Surfactant enhanced soil washing technique for removal of trace metals from contaminated soils
Bibliographic record
Abstract
Increasingly stringent environmental regulations and legislation around the world are demanding the development of environmentally friendly technologies for contaminated site remediation. Researchers have recognized soil washing as an economically promising in-situ treatment method. Despite research indicating the effectiveness of soil washing with the aid of aqueous acids or chelating agents such as HCl and EDTA, the need to minimize the environmental impact of the remediation process itself has prompted investigations into alternative soil washing agents such as surfactants, particularly degradable compounds. This thesis examines two surfactants in their effectiveness of removing cadmium (Cd) and lead (Pb) from artificially contaminated kaolinite and illite clay minerals to determine the optimum conditions (concentration of surfactants and reaction time) for removing metals from contaminated urban sediments from two locations: a Wetland and a parking lot in Vancouver B.C. Selective sequential extraction was used to investigate geochemical fractionation before and after treatment of sediments by the surfactants. The optimum effective concentration for removing Cd and Pb from kaolinite and illite was 20 mM for Rhamnolipid for 0.5 h and Texapon at a concentration of 100 mM for 0.5 h. Thus, the concentration of 20 mM Rhamnolipid and 100 mM Texapon and reaction time of 0.5 h were selected to apply to remediate the two contaminated urban sediments. The removal of total metal content (Cu, Mn, Pb, and Zn) under laboratory conditions for the Wetland was 31%, 26%, 43%, and 27% after treatment by Rhamnolipid; and 31%, 27%, 58% and 31% after treatment with Texapon. The removal of these metals was 28%, 26%, 60%, and 31% by Rhamnolipid and 39%, 18%, 86%, and 40% by Texapon. Removal of metals from exchangeable and reducible fraction which have the potential to be released into environment approached up to 100%. Determination of Rhamnolipid and Texapon concentration remaining in Wetland and parking lot sediments after treatment revealed that more than 99% of remaining surfactants were removed from the two sediments after two washes with distilled water. The residual Rhamnolipid and Texapon were below the LD50/LC50 and toxicity limits for aquatic life.
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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".