Explainable geospatial-artificial intelligence models for the estimation of PM_2.5 concentration variation during commuting rush hours in Taiwan

PM_2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM_2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM_2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM_2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.–9 a.m.) and dusk (4 p.m.–6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM_2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM_2.5 values, SO₂ concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM_2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.