RESEARCH REPORT

Children at risk: measuring racial/ethnic disparities in

potential exposure to air pollution at school and home

Jayajit Chakraborty, Paul A Zandbergen

............................................................... ............................................................... .....

See end of article for

authors’ affiliations

........................

Correspondence to:

Jayajit Chakraborty,

Department of Geography,

University of South Florida,

4202 E. Fowler Avenue,

NES107, Tampa, FL 33620,

USA; jchakrab@cas.usf.edu

Accepted 29 January 2007

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J Epidemiol Community Health 2007;61:1074–1079. doi: 10.1136/jech.2006.054130

Study objective: This paper addresses the environmental justice implications of children’s health by exploring

racial/ethnic disparities in potential exposure to air pollution, based on both school and home locations of

children and three different types of pollution sources, in Orange County, Florida, USA.

Methods: Using geocoded school and residence locations of 151 709 children enrolled in the public school

system, distribution functions of proximity to the nearest source are generated for each type of air pollution

source in order to compare the exposure potential of white, Hispanic, and black children. Discrete buffer

distances are utilised to provide quantitative comparisons for statistical testing.

Main results: At any given distance from each type of pollution source, the cumulative proportion of Hispanic

or black children significantly exceeds the corresponding proportion of white children, for both school and

home locations. Regardless of race, however, a larger proportion of children are potentially exposed to air

pollution at home than at school.

Conclusions: This study addresses the growing need to consider both daytime and nighttime activity patterns in the

assessment of children’s exposure to environmental hazards and related health risks. The results indicate a

consistent pattern of racial inequity in the spatial distribution of all types of air pollution sources examined, with

black children facing the highest relative levels of potential exposure at both school and home locations.

E

nvironmental justice provides a framework for public policy

debates on the challenging question of whether disparities in

exposure to environmental pollution and health hazards

have played a causal role in the persistent patterns of disparate

health status among minority and low-income populations in the

United States.1 2 Whereas environmental justice analysis has

focused primarily on assessing racial/ethnic or socioeconomic

inequities in the distribution of environmental hazards among the

general population,3 recent studies have begun to examine the

burdens of exposure borne by particularly vulnerable groups, such

as young children. A substantial amount of scientific evidence

indicates that children are more susceptible to the effects of

environmental pollution than adults.4–9 Air pollution from

potentially hazardous facilities and roadways, in particular, has

been documented to play a significant role in causing a variety of

health problems among school-age children.10–15

The growing need to examine the environmental justice

consequences of children’s exposure to air pollutants and related

health outcomes has been emphasised by several researchers.1 16 17

Recent studies have analysed potential exposure to toxic air

emissions at school locations based on race, ethnicity, or socioeconomic status, following concerns that children are confined to

schools for extended periods of time during the day.18–20 Time–

activity databases such as the National Human Activity Pattern

Survey indicate, however, that US youths aged from 11 to 17 years

spend approximately 61% of their time indoors at home and only

14% inside a school or other public building.21 It is necessary,

therefore, to investigate whether children are disproportionately

exposed to pollution not only at schools, but also at their places of

residence, and thus examine inequities for both their daytime and

nighttime locations.

This paper extends research on children’s environmental

health and its environmental justice implications by exploring

racial/ethnic disparities in the distribution of potential exposure

to air pollution at both the school and home locations of

children, based on a case study in a large metropolitan region

(Orange County, Florida, USA). The environmental injustice

hypothesis is investigated by comparing the exposure distributions of white children, at home and at school, with the

corresponding distributions of Hispanic and black children.

Three different types of urban air pollution sources are

examined in this study: industrial facilities from the

Environmental Protection Agency’s (EPA) toxic release inventory

(TRI), small facilities with air releases from the EPA aerometric

information retrieval system (AIRS), and major roads. TRI sites

represent large point sources of air pollution with some clustering

in industrial areas. AIRS facilities represent smaller and more

dispersed point sources of air pollution. Major roads, in contrast,

are a source of vehicular traffic emissions and thus represent a

mobile source of air pollution. Residential proximity to busy

roadways has been documented by earlier studies to cause adverse

health outcomes among young children.19 22–24 The use of three

different types of air pollution sources provides an opportunity to

explore the variability in the results and to reduce the possible bias

introduced by a unique spatial pattern of a single category of

sources.

METHODS

Locations of public schools and children

The locations of all 153 public schools in Orange County were

provided by the Orange County Schoolboard. Enrollment records

for 2005 were obtained and the home residences were geocoded

using StreetMap USA for ArcGIS 9. Of a total of 163 886 records,

95.1% were reliably geocoded. Student records that did not allow

for a determination of the school they attended were removed,

resulting in a final set of 151 709 geocoded home residences.

Racial classification was based on self-reporting by children or

their parents, and included the following mutually exclusive

categories: black, Hispanic, white, and other. Of the 151 709

children in our sample, 27.05% are black, 29.55% are Hispanic,

Abbreviations: AIRS, aerometric information retrieval system; CDF,

cumulative distribution function; EPA, Environmental Protection Agency;

TRI, toxic release inventory

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and 36.49% are white. The remaining 6.91% belonging to the other

category were excluded from the study.

Geocoded student records were used to determine the enrollment total of each school in order to avoid potential biases in

geocoding match rate across the study area and allow a direct

comparison of the student population at home with those at

school. Racial composition varied greatly among the schools. For

example, only 27.05% of the student population was black, but 38

of the 153 schools had an enrollment of 50% or more black

children and eight schools indicated an enrollment of 90% or more

black children. This variation in racial composition of the schools

reflects the racially segregated distribution of the general

population in Orange County, Florida.

As in many other metropolitan areas in the United States, race

is strongly related to socioeconomic status and educational

attainment in Orange County, Florida. For example, Census

2000 data indicate that the proportion of unemployed individuals

above 16 years of age in the labor force is 4.1% for white, 6.9% for

Hispanic and 8.2% for black individuals. The proportion of adults

above 25 years of age without a high school diploma is 14.4% for

white, 29.1% for Hispanic and 30.8% for black individuals. Finally,

the percentage of families with related children under the age of

18 years that are below the poverty line is 7.0% for white, 14.3% for

Hispanic and 21.1% for black individuals.

Locations of air pollution sources

Information on industrial facilities releasing toxic chemicals

was obtained from the EPA TRI database for the time period

2000–2003. All industrial facilities reporting point source air

emissions of at least 1000 pounds for the entire four-year period

(2000–2003) to the TRI were selected for our study. A total of 27

such facilities were identified within Orange County or within

5 miles of its boundary. The most common types of operations

at these facilities include boat building and repair (n = 4),

plastic products manufacturing (n = 4), and paint manufacturing (n = 3).

Data on facilities with air releases were obtained from the

EPA AIRS for 2005. This database contains smaller stationary

facilities that do not fall under the TRI reporting requirements.

A total of 282 AIRS facilities were identified within Orange

County or 5 miles of its boundary. These facilities include

automobile repair shops, dry cleaners, hospitals, and small

manufacturing facilities.

Locations of TRI sites and AIRS facilities were geocoded

using StreetMap USA for ArcGIS 9.

A detailed linear network of major roads at a scale of 1 : 24 000

for Florida (2005) was obtained from the Florida Department of

Transportation. From this network, only the urban and rural

arterial roads were selected for this study. The total length of these

arterial roads within Orange County is 954 km.

Figure 1 shows the locations of all TRI sites, AIRS facilities,

and major roadways in Orange County and surrounding areas.

Proximity analysis

Distance to the nearest air pollution source of each type (TRI sites,

AIRS facilities, and major roads) was determined in the

appropriate state plane coordinate system for schools and

children’s home residences and these values were rounded to

the nearest foot. These distances were summarised with a

cumulative distribution function (CDF), which represents a plot

of the number of observations falling below every threshold value.

CDF are particularly well suited for the proximity-based analysis

of potential exposure because they overcome the limitations of

choosing arbitrary discrete buffer distances.25 26 To compare the

exposure distribution of white, Hispanic, and black children, CDF

were created for each type of pollution source, plotting the

cumulative number of children within each racial category as a

percentage of the total number versus distance to the nearest

pollution source. Schools were weighted with the number of

children in each racial category associated with each school. The

cumulative number of children in each racial category was also

Figure 1 Locations of three types of air

pollution sources in Orange County, Florida,

USA. In some areas aerometric information

retrieval system (AIRS) facilities are located

in very close proximity to each other and

consequently all individual locations are not

clearly visible at this scale.

TRI, Toxic release inventory.

Children at risk 1075

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determined using discrete distances of 0.25, 0.5, 1, 2 and 4 miles.

These values represent the most widely used buffer radii in

proximity-based analyses of environmental exposure for point

sources of air pollution;27–31 buffers of 0.25–1 mile are commonly

used for vehicular sources of air pollution.32–35

Statistical analysis

The CDF were plotted against the distance from each pollution

source to identify and compare general patterns of exposure

among racial categories and between home and school locations.

The percentages of children at home and at school for various

discrete buffer distances were compared using a two-sample z-test

of proportions. The environmental injustice hypothesis was

investigated by comparing the black with the white student

population, and the Hispanic with the white student population;

this was repeated for each of the pollution sources and for school

and home locations independently, for a total of 60 z-tests.

Comparisons were also made between the home and school

student populations; these were repeated for each type of

pollution source and racial category, for a total of 45 z-tests.

RESULTS

The CDF representing exposure distributions of white,

Hispanic, and black children, respectively, at school and home,

are presented in fig 2 for each type of pollution source. The CDF

indicate how the proportion of the student population in each

racial category increases with distance to the nearest pollution

source. Almost 100% of children in all three categories can be

found within 50 000 feet from the TRI sites, 25 000 feet from

AIRS facilities, and only 15 000 feet from a major road. These

differences reflect the fact that there are fewer TRI sites than

AIRS facilities, and also that TRI sites are more spatially

clustered (fig 1). The curves for school locations reveal a more

stepwise and less continuous pattern than those based on home

locations; each step in these curves represents one of the 153

schools in Orange County, with the step height determined by

student enrollment in each racial category at that school.

Several observations regarding racial disparities can be made

from the CDF shown in fig 2. For all three types of air pollution

sources, the curves for Hispanic and black children are higher than

the curve for white children, both at school and at home. This

implies that the percentage of Hispanic and black children within

any given distance from these pollution sources is always larger

than the corresponding percentage of white children. In addition,

the curves representing black children are higher than the curves

representing Hispanic children for both TRI sites and AIRS

facilities, which suggests that a higher proportion of black

children are potentially exposed than Hispanic children. For

Table 1 Cumulative percentage of white, Hispanic, and black students at fixed distances from

air pollution sources

Buffer radius

(miles)

% % % % Difference

White

students

Hispanic

students

Black

students Hispanic–white Black–white

TRI sites

School

0.25 0.1 0.1 0.5 0.0* 0.4*

0.50 0.3 1.4 0.8 1.1* 0.5*

1.00 2.7 8.1 8.5 5.4* 5.8*

2.00 23.7 29.6 40.5 5.9* 16.8*

4.00 60.9 74.7 80.7 13.7* 19.8*

Home

0.25 0.3 0.4 0.4 0.1* 0.1*

0.50 1.1 1.9 1.7 0.8* 0.6*

1.00 5.9 9.3 8.5 3.4* 2.6*

2.00 19.4 29.1 40.4 9.7* 21.0*

4.00 59.2 73.2 84.2 14.0* 25.0*

AIRS facilities

School

0.25 4.7 5.4 7.2 0.7* 2.5*

0.50 12.1 17.4 23.3 5.3* 11.2*

1.00 42.9 54.4 69.1 11.5* 26.2*

2.00 80.5 81.7 93.5 1.2* 13.0*

4.00 96.5 98.3 99.5 1.8* 3.0*

Home

0.25 4.6 7.8 9.0 3.2* 4.4*

0.50 18.0 27.4 33.3 9.4* 15.3*

1.00 45.4 56.7 72.1 11.3* 26.7*

2.00 74.7 82.3 94.2 7.6* 19.5*

4.00 95.9 98.6 99.6 2.7* 3.7*

Arterial roads

School

0.25 38.3 45.7 45.7 7.4* 7.4*

0.50 62.1 66.7 72.2 4.6* 10.1*

1.00 82.8 91.7 92.6 8.9* 9.8*

2.00 94.3 97.2 99.0 2.9* 4.7*

4.00 100.0 100.0 100.0 0.00 0.0

Home

0.25 37.9 47.9 46.3 10.0* 8.4*

0.50 63.3 77.3 79.3 14.0* 16.0*

1.00 83.5 93.8 96.7 10.3* 13.2*

2.00 95.7 98.4 99.4 2.7* 3.7*

4.00 99.9 100.0 100.0 0.1 0.1

AIRS, aerometric information retrieval system; TRI, toxic release inventory.

*p,0.001.

z-Test results.

1076 Chakraborty, Zandbergen

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major roads, however, the gap between the curves representing

black and Hispanic children is considerably smaller.

A statistical assessment of racial disparities in the distribution

of potential exposure can be made from table 1, which presents ztest results for discrete buffer distances. At each given distance,

the cumulative percentage of white, black, and Hispanic children

inside the buffer zone was derived for both school and home

locations from the CDF in fig 2. The z-tests are based on the

difference between the proportion of white and Hispanic children,

and white and black children, respectively. As shown in table 1, 58

of the 60 comparisons revealed statistically significant differences

at the 0.001 level, confirming the consistent pattern of racial

disparities observed visually in fig 2.

Table 1 also highlights some of the differences between the

patterns observed for each type of pollution source. For TRI sites,

the cumulative proportion of children in all racial categories is less

than 10% within 1 mile and less than 2% within 0.5 mile, both at

school and home. Consequently, the largest racial disparities

(white–Hispanic or white–black) for TRI sites can be observed

only at buffer distances of 2 or 4 miles. For AIRS facilities, which

are more numerous than TRI sites, the proportion of children

within the 0.5 or 1 mile buffer is considerably higher, resulting in

larger racial disparities at smaller buffer distances. For all buffer

distances from a major road, the proportions of children are

substantially higher compared with other pollution sources. Both

at school and at home, more than 60% of children in any racial

category can be found inside the 0.5 mile buffer zone.

A more direct comparison of potential exposure at school and

home can be made for the same buffer distances in table 2. The ztests here are based on the difference between the proportion of

children at school and home locations. A negative difference

indicates that the percentage of children at home is higher than

those at school, and a positive difference indicates the opposite. As

shown in table 2, 33 of these 45 z-tests show statistically

significant differences at the 0.01 level, and 28 of these 33

significant differences are negative. This implies that a larger

proportion of children are potentially exposed to air pollution

sources at their place of residence compared with school locations,

Figure 2 Cumulative distribution functions

of exposure potential of school children. The

vertical axis in each graph depicts the

cumulative number of children as a

percentage of the group total. The horizontal

axis in each graph depicts the distance to the

nearest pollution source.

AIRS, aerometric information retrieval

system; TRI, toxic release inventory.

Children at risk 1077

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regardless of race. Minor exceptions are observed for black

children at distances of less than 2 miles from TRI sites, but these

can be partly explained by the relatively small percentage of

children and schools located in close proximity to TRI sites. The

five positive differences that are significant either occur at very

large buffer distances (2 or 4 miles), or are smaller than 1% in

magnitude.

DISCUSSION

The intersection of environmental justice and children’s health

has emerged as a critically important area of academic enquiry

and policy making in the United States. The racial/ethnic

implications of children’s potential exposure to air pollution are

addressed in our study through the analysis of proximity to

multiple air pollution sources in Orange County, Florida. Unlike

earlier studies on this topic that have examined a single type of

pollution source or facility, our analyses encompass three types

of air pollution sources. Our results indicate a persistent pattern

of racial inequity in the spatial distribution of these pollution

sources. At any given distance from these sources, the

cumulative proportion of black or Hispanic children always

exceeds the corresponding proportion of white children. In

addition, the percentage of black children in close proximity to

point sources of air pollution (TRI sites and AIRS facilities) is

higher than the corresponding percentage of Hispanic children,

suggesting that black school-age children are burdened with

the highest relative levels of potential exposure. Our results are

consistent with findings from recent studies that indicate that

non-white children are more likely to attend schools near

hazardous facilities or busy roads, and are disproportionately

exposed to airborne toxic hazards at school locations.2 18–20

Compared with studies that focus only on schools and daytime

risk, our study provides a more comprehensive view of the risk

burden imposed on young children by examining potential

exposure at both school and home locations. The comparison

between home and school locations reflects a long-standing

interest to consider both daytime and nighttime patterns in

exposure assessment.36 37 Our findings indicate that a significantly

larger proportion of children are closer to air pollution sources at

home compared with their school locations, within all three racial

categories. This pattern could be the consequence of stricter

environmental standards that have been used to locate new

suburban schools in areas that are not proximate to pollution

sources or hazardous facilities. Whereas earlier studies have

demonstrated the role of zoning in creating environmental

inequities and health problems,31 38 more research is clearly

necessary to understand the sociospatial processes that have

caused the patterns observed in Orange County.

Despite specific differences between the results at school and

home locations, the general pattern in exposure potential as

characterised by the CDF is broadly similar at home and school

for all air pollution sources considered. This similarity can be

explained partly by the fact that a majority of children attend

the school closest to their home address.

A strength of this study is the use of individual geocoded

locations of children’s home residences. This provides a more

accurate characterisation of the exposed population and reliable

comparisons among subgroups, compared with studies relying

on aggregated data at the level of census enumeration units.25

Another strength of our study is the application of CDF to

characterise proximity-based exposure potential instead of

discrete buffer distances that introduce substantial bias.25

Although discrete buffer distances were used for statistical

testing, these are complemented by the CDF, and the selected

buffer radii represent a broad range of distances of interest.

Among the limitations of the study is the use of proximity as a

measure of exposure potential. Although distance to a pollution

source is widely used as a surrogate for exposure, the actual extent

and magnitude of exposure may not be a simple function of

distance. Proximity is used in this study to focus attention on how

to summarise comparisons rather than measure the exact nature

or degree of exposure. A second limitation is the potential error in

the geocoding of schools, children, and pollution sources.

Geocoding match rates, however, were generally high and the

typical positional error for this kind of geocoding is much smaller

than the shortest buffer distance of 0.25 miles used in the

analysis.39–41 Another limitation is the use of only arterial roads as

air pollution sources, which could affect the distribution of

distances to the nearest roadway. Traffic densities on non-arterial

roads in Orange County are, however, relatively low (less than

10 000 vehicles/day) compared with arterial roads that have

traffic densities between 25 000 and 100 000 vehicles per day.

The exclusion of private schools for which individual

address information was unavailable is another limitation. Total

Table 2 Comparing potential exposure at school and home locations: cumulative percentages of students at fixed distances from

air pollution sources

Buffer % White students % Hispanic students % Black students

(miles) School Home Difference School Home Difference School Home Difference

TRI sites

0.25 0.1 0.3 20.2* 0.1 0.4 20.3* 0.4 0.4 0.0

0.50 0.3 1.1 20.8* 1.4 1.9 20.5* 0.8 1.7 20.9*

1.00 2.7 5.9 23.2* 8.1 9.3 21.2* 8.5 8.5 0.0

2.00 23.7 19.4 4.3* 29.6 29.1 0.5* 40.5 40.4 0.1

4.00 60.9 59.2 1.7* 74.7 73.2 1.5* 80.7 84.2 23.5*

AIRS facilities

0.25 4.7 4.6 0.1* 5.4 7.8 22.4* 7.2 9.0 21.8*

0.50 12.1 18.0 25.9* 17.3 27.4 210.1* 23.3 33.3 210.0*

1.00 42.9 45.4 22.5* 54.4 56.7 22.3* 69.1 72.1 23.0*

2.00 80.5 74.7 5.8* 81.7 82.3 20.6* 93.5 94.2 20.7*

4.00 96.5 95.9 0.6* 98.3 98.6 20.3* 99.5 99.6 20.1

Arterial roads

0.25 38.3 37.9 0.4* 45.7 47.9 22.2* 45.7 46.3 20.6*

0.50 62.1 63.3 21.2* 66.7 77.3 210.4* 72.2 79.3 27.1*

1.00 82.8 83.5 20.7* 91.7 93.8 22.1* 92.6 96.7 24.1*

2.00 94.3 95.7 21.4* 97.2 98.4 21.2* 99.0 99.4 20.4*

4.00 100.0 99.9 0.1 100.0 100.0 0.0 100.0 100.0 0.0

AIRS, aerometric information retrieval system; TRI, toxic release inventory.

*p,0.01.

z-Test results.

1078 Chakraborty, Zandbergen

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enrollment at private schools (grade K-12) in Orange County is

18 293 for the 2003/2004 school year according to the National

Center for Education Statistics, representing a relatively small

number compared with those enrolled in the public school system.

Finally, our analysis focuses only on race/ethnicity because

additional socioeconomic data on individual children are unavailable. Given its strong correlation with socioeconomic status in

Orange County, race may be a confounding factor masking the

association between poverty and exposure to pollution.

Within these limitations, this study provides strong evidence

of racial inequity in potential exposure to air pollution for a

large metropolitan area. This inequity is consistent between

children’s home residences and school locations, and across

different types of air pollution sources. Although more research

is necessary to characterise the actual health effects of exposure

to different pollutants, our study represents an important step

in the consideration of both daytime and nighttime activity

patterns in the assessment of children’s exposure to environmental risk and related inequities.

ACKNOWLEDGEMENTS

The authors would like to thank the Orange County School Board for

access to their school locations and student records.

Authors’ affiliations

.......................

Jayajit Chakraborty, Department of Geography, University of South

Florida, Tampa, Florida, USA

Paul A Zandbergen, Department of Geography, University of New

Mexico, Albuquerque, New Mexico, USA

Competing interests: None.

REFERENCES

1 Morello-Frosch R, Pastor M, Sadd J. Integrating environmental justice and the

precautionary principle in research and policy making: the case of ambient air

toxics exposures and health risks among schoolchildren in Los Angeles. Ann Am

Acad Pol Soc Sci 2002;584:47–68.

2 Northridge ME. Environmental equity and health: understanding complexity and

moving forward. Am J Public Health 2003;9:209–14.

3 Elliott MR, Wang Y, Lowe RA, et al. Environmental justice: frequency and severity

of US chemical industry accidents and the socioeconomic status of surrounding

communities. J Epidemiol Community Health 2004;58:24–30.

4 Plunkett LM, Turnbull D, Rodricks JV. Differences between adults and children

affecting exposure assessment. In: Guzelian PS, Henry CJ, Olin SS, eds.

Similarities and differences between children and adults: implications for risk

assessment. Washington, DC: ILSI Press, 1992:79–96.

5 Crom WR. Pharmacokinetics in the child. Environ Health Perspect,

1994;102(suppl 11), 111–17.

6 Parkinson A. Casarett and Doull’s toxicology: the basic science of poisons. New

York, NY: McGraw Hill, 1996.

7 Schettler T, Solomon G, Valenit M, et al. Generations at risk: reproductive health

and the environment. Cambridge, MA: MIT Press, 1999.

8 Pinkerton KE, Joad JP. The mammalian respiratory system and critical windows

of exposure for children’s health. Environ Health Perspect, 2000;108(suppl 3),

457–62.

9 Kaplan S, Morris J. Kids at risk: chemicals in the environment come under

scrutiny as the number of childhood learning problems soars. US News & World

Report, 2000;128(June 19), 47.

10 Ginns SE, Gatrell AC. Respiratory health effects of industrial air pollution: a study

in east Lancashire, UK. J Epidemiol Community Health 1996;50:631–5.

11 Brunekreef B, Janssen NA, de Hartog J, et al. Air pollution from truck traffic and

lung function in children living near motorways. Epidemiology 1997;8:298–303.

12 Ciccone G, Forastiere F, Agabiti N, et al. Road traffic and adverse respiratory

effects in children. SIDRIA Collaborative Group. Occup Environ Med

1998;55:771–8.

13 Gilliland FD, McConnell R, Peters J, et al. A theoretical basis for investigating

ambient air pollution and children’s respiratory health. Environ Health Perspect,

1999;107(suppl 3), 403–7.

14 Kim JJ. Ambient air pollution: health hazards to children. Pediatrics

2004;114:1699–707.

15 Schwartz J. Air pollution and children’s health. Pediatrics 2004;113:1037–43.

16 Evans GW, Marcynyszyn LA. Environmental justice cumulative environmental

risk, and health among low- and middle-income children in New York. Am J

Public Health 2004;94:1942–4.

17 Chaix B, Gustafsson S, Jerrett M, et al. Children’s exposure to nitrogen dioxide in

Sweden: investigating environmental justice in an egalitarian county. J Epidemiol

Community Health 2006;60:234–41.

18 Pastor M Jr, Sadd JL, Morello-Frosch R. Who’s minding the kids? pollution, public

schools, and environmental justice in Los Angeles. Soc Sci Quart 2002;83:263–80.

19 Green RS, Smorodinsky S, Kim JJ, et al. Proximity of California public schools to

busy roads. Environ Health Perspect 2004;112:61–6.

20 Mirabelli MC, Wing S, Marshall SW, et al. Race, poverty, and potential exposure

of middle-school students to air emissions from confined swine feeding

operations. Environ Health Perspect 2006;114:591–6.

21 Klepeis NE, Nelson WC, Ott WR, et al. The National Human Activity Pattern

Survey (NHAPS): a resource for assessing exposure to environmental pollutants.

J Expo Anal Environ Epidemiol 2001;11:231–52.

22 Janssen NAH, van Vliet PHN, Aarts F, et al. Assessment of exposure to traffic

related air pollution of children attending schools near motorways. Atmos

Environ 2001;35:3875–84.

23 Gordian ME, Haneuseb AS, Wakefield J. An investigation of the association

between traffic exposure and the diagnosis of asthma in children. J Expo Anal

Environ Epidemiol 2005;15:1–7.

24 McConnell R, Berhane K, Yao L, et al. Traffic, susceptibility, and childhood

asthma. Environ Health Perspect 2006;114:766–72.

25 Zandbergen PA, Chakraborty J. Improving environmental exposure analysis

using cumulative distribution functions and individual geocoding. Int J Health

Geographics 2006;5:23, doi: 10.1186/1476.072X-5-23.

26 Waller LA, Louis TA, Carlin BP. Environmental justice and statistical summaries of

differences in exposure distributions. J Expo Anal Environ Epidemiol

1999;9:56–65.

27 Zimmerman R. Social equity and environmental risk. Risk Anal

1993;13:649–66.

28 Glickman TS. Measuring environmental equity with Geographical Information

Systems. Renewable Resources J 1994;12:17–21.

29 Chakraborty J, Armstrong MP. Exploring the use of buffer analysis for the

identification of impacted areas in environmental equity assessment.

Cartography Geo Info Syst 1997;24:145–57.

30 Perlin SA, Sexton K, Wong DW. An examination of race and poverty for

populations living near industrial sources of air pollution. J Expo Anal Environ

Epidemiol 1999;9:29–48.

31 Maantay J. Mapping environmental injustices: pitfalls and potential of

Geographic Information Systems in assessing environmental health and equity.

Environ Health Perspect 2002;110:161–71.

32 English P, Neutra R, Scalf R, et al. Examining associations between childhood

asthma and traffic flow using a geographic information system. Environ Health

Perspect 1999;107:761–7.

33 Langholz B, Ebi KL, Thomas DC, et al. Traffic density and the risk of childhood

leukemia in a Los Angeles case–control study. Ann Epidemiol 2002;12:482–7.

34 Wilhelm M, Ritz B. Residential proximity to traffic and adverse birth outcomes in

Los Angeles County, California, 1994–1996. Environ Health Perspect

2003;111:207–16.

35 Zmirou D, Gauvin S, Pin I, et al. Traffic related air pollution and incidence of

childhood asthma: results of the Vesta case–control study. J Epidemiol

Community Health 2004;58:18–23.

36 Duan N. Model for human exposure to air pollution. Environ Int 1982;8:305–9.

37 Sexton K, Ryan PB. Assessment of human exposure to air pollution: methods,

measurement and model. In: Watson AY, Bates RR, Kennedy D, eds. Air

pollution, the automobile and public health. New York: National Academy Press,

1998:207–38.

38 Maantay R. Zoning, equity, and public health. Am J Public Health

2001;91:1033–41.

39 Dearwent SM, Jacobs RJ, Halbert JB. Locational uncertainty in georeferencing

public health datasets. J Expo Anal Environ Epidemiol 2001;11:329–34.

40 Bonner MR, Han D, Nie J, et al. Positional accuracy of geocoded addresses in

epidemiologic research. Epidemiology 2003;14:408–12.

41 Cayo MR, Talbot TO. Positional error in automated geocoding of residential

addresses. Int J Health Geographics 2003;2:10, doi: 10.1186/1476.072X-2-10.

What is already known on this subject

Previous studies on the environmental justice consequences of

children’s exposure to environmental pollution and related

health risk have focused primarily on examining: (a) school

locations or daytime exposure; and (b) a single type of air

pollution source, such as roadways or industrial facilities.

What this study adds

Our results indicate a consistent pattern of racial inequity in the

distribution of three different types of air pollution sources

(stationary and mobile), with Hispanic and black children

facing significantly higher levels of potential exposure than

white children, at both school and home. We also found a

larger proportion of children to be potentially exposed to air

pollution at home than school, regardless of race.

Children at risk 1079

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