Spatiotemporal assessment of PM2.5 concentrations and exposure in China from 2013 to 2017 using satellite-derived data
Spatiotemporal assessment of PM2.5 concentrations and exposure in China from 2013 to 2017 using satellite-derived data
复制标题
利用卫星数据对2013年至2017年中国PM2.5浓度和暴露量进行时空评估
DOI：
10.1016/j.jclepro.2020.124965
复制
发表时间：
2021-01-16
影响因子：
11.1
通讯作者：
Huang, Bo
中科院分区：
文献类型：
--
作者：
He, Qingqing;Zhang, Ming;Huang, Bo
Satellite-based estimation of fine particulate matter of 2.5 mu m or less (PM2.5) at a high spatiotemporal resolution is important to understand the detailed dynamics of PM2.5 pollution and exposure. Stricter clean air policies have been enacted in recent years to tackle China's serious problem with PM2.5 pollution, including the implementation of the Air Pollution Prevention and Control Action Plan between 2013 and 2017. However, assessment of the change in national PM2.5 exposure during this period is difficult due to the limitation of high-resolution PM2.5 data. To address this issue, a satellite-based spatiotemporal model was developed to predict daily high-resolution surface PM2.5 concentrations in China during the designated period, and quantitative analysis was then performed regarding the spatiotemporal characteristics of this critical pollutant. The corresponding changes in the population exposure to PM2.5 were also explored at a fine scale. The overall concentrations of PM2.5 declined from 2013 to 2017, with substantial decreases in eastern China but negligible decreases in western China. The national PM2.5 concentration declined remarkably from 2013 to 2014 to 2015-2017. The Beijing-Tianjin-Hebei and Pearl River Delta regions and most cities reached the goals set by the Air Pollution Prevention and Control Action Plan. However, despite the overall reduction in the PM2.5 concentration, by 2017 the vast majority of the Chinese population still lived in areas with sustained levels of high risk from fine particle pollution. The findings from this study have crucial environmental policy implications for the mitigation of PM2.5 pollution and could benefit PM2.5-related health studies in China. (C) 2020 Elsevier Ltd. All rights reserved.
