Runoff and discharge pathways of microplastics into freshwater ecosystems: A systematic review and meta-analysis
Bibliographic record
Abstract
Although many studies have focused on the importance of littering and (or) illegal dumping as a source of plastic pollution to freshwater, other relevant pathways should be considered, including wastewater, stormwater runoff, industrial effluent/runoff, and agricultural runoff. Here, we conducted a meta-analysis focused on these four pathways. We quantified the number of studies, amount and characteristics of microplastics reported, and the methods used to sample and measure microplastics from each pathway. Overall, we found 121 studies relevant to our criteria, published from 2014 to 2020. Of these, 54 (45%) quantified and characterized microplastics in discharge pathways. Although most focused on wastewater treatment plant effluent (85%), microplastic concentrations were highest in stormwater runoff (0.009 to 3862 particles/L). Morphologies of particles varied among pathways and sampling methods. For example, stormwater runoff was the only pathway with rubbery particles. When assessing methods, our analysis suggested that water filtered through a finer (<200 um) mesh and of a smaller volume (e.g., 6 L) captured more particles, and with a slightly greater morphological diversity. Overall, our meta-analysis suggested that all four pathways bring microplastics into freshwater ecosystems, and further research is necessary to inform the best methods for monitoring and to better understand hydrologic patterns that can inform local mitigation.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".