Capacity of Nerium oleander to phytoremediate mine spoils assisted by air nanobubbles and biochar
Bibliographic record
Abstract
Background Heavy metals in soil are mainly introduced from a variety of natural sources and human actions such as industrial, agricultural and mining activities. As they find their way into the environment, they persist indefinitely since they are not biodegradable. Among the methods for the remediation of soils contaminated with heavy metals, a cost effective and non-intrusive alternative is phytoremediation. A pot experiment was conducted to investigate the ability of N. oleander to phytoremediate laterite mine spoils amended with different additives including biochar produced from the pyrolysis of sewage sludge and nitrogen phosphate potassium (NPK) fertilizer. In addition, the effect of irrigation with air nanobubbles (AirNBs) as soil amendment was also investigated. Results The combination of biochar and fertilizer caused an inhibition of growth of N. oleander in laterite mine spoil, whilst its growth was not significantly affected by the addition of the biochar and the supplementation of NBs. The physiological condition of the plant species in terms of biomass, water content, chlorophyll content, protein content and antioxidant enzymes activity was examined after the end of the experimental period. Remarkably, the irrigation with NBs resulted in an increase of the bioavailability of iron (Fe), nickel (Ni) and chromium (Cr) present in laterite mine spoils. The accumulation of these metals in plant tissues was found to be significantly greater in treatments using substrates with high laterite content and the supplementation of nanobubbles. Conclusion The application of irrigation water with nanobubbles is beneficial as a soil amendment and it is recommended when phytoremediation technologies are applied in contaminated areas.
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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) | 0.001 | 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 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".