A Big Tiny Problem: Flows of Primary Microplastics in Canada
Bibliographic record
Abstract
Microplastics are ubiquitous across ecosystems, posing chemical and physical risks to wildlife and human health. To effectively monitor and manage the microplastics problem, a baseline of the mass of primary microplastics produced, used, and discarded is measured. The literature lacks a comprehensive assessment of the different flows of microplastics, especially in North America. This study quantifies the mass of seven microplastic types based on an analysis of the plastics during production and use stages and their flows to six final compartments, including surface water, soil, agricultural soil, roadside, landfill, and incineration for Canada. \n \nA material flow analysis (MFA) was conducted for Canada in 2016, using data gathered from academic literature, government reports, and industry reports. The results showcase that in Canada, 60,100 tonnes of microplastics were unintentionally and intentionally released into Canada’s environment in 2016. The top three sources of microplastics were tire wear particles (TWP), releasing a total of 51,300 tonnes; paint fragments releasing 8,000 tonnes and microfibres, releasing 913 tonnes. The flows responsible for the most microplastic emissions were direct release to roadsides, contributing 41,400 tonnes; direct release to soils, contributing 3,470 tonnes; and direct release to surface waters releasing 5,090 tonnes. Roadsides and surface waters received the most microplastics, totalling to 46,000 and 9,120 tonnes, respectively. Regarding the polymer composition of microplastics released, rubber and poly (methyl methacrylate), found in TWPs and paints, respectively, are estimated to be deposited the most commonly in the environment. \n \nThis work is the first to map the flows of primary microplastics in Canada and distinguish between polymer types. The findings of the MFA allow stakeholders to identify significant points of microplastic leakage and inform legislative, infrastructural, or technological solutions to reduce emissions upstream and downstream of the plastics lifecycle. In addition, the results of this study inform actions concerning the Canada-wide Strategy on Zero Plastic Waste and Action Plan, suggest the need for product design change, and provide insight into waste diversion and recovery. The results of this study are a foundational piece for environmental fate models to be conducted. This investigation provides a baseline to compare preventive scenarios to reduce microplastic generation in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".