Removal of Potential Toxic Inorganic and Organic Compounds from Contaminated Soils by Alkaline Leaching with Surfactant
Bibliographic record
Abstract
The objective of this study was to evaluate the influence of soil parameters (total inorganic and organic carbon, pH, particle size distribution, initial contaminant levels) on the performance of a leaching process to remove As, Cr, Cu, pentachlorophenol (PCP), and polychlorodibenzo-dioxins and furans (PCDD/F) from the fine fractions (< 0.250 mm) of various contaminated soils. The chemical treatment, including three leaching steps (pulp density (PD) = 10% (w.w−1), [BW] = 3% (w.w−1), [NaOH] = 0.85 M, retention time (t) = 2 h and temperature (T) = 80°C) followed by two rinsing steps (PD = 10% (w.w−1), T = 20°C, t = 15 min), was optimized in previous works. Five soil samples (S1 to S5 – from different location on the same industrial site) were used to study the effect of the initial contaminant levels. The results showed good performance of the leaching process used to simultaneously remove PCP (96–98%) and PCDD/F (57–81%). These results also highlighted that the initial concentration of PCP and PCDD/F slightly influenced the performance of the leaching process. Subsequently, this leaching process was applied to three different soils (F1 to F3). The results showed that this process was efficient in removing PCP (50–86%) and PCDD/F (41–45%) regardless of the nature of the soil studied. However, the results also showed that the organic matter content and the particle size slightly influenced the efficiency of the leaching process to remove PCP.
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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".