What have we known so far about microplastics in drinking water treatment? A timely review
Bibliographic record
Abstract
Microplastics (MPs) have been widely detected in drinking water sources and tap water, raising the concern of the effectiveness of drinking water treatment plants (DWTPs) in protecting the public from exposure to MPs through drinking water. We collected and analyzed the available research articles up to August 2021 on MPs in drinking water treatment (DWT), including laboratory- and full-scale studies. This article summarizes the major MP compositions (materials, sizes, shapes, and concentrations) in drinking water sources, and critically reviews the removal efficiency and impacts of MPs in various drinking water treatment processes. The discussed drinking water treatment processes include coagulation-flocculation (CF), membrane filtration, sand filtration, and granular activated carbon (GAC) filtration. Current DWT processes that are purposed for particle removal are generally effective in reducing MPs in water. Various influential factors to MP removal are discussed, such as coagulant type and dose, MP material, shape and size, and water quality. It is anticipated that better MP removal can be achieved by optimizing the treatment conditions. Moreover, the article framed the major challenges and future research directions on MPs and nanoplastics (NPs) in DWT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".