Remediation of DNAPL Source Zones with Granular Iron: Laboratory and Field Tests
Bibliographic record
Abstract
Degradation of dissolved chlorinated solvents using granular iron is an established in situ technology. This paper reports on investigations into mixing iron and bentonite with contaminated soil for in situ containment and degradation of dense nonaqueous phase liquid source zones. In the laboratory, hypovials containing soil, water, bentonite, iron, and free-phase trichloroethene (TCE) were assembled. Periodic measurement of TCE, chloride, and degradation products showed progressive degradation of TCE to nondetectable levels. Subsequently, a demonstration was conducted at Canadian Forces Base Borden near Alliston, Ontario, Canada, where, in 1991, a portion of the surficial aquifer was isolated and free-phase tetrachloroethene (PCE) was introduced. Using a drill rig equipped with large-diameter mixing blades, three mixed zones were prepared containing 0%, 5%, and 10% granular iron by volume. The bentonite was added to serve as a lubricant to facilitate injection of the iron and to isolate the contaminated zone. Analysis of core samples showed reasonably uniform distributions of iron through the mixed zones. Monitoring over a 13-month period following installation showed, relative to the control, a decline in PCE concentrations to virtually nondetectable values. Reaction rates in the laboratory tests were similar to those reported in the literature, while the rate in the field test was substantially lower. The lower rate may be a consequence of mass transfer limitations under the static conditions of the field test. Results indicate that mixing iron and bentonite into source zones may be an effective means of source-zone remediation, with the particular advantage of being relatively immune to effects of geologic heterogeneity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".