COLLABORATIVE RESEARCH: A MULTISCALE ANALYSIS OF THE TRANSPORT OF CHEMOTACTIC BACTERIA IN HETEROGENEOUS POROUS MEDIA
Roseanne M. Ford Department of Chemical Engineering, University of Virginia Brian D. Wood School of Chemical, Biological, and Environmental Engineering, Oregon State University
Chemotaxis is the ability of motile bacteria to sense chemical concentration gradients in their local surroundings and swim toward higher concentrations of pollutants that they degrade. In the subsurface, bioremediation is often hindered by the inability to achieve good mixing between injected substances and the resident contaminants. In such situations, chemotaxis might be exploited to enhance the mixing of bacterial populations within contaminated zones. The goal of this study is to connect direct experimental measurement of chemotaxis in physically and chemically heterogeneous porous media with appropriate mechanistic descriptive theory. Two main hypotheses will be used to organize the research effort. They will combine both (a) multiscale experimental observations, and (b) a multiscale upscaling analysis to develop a theoretical framework that can describe chemotaxis in chemically and physically heterogenous media at microscopic and macroscopic scales. The first hypotheses is that in porous media with trapped sources of nonaqueous phase liquid (NAPL) pollutants, chemotactic bacteria will have measurably slower transport than non-chemotactic bacteria. In particular, chemotactic bacteria will exhibit greater retardation and tailing than will non-chemotactic mutants because of selective trapping of chemotactic bacteria at the fluid-NAPL interfaces. The second hypothesis is that transport of bacteria under chemotactic versus non-chemotactic conditions are dramatically different in the presence of large-scale heterogeneities. Although chemotaxis is inherently a pore-scale process, the net influence of chemotactic bacteria on transport from high to low conductivity regions of a heterogeneous medium will be experimentally measurable, and will have relevance to bacterial transport in the field. This project will combine innovative experimental designs for data collection and upscaling to develop multiscale models for chemotaxis.
The purpose of this research is to develop an understanding of the macroscopic scale (bulk) transport behavior of chemotaxis in porous media. This necessarily involves linking the macroscale transport to the essential microscale features of the system that control chemotaxis. Improvements in the ability to measure phenomena at small scales has lead to experimental data sets that contain detail that was nearly unimaginable even a decade ago. The development of such extraordinary data sets has also frequently promulgated the question ?how does one make sense of these data? Is there a way to search for essential features of behavior in them?? New archetypes for data analysis (e.g., machine learning algorithms, data mining) have been employed as methods to assess such data sets. When applied to the problem of large data sets arising from physical systems, upscaling methods are among these data analysis archetypes. Outcomes from this proposal will result in more robust models for predicting microbial transport in groundwater systems, which will lead to improved design and implementation of bioremediation schemes.
