Modelling microplastics in bioretention systems: A review
Bibliographic record
Abstract
Urban stormwater is both a major source and a mode of transport for microplastics in the environment. Black-box field studies have found that bioretention cells, a type of low impact development (LID), consisting mainly of engineered porous media, are effective systems for capturing microplastics. However, the mechanisms of how microplastics are transported, removed from stormwater, and fragmented within bioretention cells are unclear. Additionally, the impacts of microplastics on the hydrology and microplastic removal performance of bioretention cells remain unclear. This study assesses tools to model microplastic removal using LID and reviews the literature on the mechanisms of microplastic filtration in porous media. None of the evaluated stormwater tools were found to be well-suited to model microplastic removal via bioretention. We identified 74 studies that, at times, misinterpreted “all microplastics” as colloids. We recommend using a combination of two models to evaluate the full spectrum of microplastic sizes. Currently, the best-suited models are HYDRUS and the cake-layer model described in Li and Davis (2008a), which can be adapted for this purpose. More column studies are needed to parametrize these models that use the full range of polymer types and morphologies of urban stormwater microplastics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".