What have we known so far about microplastics in drinking water treatment? A timely review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it