Analysis of colloidal activated carbon alternatives for in situ remediation of a large PFAS plume and source area
Bibliographic record
Abstract
Abstract This study evaluated optimal locations for in situ remediation of per‐ and poly‐fluoroalkyl substances (PFAS) in groundwater with colloidal activated carbon (CAC). New Freundlich isotherms for PFAS adsorption to CAC were estimated to illustrate the effect of competitive adsorption with dissolved organic carbon and other PFAS in a groundwater sample. A hypothetical model scenario was constructed based on source area characteristics similar to a site impacted by aqueous film forming foam in South Dakota. Modeling indicates that, even with high PFAS concentrations, CAC would still be capable of maintaining concentrations below proposed maximum contaminant levels in the adsorption zone for at least 30–40 years. Two‐dimensional areal modeling indicates that the future breakthrough of PFAS is likely to occur in the localized core of the plume, and that the corresponding future reinjection of CAC will only need to be conducted over a smaller portion of the original injection footprint. The benefits of implementing a phased remedial approach at PFAS sites are discussed. Source area and mid‐plume CAC treatments are shown to be ineffective at attenuating PFAS concentrations at the downgradient property boundary within a reasonable timeframe when PFAS travel time is relatively slow. Among the CAC alternatives evaluated here, a downgradient CAC permeable reactive barrier has the best performance with respect to protecting downgradient receptors.
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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) | 0.000 | 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) | 0.001 | 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".