Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

• Published in: Environmental pollution (1987) Vol. 349; p. 123974
• Main Authors: Wong, Pei-Yi, Su, Huey-Jen, Candice Lung, Shih-Chun, Liu, Wan-Yu, Tseng, Hsiao-Ting, Adamkiewicz, Gary, Wu, Chih-Da
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2024
• Subjects: Commuting periods; Dusk; Geo-AI model; Morning; PM2.5; Dusk; Geo-AI model; Commuting periods; Morning; PM2.5
• ISSN: 0269-7491; 1873-6424
• DOI: 10.1016/j.envpol.2024.123974

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 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 PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 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, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations. [Display omitted] •Rush-hour exposure to PM2.5 is high, but the spatiality is less understood.•PM2.5 variations during morning and dusk rush hours are captured by Geo-AI models.•The Geo-AI model performance for morning/dusk is 0.95 and 0.93, respectively.•PM2.5 is higher in southern Taiwan and urban areas in the morning.•Low-exposure commuting routes could be identified by the rush-hour Geo-AI models.