Abstract
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.
Natalie Ho et al. (2026). Beyond spatiotemporal modeling: a review of applications of machine learning for traffic-related air pollution toward non-exhaust emissions. npj Clean Air, 2(1). https://doi.org/10.1038/s44407-026-00078-1