Pedestrian traffic is the main driver of macro- and large microplastic debris deposition in urban stormwater drains
Bibliographic record
Abstract
Land-based sources are the greatest contributors of plastic pollution found in aquatic environments. Although plastic debris items spilling into natural watercourses from stormwater outflow sites have been investigated, this study provides details of the plastic items that can be trapped within stormwater drains prior to release. We examined macroplastic (>5 mm) and large microplastic (1–5 mm) debris that accumulated in LittaTrap TM devices at six drains over four seasonal periods in London, Ontario, Canada. Flotation, visual identification, microscopy and Fourier transform infrared spectroscopy (FTIR) were used to determine the drivers of plastic debris deposition. Macroplastics (MaPs) and microplastics (MPs) were identified in all 36 samples, and the totals ranged from 5–158 MaPs and 18–359 MPs per trap. Out of the 118 different MaPs found, the most common items were cigarette butts, wrappers, and expanded polystyrene. The main MPs were fragments, foams, and fibres. The most common macroplastic applications were “smoking”, “food/beverage packaging”, “household”, and non-food or beverage “packaging”. Microplastic particle compositions were mainly polyethylene and polypropylene, but other polymer types fall within the applications of construction (paints and resins), automotive/transportation, and electronics. The summer samples contained the greatest averages of plastic debris, and the drains located in busy pedestrian areas were associated with the highest debris counts. The results support pedestrian traffic as the main driver of plastic debris accumulation in urban London stormwater drains, which is controlled by seasonal weather conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".