Effects of Urban and Industrial Waste on Some Soil Properties of Agricultural Soils in Kano Metropolis, Kano State, Nigeria
Bibliographic record
Abstract
The potential impact of the addition of untreated sewage sludge from domestic waste and effluent from textile and tannery industries on agricultural soils is a common practice in this area of study. A proportion of heavy metals in sewage sludge will be present in the soil solution. Soil samples from three Agricultural Site were collected at three depths (0-10 cm, 10-20 cm and 20-30 cm) from the months of June to September, 2007 (Rainy season), February-May. 2008 (Dry season) for the determination of pH, electrical conductivity, organic matter, organic carbon, salinity, cation exchanged capacity. Ca, Na, K and heavy metals using standard procedures. Levels of the above parameters were higher in the dry season than in the rainy season. It was also observed from the result of this study that the levels of pH and organic carbon influenced the solubility and mobility of heavy metals. Generally. the levels of heavy metals, pH and organic carbon for the three agricultural sites increases significantly (P＜0.05) to a depth of 30 cm, while Conductivity. salinity, organic matter. CEC (Cation exchanged capacity), Ca, Na and K decreased to a depth of 30 cm. The concentrations of heavy metals in the soil samples were higher than the Interim Canadian Environmental quality criteria and FAO (Food and Agriculture Organization) for contaminated site. Based on the above results, the study underscores the need for immediate remediation programme to control the use of untreated sewage sludge and waste water from tannery and textile industries by farmers in the study areas for crop production.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".