Biogeochemical dynamics in porous media
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The goal of this work is to investigate biogeochemical dynamics in porous media, including ways to formulate microbial metabolism and its implementation into reaction transport models. First, elemental cycling in a contaminated groundwater setting is assessed using model descriptions that differ in the range of reactions taken into account and in whether microbial population dynamics are considered. Results from simulations using different reaction networks show that a relatively simple network can provide an accurate prediction of the observed distribution of dissolved substances in a contaminant plume. However, depending on the complexity of the reaction network used, distinct differences can exist in individual process rates affecting these pools. When dynamics of microbial functional groups are accounted for, our simulations show the importance of the interplay between reaction energetics and nutrient limitations. Microbial activity is also investigated in a freshwater marsh, and a set of reactions is developed describing the hydrolysis and fermentation of organic matter and terminal metabolic processes. Rates of reactions involved in organic matter breakdown were quantified with two complimentary approaches. Since the two approaches rely on different types of data, they allow for independent methods to determine process rates in the sediment. Results show that the methodologies are consistent in some predicted rates but differ significantly in others, highlighting the importance of the description of reaction kinetics used for organic matter breakdown. Explicit formulations of microbial metabolism and its implementation in reaction transport models were also investigated. A kinetic representation of Geobacter sulfurreducens central metabolism was developed which successfully reproduced measured growth efficiencies with iron as terminal electron acceptor over a wide range of extracellular acetate concentrations. Analysis of experimentally validated in silico cell models were also utilized to predict phenotypic plasticity. When environmental conditions vary, an organism adjusts its enzymatic machinery. Results show the potential importance of investigating multiple phenotypes for an organism, not only including those that are optimal under a set of environmental conditions, but also those that may be slightly less efficient under static settings. These can be better adapted to settings in which the physic-chemical environment the microbe experiences fluctuates, requiring adaptation of its metabolic machinery.