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Volume 18, issue 19
Atmos. Chem. Phys., 18, 13813–13838, 2018
https://doi.org/10.5194/acp-18-13813-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 18, 13813–13838, 2018
https://doi.org/10.5194/acp-18-13813-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Oct 2018

Research article | 01 Oct 2018

Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models

Sailaja Eluri1, Christopher D. Cappa2, Beth Friedman3, Delphine K. Farmer3, and Shantanu H. Jathar1 Sailaja Eluri et al.
  • 1Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, 80523, USA
  • 2Department of Civil and Environmental Engineering, University of California Davis, Davis, CA, 95616, USA
  • 3Department of Chemistry, Colorado State University, Fort Collins, CO, 80523, USA

Abstract. Laboratory-based studies have shown that combustion sources emit volatile organic compounds that can be photooxidized in the atmosphere to form secondary organic aerosol (SOA). In some cases, this SOA can exceed direct emissions of primary organic aerosol (POA). Jathar et al. (2017a) recently reported on experiments that used an oxidation flow reactor (OFR) to measure the photochemical production of SOA from a diesel engine operated at two different engine loads (idle, load), two fuel types (diesel, biodiesel), and two aftertreatment configurations (with and without an oxidation catalyst and particle filter). In this work, we used two different SOA models, the Volatility Basis Set (VBS) model and the Statistical Oxidation Model (SOM), to simulate the formation and composition of SOA for those experiments. Leveraging recent laboratory-based parameterizations, both frameworks accounted for a semi-volatile and reactive POA; SOA production from semi-volatile, intermediate-volatility, and volatile organic compounds (SVOC, IVOC and VOC); NOx-dependent parameterizations; multigenerational gas-phase chemistry; and kinetic gas–particle partitioning. Both frameworks demonstrated that for model predictions of SOA mass to agree with measurements across all engine load–fuel–aftertreatment combinations, it was necessary to model the kinetically limited gas–particle partitioning in OFRs and account for SOA formation from IVOCs, which were on average found to account for 70 % of the model-predicted SOA. Accounting for IVOCs, however, resulted in an average underprediction of 28 % for OA atomic O : C ratios. Model predictions of the gas-phase organic compounds (resolved in carbon and oxygen space) from the SOM compared favorably to gas-phase measurements from a chemical ionization mass spectrometer (CIMS), substantiating the semi-explicit chemistry captured by the SOM. Model–measurement comparisons were improved on using SOA parameterizations corrected for vapor wall loss. As OFRs are increasingly used to study SOA formation and evolution in laboratory and field environments, models such as those developed in this work can be used to interpret the OFR data.

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As oxidation flow reactors (OFRs) are increasingly used to study aerosol formation and evolution in laboratory and field environments, there is a need to develop models that can be used to interpret OFR data. In this work, we evaluate two coupled chemistry and thermodynamic models to simulate secondary organic aerosol formation (SOA) from diluted diesel exhaust and explore the sources, pathways, and processes important to SOA formation.
As oxidation flow reactors (OFRs) are increasingly used to study aerosol formation and evolution...
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