Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models
<p>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) re...
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doaj-362e04fe9767403489012a8a048363262020-11-25T02:30:07ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-10-0118138131383810.5194/acp-18-13813-2018Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic modelsS. Eluri0C. D. Cappa1B. Friedman2D. K. Farmer3S. H. Jathar4Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, 80523, USADepartment of Civil and Environmental Engineering, University of California Davis, Davis, CA, 95616, USADepartment of Chemistry, Colorado State University, Fort Collins, CO, 80523, USADepartment of Chemistry, Colorado State University, Fort Collins, CO, 80523, USADepartment of Mechanical Engineering, Colorado State University, Fort Collins, CO, 80523, USA<p>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); NO<sub><i>x</i></sub>-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.</p>https://www.atmos-chem-phys.net/18/13813/2018/acp-18-13813-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Eluri C. D. Cappa B. Friedman D. K. Farmer S. H. Jathar |
spellingShingle |
S. Eluri C. D. Cappa B. Friedman D. K. Farmer S. H. Jathar Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models Atmospheric Chemistry and Physics |
author_facet |
S. Eluri C. D. Cappa B. Friedman D. K. Farmer S. H. Jathar |
author_sort |
S. Eluri |
title |
Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models |
title_short |
Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models |
title_full |
Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models |
title_fullStr |
Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models |
title_full_unstemmed |
Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models |
title_sort |
modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models |
publisher |
Copernicus Publications |
series |
Atmospheric Chemistry and Physics |
issn |
1680-7316 1680-7324 |
publishDate |
2018-10-01 |
description |
<p>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); NO<sub><i>x</i></sub>-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.</p> |
url |
https://www.atmos-chem-phys.net/18/13813/2018/acp-18-13813-2018.pdf |
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