Public Health Strategies for Western Bangladesh That Address Arsenic, Manganese, Uranium, and Other Toxic Elements in Drinking Water
Bibliographic record
Abstract
BACKGROUND: More than 60,000,000 Bangladeshis are drinking water with unsafe concentrations of one or more elements. OBJECTIVES: Our aims in this study were to evaluate and improve the drinking water testing and treatment plans for western Bangladesh. METHODS: We sampled groundwater from four neighborhoods in western Bangladesh to determine the distributions of arsenic, boron, barium, chromium, iron, manganese, molybdenum, nickel, lead, antimony, selenium, uranium, and zinc, and to determine pH. RESULTS: The percentages of tube wells that had concentrations exceeding World Health Organization (WHO) health-based drinking water guidelines were 78% for Mn, 48% for U, 33% for As, 1% for Pb, 1% for Ni, and 1% for Cr. Individual tube wells often had unsafe concentrations of both Mn and As or both Mn and U. They seldom had unsafe concentrations of both As and U. CONCLUSIONS: These results suggest that the ongoing program of identifying safe drinking water supplies by testing every tube well for As only will not ensure safe concentrations of Mn, U, Pb, Ni, Cr, and possibly other elements. To maximize efficiency, drinking water testing in Bangladesh should be completed in three steps: 1) all tube wells must be sampled and tested for As; 2) if a sample meets the WHO guideline for As, then it should be retested for Mn and U; 3) if a sample meets the WHO guidelines for As, Mn, and U, then it should be retested for B, Ba, Cr, Mo, Ni, and Pb. All safe tube wells should be considered for use as public drinking water supplies.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".