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<p>Traffic-related air pollution (TRAP), increasingly shaped by non-exhaust emissions, remains a major urban health concern. This review provides a structured synthesis of over 50 studies (2020–2024) applying machine learning (ML) to TRAP, focusing on spatial modeling, contributing factor identification, non-exhaust emission characterization, and source apportionment. Key challenges include data sparsity, inconsistent features, and limited interpretability. Advancing ML integration and transparency is essential for improving exposure assessment and environmental health.</p>

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Beyond spatiotemporal modeling: a review of applications of machine learning for traffic-related air pollution toward non-exhaust emissions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月14日

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  • Natalie Ho,
  • Shashwat Dhayade,
  • Yue Zhang,
  • Yidan Zhang,
  • Jing Li,
  • Yunyao Li,
  • Yike Shen,
  • Yifang Zhu,
  • Feng Gao

摘要

Traffic-related air pollution (TRAP), increasingly shaped by non-exhaust emissions, remains a major urban health concern. This review provides a structured synthesis of over 50 studies (2020–2024) applying machine learning (ML) to TRAP, focusing on spatial modeling, contributing factor identification, non-exhaust emission characterization, and source apportionment. Key challenges include data sparsity, inconsistent features, and limited interpretability. Advancing ML integration and transparency is essential for improving exposure assessment and environmental health.