[Spatiotemporal Distribution Characteristics of Air Pollution and Health Risks in Key Cities of China].

Based on the monitoring data of five pollutants in 168 key cities under air pollution prevention and control in China from 2015 to 2020, using the MAKESENS model and the aggregate risk index(ARI), this study quantitatively analyzed the spatial and temporal distribution characteristics of air pollution and health risks in China and the six urban agglomerations. The results showed that:① PM2.5 pollution was the most serious pollution in Chinese key cities. Only 15% of the cities' six-year average concentrations of PM2.5 reached the National Secondary Standard, followed by that of NO2; 77% of the cities' six-year average concentrations of NO2 reached the National Secondary Standard. The urban agglomerations of Beijing-Tianjin-Hebei and Fenwei plain had the most serious air pollution, and the six-year average concentrations of PM2.5, SO2, CO, and NO2 were higher than those of other urban agglomerations. ② The concentrations of PM2.5, SO2, CO, and NO2 in key cities of China showed a decreasing trend, whereas the concentration of O3 in other urban agglomerations showed an increasing trend, except in the Chengdu-Chongqing urban agglomeration. The concentration of SO2 in the urban agglomerations of Beijing-Tianjin-Hebei and Fenwei plain changed the most significantly. ③ The health risk of air pollution in the key cities of China generally showed a decreasing trend, with a sharp decline from 2017 to 2018, and the population exposed to extremely high risks dropped from 160 million to 32.54 million. The urban agglomeration in the middle reaches of the Yangtze River had the most significant decline in health risks, whereas the key cities in China faced higher health risks in spring and winter seasons. ④ The Beijing-Tianjin-Hebei and Fenwei plain urban agglomerations had the highest health risks, and the urban agglomeration in the middle reaches of the Yangtze River had the lowest; O3 gradually replaced PM2.5 as the main pollutant affecting the health risk. These results can provide a reference for evaluating the effectiveness of urban air pollution control in China during the 13th Five-Year Plan period.

[Spatiotemporal Evolution Characteristics of PM2.5-O3 Compound Pollution in Chinese Cities from 2015 to 2020].

Based on the monitoring data of PM2.5 and O3 concentrations in 333 cities in China from 2015 to 2020, using spatial clustering, trend analysis, and the geographical gravity model, this study quantitatively analyzed the characteristics of PM2.5-O3 compound pollution concentrations and its spatiotemporal dynamic evolution pattern in major cities in China. The results showed that:① there was a synergistic change in PM2.5 and O3 concentrations. When ρ(PM2.5_mean) ≤ 85 µg·m-3, for every 10 µg·m-3 increase in ρ(PM2.5_mean), the peak of the mean value of ρ(O3_perc90) increased by 9.98 µg·m-3. When ρ(PM2.5_mean) exceeded the national Grade II standards of (35±10) µg·m-3, the peak of the mean value of ρ(O3_perc90) increased the fastest, with an average growth rate of 11.81%. In the past six years, on average, 74.97% of Chinese cities with compound pollution had a ρ(PM2.5_mean) in the range of 45 to 85 µg·m-3. When ρ(PM2.5_mean)>85 µg·m-3, the mean value of ρ(O3_perc90) showed a significant decreased trend. ② The spatial clustering pattern of PM2.5 and O3 concentrations in Chinese cities was similar, and hot spots of the six-year mean values of ρ(PM2.5_mean) and ρ(O3_perc90) were distributed in the Beijing-Tianjin-Hebei urban agglomeration and other cities in the Shanxi, Henan, and Anhui provinces. ③ The number of cities with PM2.5-O3 compound pollution showed an interannual variation trend of increasing first (2015-2018) and then decreasing (2018-2020) and a seasonal trend of gradually decreasing from spring to winter. Further, the compound pollution phenomenon mainly occurred in the warm season (April to October). ④ The spatial distribution of PM2.5-O3 compound polluted cities was changing from dispersion to aggregation. From 2015 to 2017, the compound polluted areas spread from the eastern coastal areas to the central and western regions of China, and by 2017, a large-scale polluted area centered on the Beijing-Tianjin-Hebei urban agglomeration, the Central Plains urban agglomeration, and surrounding areas was formed. ⑤ The migration directions of PM2.5 and O3 concentration centers were similar, and there were obvious trends of moving westward and northward. The problem of high-concentration compound pollution was concentrated and highlighted in cities in central and northern China. In addition, since 2017, the distance between the centers of gravity of PM2.5 and O3 concentrations in the compound polluted areas had been significantly reduced, with a reduction of nearly 50%.

[Spatial Variation of Surface Ozone Concentration During the Warm Season and Its Meteorological Driving Factors in China].

The concentration of surface ozone (O3) in China increased consistently from 2015 to 2018, and became an important air pollutant, followed by particulate matter. This study uses real-time O3 and meteorological data, obtained in 337 cities in China during the warm seasons (April to September) of 2015 to 2018, to determine the spatial variation of surface O3 and its meteorological driving factors in major cities in China, via trend analysis, spatial autocorrelation, hotspot analysis, and multi-scale geographically weighted regression (MGWR) modeling. The results show that: ① during the warm season, O3 concentrations showed a significant growth trend (P<0.05), with an average growth rate of 0.28 µg·(m3·a)-1, while more than 55% of urban O3 concentrations increased by 0.50 µg·m-3 annually. ② There were significant regional differences in O3 concentration. High values (>60 µg·m-3) were distributed over east China, north China, central China, and northwest China, while low values (<20 µg·m-3) were distributed over south China and southwest China. ③ The spatial agglomeration of O3 concentration has been enhanced year by year, with hotspots mainly distributed over east China and central China. In contrast, there are cold spots in northeast China, southwest China, and southern China. ④Analysis of the MGWR model indicated that temperature, wind speed, cloud coverage, and precipitation all have a significant effect on the distribution of O3, although there are also discrepancies in driving factor priorities between the different regions. Temperature was the main meteorological driving factor of O3 variation during the warm season in China, and its impact on O3 concentration was significantly higher in north China, northwest China, and northeast China than in other regions; overall, there was a significant positive correlation between O3 concentration and temperature, except in Guangxi, Yunnan, and Jiangxi. O3 concentration was negatively correlated with wind speed in most regions of south China, east China, and central China, and positively correlated with wind speed in north China and northeast China. O3 concentration was significantly negatively correlated with cloud cover, except in Liaoning, Shandong, Hebei, Gansu, Guangdong, and some areas in southwest China. O3 concentration was significantly negatively correlated with precipitation, except in the northwest and southwest regions.