Improving assessment of population exposure and health impacts to PM2.5 with high spatial and temporal data

Abstract

Exposure to ambient fine particulate matter (PM2.5) poses a significant global health challenge. However, a major obstacle for epidemiological studies and risk assessment lies in the absence of high-resolution spatiotemporal exposure estimates. Here, we present an integrated framework to achieve accurate estimations of population exposure to PM2.5 and assess related health risks by incorporating high temporal (hourly) and high spatial (1-km) PM2.5 concentrations and population density, using Beijing, China, in 2015 as an example. Firstly, hourly PM2.5 concentrations were estimated using a functional data model by integrating the gap-filled satellite-based aerosol optical depth data at 1-km resolution with hourly in-situ PM2.5 observations. Then, we calculated the population-level exposure to PM2.5 and associated health impacts incorporating both hourly PM2.5 and population information. We also investigated the bias of exposure and health impact assessment using coarse spatial or temporal PM2.5 and population data. Our findings revealed that exposure to PM2.5 resulted in 33,830, 21388, and 5,302 premature deaths in 2015 attributable to all-cause, cardiovascular, and respiratory diseases, respectively. A sensitivity analysis conducted underscored the critical importance of considering the high spatiotemporal heterogeneity of both PM2.5 concentrations and population density when investigating population-level PM2.5 exposure and related health impacts. Insights on more accurate PM2.5 exposure assessment from this study can help policymakers better assess improvements after clean air actions and analyze exposure inequality, and thereafter develop more effective pollution mitigation strategies.

Keywords

• PM2.5 exposure
• health impact
• high spatiotemporal
• multi-source
• population dynamics