Cluster analysis of PM2.5 pollution in China using the frequent itemset clustering approach

In recent years, severe air pollution has frequently occurred in China at the regional scale. The clustering method to define joint control regions is an effective approach to address severe regional air pollution. However, current cluster analysis research on the determination of joint control area...

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Vydáno v:Environmental research Ročník 204; číslo Pt B; s. 112009
Hlavní autoři: Zhang, Liankui, Yang, Guangfei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.03.2022
Témata:
air
air pollution
air quality
algorithms
China
Cluster analysis
cost effectiveness
PM2.5
Pollution control
Urban air pollution
China
Cluster analysis
Urban air pollution
Pollution control
PM2.5
ISSN:0013-9351, 1096-0953, 1096-0953
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Shrnutí:In recent years, severe air pollution has frequently occurred in China at the regional scale. The clustering method to define joint control regions is an effective approach to address severe regional air pollution. However, current cluster analysis research on the determination of joint control areas relies on the Pearson correlation coefficient as a similarity measure. Due to nonlinearity and outliers in air pollution data, the correlation coefficient cannot accurately reveal the similarity in air quality between different cities. To bridge this gap, we proposed a method to delineate spatial patterns of PM2.5 pollution and regional boundaries of polluted areas using the frequent itemset clustering approach. The frequent itemsets between cities were first mined, and the support values were employed as interestingness metrics to describe the significance of similar variation patterns between cities. Then, the hierarchical clustering method was applied to identify appropriate areas for joint pollution control. The proposed clustering algorithm exhibits the advantages of not requiring model assumptions and a robustness to the outliers, which is a cost-effective approach to define joint control regions. By analysing urban PM2.5 pollution in China from 2015 to 2018, we obtained results demonstrating that the frequent itemset clustering approach can efficiently determine pollution patterns and can effectively identify regional divisions. The clustering approach could facilitate a greater understanding of PM2.5 spatiotemporal aggregation to design joint control measures among areas. The findings and methodology of this research have important implications for the formulation of clean air policies in China.
Bibliografie:ObjectType-Article-1
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ISSN:0013-9351
1096-0953
1096-0953
DOI:10.1016/j.envres.2021.112009