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Cited by 486

The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.

Technologies for Arsenic Removal from Water: Current Status and Future Perspectives

Open access
Nicomel, Nina, Leus, Karen, Folens, Karel, Van Der Voort, Pascal, Du Laing, Gijs·2015·International Journal of Environmental Research and Public Health

Abstract

This review paper presents an overview of the available technologies used nowadays for the removal of arsenic species from water. Conventionally applied techniques to remove arsenic species include oxidation, coagulation-flocculation, and membrane techniques. Besides, progress has recently been made on the utility of various nanoparticles for the remediation of contaminated water. A critical analysis of the most widely investigated nanoparticles is presented and promising future research on novel porous materials, such as metal organic frameworks, is suggested.

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Registry

Crossref · 10.3390/ijerph13010062

Cites

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Metadata

Authors
Nicomel, N., Leus, K., Folens, K., Van Der Voort, P., & Du Laing, G.
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2015-12-22
Volume
13
Issue
1
Pages
62
ISSN
1660-4601 (ISSN-L)
OpenAlex
W2199741873
Cited by
486
References
138
FWCI
10.70
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Technologies for Arsenic Removal from Water: Current Status and Future Perspectives. https://app.cochat.ai/papers/10.3390/ijerph13010062
The paper
Nicomel, N., Leus, K., Folens, K., Van Der Voort, P., & Du Laing, G. (2015). Technologies for Arsenic Removal from Water: Current Status and Future Perspectives. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph13010062

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.