Remediation of Contaminated Soils using Supercritical Fluid Extraction: A Review (1994-2004)
Bibliographic record
Abstract
Considerable effort is being made to remediate soils contaminated with petroleum hydrocarbons, polyaromatic hydrocarbons, polychlorinated biphenyls, dioxins, heavy metals and other organic and inorganic compounds that have resulted from industrial activities, accidental spills and improper waste disposal practices. Current remediation technologies may be limited when treating certain types of contaminated soils and therefore new, efficient and cost effective technologies are being investigated. Supercritical fluid extraction is a potential remediation technology for contaminated soils. It is a simple, fast and selective solvent extraction process that uses a supercritical fluid as the solvent. A commonly used fluid is carbon dioxide at pressures and temperatures greater than 7.4 MPa and 31 degrees C, respectively. In supercritical fluid extraction, the extracted contaminants first dissolve into the supercritical solvent and then these contaminants are separated from the supercritical solvent via a simple change in pressure and temperature conditions or by using a separation process. This paper provides a review of supercritical fluid extraction and its application to the remediation of contaminated soils. This review focuses on the removal of organic contaminants (such as petroleum hydrocarbons, polyaromatic hydrocarbons, polychlorinated biphenyls and others) and inorganic contaminants (such as heavy metals and radioactive elements) from soils. Recent data (1994-2004) on the supercritical fluid extraction of spiked soils and field-contaminated soils were collected. The success of supercritical fluid extraction as a method for removing these contaminants from soils is highlighted and some of the future research needed to develop it as a commercial-scale economic remediation technology are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".