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

合作研究：趋化细菌在多相多孔介质中运输的多尺度分析弗吉尼亚大学化学工程系，俄勒冈州立大学化学，生物和环境工程学院，rian D. Wood趋化性是移动细菌感知其局部环境中的化学浓度梯度并向其降解的高浓度污染物游去的能力。在地下，由于无法实现注入物质与常驻污染物之间的良好混合，生物修复常常受到阻碍。在这种情况下，趋化性可以用来加强污染区域内细菌种群的混合。本研究的目的是将物理和化学非均质多孔介质中趋化性的直接实验测量与适当的机制描述理论联系起来。两个主要假设将用于组织研究工作。他们将结合(a)多尺度实验观察和(b)多尺度升级分析，以建立一个理论框架，可以在微观和宏观尺度上描述化学和物理异质介质中的趋化性。第一种假设是，在具有非水相液体（NAPL）污染物捕获源的多孔介质中，趋化细菌的运输速度明显低于非趋化细菌。特别是，由于趋化细菌在流体- napl界面的选择性捕获，趋化细菌将比非趋化突变体表现出更大的延迟和尾随。第二个假设是，细菌在趋化和非趋化条件下的运输在大规模异质性的存在下有显着不同。虽然趋化性本质上是一个孔隙尺度的过程，但趋化细菌对异质性介质从高电导率区域到低电导率区域运输的净影响将在实验上可测量，并且将与现场的细菌运输相关。该项目将结合数据收集和升级的创新实验设计，以开发化学趋向性的多尺度模型。本研究的目的是发展对多孔介质中趋化性的宏观尺度（体）输运行为的理解。这必然涉及到将宏观尺度的运输与控制趋化性的系统的基本微观特征联系起来。在小尺度上测量现象的能力的提高，使得实验数据集包含了甚至在十年前几乎无法想象的细节。这些非同寻常的数据集的发展也经常提出这样的问题？如何理解这些数据呢？有没有一种方法可以在他们身上找到行为的基本特征？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型数据集问题时，升级方法是这些数据分析原型之一。这一建议的结果将产生更可靠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方案的设计和实施。

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.