Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities - Zhu, Minli; Guo, Jinyuan; Zhou, Yuanyuan; Cheng, Xiangyu (Frontiers in Public Health, 2022)
From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the Three-year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5 has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further contemplate the governance measures during the 14th Five-Year Plan (2021–2025) period. Based on PM2.5 data from 2018 to 2020 and taking 366 Chinese cities as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m3 increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the PM2.5 gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran’s I and Local Geti’s G were applied to identify hotspots with a high degree of aggregation. Integrating population density, the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta and the surrounding areas were selected as the key hotspots for further GWR analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: the proportion of secondary industries in the GDP, the ownership of civilian vehicles, the annual grain planting area, the annual average population, the urban construction land area, the green space area and the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), with the goal of providing ideas and countermeasures for PM2.5 control.