Modeling Stormwater Driven Plastic Debris in the Lake Ontario Watershed
Bibliographic record
Abstract
Stormwater-driven plastic debris has emerged as a significant contributor to environmental pollution, particularly in urbanized watersheds like the Lake Ontario basin. This study focuses on developing a comprehensive framework to quantify and predict anthropogenic debris (AD) inputs and transport pathways across Monroe County, NY, using stormwater systems as a key vector. Over two years, empirical data on plastic debris were collected from storm drains retrofitted with LittaTrap™ devices across urban and suburban areas. Debris was weighed, categorized by material and use, and paired with spatial and temporal variables such as land use, rainfall, temperature, and wind events. A Support Vector Regression (SVR) model was developed to estimate daily plastic input from 30,000 storm drains, predicting a total of over 13.8 metric tonnes of debris in 2023, with approximately 6 tonnes directly entering the environment through municipal separate storm sewer systems (MS4s). Urban commercial areas were identified as major hotspots, with seasonal trends revealing higher debris loads during warmer months, influenced primarily by temperature and precipitation. Building on these findings, a watershed transport model was designed to simulate debris movement through streams, riparian zones, and stormwater ponds, integrating parameters like stream order, vegetation cover, and rainfall intensity using a modified Revised Universal Soil Loss Equation (RUSLE). The model highlights the role of precipitation in mobilizing debris from terrestrial inputs into aquatic systems, underscoring the vulnerability of higher-order streams and riparian zones during storm events. This work provides actionable insights for policymakers and watershed managers, offering a predictive tool to identify pollution hotspots, optimize cleanup interventions, and mitigate plastic debris transport. By emphasizing stormwater as a critical pathway, the framework can be adapted to other municipalities, contributing to scalable, data-driven solutions for plastic pollution in freshwater systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".