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<p>Indoor air quality (IAQ) in semi-enclosed subway environments poses critical public health concerns. This study investigates IAQ in the Shanghai subway system through year-round monitoring of PM<sub>2.5</sub> and PM<sub>10</sub> across representative stations. Concentrations of particulate matter (PM) were markedly higher indoors, peaking during winter weekday morning rush hours, with indoor/outdoor ratios exceeding unity. An interpretable machine learning model was developed to elucidate key factors affecting subway IAQ, identifying platform screen door design and train frequency as dominant influencing factors. By integrating the model with network-wide operational data and passenger boarding records, we quantified city-scale PM levels and commuter exposure. Further analysis incorporating point-of-interest data revealed that stations near residential zones exhibited the highest exposure, while commercial density intensified pollution. These findings provide a system-level assessment of PM exposure within urban rail transit and highlight the need for targeted ventilation and operational strategies to enhance IAQ and safeguard commuter health.</p>

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A citywide spatiotemporal perspective of particulate matter concentration on underground subway platforms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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  • Tianchen Qin,
  • Ran Tu,
  • An Wang,
  • Jinyitao Wang,
  • Tianyuan Li,
  • Suyang Xu,
  • Xinke Li,
  • Qiuzi Chen,
  • Jihye Kim,
  • Caiqing Yan,
  • Liubing Huang,
  • Jialiang Feng,
  • Shunyao Wang

摘要

Indoor air quality (IAQ) in semi-enclosed subway environments poses critical public health concerns. This study investigates IAQ in the Shanghai subway system through year-round monitoring of PM2.5 and PM10 across representative stations. Concentrations of particulate matter (PM) were markedly higher indoors, peaking during winter weekday morning rush hours, with indoor/outdoor ratios exceeding unity. An interpretable machine learning model was developed to elucidate key factors affecting subway IAQ, identifying platform screen door design and train frequency as dominant influencing factors. By integrating the model with network-wide operational data and passenger boarding records, we quantified city-scale PM levels and commuter exposure. Further analysis incorporating point-of-interest data revealed that stations near residential zones exhibited the highest exposure, while commercial density intensified pollution. These findings provide a system-level assessment of PM exposure within urban rail transit and highlight the need for targeted ventilation and operational strategies to enhance IAQ and safeguard commuter health.