Contamination of a calcareous soil by battery industry wastes. II. Treatment
Bibliographic record
Abstract
This study evaluated treatment of a soil that was heavily contaminated by the disposal of waste from a Pb battery industry. The site was of interest from a remediation point of view, since the soil contained substantial quantities of calcareous material. Treatment involved extraction of the metals from the soil using aqueous solutions of EDTA (ethylenediaminetetraacetic acid). The impact of pH, EDTA concentration, and reaction time on the extent of leaching of Pb was evaluated. The leaching efficiency was found to be independent of pH over a range of values from 4 to 8.2. The leaching of Pb and Ca increased with EDTA concentration with 99% of the Pb removed at an EDTA dosage of 3.2 M/kg soil. The removal efficiencies of Cu, Cr, Cd, Ni, and Zn were consistently less than that of Pb; however, with their lower initial concentrations, acceptable levels were achieved for all metals except Cd. A two-reaction model was successfully used to describe the results of a batch extraction test. The results indicated relatively rapid extraction kinetics for a substantial fraction of the Pb contamination. Treatment of the liquid effluent from the soil treatment process with nano-level filtration indicated that both the EDTA and Pb were consistently removed at efficiencies greater than 98%. The nano-filtration process was not affected by pH over a range of values from 4.8 to 7.6.Key words: soil washing, Pb, EDTA, pH effect, kinetic, leachate treatment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".