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Record W2099852683 · doi:10.3390/atmos5030536

Temporal Variability of Atmospheric Total Gaseous Mercury in Windsor, ON, Canada

2014· article· en· W2099852683 on OpenAlexafffundabout
Xiaohong Xu, Umme S. Akhtar, Kyle Clark, Xiaobin Wang

Bibliographic record

VenueAtmosphere · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsEnvironmental scienceAtmospheric sciencesMorningWindsorWind speedDiurnal temperature variationClimatologyMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Atmospheric Total Gaseous Mercury (TGM) concentrations were monitored in Windsor, Ontario, Canada, during 2007 to 2011, to investigate the temporal variability of TGM. Over five years, the average concentration was 2.0 ± 1.3 ng/m3. A gradual decrease in annual TGM concentrations from 2.0 ng/m3 in year 2007 to 1.7 ng/m3 in 2009 was observed. The seasonal means show the highest TGM concentrations during the summer months (2.4 ± 2.0 ng/m3), followed by winter (1.9 ± 1.4 ng/m3), fall (1.8 ± 0.81 ng/m3), and spring (1.7 ± 0.73 ng/m3). Diurnal patterns in summer, fall, and winter were similar. A different diurnal pattern was observed in spring with an early depletion in the morning. The TGM concentrations were lower on weekends (1.8 ± 0.77 ng/m3) than on weekdays (2.0 ± 1.5 ng/m3), suggesting 10% of TGM in Windsor was attributable to emissions from industrial sectors in the region. Directional TGM concentrations also indicated southwesterly air masses were TGM enriched due to emissions from coal-fired power plants and industrial facilities. Correlation and principal component analysis identified that combustion of fossil fuel, ambient temperature, wind speed, synoptic systems, and O3 concentrations influenced TGM concentrations significantly. Overall, inter-annual, seasonal, day-of-week, and diurnal variability was observed in Windsor. The temporal patterns were affected by anthropogenic and surface emissions, as well as atmospheric mixing and chemistry.

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 score0.085
Threshold uncertainty score0.997

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)0.0040.000

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 head0.006
GPT teacher head0.209
Teacher spread0.202 · 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

Citations12
Published2014
Admission routes3
Has abstractyes

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