Pollutants such as pesticides and their degradation products occur ubiquitously in natural aquatic environments at trace concentrations (μg L–1 and lower). Microbial biodegradation processes have long been known to contribute to the attenuation of pesticides in contaminated environments. However, challenges remain in developing engineered remediation strategies for pesticide-contaminated environments because the fundamental processes that regulate growth-linked biodegradation of pesticides in natural environments remain poorly understood. In this research, we developed a model framework to describe growth-linked biodegradation of pesticides at trace concentrations. We used experimental data reported in the literature or novel simulations to explore three fundamental kinetic processes in isolation. We then combine these kinetic processes into a unified model framework. The three kinetic processes described were: the growth-linked biodegradation of micropollutant at environmentally relevant concentrations; the effect of coincidental assimilable organic carbon substrates; and the effect of coincidental microbes that compete for assimilable organic carbon substrates. We used Monod kinetic models to describe substrate utilization and microbial growth rates for specific pesticide and degrader pairs. We then extended the model to include terms for utilization of assimilable organic carbon substrates by the specific degrader and coincidental microbes, growth on assimilable organic carbon substrates by the specific degrader and coincidental microbes, and endogenous metabolism. The proposed model framework enables interpretation and description of a range of experimental observations on micropollutant biodegradation. The model provides a useful tool to identify environmental conditions with respect to the occurrence of assimilable organic carbon and coincidental microbes that may result in enhanced or reduced micropollutant biodegradation.

Interference of coincidental carbon and indigenous bacteria on pesticide removal by biodegrading strains differs in low versus high AOC waters.

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; Michaelis-Menten; 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.

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