Influence of the presence of clay and water on the efficiency of soil vapor extraction in sand laboratory columns
Bibliographic record
Abstract
Abstract Soil vapor extraction (SVE) has been one of the most widely used technologies for remediating sites contaminated with volatile organic compounds. This technique consists of creating a depression in the soil and inducing a controlled flow of air which will entrain the volatile contaminants in the extracted gas phase. To learn the influencing factors that affect the effectiveness of removal of contaminants by the SVE method, an experimental study was performed to provide a comprehensive analysis of the SVE by tracking outgoing gases as well as the hydrodynamics of the flows. Two soil models were used: 100% sand (Soil 1) and sand mixed with 5% of Kaolin (Soil 2). Hydrodynamic tests were carried out using three mass water contents in each soil. It was shown that the quantity of mobile water is largely affected by soil composition. Experiments on soils contaminated by two tested contaminants (decane and toluene) were carried out with samples in dry and wet conditions. Results show that the SVE presented yields of 80.00% and 87.07% of the n‐decane and toluene, respectively, injected into Soil 1 against 79.88% and 86.11% of n‐decane and toluene, respectively, injected into Soil 2. The decrease of water soil saturation due to the extraction and the influence of the presence of water on the performance of the SVE were highlighted. Lower removal rates were observed for the contaminant with the lower vapor pressure (n‐decane).
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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 | 0.000 |
| 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".