Viability of small‐scale arsenic‐contaminated‐water purification technologies for sustainable development in Pakistan
Bibliographic record
Abstract
Abstract Drinking arsenic‐contaminated water leads to a series of health problems that has limited development for the largely poor rural people of Pakistan who are unable to afford bottled water, centralized treatment plants or expensive water filter systems. This paper reviews the available appropriate technologies for the removal of arsenic from drinking water to assist in just sustainable development in Pakistan. Several technologies were found to be both technically and economically viable, supporting the large‐scale deployment of these small‐scale, appropriate technologies. The economic viability determined in this study was based on both first costs and operating costs. The cost of implementing such technologies for an individual Pakistani family is made acceptable with the use of local materials, which the family may already own. For example, systems using sand and iron nails in the filters, and that are placed in plastic buckets that are already in common use in the villages, drive down the overall costs of the technology and put it in the reach of even the most destitute. This study found that complications from the variability of local supplies result in the need to identify the locally most appropriate solution from both a technical and economic standpoint. This review article should be helpful for any practitioner in determining the locally optimal solution for the removal of arsenic from drinking water in Pakistan. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".