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The overall objective is to establish a clear understanding and ability to predict the physical and chemical mechanisms that are controlling pathogen fate in porous media.
Non-Technical Summary: At present our ability to predict the migration and fate of disease-causing microorganisms (pathogens) in soil and groundwater environments is limited by our understanding of the processes that control microbe retention in soil. A review of the literature indicates that the currently accepted conceptual and mathematical framework for microbe retention (filtration theory) has serious flaws because it does not adequately account for the removal mechanisms involved in small soil pores and when microbes collide with each other and stick together. The overall research objective is therefore to establish a clear understanding and ability to predict the various physical and chemical mechanisms that are controlling microbe retention in soil. <P> Approach: Extended DLVO theory calculations and direct measurement of pathogen-pathogen interaction forces using atomic force microscopy (AFM) will be utilized to determine colloid-colloid and colloid-grain interaction energies. Real time direct observation of pore-scale deposition processes using video microscopy in impinging jet and micromodel systems will also be utilized to determine dominant deposition mechanisms. A three dimensional particle trajectory model will be developed to study pore-scale transport and deposition processes in porous media composed of spherical grains. This model will explicitly account for the presence of grain to grain contacts and interaction energies, and will provide a theoretical basis for exploring the potential role of colloid-colloid interactions. Batch and column scale experiments will be performed to provide additional information on deposition mechanisms and profiles with transport distance. Continuum based flow and transport models will be utilized and modified to simulate the breakthrough curves and deposition profiles in these column experiments, and to predict the influence of various observations at larger spatial and temporal scales.