Development of volatility distributions for organic matter in biomass burning emissions. Issue 1 (1st November 2022) - Sinha, Aditya; George, Ingrid; Holder, Amara; Preston, William; Hays, Michael; Grieshop, Andrew P.
Abstract : We present a novel filter-in-tube sorbent tube method to collect S/I-VOC samples from a range of biomass burning experiments and find that volatility distributions are relatively consistent with prior findings and across the tested combustion types. Abstract : The volatility distribution of organic emissions from biomass burning and other combustion sources can determine their atmospheric evolution due to partitioning/aging. The gap between measurements and models predicting secondary organic aerosol has been partially attributed to the absence of semi- and intermediate volatility organic compounds (S/I-VOC) in models and measurements. However, S/I-VOCs emitted from these sources and typically quantified using the volatility basis framework (VBS) are not well understood. For example, the amount and composition of S/I-VOCs and their variability across different biomass burning sources such as residential woodstoves, open field burns, and laboratory simulated open burning are uncertain. To address this, a novel filter-in-tube sorbent tube sampling method collected S/I-VOC samples from biomass burning experiments for a range of fuels and combustion conditions. Filter-in-tube samples were analyzed using thermal desorption-gas chromatography-mass spectrometry (TD/GC/MS) for compounds across a wide range of volatilities (saturation concentrations; −2 ≤ log  C * ≤ 6). The S/I-VOC measurements were used to calculate volatility distributions for each emission source. The distributions were broadly consistent across the sources with IVOCs accounting for 75–90% of the total captured organic matter, while SVOCs and LVOCs were responsible for 6–13% and 1–12%, respectively. The distributions and predicted partitioning were generally consistent with the literature. Particulate matter emission factors spanned two orders of magnitude across the sources. This work highlights the potential of inferring gas–particle partitioning behavior of biomass burning emissions using filter-in-tube sorbent samples analyzed offline. This simplifies both sampling and analysis of S/I-VOCs for studies focused on capturing the full range of organics emitted.