Remediation of Petroleum-Contaminated Loess Soil by Surfactant-Enhanced Flushing Technique
Bibliographic record
Abstract
A laboratory study was carried out to evaluate the feasibility of in situ remediation of a loess soil site contaminated with diesel oil. Six nonionic and anionic surfactants were selected and compared. In experiments of diesel oil desorption using the anionic surfactants LAS and SDS, it was shown that diesel oil solubilization increased linearly with surfactant dose at bulk aqueous concentrations of the two surfactants in excess of the relative CMC. The slope of the organic compound concentration in the micellar phase versus the concentration in the aqueous phase was used to determine the molar solubilization ratio and the diesel oil mole fraction micelle-phase/aqueous-phase partition coefficient Km. The Km values calculated by an empirical model with diesel oil octanol-water partition were very similar to that derived using the curve slope approach. Aliphatic polyethenoxy ether (AEO9) and sodium alcohol polyethoxylated ether sulfate (AES) were chosen for soil flushing. Through column tests in the laboratory, the washing effectiveness of the two selected surfactants and the relevant optimal operation conditions were examined. The results showed that AEO9 was more effective than AES in the flushing of diesel oil from contaminated loess soil, whereas AES was still more than 10 times as effective than fresh water alone. A mixed surfactant solution of 0.8% (v/v) AEO9 and 0.1% (v/v) AES could significantly increase the removal efficiency by 10% when compared with that using AEO9 alone. It was estimated that using an amount of the mixed surfactant solution equal to 60 pore volumes would be able to remove 60% of the petroleum residue remaining in the contaminated unsaturated zone within 9 days. This laboratory study provided a suitable model for a "safe" remediation alternative in the contaminated loess soil field.
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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) | 0.000 | 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 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".