Dynamic assessment of PM_2.5 exposure and health risk using remote sensing and geo-spatial big data

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM_2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM_2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM_2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM_2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM_2.5 concentration greater than 80 μg/m³ in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM_2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM_2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.