Kinetics of dechlorination by **Dehalococcoides mccartyi** using different carbon sourcesKinetics of dechlorination by **Dehalococcoides mccartyi** using different carbon sources
Faculty of Sciences. Bioscience Engineering
Sustainable Energy, Air and Water Technology
Journal of contaminant hydrology. - Oxford
157(2014), p. 25-36
University of Antwerp
Stimulated anaerobic dechlorination is generally considered a valuable step for the remediation of aquifers polluted with chlorinated ethenes (CEs). Correct simulation and prediction of this process in situ, however, require good knowledge of the associated biological reactions. The aim of this study was to evaluate the dechlorination reaction in an aquifer contaminated with trichloroethene (TCE) and its daughter products, discharging into the Zenne River. Different carbon sources were used in batch cultures and these were related to the dechlorination reaction, together with the monitored biomarkers. Appropriate kinetic formulations were assessed. Reductive dechlorination of TCE took place only when external carbon sources were added to microcosms, and occurred concomitant with a pronounced increase in the Dehalococcoides mccartyi cell count as determined by 16S rRNA gene-targeted qPCR. This indicates that native dechlorinating bacteria are present in the aquifer of the Zenne site and that the oligotrophic nature of the aquifer prevents a complete degradation to ethene. The type of carbon source, the cell number of D. mccartyi or the reductive dehalogenase genes, however, did not unequivocally explain the observed differences in degradation rates or the extent of dechlorination. Neither first-order, Michaelis-Menten nor Monod kinetics could perfectly simulate the dechlorination reactions in TCE spiked microcosms. A sensitivity analysis indicated that the inclusion of donor limitation would not significantly enhance the simulations without a clear process understanding. Results point to the role of the supporting microbial community but it remains to be verified how the complexity of the microbial (inter)actions should be represented in a model framework. (C) 2013 Elsevier B.V. All rights reserved.