Identifying the control cities of O₃ Pollution using Complex networks

Abstract. In recent years, ozone (O3) pollution has been rapidly spreading, restricting further improvement of air quality in China. Investigating the interaction of O3 concentration and identifying their driven cities are important for the prevention and control of O3 pollution in China. However, the interaction between O3 pollution between cities and their driven cities has not yet been revealed. In this study, we fill this gap based on the integration of complex network methods, the Louvain community partitioning algorithm and the maximum matching network control theory. O3 network model exhibits a structured cluster framework, such as Northeast, North China, Sichuan and Chongqing, and Southeast coastal areas. And the driver nodes are mainly concentrated in the central region, while the non-driver nodes are mainly located in the coastal periphery. We also found that the proportion of driven nodes exhibits a positive relation with the threshold. In addition, the coincidence degree of the driven node is related to the choose of threshold. A closer threshold value corresponds to a higher coincidence ratio of the driven nodes. The correlation of driven nodes predicting non-driven nodes is stronger than non-driven nodes predicting driven nodes, suggesting that driven nodes have more influence in the O3 network than non-driven nodes. The results provide scientific guidance for national O3 pollution prevention and regional synergy formatting. Furthermore, the introduced network-based approaches offer a mythological framework for the study of air pollution in key cities and clusters.