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Analysis of the Spatio-temporal Network of Air Pollution in the Yangtze River Delta Urban Agglomeration, China

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Journal PLoS One
Date 2022 Jan 11
PMID 35015793
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Abstract

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

References
1.
Shan Y, Wang X, Wang Z, Liang L, Li J, Sun J . The pattern and mechanism of air pollution in developed coastal areas of China: From the perspective of urban agglomeration. PLoS One. 2020; 15(9):e0237863. PMC: 7521894. DOI: 10.1371/journal.pone.0237863. View

2.
Posch M, Aherne J, Moldan F, Evans C, Forsius M, Larssen T . Dynamic Modeling and Target Loads of Sulfur and Nitrogen for Surface Waters in Finland, Norway, Sweden, and the United Kingdom. Environ Sci Technol. 2019; 53(9):5062-5070. DOI: 10.1021/acs.est.8b06356. View

3.
Lu Y, Wang Y, Wang L, Zhang H, Zhou S, Bi F . Provincial analysis and zoning of atmospheric pollution in China from the atmospheric transmission and the trade transfer perspective. J Environ Manage. 2019; 249:109377. DOI: 10.1016/j.jenvman.2019.109377. View

4.
Li H, Zhang M, Li C, Li M . Study on the spatial correlation structure and synergistic governance development of the haze emission in China. Environ Sci Pollut Res Int. 2019; 26(12):12136-12149. DOI: 10.1007/s11356-019-04682-5. View

5.
Lasko K, Vadrevu K, Nguyen T . Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets. PLoS One. 2018; 13(5):e0196629. PMC: 5940215. DOI: 10.1371/journal.pone.0196629. View