Abstract
Recent studies examining racial and ethnic inequities in exposure to urban air pollution have led to advances in understanding the nature and extent of overall concentration exposures by pollutant, demarcated by disadvantaged groups. However, the stability of inequities at various spatial units and the exposure by air pollution sources are often neglected. In this case study from the Dallas–Fort Worth (Texas, USA) area, we used Geographic Information Systems (GIS) and an air dispersion model to estimate environmental justice impacts at different spatial scales (i.e., zip code, census tract, block group) and by source (i.e., industrial pollution sources, vehicle pollution sources, industry and vehicle pollution sources combined). Using whites as a reference, blacks and other races were more likely to be exposed to higher sulfur dioxide (SO2) concentrations although the Odds Ratio (OR) varied substantially by pollution source type [e.g., industrial pollution source based: (OR=1.80; 95% CI (Confidence Interval): 1.79–1.80) vs. vehicle pollution source based: (OR=2.70; 95% CI: 2.68–2.71)] and varied less between spatial scales [for vehicle pollution sources, (OR=2.70; 95% CI: 2.68–2.71) at the census tract level but was (OR=2.54; 95% CI: 2.53–2.55) at the block group scale]. Similar to the pattern of racial inequities, people with less education (i.e., less than 12 years of education) and low income (i.e., per capital income below $20 000) were more likely to be exposed to higher SO2 concentrations, and those ORs also varied greatly with the pollution sources and slightly with spatial scales. It is concluded that the type of pollution source plays an important role in SO2 pollution exposure inequity assessment, while spatial scale variations have limited influence. Future studies should incorporate source–specific exposure assessments when conducting studies on environmental justice.
Highlights
Air pollution is recognized as a priority global health issue, affecting millions in both the developed and developing world (Brauer et al, 2012)
In order to reach the study objectives outlined above, we calculatedscaleconcentrationsbytwoprocedures.First,basedon broke down the entire study into three sub–processes: (1) scale– simulated SO2 concentrations at locations of discrete receptors based concenͲ (i.e.,includingregulargridreceptorsatintervalof1kmandthose trationcomputation;(2)socio–demographic(e.g.,age,race,educaͲ manually created at locations of emission sources and traffic tional attainment, income) data categorization; and (3) logistic intersections),wetestedtheperformanceofordinaryKriging(OK), regression modeling which was used to calculate Odds Ratio (OR) values that inversedistanceweighted(IDW),andSplineinterpolationavailable reveal inequities by socio–demographic characteristics
The results revealed a discrepancy of inequity in pollutionconcentrationdataprovidedbydiscretemonitoringsites
Summary
Air pollution is recognized as a priority global health issue, affecting millions in both the developed and developing world (Brauer et al, 2012). In order to reach the study objectives outlined above, we calculatedscaleconcentrationsbytwoprocedures.First,basedon broke down the entire study into three sub–processes: (1) scale– simulated SO2 concentrations at locations of discrete receptors based (i.e., scales of zip code, census tract, block group) concenͲ (i.e.,includingregulargridreceptorsatintervalof1kmandthose trationcomputation;(2)socio–demographic(e.g.,age,race,educaͲ manually created at locations of emission sources and traffic tional attainment, income) data categorization; and (3) logistic intersections),wetestedtheperformanceofordinaryKriging(OK), regression modeling which was used to calculate OR values that inversedistanceweighted(IDW),andSplineinterpolationavailable reveal inequities by socio–demographic characteristics. And Althoughthisstudyisamongthefirstthatusecurrentlypreferable air dispersion models (AERMOD) to estimate source–specific SO2 concentration surfaces over the entire study area, we still have to retired persons are less likely to live in industrial areas than the rely on ecological estimates of SO2 concentration by pollution reference group (i.e., those aged between 15 and 60) as they are sources due to the lack of individual scale demographic data.
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