Abstract
China’s coal-based energy structure and rapid economic expansion have resulted in significant air pollution, notably PM2.5 pollution, which has harmed the environment, citizens’ health, and sustainable and cleaner development of China in recent years. Traditional ground monitoring stations have certain drawbacks, such as spatial distribution that is unequal. To better understand the spatial and temporal dynamic characteristics of PM2.5 emissions, this article studied the temporal and spatial changes of night light data along PM2.5 emission at the national, regional, and provincial scales. The Chinese Academy of Science’s Earth Luminous Data Set, Dalhousie University’s PM2.5 emission dataset, and the basic national geographical dataset from National Geographic were used for analysis. We found a significant positive correlation between nightlight data and PM2.5 emission data, which resulted in an accurate fitting of PM2.5 emissions using the proposed linear regression model, and the results showed that the spatiotemporal dynamics of PM2.5 emission and night light were different in various regions. In terms of spatial distribution, PM2.5 emission over the intermediate level (44% of China’s total area) was concentrated in the Sichuan Basin, North China Plain, and Northwest China, whereas PM2.5 emission below the middle level (55% of China’s total area) was concentrated in northeast China, Xizang, and West Sichuan. In terms of geographical and temporal dynamics, more than 65% of China’s total, area mainly located in the south of the Hu line, showed negative growth from 2012 to 2018, especially the North China Plain, the Sichuan Basin, and the Yangtze River’s Plains showed a lot of negative growth. The evolution of PM2.5 emission in China from 2012 to 2018 was visually exhibited by examining spatiotemporal dynamics and the interaction linkages between PM2.5 emission and nighttime light, which was useful for China’s air pollution control and sustainable development.
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