Air quality analysis of Sichuan province based on complex network and CSP algorithm

Xiao Li Huang, Si Yu Hu, Jing Xian Chen and Wan Qi Feng
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Xiao Li Huang: College of Electrical and Electronic Information, Xihua University, Chengdu 610039, P. R. China2Department of Physics, Fribourg University, 1700 Fribourg, Switzerland
Si Yu Hu: College of Electrical and Electronic Information, Xihua University, Chengdu 610039, P. R. China
Jing Xian Chen: College of Electrical and Electronic Information, Xihua University, Chengdu 610039, P. R. China
Wan Qi Feng: College of Electrical and Electronic Information, Xihua University, Chengdu 610039, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 01, 1-18

Abstract: The air quality is directly related to people’s lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control.

Keywords: Complex network; self-organizing competitive neural network; principal component analysis algorithm; air quality; time-dependent cross-correlations (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122500073

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