Distribution and Extraction of Heavy Metals in Soil and Their Accumulation in Brassica oleracea L. after Long Term Wastewater Irrigation
Bibliographic record
Abstract
Wastewater irrigation has become a common practice especially in third world countries. Over the period of time population growth has resulted in increased domestic and industrial wastes. People produce huge quantities of vegetables and crops yields with wastewater irrigation without knowing its effects on soils, plants and ultimately on consumers. Therefore, a study was carried out to compare accumulation of heavy metals (Fe, Cu, Pb, Zn, and Cr) in wastewater irrigated soils with rain fed soils. Water soluble, and total extractable heavy metals were determined. The contents of water soluble, exchangeable and total plant essential elements (K, Ca, and Mg) were also determined. Soil samples from three different layers (0-30 cm, 30-60 cm and 60-100 cm depth) were collected from both wastewater irrigated field and rain fed field. Results indicated that water soluble heavy metals varied in soil in order Fe> Cr > Pb > Zn > Cu irrespective of the depth and irrigation management. Total heavy metals in all layers of soil were noted as Cr > Zn > Pb > Cu > Fe for wastewater irrigated field and Cr > Zn > Pb > Fe > Cu for rain-fed field. On the other hand the concentrations of water soluble essential elements varied as K > Mg > Ca for both rain-fed and wastewater irrigated soils. The study clearly indicated that wastewater irrigation caused heavy metal accumulation in both soils and plants. The use of wastewater for agriculture may be economically productive due to abundance of nutrients present in it but have adverse effects on soil, plant and ultimately its consumers. Although heavy metals in plant were found within the standard limits, however, continuation of such practices for a longer period of time may escalate their levels beyond the safe limits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".