Environmental Health Perspectives • VOLUME 112 | NUMBER 1 | January 2004 61

Vehicular-traffic–related emissions are a major

source of air pollution, especially in urban

areas. Epidemiologic studies of the relationship between asthma prevalence or morbidity

and traffic-related exposures have recently

been reviewed (Delfino 2002). Residential

proximity to busy roads has been associated

with respiratory symptoms (Ciccone et al.

1998; van Vliet et al. 1997; Venn et al. 2000,

2001), asthma hospitalizations (Edwards et al.

1994), and decreased lung function in children (Brunekreef et al. 1997). A study in San

Diego, California (English et al. 1999), found

increased risks for medical visits for asthmatic

children associated with residence near at least

one busy street. Some studies have found associations between proximity to traffic and

higher rates of childhood cancer (Pearson et al.

2000; Raaschou-Nielsen et al. 2001; Savitz

and Feingold 1989), whereas others have failed

to show any association (Langholz et al. 2002;

Reynolds et al. 2002).

A study of vehicular traffic density in census block groups in California (Gunier et al.

2003) found that children of color were

about three times more likely to live in 

high-traffic areas than were white children.

Researchers examining stationary sources of

air pollution, such as Toxic Release Inventory

(TRI) facilities, have found evidence of disproportionate exposures by race and socioeconomic status (Maantay 2002; Morello-Frosch

et al. 2002a). Another study found that nonwhite students in southern California were

more likely than non-Hispanic whites to

attend schools in locations with higher respiratory risks, estimated from modeled concentrations of hazardous outdoor air pollutants

emitted by both mobile and stationary

sources (Morello-Frosch et al. 2002b).

School location may be an important

determinant of exposure to traffic-related pollutants because children spend much of their

time at school. There are more than 8,000

public schools in California, but to date no

one has assessed either the spatial distribution

or the demographic profiles of schools in relation to nearby vehicular traffic volume. The

goal of this study was to examine the characteristics of public schools in California by

proximity to busy roads and to determine

whether there are disparities in proximity of

schools to high traffic volumes by race/ethnicity and socioeconomic status.

Materials and Methods

School data and selection. For this study, we

used the most current database available from

the California Department of Education

(CDE) as of December 2000 (CDE 2000).

From this statewide school database of approximately 8,200 schools, we selected all open

public schools, grades K–12, excluding alternative and special education; 7,515 schools met

the eligibility criteria. The school database also

included a) school address, b) school district,

c) county, d) census tract in which the school

was located, e) type of school (e.g., elementary

school, middle school, high school), f) total

enrollment by grade and race/ethnicity,

g) number of students eligible for free/reducedprice meals (which is related to household

income and size), h) number of children

5–17 years old residing in the school’s attendance area who are eligible for the California

Work Opportunity and Responsibility to Kids

program (CalWORKS; aid for families and

welfare-to-work program), and i) total number

and native language of English learners (i.e.,

children whose primary language is not

English and who have been assessed to lack

English language skills). Except for the

CalWORKS indicator, all other demographic

data are based on student enrollment information obtained by the school.

Census data. We downloaded the Census

2000 Summary File 3 data from the U.S.

Census Bureau American FactFinder website

(U.S. Census Bureau 2002). We compiled

demographic, social, economic, housing, and

population density data and calculated proportions of individuals in a census tract possessing

each characteristic (e.g., race, income below

poverty level, birthplace outside the United

States). These data were used to delineate the

sociodemographic composition of the census

tracts in which the schools were located.

Geocoding of schools. The addresses of all

the schools were geocoded using Maptitude,

version 4.2, and a street network provided

with the software (Caliper Corporation,

Newton, MA). We used an offset of 25 feet

(~8 m) from the street centerline to locate the

school. The geographic information system

(GIS) software identifies the street block that

matches the address and uses an interpolation

method to place the point within an address

range. Schools still unmatched after the batch

process (468 total) were then checked against

other street address resources, such as Street

Atlas USA, version 9.0 (DeLorme, Yarmouth,

ME) and ZP4 (Semaphore Corporation,

Address correspondence to B. Ostro, Office of

Environmental Health Hazard Assessment, 1515

Clay Street, 16th Fl., Oakland, CA 94612 USA.

Telephone: (510) 622-3157. Fax: (415) 622-3210.

E-mail: bostro@oehha.ca.gov

The contents and opinions expressed in this manuscript are those of the authors and do not represent the

official position of the Office of Environmental Health

Hazard Assessment, the California Environmental

Protection Agency, the California Department of

Health Services, or the State of California.

The authors declare they have no competing

financial interests.

Received 7 July 2003; accepted 6 October 2003.

Proximity of California Public Schools to Busy Roads

Rochelle S. Green,1 Svetlana Smorodinsky,2 Janice J. Kim,1 Robert McLaughlin,2 and Bart Ostro1

1Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, California, USA;

2Environmental Health Investigations Branch, California Department of Health Services, Oakland, California, USA

Residential proximity to busy roads has been associated with adverse health outcomes, and school

location may also be an important determinant of children’s exposure to traffic-related pollutants.

The goal of this study was to examine the characteristics of public schools (grades K–12) in

California (n = 7,460) by proximity to major roads. We determined maximum daily traffic counts

for all roads within 150 m of the school using a statewide road network and a geographic information system. Statewide, 173 schools (2.3%) with a total enrollment of 150,323 students were

located within 150 m of high-traffic roads (≥ 50,000 vehicles/day); 536 schools (7.2%) were within

150 m of medium-traffic roads (25,000–49,999 vehicles/day). Traffic exposure was related to

race/ethnicity. For example, the overall percentage of nonwhite students was 78% at the schools

located near high-traffic roads versus 60% at the schools with very low exposure (no streets with

counted traffic data within 150 m). As the traffic exposure of schools increased, the percentage of

both non-Hispanic black and Hispanic students attending the schools increased substantially.

Traffic exposure was also related to school-based and census-tract–based socioeconomic indicators,

including English language learners. The median percentage of children enrolled in free or reducedprice meal programs increased from 40.7% in the group with very low exposure to 60.5% in the highest exposure group. In summary, a substantial number of children in California attend schools close

to major roads with very high traffic counts, and a disproportionate number of those students are

economically disadvantaged and nonwhite. Key words: air pollution, children’s health, environmental

justice, ethnicity, schools, socioeconomic status, traffic. Environ Health Perspect 112:61–66 (2004).

doi:10.1289/ehp.6566 available via http://dx.doi.org/ [Online 7 October 2003]

Children’s Health | Article
Aptos, CA); we then manually transferred

this information to Maptitude for geocoding.

Ultimately, 7,460 of the 7,515 schools

(> 99%) were successfully located and

assigned a longitude and latitude. A number

of the nongeocoded schools did not have

addresses in the CDE database. Others were

in rural areas without numerical addresses or

in fast-growing areas with new streets. Finally,

the schools master file was converted to a

point shape file for use in ArcView 3.2a GIS

software (Environmental Systems Research

Institute, Redlands, CA).

Traffic database. The traffic database,

obtained from the California Department of

Transportation (CalTrans), contained traffic

and various road data for discrete road segments

in California. The source of these data was the

1997 Highway Performance Monitoring

System (HPMS) maintained by CalTrans

(CalTrans 1997). The HPMS traffic database

has been described previously in several recent

epidemiologic studies of traffic and adverse

health outcomes (English et al. 1999; Gunier

et al. 2003; Wilhelm and Ritz 2003). For this

study, we used the average annual daily traffic

(AADT) of the road segment, which is twoway traffic volume adjusted for day-of-week,

seasonal, and growth factors. Only freeways,

highways, major arterial roads, and large collector roads are included in the HPMS database.

Collector roads are main streets in communities that lead to the residential streets. CalTrans

periodically undertakes traffic counts on some

roads and then estimates or extrapolates traffic

flow to others, assigning AADT to each road

segment. There are no traffic flow data for

local residential streets.

GIS analysis. All georeferenced data were

stored in decimal degrees and were standardized to the North American Datum for 1983, a

set of parameters and control points used to

accurately define the three-dimensional shape

of Earth. The schools and CalTrans traffic files

were overlaid in ArcView, and an equidistant

projection was performed in order to avoid distance distortion. Then the distances from each

school to all road segments within a 150-m

buffer around the school were calculated using

ArcView 3.2a. Distance was estimated as the

shortest planar distance between the point

(school) and the line (road segment with traffic

counts; Figure 1). The last step was to overlay

the full schools file with 2000 census tracts and

assign each school a census tract number.

Exposure assessment. Several studies have

shown that the concentration of traffic pollutants drops off rapidly with increasing distance

downwind from the road, with the most

rapid decline occurring within 150 m of the

road (Hitchins et al. 2000; Zhu et al. 2002a,

2002b). A recent study in Los Angeles (Zhu

et al. 2002b) found that concentrations of

ultrafine particles, black carbon, and carbon

monoxide decreased exponentially with

distance downwind of a major freeway.

Concentrations measured 150 m downwind

of the freeway were close to background levels, whereas those measured 300 m downwind were indistinguishable from upwind

background concentrations. Additionally, epidemiologic studies have reported associations

between increasing traffic counts on nearby

roads and decreased lung function, respiratory

disease, and asthma hospitalizations in children (Brunekreef et al. 1997; Ciccone et al.

1998; Edwards et al. 1994; English et al.

1999; Pearson et al. 2000; Raaschou-Nielsen

et al. 2001; Savitz and Feingold 1989;

van Vliet et al. 1997; Venn et al. 2000, 2001;

Wjst et al. 1993). Distance to the nearby road

varied by study but generally ranged from 100

m (van Vliet et al. 1997) to 500 m (Edwards

et al. 1994), and adverse effects were reported

for traffic counts starting at about 24,000

vehicles/day (Edwards et al. 1994; Wjst et al.

1993).

On the basis of these findings, we selected

the road segment with the maximum AADT

within 150 m of each school as an estimate of

exposure to traffic (Figure 2). High exposure

to nearby traffic was defined as ≥ 50,000 vehicles/day on any road segment within 150 m of

the school; medium exposure 25,000–49,999

vehicles; and low exposure < 25,000 vehicles.

Schools not having any streets with counted

traffic volume within 150 m were assigned to

the very-low-exposure category. The school in

Figure 2 would be in the low-traffic-exposure

category.

Statistical analyses. We first evaluated associations between variables obtained from the

school and traffic databases [e.g., urban/rural

location, school demographics (race, ethnicity,

economic disadvantage), and type of school]

and the four traffic categories (i.e., very low,

low, medium, and high exposures). Although

there were few measures of economic disadvantage in the school database, several socioeconomic variables were available from the

census-tract–level data. Because there were

strong associations between the continuous

school-level and corresponding census-tract–

level demographic variables, as calculated with

Pearson’s product moment correlations, we

also used the following census-tract–level data

in our analyses: population density, percentage

of households with income below the poverty

level, percentage of people 25 years or older

with no high school diploma, and percentage

of persons born abroad.

To evaluate relationships between traffic

categories and both school-level and censustract–level variables, we performed polytomous logistic regression using SAS software

(version 8.2 for Windows; SAS Institute Inc.,

Cary, NC). Specifically, using race, ethnicity,

and socioeconomic status as explanatory variables, we modeled a) the odds of a school

belonging to the high traffic category compared with the low and very low categories

combined and b) the odds of a school belonging to the medium traffic category compared

with the very low and low categories combined. We computed odds ratios (ORs) for a

25% increase in each demographic variable

except population density, which was log transformed because of its skewed distribution.

We also examined the association between

traffic and race/ethnicity after stratifying schools

into two groups: those above and those below

the median percentage of students receiving

free/reduced-price meals. Finally, we performed

a separate analysis of the data after restricting

schools to those located in Los Angeles County

census tracts with a population density of at

least 5,000 people per square mile. In this way,

we could examine whether the relationships

between exposure to traffic at school and

race/ethnicity or economic disadvantage in a

highly urbanized area were similar to those

seen statewide.

Results

Statewide, 173 schools (2.3%) with a total

enrollment of 150,323 students (2.6%) had

high exposure to nearby traffic, 536 schools

(7.2%) had medium exposure, 4,484 schools

Children’s Health | Green et al.

62 VOLUME 112 | NUMBER 1 | January 2004 • Environmental Health Perspectives

Figure 1. Schematic diagram of distance calculations (D1–D3) between a school and three road

segments (S1–S3). This example shows that the

distance between the school and Si is Di.

D1

D2

D3

S1 S2

S3

School

Schematic for distance calculations

Figure 2. Schematic diagram of a school and three

road segments with attributed traffic volume (number of vehicles per day) within a 150-m buffer of the

school. Segment A would be chosen for the analysis, because it has the highest AADT.

School

11,235

5,299

11,081

150 m buffer

A

B

C
(60.1%) had low exposure, and 2,267 schools

(30.4%) had very low exposure (Table 1).

The percentage of children enrolled in schools

in each of the traffic exposure categories,

except the category with no attributable traffic, was somewhat larger than the percentage

of schools with these levels of traffic (Figure

3). Similar proportions of elementary and

middle schools were close to medium or high

traffic. A higher percentage of high schools

than elementary or middle schools was close

to medium traffic (12.2%). Of the 49 schools

that combined grades K–12, 20.4% had

medium or high exposure to nearby traffic

versus 9.5% of schools statewide.

Most schools close to high traffic volume

were in large urban areas, but several schools

in small urban and rural areas were also close

to busy roads. The percentage of schools near

medium and high traffic increased with

increasing population density per square mile

(Table 1). However, some schools in the lowest quartile of population density were close

to medium or high traffic.

Race/ethnicity was related to traffic exposure (Figure 4). As the traffic exposure category increased, the percentages of Hispanic

and of non-Hispanic black children attending

schools in those categories increased, and the

percentage of non-Hispanic white children

decreased. There was little difference in the

percentages of Asian children across traffic categories. Economic disadvantage was also

related to traffic exposure (Figure 5). The

median percentage of children participating in

CalWORKS in the schools’ attendance areas

increased steadily from 8.9% in schools with

very low exposure to 15.5% in those with high

exposure. Similarly, the median percentage of

children enrolled in free or reduced-price meal

programs increased from 40.7% in the group

with very low exposure to 60.5% in the highest exposure group. The median percentage of

children whose primary language was not

English and who had been assessed to lack

English language skills also rose steadily with

increasing traffic. Similar patterns were also

observed when the analysis was restricted to

schools in Los Angeles County census tracts

with population densities of at least 5,000

people per square mile.

The school-level and corresponding census-tract–level demographic variables were

highly correlated. The Pearson correlation

coefficients for percent Hispanic, percent nonHispanic black, percent non-Hispanic white,

percent Asian, and percent English learners at

school versus in the host census tract were,

respectively, 0.88, 0.82, 0.87, 0.77, and 0.76.

The correlation coefficient for percentage

receiving free or reduced-price meals, a schoolbased variable, and percentage with family

income below poverty, a census tract variable,

was 0.61. Based on these results, we considered

it reasonable to use several census-tract–level

variables not available in the school database in

additional analyses.

Table 2 shows the results of the polytomous

logistic regression analyses modeling the odds

of belonging to the high-traffic and to the

medium-traffic categories compared with the

combined low and very low categories, given

population density in the surrounding tract,

ethnic composition of the school, and the

school-level and census-tract–level socioeconomic status variables. Except for population

density, the ORs shown are for a 25% change

in the independent variable. Based on the standardized regression coefficients, measured as the

change in the log-odds of traffic category for a

one standard deviation increase in the independent variable (Selvin 1996), population density

per square mile of land in the surrounding census tract was the strongest predictor of traffic

category, followed by percentage born abroad,

another tract-level variable. The OR for a 25%

increase in percentage born abroad was 2.52

[95% confidence interval (CI), 2.00–3.17] for

50,000 vehicles compared with < 25,000

vehicles; the OR for 25,000–49,999 vehicles

versus < 25,000 vehicles was 2.23 (95% CI,

1.94–2.56). All the measures of low socioeconomic status—including percentage receiving

free/reduced-price meals at school, percentage

with income below poverty level in the census

tract, and percentage with no high school

diploma in the census tract—were also

strongly positively associated with high and

medium traffic within 150 m of school.

Percent black and percent Hispanic at the

school were strongly related to traffic category, whereas percent Asian was positively

associated with exposure to medium traffic

(AADT = 25,000–49,000) but not with

exposure to high traffic. The OR for a 25%

increase in percent black was 1.51 (95% CI,

1.21–1.89) for 50,000 vehicles compared

with < 25,000 vehicles; the OR for

25,000–49,999 vehicles versus < 25,000 vehicles was 1.38 (95% CI, 1.19–1.59). The OR

for a 25% increase in percent Hispanic was

1.45 (95% CI, 1.28–1.64) for 50,000 vehicles

compared with < 25,000 vehicles; the OR for

25,000–49,999 vehicles versus < 25,000 vehicles was 1.25 (95% CI, 1.16–1.35).

Environmental Health Perspectives • VOLUME 112 | NUMBER 1 | January 2004 63

Table 1. Maximum AADT within 150 m of California public schools (K–12) in 2000 by enrollment, school type, degree of urbanization, and population density.

No attributable < 25,000 25,000–49,999 ≥ 50,000

traffic within 150 m vehicles vehicles vehicles

n (%) n (%) n (%) n (%)

Total enrollment 1,518,205 (26.1) 3,582,263 (61.5) 571,040 (9.8) 150,323 (2.6)

Total schools 2,267 (30.4) 4,484 (60.1) 536 (7.2) 173 (2.3)

School type

Elementary 1,781 (33.5) 3,090 (58.1) 326 (6.1) 122 (2.3)

Middle school 309 (26.4) 740 (63.2) 91 (7.8) 31 (2.7)

High school 161 (17.5) 631 (68.5) 112 (12.2) 17 (1.9)

K–12 16 (32.7) 23 (46.9) 7 (14.3) 3 (6.1)

Degree of urbanization

Rural (< 5,000 people) 369 (35.1) 670 (63.8) 8 (0.8) 4 (0.4)

Small urban (5,000–50,000 people) 182 (25.6) 524 (73.7) 4 (0.6) 1 (0.1)

Urban (50,000–200,000 people) 164 (26.8) 418 (68.2) 24 (3.9) 7 (1.1)

Large urban (> 200,000 people) 1,442 (29.0) 2,872 (57.7) 500 (10.1) 161 (3.2)

Population densitya

Quartile 1 (< 1,211) 711 (38.1) 1,104 (59.2) 32 (1.7) 18 (1.0)

Quartile 2 (1,211–4,849) 603 (32.3) 1,133 (60.8) 98 (5.3) 31 (1.7)

Quartile 3 (4,850–8,371) 531 (28.5) 1,129 (60.5) 149 (8.0) 56 (3.0)

Quartile 4 (8,372–96,271) 421 (22.6) 1,118 (60.0) 257 (13.8) 68 (3.7)

aPopulation per square mile of land in surrounding census tract in 2000.

Figure 3. Percentage of schools and percentage of

total enrollment for California public schools (K–12)

in 2000, by maximum AADT within 150 m of school.

70

60

50

40

30

20

10

0

Percent

No attributable

traffic within

150 m

< 25,000

vehicles

25,000–49,999

vehicles

≥ 50,000

vehicles

30.3926.08

60.11 61.53

7.18 9.81

2.30 2.58

Schools

Total enrollment

Children’s Health | Proximity of California schools to busy roads
When we stratified the analyses by percentage of students receiving free/reducedprice meals (above and below the median

value of 46.9%), the ORs within strata were

very similar to those based on all schools for

all ethnic groups and for English learners. For

example, the OR for a 25% increase in percent Hispanic at schools where < 46.9% of

students received free/reduced-price meals

was 1.59 (95% CI, 1.26–2.0) for ≥ 50,000

vehicles compared with < 25,000 vehicles,

whereas the OR for the same traffic category

comparison at schools above the median was

1.31 (95% CI, 1.09–1.58).

Discussion

This is the first study to examine proximity of

California public schools to busy roads. We

found that close to 10% of California public

schools, enrolling 721,363 children (12.4% of

students), are close to medium or high traffic

volume, defined as being within 150 m of a

road segment with AADT of 25,000 or more

vehicles. Most schools near high traffic are

located in large urban areas; however, this phenomenon can be observed in small urban and

rural locations as well. Furthermore, disproportionate numbers of nonwhite and economically

disadvantaged students attend schools near

high traffic volumes. In addition, we found

that schools that are in close proximity to high

traffic volumes are more likely than those farther away from busy roads to be located in economically disadvantaged census tracts. We also

found a strong association between proximity

of a school to high traffic volume and both

percentage of English learners at the school and

percentage of foreign-born persons living in

the surrounding census tract. This is the first

published study of which we are aware that

suggests that immigrants are more likely than

U.S.-born citizens to live or attend schools near

a source of environmental pollution. The positive associations of proximity to high traffic

volume with race/ethnicity and English language competency persisted even after stratifying by a school-based measure of economic

disadvantage. Our findings support those of

the California study (Gunier et al. 2003),

which found that, on the basis of residential

exposure profiles, nonwhites are more likely to

be exposed to high traffic density. Other

researchers have found evidence of disproportionate exposures by race or socioeconomic status in the siting of stationary sources of air

pollutants, such as TRI facilities and treatment,

storage, and disposal facilities (TSDFs). A

review of 13 GIS-based environmental equity

studies conducted from 1993 to 1999 either

nationwide or in several individual states

(Maantay 2002) found that all the studies

showed a disproportionate number of nonwhite persons living near environmental

hazards such as TRI facilities and TSDFs.

In several of these studies, income was also

inversely related to proximity to environmental

hazards.

A study in southern California (MorelloFrosch et al. 2002a) reported significant demographic differences between census tracts in

Los Angeles hosting or located within a 1-mile

radius of a TSDF and those without TSDFs.

Those with TSDFs had significantly greater

percentages of nonwhite residents, especially

Hispanics; lower per capita income; and lower

household income. The authors of that study

also found that minority students, especially

Hispanics, were more likely than non-Hispanic

whites to attend schools in locations with

higher estimated respiratory risks modeled

from concentrations of outdoor air pollutants.

Children attending these schools had lower academic performance scores than did those

attending schools in locations with low respiratory risk, even after controlling for several measures of poverty, English-language competence,

Children’s Health | Green et al.

64 VOLUME 112 | NUMBER 1 | January 2004 • Environmental Health Perspectives

Figure 4. Percentage of all California public school (K–12) students in 2000, by ethnicity, in relation to maximum AADT within 150 m of school. Students (%)

60

50

40

30

20

10

0

Non-Hispanic white

Asian

Hispanic

Non-Hispanic black

No roads with

count data

(2,267 schools)

60–24,999

vehicles (4,484 schools)

25,000–49,999

vehicles

(536 schools)

≥ 50,000

vehicles

(173 schools)

Figure 5. Median percentage of California public school (K–12) students participating in programs for economically disadvantaged students and whose primary language is not English, in 2000, by maximum AADT

within 150 m of school. Students (%)

Reduced-price meals

English learners

CalWORKS No roads with

count data

(2,267 schools)

60–24,999

vehicles

(4,484 schools)

25,000–49,999

vehicles

(536 schools)

≥ 50,000

vehicles

(173 schools)

70

60

50

40

30

20

10

0
and parental educational attainment (MorelloFrosch et al. 2002b).

Our finding that about 721,363 California

students (12.4% of the total statewide) attend

public schools located within 150 m of a busy

road has important health implications. Several

European studies have found adverse health

effects in children residing or attending school

in close proximity to roads with AADT of

25,000 or more vehicles. A study of German

school children (Wjst et al. 1993) found a

decrease in peak expiratory flow of 0.71%

(95% CI, 0.33–1.08%) per increase of 25,000

cars daily on roads with the highest traffic

volume passing through the school district.

That study also found that increasing traffic

volume was associated with recurrent wheezing

(adjusted OR/25,000 cars = 1.08; 95% CI,

1.01–1.16) and recurrent dyspnea (adjusted

OR/25,000 cars = 1.10; 95% CI, 1.00–1.20).

A case–control study of hospital admissions for

asthma in Birmingham (U.K.; Edwards et al.

1994) found that cases were more likely to

live near a road with high traffic (> 24,000

cars/day) than were community controls

(OR = 1.4; 95% CI, 1.13–1.74). Furthermore,

they found a significant linear trend in hospital

admissions with increasing traffic for subjects

living less than 500 m from a major road (p <

0.006). However, only unadjusted results were

reported. A study conducted in San Diego,

California (English et al. 1999), found an

almost 3-fold increase (OR = 2.91; 95% CI,

1.23–6.91) in medical care visits for asthmatic

children for whom the traffic flow on the

nearest street within 168 m was 41,000 vehicles per day and a moderately increased OR

(1.85, 95% CI; 0.92–3.71) for 21,200 or more

vehicles per day. Because children spend many

hours per day at school, both in the classroom

and outside at play or participating in rigorous

sports, school proximity to large traffic volumes may increase children’s risk of adverse

respiratory and other health outcomes.

The traffic volume and distance cut points

used in this analysis are based on the results of

both epidemiologic studies and studies monitoring the air dispersion of traffic-related pollutants, and they represent a reasonable basis for

an initial categorization of California public

schools in relation to traffic. Other traffic metrics, such as the sum of traffic within a buffer,

distance-weighted traffic density, or the traffic

count at the nearest road have been used in

epidemiologic studies (English et al. 1999;

Wilhelm and Ritz 2003). However, we felt

that the maximum traffic within 150 m would

be a reasonable, easy-to-visualize metric for a

descriptive study of proximity to traffic and

would be a more practical metric for school site

planning. To date, no good studies have compared the relation of different traffic metrics to

actual exposure to traffic-related pollutants.

Most of the epidemiologic studies have been

conducted in Europe, where fleet composition and other factors affecting traffic emissions differ, and further studies are needed

to quantify traffic volumes that may be

associated health effects in children in the

United States.

Several studies suggest that residential

proximity to truck traffic may be more strongly

associated with adverse health effects compared

with total vehicular traffic (Brunekreef et al.

1997; Duhme et al. 1996; van Vliet et al.

1997; Weiland et al. 1994). Unfortunately, the

CalTrans database did not include complete

data on truck or bus traffic. Additionally, we

were not able to take into account prevailing

wind direction, topography, or climate, all of

which can affect exposure to traffic-related

pollutants.

Although the development of GIS software

allowed us to determine the traffic volume

nearby for more than 98% of schools that were

eligible for study, this method does have some

limitations. First, the street linework layer used

to geocode the school addresses did not align

perfectly with the street linework from

CalTrans, which contains the traffic volume

data. This would cause some incorrect distance

calculations. The misalignment is difficult to

quantify on a statewide basis because it varies

by region of the state with no consistent pattern. It would have been too labor intensive to

actually determine the degree of misalignment

between the CalTrans street linework and the

street linework provided with Maptitude for

each of the nearly 8,000 schools in this study.

If this error were randomly distributed, it

would not cause differential misclassification.

The associations we saw between traffic and

race/ethnicity statewide were similar to those

we saw when we restricted our analysis to

schools in Los Angeles County census tracts

with population density of at least 5,000 people

per square mile. This suggests that there were

no systematic errors in calculating distances to

roads in urban areas. However, random errors

could have biased toward the null the associations we found between traffic and factors

such as ethnicity and socioeconomic status.

Second, in reality a school is not a point (as

assumed in the geocoding process) but extends

over a large area (i.e., is a polygon). Similarly, a

road is not a line but a “ribbon.” Schools are

geocoded based on the address of the administration building, but this point does not represent the actual location of children at school,

which can vary by classroom and activity.

Consequently, the distance from a school to a

road as calculated by GIS is an approximation

of the distance from where the students are to

the road. If we had increased the buffer size, we

may have captured some road segments that

were actually close to one boundary of the

school property, but we would have also

included roads that were potentially much

farther from the schools.

Third, there are problems inherent in

the geocoding process itself. The offset we

used assumes that all school buildings are

located 25 feet from the center of the street.

Depending on where different school campus

Children’s Health | Proximity of California schools to busy roads

Environmental Health Perspectives • VOLUME 112 | NUMBER 1 | January 2004 65

Table 2. Odds of close proximity to high and to medium traffic by demographic characteristics of California

public schools (K–12) and surrounding census tracts in 2000.

Standardized

Variables Maximum AADTa coefficientb ORc (95% CI)

School-level variables

Percent nonwhited ≥ 50,000 0.34 1.69 (1.46–1.97)

25,000–49,999 0.25 1.48 (1.36–1.61)

Percent blackd ≥ 50,000 0.11 1.51 (1.21–1.89)

25,000–49,999 0.09 1.38 (1.19–1.59)

Percent Hispanicd ≥ 50,000 0.24 1.45 (1.28–1.64)

25,000–49,999 0.14 1.25 (1.16–1.35)

Percent Asiand ≥ 50,000 –0.01 0.96 (0.68–1.35)

25,000–49,999 0.12 1.57 (1.35–1.82)

Percent reduced-price or free mealsd ≥ 50,000 0.21 1.39 (1.21–1.60)

25,000–49,999 0.10 1.17 (1.09–1.27)

Percent English learnersd ≥ 50,000 0.26 1.71 (1.48–1.97)

25,000–49,999 0.18 1.43 (1.31–1.56)

Census 2000 tract-level variables

Natural log of population densitye ≥ 50,000 0.43 1.46 (1.29–1.66)

25,000–49,999 0.73 1.90 (1.72–2.09)

Percent below povertyd ≥ 50,000 0.13 1.73 (1.26–2.38)

25,000–49,999 0.14 1.84 (1.52–2.21)

Percent without high school diplomad ≥ 50,000 0.13 1.37 (1.13–1.66)

25,000–49,999 0.11 1.32 (1.18–1.48)

Percent born abroadd ≥ 50,000 0.30 2.52 (2.00–3.17)

25,000–49,999 0.26 2.23 (1.94–2.56)

aAADT is the daily number of vehicles within 150 m of school. bChange in the log-odds of traffic category for a one standard

deviation increase in the independent variable. cOdds of location within 150 m of high traffic (maximum AADT ≥ 50,000) versus low or very low traffic (AADT < 25,000) and odds of location within 150 m of medium traffic (maximum AADT =

25,000–49,999) versus low or very low traffic. dORs and confidence limits for a 25% change. ePopulation density = population (in thousands) per square mile of land in 2000. ORs and confidence limits are given for a one unit change in the natural

log of population density.
Children’s Health | Green et al.

66 VOLUME 112 | NUMBER 1 | January 2004 • Environmental Health Perspectives

buildings are located, that offset may be incorrect. The interpolation of points based on

address ranges may lead to two potential

errors: a) if the address range is inaccurate, the

point will not represent the true location of an

address; b) if there is uneven spatial distribution of properties along the block, the interpolation will place the point in the wrong

space. This is more of a problem in rural areas

because the road segments are longer there.

These problems probably did not cause differential misclassification of exposure, however,

because we assume a random effect statewide.

Finally, the CalTrans database does not

include local roads. However, traffic counts on

local roads are relatively small. For example, in

San Diego County, traffic volume on local

roads was estimated to be 700 vehicles/day

(San Diego Association of Governments 2001).

Therefore, nearby local roads are unlikely to

have a large impact on the concentration of

traffic-related pollutants at the school.

Conclusion

Despite the potential limitations, this study

describes the distributions of California public

schools in relation to busy roads and indicates

that large numbers of children may be regularly

exposed to elevated levels of traffic-related emissions while at school, and that a disproportionate number of those students are economically

disadvantaged and nonwhite. The research

exploring associations between exposure to

traffic-related emissions and children’s health

raises the issue of siting new schools with

respect to busy roads and mitigating exposures

to traffic-related pollutants at existing schools.

Many environmental factors are already

considered when siting schools within

California, such as proximity to hazardous

waste sites and stationary sources of toxic air

contaminants. Our results suggest that educators and policy makers, particularly those interested in environmental justice and children’s

health, should at least consider local traffic

emissions as a factor to be considered in school

siting decisions. Recently, California introduced legislation, SB 352 Escutia, to prohibit

siting new schools within 500 feet (168 m) of a

busy road (State of California 2003).

Once schools are located near busy roads,

mitigation of exposure may be difficult.

Possible measures might include regular

maintenance of the heating, ventilation, and

air conditioning (HVAC) systems, use of the

HVAC system during commute hours in

addition to normal use for heating and cooling, use of a filter with good efficiency (e.g.,

60–90% efficiency at removal of particles;

California Air Resources Board 2003), fixing

building air leaks, and avoiding placement of

air-intake vents close to busy roads, loading

docks, or parking areas. If possible, schools

should locate outdoor school activities on a

part of the campus farther from the traffic

corridor. More important, policy makers

must continue their efforts to reduce motor

vehicle emissions so that exposures can be

mitigated at the source.

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