High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are 35 mu g/m(3) close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation(.) Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5