Back to Search Start Over

The sequential application of macroalgal biosorbents for the bioremediation of a complex industrial effluent

Authors :
Joel T. Kidgell
David A. Roberts
Rocky de Nys
Nicholas A. Paul
Source :
PLoS ONE, Vol 9, Iss 7, p e101309 (2014), PLoS ONE
Publication Year :
2014
Publisher :
Public Library of Science (PLoS), 2014.

Abstract

Fe-treated biochar and raw biochar produced from macroalgae are effective biosorbents of metalloids and metals, respectively. However, the treatment of complex effluents that contain both metalloid and metal contaminants presents a challenging scenario. We test a multiple-biosorbent approach to bioremediation using Fe-biochar and biochar to remediate both metalloids and metals from the effluent from a coal-fired power station. First, a model was derived from published data for this effluent to predict the biosorption of 21 elements by Fe-biochar and biochar. The modelled outputs were then used to design biosorption experiments using Fe-biochar and biochar, both simultaneously and in sequence, to treat effluent containing multiple contaminants in excess of water quality criteria. The waste water was produced during ash disposal at an Australian coal-fired power station. The application of Fe-biochar and biochar, either simultaneously or sequentially, resulted in a more comprehensive remediation of metalloids and metals compared to either biosorbent used individually. The most effective treatment was the sequential use of Fe-biochar to remove metalloids from the waste water, followed by biochar to remove metals. Al, Cd, Cr, Cu, Mn, Ni, Pb, Zn were reduced to the lowest concentration following the sequential application of the two biosorbents, and their final concentrations were predicted by the model. Overall, 17 of the 21 elements measured were remediated to, or below, the concentrations that were predicted by the model. Both metalloids and metals can be remediated from complex effluent using biosorbents with different characteristics but derived from a single feedstock. Furthermore, the extent of remediation can be predicted for similar effluents using additive models.

Details

Language :
English
ISSN :
19326203
Volume :
9
Issue :
7
Database :
OpenAIRE
Journal :
PLoS ONE
Accession number :
edsair.doi.dedup.....ee0769e21f5b2d79e1325ea45c80c93e