Reconciling the total carbon budget for boreal forest wildfire emissions using airborne observations

Wildfire impacts on air quality and climate are expected to be exacerbated by climate change with the most pronounced impacts in the boreal biome. Despite the large geographic coverage, there is limited information on boreal forest wildfire emissions, particularly for organic compounds, which are 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 provide the most detailed characterization, to date, of boreal forest wildfire emissions. Highly speciated measurements showed a large diversity of chemical classes highlighting the complexity of emissions. Using measurements of the total NMOG carbon (NMOG(T)), the Sigma NMOG was found to be 50 % +/- 3 % to 53 % +/- 3 % of NMOG(T), of which, the intermediate- and semi-volatile organic compounds (I/S VOCs) were estimated to account for 7 % to 10 %. These estimates of I/SVOC emission factors expand the volatility range of NMOG typically reported. Despite extensive speciation, a substantial portion of NMOG(T) remained unidentified (47 % +/- 15 % to 50 % +/- 15 %), with expected contributions from more highly-functionalized VOCs and I/SVOCs. The emission factors derived in this study improve wildfire chemical speciation profiles and are especially relevant for air quality modelling of boreal forest wildfires. These aircraft-derived emission estimates were further linked with those derived from satellite observations demonstrating their combined value in assessing variability in modelled emissions. These results contribute to the verification and improvement of models that are essential for reliable predictions of near-source and downwind pollution resulting from boreal forest wildfires.

Automated summary

This study provides the most detailed characterization to date of emissions from boreal forest wildfires, highlighting the complexity of the emissions and their importance for air quality modeling. The results improve the understanding of wildfire emissions and contribute to the verification and improvement of models that predict pollution from such wildfires.