Year‐Long High‐Frequency Observations of 16 Regulated ODS and HFCs in Urban Guangzhou, South China: Implications for Regional Emissions
Bibliographic record
Abstract
Abstract The Pearl River Delta is the worlds largest urban‐industrial agglomeration, yet continuous measurements of ozone‐depleting substances and Hydrofluorocarbons (HFCs) remain scarce. Here we present 1‐year (April 2022 to March 2023), 3‐hr resolution record of 16 regulated halogenated gases from central Guangzhou. Our results show that urban baseline of phased‐out chlorofluorocarbons and halons exceeded hemispheric baseline values by only 1%–5% and continued to decline (e.g., −4.7 ± 0.3 ppt yr −1 for CFC‐11 and −4.3 ± 0.4 ppt yr −1 for CFC‐12), confirming the effectiveness of China's production bans. In contrast, frequent wintertime plumes of CFC‐113 and CFC‐13, often accompanied by HFC‐23, revealed unresolved emissions linked to fluoropolymer and HCFC‐134a manufacture; tracer inversions yield 2.4 ± 0.7 Gg yr −1 of CFC‐113 and 0.07 ± 0.04 Gg yr −1 of CFC‐13, together representing ∼31% of current global totals. Summer heat triggered large enhancements of HCFC‐22, HCFC‐141b, and R‐410A components HFC‐32 and HFC‐125, by up to twofold relative to winter, implicating leaking during room air‐conditioning and refrigeration equipment operation/servicing. Tracer‐ratio inversions reveal that HFC‐32 and HFC‐125 emissions from southeastern China amounted to 39 ± 8 Tg yr −1 CO 2 ‐equivalent. These observations demonstrate that dense urban monitoring can detect clandestine by‐product release, and expanding similar high‐frequency networks cross China's industrial corridors would enable targeted mitigation of most climate‐ and ozone‐relevant emissions from rapidly growing megacities. Strengthened HFC quota enforcement, accelerated adoption of lower‐GWP refrigerants, improved bank management and destruction, and tighter by‐product emission controls are critical to align China's emissions with the Montreal Protocol and Kigali Amendment.
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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 | 0.000 | 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) | 0.001 | 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".