Machine Learning-Based Ground-Level NO2 Estimation in Istanbul: A Comparative Analysis of Sentinel-5P and GEOS-CF

Nitrogen dioxide (NO2) poses severe risks to human health and the environment, especially in densely populated megacities. Ground-based air quality monitoring stations provide high-temporal-resolution data but are spatially limited, while satellite observations offer broad coverage but measure column densities rather than surface concentrations. To overcome these limitations, this study integrates ground-based observations with satellite-derived NO2 from Sentinel-5P TROPOMI and GEOS-CF products to estimate ground-level NO2 in Istanbul using machine learning (ML) approaches. Three ML algorithms (RF, XGB, and CB) were tested on two datasets spanning 2019–2024 at ~1 km resolution, incorporating 20 features, including topographic, meteorological, environmental, and demographic variables. Among models, CB achieved the best performance (R: 0.686, RMSE: 16.23 µg/m3, and MAE: 11.75 µg/m3 in the test dataset) with the Sentinel-5P dataset, successfully capturing spatial and seasonal variations in ground-level NO2 both quantitatively and qualitatively. SHAP analysis revealed that regarding satellite-derived NO2, anthropogenic indicators such as population density, road length, and digital elevation model were the most influential features, while meteorological factors contributed secondarily. Despite the lower spatial resolution of GEOS-CF data, both Sentinel-5P and GEOS-CF datasets supported reliable model outputs. This study provides the first ML-based ground-level NO2 estimation framework for the Istanbul Metropolitan City.