Development and Application of Sampling and Extraction Methods for Microplastics in Drinking Water
Bibliographic record
Abstract
To-date, no standardized methods have been proposed for analyzing microplastics in drinking waters. This study assessed known methods to collect and extract microplastics from treated drinking waters and identified two common limitations: use of insufficient water, and lack of method recovery assessment. In response, this study developed an in-line filtration method, which improved accuracy when compared to in-laboratory filtration methods. In-line filtration was shown to have higher recoveries for the reference microplastics examined (+37% for PVC fragments, +23% for PET fragments, +22% for nylon fibers and +7% for PET fibers) and a greater potential to reduce microplastic contamination. The filtration capacity of in-line filtration method was observed to exceed 350 L of treated water. Application of in-line filtration was validated using ultrafiltration (UF) influent and effluent from two full-scale drinking water treatment facilities. UF represents an effective technology which is capable of removing ~95% of microplastics from drinking water.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".