Spatial and Temporal Trends of Perfluoroalkyl Substances in Global Ocean and Coastal Waters
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) have been widely detected in global surface waters since the early 2000s. Here, we have compiled and analyzed the published data for perfluorocarboxylates (PFCAs) and perfluorosulfonates (PFSAs) in surface waters of coastal seas, the Great Lakes, and open oceans to examine temporal and geospatial trends. Mass discharges from major rivers were also estimated. A large number of measurements of individual PFAS have been made in these surface waters (29 500 values), with seven C4-C10 PFSAs and nine C4-C12 PFCAs accounting for 83% of all data. However, most results (85% for PFSAs; 80% for PFCAs) were for the coastal seas of Western Europe, China, Korea, and Japan, while results were limited for coastal North America and lacking for South America and Africa. Highest median concentrations of PFCAs and PFSAs were reported in the Bohai and Yellow Seas region of China as well as in the North and Baltic seas in Europe. Significant declines in median PFSAs and C7-C12 PFCAs were also observed for the period 2012-2018 in these same regions, and for 2004-2017 in the Great Lakes. Mass discharge estimates indicated continued substantial riverine emissions of long chain (C7-C12) PFCAs in the period 2015-2019 for the coastal seas of China and reductions in emissions for Western European rivers compared to earlier time periods.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".