Widespread Occurrence of Non-Extractable Fluorine in Artificial Turfs from Stockholm, Sweden
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are frequently used in the production of rubber and plastic, but little is known about the identity, concentration, or prevalence of PFAS in these products. In this study, a representative sample of plastic- and rubber-containing artificial turf (AT) fields from Stockholm, Sweden, were subjected to total fluorine (TF), extractable organic fluorine (EOF) and target PFAS analysis. TF was observed in all 51 AT samples (range: 16-313, 12-310, and 24-661 µg F/g in backing, filling, and blades, respectively), while EOF and target PFAS occurred in <42% of all samples (<200 and <1 ng F/g, respectively). A subset of samples extracted with water confirmed the absence of fluoride. Moreover, application of the total oxidizable precursor assay revealed negligible perfluoroalkyl acid (PFAA) formation across all three sample types, indicating that the fluorinated substance(s) in AT are not low molecular weight PFAA-precursors. Collectively, these results point towards polymeric organofluorine (e.g. fluoroelastomer, polytetrafluoroethylene, polyvinylidene fluoride), consistent with patent literature. The combination of poor extractability and recalcitrance towards advanced oxidation suggests that the fluorine in AT does not pose an imminent risk to users. However, concerns remain surrounding the production and end-of-life of ATs, as well as the contribution of filling to environmental microplastic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".