| 摘 要： |
Having high spatio-temporal resolution data of pollutants is critical to understand environmental pollution patterns and their mechanisms. Our research employs the hourly average concentration data on the air quality index (AQI) and its six component pollutants (PM2.5, PM10, SO2, NO2, CO, and O-3) in 336 Chinese cities from 2014 to 2019. We analyze annual, seasonal, monthly, hourly, and spatial variations of different air pollutants and their socioeconomic factors. The results are as follows. (1) Air pollutants' concentration in Chinese cities decreased year by year during 2014-2019. Among the primary pollutants, PM(2.)5 dominated pollution days, accounting for 38.46%, followed by PM10. Monthly concentration curves of AQI, PM2.5, NO2, SO2, and CO showed a U-shaped trend from January to December, while that of O-3 presented an inverted U-shaped unimodal pattern. Regarding daily variation, urban air quality tended to be worse around sunrise compared with sunset. (2) Chinese cities' air quality decreased from north to south and from inland to coastal areas. Recently, air quality has improved, and polluted areas have shrunk. The six pollutant types showed different spatial agglomeration characteristics. (3) Industrial pollution emissions were the main source of urban air pollutants. Energy-intensive industries, dominated by coal combustion, had the greatest impact on SO2 concentration. A pollution shelter was established in China because foreign investment introduced more pollution intensive industries. Thus, China has crossed the Kuznets U-curve inflection point. In addition, population agglomeration contributed the most to PM2.5 concentration, increasing the PM2.5 exposure risk and causing disease, and vehicle exhaust aggravated the pollution of NO2 and CO. The higher China's per capita gross domestic product, the more significant the effect of economic development is on reducing pollutant concentration. |
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