A Global Mass Balance Analysis of the Source of Perfluorocarboxylic Acids in the Arctic Ocean
Bibliographic record
Abstract
Whereas the pervasive and abundant presence of perfluorinated carboxylic acids (PFCAs) in the Arctic marine food chain is clearly established, their origin and transport pathway into the Arctic Ocean are not. Either the atmospheric oxidation of volatile precursor compounds, such as the fluorotelomer alcohols (FTOHs), or the long-range oceanic transport of directly emitted PFCAs is seen as contributing the bulk of the PFCA input to the Arctic. Here simulations with the zonally averaged global fate and transport model Globo-POP, in combination with historical emission estimates for FTOHs and perfluorooctanoic acid (PFOA), are used to evaluate the relative efficiency and importance of the two transport pathways. Estimates of the emission-independent Arctic Contamination Potential reveal that the oceanic transport of directly emitted PFCAs is more than 10-fold more efficient than the atmospheric degradation of FTOHs in delivering PFCAs to the Arctic, mostly because of the low yield of the reaction. The cumulative historic emissions of FTOHs are lower than those estimated for PFOA alone by a factor of 2-3, further limiting the contribution that precursor oxidation makes to the total PFCAs load in the Arctic Ocean. Accordingly, when fed only with FTOH emissions, the model predicts FTOH air concentrations in agreement with the reported measurements, but yields Arctic seawater concentrations for the PFOA that are 2 orders of magnitude too low. Whereas ocean transport is thus very likely the dominant pathway of PFOA into the Arctic Ocean, the major transport route of longer chain PFCAs depends on the size of their direct emissions relative to those of 10:2 FTOH. The predicted time course of Arctic seawater concentrations is very similar for directly emitted and atmospherically generated PFCAs, implying that neither past doubling times of PFCA concentrations in Arctic marine mammals nor any future time trends are likely to resolve the question of the dominant source of PFCAs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".