Isomer Profiles of Perfluoroalkyl Substances in Water and Soil Surrounding a Chinese Fluorochemical Manufacturing Park
Bibliographic record
Abstract
Despite that China is the largest global manufacturer of perfluoroalkyl substances (PFASs), the manufacturing methods and isomer purity of these chemicals are generally unknown. Here, sampling was conducted around a major fluorochemical manufacturing park in China in 2012, including soil and water collection inside the park, including from a wastewater treatment plant (WWTP), as well as in surrounding rivers and soil (∼15 km radius). Perfluoroalkyl sulfonates (PFSAs) were lower than perfluoroalkyl carboxylates (PFCAs) in all samples, and short-chain (C4-C6) PFCAs were predominant. Perfluoroalkyl phosphonates and phosphate diesters were occasionally detected, but at low detection frequency. Branched isomers of perfluorobutanesulfonate (PFBS) are reported for the first time, accounting for 15-27% of total PFBS in water. An enrichment of isopropyl-PFOA (28%) was found in WWTP influent, suggesting its manufacturing primarily by isopropyl telomerization. More numerous branched isomers were observed for the longer C9-C13 PFCAs (e.g., C12 PFCA had 16 branched isomers), including high proportions of one major branched isomer (likely isopropyl), possibly as impurities from isopropyl-PFOA manufacturing. Overall, short-chain perfluorinated acids were the predominant PFASs being released, but PFOA was still a major chemical in use at this site, primarily from isopropyl telomerization.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".