The influence of flow on the amount, retention and loss of plastic pollution in an urban river
Bibliographic record
Abstract
Urban rivers are hypothesized to be major transporters of plastic pollution into lakes and oceans, with storm events playing a pivotal role. However, few studies investigate microplastic and macroplastic contamination and transport across a river basin, and how it varies with flow. Here, we sampled microplastic (less than 5 mm) and macroplastic (greater than 5 mm) from four sites along an urban river in Ontario, Canada, during baseflow and stormflow. To contextualize their fate and transport through river reaches, we sampled macroplastic stored in the riparian zone, overhanging vegetation, floating in surface water and riverbed and sampled microplastic from the surface water, water column and sediment. At baseflow, most macroplastic was found in the riparian zone (ranging from 0.1 to 4.7 pieces per m 2 ). During stormflow, concentrations (micro and macro) rise and fall with discharge. Moreover, the composition of microplastics in the water column shifts from fibre- to rubber-dominated during higher flows. The mobilization of denser (e.g. rubber) particles during flow is consistent with greater water velocities during storms. Finally, using our data and flow patterns from 2022 to 2023, we estimate that approximately 522 billion microplastic particles and 20 754 macroplastic items, equalling approximately 36 000 and 160 kg by mass, respectively, are transported to Lake Ontario annually. This article is part of the Theo Murphy meeting issue ‘Sedimentology of plastics: state of the art and future directions’.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".