Global Distribution of Polyfluoroalkyl and Perfluoroalkyl Substances and their Transformation Products in Environmental Solids
Bibliographic record
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFASs) have been ubiquitously detected in environmental solids, like sediments, wastewater treatment plant (WWTP) sludge, and soil, with perfluorooctanoate (PFOA, C8) and perfluorooctane sulfonate (PFOS, C8) typically observed as the dominant perfluoroalkyl acids (PFAAs). Urban and industrial discharges have been identified as major contributors to current ambient levels of PFASs in near-source environments, while a number of studies have also highlighted the contribution of known fluorochemical point sources to regional contamination hotspots. In these near-source regions, the high PFAS contamination observed in sediments and soils has been attributed to the proximity of airports and fire-training facilities using aqueous film-forming foams (AFFFs), discharges from nearby fluorochemical production facilities, accidental spills, and application of contaminated WWTP biosolids to agricultural farmlands. In the case of the biosolids-applied farmlands, significant PFAS contamination was limited not only to soils, but was also observed in the plants and groundwater collected in the vicinity. In addition, since the early 2000s, China has emerged as a major fluorochemical producer, especially after the production phase-out of perfluorooctylsulfonyl (POSF)-based materials in North America in 2000–2002. This shift in the fluorochemical industry is reflected in global environmental surveys in which sediments, WWTP sludge, and soil sampled in China and other Asian-Pacific countries often exhibit the highest PFAS concentrations compared to those observed in Europe and North America.
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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 | 0.002 | 0.003 |
| 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".