Per- and polyfluoroalkyl substances and volatile methyl siloxanes in global air: Spatial and temporal trends
Bibliographic record
Abstract
The study reports on the atmospheric concentrations of per- and polyfluoroalkyl substances (PFAS) and volatile methyl siloxanes (VMS) measured using sorbent-impregnated polyurethane foam disks (SIPs) passive air samplers. New results are reported for samples collected in 2017, which extends temporal trend information to the period 2009–2017, for 21 sites where SIPs have been deployed since 2009. Among neutral PFAS, fluorotelomer alcohols (FTOHs) had higher concentrations than perfluoroalkane sulfonamides (FOSAs) and perfluoroalkane sulfonamido ethanols (FOSEs) with levels of ND‒228, ND‒15.8, ND‒10.4 pg/m3, respectively. Among ionizable PFAS, the sum of perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) in air were 0.128–781 and 6.85–124 pg/m3, respectively. Longer-chain i.e. C9–C14 PFAS, which are relevant to the recent proposal by Canada for a listing of long-chain (C9–C21) PFCAs to the Stockholm Convention, were also detected in the environment at all site categories including Arctic sites. Cyclic and linear VMS ranged between 1.34‒452 and 0.01–12.1 ng/m3, respectively, showing dominance in urban areas. Despite the wide range of levels observed across different site categories, geometric means of the PFAS and VMS groups were fairly similar when grouped according to the five United Nations regions. Variable temporal trends in air (2009–2017) were observed for both PFAS and VMS. PFOS, which has been listed in the Stockholm Convention since 2009, is still showing increasing tendencies at several sites, indicating constant input from direct and/or indirect sources. These new data inform international chemicals management for PFAS and VMS.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".