Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models
Date
2017
Authors
Eluri, Sailaja, author
Jathar, Shantanu, advisor
Volckens, John, committee member
Pierce, Jeffrey, committee member
Farmer, Delphine, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
Laboratory-based studies have shown that diesel-powered sources emit volatile organic compounds that can be photo-oxidized in the atmosphere to form secondary organic aerosol (SOA); in some cases, this SOA can exceed direct emissions of particulate matter (PM); PM is a criteria pollutant that is known to have adverse effects on air quality, climate, and human health. However, there are open questions surrounding how these laboratory experiments can be extrapolated to the real atmosphere and how they will help identify the most important species in diesel exhaust that contribute to SOA formation. Jathar et al. (2017) recently performed experiments using 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 will use two different SOA models, namely the volatility basis set (VBS) model and the statistical oxidation model (SOM), to simulate the formation, evolution and composition of SOA from the experiments of Jathar et al. (2017). 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 multigenerational gas-phase chemistry, and kinetic gas/particle partitioning. Both frameworks demonstrated that for model predictions of SOA mass and elemental composition to agree with measurements across all engine load-fuel-aftertreatment combinations, it was necessary to (a) model the kinetically-limited gas/particle partitioning likely in OFRs and (b) account for SOA formation from IVOCs (IVOCs were found to account for more than four-fifths of the model-predicted SOA). Model predictions of the gas-phase organic compounds (resolved in carbon and oxygen space) from the SOM compared favorably to gas-phase measurements made using a Chemical Ionization Mass Spectrometer (CIMS) that, qualitatively, substantiated the semi-explicit chemistry captured by the SOM and the measurements made by the CIMS. Sensitivity simulations suggested that (a) IVOCs from diesel exhaust could be modeled using a single surrogate species with an SOA mass yield equivalent to a C15 or C17 linear alkane for use in large-scale models, (b) different diesel exhaust emissions profiles in the literature resulted in the same SOA production as long as IVOCs were included and (c) accounting for vapor wall loss parameterizations for the SOA precursors improved model performance. As OFRs are increasingly used to study SOA formation and evolution in laboratory and field environments, there is a need to develop models that can be used to interpret the OFR data. This work is one example of the model development and application relevant to the use of OFRs.