Arsenic in groundwater in the southern lowlands of Nepal and its mitigation options: a review
Bibliographic record
Abstract
As in several other countries of Southeast Asia (namely Bangladesh, India, Myanmar, China, Vietnam, and Cambodia) arsenic (As) concentrations in the groundwater of the lowlands of Nepal (the so called Terai) can reach concentrations that are unsafe to humans using the groundwater as drinking water. Whereas Bangladesh has received much international attention concerning the As crisis, Nepal was more or less neglected. The first report about As contamination of the groundwater above toxic levels in Nepal was published in 1999. Twenty-four percent of samples analyzed (n = 18 635) from the Terai Basin exceeded the WHO guideline of 10 μg/L. Since the first overall survey from 2001, only sporadic information on the situation has been published. The geological and geochemical conditions favour the release of the contaminant as As can be easily solubilized in groundwaters depending on pH, redox conditions, temperature, and solution composition. The thin alluvial aquifers of the Terai are some of the most severely As contaminated. These sediments constituting a hugh proportion of the Terai aquifers are derived from two main sources: (i) sediments deposited by large rivers that erode the upper Himalayan crystalline rocks, and (ii) weathered meta-sediments carried by smaller rivers originating in the Siwalik forehills. The generally low redox potential and low SO 4 2− and high DOC, PO 4 3− , and HCO 3 − concentrations in groundwater signify ongoing microbial-mediated redox processes favoring As mobilization in the aquifer. Other geochemical processes, e.g., Fe-oxyhydroxide reduction and carbonate dissolution, are also responsible for high As occurrence in groundwaters. Originally, gagri filters (a two-filter system with chemical powder) and later iron (Fe)-assisted biosand filters were commonly used to remove As and Fe from well water in Nepal—these two options were believed to be the best treatment option at household levels. This review focus on the description of the overall situation, including geogenic issues, occurrence of As in the sediments of the Terai, mechanisms for the release of As to the groundwater, and mitigation options.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".