Uranium in groundwater in parts of India and world: A comprehensive review of sources, impact to the environment and human health, analytical techniques, and mitigation technologies
Bibliographic record
Abstract
Uranium concentration/contamination in groundwater is currently a subject of concern all over the world due to related severe health problems to humans, as groundwater is the main drinking water source in rural and urban India and also in several parts of the world. Uranium concentration in groundwater in shallow aquifers in various states such as Punjab, Rajasthan, Karnataka Telangana, and Madhya Pradesh of India varies from 0 to 1443 ng/ml exceeding the permissible levels by WHO for drinking water (30 ng/ml), at several places. Very high concentrations ranging up to 1400 ng/ml were reported in some areas in other countries such as Canada, the USA, Mongolia, Burundi, Zambia, Nigeria, South Korea, Pakistan, Jordon, Afghanistan, China, and Myanmar. Various natural aspects which influence the uranium concentration in groundwater such as bedrock geology, water chemistry, and redox conditions, and anthropogenic sources such as mining activities (uranium, coal, and phosphate rock), nuclear activities, agricultural practices of using phosphate fertilizers, and prevalence of excessive nitrate in some areas, are described with examples. Some of the important analytical techniques for the precise and accurate determination of elemental and isotopic concentrations of uranium in water samples, such as LED fluorimetry, Raman spectroscopy, inductively coupled plasma mass spectrometry (ICP-MS), high-resolution ICP-MS (HR-ICP-MS), and multi-collector ICP-MS (MC-ICP-MS), are described. A number of advancements have taken place in remediation technologies for the removal of uranium in drinking water using different physical, chemical, and biological methods including rainwater harvesting. Various mitigation strategies for the effective removal of uranium from water during treatment, such as bioremediation using biochars from different sources, nanoparticle technology, and adsorption by magnesium (Mg)-iron (Fe)-based hydrotalcite-like compounds (MF-HT), are described in detail.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".