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.

No abstract

IntroductionGlobally, exposure to air pollution is estimated to cause millions of deaths annually as well as loss of healthy years of life (Li et al., 2021). PM 2.5 pollution has become a worldwide challenge, especially in developing countries such as China. PM 2.5 exposure not only affects the mortality rates of residents as well as outpatient and hospitalization rates for specific diseases but also increases cancer risk and death burden (Tan et al., 2018;Li et al., 2019). In addition, PM 2.5 pollution can cause economic losses by increasing health-related expenditures. Hence, evaluating the urban PM 2.5 -related health economic losses across China is of significance for environmentally sustainable and equitable decision-making developments.Various scholars have studied PM 2.5 -related health economic losses in China for different spatial scales. The average PM 2.5 -related economic loss was 0.3% (amended human capital: AHC) to 1% (value of statistical life: VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016 (Yang et al., 2018). In the Beijing-Tianjin-Hebei (BTH) region of China, the PM 2.5 economic loss was 6,081 million USD in 2017 (Wang et al., 2020). Most of the previous studies on this topic were mainly targeted at limited and individual areas or cities, such as 190 major cities in China (Yang et al., 2018), the BTH region (Wang et al., 2020), and Shanghai (Wu et al., 2017). However, owing to the spatial spillover effects of PM 2.5 and regional joint management requirements, it is important to systematically analyze the urban PM 2.5 -related health economic losses from the national-regional perspectives.Owing to regional differences in the economic, social, and environmental perspectives, the factors driving PM 2.5 -related health economic losses in different regions need to be analyzed further. The geographically weighted regression (GWR) is often used to identify such factors because of its flexibility in identifying relationships between different geospatial influences. Fu et al. ( 2014) used the GWR to analyze the factors influencing PM 2.5 health risks; the population, social, and economic factors accounted for about 45% of the PM 2.5 health risks. It is therefore helpful to identify

IntroductionWith the promulgation of air pollution control policies, there are still many cities where the PM2.5 concentration exceeds 35 μg/m3, and O3 pollution is increasingly apparent.MethodsThe spatio-temporal evolution and differentiation characteristics of PM2.5 and O3 pollution were explored, and then compound pollution hotspot urban agglomerations were screened out. A weather normalization technique was used to identify the driving amount, the influence of meteorological factors, and the anthropogenic emissions quantitatively, on pollution in hotspot urban agglomeration. Furthermore, the health and economic losses due to PM2.5 and O3 in hot cities in 2015–2020 were quantified. Finally, a natural break-point classification method was used to establish the health loss rating systems for PM2.5 and O3.Results and DiscussionThe results showed the following: (1) From 2015 to 2020, 78%, 72%, 69%, 58%, 50%, and 41% of the annual mean PM2.5 concentration had exceeded 35 μg/m3, respectively, and 17%, 18%, 31%, 33%, 30%, and 17% of the annual mean O3 concentration exceeded 160 μg/m3, respectively, in 337 cities in China. (2) From 2015 to 2020, the health losses caused by PM2.5 and O3 were ranked as follows: Beijing–Tianjin–Hebei (BTH; 1968, 482 people) > Shandong Peninsula (SDP; 1,396, 480 people) > Central Plains (CP; 1,302, 314 people) > Yangtze River Delta (YRD; 987, 306 people) > Triangle of Central China (TC; 932, 275 people) > Guanzhong Plain (GZP; 869, 189 people). (3) The average economic losses associated with public health of the PM2.5 and O3 were ranked as follows: BTH (2.321 billion, 3.218 billion RMB, 1 RMB = 0.0.1474 USD on 20 January 2023) > SDP (1.607, 2.962 billion RMB) > YRD (1.075, 1.902 billion RMB) > TC (1.016 billion, 1.495 billion RMB) > CP (1.095, 1.453 billion RMB) > GZP (0.69, 0.828 billion RMB). Therefore, combining hot pollution factors, the regional characteristics of the priority control areas, and the national 14th 5-Year Plan, targeted control countermeasures were proposed.