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PM2.5 pollution control in the Guangdong–Hong Kong–Macao Greater Bay Area requires a clearer understanding of how PM2.5-related linkages are organized across closely connected cities and jurisdictions. This study develops a CCM-based directional-network framework using PM2.5 pollution data from nine cities and two special administrative regions in the Greater Bay Area. Convergent cross mapping is first applied to identify nonlinear directional associations among cities, based on which an intercity PM2.5 pollution network is constructed. Social network analysis and motif analysis are then used to reveal the network’s macro-level connectivity and micro-level interaction patterns. An exponential random graph model is further introduced to identify the natural and socioeconomic factors associated with the emergence of intercity PM2.5 pollution linkages. The results show that PM2.5 levels in the Greater Bay Area generally declined across the selected years, with high-value areas becoming more localized, while the CCM results revealed heterogeneous nonlinear directional linkages among cities. The strongest CCM linkage was observed from Foshan to Guangzhou (0.8978), whereas the weakest linkage was observed from Macao SAR to Zhaoqing (0.4993). The results derived from social network analysis indicate an east–west cross-regional linkage pattern, with most cities serving as bridging nodes in the network. Natural factors, including temperature and precipitation, as well as socioeconomic factors, including economic development and population density, were significantly associated with the formation of intercity PM2.5 pollution linkages. These findings highlight the need for an integrated governance approach that combines source-oriented control, coordinated management of intercity PM2.5-related linkages, and public participation to improve collaborative PM2.5 pollution management in the Greater Bay Area.
He et al. (Wed,) studied this question.