Assessing the success of the Montreal Protocol: trends of halogenated gases from ground-based, satellite, and model data
Bibliographic record
Abstract
Our atmosphere protects life on Earth, but its current state and changes over the past century are major concerns, especially regarding ozone layer depletion and global warming. The Montreal Protocol (1987) successfully reduced ozone-depleting substances like CFCs, which are synthetic compounds mainly used as refrigerants. To address this, substitutes such as HCFCs and HFCs were introduced, with HFCs not contributing to ozone depletion. However, HFCs are potent greenhouse gases, leading to the Kigali Amendment (2016) to control their use. Monitoring gases regulated by the Montreal Protocol requires long-term, high-quality data. This research analysed halogenated gases using FTIR spectroscopy, 3-D model simulations, in situ measurements, and satellite observations, as well as advanced statistical tools for trend analysis. We found that the decline of atmospheric CFC-11 had slowed since 2011 in the Northern Hemisphere and since 2014 in the Southern Hemisphere, likely due to undeclared emissions. Additionally, this thesis studies, for the first time using ground-based FTIR spectra, HFC-134a, the most abundant HFC, revealing a continuous rise of about 7% per year since the early 2000s. These findings highlight the importance of continuous and global atmospheric monitoring to better understand changes in atmospheric composition and detect potential undeclared emissions of harmful substances, as a support to international regulations.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".