Plastic Pollution in the Laurentian Great Lakes: Informing a Coordinated, Regional Management Response
Bibliographic record
Abstract
The past decade has seen substantial progress in understanding and managing plastic pollution in the Laurentian Great Lakes. Key sources—such as litter, pre-production resin, textiles, and paint—and transport pathways, including stormwater runoff and treated wastewater effluent, are now better characterized. Microplastics are now understood to be widely distributed in water, sediment, and biota across the basin, raising concerns about ecological and human health risks. In response, governments in Canada and the United States have implemented measures from regulatory bans to cleanup initiatives. However, a basin-wide strategy remains absent. The central objective of this thesis is to synthesize scientific and policy information to support more unified and regionally tailored government action on plastic pollution within the framework of the Great Lakes Water Quality Agreement. Chapter 2 reviews active public policies on the Canadian side of the basin relevant to reducing plastic pollution, identifying trends, gaps, and opportunities. I systematically searched legal databases and government websites, compiling and categorizing 195 policies across municipal, provincial, federal, and international levels. Findings reveal a predominance of non-regulatory policies and a focus on downstream management over upstream prevention. Chapters 3 and 4 explore the application and adaptation of novel ecological risk assessment and management frameworks for microplastics to the Great Lakes context. Chapter 3a introduces these frameworks regionally—drawing on applications in other jurisdictions—by comparing recommended risk thresholds to microplastic concentrations reported in Great Lakes water and sediment. Results indicate that while risks are not widespread at the lake or basin scale, localized hotspots exist, particularly in Lakes Ontario and Michigan. Chapter 3b contributes to ongoing discussions about adapting these frameworks, focusing on methodological implications and balancing imperfect data and uncertainty with timely action. Chapter 4 presents a Great Lakes specific adaptation developed through a regional workshop. The resulting management framework comprises three tiers representing different ecosystem health statuses based on microplastic concentrations. Two risk thresholds—corresponding to concentrations at which 95% and 70% of aquatic species are predicted to be protected—differentiate the tiers. While workshop participants identified areas for improvement, confidence ratings and data quality assessments suggest that these outputs offer credible foundation for further refinement and future application. Collectively, this thesis provides original scientific and policy evidence to inform the future direction of regional plastic pollution management.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".