A14A-03
Incorporating Detailed Chemical Characterization of Biomass Burning Emissions into Air Quality Models

Monday, 14 December 2015: 16:30
3010 (Moscone West)
Kelley Barsanti, University of California Riverside, Riverside, CA, United States
Abstract:
Approximately 500 Tg/yr of non-methane organic compounds (NMOCs) are emitted by biomass burning (BB) to the global atmosphere, leading to the photochemical production of ozone (O3) and secondary particulate matter (PM). Until recently, in studies of BB emissions, a significant mass fraction of NMOCs (up to 80%) remained uncharacterized or unidentified. Models used to simulate the air quality impacts of BB thus have relied on very limited chemical characterization of the emitted compounds. During the Fourth Fire Lab at Missoula Experiment (FLAME-IV), an unprecedented fraction of emitted NMOCs were identified and quantified through the application of advanced analytical techniques. Here we use FLAME-IV data to improve BB emissions speciation profiles for individual fuel types. From box model simulations we evaluate the sensitivity of predicted precursor and pollutant concentrations (e.g., formaldehyde, acetaldehyde, and terpene oxidation products) to differences in the emission speciation profiles, for a range of ambient conditions (e.g., high vs. low NOx). Appropriate representation of emitted NMOCs in models is critical for the accurate prediction of downwind air quality. Explicit simulation of hundreds of NMOCs is not feasible; therefore we also investigate the consequences of using existing assumptions and lumping schemes to map individual NMOCs to model surrogates and we consider alternative strategies. The updated BB emissions speciation profiles lead to markedly different surrogate compound distributions than the default speciation profiles, and box model results suggest that these differences are likely to affect predictions of PM and important gas-phase species in chemical transport models. This study highlights the potential for further BB emissions characterization studies, with concerted model development efforts, to improve the accuracy of BB predictions using necessarily simplified mechanisms.