A novel near real-time approach to forecast high resolution NO2 concentrations in southeastern China by incorporating multi-source satellite data

Nitrogen dioxide, designated as NO₂, is a critical yet harmful trace gas in Earth's atmospheric composition. NO₂ poses significant threats to human health, ecosystems, and agricultural productivity. Accurate NO₂ forecasts at high spatial resolution enable authorities to safeguard public health through targeted mitigation efforts. Conventional NO₂ forecasting approaches, such as time series analysis and chemical transport models (CTMs), often suffer from significant uncertainty or lack fine spatial details. This study presents a novel NO₂ forecast model that combines Random Forest techniques with multi-source satellite data and NASA's Goddard Earth Observing System "Composing Forecasting" (GEOS-CF) product to provide spatially continuous, five-day forecasts of NO₂ concentrations at 1 km resolution across southeastern China. The superior capabilities of our forecast framework were confirmed through multiple validation methods, consistently surpassing the performance of the original GEOS-CF model. Notably, the new framework achieved substantial error reductions and resolution enhancements in GEOS-CF forecasts, outperforming the initial product across all validation metrics. The developed model facilitates the generation of NO₂ forecasts characterized by near-real-time delivery, great precision, and high spatial resolution.