COLLABORATIVE RESEARCH: A Multiscale Analysis of Chemotactic Bacteria Transport in Heterogeneous Porous Media

合作研究：趋化性细菌在非均质多孔介质中传输的多尺度分析M.Ford弗吉尼亚大学化学工程系俄勒冈州立大学布莱恩·D·伍德化学、生物和环境工程学院趋化性是指可移动的细菌感知其当地环境中的化学浓度梯度并游向它们可降解的更高浓度的污染物的能力。在地下，生物修复往往因为无法在注入物质和残留污染物之间实现良好的混合而受到阻碍。在这种情况下，可以利用趋化性来加强受污染区域内细菌种群的混合。这项研究的目的是将物理和化学非均质多孔介质中化学趋化性的直接实验测量与适当的机械描述理论联系起来。两个主要的假设将被用来组织研究工作。他们将结合(A)多尺度实验观察和(B)多尺度放大分析来开发一个理论框架，可以在微观和宏观尺度上描述化学和物理非均质介质中的趋化性。第一个假设是，在具有非水相液体(NAPL)污染物捕获源的多孔介质中，趋化性细菌的运输速度明显慢于非趋化性细菌。特别是，趋化性细菌将表现出比非趋化性突变体更大的迟滞和拖尾，因为趋化性细菌选择性地捕获在液体-NAPL界面上。第二个假设是，在大规模异质性存在的情况下，趋化和非趋化条件下细菌的运输有很大的不同。虽然趋化性本质上是一个孔隙尺度的过程，但趋化性细菌对从非均质介质的高电导率区域到低电导率区域的运输的净影响将是可以实验测量的，并将与细菌在现场的运输有关。该项目将结合数据收集和放大的创新实验设计，开发多尺度趋化模型。这项研究的目的是加深对趋化性在多孔介质中的宏观尺度(整体)传输行为的理解。这必然涉及将宏观尺度的运输与控制趋化性的系统的基本微观尺度特征联系起来。测量小尺度现象的能力的提高导致了实验数据集的出现，其中包含的细节在十年前几乎是不可想象的。如此非凡的数据集的发展也经常揭示这样一个问题：人们如何理解这些数据？有没有办法寻找他们行为的基本特征？？用于数据分析的新原型(例如，机器学习算法、数据挖掘)已被用作评估此类数据集的方法。当应用于物理系统产生的大数据集问题时，提升方法是这些数据分析原型中的一种。这一提议的结果将产生更强大的模型来预测地下水系统中的微生物迁移，这将导致改进生物修复计划的设计和实施。

COLLABORATIVE RESEARCH: A MULTISCALE ANALYSIS OF THE TRANSPORT OF CHEMOTACTIC BACTERIA IN HETEROGENEOUS POROUS MEDIARoseanne M. Ford Department of Chemical Engineering, University of VirginiaBrian D. Wood School of Chemical, Biological, and Environmental Engineering, Oregon State UniversityChemotaxis 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.