Spatiotemporal characteristics and influencing factors of heterogeneity in human dynamic exposure risk to particulate matters

Abstract

Urban residents face serious health issues owing to air pollution, especially from particulate matter (PM). The dynamic exposure risk of PM exhibits intricate spatiotemporal fluctuations influenced by resident activity and urban patterns. Therefore, high spatiotemporal resolution assessments and researches are needed. In this study, high-resolution dynamic exposure risk was assessed using mobile monitoring of three types of PM (PM1, PM2.5, and PM10) and cell phone signaling data in the center of Shenyang, China, combined with geographically weighted regression model and dynamic exposure risk model. And influencing factors of dynamic exposure risks were explored by boosted regression tree model. The results showed that high-risk areas were concentrated along the main roads. Residents suffered greater risks during the morning peak than evening peak, and weekday than weekend. The dynamic exposure risk was significantly affected by the speed of population mobility (relative influence>55.49), surpassing the effect of POI (Point of Interest) density (relative influence<36.55), except during the weekday morning peak. POI density more pronounced affected on dynamic exposure risk of PM2.5, except during the weekend evening peak. Leveraging diverse data with model simulations to independently analyses based on human activity enables a cost-effective assessment and better understanding of the spatiotemporal variability of dynamic exposure risks.