Modeling Influence of Population Mobility to Airborne PM2.5 Exposure

Abstract

Recent studies have shown that both spatiotemporal variability in air pollutant concentrations and people’s mobility play important roles in estimating personal exposure to air pollutants. However, several models consider average and fixed pollutant concentrations and exposure times. We present an innovative model that assesses PM2.5 dynamic exposure based on population commuting and daily activities, providing a more realistic and accurate assessment of exposure than traditional models. This approach captures the significant impact of daily mobility and spatiotemporal variability of pollutant concentrations on total exposure. The model was tested in a well-defined case study, where movement patterns and hourly PM2.5 concentrations were analyzed to estimate population exposure in the Mendoza Metropolitan Area (Argentina). We foundan average increase of more than 200% in total exposure for individuals living in areas with better air quality and moving to areas with poorer air quality. Total exposure decreased by an average of ~ 17% for those living in areas with poorer air quality and moving to areas with better air quality. Although the highest exposures occur during the commuting period, their contribution to the total daily exposure is ~ 10%. This study demonstrates that ignoring human mobility and the spatiotemporal variability of air pollution could lead to erroneous total exposure estimations, potentially biasing health risk assessments. This innovative approach is a more realistic evaluation of personal exposure levels and potential health effects than models that do not consider urban mobility, providing a better approximation of the geographic and temporal context in which individuals are exposed to air pollution.