Wastewater irrigation and health: assessing and mitigating risk in low-income countries
Bibliographic record
Abstract
In most developing countries wastewater treatment systems have very low coverage or function poorly, resulting in large-scale water pollution and the use of poor-quality water for crop irrigation, especially in the vicinity of urban centres. This can pose significant risks to public health, particularly where crops are eaten raw. Wastewater Irrigation and Health approaches this serious problem from a practical and realistic perspective, addressing the issues of health risk assessment and reduction in developing country settings. The book therefore complements other books on the topic of wastewater which focus on high-end treatment options and the use of treated wastewater. This book moves the debate forward by covering also the common reality of untreated wastewater, greywater and excreta use. It presents the state-of-the-art on quantitative risk assessment and low-cost options for health risk reduction, from treatment to on-farm and off-farm measures, in support of the multiple barrier approach of the 2006 guidelines for safe wastewater irrigation published by the World Health Organization. The 38 authors and co-authors are international key experts in the field of wastewater irrigation representing a mix of agronomists, engineers, social scientists and public health experts from Africa, Asia, Europe, North America and Australia. The chapters highlight experiences across the developing world with reference to various case studies from sub-Saharan Africa, Asia, Mexico and the Middle East. The book also addresses options for resource recovery and wastewater governance, thus clearly establishes a connection between agriculture, health and sanitation, which is often the missing link in the current discussion on ‘making wastewater an asset’.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".