- Mapping air pollution across space remains challenging, even in countries with dense monitoring networks. In Germany, pollutant levels can change over short distances because of traffic, land use, and meteorological conditions, while national assessments often rely on unevenly distributed monitoring stations. This study examines how openly available satellite observations and reanalysis data can support annual modelling of NO2 and PM2.5 across Germany from 2019 to 2024. Sentinel-5P (NO2 and CO), MODIS Normalized Difference Vegetation Index (NDVI) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD), and ERA5-Land meteorological variables were combined with EuroAirnet observations, and seven machine-learning algorithms were evaluated. Model performance was assessed using random cross-validation, an independent test set, and spatial cross-validation, while SHAP (Shapley Additive Explanations) values were used to interpret predictor contributions.Mapping air pollution across space remains challenging, even in countries with dense monitoring networks. In Germany, pollutant levels can change over short distances because of traffic, land use, and meteorological conditions, while national assessments often rely on unevenly distributed monitoring stations. This study examines how openly available satellite observations and reanalysis data can support annual modelling of NO2 and PM2.5 across Germany from 2019 to 2024. Sentinel-5P (NO2 and CO), MODIS Normalized Difference Vegetation Index (NDVI) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD), and ERA5-Land meteorological variables were combined with EuroAirnet observations, and seven machine-learning algorithms were evaluated. Model performance was assessed using random cross-validation, an independent test set, and spatial cross-validation, while SHAP (Shapley Additive Explanations) values were used to interpret predictor contributions. For NO2, Random Forest achieved the highest accuracy (R2 = 0.68; RMSE = 5.87 μg m−3), with SHAP analysis identifying tropospheric NO2 and vegetation structure (NDVI) as the most influential predictors. PM2.5 proved more difficult to model at the annual scale: Gradient Boosting performed best (R2 = 0.50; RMSE = 11.53 μg m−3), with surface pressure, NDVI, and co-emitted gases emerging as key variables, while MAIAC AOD contributed little independent information when aggregated annually. A sensitivity analysis showed that including a static road-density layer improved NO2 estimates near monitoring sites but provided limited gains under spatial validation. The resulting concentration maps reproduce the main national patterns observed in the monitoring network, showing a decline in NO2 and more regionally variable behaviour for PM2.5. Although annual predictors cannot capture short-term variability or highly localised emission sources, the study provides a transparent and reproducible framework for national-scale air-quality assessment based entirely on open global datasets and highlights the potential to integrate additional Earth observation and climate reanalysis products in future research.…
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