Unravelling the magnitude and drivers of PFAS trophic magnification: a meta-analysis
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) threaten ecosystems worldwide due to their persistence, bioaccumulation, and toxicity. Through a global-scale meta-analysis of 119 aquatic and terrestrial food webs from 64 studies, we analyse 1009 trophic magnification factors (TMFs) for 72 PFAS and identify key variability drivers. On average, PFAS concentrations double with each trophic level increase (mean TMF = 2.00, 95% CI:1.64-2.45), though magnification varies considerably by compound. Notably, the industrial alternative F-53B exhibits the highest magnification (TMF = 3.07, 95% CI:2.41-3.92), a critical finding given its expanding use and minimal regulatory scrutiny. Methodological disparities across studies emerge as the dominant source of TMF variability. Our models explain 85% of the variation in TMFs, underscoring predictive capacity. This synthesis establishes PFAS as persistent trophic multipliers and provides a framework to prioritise high-risk compounds and harmonise biomagnification assessments. Our results call for consideration of stricter PFAS regulation to curb cascading ecological and health impacts. PFAS are “forever chemicals” that build up in living things and can move through food webs. This study shows their levels roughly double with each step up the food chain, highlighting widespread chemical magnification in nature.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".