Geogenic Arsenic and Microbial Contamination in Drinking Water Sources: Exposure Risks to the Coastal Population in Bangladesh
Bibliographic record
Abstract
The study aimed to investigate the most usable drinking water sources quality and the dependent population’s exposure to potentially contaminated water. The specific area chosen for the study was the coastal area in Satkhira district’s Tala Upazila. 649 most usable drinking water sources were selected that included Deep Tubewell (DTW), Shallow Tubewell (STW) and Pond Sand Filter (PSF) for drinking water sampling. Following standard sampling procedures, in-situ measurements were taken for seven important water quality parameters: Arsenic-As, Iron-Fe, Electrical Conductivity-EC, Temperature-Temp, Total Coliform- TC, E-coli and Fecal Coliform-FC. In addition, semi-structured questionnaire surveys were conducted at corresponding dependent households (HH). Weighted arithmetic water quality index (WQI) was used to calculate the suitability of the derived water for drinking purposes. In the tested water sources, As, Fe and EC range were found 0-500 µg/L, 0-18 mg/L and 165-8715 µS/cm, respectively. Of all tested water sources, 74% exceeded permissible limit for As, 83% for Fe and 99% for EC, according to WHO standards. Comparatively higher percentage of Point of Uses (PoU) were found to be more contaminated than Point of Sources (PoS) such as TC found in 38% PoS and 54% of corresponding PoU, E.coli found in 24% PoS and 35% of PoU and FC found in 45% PoS and 55% of PoU. WQI suggested that the majority (72%) of most usable drinking water sources were found to be unsuitable for drinking. Thus, 40% of the population (0.12 million) in the study area were directly consuming contaminated water, dependent household members most frequently suffered from fever, diarrhea and high blood pressure, resulting in the average household to spend USD 3-13 per month/HH for health-related expenditures, which is higher than national average. To acquire safe drinking water, the majority (58%) of the dependent HH expressed willingness to pay USD 1 per month/ HH which is costly for them. The situation can be improved by installing deep tube well for safe drinking water, periodically testing of the water quality, educating public for better hygiene practices, and providing entrepreneurial incentives to help deliver safe water to the public at lower cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".