Associations of street morphology and local wind environment with annual cumulative ambient PM2.5 exceedance: Evidence from Wuhan, China

Abstract

Streets are important urban microenvironments where residents encounter ambient PM_2.5, while their pollution conditions are shaped by coupled spatial and aerodynamic processes. However, how street morphology, grey–green elements, and local ventilation are jointly associated with cumulative PM_2.5 exceedance remains insufficiently understood. Using 6785 streets in Wuhan, we classified streets into five typologies and developed a multidimensional indicator system integrating street geometry, grey–green elements, and the local wind environment. Annual cumulative excess exposure (ACEE) was defined as an ambient PM_2.5 exceedance indicator. Interpretable machine learning (CatBoost-SHAP) and partial least squares path modeling were combined to quantify type-specific predictive contributions, nonlinear response patterns, and direct and indirect pathways. Open street canyons accounted for more than 75% of the sampled streets, and the models explained over 70% of ACEE variation on average. Street geometry and wind environment jointly contributed more than 50%, although their relative importance varied across street types. In enclosed canyons, ACEE was mainly associated with the Building View Index and Sky View Factor, whereas wind-related indicators were more influential in open canyons, with street–wind angle (WA) and street wind speed (WS) contributing 25.28% on average. Model-derived WS breakpoints occurred at approximately 0.33 m/s (95% CI: 0.31-0.44 m/s) in enclosed canyons and 1.19 m/s (95% CI: 0.78-1.31 m/s) in open canyons. Green-space associations were context-dependent: higher visible greenery was positively associated with ACEE in enclosed canyons, whereas canopy ratio showed more consistent negative associations. These findings provide context-specific evidence for typology-sensitive street planning and air-quality management.