Journal
JOURNAL OF CLEANER PRODUCTION
Volume 190, Issue -, Pages 703-711Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.04.174
Keywords
Emerging pollutants; Clay-polymer composites; Cationic starch; Filtration Modeling
Categories
Funding
- NANOWAT within the ENPI-CBC-MED programme of the European Union [I-B/2.1/049]
- Spanish Ministry of Economy and Competitiveness - European Regional Development Fund (FEDER) [CTM2013-42306-R, CTM2016-77168-R]
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Significant concerns have been raised up due to the presence of organic micropollutants in surface waters. The ability of two polymer-clay sorbents based on a functionalized cationic starch was examined for the removal of three pharmaceuticals: atenolol, sulfamethoxazole and diclofenac sodium. In batch experiments, the complex which exhibited a planar conformation of the polymer on the clay surface and higher cationic charge density showed higher sorption of diclofenac and sulfamethoxazole over those of the composite with a loops and tails configuration, but similar with atenolol. The introduction of functional moieties on the polymers that are capable to create a network of hydrogen-bonds with the pollutants promoted their removal as revealed by thermal and infrared techniques: diclofenac molecules formed an ion pair including hydrogen bonds through their secondary amine groups; sulfamethoxazole sorbed by strong electrostatic interactions followed by proton transfer involving its sulphon-nitrogen group and the hydroxyl moieties of the composite. Filtration experiments showed a better performance of the columns made of the composite with higher cationic charge density on the removal of diclofenac and sulfamethoxazole over that of granular activated carbon. The filtration processes were successfully modeled by using an adsorption-convection model which enabled predictions under different operational conditions used in drinking water plants. Experimental removal of diclofenac by filtration within the range found in environmental concentrations was in good agreement with the predicted amounts. (C) 2018 Elsevier Ltd. All rights reserved.
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