Bench‐scale testing of a novel soil PFAS treatment train for informed remedial planning and decision‐making
Bibliographic record
Abstract
Abstract Bench‐scale batch tests were conducted to assess the potential applicability of a combined separation/concentration/destruction treatment train to address soils and sediments impacted by per‐ and polyfluoroalkyl substances (PFAS) contamination at Schriever Space Force Base with historic aqueous film‐forming foam (AFFF). Specifically, a novel treatment train coupling soil washing (for treatment of impacted soil/sediment) with foam fractionation (for treatment of the wash water [WW] generated during soil washing) and electrochemical oxidation (ECO, for treatment of the foam fractionate generated during foam fractionation) was evaluated at the bench scale using site‐specific materials. Results presented herein show that the AFFF‐impacted sandy soils with low organic content were amenable to treatment via soil washing. However, the removal of hydrophobic PFAS, such as perfluorooctanesulfonic acid (PFOS), from the organic‐rich sediments was challenging. Results from batch desorption experiments were within a factor of 2 of those generated by soil washing bench studies, suggesting that simple batch tests can potentially be used to reasonably predict the treatment efficacy of soil washing. Long‐chained perfluoroalkyl acids (PFAAs) within the WW were removed more effectively in the foam fractionation studies as compared to short‐chain PFAAs. Addition of a surfactant, such as cetrimonium bromide (CTAB), enhanced foaming but only marginally improved the treatment of short‐chained PFAAs and in some cases inhibited PFOS removal. ECO reduced PFAS concentrations in the foam fractionate generated during foam fractionation by several orders of magnitude. However, generation of unwanted byproducts may warrant further treatment and/or disposal. Overall, results from this study provide a novel data set highlighting the site‐dependent nature of these PFAS remedial technologies and how simple, low‐cost bench tests can be reliably leveraged for informed decision‐making during PFAS remedial planning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".