Air pollution in China: Spatial patterns and spatial coupling with population and economy - Yan, Dachuan; Zhou, Meichun; Diao, Yiwei; Yang, Meng (Frontiers in Environmental Science, 2022)
This paper analyzes the daily data of China's air quality index (AQI) from 2015-2020 using kriging, spatial autocorrelation, and gravity center method. The results confirm that China's air pollution is gradually controlled. From 2015 to 2020, the AQI, the proportion of air pollution days and the city air quality exceedance rate decreased by about 29%, 44% and 49% respectively. The spatial distribution of air pollution changes with seasons. The heavily polluted area (AQI > 200) is largest in spring, which accounts for 0.48% of the total area, while the polluted area (AQI > 100) is largest in winter due to large area pollution in the North China Plain. The air pollution has strong spatial autocorrelation with a global Moran's Index larger than 0.7. The Beijing-Tianjin-Hebei region and southern Xinjiang are high-pollution clustering areas. The former is dominated by PM2.5 pollution caused by anthropogenic activities, while the latter is dominated by PM10 pollution from natural sources. Using gravity center method, it works out the air pollution gravity center (APGC), economic gravity center (EGC), the secondary industry gravity center (SIGC), the tertiary industry gravity center (TIGC) and the population gravity center (PGC) in the Beijing-Tianjin-Hebei region and its surrounding areas. Results show that the spatial overlapping indicator of the APGC with the PGC is the highest, and the APGC and the SIGC has the highest shifting consistency indicator. When combining the two indicators, it is obtained that air pollution has the highest degree of spatial coupling with the secondary industry, but low coupling with economic volume. It is indicated that the driving factors of air pollution is the secondary industry. Therefore, it is suggested that the regulation of air pollution should start from optimizing the industrial structure in China.