MétaCan
Menu
← Back to cohort
Record W4238649008 · doi:10.5194/acp-2016-924

Multi-model study of mercury dispersion in the atmosphere: Atmospheric processes and model evaluation

2016· preprint· en· W4238649008 on OpenAlexaff
Oleg Travnikov, Hélène Angot, Paulo Artaxo, Mariantonia Bencardino, Johannes Bieser, Francesco D’Amore, Ashu Dastoor, Francesco De Simone, María del Carmen Diéguez, Aurélien Dommergue, Ralf Ebinghaus, Xin Feng, Christian N. Gencarelli, Ian M. Hedgecock, Olivier Magand, Lynwill Martin, Volker Matthias, Nikolay Mashyanov, Nicola Pirrone, R. Ramesh, Katie Read, Andrei Ryjkov, Noelle E. Selin, Fabrizio Sena, Shaojie Song, Francesca Sprovieri, Dennis Wip, Ingvar Wängberg, Xin Yang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersSeventh Framework ProgrammeInstitut Polaire Français Paul Emile VictorNational Science Foundation
KeywordsAtmosphere (unit)Atmospheric sciencesDeposition (geology)Environmental scienceAtmospheric chemistryFlux (metallurgy)Chemical transport modelEnvironmental chemistryMercury (programming language)AerosolChemistryTroposphereMeteorologyOzoneGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. Current understanding of mercury (Hg) behaviour in the atmosphere contains significant gaps. Some key characteristics of Hg processes including anthropogenic and geogenic emissions, atmospheric chemistry, and air-surface exchange are still poorly known. This study provides a complex analysis of processes governing Hg fate in the atmosphere involving both measurement data from ground-based sites and simulation results of chemical transport models. A variety of long-term measurements of gaseous elemental Hg (GEM) and reactive Hg (RM) concentration as well as Hg wet deposition flux has been compiled from different global and regional monitoring networks. Four contemporary global-scale transport models for Hg were applied both in their state-of-the-art configurations and for a number of numerical experiments aimed at evaluation of particular processes. Results of the model simulation were evaluated against measurements. As it follows from the analysis the inter-hemispheric gradient of GEM is largely formed by the spatial distribution of anthropogenic emissions which prevail in the Northern Hemisphere. Contribution of natural and secondary emissions enhances the south-to-north gradient but their effect is less significant. The atmospheric chemistry does not affect considerably both spatial distribution and temporal variation of GEM concentration in the surface air. On the other hand, RM air concentration and wet deposition are largely defined by oxidation chemistry. The Br oxidation mechanism allows successfully reproducing observed seasonal variation of the RM / GEM ratio in the near-surface layer, whereas it predicts maximum in wet deposition in spring instead of summer as observed at monitoring sites located in North America and Europe. Model runs with the OH chemistry correctly simulate both the periods of maximum and minimum values and the amplitude of observed seasonal variation but lead to shifting the maximum RM / GEM ratios from spring to summer. The O3 chemistry does not provide significant seasonal variation of Hg oxidation. Thus, performance of the considered Hg oxidation mechanisms differs in reproduction of different observed parameters that can imply possibility of more complex chemistry and multiple pathways of Hg oxidation occurring concurrently in various parts of the atmosphere.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.321
Teacher spread0.268 · 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
GenreEmpirical

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

Citations14
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicMercury impact and mitigation studies→French-language works237,207→