Predicting Fine-Scale Daily NO₂ for 2005-2016 Incorporating OMI Satellite Data Across Switzerland

Nitrogen dioxide (NO₂) remains an important traffic-related pollutant associated with both short- A nd long-term health effects. We aim to model daily average NO₂ concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining â58% (R² range, 0.56-0.64) of the variation in measured NO₂ concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained â73% (R² range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO₂ across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO₂. The predicted NO₂ concentrations will be made available to facilitate health research in Switzerland.