Spatial distribution and risk assessment of microplastics in surface waters of the St. Lawrence Estuary
Bibliographic record
Abstract
Development of effective prevention and mitigation strategies for marine plastic pollution requires a better understanding of the pathways and transport mechanisms of plastic waste. Yet the role of estuaries as a key interface between riverine inputs of plastic pollution and delivery to receiving marine environments remains poorly understood. This study quantified the concentration and distribution of microplastics (MPs) (50–3200 μm) in surface waters of the St. Lawrence Estuary (SLE) in eastern Canada. Microplastics were identified and enumerated based on particle morphology, colour, and size class. Fourier Transform Infrared (FTIR) spectroscopy was used on a subset of particles to identify polymers. Generalized linear models (Gamma distribution with log-link) examined the relationship between MP concentrations and oceanographic variables and anthropogenic sources. Finally, a risk assessment model, using MP concentrations and chemical hazards based on polymer types, estimated the MP pollution risk to ecosystem health. Mean surface MP concentration in the SLE was 120 ± 42 SD particles m−3; MP concentrations were highest in the fluvial section and lowest in the Northwest Gulf of St. Lawrence. However, MP concentrations exhibited high heterogeneity along the length and width of the SLE. Microplastics were elevated at stations located closer to wastewater treatment plant outflows and downstream sites with more agricultural land. Black, blue, and transparent fibers and fragments ≤250 μm were most commonly encountered. Predominant polymer types included polyethylene terephthalate, regenerated cellulose, polyethylene, and alkyds. While the overall risk to ecosystem health in the entire estuary was considered low, several stations, particularly near urban centres were at high or very high risk. This study provides new insights into the quantification and distribution of MPs and first estimates of the risk of MP pollution to ecosystem health in one of the world's largest estuaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".