Urban Stormwater Runoff: A Major Pathway for Anthropogenic Particles, Black Rubbery Fragments, and Other Types of Microplastics to Urban Receiving Waters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Stormwater runoff has been suggested to be a significant pathway of microplastics to aquatic habitats; yet, few studies have quantified microplastics in stormwater. Here, we quantify and characterize urban stormwater runoff from 12 watersheds surrounding San Francisco Bay for anthropogenic debris, including microplastics. Depth-integrated samples were collected during wet weather events. All stormwater runoff contained anthropogenic microparticles, including microplastics, with concentrations ranging from 1.1 to 24.6 particles/L. These concentrations are much higher than those in wastewater treatment plant effluent, suggesting urban stormwater runoff is a major source of anthropogenic debris, including microplastics, to aquatic habitats. Fibers and black rubbery fragments (potentially tire and road wear particles) were the most frequently occurring morphologies, comprising ∼85% of all particles across all samples. This suggests that mitigation strategies for stormwater should be prioritized. As a case study, we sampled stormwater from the inlet and outlet of a rain garden during three storm events to measure how effectively rain gardens capture microplastics and prevent it from contaminating aquatic ecosystems. We found that the rain garden successfully removed 96% of anthropogenic debris on average and 100% of black rubbery fragments, suggesting rain gardens should be further explored as a mitigation strategy for microplastic pollution.
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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 | 0.000 |
| 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) | 0.001 | 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".