Detections of Commercial Fluorosurfactants in Hong Kong Marine Environment and Human Blood: A Pilot Study
Bibliographic record
Abstract
Previously, much of the perfluoroalkyl and polyfluoroalkyl substance (PFAS) research has focused on perfluoroalkyl carboxylates (PFCAs) or perfluoroalkane sulfonates (PFSAs). Recent studies indicate that known PFCAs and PFSAs accounted for 5-95% of the organofluorine (OF) in human and wild rat blood samples suggesting that a relatively large proportion of OF remained unknown. Until recently, some studies reported commercially available compounds such as polyfluoroalkyl phosphate diesters (diPAPs) and fluorotelomer sulfonates (FTSAs) in human blood and sludge samples. The present investigation is a pilot study aiming at surveying some newly identified PFASs such as diPAPs, FTSAs, and perfluorinated phosphinates (PFPiAs) in different environmental samples including surface water, sediment, sewage treatment plant influent and effluent, sludge, benthic worm, and human blood from Hong Kong. DiPAPs (6:2, 6:2/8:2, and 8:2) were detected in some of the samples at part-per-billion (ppb) levels in sludge, sub ppb levels in influent and effluent, sediment, worm, and human blood samples, and sub part-per-trillion (ppt) levels in surface waters. Sub ppt to ppb levels of 6:2 and 8:2 FTSAs were observed in worm, surface water, and human blood samples. PFPiAs were only observed in worm samples. The detected "new PFASs" accounted for a minor proportion (less than 5%) of the total PFASs in benthic worm and human blood, but up to 95% in sewage sludge samples from Hong Kong. This is the first report of commercial fluorosurfactants (PFPiAs, diPAPs, and FTSAs) in the samples from the environment and human blood in Hong Kong; further information on the distribution, fate, and transport of "new PFASs" in other Asian cities, as well as toxicity, is needed for further assessing the human exposure and risk.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".