Biochar Amendment in Remediation of Heavy Metals in Paddy Soil: A Case Study in Nobewam, Ghana
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Bibliographic record
Abstract
Biochar is a stabilised, carbon-rich material created when biomass is heated to temperatures usually between 450 and 550 °C, under low-oxygen concentrations. This study evaluated the effectiveness of sawdust, cocoa pod ash and rice husk biochars in remediating metal-contaminated paddy soil in Nobewam, Ghana. Biochar was applied 21 days before cultivating the rice for 120 days, followed by soil sampling and rice harvesting for metals and physicochemical analyses. Compared to the untreated soils, biochar treatments exhibited an enhancement in soil quality, characterised by an increase in pH of 1.01–1.20 units, an increase in available phosphorus (P) concentration of 6.76–13.05 mg/kg soil and an increase in soil total nitrogen (N), and organic carbon (OC) concentration, ranging from 0.02% to 0.12%. Variabilities in electrical conductivity and effective cation exchange capacity were observed among the treated soils. Concentrations of potentially toxic metals (arsenic, cadmium, copper, mercury, lead and zinc) in paddy soils and rice analysed by atomic absorption spectroscopy showed significant differences (p < 0.05) among the sampled soils. The concentrations of arsenic and lead in all soil samples exceeded the Canadian Council of Ministers of the Environment soil quality guideline for agricultural soils, with untreated soils having the highest levels among all the soils. Cadmium had a potential ecological risk index > 2000 and a geoaccumulation index above 5, indicating pollution in all samples. In contrast, arsenic and mercury contamination were only found in the untreated soils. Among the tested treatments, rice husk and its combinations, particularly with cocoa pod ash, showed significant efficacy in reducing metal concentrations in the soils. The potential non-carcinogenic human health risks associated with the consumption of rice grown in biochar-treated soils were lower for all the metals compared to the control samples. Future research should focus on long-term field studies to validate these findings and explore the underlying mechanisms governing metal immobilization in paddy fields.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it