Advancing sectoral emission estimates using TEMPO and LEO satellite observations (Invited Presentation)

Accurate quantification of air pollutant emissions from each human activity is critical for the planning and verification of emission reduction efforts. Top-down estimates with satellite data provide important information on the sources of air pollutants. We apply a newly developed sector-based inversion method based on 4D-Var to quantify NOx, SO2, and CO emissions from various activities, including transportation, industry, residential, aviation, shipping, energy, and biomass burning over the US. We incorporate TEMPO NO2, TROPOMI NO2 and SO2, and MOPITT CO observations and leverage the co-emission of these gases to identify the source sectors. The framework improves emission estimates at the process level by optimizing emission factors and activity rates without relying on explicit knowledge of their values and resolves discrepancies with bottom-up inventories at the sector level. Previous applications of this approach show reduced normalized mean bias (NMB) by 7% (NO2)–15% (SO2) and normalized mean square error (NMSE) by 8% (SO2)–9% (NO2) compared to a species-based inversion when evaluated with surface measurements. We will evaluate the sectoral profile and temporal variations of our posterior emissions with bottom-up inventories (e.g., EPA NEI and CEDS) and EPA AQS measurements.