Arsenic removal from drinking water using granular ferric hydroxide
Bibliographic record
Abstract
This paper examines the use of granular ferric hydroxide (GFH) to remove both arsenate [As(V)] and arsenite [As(III)] present in drinking water by conducting batch and column studies. The kinetic studies were conducted as a function of pH, and less than 5 mg/l was achieved from an initial concentration of 100 mg/l for both As(III) and As(V) with GFH at a pH of 7.6, which is in the pH range typically encountered in drinking water supplies. In the isotherm studies, the observed data fitted well with both the Freundlich and the Langmuir models. In continuous column tests (five cycles) with tap water using GFH, consistently less than 5 mg/l of arsenic was achieved in the finished water for 38 to 42 hours of column operation, where the influent had a spiked arsenic concentration of 500 mg/l. High bed volumes (1260 and 1140) up to a breakthrough concentration of 5 mg/l were achieved in the column studies. The adsorptive capacities for GFH estimated from the column studies were higher than that of activated alumina reported in the previous studies. Speciation of a natural water sample with arsenic showed the dominance of As(III) species over As(V). Batch and column studies showed that granular ferric hydroxide (GFH) can be effectively used in small water utilities to achieve less than 5 mg As/l in drinking water. Keywords: Adsorption, Drinking water, Arsenic removal, Granular ferric hydroxide, Arsenic speciation (WaterSA: 2003 29(2): 161-170)
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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) | 0.000 | 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) | 0.000 | 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".