Bioaccumulation and Transfer of Per- and Polyfluoroalkyl Substances (PFAS) in a Stream and Riparian Food Web Contaminated by Food Processing Wastewater
Bibliographic record
Abstract
We evaluated the bioaccumulation and transfer of per- and polyfluoroalkyl substances (PFAS) in a stream food web contaminated by a food processing facility. Abiotic (i.e., water, sediment, and foam) and biotic (i.e., algae, aquatic insect larvae and adults, fish, and riparian spiders) matrices were sampled upstream and downstream of the facility’s wastewater outfall. Compared with upstream, PFAS concentrations were 600-fold higher in downstream water (mean ∑ 40 PFAS 3.67 ng mL –1 ± 0.48 (standard error)) and reflected inputs from the outfall, with 6:2 fluorotelomer sulfonate (6:2 FTS) dominating the PFAS profile. Within the aquatic food web, perfluorooctanesulfonate (PFOS) was the most biomagnified, and 6:2 FTS was the most biodiluted. In contrast, insect-mediated transfer of PFAS to riparian spiders showed trophic enrichment of 6:2 FTS and dilution of PFOS. We observed significant positive associations between phospholipid membrane-water partition coefficient (log K MW ) and perfluoroalkyl carboxylate (PFCA) chain length on bioaccumulation across most biological matrices, demonstrating that these chemical parameters are predictive of PFAS bioaccumulation potential in the field. Our research reveals important differences in aquatic versus terrestrial exposure for certain PFAS and that biological processes (e.g., trophic interactions and metamorphosis) and chemical properties (e.g., chain length, log K MW, and concentration) control PFAS uptake, bioaccumulation, and transfer in linked freshwater and terrestrial ecosystems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".