Pavement wear generates microplastics in stormwater runoff
Bibliographic record
Abstract
Tire and road wear particles are a major source of microplastics to urban stormwater. They are composed of hetero-aggregates of abraded tire and pavement particles that are difficult to distinguish. While tire wear is a known source of microplastics, little is known on the contribution of pavement wear. This two-year field study with complementary lab testing evaluates the effects of pavement degradation on microplastic generation in stormwater from different pavement types: asphalt, concrete, and recycled rubber pavers. Pavement specimens from each site were collected and underwent degradation testing. We directly demonstrated that pavement wear is a source of microplastics in stormwater separate from tire wear. We showed that the rubber pavement released the most microplastics in lab testing, suggesting that the formulation of such novel recycled-tire pavers must undergo thorough testing before wide application. The asphalt pavement was the most susceptible to rutting and released the most microplastics in the field, including a large proportion of tire wear particles. Both land-use and pavement surface characteristics influenced microplastic generation. These results demonstrate the need to consider microplastic generation during pavement material selection and mitigate the spread of microplastics from pavement wear to nearby environments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".