Microplastic Emissions and Retention in Urban Catchments and Stormwater Ponds
Bibliographic record
Abstract
The rapid growth in plastic production and mismanagement of plastic waste streams have raised environmental concerns, with microplastics (MPs) emerging as pervasive pollutants. This study quantifies stormwater MP exports from urban areas by examining five stormwater management ponds (SWPs) and their representative catchments in Kitchener, Ontario, Canada, using field sampling, laboratory extraction, and modeling approaches. Using Laser Direct Infrared (LDIR) spectroscopy, MP concentrations were determined for different MP shapes and polymer compositions, enabling the calculation of both particle- and mass-based fluxes. A hydrology model coupled with a mass balance approach was employed to estimate MP emission factors (i.e. export coefficients) and retention efficiencies in both particle- and mass-based units. Land use impacts were examined by classifying stormwater catchments through machine learning-aided analysis of aerial imagery. Sediment emissions were also quantified through surveys and samplings to explore potential correlations with MP exports. Industrial catchments showed the highest MP emission factor at 8.7×1011 particles ha-1 year-1 (19.6 kg ha-1 year-1), whereas residential areas exhibited the lowest emissions at 1.7×1011 particles ha-1 year-1 (2.3 kg ha-1 year-1). Fibrous MPs accounted for 2–6% of particle-based emissions but 10–24% by mass, highlighting differences in composition across land use types. Parking lots and traffic were key contributors to MP pollution, consistent with polymer composition analysis. SWP retention efficiencies ranged from 73–97% for total loads but varied for specific polymers, from minimal to complete retention. Retention performance was influenced by SWP design features such as inlet and outlet configurations, catchment wash-off dynamics, and hydraulic residence time. These findings emphasize the critical role of land use and SWP design in urban stormwater MP mitigation, with industrial and high-traffic areas contributing significantly to pollution. Understanding these dynamics provides actionable insights for mitigating MP emissions and optimizing SWP retention performance to protect aquatic ecosystems.
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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 | 0.000 | 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) | 0.000 | 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".