Estimating Hourly Surface NO₂ Across the Contiguous United States Using TEMPO Observations and Machine Learning

Abstract. Ground-level nitrogen dioxide (NO₂) poses significant health risks, yet hourly concentrations have not been estimated due to the limited temporal sampling of polar-orbiting satellites. NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary instrument provides the first hourly tropospheric NO₂ observations over North America, creating new opportunities for estimating surface NO₂ throughout the daytime. We develop XGBoost models using TEMPO tropospheric NO₂, ERA5 meteorology, road density, impervious surface percentage (ISP), and temporal features to predict hourly surface NO₂ at 474 EPA stations across the contiguous United States from August 2023 through June 2025, and map concentrations at TEMPO’s L3 resolution. Separate TEMPO (daylight) and no-TEMPO (nighttime/imputation) models achieve test R² = 0.78 (RMSE = 3.33 ppb) and R² = 0.76 (RMSE = 4.36 ppb), respectively. TEMPO column NO₂ is the strongest predictor by gain, while ISP dominates spatial prediction and boundary layer height is the most influential meteorological predictor. Importantly, TEMPO provides complementary spatial information beyond meteorological and temporal predictors, increasing R² across all spatial cross-validation folds by an average of 17% and improving performance during data-scarce periods. Spatial cross-validation (R² = 0.48) further highlights opportunities to improve predictions at unseen locations through higher-resolution spatial inputs, while extending satellite coverage into the evening could improve representation of the evening NO₂ peak. To our knowledge, this is the first study to predict hourly surface NO₂ across CONUS using geostationary satellite data, demonstrating the potential of TEMPO to support acute exposure assessments at previously infeasible temporal resolutions.