Micro and Nanoplastic Contamination and Its Effects on Freshwater Mussels Caged in an Urban Area
Bibliographic record
Abstract
Plastic-based contamination has become a major cause of concern as it pervades many environments such as air, water, sediments, and soils. This study sought to examine the presence of microplastics (MPs) and nanoplastics (NPs) in freshwater mussels placed at rainfall/street runoff overflows, downstream (15 km) of the city centre of Montréal, and 8 km downstream of a municipal effluent dispersion plume. MPs and NPs were determined using flow cytometry and size exclusion chromatography using fluorescence detection. Following 3 months of exposure during the summer season, mussels contained elevated amounts of both MPs and NPs. The rainfall overflow and downstream of the city centre were the most contaminated sites. Lipid peroxidation, metallothioneins, and protein aggregates (amyloids) were significantly increased at the most contaminated sites and were significantly correlated with NPs in tissues. Based on the levels of MPs and NPs in mussels exposed to municipal effluent, wastewater treatment plants appear to mitigate plastic contamination albeit not completely. In conclusion, the data support the hypothesis that mussels placed in urbanized areas are more contaminated by plastics, which are associated with oxidative damage. The highest responses observed at the overflow site suggest that tire wear and/or asphalt (road) erosion MPs/NPs represent important sources of contamination for the aquatic biota.
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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 | 0.000 | 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".