Concentration Rebound Following In Situ Chemical Oxidation in Fractured Clay
Bibliographic record
Abstract
A two-dimensional, transient-flow, and transport numerical model was developed to simulate in situ chemical oxidation (ISCO) of trichloroethylene and tetrachloroethylene by potassium permanganate in fractured clay. This computer model incorporates dense, nonaqueous phase liquid dissolution, reactive aquifer material, multispecies matrix diffusion, and kinetic formulations for the oxidation reactions. A sensitivity analysis for two types of parameters, hydrogeological and engineering, including matrix porosity, matrix organic carbon, fracture aperture, potassium permanganate dosage, and hydraulic gradient, was conducted. Remediation metrics investigated were the relative rebound concentrations arising from back diffusion and percent mass destroyed. No well-defined correlation was found between the magnitude of rebound concentrations during postremedy monitoring and the amount of contaminant mass destroyed during the application. Results indicate that all investigated parameters affect ISCO remediation in some form. Results indicate that when advective transport through the fracture is dominant relative to diffusive transport into the clay matrix (large System Peclet Number), permanganate is more likely to be flushed out of the system and treatment is not optimal. If the System Peclet Number is too small, indicating that diffusion into the matrix is dominant relative to advection through the fracture, permanganate does not traverse the entire fracture, leading to postremediation concentration rebound. Optimal application of ISCO requires balancing advective transport through the fracture with diffusive transport into the clay matrix.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".