Investigation of PFAS rejection by closed-circuit reverse osmosis and nanofiltration and sorption to treatment materials during groundwater treatment: a pilot demonstration
Bibliographic record
Abstract
The rejection of a broad range of per- and polyfluoroalkyl substances (PFAS) by reverse osmosis (RO) and nanofiltration (NF) was evaluated using a pilot closed-circuit membrane system operating at three recoveries (80, 85, 90 %) treating aqueous film-forming foam (AFFF) impaired groundwater (total PFAS ∼ 14.3 µg/L). Evaluation of the membranes focused on 15 PFAS measured in the groundwater above 75 ng/L including carboxylates, sulfonates, fluorotelomer sulfonates, and sulfonamides, dominated by perfluorooctane sulfonate (PFOS). RO required higher pressures and energy to reach recovery setpoints than NF, in exchange for PFAS specific rejections greater than 99 %. Rejection by NF ranged from 97.9 to 99.8 % and was impacted by functional group (carboxylates > sulfonates, fluorotelomer sulfonates > sulfonamides) and increased by increasing chain lengths. Overall PFAS rejection by RO decreased between 85 and 90 % recovery, with discrete sampling demonstrating a decrease in rejection after 87 % recovery, indicating that the tradeoff between reduced retentate volume and decreased permeate quality is an important operational consideration. To evaluate PFAS sorption to treatment materials, methanol extractions were performed on pretreatment materials and one BW30 element. Adsorbed total PFAS mass was dominated by PFOS, with long-chain PFAS exhibiting preferential adsorption. Per gram of material extracted, a 0.2 µm cartridge filter accumulated the most PFAS. The membrane and IX softening resin had similar accumulation of PFAS, while accumulation was the lowest on greensand. PFAS chain length had the greatest impact on adsorption to the filter, membrane, and IX softening resin, sorption to greensand was more impacted by functional group.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".