Critical Evaluation and Meta-Analysis of Ecotoxicological Data on Per- and Polyfluoroalkyl Substances (PFAS) in Freshwater Species
Bibliographic record
Abstract
Despite the increasing concern regarding the ecological risks posed by per- and polyfluoroalkyl substances (PFAS), a lack of comprehensive understanding of their actual ecotoxicity remains. Through a meticulous examination of 91 peer-reviewed studies investigating effects at a population level and constructing probabilistic species sensitivity distributions (PSSDs), we present a state-of-the-science hazard assessment of PFAS in freshwater species. Using data subsets containing suboptimal data led to an overestimation of the predicted no-effect concentrations (PNECs) of PFAS. We report PNECs of perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonates (PFSAs) in freshwater to be 4.8–2000 μg/L and 0.4–8.9 μg/L, respectively, derived from high-quality data. Statistical analyses revealed that both functional groups and carbon chain length significantly influenced ( p < 0.05) the variations in toxicity observed among different PFAS. This study underscores the importance of obtaining high-quality PFAS ecotoxicity data to comprehend associated hazards. The PNECs of PFAS derived in this study are higher compared to those of micro/nanoplastics and persistent organic pollutants. Our research offers valuable insights into prioritizing the regulation of more toxic PFAS.
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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 | 0.138 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.041 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".