Analysis of influential factors on air quality from global and local perspectives in China

Abstract

Regional haze pollution has frequently occurred in China over the past several years, and this haze has hindered the development of the economy and harmed the health of people in China. Currently, several studies have analyzed the impact of different influencing factors on haze. However, few studies have comprehensively analyzed the influential factors of haze from different perspectives. In this paper, we utilized global and local regression models to explore the main influential factors on air quality index (AQI) in China from global and local perspectives. The results are as follows: (1) the AQIs of Chinese cities have significant positive spatial correlation, and higher values of AQI were typically found in Beijing-Tianjin-Hebei, Shandong, Henan, Shanxi and Shaanxi Province; (2) from a global perspective, as there is one unit of increase in the average AQI of one city's neighbors, the city's AQI will increase by 0.827 unit. An increase in the industrial structures and the number of civilian vehicles will also lead to an increase in the AQI, but the impact of precipitation is reversed; and (3) from a local perspective, there are spatial differences in the effects of different factors on the AQI. In northern China, an appropriate temperature reduction and an appropriate increase in atmospheric pressure is helpful for reducing haze pollution; however, opposing conditions are found in southern China. Compared with China's coastal cities, the increase in precipitation is more effective at reducing the AQI in inland cities. Compared with other cities, reducing the industrial structure and the number of civilian vehicles was more effective for haze management in Beijing, Tianjin, Shandong, Henan, Shanxi, and Shaanxi provinces. These results of this paper are helpful for government departments to formulate regionally differentiated governance policies regarding haze.

Keywords: Air quality index; Global regression model; Haze pollution; Influential factors; Local regression model.