Permanganate Treatment of an Emplaced DNAPL Source
Bibliographic record
Abstract
Abstract In situ chemical oxidation (ISCO) using permanganate is one of the few promising technologies that have recently appeared with the capability of aggressively removing mass from nonaqueous phase liquid (NAPL) source zones. While NAPL mass in regions of the treatment zone where delivery is dominated by advection can be removed rather quickly, the rate of mass removal from stagnant zones is diffusion controlled. This gives rise to partial mass removal and a concomitant reduction in the NAPL mass, downgradient ground water concentrations, and the dissolution rate associated with the source zone. Therefore, monitoring the performance of a permanganate ISCO treatment system is important to maintain the desired efficiency and to establish a treatment end point. In this paper, we illustrate the use of various monitoring approaches to assess the performance of a pilot‐scale investigation that involved treatment of a multicomponent NAPL residual source zone with permanganate using a ground water recirculation system for 485 d. Ongoing treatment performance was assessed using permanganate and chloride concentration data obtained from extraction wells, 98 piezometers located approximately 1 m downgradient from the source, and ground water profiling. At the completion of treatment, 23 intact soil cores were extracted from the source zone and used to determine the remaining NAPL mass and manganese deposition. Based on the data collected, more than 99% of the initial NAPL mass was removed during treatment; however, remnant NAPL was sufficient to generate a small but measurable dissolved phase trichloroethene (TCE) and perchloroethene (PCE) plume. As a result of treatment, the ambient‐gradient discharge rates were reduced by 99% for TCE and 89% for PCE relative to baseline conditions. The lack of complete source zone oxidation was presumed to be the result of dissolution fingers, which channeled the permanganate solution through the source zone preventing direct contact with the NAPL and giving rise to diffusion‐limited mass removal.
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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".