Per-and polyfluoroalkyl substances removal in water and wastewater treatment plants: overall efficiency and performance of adsorption
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it