In situ occurrence and mobility of per and polyfluoroalkyl substances in soils amended with organic waste products
Bibliographic record
Abstract
We evaluated the in situ occurrence and soil-water distribution of 75 anionic, zwitterionic, and cationic per- and polyfluoroalkyl substances (PFAS) in soils from five field experimental sites distributed in different climatic regions in France. Four sites received agronomical doses of organic waste products (OWPs, ∼5-10 t/ha per application) with repeated inputs between 1974 and 1996 (2 historical sites) and 1998-2018 (2 on-going sites), while one site received about two-fold larger amounts. Control soils without OWP application had detectable yet low PFAS levels, the Σ75PFAS remaining in most cases below 1 μg/kg. Soils amended with municipal sludge or urban composts exhibited the largest Σ75PFAS increase relative to controls (∼2-20 μg/kg), with soils receiving biowaste composts displaying the lowest Σ75PFAS (∼2 μg/kg). In most cases, Σ75PFAS increased significantly with time. While perfluorooctane sulfonate (PFOS) typically dominated the PFAS profiles in municipal sludge-amended soils, the other prevalent PFAS classes varied with sites and years: soils from older sites also had anionic and cationic electrochemical fluorination-derived precursors (e.g., EtFOSAA and PFOSAmS), while on-going sites had increased prevalence of short-chain perfluoroalkyl carboxylates (PFCAs) (3 sites) and perfluoroalkyl phosphinates (1 site). Interestingly, 6:2 fluorotelomer sulfonamidopropyl betaine (6:2 FTAB), a major zwitterionic precursor found in French municipal sludge, was only detected at low levels in soils, indicating its transformation to degradation products. Leaching waters at a depth of 45 cm in the experimental plots had high levels of short-chain PFCAs (Mean C3-C5: 120-160 ng/L; Max C3-C5: 900-1600 ng/L), suggesting that land applied OWP containing PFCA precursors are important contributors to long-term groundwater contamination.
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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 | 0.001 | 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".