Removal of Arsenic in Drinking Water by Iron Oxide-Coated Sand and Ferrihydrite — Batch Studies
Why this work is in the frame
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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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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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 it