Relationships among six urban air pollutants and identification of pollution types – A case study of Chinese cities above prefecture level

• Published in: Atmospheric pollution research Vol. 15; no. 7; p. 102160
• Main Authors: Chang, Yinghui, Li, Guanghui, Zhang, Pengyan, Liu, Yu, Chen, Zhuo, Xing, Guangrui, Li, Mengfan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2024
• Subjects: Air pollutants; China; Entropy weight TOPSIS; ESDA; Gaussian mixture model; Random forest regression model; Entropy weight TOPSIS; ESDA; Air pollutants; China; Gaussian mixture model; Random forest regression model
• ISSN: 1309-1042; 1309-1042
• DOI: 10.1016/j.apr.2024.102160

Urban air pollution is caused by the interplay of urban development and natural conditions, but it's unclear which factor has a greater impact. Studying the link between air pollutants and identifying the types of pollution causes is crucial for understanding the pollution mechanisms and formulating efficient management measures. During the research period from 2015 to 2020, this study analyzed the relationships between six air pollutants, calculated the city capacity (CC) and natural condition (NC) scores of 285 cities with the help of the index system and entropy weight TOPSIS model, and employed Gaussian Mixture Model (GMM) and random forest regression model to identify air pollution types. The results show: During the study period, the concentrations of most pollutants decreased, with SO2 experiencing the largest decline, reflecting the significant effectiveness of China's recent atmospheric pollution control efforts. In terms of spatial distribution, the North China Plain (NCP) and the Loess Plateau (LP) remain focal areas of pollution. The correlation analysis results indicate a significant positive correlation among all air pollutants except O3, underscoring the necessity of coordinated pollution control efforts, but the complexity of O3 pollution should not be overlooked. High-value agglomerations of CC scores calculated by the model are predominantly located in the eastern coastal region, while NC scores exhibit two high-value agglomerations in the north and south. Utilizing the GMM, the 285 cities were classified into three categories, and the random forest regression model was employed to identify them as CC dominate, NC dominate, and intermediate types. This paper proposes a novel approach for categorizing city air pollution types, aiming to tailor prevention measures and foster sustainable urban development.
•The concentration of most air pollutants shows a significant downward trend, and the complexity of ozone pollution cannot be overlooked.•Except for O3, urban air pollutants showed a significant positive correlation, which verified the necessity of collaborative governance.•The city capacity score and natural condition score were calculated, and the cities were clustered using the GMM model.•The random forest regression model identifies the types of urban pollution as CC dominate, NC dominate, and intermediate types.