Traffic, air pollution, minority and socio-economic status: addressing inequities in exposure and risk

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.

Keywords: air pollution risk; environmental justice; socio-economic status; traffic.

Figure 1

Kendall’s Tau-B nonparametric correlations for 2085 block groups in the Metro Area. The first seven rows and columns are variables describing air pollution exposure and risk. The remaining columns are demographic variables. All reported coefficients are statistically significant with p < 0.05 for the lightest shading and decreasing to p < 0.00001 for the heaviest shading.

Figure 2

Factor loading plot for the first two components identified in the principal components analysis. The prefix HI refers to non-cancer hazard index.

Figure 3

The Minneapolis-St. Paul Metropolitan Area with block groups colored according to (a) the fraction non-white population, (b) traffic density, (c) vehicles per household, and (d) cancer risk from on-road sources. The color palette in all four maps increases from blue to red by decile of the metric. The seven counties in the Metro Area are shown in bold outline.

Figure 3

The Minneapolis-St. Paul Metropolitan Area with block groups colored according to (a) the fraction non-white population, (b) traffic density, (c) vehicles per household, and (d) cancer risk from on-road sources. The color palette in all four maps increases from blue to red by decile of the metric. The seven counties in the Metro Area are shown in bold outline.

Figure 4

Each column represents one regression equation with the dependent variable at the top of the column and the independent variables listed down the side in column one. The reported values are the statistically significant (p < 0.05) regression coefficients with R² values in the bottom row. Blank cells were insignificant in the model. Positive coefficients are highlighted in green and negative coefficients in red with p < 0.05 for the lightest shading to p < 0.0001 for the darkest shading.

Figure 5

Breakdown of the Metro Area traffic density, cancer risks, and non-cancer hazard indices by demographic group and pollution source category.

Figure 6

Estimated resident cancer risks from the MNRiskS system by major source groups for selected demographic groups in the Minneapolis-St. Paul metropolitan area.

Figure 7

Estimated resident non-cancer hazard indices from the MNRiskS system by major source groups for selected demographic groups in the Minneapolis-St. Paul metropolitan area.