Secondary organic aerosol formation from smoldering and flaming combustion of biomass: a box model parametrization based on volatility basis set

Abstract. Box model simulations based on the volatility basis set (VBS) approach were used to assess secondary organic aerosol (SOA) precursors and volatility distributions from residential wood combustion. Emissions were sampled from three different residential stoves at different combustion conditions (flaming vs. smoldering-dominated), aging temperatures (−10 °C, 2 °C and 15 °C), and emission loads, then exposed to hydroxyl (OH) radicals in a smog chamber. Primary emissions of SOA precursor compounds, organic aerosol and their evolution during aging in the smog chamber were monitored by a comprehensive suite of gas and particle instrumentation, including a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) and a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). SOA precursors were classified according to their chemical composition and the identification of the nature of the precursors revealed useful to better constrain model parameters, in particular SOA production rates and molecular characteristics of the condensable gases formed. The general aim of the model was the determination of the parameters describing the volatility distributions of the oxidation products from the different chemical classes considered and their temperature dependence. Novel parameterization methods based on a genetic algorithm (GA) approach allowed estimation of precursor class contributions to SOA and evaluation of the effect of emission variability on SOA yield predictions. Significant differences were observed in the gas-phase composition between smoldering and flaming emissions. Smoldering phase emissions were dominated by oxidized VOCs with less than six carbon atoms family (OVOC c 6 ) while the flaming phase exhibited higher contributions by the single-ring aromatic hydrocarbon (SAH) and polycyclic aromatic hydrocarbon (PAH) classes. For both phases studied, cresol and phenolic species provided a major contribution to SOA formation. In combination with state-of-the-art mass spectrometry analysis, the model framework developed herein may be generalizable for other complex emissions sources, allowing determination of the contributions to SOA of different precursor classes at a level of complexity suitable for implementation in regional air quality models.