Field Evidence of Dissolution and Degradation Rates Enhancement During ISCR and ENA Treatments of Chlorinated Solvents
Bibliographic record
Abstract
Abstract This article presents field tests comparing two methods of treatment of chlorinated solvents undertaken at the same site. The site is an automobile factory where two chlorinated solvents (CS) plumes were identified. At the first source, in situ chemical reduction (ISCR) was applied, while at the second one, enhanced natural attenuation (ENA) was used. A set of specific multilevel sampling wells were installed approximately 20 m downgradient of the sources to estimate the efficiency of the treatments. The presence of a low‐permeability layer (source 1) or a thick oil lens (source 2) in the top part of the aquifer prevented the CS from reaching the bottom of the aquifer. These layers led to difficulties treating the contamination. At the ISCR and ENA treatment zones, the concentrations of tetrachloroethene (PCE) and trichloroethene (TCE) did not change significantly, while the concentration of metabolites ( cis ‐1,2‐DCE, vinyl chloride, and ethene) significantly increased 50 to 150 days after treatment. Due to high concentration of CS in the source zone, a mass balance calculation, including chlorine, was possible. It showed that around 1 to 2 percent of the injected products were used to reduce the CS. A detailed analysis and 1D analytical modeling of CS concentrations showed that the treatment led to a large (two to three times) increase in dissolution of the organic phase. This explains why, despite an efficient treatment, the PCE and TCE concentrations remained virtually unchanged. Degradation rates also increased due to the treatment. Due to some differences in the source‐zone chemistry, it was not possible to differentiate between the ISCR and ENA efficiencies. © 2013 Wiley Periodicals, Inc.
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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 | 0.001 |
| 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".