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Record W2794110565 · doi:10.5194/acp-2018-220

Toward resolving the mysterious budget discrepancy of ozone-depleting CCl <sub>4</sub> : An analysis of top-down emissions from China

2018· article· en· W2794110565 on OpenAlexaboutno aff
Sunyoung Park, Shanlan Li, Jens Mühle, Simon O’Doherty, Ray F. Weiss, Xuekun Fang, Stefan Reimann, Ronald G. Prinn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationMinistry of Science and ICT, South KoreaMinistry of EnvironmentNational Research FoundationUniversity of BristolDepartment of Energy and Climate ChangeNational Aeronautics and Space AdministrationNational Research Foundation of KoreaU.S. Department of Energy
KeywordsOzoneMontreal ProtocolRaw materialEnvironmental scienceChinaProduction (economics)Factory (object-oriented programming)Environmental chemistryEnvironmental engineeringChemistryMeteorologyOzone layerGeographyEconomics

Abstract

fetched live from OpenAlex

Abstract. Emissive production and use of carbon tetrachloride (CCl4), one of the first-generation ozone-depleting substances, have been banned by the Montreal Protocol with the 2010 phase-out for developing countries, but production and consumption for non-dispersive use as a chemical feedstock and as process agent are still allowed. Here, we present evidence that significant unreported emissions of CCl4 still persist, based on the high frequency and magnitude of CCl4 pollution events observed in the 8-year real time atmospheric measurement record at Gosan station, a regional background monitoring site in East Asia. From this we estimate top-down emissions of CCl4 amounting to 23.6 ± 7.1 Gg yr−1 from 2011 to 2015 for China, in contrast to the 4.3–5.2 Gg yr−1 reported as the most up-to-date post-2010 Chinese bottom-up emissions. The missing emissions (~ 19 Gg yr−1) for China are highly significant, contributing about 54 % of global CCl4 emissions. We show that 89 ± 6 % of the CCl4 enhancements observed at Gosan can be accounted for by fugitive emissions of CCl4 occurring at the factory level during the production of CH3Cl, CH2Cl2, CHCl3 and C2Cl4 (PCE) and feedstock and solvent use in chemical manufacturing industries. Thus, it is crucial to implement technical improvements and better regulation strategies to reduce the evaporative losses of CCl4 occurring at the factory and/or process level.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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