Identification of Microplastic Accumulation Zones in a Tidal River: A Case Study of the Fraser River, British Columbia, Canada
Bibliographic record
Abstract
Sustainable management of aquatic ecosystems requires effective strategies to monitor and mitigate microplastic pollution, particularly in vulnerable tidal river systems. Microplastic accumulation in these environments poses significant environmental risks, threatening biodiversity, ecosystem health, and long-term water quality. This study employs a three-dimensional hydrodynamic model (TELEMAC-3D—v8p5) coupled with a Lagrangian particle tracking model (CaMPSim-3D—v1.2.1) to simulate microplastic transport dynamics in the lower Fraser River, British Columbia, Canada. The model incorporates tidal forcing, riverine hydrodynamics, and mixing processes, and was validated with good agreement against observed water levels. This model provides a high-resolution representation of microplastic dispersion under varying release scenarios, including emissions from combined sewer overflows (CSOs) and wastewater treatment plants (WWTPs). A novel approach is proposed to identify microplastic accumulation zones using the OPTICS (Ordering Points to Identify the Clustering Structure) clustering algorithm. Accumulation zone locations remain spatially consistent despite variations in release volume. Persistent clusters occurred near channel constrictions and shoreline segments associated with flow deceleration. These findings demonstrate the robustness of the method and provide a systematic framework for prioritizing high-risk areas, supporting targeted monitoring and informing sustainable estuarine management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".