The WHO Guidelines for Safe Wastewater Use in Agriculture: A Review of Implementation Challenges and Possible Solutions in the Global South
Bibliographic record
Abstract
Globally, the use of untreated, often diluted, or partly treated wastewater in agriculture covers about 30 million ha, far exceeding the area under the planned use of well-treated (reclaimed) wastewater which has been estimated in this paper at around 1.0 million ha. This gap has likely increased over the last decade despite significant investments in treatment capacities, due to the even larger increases in population, water consumption, and wastewater generation. To minimize the human health risks from unsafe wastewater irrigation, the WHO’s related 2006 guidelines suggest a broader concept than the previous (1989) edition by emphasizing, especially for low-income countries, the importance of risk-reducing practices from ‘farm to fork’. This shift from relying on technical solutions to facilitating and monitoring human behaviour change is, however, challenging. Another challenge concerns local capacities for quantitative risk assessment and the determination of a risk reduction target. Being aware of these challenges, the WHO has invested in a sanitation safety planning manual which has helped to operationalize the rather academic 2006 guidelines, but without addressing key questions, e.g., on how to trigger, support, and sustain the expected behaviour change, as training alone is unlikely to increase the adoption of health-related practices. This review summarizes the perceived challenges and suggests several considerations for further editions or national adaptations of the WHO guidelines.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".