Experimental Open Air Burning of Vegetation Enhances Organic Matter Chemical Heterogeneity Compared to Laboratory Burns.

Wildfires produce solid residuals that have unique chemical and physical properties compared to unburned materials, which influence their cycling and fate in the natural environment. Visual burn severity assessment is used to evaluate post-fire alterations to the landscape in field-based studies, yet muffle furnace methods are commonly used in laboratory studies to assess molecular scale alterations along a temperature continuum. Here, we examined solid and leachable organic matter characteristics from chars visually characterized as low burn severity that were created either on an open air burn table or from low-temperature muffle furnace burns. We assessed how the different combustion conditions influence solid and dissolved organic matter chemistries and explored the potential influence of these results on the environmental fate and reactivity. Notably, muffle furnace chars produced less leachable carbon and nitrogen than open air chars across land cover types. Organic matter produced from muffle furnace burns was more homogeneous than open air chars. This work highlights chemical heterogeneities that exist within a single burn severity category, potentially influencing our conceptual understanding of pyrogenic organic matter cycling in the natural environment, including transport and processing in watersheds. Therefore, we suggest that open air burn studies are needed to further advance our understanding of pyrogenic organic matter's environmental reactivity and fate.

Model-based analysis of the role of biological, hydrological and geochemical factors affecting uranium bioremediation.

Uranium contamination is a serious concern at several sites motivating the development of novel treatment strategies such as the Geobacter-mediated reductive immobilization of uranium. However, this bioremediation strategy has not yet been optimized for the sustained uranium removal. While several reactive-transport models have been developed to represent Geobacter-mediated bioremediation of uranium, these models often lack the detailed quantitative description of the microbial process (e.g., biomass build-up in both groundwater and sediments, electron transport system, etc.) and the interaction between biogeochemical and hydrological process. In this study, a novel multi-scale model was developed by integrating our recent model on electron capacitance of Geobacter (Zhao et al., 2010) with a comprehensive simulator of coupled fluid flow, hydrologic transport, heat transfer, and biogeochemical reactions. This mechanistic reactive-transport model accurately reproduces the experimental data for the bioremediation of uranium with acetate amendment. We subsequently performed global sensitivity analysis with the reactive-transport model in order to identify the main sources of prediction uncertainty caused by synergistic effects of biological, geochemical, and hydrological processes. The proposed approach successfully captured significant contributing factors across time and space, thereby improving the structure and parameterization of the comprehensive reactive-transport model. The global sensitivity analysis also provides a potentially useful tool to evaluate uranium bioremediation strategy. The simulations suggest that under difficult environments (e.g., highly contaminated with U(VI) at a high migration rate of solutes), the efficiency of uranium removal can be improved by adding Geobacter species to the contaminated site (bioaugmentation) in conjunction with the addition of electron donor (biostimulation). The simulations also highlight the interactive effect of initial cell concentration and flow rate on U(VI) reduction.

Contributions of biofilm-induced flow heterogeneities to solute retention and anomalous transport features in porous media.

Microbial biofilms are ubiquitous within porous media and the dynamics of their growth influence surface and subsurface flow patterns which impacts the physical properties of porous media and large-scale transport of solutes. A two-dimensional pore-scale numerical model was used to evaluate the impact of biofilm-induced flow heterogeneities on conservative transport. Our study integrates experimental biofilm images of Paenibacillus 300A strain in a microfluidic device packed with cylindrical grains in a hexagonal distribution, with mathematical modeling. Biofilm is represented as a synthetic porous structure with locally varying physical properties that honors the impact of biofilm on the porous medium. We find that biofilm plays a major role in shaping the observed conservative transport dynamics by enhancing anomalous characteristics. More specifically, when biofilm is present, the pore structure in our geometry becomes more spatially correlated. We observe intermittent behavior in the Lagrangian velocities that switches between fast transport periods and long trapping events. Our results suggest that intermittency enhances solute spreading in breakthrough curves which exhibit extreme anomalous slope at intermediate times and very marked late solute arrival due to solute retention. The efficiency of solute retention by the biofilm is controlled by a transport regime which can extend the tailing in the concentration breakthrough curves. These results indicate that solute retention by the biofilm exerts a strong control on conservative solute transport at pore-scale, a role that to date has not received enough attention.

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River.

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

WHONDRS-GUI: a web application for global survey of surface water metabolites.

BACKGROUND: The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a consortium that aims to understand complex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. RESULTS: The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well-structured, discoverable dataset for WHONDRS. Together with a context-aware dynamic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1484811, which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server (https://xmlin.shinyapps.io/whondrs/).

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling.

Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling-termed substrate-explicit modeling-that enables parameterizing OM-specific oxidative degradation pathways and reaction rates based on the thermodynamic properties of OM pools. Based on previous developments in the literature, we characterized the resulting kinetic models by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. The two parameters include maximal growth rate (µmax) and harvest volume (Vh) (i.e., the volume that a microbe can access for harvesting energy). For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics using ultra high-resolution metabolomics datasets derived from Fourier transform ion cyclotron resonance mass spectrometry analyses, our method predicted how oxidative degradation is primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements (as shown by correlation coefficients of up to 0.61), and how biogeochemical reactions can vary in response to carbon and/or oxygen limitations. Lastly, we showed that incorporation of enzymatic regulations into substrate-explicit models is critical for more reasonable predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.

Subsurface biogeochemistry is a missing link between ecology and hydrology in dam-impacted river corridors.

Global investment in hydropower is rapidly increasing, fueled by a need to manage water availability and by incentives promoting renewable energy sources. This expansion poses unrecognized risks to the world's vulnerable freshwater ecosystems. While many hydropower impacts have been investigated, dam-induced alterations to subsurface processes influence river corridor ecosystem health in ways that remain poorly understood. We advocate for a better understanding of dam impacts on subsurface biogeochemical activity, its connection to hydrology, and follow-on trophic cascades within the broader river corridor. We delineate an integrated view of hydropower impacts in which dam-induced changes to surface water flow regimes generate changes in surface-subsurface hydrologic exchange flows (HEFs) that subsequently (1) regulate resource availability for benthic microorganisms at the base of aquatic food webs and (2) impose kinetic constraints on biogeochemical reactions and organismal growth across a range of trophic levels. These HEF-driven effects on river corridor food webs, as mediated by subsurface biogeochemistry, are a key knowledge gap in our assessment of hydropower sustainability and putatively combine with other, more well-known dam impacts to result in significant changes to river corridor health. We suggest targeted laboratory and field-based studies to link hydrobiogeochemical models used to predict heat transport, biogeochemical rates, and hydrologic flow with ecological models that incorporate biomass changes in specific categories of organisms. Doing so will enable predictions of feedbacks among hydrology, temperature, biogeochemical rates, organismal abundances, and resource transfer across trophic levels. This understanding of dam impacts on subsurface hydrobiogeochemistry and its connection to the broader aquatic food web is fundamental to enabling mechanism-based decision making for sustainable hydropower operations.

On Modeling Ensemble Transport of Metal Reducing Motile Bacteria.

Many metal reducing bacteria are motile with their run-and-tumble behavior exhibiting series of flights and waiting-time spanning multiple orders of magnitude. While several models of bacterial processes do not consider their ensemble motion, some models treat motility using an advection diffusion equation (ADE). In this study, Geobacter and Pelosinus, two metal reducing species, are used in micromodel experiments for study of their motility characteristics. Trajectories of individual cells on the order of several seconds to few minutes in duration are analyzed to provide information on (1) the length of runs, and (2) time needed to complete a run (waiting or residence time). A Continuous Time Random Walk (CTRW) model to predict ensemble breakthrough plots is developed based on the motility statistics. The results of the CTRW model and an ADE model are compared with the real breakthrough plots obtained directly from the trajectories. The ADE model is shown to be insufficient, whereas a coupled CTRW model is found to be good at predicting breakthroughs at short distances and at early times, but not at late time and long distances. The inadequacies of the simple CTRW model can possibly be improved by accounting for correlation in run length and waiting time.

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process.

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.

Change of collision efficiency with distance in bacterial transport experiments.

Understanding the mechanisms of bacterial transport in aquifers is important in developing bioremediation strategies. Collision efficiency (alpha) is one important parameter used in modeling bacterial transport. This study was undertaken to measure change in alpha with distance by performing a bacterial transport experiment in Oyster, Virginia. Following injection of a bacterium, Comamonas sp., into a well, water samples were collected at various distances along the flowpath and injected into columns packed with homogenized South Oyster focus area sediment. Zeta potentials of the bacteria in the samples were measured. Values of alpha were determined at various locations in the field in two ways: based on field breakthrough concentrations at the sampling points and based on column breakthrough concentrations. The alpha values estimated from field breakthrough decreased with distance, whereas those estimated from column breakthrough increased with distance. Bacterial cell surface charge became progressively more negative with distance in the field. We hypothesize that the apparent contradiction between field and column alpha values was caused by differences in the flow of the two systems. Flow in the columns was forced to occur through fine-grained zones of iron and aluminum hydroxide coatings that selectively removed the most negatively charged bacteria. In contrast, in the field, the injected cells did not come into contact with the positively charged coatings because the bulk solution bypassed them due to heterogeneous hydraulic properties. These results suggest that laboratory-based models may underestimate bacterial transport distance in the field. A more realistic approach may be necessary to capture the degree of heterogeneity.

Conceptual and numerical model of uranium(VI) reductive immobilization in fractured subsurface sediments.

A conceptual model and numerical simulations of bacterial U(VI) reduction in fractured subsurface sediments were developed to assess the potential feasibility of biomineralization at the fracture/matrix interface as a mechanism for immobilization of uranium in structured subsurface media. The model envisions flow of anaerobic groundwater, with or without acetate as an electron donor for stimulation of U(VI) reduction by dissimilatory metal-reducing bacteria (DMRB), within mobile macropores along a one-dimensional flow path. As the groundwater moves along the flow path, U(VI) trapped in the immobile mesopore and micropore domains (the sediment matrix) becomes desorbed and transferred to the mobile macropores (fractures) via a first-order exchange mechanism. By allowing bacterial U(VI) reduction to occur in the mesopore domain (assumed to account for 12% of total sediment pore volume) according to experimentally-determined kinetic parameters and an assumed DMRB abundance of 10(7) cells per cm3 bulk sediment (equivalent to 4 mg of cells per dm3 bulk sediment), the concentration of U(VI) in the macropore domain was reduced ca. 10-fold compared to that predicted in the absence of mesopore DMRB activity after a 6-month simulation period. The results suggest that input of soluble electron donors over a period of years could lead to a major redistribution of uranium in fractured subsurface sediments, converting potentially mobile sorbed U(VI) to an insoluble reduced phase (i.e. uraninite) in the mesopore domain that is expected to be permanently immobile under sustained anaerobic conditions.

An analysis platform for multiscale hydrogeologic modeling with emphasis on hybrid multiscale methods.

One of the most significant challenges faced by hydrogeologic modelers is the disparity between the spatial and temporal scales at which fundamental flow, transport, and reaction processes can best be understood and quantified (e.g., microscopic to pore scales and seconds to days) and at which practical model predictions are needed (e.g., plume to aquifer scales and years to centuries). While the multiscale nature of hydrogeologic problems is widely recognized, technological limitations in computation and characterization restrict most practical modeling efforts to fairly coarse representations of heterogeneous properties and processes. For some modern problems, the necessary level of simplification is such that model parameters may lose physical meaning and model predictive ability is questionable for any conditions other than those to which the model was calibrated. Recently, there has been broad interest across a wide range of scientific and engineering disciplines in simulation approaches that more rigorously account for the multiscale nature of systems of interest. In this article, we review a number of such approaches and propose a classification scheme for defining different types of multiscale simulation methods and those classes of problems to which they are most applicable. Our classification scheme is presented in terms of a flowchart (Multiscale Analysis Platform), and defines several different motifs of multiscale simulation. Within each motif, the member methods are reviewed and example applications are discussed. We focus attention on hybrid multiscale methods, in which two or more models with different physics described at fundamentally different scales are directly coupled within a single simulation. Very recently these methods have begun to be applied to groundwater flow and transport simulations, and we discuss these applications in the context of our classification scheme. As computational and characterization capabilities continue to improve, we envision that hybrid multiscale modeling will become more common and also a viable alternative to conventional single-scale models in the near future.

An evaluation of conditioning data for solute transport prediction.

The large and diverse body of subsurface characterization data generated at a field research site near Oyster, Virginia, provides a unique opportunity to test the impact of conditioning data of various types on predictions of flow and transport. Bromide breakthrough curves (BTCs) were measured during a forced-gradient local-scale injection experiment conducted in 1999. Observed BTCs are available at 140 sampling points in a three-dimensional array within the transport domain. A detailed three-dimensional numerical model is used to simulate breakthrough curves at the same locations as the observed BTCs under varying assumptions regarding the character of hydraulic conductivity spatial distributions, and variable amounts and types of conditioning data. We present comparative results of six cases ranging from simple (deterministic homogeneous models) to complex (stochastic indicator simulation conditioned to cross-borehole geophysical observations). Quantitative measures of model goodness-of-fit are presented. The results show that conditioning to a large number of small-scale measurements does not significantly improve model predictions, and may lead to biased or overly confident predictions. However, conditioning to geophysical interpretations with larger spatial support significantly improves the accuracy and precision of model predictions. In all cases, the effects of model error appear to be significant in relation to parameter uncertainty.

Dissipative-particle-dynamics model of biofilm growth.

A dissipative-particle-dynamics model for the quantitative simulation of biofilm growth controlled by substrate (nutrient) consumption, advective and diffusive substrate transport, and hydrodynamic interactions with fluid flow (including fragmentation and reattachment) is described. The model was used to simulate biomass growth, decay, and spreading. It predicts how the biofilm morphology depends on flow conditions, biofilm growth kinetics, the rheomechanical properties of the biofilm, and adhesion to solid surfaces. The morphology of the model biofilm depends strongly on its rigidity and the magnitude of the body force that drives the fluid over the biofilm.

Lessons learned from bacterial transport research at the South Oyster Site.

This paper provides a review of bacterial transport experiments conducted by a multiinvestigator, multiinstitution, multidisciplinary team of researchers under the auspices of the U.S. Department of Energy (DOE). The experiments were conducted during the time period 1999-2001 at a field site near the town of Oyster, Virginia known as the South Oyster Site, and included four major experimental campaigns aimed at understanding and quantifying bacterial transport in the subsurface environment. Several key elements of the research are discussed here: (1) quantification of bacterial transport in physically, chemically, and biologically heterogeneous aquifers, (2) evaluation of the efficacy of conventional colloid filtration theory, (3) scale effects in bacterial transport, (4) development of new methods for microbial enumeration and screening for low adhesion strains, (5) application of novel hydrogeophysical techniques for aquifer characterization, and (6) experiences regarding management of a large field research effort. Lessons learned are summarized in each of these areas. The body of literature resulting from South Oyster Site research has been widely cited and continues to influence research into the controls exerted by aquifer heterogeneity on reactive transport (including microbial transport). It also served as a model (and provided valuable experience) for subsequent and ongoing highly-instrumented field research efforts conducted by DOE-sponsored investigators.

Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.

The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The modeling system is designed in such a way that constraint-based models targeting different microorganisms or competing organism communities can be easily plugged into the system. Constraint-based modeling is very costly given the size of a genome-scale reaction network. To save computation time, a binary tree is traversed to examine the concentration and solution pool generated during the simulation in order to decide whether the constraint-based model should be called. We also show preliminary results from the integrated model including a comparison of the direct and indirect coupling approaches and evaluated the ability of the approach to simulate field experiment.

Modeling and sensitivity analysis of electron capacitance for Geobacter in sedimentary environments.

In situ stimulation of the metabolic activity of Geobacter species through acetate amendment has been shown to be a promising bioremediation strategy to reduce and immobilize hexavalent uranium [U(VI)] as insoluble U(IV). Although Geobacter species are reducing U(VI), they primarily grow via Fe(III) reduction. Unfortunately, the biogeochemistry and the physiology of simultaneous reduction of multiple metals are still poorly understood. A detailed model is therefore required to better understand the pathways leading to U(VI) and Fe(III) reduction by Geobacter species. Based on recent experimental evidence of temporary electron capacitors in Geobacter we propose a novel kinetic model that physically distinguishes planktonic cells into electron-loaded and -unloaded states. Incorporation of an electron load-unload cycle into the model provides insight into U(VI) reduction efficiency, and elucidates the relationship between U(VI)- and Fe(III)-reducing activity and further explains the correlation of high U(VI) removal with high fractions of planktonic cells in subsurface environments. Global sensitivity analysis was used to determine the level of importance of geochemical and microbial processes controlling Geobacter growth and U(VI) reduction, suggesting that the electron load-unload cycle and the resulting repartition of the microbes between aqueous and attached phases are critical for U(VI) reduction. As compared with conventional Monod modeling approaches without inclusion of the electron capacitance, the new model attempts to incorporate a novel cellular mechanism that has a significant impact on the outcome of in situ bioremediation.

Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation.

The increasing availability of the genome sequences of microorganisms involved in important bioremediation processes makes it feasible to consider developing genome-scale models that can aid in predicting the likely outcome of potential subsurface bioremediation strategies. Previous studies of the in situ bioremediation of uranium-contaminated groundwater have demonstrated that Geobacter species are often the dominant members of the groundwater community during active bioremediation and the primary organisms catalysing U(VI) reduction. Therefore, a genome-scale, constraint-based model of the metabolism of Geobacter sulfurreducens was coupled with the reactive transport model HYDROGEOCHEM in an attempt to model in situ uranium bioremediation. In order to simplify the modelling, the influence of only three growth factors was considered: acetate, the electron donor added to stimulate U(VI) reduction; Fe(III), the electron acceptor primarily supporting growth of Geobacter; and ammonium, a key nutrient. The constraint-based model predicted that growth yields of Geobacter varied significantly based on the availability of these three growth factors and that there are minimum thresholds of acetate and Fe(III) below which growth and activity are not possible. This contrasts with typical, empirical microbial models that assume fixed growth yields and the possibility for complete metabolism of the substrates. The coupled genome-scale and reactive transport model predicted acetate concentrations and U(VI) reduction rates in a field trial of in situ uranium bioremediation that were comparable to the predictions of a calibrated conventional model, but without the need for empirical calibration, other than specifying the initial biomass of Geobacter. These results suggest that coupling genome-scale metabolic models with reactive transport models may be a good approach to developing models that can be truly predictive, without empirical calibration, for evaluating the probable response of subsurface microorganisms to possible bioremediation approaches prior to implementation.

Apparent decreases in colloid deposition rate coefficients with distance of transport under unfavorable deposition conditions: a general phenomenon.

The transport of polystyrene microspheres was examined in packed glass beads under a variety of environmentally relevant ionic strength and flow conditions. The observed profiles of numbers of retained microspheres versus distance from the column entrance were much steeper than expected based on a constant rate coefficient of deposition acrossthe length of the column, indicating apparent decreases in deposition rate coefficients with transport distance. Deviation in the profile from log-linear decreases with distance was greatest under highly unfavorable conditions (low ionic strength), relatively reduced under mildly unfavorable conditions (high ionic strength), and was eliminated under favorable conditions. The generality of apparent decreases in deposition rate coefficients with distance of transport among microspheres, bacteria, and viruses leads to the conclusion that such effects reflect processes that are fundamental to filtration under unfavorable conditions. Numerical simulations of experiments that were performed under unfavorable conditions utilized a log-normal distribution of deposition rate coefficients among the colloid population in orderto simulate the effluent curves and retained profiles simultaneously. It is shown that while straining could be a significant contributor to the steep retained profiles at low ionic strength, where overall retention is low, distribution in interaction potentials among the population was a viable mechanism that can yield apparent decreases in deposition rate coefficients with distance of transport.

Relative dominance of physical versus chemical effects on the transport of adhesion-deficient bacteria in intact cores from South Oyster, Virginia.

Bacterial transport experiments were conducted using intact sediment cores collected from sites on the Delmarva Peninsula near South Oyster, VA, to delineate the relative importance of physical and chemical heterogeneity in controlling transport of an adhesion-deficient bacterial strain. Electron microscopy revealed that the sediments consisted of quartz and feldspar with a variable amount of clay and iron and aluminum hydroxide coatings on the grains. A nonmotile, gram-negative indigenous groundwater strain, designated as Comamonas sp. DA001, was injected into the cores along with a conservative tracer bromide (Br). DA001 cells were 1.2 x 0.6 microm in size with a hydrophilic surface and a slightly negative surface charge. Bacterial breakthrough preceded that of Br. This differential advection phenomenon can be accounted for by reduction of the effective porosity for the bacteria relative to Br. The distribution of cells remaining in the core as determined by scintillation counting and phosphor imaging techniques was variable, ranging from nearly uniform concentrations throughout the core to exponentially decreasing concentrations with distance from the point of injection. The fraction of bacterial retention in the core was positively correlated with the abundance of the metal hydroxides and negatively correlated with grain size. Because grain size was inversely correlated with the abundance of the metal hydroxide coatings, it was necessary to separate the effects of grain size and mineralogy. The fraction of the bacterial retention accounting for the effect of grain size, the collision efficiency, exhibited no correlation with the abundance of the metal hydroxides, indicating that the bacterial retention was primarily controlled by grain size. Reasons for the lack of influence of mineralogy on bacterial transport include (i) the slightly negatively charged bacterial surfaces; (ii) an insufficient heterogeneity of sediment surface properties; and (iii) the masking of the positive charge of the metal hydroxide surfaces by adsorbed organic carbon (up to 1180 ppm). This study demonstrates that the laboratory-based bacterial transport experiments are effective in delineating physical versus chemical controlling factors and provide an important link to field-based bacterial transport studies.