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Environ Res. 2013 Oct;126:152-8. doi: 10.1016/j.envres.2013.06.005. Epub 2013 Jul 11.

Race, socioeconomic status, and air pollution exposure in North Carolina.

Author information

1
Children's Environmental Health Initiative, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA. Electronic address: simonegray@cdc.gov.

Abstract

BACKGROUND:

Although studies suggest that exposure to pollutants is associated with race/ethnicity and socio-economic status (SES), many studies are limited to the geographic regions where monitoring stations are located.

OBJECTIVES:

This study uses modeled predictive surfaces to examine the relationship between air pollution exposure, race/ethnicity, and measures of SES across the entire State of North Carolina.

METHODS:

The daily predictions of particulate matter <2.5 µm in aerodynamic diameter (PM2.5) and ozone (O3) were determined using a spatial model that fused data from two sources: point air monitoring data and gridded numerical output. These daily predicted pollution levels for 2002 were linked with Census data. We examine the relationship between the census-tract level predicted concentration measures, SES, and racial composition.

RESULTS:

SES and race/ethnicity were related to predicted concentrations of both PM2.5 and O3 for census tracts in North Carolina. Lower SES and higher proportion minority population were associated with higher levels of PM2.5. An interquartile range (IQR) increase of median household income reduced the predicted average PM2.5 level by 0.10 µg/m3. The opposite relationship was true for O3. An IQR increase of median household income increased the predicted average O3 measure by 0.11 ppb.

CONCLUSIONS:

The analyses demonstrate that SES and race/ethnicity are related to predicted estimates of PM2.5 and O3 for census tracts in North Carolina. These findings offer a baseline for future exposure modeling work involving SES and air pollution for the entire state and not just among the populations residing near monitoring networks.

KEYWORDS:

AQS; Air Quality System; Air pollution; CMAQ; Community Multi-Scale Air Quality Model; Data fusion; EJ; Environmental justice; Exposure predictions; NDI; NHB; Neighborhood deprivation index; Non-Hispanic black; O(3); Ozone; PM2.5; Particulate matter <2.5μm in aerodynamic diameter; SES; SHEDS; Socio-economic status; Socioeconomic status; Stochastic human exposure and dose simulation; US Environmental Protection Agency; USEPA

PMID:
23850144
DOI:
10.1016/j.envres.2013.06.005
[Indexed for MEDLINE]

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