Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-Use Regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess the complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving a CV R2 of 0.81 (95 % CI: 0.75–0.86) for the long-term PM2.5 prediction and 0.90 (95 % CI: 0.87–0.91) for a short-term one. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m–225 m) particles, while 21 % was associated with upper-level (825 m–945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan–22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec–24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.