Assessing Air Quality in Tehran: Explainable Artificial Intelligence for Megacities

Urban air pollution poses a persistent threat to public health and environmental sustainability in megacities like Tehran, where complex emission sources and topographical constraints amplify exposure risks. This study presents a transparent, interpretable, and highly accurate machine learning framework for district-level air quality assessment, leveraging a decade of high-resolution hourly monitoring data (April 2015–April 2025) across six socio-spatially stratified districts. Using an ensemble of tree-based models—XGBoost, CatBoost, and LightGBM—the framework forecasts concentrations of six key pollutants (PM₂.₅, PM₁₀, NO₂, CO, SO₂, O₃) with exceptional fidelity (MAE: 3.0–9.0 µg/m³; R²: 0.65–0.91). Through SHAP-based explainable Artificial Intelligence, the model identifies seasonally dynamic, district-specific pollution drivers—such as CO/NO₂ dominance in traffic corridors during winter and O₃-driven photochemical regimes in receptor basins during summer—revealing signatures that align with Tehran’s known emission geography. By converting pollutant forecasts into U.S. EPA Air Quality Index (AQI) categories, the framework provides spatially resolved estimates of population-level health risk exposure—from Moderate to Hazardous conditions—enabling targeted public health interventions. Designed for scalability and immediate policy utility, this work delivers a reproducible blueprint for data-driven air quality governance. By demonstrating that high-impact forecasting is achievable even in data-constrained settings, the study offers a globally transferable model for healthier, more resilient megacities—particularly in low- and middle-income regions where monitoring infrastructure is limited but governance needs are urgent.