Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities

Abstract

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the 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 period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China 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 pollution's 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 national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) 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), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China

“…The extremely high levels of PM 2.5 exposure have become a major public concern in China ( 1 ). In 2019, over 90% of the world's population was exposed to annual average PM 2.5 concentrations that exceeded the WHO Air Quality Guideline of 10 μg/m 3 ( 2 ).…”

Causal Associations of Air Pollution With Cardiovascular Disease and Respiratory Diseases Among Elder Diabetic Patients

“…Evidence emerges that air pollution may be one of the common modifiable risk factors for CVD, RD, and DM (Burkart et al., 2022 ; Liu, Cai, et al., 2022 ; Ratajczak et al., 2021 ; Xu, Liu, et al., 2022 ). Although air pollution has been improved year by year, there are still heavily polluted areas in northern China, such as Beijing (Maji et al., 2020 ; Zhu et al., 2022 ). Epidemiologic studies and laboratory experiments suggests that the smaller the particle size, the greater risk to health, that is the effect size of PM 1 (particulate matter <1 μm in diameter) may be larger than PM 2.5 (particulate matter <2.5 μm in diameter) and PM 10 (particulate matter <10 μm in diameter) (Huang et al., 2018 ; Zou et al., 2017 ).…”

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

“…The goal of pertinent study should be to investigate the spatiotemporal distribution properties of PM 2.5 and the underlying causes of its existence, according to present research findings. From the studies of the spatiotemporal distribution characteristics of PM 2.5 , scholars have mainly explored PM 2.5 spatiotemporal variation patterns and spatial aggregation characteristics through spatial center of gravity shift evaluation, cold and hot spot analysis, and spatial autocorrelation analysis (29)(30)(31)(32)(33). Among them, the spatial center of gravity shift can be realized by the standard deviation ellipsoid (SDE) model (34,35), which can not only calculate the center of gravity of the PM 2.5 concentration spatial distribution but also effectively reflect the spatial aggregation trend of PM 2.5 .…”