Arsenic removal from water through adsorption-A Review
Bibliographic record
Abstract
Arsenic is a commonly occurring toxic metal in natural systems. It is ahuman carcinogen in water over a wide range of pH values, having harmful effects on both human health andenvironment, even at low concentration. Because ofthis effect, the World Health Organization (WHO) and the US Environmental Protection Agency (USEPA) set the arsenic standard for drinking water at .010 ppm to protect consumers served by public water systems.Occurrence of arsenic contaminated water is reported in several countries like USA, China, Bangladesh, Mexico, Argentina, Poland, Canada, Hungary, Japan and India. The largest population at risk is in Bangladesh followed by India (West Bengal).There is no effective treatment for arsenic toxicity. Onlythe removal of arsenic from water can prevent the toxicity. A great deal of research over recent decades has been done to lower the concentration of arsenic in drinking water and still there is a need to develop low cost viable techniques. Existing major arsenic removal technologies include oxidation, adsorption, precipitation, coagulation andmembrane separation. This paper presents the review of current status of research in the area of arsenic removal from contaminated water and comparison of all technologies available with more emphasis on adsorption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".