Last-mile transit PM2.5 under ultra low emission zone: a network-based exposure assessment

Evaluating the hyper-local effectiveness of transport policies like the Ultra Low Emission Zone (ULEZ) on commuter air pollution exposure is critical for urban health yet remains challenging. Existing studies often lack high-resolution pollution data integrated with realistic pedestrian pathways, which constrains precise exposure evaluation. This study proposes an innovative framework that combines machine learning-based spatial–temporal street-level air pollution (SLAP) estimation with a network-based metric, the Public Transit-oriented Exposure Value (PTEV), to quantify last-mile cumulative PM2.5 exposure within a station's walkable service areas. Applying this framework to pre- and post-ULEZ periods in London, we find that while the policy reduced overall exposure, a central transit hub persisted as a high-risk hotspot, revealing significant spatial heterogeneity in policy impact. Our research contributes a replicable, fine-scale methodology for assessing transport-environment interactions, providing critical evidence for targeted planning to mitigate commuter health risks within complex urban transport systems.