Geo-AI-Based Assessment of 3-D Ultrafine Particle Distribution and Population Exposure: A Digital Twin Approach in Taichung, Taiwan

This study developed a Geospatial Artificial Intelligence (Geo-AI)–based framework to estimate and visualize the three-dimensional (3-D) distribution of ultrafine particles (PM₀.₁) and associated population exposure across Taichung City, Taiwan. An unmanned aerial vehicle (UAV) platform equipped with a P-Trak Ultrafine Particle Counter was deployed to collect high-resolution 3-D PM₀.₁ concentration data across varying altitudes and land-use types. These 3-D PM₀.₁ data were integrated with multi-source geospatial datasets, including 3-D building models, meteorological variables, and emission inventories. The SHapley Additive exPlanations (SHAP) method was then employed to identify key predictors for machine-learning modeling. The optimized model was applied to map the continuous 3-D pollution field and used to estimate and visualize population exposure for each floor level. The resulting Geo-AI model achieved strong predictive performance, with R² values of 0.95 for training and above 0.85 for validation, demonstrating high robustness and predictive capability. Visualizations reveal a nonlinear vertical structure of PM₀.₁ in 3-D space, characterized by near-ground peaks in industrial and traffic zones alongside persistent localized hotspots at mid-to-high elevations. Population exposure assessments highlighted that, despite lower concentrations at higher elevations, the total exposure burden remains significant in mid-to-high-rise residential buildings due to higher population density. This research presents an advanced framework for assessing 3-D air pollution exposure risks in dense urban environments, demonstrating the potential of Digital Twin technologies in supporting air quality management and public health decision-making.