Effect of soil texture on surfactant-based remediation of hydrophobic organic-contaminated soil
Bibliographic record
Abstract
Surfactants may be used in remediation of subsoil and aquifer contaminated with hydrophobic compounds. The objectives of this study were to examine the effect of soil texture on hydrophobic organic contaminant (HOC; toluene, or 1,2,4-trichlorobenzene [TCB]) removal from six soils and to evaluate the optimal composition of soil texture for maximum HOC removal using aqueous surfactant solution. Selected surfactants were 4 % (vol/vol) sodium diphenyl oxide disulfonate (DOSL) and 4 % (wt/vol) sodium lauryl sulfate (LS). Toluene and TCB were selected as the lighter-than-water nonaqueous phase liquid (LNAPL) and denser-than-water nonaqueous phase liquid (DNAPL) model substances, respectively. Soil types used for this study were Ottawa sand and five Iowa soils (Fruitfield, Keomah, Crippin, Webster, and Galvar). The greatest recovery of toluene and TCB in batch tests was 73 % and 84%, respectively, which was obtained with DOSL surfactant in Ottawa sand. The toluene removal of 95 % in column tests has been achieved in the Ottawa sand and three Iowa soils (Fruitfield, Keomah, Crippin) with DOSL after effluent volume of 3750 ml (about 32 pore volume) passed. TCB removal of 98 % in column tests has been achieved in Ottawa sand and three Iowa soils (Fruitfield, Keomah, Crippin) with DOSL after effluent volume of 2500 ml (about 21 pore volume) passed. These results were related with soil texture (clay content 30%), clay mineralogy (kaolinite and smectite), as a function of
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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 | 0.001 | 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".