A survey of arsenic, manganese, boron, thorium, and other toxic metals in the groundwater of a West Bengal, India neighbourhood
Bibliographic record
Abstract
Around 150 million people are at risk from arsenic-contaminated groundwater in India and Bangladesh. Multiple metal analysis in Bangladesh has found other toxic elements above the World Health Organization (WHO) health-based drinking water guidelines which significantly increases the number of people at risk due to drinking groundwater. In this study, drinking water samples from the Bongaon area (North 24 Parganas district, West Bengal, India) were analyzed for multiple metal contamination in order to evaluate groundwater quality on the neighbourhood scale. Each sample was analyzed for arsenic (As), boron (B), barium (Ba), chromium (Cr), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), and uranium (U). Arsenic was found above the WHO health-based drinking water guideline in 50% of these tubewells. Mn and B were found at significant concentrations in 19% and 6% of these tubewells, respectively. The maps of As, Mn, and B concentrations suggest that approximately 75% of this area has no safe tubewells. The concentrations of As, Mn, B, and many other toxic elements are independent of each other. The concentrations of Pb and U were not found above WHO health-based drinking water guidelines but they were statistically related to each other (p-value = 0.001). An analysis of selected isotopes in the Uranium, Actinium, and Thorium Radioactive Decay Series revealed the presence of thorium (Th) in 31% of these tubewells. This discovery of Th, which does not have a WHO health-based drinking water guideline, is a potential public health challenge. In sum, the widespread presence and independent distribution of other metals besides As must be taken into consideration for drinking water remediation strategies involving well switching or home-scale water treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".