Satellite Retrieval of Tropospheric NO₂ under Fire Conditions

Abstract. Open fires, including wildfires and planned fires, emit large amounts of nitrogen oxides (NO_x = NO + NO₂) into the atmosphere, and their environmental impacts are becoming increasingly severe under climate change. Satellite-based retrievals of tropospheric nitrogen dioxide (NO₂) vertical column densities (VCDs) provide broad spatial coverage and continuous monitoring capabilities for assessing NO_x pollution under fire conditions. However, due to the lack of explicit fire-related a priori information in current satellite NO₂ retrieval algorithms, the resulting data products exhibit large uncertainties under fire conditions. Here, we use the Peking University OMI NO₂ (POMINO) algorithm to investigate the impact of including fire-related a priori information on the tropospheric NO₂ retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) sensor. We conduct sensitivity experiments by including and excluding fire-related a priori information in the NO₂ retrieval process over the western United States in September 2020, a period of intense wildfire activities. The a priori information is taken from GEOS-Chem simulations with and without fire emissions, as well as with different fire emission injection heights. In addition, NO₂ retrieval based on a priori information from the Global Earth Observing System Composition Forecast (GEOS-CF) data is also conducted for comparison. Our results show that including fire-related a priori information in the retrieval significantly increases tropospheric NO₂ VCDs, primarily due to enhanced NO₂ concentrations in the lower layers of the a priori NO₂ profile. Retrieved tropospheric NO₂ VCDs increase by up to 100 % at locations greatly impacted by fires and by about 80 % in surrounding areas. Differences in fire emission injection height lead to up to ~30 % variations in the retrieved VCDs in fire regions, indicating a secondary but non-negligible effect. Validation against EPA surface NO₂ measurements shows improved agreement when fire-related a priori information is included, particularly with lower biases (-12 % versus -44 %) over fire-affected regions. These results highlight the importance of incorporating fire-related a priori information in satellite NO₂ retrievals to obtain more accurate data for air quality assessments under fire conditions.