Pre-regulatory actions as a driver for reduced PFAS emissions? Long-term trends and change points for human and environmental samples from Germany
Bibliographic record
Abstract
Abstract Per- and polyfluoroalkyl substances (PFAS) contamination is a long-term global problem for human health and the environment. Due to their hazardous properties and high risks to humans and the environment, certain PFAS such as perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) were phased out by their main manufacturers in the first decade of the 2000s and have been regulated on a regional and international scale by the Stockholm Convention. So far, the effectiveness of PFAS management—defined as the sum of all regulatory and voluntary actions—is not well understood. We conducted a comprehensive time trend analysis with samples from the German Environmental Specimen Bank dating back to the 1980s and early 1990s. These samples covered human samples (blood plasma) as well as samples from the terrestrial (deer liver), freshwater (bream muscle/liver, mussel soft tissue) and coastal environment (herring gull eggs). The aim of this study was to statistically assess the change points (CPs) of exposure and to quantify the changes before and after the CP in comparison with the evolving PFAS management. The analysis revealed remarkably similar CP estimates across human and environmental matrices investigated. The results show that the maximum concentrations of PFOS were already reached before (late 1990s) the announcement of the phase-out by their main manufacturer. These results are similar for all PFSA tested and indicate an earlier shift to PFAS substitutes than previously assumed. For PFCAs, CPs were generally later (mid-2000s to late 2000s), possibly due to later regulatory action on PFCAs and their preceding production shifts, respectively. In general, regulatory actions were introduced around 10 to 15 years after the respective CPs, which indicates market shifts for PFAS in anticipation of compound specific regulatory measures and not as a reaction to them and demonstrates the need for faster regulatory actions to effectively protect humans and the environment. Graphical Abstract
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".