Characteristics of the PM2.5 Pollution Association Network and Its Meteorological Drivers in the Beijing-Tianjin-Hebei Region: A Complex Network Perspective

Air pollution remains a major global environmental challenge, posing serious threats to ecosystems and human health. PM2.5 pollution is particularly severe in the Beijing-Tianjin-Hebei (BTH) region and is characterized by strong regional associations. In this study, a time-lagged PM2.5 association network was constructed for the BTH region during 2015-2023 based on complex network theory. Seasonal and interannual variations in network structure were examined, key nodes, major association pathways, and dynamic community structures were identified, and the influence of meteorological conditions on network topology under different winter weather types was further investigated by combining network analysis with a self-organizing map (SOM) approach. The results show that the PM2.5 association network in the BTH region exhibits pronounced seasonal and interannual variability. Network density and clustering coefficient are highest in winter, and overall connectivity increases over time. Langfang, Baoding, Tianjin, Beijing, and Tangshan are identified as key nodes in the network, and the major association pathways linked to these cities remain structurally robust across seasons and winter weather types. The PageRank hierarchy of the network remains broadly stable during the study period, although the distribution of node influence shows a tendency to evolve from concentration toward relative homogenization. The community structure exhibits a clear south-north contrast, with denser internal connections in southern communities and relatively weaker connections in northern communities, while some cities show high stability in community affiliation. Multi-threshold sensitivity analyses further confirm the robustness of key-node identification and community partitioning. Network topology also differs substantially among winter weather types: high-stagnation conditions correspond to stronger connectivity and clustering, suggesting enhanced regional pollutant retention and mixing, whereas strong-wind conditions are associated with sparser networks and greater community separation. Overall, within the winter SOM analysis, wind speed and the stagnation index are identified as the key meteorological factors associated with network connectivity and the strength of regional PM2.5 associations. These findings provide a transferable analytical framework for investigating regional PM2.5 association networks in other areas and offer a scientific basis for season-specific, region-specific, and weatherspecific air pollution prevention and control in the BTH urban agglomeration.