Efficiency of EDTA-Na2 and Oxalic Acid Mixture in Removing Lead from Calcareous and Gypsum Soils
Bibliographic record
Abstract
The soil washing method is an effective approach for treating polluted soils with high heavy metals concentrations.Oxalic acid and EDTA are both chelating agents that can form stable complexes with heavy metals and increase their solubility and mobility in the soil solution.By mixing oxalic acid and EDTA, the synergistic effect of the two agents can be achieved.In a recent study, two types of soils, calcareous and gypsum, were selected from agricultural lands (depth 0-30 cm).The washing solution comprising an equal ratio (1:1) of EDTA-Na2 (0.05 M) and oxalic acid (0.05 M) was used to remove lead (Pb) from the soil samples.Five factors were selected to evaluate their impact on lead removal efficiency, which include pH (2 -4 -6 -8), washing time (15 min -30 min -60 min -90 min), lead concentrations (0 mg.L --300 mg.L --400 mg.L --500 mg.L --600 mg.L -), temperature (20℃ -40℃ -60℃ -80℃), and liquid/solid ratio (L/S) (5/1 ml/g -10/1 ml/g -15/1 ml/g -30/1 ml/g).The results indicated that the pH level significantly influenced the removal of Pb, with higher removal at lower pH levels.Additionally, the removal percentage increased with higher temperature, Pb concentrations, washing time, and L/S ratio.The results of this study can be summarized, that is Pb removal efficiency of 60.19% at pH 2, 94.24% at 30 min of washing time, 78.25% at 0 mg.L -of Pb concentrations, 66.47% at 20℃, and 84.77% at 15/1 ml/g of liquid/solid ratio (L/S).However, the presence of used engine oil (UEO) reduced the removal of Pb.There is no clear difference in the efficiency of removing lead from both types of soil (gypsum and calcareous).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".