Spatiotemporal variability of remotely sensed pm2.5 concentrations in China from 1998 to 2014 based on a bayesian hierarchy model

Abstract

With the rapid industrial development and urbanization in China over the past three decades, PM2.5 pollution has become a severe environmental problem that threatens public health. Due to its unbalanced development and intrinsic topography features, the distribution of PM2.5 concentrations over China is spatially heterogeneous. In this study, we explore the spatiotemporal variations of PM2.5 pollution in China and four great urban areas from 1998 to 2014. A space-time Bayesian hierarchy model is employed to analyse PM2.5 pollution. The results show that a stable “3-Clusters” spatial PM2.5 pollution pattern has formed. The mean and 90% quantile of the PM2.5 concentrations in China have increased significantly, with annual increases of 0.279 ĉg/m3 (95% CI: 0.083-0.475) and 0.735 ĉg/m3 (95% CI: 0.261-1.210), respectively. The area with a PM2.5 pollution level of more than 70 ĉg/m3 has increased significantly, with an annual increase of 0.26 percentage points. Two regions in particular, the North China Plain and Sichuan Basin, are experiencing the largest amounts of PM2.5pollution. The polluted areas, with a high local magnitude of more than 1.0 relative to the overall PM2.5concentration, affect an area with a human population of 949 million, which corresponded to 69.3% of the total population in 2010. North and south differentiation occurs in the urban areas of the Jingjinji and Yangtze Delta, and circular and radial gradient differentiation occur in the urban areas of the Cheng-Yu and Pearl Deltas. The spatial heterogeneity of the urban Jingjinji group is the strongest. Eighteen cities located in the Yangtze Delta urban group, including Shanghai and Nanjing, have experienced high PM2.5 concentrations and faster local trends of increasing PM2.5. The percentage of exposure to PM2.5 concentrations greater than 70 ĉg/m3 and 100 ĉg/m3 is increasing significantly.