Comparative Assessment of Powdered versus Granular Activated Carbon for PFAS Removal in Drinking Water Treatment Plants
Bibliographic record
Abstract
Since the acceptable PFAS levels in drinking water vary among regulatory agencies, drinking water treatment plants (DWTPs) are urged to adapt their processes to improve their removal. This study’s objective was to assess the performance of powdered and granular activated carbon (PAC and GAC) for PFAS removal and evaluate their applications in DWTPs. Raw and filtered waters were used to examine different types of PAC and GAC in batch and rapid small-scale column tests, respectively. A conventional PAC dose (10 mg/L) eliminated 40% of the total PFAS 76 and 25% of long-chain PFAS after 10 min. It would, however, transfer 24 ppb of PFAS 76 daily to the biosolids. A comparable GAC dose (equivalent to 27,000 BV) removed 43% of PFAS 76 and 80% of long-chain PFAS. Considering a medium-sized DWTP with a long-chain PFAS removal target of 80%, a pretreatment with PAC would require an elevated AC dose of 29 mg/L. It will incur the total equivalent cost of a post-treatment with six GAC columns, while remarkably increasing the mass of dry sludge by 46%. Hence, the pretreatment with PAC emerges as better suited for an instant intervention to mitigate PFAS contaminations without revoking the need for a long-term solution.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".