Evaluating Secondary Organic Aerosol Formation from Wildfire Emissions

The frequency and intensity of wildfires have increased globally, significantly impacting air quality and climate. Emitted gas-phase organic compounds and those evaporated from intermediate and semi-volatile primary organic aerosols (POA) can undergo photochemical aging to form secondary organic aerosols (SOA). Factors such as dilution, temperature variations during smoke transport, background OA concentration, and ambient temperature significantly influence SOA formation from biomass burning origin aerosol (BBOA), which is the primary source of 87% of SOA from wildfires.

Although the volatility distribution of biomass burning emissions has been studied in various ground-based field and laboratory settings, its applicability to real wildfire smoke at different atmospheric levels has not been thoroughly evaluated. Recent studies have revealed discrepancies between laboratory experiments and field measurements, potentially due to differences in dilution and gas-particle partitioning in wildfire plumes. However, questions remain about how these discrepancies affect SOA formation at different altitudes and over extended aging periods.

New measurements have quantified the volatility of bulk BBOA and key molecular components of biomass burning particulate matter, providing critical constraints on BBOA volatility and demonstrating its dependence on altitude. In this study, KinSim box model is used to evaluate the impact of different volatility bins on SOA formation in wildfire plumes and investigate the effects of long-term aging. The SOA formation based on different bin settings after 100 hours of aging was compared to data from the AEROMMA 2023 aircraft campaign to validate the model's performance.

This approach comprehensively evaluates SOA formation using long-term aging scenarios and altitude-dependent volatility measurements, offering new insights into the mechanisms driving SOA formation in wildfire plumes. The results highlight the importance of accurately representing atmospheric conditions in global models to improve predictions of wildfire impacts on air quality and climate