Small-Size Microplastics in Urban Stormwater Runoff are Efficiently Trapped in a Bioretention Cell
Bibliographic record
Abstract
As they decrease in size, microplastics pose increasing environmental and health risks. Previous work showed that bioretention cells, a type of low impact development (LID), are effective at removing microplastics greater than approximately 100 μm from urban stormwater runoff. This two-year field study investigates whether bioretention cells provide similar benefits by removing microplastics as small as 25 μm in size from urban stormwater. The use of automated μFTIR mapping allowed for the analysis of smaller microplastics, less than 100 μm, which, until recently, have rarely been analyzed in stormwater due to the difficulty of their identification. A 71% concentration decrease was observed in the bioretention cell. In this 25–100 μm size range, the median microplastic concentrations were 227 microplastics/L in the stormwater (i.e., the bioretention inlet) and 66.5 microplastics/L at the outlet. The most prevalent synthetic polymers were polypropylene and polyethylene. Rubber and fibers were not analyzed due to method limitations. No correlations between hydrologic characteristics and microplastic quantities were observed, highlighting that other factors are likely involved in the fate and transport of microplastics in stormwater, like weather-induced particle fragmentation. These results demonstrate that this filtration-based LID system continues to provide effective microplastic removal down to 25 μm.
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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 | 0.001 | 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".