Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data
Publication in refereed journal


摘要In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM2.5 (particulate matters with aerodynamic diameters less than 2.5 mu m) 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 PM2.5 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 PM2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM2.5 concentration greater than 80 mu g/m(3) in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM2.5 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 PM2.5 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.
著者Song YM, Huang B, He QQ, Chen B, Wei J, Mahmood R
期刊名稱Environmental Pollution
頁次288 - 296
關鍵詞Human mobility, Spatiotemporal heterogeneity, Remote sensing, Big data, Environmental health
Web of Science 學科類別Environmental Sciences;Environmental Sciences & Ecology

上次更新時間 2020-01-06 於 23:47