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Arsenic in Water: Understanding the Chemistry, Health Implications, Quantification and Removal Strategies

2024· article· en· W4401209221 on OpenAlexaboutno aff
Muhammad Murtaza Chaudhary, Saqib Hussain, Chenyu Du, Barbara R. Conway, Muhammad Usman Ghori

Bibliographic record

VenueChemEngineering · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsArsenicMetalloidGroundwaterArsenic contamination of groundwaterEnvironmental scienceArsenateContaminationHuman healthArsenic toxicityEnvironmental healthEnvironmental chemistryWater resource managementEnvironmental protectionChemistryEcologyBiologyMedicineGeology

Abstract

fetched live from OpenAlex

Arsenic, the 20th most common element in Earth’s crust and historically regarded as the King of Poisons, occurs naturally in two oxidation states, Arsenate (V) and Arsenite (III), and is prevalent worldwide through natural and anthropogenic means. The cations of the metalloid exhibit unique chemical behaviour in water and are found to be components of approximately 245 natural minerals, making its occurrence in drinking water a compelling challenge, especially in groundwater. This comprehensive review collates information regarding the prevalence of arsenic contamination in water worldwide and its impact on human health, its chemical behaviour, methods for detection and quantification, and treatment strategies. A comprehensive search was conducted, and the selection of eligible studies was carried out using the PRISMA (the preferred reporting items for systematic reviews and meta-analyses) guidelines. Essential characteristics of eligible research studies were extracted based on geographical areas, origins, concentration levels and the magnitude of populations vulnerable to arsenic contamination in groundwater sources. Arsenic contamination of water affects over 100 countries including Canada, the United States, Pakistan, China, India, Brazil and Bangladesh, where hydrogeological conditions favour prevalence and groundwater is the primary water source for food preparation, irrigation of food crops and drinking water. This leads to human exposure through absorption, ingestion and inhalation, causing numerous health disorders affecting nearly all systems within the human body, with acute and chronic toxicity including cancers. The presence of arsenic in water poses a considerable challenge to humanity, prompting scientists to devise diverse mitigation approaches categorized as (a) oxidation processes, (b) precipitation methods, (c) membrane technologies, (d) adsorption and ion exchange methods, and (e) social interventions. This comprehensive review is expected to be a valuable source for professionals in the water industry, public management, and policymaking, aiding their ongoing and future research and development efforts.

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)0.012
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0030.006
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.254
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2024
Admission routes1
Has abstractyes

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