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How the sorption of benzene in soils contaminated with aromatic hydrocarbons is affected by the presence of biofuels
Author(s) -
Manuela Carvalho,
Maria Cristina Vila,
Fernanda Rohden,
Mónica Rosas,
Joana Maia Dias,
M. Teresa Oliva-Teles,
Anthony S. Danko,
Cristina DelerueMatos,
António Fiúza
Publication year - 2015
Publication title -
eurasian journal of soil science (ejss)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.24
H-Index - 5
ISSN - 2147-4249
DOI - 10.18393/ejss.42200
Subject(s) - sorption , soil water , environmental chemistry , environmental science , benzene , biofuel , contamination , soil contamination , chemistry , waste management , soil science , adsorption , organic chemistry , ecology , engineering , biology
The increasing use of biofuels as additives to gasoline may have potential indirect effects on the efficiency of soil remediation technologies used to remediate fuel spills.  This problem has not yet been studied. Sorption is one of the controlling processes in soil remediation. The effect of biofuels on sorption and phase distribution of contaminants by different natural soils has not been reported on the literature. The present work examines how two different biofuels, n-butanol and soybean biodiesel, affect benzene sorption in two naturally occurring subsoils (granite and limestone). Sorption isotherms were made with soils deliberately contaminated with benzene, benzene and n-butanol and benzene plus biodiesel, using lab-scale reactors operated at constant temperature, each one loaded with 700 grams of wet sterilized soil. For each type of soil, five isotherms were determined corresponding to different contamination profiles. It was concluded that sorption was strongly affected by the nature of the soil. The partition of benzene into the different phases of the soil was significantly affected by the presence of biofuels. The experimental data was fitted to conventional sorption models, Freundlich, Langmuir and a second order polynomial. Model parameters were determined using a non-linear least squares (NLLS) optimization algorithm and showed a good agreement between experimental and fitted data.

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