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Record W4243014184 · doi:10.5194/acpd-10-4719-2010

Global mercury emissions to the atmosphere from anthropogenic and natural sources

2010· preprint· en· W4243014184 on OpenAlexaff
Nicola Pirrone, Sergio Cinnirella, Xinbin Feng, Robert B. Finkelman, H. Friedli, Joy Leaner, Robert P. Mason, Arun B. Mukherjee, Glenn B. Stracher, David G. Streets, Kevin Telmer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Victoria
FundersMinistry of Environment
KeywordsMercury (programming language)Environmental scienceNatural gasFossil fuelEnvironmental chemistryEmission inventoryEnvironmental protectionEnvironmental engineeringAir pollutionWaste managementChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract. This paper provides an up-to-date assessment of global mercury emissions from anthropogenic and natural sources. On an annual basis, natural sources account for 5207 Mg of mercury released to the global atmosphere, including the contribution from re-emission processes, which are emissions of previously deposited mercury originating from anthropogenic and natural sources, and primary emissions from natural reservoirs. Anthropogenic sources, which include a large number of industrial point sources, are estimated to account for 2320 Mg of mercury emitted annually. The major contributions are from fossil-fuel fired power plants (810 Mg yr−1), artisanal small scale gold mining (400 Mg yr−1), non-ferrous metals manufacturing (310 Mg yr−1), cement production (236 Mg yr−1), waste disposal (187 Mg yr−1) and caustic soda production (163 Mg yr−1). Therefore, our current estimate of global mercury emissions suggests that the overall contribution from natural sources (primary emissions+re-emissions) and anthropogenic sources is nearly 7527 Mg per year, the uncertainty associated with these estimates are related to the typology of emission sources and source regions.

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 score0.207
Threshold uncertainty score0.993

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 science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.013
GPT teacher head0.278
Teacher spread0.265 · 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

Citations107
Published2010
Admission routes1
Has abstractyes

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