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Record W3139237125 · doi:10.5194/gmd-14-5093-2021

Extension of a gaseous dry deposition algorithm to oxidized volatile organic compounds and hydrogen cyanide for application in chemistry transport models

2021· article· en· W3139237125 on OpenAlexaff
Zhiyong Wu, Leiming Zhang, John T. Walker, Paul A. Makar, Judith A. Perlinger, Xuemei Wang

Bibliographic record

VenueGeoscientific model development · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaElectric Power Research InstituteSouthern Company
KeywordsChemistryFlux (metallurgy)Deposition (geology)Environmental chemistryOxidizing agentHydrogen cyanidePollutantAtmospheric chemistryOzoneInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract. The dry deposition process refers to flux loss of an atmospheric pollutant due to uptake of the pollutant by the Earth's surfaces, including vegetation, underlying soil, and any other surface types. In chemistry transport models (CTMs), the dry deposition flux of a chemical species is typically calculated as the product of its surface layer concentration and its dry deposition velocity (Vd); the latter is a variable that needs to be highly empirically parameterized due to too many meteorological, biological, and chemical factors affecting this process. The gaseous dry deposition scheme of Zhang et al. (2003) parameterizes Vd for 31 inorganic and organic gaseous species. The present study extends the scheme of Zhang et al. (2003) to include an additional 12 oxidized volatile organic compounds (oVOCs) and hydrogen cyanide (HCN), while keeping the original model structure and formulas, to meet the demand of CTMs with increasing complexity. Model parameters for these additional chemical species are empirically chosen based on their physicochemical properties, namely the effective Henry's law constants and oxidizing capacities. Modeled Vd values are compared against field flux measurements over a mixed forest in the southeastern US during June 2013. The model captures the basic features of the diel cycles of the observed Vd. Modeled Vd values are comparable to the measurements for most of the oVOCs at night. However, modeled Vd values are mostly around 1 cm s−1 during daytime, which is much smaller than the observed daytime maxima of 2–5 cm s−1. Analysis of the individual resistance terms and uptake pathways suggests that flux divergence due to fast atmospheric chemical reactions near the canopy was likely the main cause of the large model–measurement discrepancies during daytime. The extended dry deposition scheme likely provides conservative Vd values for many oVOCs. While higher Vd values and bidirectional fluxes can be simulated by coupling key atmospheric chemical processes into the dry deposition scheme, we suggest that more experimental evidence of high oVOC Vd values at additional sites is required to confirm the broader applicability of the high values studied here. The underlying processes leading to high measured oVOC Vd values require further investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.196
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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