat/long coordinates and as a result this monitor may not actually be near a busy road. A visual inspection of the

lat/long from Google Maps imagery did not reveal a monitoring station; the street address location was in a shopping center

and office park complex and we could not determine the presence of a monitoring station.  

Table 8 Location and description of near very busy roads (> 100,000 AADT) air quality monitors in California

Distance

(m)

Precisiona

Monitor Site ID City (m) CO NO2 Ozone PM10 PM2.5

06‐041‐0001 San Rafael 0‐100 ±2 x x x x x

06‐029‐0014 Bakersfield 101‐300b ±151 x x x x

06‐061‐0006 Roseville 101‐300 ±151     x x x x

06‐001‐2004 Berkeley 301‐500 ±15 x x x x

06‐037‐4002 Long Beach 301‐500 ±0 x x x x x

06‐059‐0007 Anaheim 301‐500 ±20 x x x x x

06‐065‐0012 Banning 301‐500 ±151 x x

06‐073‐0006 San Diego 301‐500 ±0 x x x

06‐073‐1008

Camp Pendleton

South 301‐500 ±54 x x

06‐073‐1010 San Diego 301‐500 ±10 x x x x x

06‐073‐1016 San Diego 301‐500 ±1 x

06‐075‐0005 San Francisco 301‐500 ±0 x x x x x

06‐077‐1002 Stockton 301‐500 ±0 x x x x x

06‐087‐0007 Santa Cruz 301‐500 ±13 x x

06‐111‐2002 Simi Valley 301‐500 ±0     x x x x a Accuracy of reported latitude and longitude coordinates.   b The reported latitude and longitude coordinates for this site appear to be in error. The reported street address is over 2,000m

from the lat/long coordinates and as a result this monitor may not actually be near a busy road. A visual inspection of the

lat/long from Google Maps imagery did not reveal a monitoring station; the street address location was in a shopping center

and office park complex and we could not determine the presence of a monitoring station.  
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28

Figure 10 Location6 of active air quality monitors (during 2010) that are within 500m of a road with greater than 50,000 AADT

                                                           

6 The precision of air quality monitor locations varies by monitoring site. See Table 7 for details.  
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29

Figure 11 Figure 12 Location7 of active air quality monitors (during 2010) that are within 500m of a road with greater than

100,000 AADT

To protect the public health of near road populations and address environmental justice concerns more

near road monitors are needed. The current network is too sparse considering the many factors that

influence near roadway air pollutant concentrations and the wide range of concentrations and distance

decay gradients observed in monitoring studies. For example, to record air pollutants from highway

vehicles a monitor must be placed downwind of a busy road. However, in some places the direction that

is downwind may change throughout the day, from day to day, or seasonally. Additionally, the local

                                                           

7 The precision of air quality monitor locations varies by monitoring site. See Table 8 for details.
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30

environment including trees, walls and, buildings can affect near road concentrations. It is not evident

that the placement of near road monitors has considered these factors. Additional monitors should be

strategically sited to detect the maximum concentrations that near road populations are exposed to in

each region or community. The location and number of additional monitors should consider prevailing

wind patterns, traffic volumes, amounts of diesel truck traffic, congestion, and physical barriers between

the monitoring site and the road.  

The ambient air quality network and NAAQS also fail to measure ultra fine particles. A number of studies

have measured elevated concentrations of these particles near roadways, with gradients sometime

extending far downwind from highways. The health literature also suggests that ultra fine particles are

correlated with a number of negative health outcomes. EPA and other air quality regulatory agencies

should seriously consider monitoring concentrations of ultra fine particulate matter along roadways and

adopt measure to limit exposure.

  
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31

References

Apelberg, B.J., Buckley, T.J. & White, R.H., 2005. Socioeconomic and Racial Disparities in 

Cancer Risk from Air Toxics in Maryland. Environmental Health Perspectives, 113(6), 

pp.693-699. 

Appatova, A. et al., 2008. Proximal exposure of public schools and students to major roadways: 

a nationwide US survey. Journal of Environmental Planning and Management, 51(5), 

pp.631-646. 

Baldauf, R. et al., 2008. Impacts of noise barriers on near-road air quality. Atmospheric 

Environment, 42(32), pp.7502-7507. 

Bowker, G.E. et al., 2007. The effects of roadside structures on the transport and dispersion of 

ultrafine particles from highways. Atmospheric Environment, 41(37), pp.8128-8139. 

CARB, 2008. Almanac Emission Projection Data, Sacramento, CA: California Air Resources 

Board. Available at: http://www.arb.ca.gov/app/emsinv/emssumcat_query.php?F_DIV=-

4&F_DD=Y&F_YR=2008&F_SEASON=A&SP=2009&F_AREA=CO&F_CO=19. 

Cesaroni, G. et al., 2010. Socioeconomic position and health status of people who live near busy 

roads: the Rome Longitudinal Study (RoLS). , 9, pp.41-41. 

Chakraborty, J., 2009. Automobiles, Air Toxics, and Adverse Health Risks: Environmental 

Inequities in Tampa Bay, Florida. Annals of the Association of American Geographers, 

99(4), pp.674-697. 

DOT, 2010. National Transportation Atlas Database, Washington, DC: U.S. Department of 

Transportation, Bureau of Transportation Statistics. Available at: 

http://www.bts.gov/publications/national_transportation_atlas_database/2010/. 

Durant, J.L. et al., 2010. Short-term variation in near-highway air pollutant gradients on a winter 

morning. Atmospheric Chemistry and Physics, 10(17), pp.8341-8352. 

EPA, 2002. Health Assessment Document for Diesel Engine Exhaust, Washington, DC: 

Environmental Protection Agency. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060#Download. 

EPA, 2009a. Integrated Science Assessment for Particulate Matter (Final Report), Washington, 

DC: U.S. Environmental Protection Agency. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546. 

EPA, 2009b. National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data. 

Available at: http://www.epa.gov/ttn/chief/trends/ [Accessed May 17, 2011]. 

EPA, 2010. Our Nation’s Air - Status and Trends through 2008, Research Triangle Park, NC: 

Office of Air Quality Planning and Standards. Available at: 

http://www.epa.gov/airtrends/2010/. 
***DRAFT*** 6/30/2011 ***DRAFT***

32

Finlayson-Pitts, B.J., 1997. Tropospheric Air Pollution: Ozone, Airborne Toxics, Polycyclic 

Aromatic Hydrocarbons, and Particles. Science, 276(5315), pp.1045-1051. 

Fujii, E. et al., 2008. Removal Rates of Particulate Matter onto Vegetation as a Function of 

Particle Size, Sacramento, CA: Prepared for Breathe California. Available at: 

http://www.sacbreathe.org/Local%20Studies/Vegetation%20Study.pdf. 

Garshick, E. et al., 2004. Lung Cancer in Railroad Workers Exposed to Diesel Exhaust. 

Environmental Health Perspectives, 112(15), pp.1539-1543. 

Green, R.S. et al., 2004. Proximity of California public schools to busy roads. Environmental 

Health Perspectives, 112(1), pp.61-66. 

Gunier, R.B. et al., 2003. Traffic density in California: Socioeconomic and ethnic differences 

among potentially exposed children. J Expo Anal Environ Epidemiol, 13(3), pp.240-246. 

Havard, S. et al., 2009. Traffic-Related Air Pollution and Socioeconomic Status. Epidemiology, 

20(2), pp.223-230. 

Hitchins, J., 2000. Concentrations of submicrometre particles from vehicle emissions near a 

major road. Atmospheric Environment, 34(1), pp.51-59. 

Houston, D. et al., 2004. Structural Disparities of Urban Traffic in Southern California: 

Implications for Vehicle-Related Air Pollution Exposure in Minority and High-Poverty 

Neighborhoods. Journal of Urban Affairs, 26(5), pp.565-592. 

Hu, S. et al., 2009. A wide area of air pollutant impact downwind of a freeway during pre-sunrise 

hours. Atmospheric Environment, 43(16), pp.2541-2549. 

ICF International, 2011. An Overview of Methods for EPA’s National-Scale Air Toxics 

Assessmen. Available at: http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf 

[Accessed May 27, 2011]. 

Jerrett, M. & Buzzelli, M., 2007. Geographies of Susceptibility and Exposure in the City: 

Environmental Inequity of Traffic-Related Air Pollution in Toronto. Canadian Journal of 

Regional Science, 30(2), pp.195-210. 

Karner, A.A., Eisinger, D.S. & Niemeier, D.A., 2010. Near-Roadway Air Quality: Synthesizing 

the Findings from Real-World Data. Environmental Science & Technology, 44(14), 

pp.5334-5344. 

Kingham, S., Pearce, J. & Zawarreza, P., 2007. Driven to injustice? Environmental justice and 

vehicle pollution in Christchurch, New Zealand. Transportation Research Part D: 

Transport and Environment, 12(4), pp.254-263. 

Laden, F. et al., 2006. Historical estimation of diesel exhaust exposure in a cohort study of U.S. 

railroad workers and lung cancer. Cancer Causes & Control, 17(7), pp.911-919. 
***DRAFT*** 6/30/2011 ***DRAFT***

33

Lidman, J.K., 1985. Effect of a noise wall on snow accumulation and air quality. Transportation 

Research Record, 1033, pp.79–88. 

Lough, G.C. et al., 2005. Emissions of Metals Associated with Motor Vehicle Roadways. 

Environmental Science & Technology, 39(3), pp.826-836. 

Micha, F., Bombardelli, F. & Niemeier, D., 2009. Particulate Matter Modeling in Near-road 

Vegetation Environments, Davis, CA: University of California. Available at: 

http://dn.engr.ucdavis.edu/images/AQMit-Report6.pdf. 

Palmgren, F. et al., 1996. Effects of reduction of NOx on the NO2 levels in urban streets. Science 

of The Total Environment, 189-190, pp.409-415. 

Pastor, M., Sadd, J. & Hipp, J., 2001. Which Came First? Toxic Facilities, Minority MoveIn, 

and Environmental Justice. Journal of Urban Affairs, 23(1), pp.1-21. 

Reponen, T. et al., 2003. Concentration gradient patterns of aerosol particles near interstate 

highways in the Greater Cincinnati airshed. Journal of Environmental Monitoring, 5(4), 

p.557. 

Schwartz, J., 2004. Air Pollution and Children’s Health. Pediatrics, 113, pp.1037-1043. 

Seinfeld, J.H., 1989. Urban Air Pollution: State of the Science. Science, 243(4892), pp.745-752. 

Shi, J., 1999. Measurements of ultrafine particle concentration and size distribution in the urban 

atmosphere. The Science of The Total Environment, 235(1-3), pp.51-64. 

United Church of Christ, 1987. Toxic Wates and Race in The United States, New York, NY: 

Prepared by Public Data Access Inc. for the Commission of Racial Justice United Church 

of Christ. Available at: 

https://vpn.lib.ucdavis.edu/,DanaInfo=www.google.com+url?sa=t&source=web&cd=1&

ved=0CCEQFjAA&url=http%3A%2F%2Fwww.ucc.org%2Faboutus%2Farchives%2Fpdfs%2Ftoxwrace87.pdf&ei=6J3_Ta6zO4nEsAOByfTdBQ&usg=AF

QjCNGnypsXmF9JRTRSpN807KZAZa4dlA. 

Vardoulakis, S., 2003. Modelling air quality in street canyons: a review. Atmospheric 

Environment, 37(2), pp.155-182. 

Zhou, Y. & Levy, J., 2007. Factors influencing the spatial extent of mobile source air pollution 

impacts: a meta-analysis. BMC Public Health, 7(1), p.89. 

Zhu, Y. et al., 2006. Comparison of Daytime and Nighttime Concentration Profiles and Size 

Distributions of Ultrafine Particles near a Major Highway. Environmental Science & 

Technology, 40(8), pp.2531-2536. 

Zhu, Y. et al., 2009. Air pollutant concentrations near three Texas roadways, Part I: Ultrafine 

particles. Atmospheric Environment, 43(30), pp.4513-4522.
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Appendix A

Table 9 Population within 100m, 300m, and 500m of roads with greater than 50,000 AADT in California counties.

Distance to Road (>50,000 AADTa)

State County 100m 300m 500m

California Alameda 55,742 3.86%b 194,017 13.44% 343,266 23.78%

California Contra Costa 28,846 3.04% 109,261 11.52% 194,869 20.54%

California El Dorado 573 0.37% 2,690 1.72% 5,132 3.28%

California Fresno 9,100 1.14% 35,531 4.44% 66,538 8.32%

California Kern 6,731 1.02% 27,308 4.13% 55,489 8.39%

California Los Angeles 329,906 3.47% 1,248,158 13.11% 2,220,631 23.33%

California Madera 1,891 1.54% 5,034 4.09% 10,120 8.22%

California Marin 6,946 2.81% 25,530 10.32% 48,801 19.73%

California Merced 986 0.47% 6,871 3.26% 14,036 6.67%

California Monterey 4,759 1.18% 13,975 3.48% 22,675 5.64%

California Napa 3,082 2.48% 12,137 9.77% 22,453 18.07%

California Orange 103,654 3.64% 385,325 13.54% 673,478 23.66%

California Placer 3,440 1.38% 12,095 4.87% 23,063 9.28%

California Riverside 17,572 1.14% 77,495 5.01% 154,118 9.97%

California Sacramento 42,952 3.51% 141,309 11.55% 254,111 20.77%

California San Benito 83 0.16% 262 0.49% 433 0.81%

California San Bernardino 26,595 1.56% 103,088 6.03% 188,266 11.01%

California San Diego 98,667 3.51% 361,360 12.84% 643,364 22.86%

California San Francisco 35,467 4.57% 131,495 16.93% 228,631 29.43%

California San Joaquin 17,318 3.07% 55,792 9.90% 95,397 16.93%

California San Luis Obispo 4,274 1.73% 15,522 6.29% 27,915 11.32%

California San Mateo 23,001 3.25% 87,053 12.31% 157,027 22.21%

California Santa Barbara 11,505 2.88% 45,797 11.47% 73,239 18.34%

California Santa Clara 108,599 6.45% 354,113 21.05% 588,216 34.96%

California Santa Cruz 5,323 2.08% 19,413 7.60% 34,122 13.35%

California Shasta 1,904 1.17% 7,424 4.55% 13,036 7.99%

California Sierra 0 0.00% 0 0.01% 1 0.02%

California Solano 10,232 2.59% 36,649 9.29% 68,324 17.32%

California Sonoma 8,899 1.94% 29,457 6.42% 53,246 11.61%

California Stanislaus 6,569 1.47% 19,620 4.39% 36,890 8.25%

California Tulare 1,657 0.45% 7,755 2.11% 15,279 4.15%

California Ventura 11,117 1.48% 45,050 5.98% 85,791 11.39%

California Yolo 1,530 0.91% 6,412 3.80% 13,213 7.83% aAnnual Average Daily Trips

b Percentage of county population
***DRAFT*** 6/30/2011 ***DRAFT***

35

Table 10 Population within 100m, 300m, and 500m of roads with greater than 100,000 AADT in California counties.

Distance to Road (>100,000 AADTa)

State County 100m 300m 500m

California Alameda 45,072 3.12%b 158,592 10.98% 283,969 19.67%

California Contra Costa 20,680 2.18% 79,316 8.36% 144,327 15.21%

California Fresno 5,184 0.65% 19,594 2.45% 37,769 4.72%

California Kern 1,236 0.19% 6,618 1.00% 14,230 2.15%

California Los Angeles 225,445 2.37% 908,718 9.55% 1,639,205 17.22%

California Marin 5,627 2.28% 21,417 8.66% 40,049 16.20%

California Napa 1 0.00% 8 0.01% 14 0.01%

California Orange 60,877 2.14% 239,659 8.42% 421,817 14.82%

California Placer 1,367 0.55% 4,961 2.00% 10,007 4.03%

California Riverside 14,133 0.91% 64,088 4.15% 127,260 8.23%

California Sacramento 25,996 2.12% 91,382 7.47% 168,493 13.77%

California San Bernardino 17,408 1.02% 71,975 4.21% 134,411 7.86%

California San Diego 67,197 2.39% 258,020 9.17% 469,429 16.68%

California San Francisco 16,396 2.11% 67,001 8.63% 120,835 15.56%

California San Joaquin 9,025 1.60% 30,778 5.46% 54,034 9.59%

California San Mateo 19,241 2.72% 74,857 10.59% 136,348 19.28%

California Santa Barbara 5,734 1.44% 26,469 6.63% 43,845 10.98%

California Santa Clara 61,737 3.67% 211,752 12.58% 360,060 21.40%

California Santa Cruz 2,427 0.95% 8,968 3.51% 15,422 6.03%

California Solano 6,757 1.71% 23,686 6.00% 45,730 11.59%

California Sonoma 2,106 0.46% 4,971 1.08% 8,007 1.75%

California Stanislaus 5,842 1.31% 16,878 3.78% 31,050 6.95%

California Ventura 8,004 1.06% 34,372 4.56% 68,007 9.03%

California Yolo 716 0.42% 3,590 2.13% 8,259 4.90% aAnnual Average Daily Trips

b Percentage of county population
***DRAFT*** 6/30/2011 ***DRAFT***

36

Appendix B

Table 11 Correlation coefficients and odds ratios estimated for the Latino population near roads with greater than 50,000

AADT

Correlation

Coefficienta

Odds Ratio (Latino/white)

County min median max

Alameda  ‐0.89 1.0 1.1 1.3

Contra Costa 0.22 1.1 1.1 1.2

El Dorado  ‐0.25 0.6 0.7 1.1

Fresno  ‐0.91 1.2 1.4 1.5

Kern  ‐0.52 2.1 2.2 2.3

Los Angeles  ‐0.80 1.1 1.2 1.2

Madera  ‐0.09 3.2 3.8 4.1

Marin 0.74 1.3 2.4 2.8

Merced 0.30 1.1 2.1 2.2

Monterey  ‐0.68 0.4 0.4 0.6

Napa  ‐0.75 1.2 1.6 2.0

Orange  ‐0.76 1.2 1.3 1.3

Placer  ‐0.87 1.1 1.1 1.3

Riverside  ‐0.77 1.3 1.3 1.7

Sacramento  ‐0.96 1.0 1.0 1.2

San Benito  ‐0.71 0.6 0.8 1.1

San Bernardino  ‐0.31 1.4 1.4 1.5

San Diego  ‐0.88 1.4 1.5 1.5

San Francisco  ‐0.45 1.9 2.1 2.3

San Joaquin  ‐0.83 1.2 1.3 1.4

San Luis Obispo  ‐0.81 0.7 0.8 1.5

San Mateo  ‐0.98 1.5 1.9 2.3

Santa Barbara  ‐0.89 1.2 1.5 1.7

Santa Clara  ‐0.98 1.3 1.4 1.6

Santa Cruz  ‐0.91 0.3 0.4 0.6

Shasta  ‐0.88 1.1 1.3 1.3

Sierra 0.77 0.0 0.0 0.1

Solano 0.16 1.0 1.0 1.0

Sonoma  ‐0.06 2.1 2.1 2.2

Stanislaus 0.95 1.4 1.7 1.9

Tulare  ‐0.55 0.4 0.5 0.5

Ventura  ‐0.97 0.7 0.8 0.9

Yolo 0.63 0.7 0.8 0.9 a Pearson correlation coefficient estimated for buffer distance and odds ratio. Negative values indicate

that Latinos are more likely to live closer to roads with greater than 50,000 AADT.
***DRAFT*** 6/30/2011 ***DRAFT***

37

Table 12 Correlation coefficients and odds ratios estimated for the Latino population near roads with greater than 100,000

AADT

Correlation

Coefficienta

Odds Ratio (Latino/white)

County min median max

Alameda  ‐0.77 1.1 1.1 1.3

Contra Costa 0.74 1.1 1.2 1.3

Fresno  ‐0.61 1.3 1.5 1.5

Kern  ‐0.97 0.7 0.8 0.9

Los Angeles  ‐0.96 1.4 1.5 1.7

Marin 0.37 1.5 2.3 3.3

Napa 0.97 0.2 0.2 0.3

Orange  ‐0.90 1.3 1.3 1.5

Placer  ‐0.70 1.1 1.2 1.6

Riverside  ‐0.78 1.4 1.4 1.9

Sacramento  ‐0.97 1.2 1.3 1.6

San Bernardino  ‐0.43 1.5 1.6 1.7

San Diego  ‐0.98 1.6 1.8 2.0

San Francisco 0.99 1.9 2.4 3.1

San Joaquin  ‐0.52 1.7 1.8 2.2

San Mateo  ‐0.98 1.6 2.1 2.5

Santa Barbara  ‐0.83 1.3 1.7 2.2

Santa Clara  ‐0.97 1.3 1.5 1.7

Santa Cruz  ‐0.89 0.4 0.5 0.8

Solano  ‐0.54 1.1 1.1 1.2

Sonoma 0.13 1.1 1.3 1.9

Stanislaus 0.96 1.5 1.7 2.0

Ventura  ‐0.97 0.7 0.9 1.1

Yolo 0.90 0.4 0.5 0.6 a Pearson correlation coefficient estimated for buffer distance and odds ratio. Negative values indicate

that Latinos are more likely to live closer to roads with greater than 100,000 AADT.
***DRAFT*** 6/30/2011 ***DRAFT***

38

Table 13 Correlation coefficients and odds ratios estimated for the black population near roads with greater than 50,000

AADT

Correlation

Coefficienta

Odds Ratio (black/white)

County min median max

Alameda 0.09 1.3 1.3 1.4

Contra Costa 0.33 1.4 1.5 1.6

El Dorado  ‐0.01 0.3 1.5 1.9

Fresno  ‐0.56 1.6 1.8 2.1

Kern  ‐0.67 1.6 1.8 2.0

Los Angeles  ‐0.81 1.1 1.1 1.2

Madera 0.92 1.7 2.1 2.5

Marin 0.97 1.1 1.7 3.1

Merced 0.91 1.2 1.4 1.7

Monterey 0.22 0.5 0.6 0.8

Napa 0.94 0.3 0.5 0.8

Orange  ‐0.99 1.3 1.5 1.7

Placer  ‐0.55 0.8 0.8 1.1

Riverside  ‐0.96 1.4 1.5 1.7

Sacramento  ‐0.84 1.0 1.0 1.2

San Benito  ‐0.46 0.2 0.2 0.3

San Bernardino  ‐0.99 1.3 1.4 1.6

San Diego  ‐0.97 1.3 1.4 1.5

San Francisco 0.86 1.0 1.2 1.6

San Joaquin  ‐1.00 0.9 1.3 1.8

San Luis Obispo  ‐0.91 0.4 0.5 0.7

San Mateo  ‐0.96 1.7 2.4 3.1

Santa Barbara  ‐0.93 0.6 0.7 0.9

Santa Clara  ‐0.97 1.2 1.4 1.9

Santa Cruz  ‐0.98 0.8 1.0 1.2

Shasta  ‐0.90 1.3 2.1 3.9

Solano 0.71 1.2 1.2 1.3

Sonoma  ‐0.87 1.9 2.4 2.4

Stanislaus 0.62 1.5 1.7 1.9

Tulare  ‐0.97 1.0 1.2 1.5

Ventura 0.03 0.7 0.8 0.8

Yolo  ‐0.95 1.7 1.9 2.0 a Pearson correlation coefficient estimated for buffer distance and odds ratio. Negative values indicate

that blacks are more likely to live closer to roads with greater than 50,000 AADT.
***DRAFT*** 6/30/2011 ***DRAFT***

39

Table 14 Correlation coefficients and odds ratios estimated for the black population near roads with greater than 100,000

AADT

Correlation

Coefficienta

Odds Ratio (black/white)

County min median max

Alameda 0.51 1.1 1.3 1.3

Contra Costa 0.81 1.1 1.4 1.5

Fresno 0.16 1.9 2.0 2.3

Kern  ‐0.97 1.0 1.3 2.0

Los Angeles  ‐0.97 1.2 1.3 1.3

Marin 0.64 1.3 1.8 2.6

Napa 0.80 2.9 3.9 4.3

Orange  ‐0.97 1.4 1.5 1.7

Placer 0.96 1.0 1.1 1.4

Riverside  ‐0.26 1.5 1.5 1.6

Sacramento  ‐0.92 1.4 1.5 1.8

San Bernardino  ‐0.98 1.3 1.4 1.6

San Diego  ‐0.99 1.6 1.8 1.9

San Francisco 0.80 1.6 2.1 3.0

San Joaquin  ‐0.94 1.7 1.9 2.4

San Mateo  ‐0.97 1.7 2.6 3.4

Santa Barbara  ‐0.80 0.8 0.9 1.3

Santa Clara  ‐0.97 1.3 1.5 1.8

Santa Cruz  ‐0.96 1.0 1.1 1.4

Solano  ‐0.03 1.0 1.1 1.3

Sonoma  ‐0.63 2.3 3.1 3.7

Stanislaus 0.60 1.7 1.9 2.1

Ventura  ‐0.66 0.7 0.8 0.9

Yolo 0.05 1.4 1.8 2.1 a Pearson correlation coefficient estimated for buffer distance and odds ratio. Negative values indicate

that blacks are more likely to live closer to roads with greater than 100,000 AADT.