Spatial correlation of PM_2.5 pollution in Beijing-Tianjin-Hebei and surrounding cities based on complex networks and their motif

In recent years, with the increase of pollution control and ecological environmental protection, the quality of the ecological environment in Beijing-Tianjin-Hebei region has continued to improve, but the problem of air pollution with PM_2.5 characteristics should not be underestimated. PM_2.5 data of 31 cities in Beijing-Tianjin-Hebei and surrounding areas from January 1, 2015 to November 30, 2022 were selected, combined with the gravity model and Pearson correlation index, to construct a spatial correlation network of PM_2.5 pollution in Beijing-Tianjin-Hebei and surrounding cities, and the overall characteristics of the network and the seasonal changes of the motif were analyzed statistically. The results showed that Xingtai, Shijiazhuang and Handan ranked top 3 in degree value, betweenness centrality and closeness centrality. They were at the core of the network and had strong spatial correlation with PM_2.5 pollution in multiple cities, which played an important role in controlling the spatial spillover of PM_2.5 pollution. The density and average degree of the four seasons correlation network of the motif were not different, but both were high. There was a strong spatial correlation between PM_2.5 among cities, and the network density was the highest in winter. Shijiazhuang, Xingtai and Hengshui played an important role in the four seasons correlation network of the motif. For the cities in the core position of the spatial correlation network of PM_2.5 pollution in Beijing-Tianjin-Hebei and surrounding cities, while strengthening their own PM_2.5 control, policies should be formulated to reduce the spatial spillover effect of PM_2.5 pollution on neighboring cities. According to the correlation of the motif in different seasons, corresponding collaborative governance policies should be formulated to strengthen the collaborative governance of urban PM_2.5 pollution of the motif, while weakening the spatial correlation and spillover effect of PM_2.5 pollution between them.