Big mobility data reveals hyperlocal air pollution exposure disparities in the Bronx, New York

Abstract

Air pollution disproportionately affects socially disadvantaged populations. Our study bridges the existing gap in quantifying mobility-based exposure and its associated disparity issues. We combined the granular mobility of over 500,000 unique anonymized users daily and hyperlocal air pollution data in 100 × 100-m grid cells to quantify disparities in particulate matter exposure in a racially diverse and dense urban area of New York City. Our approach advances the study of exposure and its disparity from individualized exposure tracking to a population-representative scale. We observed apparently different spatial patterns between personal exposure and exposure disparities, noting that people from Hispanic-majority and low-income neighborhoods were those most severely and disproportionately exposed to fine particulate matter (PM2.5) pollution. We reveal that race and ethnicity are much stronger indicators of exposure disparity than income. Our study further demonstrates that within-group variation contributes a major portion to exposure disparities, suggesting more granular mitigation plans are needed to target high-exposure individuals from socially disadvantaged groups in addition to generic air quality improvement. This study used mobility data and air pollution data from the Bronx, NY, to observe the links between social disadvantage and air pollution exposure. It found that, more than income, race and ethnicity have a greater influence on air pollution exposure, with Hispanic people having the highest risk.