Ones That Get Away: Investigating the Leaching of Persistent, Mobile, and Toxic Plastic Additives from New and Environmentally Sampled Plastic Items
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plastic additives are substances used to aid in plastic manufacturing or to impart unique properties (e.g., colorants, plasticizers, etc.). However, some plastic additives are persistent, mobile, and toxic (PMT). PMT substances can pose a substantial risk to water quality, as their stability and low adsorption potential enable them to pass through water treatment processes and remain in the environment long after their release. Importantly, many additives can leach out of plastics during environmental weathering. Despite these known risks, there has yet to be any work studying PMT plastic additive release from different plastic items or how weathering may impact their leaching. Herein, PMT plastic additive leaching from store-bought and environmentally sampled plastic items was investigated. The leachates of 68 plastic items were analyzed by using high-performance liquid chromatography with quantitative time-of-flight mass spectrometry. Significantly higher ( p = 0.05) numbers and levels of PMT substances were observed in the environmental samples when compared to store-bought. Furthermore, item categories such as toys and hardware supplies had higher numbers or levels of PMT substances than other items. These results discuss the role that weathering can play in PMT leaching and highlight items and compounds with high amounts or numbers of PMT substances, which can inform future monitoring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".