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Abstract
Higher levels of nearby traffic increase exposure to air pollution and adversely affect health outcomes. Populations with lower socio-economic status (SES) are particularly vulnerable to stressors like air pollution. We investigated cumulative exposures and risks from traffic and from MNRiskS-modeled air pollution in multiple source categories across demographic groups. Exposures and risks, especially from on-road sources, were higher than the mean for minorities and low SES populations and lower than the mean for white and high SES populations. Owning multiple vehicles and driving alone were linked to lower household exposures and risks. Those not owning a vehicle and walking or using transit had higher household exposures and risks. These results confirm for our study location that populations on the lower end of the socio-economic spectrum and minorities are disproportionately exposed to traffic and air pollution and at higher risk for adverse health outcomes. A major source of disparities appears to be the transportation infrastructure. Those outside the urban core had lower risks but drove more, while those living nearer the urban core tended to drive less but had higher exposures and risks from on-road sources. We suggest policy considerations for addressing these inequities.
Highlights
It has been known for at least 50 years that economic status can be a modifier of the effects of air pollution on health [1]
Since numerous studies have shown a disproportionate exposure to traffic and air pollution among non-whites and populations with lower socio-economic status (SES) [2,3,4,5,6,7,8,9,10,11,12,13,14,15]
We infer from our results that race, income, housing, education, age, and transportation utilization are all significantly related to traffic exposure and air pollution risk to varying degrees
Summary
It has been known for at least 50 years that economic status can be a modifier of the effects of air pollution on health [1]. Many of the above-cited studies used exposure metrics that were limited to a single pollutant and were not highly spatially resolved Those metrics included modeled county, zip-code, or census tract level estimates of air pollution concentrations, measurements at the nearest monitoring site (often widely spaced), proximity to air pollution point sources, and proximity to high traffic corridors, among others. (1) exploring features of SES (like housing and transportation) that have not been previously examined at this level of detail; (2) working in a northern US metropolitan area that is racially and economically homogenous; (3) using highly spatially resolved estimates of exposure and risk; and (4) evaluating impacts by subcategories of sources as well as cumulative impacts across all sources and 235 pollutants
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