Smoldering Treatment of PFAS: Investigation of Mass Balance and Volumetric Scale Up for Field Implementation
Bibliographic record
Abstract
Abstract The chemical properties of perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant remediation challenge. This study investigated smoldering combustion to destroy PFAS while scaling up from the lab to field implementation. The first phase consisted of bench-scale tests using a model soil system. Calcium oxide (CaO) was used as a soil amendment in some of the test cases. For all test conditions, greater than 99.9% removal of PFAS was achieved. Post-treatment soils without CaO amendments were found to have a significant reduction in total fluorine concentrations, while the fluorine concentrations in soils with CaO amendments were similar following treatment. This suggests that fluorine emissions from smoldering treatment of PFAS are captured by the presence of the calcium ion (Ca2+). Using new analytical methods, we better characterized the mass balance of the system. These lab results were carried out in a pilot study using soils from a PFAS-impacted site. Two large-scale tests treating 10 m3 of soil were completed. Results of the large-scale smoldering tests agreed with the results from the lab phase. The results from both phases provide a greater understanding of the fate of PFAS when it is treated by smoldering and detail the first large-scale demonstration of smoldering treatment for PFAS-impacted soils.
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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 | 0.001 |
| 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".