[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.

[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.

Global, continental, and national variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-19 lockdown.

Lockdowns implemented in response to COVID-19 have caused an unprecedented reduction in global economic and transport activity. In this study, variation in the concentration of health-threatening air pollutants (PM2.5, NO2, and O3) pre- and post-lockdown was investigated at global, continental, and national scales. We analyzed ground-based data from >10,000 monitoring stations in 380 cities across the globe. Global-scale results during lockdown (March to May 2020) showed that concentrations of PM2.5 and NO2 decreased by 16.1% and 45.8%, respectively, compared to the baseline period (2015-2019). However, O3 concentration increased by 5.4%. At the continental scale, concentrations of PM2.5 and NO2 substantially dropped in 2020 across all continents during lockdown compared to the baseline, with a maximum reduction of 20.4% for PM2.5 in East Asia and 42.5% for NO2 in Europe. The maximum reduction in O3 was observed in North America (7.8%), followed by Asia (0.7%), while small increases were found in other continents. At the national scale, PM2.5 and NO2 concentrations decreased significantly during lockdown, but O3 concentration showed varying patterns among countries. We found maximum reductions of 50.8% for PM2.5 in India and 103.5% for NO2 in Spain. The maximum reduction in O3 (22.5%) was found in India. Improvements in air quality were temporary as pollution levels increased in cities since lockdowns were lifted. We posit that these unprecedented changes in air pollutants were mainly attributable to reductions in traffic and industrial activities. Column reductions could also be explained by meteorological variability and a decline in emissions caused by environmental policy regulations. Our results have implications for the continued implementation of strict air quality policies and emission control strategies to improve environmental and human health.