title: Identifying the Correct Biotransformation Model from Polychlorinated Biphenyl and Dioxin Dechlorination Batch Studies.
abstract: We performed Monte Carlo simulations of batch transformations of hydrophobic compounds using typical numbers of data points, extent of reaction, and measurement error, to identify the most appropriate biotransformation model to describe such data under different conditions. Highly hydrophobic compounds such as polychlorinated biphenyls (PCBs) and dioxins present special challenges for parameterization due to low environmental concentrations and slow biotransformation rates, which result in high sample variability, few samples, and limited substrate concentration range. Four models of varying complexity (zero-order, first-order, Monod, and Best) were fit to simulated data. Various combinations of initial concentration (S0), half saturation concentration (KS), maximum substrate utilization rate (qmax), measurement error, number of data points per batch run, and extent of biotransformation were simulated. One thousand Monte-Carlo runs were performed for each parameter combination, and AICc(Akaike's information criterion corrected for small numbers of data points) was used to determine the most appropriate model. Neither the Best model nor the zero-order model ever produced the lowest AICcfor a majority of simulations under any combination of test conditions. With 10% measurement error, the first-order model always outperformed the others. In the case of 1% measurement error with 10 evenly-spaced data points, the Monod model was the better choice whenS0>KSand the system was not mass transfer limited [Formula: see text] otherwise, the first-order model was indicated.S0is constrained by the compound's aqueous solubility; therefore, for highly hydrophobic compounds such as PCBs or polychlorinated dibenzo-p-dioxins and dibenzofurans, a first-order model is likely to fit batch biotransformation data as well or better than a more complicated model.
authors.affiliation: Department of Environmental Sciences, Rutgers, The State University of New Jersey , New Brunswick, New Jersey.
authors.forename: Valdis
authors.initials: V
authors.lastname: Krumins
authors.affiliation: Department of Environmental Sciences, Rutgers, The State University of New Jersey , New Brunswick, New Jersey.
authors.forename: Donna E
authors.initials: DE
authors.lastname: Fennell
keywords: PCBs; PCDD/Fs; batch; kinetics; low solubility compounds; model selection