Extending the Monitoring of Perfluoroalkyl Substances in Arctic Air Reveals a High Abundance of Both Short Acids and Neutral Compounds
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Interest in per- and polyfluoroalkyl substances (PFASs) in the remote atmosphere now extends to perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) with short ( n C < 4), medium (3 < n C < 13), and long ( n C > 12) alkyl chains. A liquid chromatography–mass spectrometry method for the combined analysis of PFASs of variable chain length was applied to 204 high volume active air samples collected at Alert, Nunavut (82° 30′ N 62° 20′ W) between March 2014 and October 2023. Short-chain PFASs (scPFASs) were detected frequently (>75%) and at the highest median concentrations (trifluoroacetic acid (TFA): 20 pg/m 3, perfluoropropionic acid (PFPrA): 1.1 pg/m 3, perfluorobutanoic acid (PFBA): 3.7 pg/m 3 ), while n C > 10 PFAS were sparsely detected (detection frequency [DF] < 20%). Using a suspect-screening approach, hexafluoro-2,2-propanediol (HF2OH) and hexafluoroisopropanol (HFIPA) were confirmed in Arctic air at DF exceeding 75%. We find that concentrations of TFA and HF2OH were significantly correlated with temperature and increased during snowmelt periods, suggesting local emission or precursor release followed by degradation processes. The modified OECD LRTP and Pov assessment tool supported the potential of HFIPA and HF2OH to undergo long-range atmospheric transport. Time trend analysis reveals that after a short period of stable or declining levels in the mid-2010s, concentrations of PFBA, PFOA, and PFOS in Arctic air are increasing again since 2019, which may be a useful consideration when evaluating the effectiveness of the Stockholm Convention’s listing of PFOA and PFOS.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".