Occurrence and spatial distribution of neutral perfluoroalkyl substances and cyclic volatile methylsiloxanes in the atmosphere of the Tibetan Plateau
Bibliographic record
Abstract
Due to their properties of bioaccumulation, toxicity, and long-range atmospheric transport, polyfluoroalkyl and perfluoroalkyl substances (PFASs) and cyclic volatile methylsiloxanes (cVMSs) are currently being considered as emerging persistent organic pollutants (POPs) for regulation. To date, there are limited data on PFASs and cVMSs in the atmosphere of the Tibetan Plateau (TP), a remote environment which can provide information on global background conditions. Sorbent-impregnated polyurethane foam (SIP) disc passive air samplers were therefore deployed for 3 months (May to July 2011 and 2013) at 16 locations across the TP. Using previously reported methods for estimating the air volume sampled by SIP discs, the derived atmospheric concentrations ranged as follows: 18–565 ng m −3 for ΣcVMS (including D3, D4, D5, and D6); 65–223 pg m −3 for fluorotelomer alcohols (ΣFTOHs); 1.2–12.8 pg m −3 for fluorinated sulfonamides (ΣFOSA); and 0.29–1.02 pg m −3 for fluorinated sulfonamidoethanols (ΣFOSE). The highest ΣcVMS occurred at Lhasa, the capital city of the TP, indicating the local contribution to the emerging pollutants. Higher levels of ΣFTOHs were observed at sites close to the transport channel of the Yarlung Tsangpo Grand Canyon, indicating possible long-range atmospheric transport (LRAT). Elevated concentrations of shorter-chain volatile PFAS precursors (4 : 2 FTOH and fluorobutane sulfonamidoethanol) were found in most air samples, reflecting the shift in production from long- to short-chain PFASs in Asia. Overall, concentrations of emerging POPs at background sites of the TP were 1–3 orders of magnitude higher than those reported for legacy POPs.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".