Sewage water irrigation effects on some potentially toxic trace elements in soil and potato plants in northwestern India
Bibliographic record
Abstract
Sewage water is used for irrigation to improve crop yields, but it may affect the soil and crop quality. The effects of discharging the effluents of a leather complex on the concentrations of some potentially toxic elements (Cu, Fe, Mn, Zn, Al, As, Cr and Ni) in sewage water and the effects of irrigation with contaminated sewage water on the concentrations of these elements in soils and in potato (Solanum tuberosum L.) leaves and tubers were studied near Jalandhar city, Punjab, in northwestern India. Two treatments were 10 yr of irrigation with 300 mm of sewage water + 300 mm of ground water per annum (SW) and with 600 mm of ground water per annum (GW). Soils of the study fields were Typic Ustochripts with sandy to loamy sand texture. The concentrations of all elements except As increased following the addition of leather complex effluents in the sewage water, with the biggest increase in the concentration of Cr (from 2.7 mg to 14.0 mg Cr L −1 ). The SW treatment increased concentrations of all elements in soil except As, and the increase was significant to 60 cm depth for Fe, Mn, Zn, Al and Ni and to 30 cm depth for Cu and Cr. Irrigation with SW also increased the concentrations of these elements in potato leaves and tubers and the increase was generally higher in leaves than in tubers. The proportional increase of Cu, Fe, Zn and Al was less in plants than in soils, that of Mn and Cr was almost similar in plants and in soil, and that of Ni was more in plants than in soil. These elements, which accumulate in soils and crops, may become health hazards to humans and/or animals. Therefore, continued monitoring of the concentrations of potentially toxic elements in soil and plants and/or treatment of sewage water before using for irrigation is needed. Key words: Irrigation, leather industry effluent, Solanum tuberosum, potentially toxic elements, sewage water
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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 | 0.001 |
| 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 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".