Hidden Contaminants: The Presence of Per- and Polyfluoroalkyl Substances in Remote Regions
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are increasingly detected in remote environments. This review aims to provide a comprehensive overview of the types and concentrations of PFAS found in the air, water, soil, sediments, ice, and precipitation across different remote environments globally. Most of the recent studies on PFAS remote occurrence have been conducted for the Arctic, the Antarctica, and the remote regions of China. Elevated perfluorooctane sulfonate (PFOS) in Meretta and Resolute Lakes reflects the impact of local sources like airports, while PFAS in lakes located in remote regions such as East Antarctica and the Canadian High Arctic suggest atmospheric deposition as a primary PFAS input. Long-chain PFAS (≥C7) accumulate in sediments, while short-chain PFAS remain in water, as shown in Hulun Lake. Oceanic PFAS are concentrated in surface waters, driven by atmospheric deposition, with PFOA and PFOS dominating across oceans due to current emissions and legacy contamination. Coastal areas display higher PFAS levels from local sources. Arctic sediment analysis highlights atmospheric deposition and ocean transport as significant PFAS contributors. PFAS in Antarctic coastal areas suggest local biological input, notably from penguins. The Tibetan Plateau and Arctic atmospheric data confirm long-range transport, with linear PFAS favoring gaseous states, while branched PFAS are more likely to associate with particulates. Climatic factors like the Indian monsoon and temperature fluctuations affect PFAS deposition. Short-chain PFAS are prevalent in snowpacks, serving as temporary reservoirs. Mountainous regions, such as the Tibetan Plateau, act as cold traps, accumulating PFAS from atmospheric precursors. Future studies should focus on identifying and quantifying primary sources of PFAS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".