499583 Gas-Particle Partitioning of Alkyl Nitrates from Anthropogenic Alkanes

Dongyu S. Wang1, Sahil Bhandari1, Felipe Cardoso-SaldaƱa1, Surya Venkatesh Dhulipala1, Jeff K. Bean2 and Lea Hildebrandt Ruiz1, (1)Chemical Engineering, The University of Texas at Austin, Austin, TX, (2)Phillips 66, Bartlesville, OK

Oxidation of volatile organic compounds (VOC) produces secondary organic aerosol (SOA), increasing the overall fine particle mass and aggravating the associated negative health effects. Studies have shown that long-chain anthropogenic VOCs, despite their low ambient abundance relative to biogenic VOCs, can contribute significantly to the overall SOA mass. Typical anthropogenic emission sources, such as oil and gas extraction sites, tend to be environments rich in nitrogen oxides (NOx) and therefore favorable for the formation of alkyl nitrates (AN), which can partition to the particle phase and therefore contribute to SOA. The gas-particle partitioning of AN is known to aid NOx cycling and transport, contributing to the regional nature of NOx pollution. The gas-particle partitioning behavior of SOA derived from anthropogenic VOCs remains poorly characterized.

Here we report results from environmental chamber experiments conducted to investigate SOA formation from select linear, branched, and cyclic C8, C10, and C12 anthropogenic alkanes under high NOx conditions. SOA formation was significantly higher for cyclic alkanes, consistent with previous findings. Aerosol volatility was determined using a thermodenuder and an aerosol evaporation kinetics model. The partitioning behavior of ANs was determined based on particle-phase measurements and total AN concentration estimated using an updated SAPRC box model with carbon-bond-based reaction mechanisms. The molecular composition of gas- and particle-phase products was determined using a Filter Inlet for Gases and AEROsols (FIGAERO) coupled to a High-Resolution Time-of-Flight Chemical Ionization Mass Spectrometer, where evidence of oligomer formation was observed. Results from this study can help improve air quality models with respect to SOA loading, NOx cycling, and NOx transport.


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