Assessment of exposure to ambient PM2.5 based on gap-filled aerosol optical depth at urban scale

Abstract

Air pollution has been a crucial issue affecting human health and has drawn more and more attention in the world. The assessment of exposure to PM2.5 of urban residents based on remote sensing is challenging because of the data deficiency in aerosol optical depth (AOD) and the low spatial resolution. This article is devoted to adopt an approach with 2 gradient boosting decision tree (GBDT) models to fill the gaps in AOD and derive continuous PM2.5 distribution at urban scale. Then, the assessment of exposure to PM2.5 in Beijing was conducted. First, Simplified High Resolution MODIS Aerosol Retrieval Algorithm (SARA) was employed to obtain daily AOD from September 2016 to February 2017 at 500m resolution. Then we used the first GBDT to derive the gap-filled SARA AOD and the second GBDT to estimate PM2.5 spatial distribution based on multi-source data. Furthermore, population weighted exposure (PWE) levels of PM2.5 and population proportion exposed to PM2.5 concentration were estimated by PM2.5 distribution and population density data. The result demonstrates that both two GBDT models performed well with cross validation (CV) R2 of 0.86 and 0.85 on AOD gap-filling and PM2.5 concentration estimation respectively. The areas with high PM2.5 concentration are mainly distributed in the east and south of the city but the areas with higher PM2.5 exposure are mainly distributed in the urban centre. Moreover, it is found that over 80% people in Beijing are affected by PM2.5 pollution in autumn and winter. Overall, the approach this article applied and the analysis results are very useful for epidemiological investigation and air pollution control policy formulation.