Manufacturing Origin of Perfluorooctanoate (PFOA) in Atlantic and Canadian Arctic Seawater
Bibliographic record
Abstract
The extent to which different manufacturing sources and long-range transport pathways contribute to perfluorooctanoate (PFOA) in the world's oceans, particularly in remote locations, is widely debated. Here, the relative contribution of historic (i.e., electrochemically fluorinated) and contemporary (i.e., telomer) manufacturing sources was assessed for PFOA in various seawater samples by an established isomer profiling technique. The ratios of individual branched PFOA isomers were indistinguishable from those in authentic historic standards in 93% of the samples examined, indicating that marine processes had little influence on isomer profiles, and that isomer profiling is a valid source apportionment tool for seawater. Eastern Atlantic PFOA was largely (83-98%) of historic origin, but this decreased to only 33% close to the Eastern U.S. seaboard. Similarly, PFOA in the Norwegian Sea was near exclusively historic, but the relative contribution decreased to ∼50% near the Baltic Sea. Such observations of contemporary PFOA in coastal source regions coincided with elevated concentrations, suggesting that the continued production and use of PFOA is currently adding to the marine burden of this contaminant. In the Arctic, a spatial trend was observed whereby PFOA in seawater originating from the Atlantic was predominantly historic (up to 99%), whereas water in the Archipelago (i.e., from the Pacific) was predominantly of contemporary origin (as little as 17% historic). These data help to explain reported temporal and spatial trends from Arctic wildlife biomonitoring, and suggest that the dominant PFOA source(s) to the Pacific and Canadian Arctic Archipelago are either (a) from direct emissions of contemporary PFOA via manufacturing or use in Asia, or (b) from atmospheric transport and oxidation of contemporary PFOA-precursors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".