Evaluating Alternate Biokinetic Models for Trace Pollutant Cometabolism

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Abstract

Mathematical models of cometabolic biodegradation kinetics can improve our understanding of the relevant microbial reactions and allow us to design in situ or in-reactor applications of cometabolic bioremediation. A variety of models are available, but their ability to describe experimental data has not been systematically evaluated for a variety of operational/experimental conditions. Here five different models were considered: first-order; MichaelisMenten; reductant; competition; and combined models. The models were assessed on their ability to fit data from simulated batch experiments covering a realistic range of experimental conditions. The simulated observations were generated by using the most complex model structure and parameters based on the literature, with added experimental error. Three criteria were used to evaluate model fit: ability to fit the simulated experimental data, identifiability of parameters using a colinearity analysis, and suitability of the model size and complexity using the Bayesian and Akaike Information criteria. Results show that no single model fits data well for a range of experimental conditions. The reductant model achieved best results, but required very different parameter sets to simulate each experiment. Parameter nonuniqueness was likely to be due to the parameter correlation. These results suggest that the cometabolic models must be further developed if they are to reliably simulate experimental and operational data.
Original languageEnglish
JournalEnvironmental Science and Technology
Volume49
Issue number4
Pages (from-to)2230-2236
Number of pages7
ISSN0013-936X
DOIs
Publication statusPublished - 2015

Keywords

  • Biodegradation
  • Akaike information criterion
  • Co-metabolic models
  • Cometabolic biodegradations
  • Cometabolic bioremediation
  • Experimental conditions
  • Experimental errors
  • Parameter correlation
  • Parameter nonuniqueness
  • Bioremediation
  • cometabolism
  • oxygenases

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