An In-Situ Soil Washing System for Diesel Fuel Contaminated Soils
Bibliographic record
Abstract
The increasing production and use of fossil fuels increase the probability of soil source contamination by oil and petroleum products, and pose a hazard to life. There are over 43,000 chemical contaminated sites in Canada and USA, with 2,200 of them classified as high risk. In the US, over $10 billion are spent annually on the cleanup of contaminated sites. The primary objective of this study was to develop and test an innovative in situ soil washing system that could extract contaminants from the soil, quickly and effectively, without causing any threat to water sources. We proposed the use of an organic solvent in our system that is not only lighter than water but is also immiscible with water. It was introduced into the soil profile from below using a system of horizontal, underground perforated pipes, and collected from the soil surface. Since the solvent was being introduced from the bottom, it “wete” the soil completely and thus removed the contaminant effectively. To understand and evaluate the performance of the system for remediation, a preliminary experiment was performed using two stainless steel columns (1m long x 0.2m diameter) packed with two diesel fuel contaminated soils (fresh and old contamination). The proposed technique is innovative, unique and very fast for the remediation of diesel fuel contaminated soils. It is the first study of its kind employing a subirrigation system for delivering organic solvents (lighter than water and immiscible in water) for in situ soil washing. Some preliminary applications of the system to diesel fuel contaminated sand soils have proved very promising for both fresh and old contaminated sites. The method could be equally suited for other contaminants also.
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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 | 0.000 |
| 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) | 0.001 | 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".