Source activation or fluvial transport – dynamic controls on spatial patterns and temporal dynamics of plastic pollution in river corridors
Bibliographic record
Abstract
Microplastic pollution has been found to be ubiquitous in freshwater ecosystems around the world, with global models predicting river network contributions to the oceans to present major and still increasing sources of marine plastic waste. While previous research has to a large degree focussed on identifying potential sources of plastic pollution to freshwater ecosystems (such as wastewater treatment plants, storm sewers, urban areas), and attributing these to observed microplastic pollution patterns in river corridors, little is known under what conditions potential pollution sources become activated and connected to surface waters, and how the fluvial transport of different micro- and nanoplastic size fractions determines spatial patterns of plastics along river networks, including long-term deposition, storage and potential resuspension. This paper integrates field-based evidence of our global river microplastic survey and several comparative large river network studies (including the rivers Ganges, Boulder Creek, Rhone, and others) with river basin to global scale plastic fate and transport models to identify major drivers of hotspots and hot moments of riverine plastic pollution. Our results highlight under what conditions prior knowledge of the source distributions of plastic pollution carries significant predictive capacity for expected river corridor microplastic concentrations and when (and where) these patters can get transformed substantially by fluvial transport (and transformation) processes. Fusing this experimental evidence with our model predictions revealed significant differences in the downstream footprint, longevity and legacy of dominant sources and transport controls of plastics in the water column and in streambed sediments, driven by gravitational settling, hyporheic exchange flow and resuspension processes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".