Mechanistic insights into the (im)mobilization of arsenic, cadmium, lead, and zinc in a multi-contaminated soil treated with different biochars
Bibliographic record
Abstract
The effect and mechanistic evidence of biochar on the (im)mobilization of potentially toxic elements (PTEs) in multi-contaminated soils, with respect to the role of surface-functional groups and organic/inorganic compounds of biochar, are poorly understood. Herein, biochars produced from grass residues, rice straw, and wood were applied to a mining-soil contaminated with As, Cd, Pb, and Zn for 473-d. Biochars did not reduce the mobilization of Cd and Zn, whereas they simultaneously exhibited disparate effects on As and Pb mobilization. The phenolic hydroxyl and carboxylic groups on the wood biochar’s surfaces promoted the conversion of Pb2+ into PbCO3/Pb(OH)2 and/or PbO, minimally by the rice and grass biochars. Rice and grass biochars led to the dissolution of scorodite and the formation of less stable forms of Fe-oxide-bound As (i.e., goethite and ferrihydrite); furthermore, it resulted in the reduction of As(V) to As(III). The PTEs mobilization and phytoavailability was mainly governed by the release of dissolved aliphatic- and aromatic-carbon, chloride, sulfur chemistry, phosphate competition, and the electrostatic repulsion in biochar-treated soils. In conclusion, pristine-biochar has a limited impact on the remediation of multi-contaminated soils, and the use of modified-biochar, possessing higher surface areas and functionality and active exchange sites, are preferred under such conditions.
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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 | 0.000 |
| 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 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".