Treated wastewater reuse for irrigation in a semi-arid region
Bibliographic record
Abstract
Globally, agriculture consumes 72 % of the freshwater resources. In this sector, the water supply-demand gap is predicted to increase with the elevated and unmet water demand and the exacerbating water supply pressure. Treated wastewater reuse (TWWR), as an alternative water source for irrigation, increases water availability and reduces the overexploitation of freshwater. Nonetheless, its safe quality and social acceptability are prerequisites for its application. This study aims to determine the willingness of Lebanese farmers in the Bekaa Valley, as well as consumers from the general Lebanese communities toward safe TWWR in agriculture. The study further characterizes the TWW quality for reuse in irrigation from two wastewater treatment plants (WWTPs) in Bekaa to evaluate their environmental and health risks. The results revealed that farmers and consumers have a very high willingness to use and accept the use of safe TWW for irrigation. In contrast, the majority do not trust the authorities to ensure safe effluents for irrigation. This perception matches the reality due to the restrictive quality effluent of two WWTPs in the region influenced by chronic poor national governance, corruption, devastating economic crisis, and fuel shortage. Despite these restrictions, some farmers are irrigating with these outlets irrespective of the guidelines. Hence, some farmers are not fully aware of the risks of unsafe TWW irrigation, but they might not have another alternative with the worsened water security issues. Despite the challenges, TWW remains a sustainable alternative improved in quality upon abiding by the proposed technical recommendations, decided upon further monitoring and investigation assuming funding availability and improvement in electricity provisioning, complemented by recommendations at the irrigation level to mitigate its remaining risks. Most importantly, recommendations to promote water reuse and overcome public opposition are suggested. This work provides insights for the future of water for arid areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".