Rising Trifluoroacetic Acid Levels: Evaluating Contributions from long-lived CFC replacements and anaesthetics
Bibliographic record
Abstract
Trifluoroacetic acid (TFA), a short chain perfluorocarboxylic acid (scPFCA), is a contaminant of emerging concern because its emissions are projected to rapidly increase, it is highly persistent, and remediation is challenging. Recent studies based on ice core records report large increases (up to a factor of ~10) in Arctic TFA deposition since the 1970s. The ice core temporal trends suggest that CFC replacement gases introduced following the Montreal Protocol could be an important source. However, TFA is a “substance from multiple sources” and their relative importance remains poorly quantified; a challenge which needs to be addressed for the emission trend to be reversed through regulation. Here we use a chemical transport model (FRSGC/UCI-CTM) to examine the global TFA budget from the production of long-lived source gases, namely, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), and inhalation anaesthetics. A detailed degradation scheme describing TFA production from each precursor was added to the model and simulations performed using time-varying loadings of its major emitted precursors. Model results showed that TFA production from CFC-replacements increased by a factor of four from 2000 (6.3 Gg/yr) to 2016 (25.4 Gg/yr), with cumulative deposition over this period reaching 226 Gg/yr. HCFC-123, HCFC-124, and HFC-134a account for the majority of this production. TFA deposition shows a latitudinal dependence with the majority occurring in extrapolar regions. Model results are compared to measurements from ice core data and precipitation concentrations. While demonstrating the increasing contribution of CFC replacements to TFA, we highlight the challenges in elucidating their significance against other sources from sparse TFA measurements records, particularly in regions where TFA deposition is highest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".