title: 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 μg/m³ (95% CI: 0.083-0.475) and 0.735 μg/m³ (95% CI: 0.261-1.210), respectively. The area with a PM2.5 pollution level of more than 70 μg/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 PM2.5 pollution. The polluted areas, with a high local magnitude of more than 1.0 relative to the overall PM2.5 concentration, 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 μg/m³ and 100 μg/m³ is increasing significantly.
authors.affiliation: Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Xudong Street 340, Wuhan 430077, China. lijunming_dr@l26.com.|University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China. lijunming_dr@l26.com.
authors.forename: Junming
authors.initials: J
authors.lastname: Li
authors.affiliation: College of Architecture and Civil Engineering, Taiyuan University of Technology, Yingze Street 79, Taiyuan 030024, China. jinmeijun@tyut.edu.cn.
authors.forename: Meijun
authors.initials: M
authors.lastname: Jin
authors.affiliation: Henan University, Jin Ming Avenue, Kaifeng 475001, China. XuZheng_Geo@163.com.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Xu
mesh_terms: D000393:Air Pollutants / Q000032:analysis; D000397:Air Pollution / Q000032:analysis; D001499:Bayes Theorem; D002681:China; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods; D004787:Environmental Pollution / Q000032:analysis; D006801:Humans; D052638:Particulate Matter / Q000032:analysis*; D058998:Remote Sensing Technology; D012621:Seasons; D014507:Urbanization / Q000639:trends
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Bayesian hierarchy model; PM2.5 concentrations; spatiotemporal variation