Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 250

The DOI matches its Crossref record.

Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model

Open access
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., Zhang, Q.·2014·Atmospheric Chemistry and Physics

Abstract

Abstract. New pathways to form secondary organic aerosol (SOA) have been postulated recently. Glyoxal, the smallest dicarbonyl, is one of the proposed precursors. It has both anthropogenic and biogenic sources, and readily partitions into the aqueous phase of cloud droplets and deliquesced particles where it undergoes both reversible and irreversible chemistry. In this work we extend the regional scale chemistry transport model WRF-Chem to include detailed gas-phase chemistry of glyoxal formation as well as a state-of-the-science module describing its partitioning and reactions in the aerosol aqueous-phase. A comparison of several proposed mechanisms is performed to quantify the relative importance of different formation pathways and their regional variability. The CARES/CalNex campaigns over California in summer 2010 are used as case studies to evaluate the model against observations. A month-long simulation over the continental United States (US) enables us to extend our results to the continental scale. In all simulations over California, the Los Angeles (LA) basin was found to be the hot spot for SOA formation from glyoxal, which contributes between 1% and 15% of the model SOA depending on the mechanism used. Our results indicate that a mechanism based only on a reactive (surface limited) uptake coefficient leads to higher SOA yields from glyoxal compared to a more detailed description that considers aerosol phase state and chemical composition. In the more detailed simulations, surface uptake is found to give the highest SOA mass yields compared to a volume process and reversible formation. We find that the yields of the latter are limited by the availability of glyoxal in aerosol water, which is in turn controlled by an increase in the Henry's law constant depending on salt concentrations ("salting-in"). A time dependence in this increase prevents substantial partitioning of glyoxal into aerosol water at high salt concentrations. If this limitation is removed, volume pathways contribute > 20% of glyoxal-SOA mass, and the total mass formed (5.8% of total SOA in the LA basin) is about a third of the simple uptake coefficient formulation without consideration of aerosol phase state and composition. Results from the continental US simulation reveal the much larger potential to form glyoxal-SOA over the eastern continental US. Interestingly, the low concentrations of glyoxal-SOA over the western continental US are not due to the lack of a potential to form glyoxal-SOA here. Rather these small glyoxal-SOA concentrations reflect dry conditions and high salt concentrations, and the potential to form SOA mass here will strongly depend on the water associated with particles.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.5194/acp-14-6213-2014

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 250

Metadata

Authors
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., & Zhang, Q.
Venue
Atmospheric Chemistry and Physics
Published
2014-06-24
Volume
14
Issue
12
Pages
6213-6239
ISSN
  • 1680-7316 (ISSN-L)
  • 1680-7324
OpenAlex
W2020857572
Cited by
250
References
0
FWCI
10.37
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model. https://app.cochat.ai/papers/10.5194/acp-14-6213-2014
The paper
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., & Zhang, Q. (2014). Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model. Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-14-6213-2014

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.