Kinetic Extractions of Nickel and Lead from Some Contaminated Calcareous Soils
Bibliographic record
Abstract
The mobility of heavy metals in contaminated soils is dependent on the kinetics release from soils. Metal extraction over time is commonly used to distinguish two or more fractions of metal based on differences of extraction or release rates. Kinetic studies using 0.01 M CaCl2, 0.01 M malic acid, and 0.01 M EDTA extractions were performed to characterize nickel (Ni) and lead (Pb) kinetic release in 10 contaminated calcareous soils. Proportions of Ni and Pb extracted with EDTA were higher than when using malic acid and CaCl2, respectively. The release of Ni and Pb was characterized by an initial fast rate followed by a slower rate and could best be described by a two first-order reactions model with rate constants k1 and k2 and two metal pools: readily labile (Q 1) and less labile (Q 2). In an EDTA extractant, different Q1 /Q2 ratios for Ni and Pb were observed, indicating binding energies to soil constituents is not comparable. The k1 of the model for Ni (average of 10 soils: 0.2204 h−1 and 0.2359 h−1 for 0.01 M CaCl2 and 0.01 EDTA, respectively) was higher than Pb (0.1044 h−1 and 0.1631 h−1 for 0.01 M CaCl2 and 0.01 M EDTA, respectively), indicating a higher potential of Ni for leaching and groundwater contamination in contaminated calcareous soils. Relationships between the fraction of Ni and Pb determined with the two first-order reactions model and the soil composition and Pb fractions were established. The results indicated that the efficiency of the extractions Ni and Pb from soils depends both on the Ni and Pb content and soil composition. Overall, the results indicated that the use of a 0.01 M malic acid washing solution would be preferred in the field condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".