The potential for a plastic recycling facility to release microplastic pollution and possible filtration remediation effectiveness
Bibliographic record
Abstract
With current plastic production and the growing problem of global plastic pollution, an increase and improvement in plastic recycling is needed. There is limited knowledge or assessment of microplastic pollution from point sources such as plastic recycling facilities globally. This pilot study investigates microplastic pollution from a mixed plastics recycling facility in the UK to advance current quantitative understanding of microplastic (MP) pollution release from a plastic recycling facility to receiving waters. Raw recycling wash water were estimate to contain microplastic counts between 5.97 106 – 1.12 × 108 MP m−3 (following fluorescence microscopy analysis). The microplastic pollution mitigation (filtration installed) was found to remove the majority of microplastics >5µm, with high removal efficiencies for microplastics >40µm. Microplastics <5µm were generally not removed by the filtration and subsequently discharged, with 59-1184 tonnes potentially discharged annually. It is recommended that additional filtration to remove the smaller microplastics prior to wash discharge is incorporated in the wash water management. Evidence of microplastic wash water pollution suggest it may be important to integrate microplastics into water quality regulations. Further studies should be conducted to increase knowledge of microplastic pollution from plastic recycling processes.
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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".