Abstract
China has extensively implemented the Joint Prevention and Control of Atmospheric Pollution (JPCAP) strategy to address regional air pollution challenges; however, the lack of accurate scope and regional priority control sequence has resulted in reduced pollution control efficiency and high air quality improvement costs. We proposed the following new solutions to these problems: (i) constructing inter-regional network correlation models based on three key network topology parameters and (ii) subdividing large regions into sub-regions using the Girvan Newman (GN) algorithm. To test these methods, we applied them to a case study of China's provincial-level JPCAP areas using hourly air resource endowment (ARE) data obtained from 2013 to 2017. We found that the modularity (Q) values of ARE network partitioning in different seasons were approximately 0.5, indicating the well-structured association of the network constructed in this study. Seven areas were identified based on annual values, while eight, five, four, and six areas were classified for spring, summer, autumn, and winter, respectively. The strength of the interconnectedness of the provinces in the region, as measured by the clustering coefficient (C˜) value, is ordered as follows: Autumn > winter > spring > summer. For a specific JPCAP region, the priority control level can be dynamically adjusted based on the Pearson correlation coefficient (r-value) between the target city or province and the surrounding region. The regional division results, priority control sequences, and key control seasons identified in this study can provide technical and methodological reference for delineating and optimizing China's JPCAP policy.
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