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Wildfire impacts on air quality and climate are expected to be exacerbatedby climate change with the most pronounced impacts in the boreal biome.Despite the large geographic coverage, there is limited information onboreal forest wildfire emissions, particularly for organic compounds, whichare critical inputs for air quality model predictions of downwind impacts.In this study, airborne measurements of 193 compounds from 15 instruments,including 173 non-methane organics compounds (NMOG), were used to providethe most detailed characterization, to date, of boreal forest wildfireemissions. Highly speciated measurements showed a large diversity ofchemical classes highlighting the complexity of emissions. Usingmeasurements of the total NMOG carbon (NMOGT), the ΣNMOG wasfound to be 50 % ± 3 % to 53 % ± 3 % of NMOGT, of which, theintermediate- and semi-volatile organic compounds (I/SVOCs) were estimatedto account for 7 % to 10 %. These estimates of I/SVOC emission factorsexpand the volatility range of NMOG typically reported. Despite extensivespeciation, a substantial portion of NMOGT remained unidentified(47 % ± 15 % to 50 % ± 15 %), with expected contributions from morehighly-functionalized VOCs and I/SVOCs. The emission factors derived in thisstudy improve wildfire chemical speciation profiles and are especiallyrelevant for air quality modelling of boreal forest wildfires. Theseaircraft-derived emission estimates were further linked with those derivedfrom satellite observations demonstrating their combined value in assessingvariability in modelled emissions. These results contribute to theverification and improvement of models that are essential for reliablepredictions of near-source and downwind pollution resulting from borealforest wildfires.

 

Abstract. Smoke from wildfires is a significant source of air pollution, which can adversely impact air quality and ecosystems downwind. With the recently increasing intensity and severity of wildfires, the threat to air quality is expected to increase. Satellite-derived biomass burning emissions can fill in gaps in the absence of aircraft or ground-based measurement campaigns and can help improve the online calculation of biomass burning emissions as well as the biomass burning emissions inventories that feed air quality models. This study focuses on satellite-derived NOx emissions using the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI) NO2 dataset. Advancements and improvements to the satellite-based determination of forest fire NOx emissions are discussed, including information on plume height and effects of aerosol scattering and absorption on the satellite-retrieved vertical column densities. Two common top-down emission estimation methods, (1) an exponentially modified Gaussian (EMG) and (2) a flux method, are applied to synthetic data to determine the accuracy and the sensitivity to different parameters, including wind fields, satellite sampling, noise, lifetime, and plume spread. These tests show that emissions can be accurately estimated from single TROPOMI overpasses.The effect of smoke aerosols on TROPOMI NO2 columns (via air mass factors, AMFs) is estimated, and these satellite columns and emission estimates are compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America. Our results indicate that applying an explicit aerosol correction to the TROPOMI NO2 columns improves the agreement with the aircraft observations (by about 10 %–25 %). The aircraft- and satellite-derived emissions are in good agreement within the uncertainties. Both top-down emissions methods work well; however, the EMG method seems to output more consistent results and has better agreement with the aircraft-derived emissions. Assuming a Gaussian plume shape for various biomass burning plumes, we estimate an average NOx e-folding time of 2 ±1 h from TROPOMI observations. Based on chemistry transport model simulations and aircraft observations, the net emissions of NOx are 1.3 to 1.5 times greater than the satellite-derived NO2 emissions. A correction factor of 1.3 to 1.5 should thus be used to infer net NOx emissions from the satellite retrievals of NO2.