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Record W2132558570 · doi:10.2166/wqrj.2001.004

Removal of Arsenic in Drinking Water by Iron Oxide-Coated Sand and Ferrihydrite — Batch Studies

2001· article· en· W2132558570 on OpenAlexafffund
O. S. Thirunavukkarasu, T. Viraraghavan, K. S. Subramanian

Bibliographic record

VenueWater Quality Research Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsHealth CanadaUniversity of Regina
FundersHealth Canada
KeywordsArsenicFerrihydriteChemistryAdsorptionEnvironmental chemistryArsenateWater treatmentRaw waterMetalloidIron oxideFreundlich equationArsenic contamination of groundwaterLangmuirArseniteEnvironmental engineeringEnvironmental scienceMetalOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.392
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations140
Published2001
Admission routes2
Has abstractyes

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