Providing Water Quality Index for Water use in Agriculture: A Case Study
Bibliographic record
Abstract
Background: Most countries face water scarcity, population growth, climate change, uneven water distribution, excessive water use, and biological, agricultural, and industrial water pollution. Using wastewater and brackish waterways for cultivation reduces pollution. Polluted water impacts human biology. Thus, they must be adequately studied before irrigating crops. Methods: This study used the analytical hierarchy process (AHP) to assess the quality of nonconventional agricultural waters from the Karaj and Anbaj wastewater treatment plants in Iran. Water quality was evaluated based on 7 primary criteria and 52 sub-criteria. The Canadian Water Quality Index (CWQI) and the proposed model were developed from the measured parameters of the effluent from both the Anbaj and Karaj treatment plants. The data were analyzed using the Expert Choice software. Results: In this study, chloride, fecal coliforms, and intestinal parasite eggs received the highest scores, while arsenic (As) and molybdenum (Mo) were assigned the lowest scores. The findings indicated that the effluent from the Anbaj wastewater treatment plant requires extensive treatment before being suitable for agricultural use. In contrast, the effluent from the Karaj wastewater treatment plant was of moderate quality and requires minimal treatment. This study recommends applying the proposed model to evaluate wastewater quality for agricultural purposes. Conclusion: Researches of soil and wastewater interactions suggests that crops irrigated with wastewater may pose risks to both ecosystems and human health due to physical, chemical, and microbiological factors. These impacts can compromise soil fertility and productivity. Therefore, the use of wastewater in agricultural practices should be implemented with appropriate safeguards.
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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 | 0.004 | 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 | 0.001 |
| 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".