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Record W2107555889 · doi:10.5194/acp-12-6095-2012

Nested-grid simulation of mercury over North America

2012· article· en· W2107555889 on OpenAlexaffabout
Y. Zhang, Lyatt Jaeglé, Aaron van Donkelaar, Randall V. Martin, Christopher D. Holmes, Helen M. Amos, Qiaoqiao Wang, R. W. Talbot, Richard S. Artz, Steve Brooks, Winston T. Luke, Thomas M. Holsen, Dirk Felton, E. K. Miller, K. D. Perry, D. Schmeltz, A. Steffen, R. Tordon, P. S. Weiss‐Penzias, R. Zsolway

Bibliographic record

VenueAtmospheric chemistry and physics · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change CanadaDalhousie University
Fundersnot available
KeywordsLongitudeNested set modelMercury (programming language)Environmental scienceAtmospheric sciencesLatitudeClimatologyPlumeMeteorologyAerosolPrecipitationDeposition (geology)GeologyGeography

Abstract

fetched live from OpenAlex

Abstract. We have developed a new nested-grid mercury (Hg) simulation over North America with a 1/2° latitude by 2/3° longitude horizontal resolution employing the GEOS-Chem global chemical transport model. Emissions, chemistry, deposition, and meteorology are self-consistent between the global and nested domains. Compared to the global model (4° latitude by 5° longitude), the nested model shows improved skill at capturing the high spatial and temporal variability of Hg wet deposition over North America observed by the Mercury Deposition Network (MDN) in 2008–2009. The nested simulation resolves features such as higher deposition due to orographic precipitation, land/ocean contrast and and predicts more efficient convective rain scavenging of Hg over the southeast United States. However, the nested model overestimates Hg wet deposition over the Ohio River Valley region (ORV) by 27%. We modify anthropogenic emission speciation profiles in the US EPA National Emission Inventory (NEI) to account for the rapid in-plume reduction of reactive to elemental Hg (IPR simulation). This leads to a decrease in the model bias to −2.3% over the ORV region. Over the contiguous US, the correlation coefficient (r) between MDN observations and our IPR simulation increases from 0.60 to 0.78. The IPR nested simulation generally reproduces the seasonal cycle in surface concentrations of speciated Hg from the Atmospheric Mercury Network (AMNet) and Canadian Atmospheric Mercury Network (CAMNet). In the IPR simulation, annual mean gaseous and particulate-bound Hg(II) are within 140% and 11% of observations, respectively. In contrast, the simulation with unmodified anthropogenic Hg speciation profiles overestimates these observations by factors of 4 and 2 for gaseous and particulate-bound Hg(II), respectively. The nested model shows improved skill at capturing the horizontal variability of Hg observed over California during the ARCTAS aircraft campaign. The nested model suggests that North American anthropogenic emissions account for 10–22% of Hg wet deposition flux over the US, depending on the anthropogenic emissions speciation profile assumed. The modeled percent contribution can be as high as 60% near large point sources in ORV. Our results indicate that the North American anthropogenic contribution to dry deposition is 13–20%.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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 teacher head, not a consensus.

Study designObservational
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

Citations123
Published2012
Admission routes2
Has abstractyes

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