Superfund Research Program

Ecology, Physiology, Molecular Genetics and Evolution of Microorganisms that Degrade Aromatic Xenobiotic Pollutants

Project Leaders: Ronald H. Olsen (University of Michigan), Jerome J. Kukor (Rutgers University)
Grant Number: P42ES004911
Funding Period: 1995 - 2000

Project-Specific Links

Final Progress Reports

Year:   1999 

Project 1A researchers previously investigated the regulation of the discrete catabolic pathways allowing for hydrocarbon degradation in oxic and hypoxic environments. Currently, the investigators are studying the processes which limit the biotransformation of either the residual or eluted contaminant mass under natural subsurface conditions. To address this issue, Drs. Kukor and Abriola are exploring microbial transformations of organic substrates under flowing conditions--focusing on hydrocarbon transformation by the model microbe, Ralstonia pickettii PKO1. Laboratory observations from batch and soil column experiments, as well as model simulations, have provided evidence of substrate exposure history dependence. Cell deactivation occurred below a certain threshold, and biodegradative activity was re-activated after a certain lag period, during which the cells were continuously exposed to substrate concentrations above a threshold for the re-activation. Increase in the exposure time to low substrate concentrations below a certain threshold resulted in a longer lag period or reduced biodegradative activity. As a consequence of the history dependence of substrate exposure, the spatial gradient of biodegradative activity was controlled by substrate accessibility, which was significantly influenced by flowing conditions. Since the Michaelis-Menten type equation was unable to describe the observed substrate exposure history dependence, batch measured biotransformation kinetic parameters for cells grown on a high concentration of substrate were not applicable to the predictions of biotransformation rates under low substrate and flowing conditions. These findings may have significant implications for the accurate prediction of intrinsic bioremediation rates under low concentrations typical of many contaminant plumes.