Global emissions of refrigerants HCFC-22 and HFC-134a: Unforeseen seasonal contributions
Bibliographic record
Abstract
HCFC-22 (CHClF2) and HFC-134a (CH2FCF3) are two major gases currently used worldwide in domestic and commercial refrigeration and air conditioning. HCFC-22 contributes to stratospheric ozone depletion, and both species are potent greenhouse gases. In this work, we study in situ observations of HCFC-22 and HFC-134a taken from research aircraft over the Pacific Ocean in a 3-y span [HIaper-Pole-to-Pole Observations (HIPPO) 2009-2011] and combine these data with long-term ground observations from global surface sites [National Oceanic and Atmospheric Administration (NOAA) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks]. We find the global annual emissions of HCFC-22 and HFC-134a have increased substantially over the past two decades. Emissions of HFC-134a are consistently higher compared with the United Nations Framework Convention on Climate Change (UNFCCC) inventory since 2000, by 60% more in recent years (2009-2012). Apart from these decadal emission constraints, we also quantify recent seasonal emission patterns showing that summertime emissions of HCFC-22 and HFC-134a are two to three times higher than wintertime emissions. This unforeseen large seasonal variation indicates that unaccounted mechanisms controlling refrigerant gas emissions are missing in the existing inventory estimates. Possible mechanisms enhancing refrigerant losses in summer are (i) higher vapor pressure in the sealed compartment of the system at summer high temperatures and (ii) more frequent use and service of refrigerators and air conditioners in summer months. Our results suggest that engineering (e.g., better temperature/vibration-resistant system sealing and new system design of more compact/efficient components) and regulatory (e.g., reinforcing system service regulations) steps to improve containment of these gases from working devices could effectively reduce their release to the atmosphere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".