Biochar and alternate partial root-zone irrigation greatly enhance the effectiveness of mulberry in remediating lead-contaminated soils
Bibliographic record
Abstract
Abstract Aims Soil lead contamination has become increasingly serious and phytoremediation can provide an effective way to reclaim the contaminated soils. This study aims to examine the growth, lead resistance and lead accumulation of mulberry (Morus alba L.) seedlings at four levels of soil lead contamination with or without biochar addition under normal or alternative partial root-zone irrigation (APRI). Methods We conducted a three-factor greenhouse experiment with biochar (with vs. without biochar addition), irrigation method (APRI vs. normal irrigation) and four levels of soil lead (0, 50, 200 and 800 mg·kg−1). The performance of the seedlings under different treatments was evaluated by measuring growth traits, osmotic substances, antioxidant enzymes and lead accumulation and translocation. Important Findings The results reveal that mulberry had a strong ability to acclimate to soil lead contamination, and that biochar and APRI synergistically increased the biomass and surface area of absorption root across all levels of soil lead. The seedlings were able to resist the severe soil lead contamination (800 mg·kg−1 Pb) by adjusting glutathione metabolism, and enhancing the osmotic and oxidative regulating capacity via increasing proline content and the peroxidase activity. Lead ions in the seedlings were primarily concentrated in roots and exhibited a dose–effect associated with the lead concentration in the soil. Pb, biochar and ARPI interactively affected Pb concentrations in leaves and roots, translocation factor and bioconcentration. Our results suggest that planting mulberry trees in combination with biochar addition and APRI can be used to effectively remediate lead-contaminated soils.
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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 | 0.001 | 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 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".