Emissions of Ozone-Layer-Depleting Methyl Chloroform (CH<sub>3</sub>CCl<sub>3</sub>) in China Inferred from High-Frequency In-Situ Observations
Bibliographic record
Abstract
Methyl chloroform (CH 3 CCl 3, TCA), a first-generation ozone-depleting substance leading to ozone depletion, is regulated under the Montreal Protocol. However, recent atmospheric observations and emission estimates for TCA in China are lacking, leaving the effectiveness of the Montreal Protocol’s implementation unclear. In this study, we collected 2727 atmospheric samples at the Shanghuang site in eastern China from August 2023 to July 2024. Using these observations with a Bayesian inversion algorithm, we quantify emissions of TCA in eastern China during August 2023–July 2024 at 0.21 ± 0.04 gigagram per year (Gg yr –1 ), comprising about 10.2% of global emissions. Moreover, TCA emissions in whole China have declined from 6.0–10.5 Gg yr –1 in 2003 to 0.30 ± 0.06 Gg yr –1 during August 2023–July 2024, demonstrating effective phase-out under the Montreal Protocol. Nonzero emissions likely arise from fugitive emissions in industrial processes (e.g., iron and steel industry, refined petroleum industry, and coking industry) and nonindustrial sources (e.g., biomass combustion), which are not controlled by the Protocol. Continued monitoring of TCA mole fractions and emissions would be valuable for confirming the sustained success of China’s phase-out efforts and long-term compliance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".