A comprehensive review on arsenic contamination in groundwater: Sources, detection, mitigation strategies and cost analysis
Bibliographic record
Abstract
While groundwater is commonly perceived as safe, the excessive presence of trace metals, particularly arsenic (As), can pose significant health hazards. This review examines the current scenario of pollutants and their mitigations focusing on As contamination in groundwater across multiple nations, with a specific emphasis on the Indian Peninsula. Arsenic pollution surpasses the WHO limit of 10 ppb in 107 countries, impacting around 230 million people worldwide, with a substantial portion in Asia, including 20 states and four union territories in India. Analysis of the correlation between the aquifer and arsenic poisoning highlights severe contamination in groundwater originating from loose sedimentary aquifer strata, particularly in recently formed mountain ranges with geological sources presumed to contribute over 90% of arsenic pollution, i.e. a big environmental challenge. A myriad of techniques, including chromatographic, electrochemical, biological, spectroscopic, and colorimetric methods among others, are available for the detection and removal of arsenic from groundwater. Removal strategies encompass a wide array of approaches such as bioremediation, adsorption, coagulation/flocculation, ion exchange, biological processes, membrane treatment, and oxidation techniques specifically tailored for affected areas. Constructed wetlands help to eliminate heavy metal impurities such as As, Zn, Cd, Cu, Ni, Fe, and Cr. Their efficiency is influenced by design and environmental factors. Nanotechnology and nanoparticles have recently been studied to remove arsenic and toxic metal ions from water. Cost-effective solutions including community-based mitigation initiatives, alongside policy and regulatory frameworks addressing arsenic contamination, are essential considerations.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".