Detection of Atmospheric Hydrofluorocarbon-22 with Ground-Based Remote High-Resolution Fourier Transform Spectroscopy over Hefei and an Estimation of Emissions in the Yangtze River Delta
Bibliographic record
Abstract
Under the control of the Montreal Protocol and its amendments, hydrofluorocarbons (HCFCs) are used as temporary substitutes for ozone-depleting substances, such as chlorofluorocarbons, and are regulated for consumption and production. China plans to phase out HCFCs by 2030, and HCFC-22 (CHClF2) is currently the most abundant HCFC in the atmosphere. This study measures the vertical profiles and total columns of atmospheric HCFC-22 from January 2017 to December 2022, based on the mid-infrared solar spectra recorded by the ground-based high-resolution Fourier transform infrared (FTIR) spectrometer at the Hefei remote sensing station. The HCFC-22 total columns over Hefei increased from 2017–2018 and gradually decreased in 2018–2022, with an annual variation rate of 5.98% and −1.02% ± 0.02%, respectively. Compared with the ACE-FTS satellite independent dataset, the FTIR data indicate good consistency with the ACE-FTS data at a 5–25 km altitude, with an average relative difference of −4.38 ± 0.83% between the vertical profiles. HCFC-22 emissions in the Yangtze River Delta from 2017 to 2022 are estimated, derived from measured total columns combined with the Lagrangian transport model and the Bayesian inversion technique. In the Yangtze River Delta, HCFC-22 emissions were high in 2017, with a value of 33.3 ± 16.8 kt, and decreased from 2018 to 2022, with a minimum of 27.3 ± 13.6 kt in 2022 during the observations.
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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 | 0.001 | 0.001 |
| 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".