Microplastic contamination in fish from the St. Lawrence River and Estuary: Roles of semisynthetic polymers, passive uptake, and wastewater inputs
Bibliographic record
Abstract
Semisynthetic polymers, such as rayon, are inconsistently reported in microplastic monitoring as most studies focus on synthetic polymers. However, evidence is growing for their ecological impacts. We measured microplastics, including semisynthetic polymers, in water and four fish species from the St. Lawrence River and Estuary (SLRE, Canada), a freshwater-marine corridor and good model for large river-estuary systems. Microplastic abundance was 0.44 ± 1.13 (mean ± SD) in fish gastrointestinal tract, 1.34 ± 2.12 (n/sample) in fish gills, and 2.17 ± 3.68 (n/L) in water. Rayon was the dominant microplastic in both water (41 %) and fish (40-100 %), revealing an underreported but significant contribution of semisynthetic polymers to aquatic microplastic burdens. This finding underscores the need to integrate semisynthetic polymers into future monitoring frameworks. In large piscivorous fish, gill uptake contributed more to microplastic accumulation than oral ingestion, unlike most non-piscivorous species reported in the literature, which accumulate more microplastics in the gastrointestinal tract. Comparisons of sites upstream and downstream of wastewater treatment plants (WWTPs) showed no significant difference in total microplastic abundance, but downstream waters contained more particles <100 μm and a broader diversity of polymers and colors. Given the greater environmental risks of smaller microplastics, these patterns highlight gaps in WWTP performance metrics that focus solely on total counts. Our findings provide evidence to expand monitoring frameworks to include semisynthetic polymers, incorporate non-oral exposure pathways into risk assessments, and improve WWTP metrics to inform global policy against microplastic pollution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".