Reductions of\nPlastic Microbeads from Personal Care\nProducts in Wastewater Effluents and Lake Waters Following Regulatory\nActions
Bibliographic record
Abstract
Plastic microbeads were widely used as exfoliants in personal care products (PCPs; e.g., hand/body washes) in North America, but restrictions were imposed on their use in PCPs in the U.S. (2017) and Canada (2018). We provide the first assessment of whether restrictions are effectively reducing microbeads entering surface waters. We examined their abundance, character, and trends in wastewater treatment plant (WWTP) effluents in Toronto, Canada, from 2016 to 2019, and in adjacent Lake Ontario surface waters (2015 and 2018), encompassing the period before and after the bans. Microbeads isolated from PCPs purchased in 2015 provided a visual morphological key with “irregular” and “spherical” microbead categories. Median concentrations of irregular microbeads, composed of polyethylene plastic, declined by up to 86% in WWTP effluents from 8.4 to 14.3 particles/m<sup>3</sup> before to 2.0–2.2 particles/m<sup>3</sup> after the bans, while those of spherical microbeads, predominantly synthetic/polyethylene wax, ranged within 0.5–2.3 particles/m<sup>3</sup> and did not differ before and after the bans since, as nonplastic, they were not regulated. Similarly, amounts of irregular microbeads declined relative to spherical microbeads in Lake Ontario, indicating that product changes may be influencing observations in lake waters. The results suggest that the Canadian and U.S. restrictions effectively and rapidly reduced plastic microbeads entering waters via WWTPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".