A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California

This study elucidates PM2.5 exposure disparities in a socioeconomically diverse air basin that is heavily burdened by air pollution. A novel spatial clustering approach is applied to classify the microenvironments of more than 900,000 high temporal resolution personal exposure data points. Results from the study indicate that participants from the lowest socioeconomic status community experienced overall higher personal exposures over consecutive 24-hr monitoring periods, despite high participant mobility and low variability in ambient PM2.5 during the study. Our inclusive monitoring protocol minimizes participant fatigue and is well-suited for real-time, long-term characterization of PM2.5 exposure disparities in underserved communities.