Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Shengyong Zhang, Yunhao Chen, Yudong Li, Xing Yi and Jiansheng Wu
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Shengyong Zhang: Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China
Yunhao Chen: School of Urban Planning and Design, Peking University, Shenzhen 518055, China
Yudong Li: School of Urban Planning and Design, Peking University, Shenzhen 518055, China
Xing Yi: School of Urban Planning and Design, Peking University, Shenzhen 518055, China
Jiansheng Wu: Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China

IJERPH, 2022, vol. 19, issue 8, 1-15

Abstract: Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O 3 , NO 2 , SO 2 , PM 2.5 and PM 10 of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM 2.5 , PM 10 and CO are the main pollutants affecting air quality and fluctuation, followed by SO 2 , NO 2 and O 3 .

Keywords: air quality and fluctuation; clustering analysis; kriging interpolation; principal component analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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