Microplastics Characterization in Stormwater: Pavement Source Evaluation and Treatment Efficiency of a Bioretention Cell
Bibliographic record
Abstract
Due to the widespread use of plastic in numerous disciplines, microplastics, a suite of environmental contaminants, are found globally in increasingly large quantities. It is important to characterize all microplastics sources and pathways to better identify solutions that reduce their presence and mitigate their spread in the environment. This thesis had three main objectives: (i) to characterize microplastics in urban stormwater runoff; (ii) to evaluate a bioretention cell's efficiency to remove microplastics from urban stormwater runoff; and (iii) to review the ability of stormwater engineering tools and porous media models to evaluate microplastic removal from stormwater.First, a two-year field study was conducted where stormwater was collected from four different pavement locations including an asphalt road, asphalt lot, concrete lot, and rubber lot. Study findings identified microplastics concentrations, polymer types, and morphologies from these different pavement types. Though generally lumped together, pavement was found to be a distinct source of microplastics from tire wear. Pavement surface characteristics were also found to influence microplastics generation in stormwater. Second, during the same two-year field study, autosamplers were used to collect stormwater at the inlet and outlet of a bioretention cell. Findings showed that the bioretention cell effectively captured both large (i.e., between 106 µm to 5 mm) and smaller (i.e., between 25 to 106 µm) dimensioned microplastics at rates of 84% and 71% respectively. Correlations between microplastics concentrations and hydrologic factors were evaluated. Third, a literature review was conducted of existing stormwater engineering tools and porous media studies evaluating microplastics filtration. Findings showed no current engineering tools are well-equipped to model all microplastics. Instead, two models are needed based on different particle size ranges. More studies are needed to parameterize these models for the full suite of stormwater-derived microplastics. In characterizing microplastics in stormwater, evaluating their capture via a bioretention cell, and investigating the capacity to model these systems, this thesis supports planning strategies and policies that reduce microplastics production and mitigate their spread in urban stormwater.
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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".