Air quality impacts of observationally constrained biomass burning heat flux inputs

Biomass burning is a major contributor to ambient air pollution worldwide, and the accurate characterization of biomass burning plume behavior is an important consideration for air quality models that attempt to reproduce these emissions. Smoke plume injection height, or the vertical level into which the combustion emissions are released, is an important consideration for determining plume behavior, transport, and eventual impacts. This injection height is dependent on several fire properties, each with estimates and uncertainties in terms of historical fire emissions inventories. One such property is the fire heat flux, a fire property metric sometimes used to predict and parameterize plume injection heights in current chemical transport models. Although important for plume behavior, fire heat flux is difficult to predict and parameterize efficiently, and is therefore often held to fixed, constant values in these models, leading to potential model biases relative to real world conditions. In this study we collect observed heat flux estimates from satellite data products for three wildfire events over northern California and use these estimates in a regional chemical transport model to investigate and quantify the impacts of observationally constrained heat fluxes on the modeled injection height and downwind air quality. We find large differences between these observationally derived heat flux estimates and fixed model assumptions, with important implications for modeled behavior of plume dynamics and surface air quality impacts. Overall, we find that using observationally constrained heat flux estimates tends to reduce modeled injection heights for our chosen fires, resulting in large increases in surface particulate matter concentrations. While local wind conditions contribute to variability and additional uncertainties in the impacts of modified plume injection heights, we find observationally constrained heat fluxes to be an impactful and potentially useful tool towards the improvement of emissions inventory assumptions and parameterizations.