Estimation of the Maximum Consumption of Permanganate by Aquifer Solids Using a Modified Chemical Oxygen Demand Test
Bibliographic record
Abstract
Knowledge of the consumption of permanganate by naturally occurring reduced species associated with aquifer materials is required for site screening and design purposes to support permanganate in situ chemical oxidation (ISCO) applications. It has been established that this consumption is not a singled-valued quantity, but rather is kinetically controlled. Current methods to determine this permanganate natural oxidant demand (NOD) involve the use of well-mixed batch tests, which are time consuming and subject to test variables (e.g., concentration, mass of oxidant to solid ratio, reaction duration, and mixing conditions) that significantly affect the results. In this paper, we propose a modified chemical oxygen demand (COD) test using permanganate, which can be used to determine the maximum permanganate NOD of an aquifer material. As an initial point of comparison, we tested aquifer materials collected from eight potential ISCO sites using this modified or permanganate COD method, the traditional dichromate COD method, and a method based on well-mixed batch reactors. The results from this comparison indicated that there was no statistically significant difference (α=5%) between the results of the permanganate COD test and the maximum NOD from the well-mixed batch reactors, while on average the dichromate COD test overestimated the maximum NOD by 100%. The permanganate COD test results were highly correlated to the batch-test maximum NOD data (r=0.996), and to the total organic carbon and amorphous Fe content of the aquifer materials (r=0.91). A limited sensitivity investigation of this proposed permanganate COD test revealed that the suspected formation of manganese oxides, a reaction byproduct, may lead to increased experimental variability. However, in spite of this concern we recommend that this proposed permanganate COD method is a quick and economical approach for estimating the maximum permanganate NOD for aquifer materials to support permanganate ISCO site screening and initial design purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".