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Performance and mechanism of arsenic removal from water by a nanofiltration membrane

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Abstract
The removal of arsenic by a commercial nanofiltration membrane was evaluated with respect to the performance and mechanism. The important membrane properties governing the separation of arsenic, such as molecular weight cut-off (MWCO), electrokinetic charge and individual salts rejection characteristics, were determined. The mechanisms of arsenic rejection were directly investigated by measuring the effects of the initial arsenic concentration, arsenic speciation and various electrolytes on the removal of arsenic. Arsenate [As(V)] was rejected at a much higher rate than arsenite [As(III)], which resulted from the dominance of Donnan exclusion over steric exclusion in controlling the arsenic removal capacity of the membrane. In addition, the rejection of monovalent As(V) was enhanced by the presence of Cl-, but reduced by the presence of SO4 2- due to mutual interactions between anions. Subsequently, the functionalities of Donnan exclusion and mutual ions interactions were validated by testing the removal of arsenic from synthetic water (1 mN NaHCO3 + 10 mN NaCl) over a wide pH range: 4 to 10. With initial As(V) and As(III) concentrations of 50 μg l-1, the removal of As(V) increased with increasing pH over the entire range, while that of As(III) significantly increased over the pH range 8 to 10. The arsenic rejection efficiency of the membrane was also evaluated using the synthetic water. Approximately 89-96% of As(V) was removed at pH 7, whereas 44-41% of As (III) was rejected from feed solutions containing 20, 50, 70 and 100 μg l-1 As(III) and As(V). In addition, the arsenic separation efficiency increased by less than 3% with increasing applied pressure from 138 to 552 kPa (20-80 psi). © 2009.
Author(s)
Nguyen, Cuong ManhBang, SunbaekCho, Jae WeonKim, Kyoung-Woong
Issued Date
2009-09
Type
Article
DOI
10.1016/j.desal.2008.04.047
URI
https://scholar.gist.ac.kr/handle/local/16978
Publisher
Elsevier BV
Citation
Desalination, v.245, no.1-3, pp.82 - 94
ISSN
0011-9164
Appears in Collections:
Department of Environment and Energy Engineering > 1. Journal Articles
Graduate School of AI Policy and Strategy > 1. Journal Articles
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