Removal of Arsenic in Drinking Water by Iron Oxide-Coated Sand and Ferrihydrite — Batch Studies
Bibliographic record
Abstract
Abstract Arsenic, a common toxic element is mainly transported in the environment by water. Arsenic in drinking water is of major concern to many of the water utilities in the world. Numerous studies have examined the removal of arsenic from drinking water through treatment processes such as coagulation-precipitation, reverse osmosis and ion exchange. The focus of research has now shifted to solve the problems using suitable adsorbents to achieve low level As in drinking water for communities with high raw water arsenic concentration. The determination of arsenic species is also essential for a better understanding and prediction of the toxic and carcinogenic nature of the species present in natural water systems. It is generally known that As(III) is more toxic than As(V) and inorganic arsenicals are more toxic than organic derivatives. The objective of this study was to study the arsenic adsorption behaviour on iron oxide-coated sand (IOCS) and ferrihydrite (FH). Batch studies were conducted using these adsorbents with natural water containing 325 μg/L arsenic, and the removal of approximately 90% was obtained. The adsorption capacity of the IOCS and FH used in this study for arsenic was estimated as 18.3 and 285 μg/g, respectively. The experimental data fitted well with the well-known isotherms, namely, Freundlich, Langmuir and BET, indicating a favourable adsorption by these adsorbents. Speciation studies were also conducted with natural water containing arsenic. Particulate and soluble arsenic in water were determined, and As(III) in the sample was determined by passing the sample containing arsenic through anion exchange resin (Dowex 1X8-100; acetate form) packed in the column. Speciation studies with natural water showed that the particulate and soluble arsenic contributed 11.4 and 88.6% of the total arsenic present in the natural water, respectively. In the case of soluble arsenic, As(III) and As(V) were 47.3 and 52.7%, respectively.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".