Spatiotemporal heterogeneity of PM2.5 exposure risk: An assessment framework based on residents’ travel behavior using mobile phone data

Abstract

Exposure assessment is a crucial method for evaluating the impact of environmental pollution on human health. However, existing methodological frameworks for air pollution exposure risk assessment have failed to adequately integrate individual mobility patterns and environmental media, and encounter limitations in performing macro-scale evaluations and spatially mapping outcomes. To address these limitations, this study proposes a dynamic exposure risk assessment framework that incorporates individual travel behavior using mobile phone data at a macro scale, building upon two novel indicators—travel aggregation and travel regularity. The proposed framework provides innovative insights into how residents’ travel behaviors affect specific spatial exposure risks. We assessed PM_2.5 exposure risk in Shanghai, examining its spatiotemporal heterogeneity and dominant influencing factors. The results indicated that travel activity characteristics significantly affected the spatial distribution of PM_2.5 exposure risk during peak travel periods. In the morning, PM_2.5 exposure was highly correlated with travel regularity, while high PM_2.5 concentration and high travel aggregation further increased the exposure risk. High Exposure Risk Areas (HERAs) accounted for 29% of the total area, mainly distributed in central urban districts, major transportation corridors, and industrial clusters. We also found that dominant factors and built environment conditions of HERAs varied across locations, leading to the proposal of differentiated planning and governance strategies to address particular exposure problems. This study’s framework incorporates residents’ travel behavior into assessing air pollution exposure risk in specific spaces at a macro scale, providing decision support for air quality management and healthy urban planning.