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<p>Despite recent progress in controlling PM<sub>2.5</sub> pollution, identifying its spatiotemporal distribution characteristics, key pollution sources, and regional transmission patterns remains a critical task in addressing PM<sub>2.5</sub> pollution in Jiangxi Province. However, accurately identifying the seasonal community divisions, core contribution sources, and transmission hub cities for PM<sub>2.5</sub> pollution in this region still poses significant challenges. This study employs the optimized community detection method of Community Detection by Motif-aware Label Propagation (MWLP) to explore the seasonal community segmentation of PM<sub>2.5</sub> pollution in Jiangxi Province and the key pollution sources within these communities. Additionally, the Hungarian algorithm is used to identify the hub cities involved in the PM<sub>2.5</sub> pollution transmission process. The results show that the PM<sub>2.5</sub> pollution network in Jiangxi Province is segmented into 4, 6, 4, and 3 communities during spring, summer, autumn, and winter, respectively; the key pollution sources for each season are Nanchang, Xinyu, Ganzhou, and Shangrao; Pingxiang, Yingtan, Ganzhou, Shangrao, Jiujiang, and Jingdezhen; Jingdezhen, Pingxiang, Ganzhou, and Nanchang; and Pingxiang, Ganzhou, and Nanchang, respectively. Additionally, the core cities driving long-distance PM<sub>2.5</sub> transmission are primarily concentrated in central Jiangxi Province, while cities with lesser transmission roles are distributed along the periphery. Further analysis indicates that PM<sub>2.5</sub> pollution in Jiangxi Province exhibits significant spatial variability and seasonal changes, with clear spatiotemporal correlations between polluted cities.</p>

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Characteristics of Spatial and Temporal Variations of PM2.5 Pollution Based on Complex Network

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Hao Huang,
  • Yuanhao Hu,
  • Zuhan Liu,
  • Yang Liu,
  • Lili Wang

摘要

Despite recent progress in controlling PM2.5 pollution, identifying its spatiotemporal distribution characteristics, key pollution sources, and regional transmission patterns remains a critical task in addressing PM2.5 pollution in Jiangxi Province. However, accurately identifying the seasonal community divisions, core contribution sources, and transmission hub cities for PM2.5 pollution in this region still poses significant challenges. This study employs the optimized community detection method of Community Detection by Motif-aware Label Propagation (MWLP) to explore the seasonal community segmentation of PM2.5 pollution in Jiangxi Province and the key pollution sources within these communities. Additionally, the Hungarian algorithm is used to identify the hub cities involved in the PM2.5 pollution transmission process. The results show that the PM2.5 pollution network in Jiangxi Province is segmented into 4, 6, 4, and 3 communities during spring, summer, autumn, and winter, respectively; the key pollution sources for each season are Nanchang, Xinyu, Ganzhou, and Shangrao; Pingxiang, Yingtan, Ganzhou, Shangrao, Jiujiang, and Jingdezhen; Jingdezhen, Pingxiang, Ganzhou, and Nanchang; and Pingxiang, Ganzhou, and Nanchang, respectively. Additionally, the core cities driving long-distance PM2.5 transmission are primarily concentrated in central Jiangxi Province, while cities with lesser transmission roles are distributed along the periphery. Further analysis indicates that PM2.5 pollution in Jiangxi Province exhibits significant spatial variability and seasonal changes, with clear spatiotemporal correlations between polluted cities.