Urban air pollution from road traffic remains a major public health concern, with its spatial variability at neighbourhood scales strongly influenced by urban morphology. This study investigates how urban form affects the dispersion of traffic-related PM2.5 in four Italian cities (Lecce, Bari, Milan and Rome) representing diverse climatic and morphological contexts. Seasonal simulations were conducted using the ADMS-Roads dispersion model, integrating detailed road geometries, standardized traffic emissions, and city-level meteorological data for 2019–2021. Urban morphology was characterized at 100 m resolution using building plan area fraction (λp), street-canyon aspect ratio and mean building height derived from GIS analyses. Statistical analysis combined random forest regression with partial dependence plots and quantile regression to explore both average and distributional effects. Results reveal a generally negative association between λp and PM2.5 in Lecce, Milan, and Rome, particularly at higher concentration quantiles, suggesting that denser urban fabrics may mitigate extreme pollution episodes. Bari exhibits a weaker and more heterogeneous response, highlighting the influence of local wind regimes and traffic distribution. Wind speed and temperature consistently reduce PM2.5 across all cities, while street geometry effects are non-linear and season-dependent. These findings demonstrate the importance of considering urban morphology alongside traffic and meteorology when designing strategies to reduce exposure. Importantly, the methodological framework presented here, combining high-resolution dispersion modelling with interpretable machine-learning analyses, is transferable to other urban contexts, providing a robust approach to assess morphology–pollution interactions beyond the studied cities.
Metrangolo et al. (Wed,) studied this question.