The role of biochar in enhancing safe use of untreated wastewater in agriculture
Bibliographic record
Abstract
In many developing countries, water scarcity and the growing population are becoming problematic.Therefore, the reuse of wastewater for irrigation provides an alternative management option.Irrigation with poorly treated or untreated wastewater could, however, pose risk to human health due to the presence of a wide range of contaminants, including heavy metals, which can move into the edible parts of various crops such as potatoes (Solanum tuberosum L.) and spinach (Spinacia oleracea L.).The study aimed to investigate biosorbent role in the remediation of heavy metals in soil and crops irrigated with untreated wastewater.Both aboveground and belowground crops were selected to better assess the effect of rooting system on the plant uptake of heavy metals.To achieve this goal, a field lysimeter experiment was undertaken to elucidate the fate and transport of six water-borne heavy metals (Cd, Cr, Cu, Fe, Pb and Zn) in irrigation water applied to potatoes (cv.Russet Burbank) and spinach grown on a sandy soil.Plantain peel biochar (1% w/w) was incorporated in the top 0.1 m of soil.All the control and biochar treatments were replicated three times in a completely randomized design carried out on nine outdoor PVC lysimeters (1.0 m height 0.45 m diameter).In a two-year study, potatoes were planted, irrigated at 10-day intervals, leachate samples were collected, followed by soil samples collected two days after each irrigation.Results showed that all heavy metals accumulated in the top soil; Fe, Pb and Zn were detected at 0.1 m depth; while lower than those in the peel, suggesting that when consuming potatoes grown under wastewater irrigation, the peel poses a higher health risk than the flesh.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".