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

Kidgell, Joel T., de Nys, Rocky, Paul, Nicholas A., and Roberts, David A. (2014) The sequential application of macroalgal biosorbents for the bioremediation of a complex industrial effluent. PLoS ONE, 9 (7).

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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.

Item ID: 34311
Item Type: Article (Refereed Research - C1)
Additional Information:

© 2014 Kidgell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ISSN: 1932-6203
Funders: Advanced Manufacturing Cooperative Research Centre (AMCRC), Australian Renewable Energy Agency (ARENA)
Date Deposited: 07 Aug 2014 03:26
FoR Codes: 10 TECHNOLOGY > 1002 Environmental Biotechnology > 100203 Bioremediation @ 100%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960912 Urban and Industrial Water Management @ 100%
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