Projections of emission, fate and impact of HFO-1234yf in China
Bibliographic record
Abstract
Hydrofluoroolefins (HFOs) are being used as substitutes for potent greenhouse gases hydrofluorocarbons (HFCs). However, the use and environmental impacts of HFOs are of great concern due to the rapid degradation of HFOs to produce persistent and phytotoxic trifluoroacetic acid (TFA), one of the per- and polyfluoroalkyl substances (PFAS). HFO-1234yf is the most widely used HFO and has the greatest formation potential of TFA. Here, we provided a comprehensive projection of HFO-1234yf emission in China during 2025-2060. GEOS-Chem was applied to simulate the atmospheric processes of HFO-1234yf and to characterize the distribution of the degradation product TFA. A water quality model was further adopted to assess the impact of HFO-1234yf emissions on surface terminal water body TFA concentrations in China. Under the Kigali Amendment to the Montreal Protocol, HFO-1234yf emission in China was estimated to increase from 1.5 to 79.0 kt in 2025-2060 with cumulative emission of 1.7 Mt. The annual deposition flux (dry plus wet) of TFA due to HFO-1234yf emission was expected to grow from 0.02 kg/km2/year in 2025 to 0.9 kg/km2/year in 2060, dominated by wet deposition. After continuous emission of HFO-1234yf from 2025 to 2060, the average concentration of TFA in terminal waters in China was projected to increase by 7.4 μg/L. The results of this study can provide scientific support for evaluating the environmental risks of HFOs uses and help in developing HFCs phase-out pathways for addressing climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".