Per-and polyfluoroalkyl substances removal in water and wastewater treatment plants: overall efficiency and performance of adsorption
Bibliographic record
Abstract
Abstract Per- and polyfluoroalkyl substances (PFAS) in aqueous environment attracted prodigious attention due to the deleterious effects and environmental persistence. Many studies suggested that adsorption is an economical and efficient method to remove PFAS and a variety of adsorbents were developed. However, few adsorbents were conveniently applicable in real wastewater treatment plants (WWTPs) or drinking water treatment plants (DWTPs). This review discusses the gap between laboratory results of PFAS removal by adsorbents and the realistic efficiency in water treatment. First, the overall performance of PFAS removal by conventional WWTPs and DWTPs was discussed. Second, PFAS removal efficiencies by different units along the treatment trains of DWTPs were compared and summarized. Third, benchtop results for the efficiency of different adsorbents including activated carbon, ion exchange resin, minerals, and metal–organic frameworks were reviewed. These studies collectively concluded that dissolved organic matter in water is the most consequential component influencing the absorptive removal of PFAS; PFAS removal efficacy was discounted in water enriched in organic matter due to competitive absorption. To obtain application implications, research on novel adsorbents of high selectivity is suggested to couple with realistic demonstration. As the battle with ‘forever chemicals’ escalates, this is a timely and insightful review to help future research efforts bridge the gaps between laboratory performance and realistic removal of PFAS applying adsorbents.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".