Understanding Wildfire Emissions: From Composition to Variability, and their Link to Fire Characteristics

Wildfires emit a complex mixture of trace gases and aerosols that significantly impact air quality, climate, and atmospheric chemistry. Key trace gases include carbon dioxide (CO₂), carbon monoxide (CO), nitric oxide (NO), methane (CH₄), and volatile organic compounds (VOCs). Wildfire-generated aerosols predominantly consist of organic carbon (OC), black carbon (BC), and secondary organic aerosols (SOA). Over recent decades, the frequency and intensity of wildfires, particularly in the western United States, have risen due to warmer temperatures and prolonged periods of drought. This trend has led to increased fire activity and smoke emissions, causing wildfires to be a growing contributor to regional and global aerosol forcing, in turn affecting the Earth's radiation budget and climate system. However, substantial uncertainties remain in estimating the composition and quantity of wildfire emissions.

Large variability in biomass burning aerosol estimates across different fire emission inventories poses challenges for accurate air quality and climate impact assessments. To address these challenges, we leverage observational data from the FIREX-AQ and WE-CAN campaigns to investigate how wildfire characteristics such as individual fire size, fire radiative power, and fuel composition influence the chemical composition of wildfire emissions, particularly VOCs. We then develop and apply an artificial neural network in tandem with dimensionality reduction methods to estimate smoke chemistry utilizing fire characteristics. Our machine learning model's results are compared with existing observations and current fire emission inventories to improve our understanding of wildfire emissions and their impacts.