Routing algorithms as tools for integrating social distancing with emergency evacuation.

One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

Aquifer Imaging with Oscillatory Hydraulic Tomography: Application at the Field Scale.

Modeling and laboratory experiments have demonstrated the ability of oscillatory hydraulic tomography (OHT) to characterize heterogeneity in aquifer hydraulic properties. In OHT, a location is stressed via periodic pumping/injection at a set frequency, and the resulting head signal is measured at a number of monitoring locations. The source of oscillations is repeatedly moved, allowing tomographic imaging of aquifer properties. Changing the period of oscillation also results in observations with additional information. In theory, OHT is comparable to other hydraulic tomography methods in that distributed pressure change measurements provide characterization information. In practice, OHT has several benefits including: (1) little to no water injected into or extracted from the aquifer; and (2) an observational signal at a set period that can be easily extracted in the presence of noise. We report the first field application of OHT, carried out at the Boise Hydrogeophysical Research Site (BHRS) using an oscillating signal generator with a very small cycling volume of <2 L, and a period range of 5 to 70 s. For these tests, signals were detected at distances of over 15 m. After processing to extract periodic signal properties, we perform tomography using a frequency-domain numerical model for groundwater flow. In comparing results against prior characterization results from the BHRS, we find moderate to strong positive correlations between K profiles estimated via different methods at multiple wells, with moderate overall correlation between estimated three-dimensional (3D) K volumes.

Longitudinal dispersion coefficients for numerical modeling of groundwater solute transport in heterogeneous formations.

Most recent research on hydrodynamic dispersion in porous media has focused on whole-domain dispersion while other research is largely on laboratory-scale dispersion. This work focuses on the contribution of a single block in a numerical model to dispersion. Variability of fluid velocity and concentration within a block is not resolved and the combined spreading effect is approximated using resolved quantities and macroscopic parameters. This applies whether the formation is modeled as homogeneous or discretized into homogeneous blocks but the emphasis here being on the latter. The process of dispersion is typically described through the Fickian model, i.e., the dispersive flux is proportional to the gradient of the resolved concentration, commonly with the Scheidegger parameterization, which is a particular way to compute the dispersion coefficients utilizing dispersivity coefficients. Although such parameterization is by far the most commonly used in solute transport applications, its validity has been questioned. Here, our goal is to investigate the effects of heterogeneity and mass transfer limitations on block-scale longitudinal dispersion and to evaluate under which conditions the Scheidegger parameterization is valid. We compute the relaxation time or memory of the system; changes in time with periods larger than the relaxation time are gradually leading to a condition of local equilibrium under which dispersion is Fickian. The method we use requires the solution of a steady-state advection-dispersion equation, and thus is computationally efficient, and applicable to any heterogeneous hydraulic conductivity K field without requiring statistical or structural assumptions. The method was validated by comparing with other approaches such as the moment analysis and the first order perturbation method. We investigate the impact of heterogeneity, both in degree and structure, on the longitudinal dispersion coefficient and then discuss the role of local dispersion and mass transfer limitations, i.e., the exchange of mass between the permeable matrix and the low permeability inclusions. We illustrate the physical meaning of the method and we show how the block longitudinal dispersivity approaches, under certain conditions, the Scheidegger limit at large Péclet numbers. Lastly, we discuss the potential and limitations of the method to accurately describe dispersion in solute transport applications in heterogeneous aquifers.

Hydraulic Tomography: Continuity and Discontinuity of High-K and Low-K Zones.

Hydraulic tomography is an emerging field and modeling method that provides a continuous hydraulic conductivity (K) distribution for an investigated region. Characterization approaches that rely on interpolation between one-dimensional (1D) profiles have limited ability to accurately identify high-K channels, juxtapositions of lenses with high K contrast, and breaches in layers or channels between such profiles. However, locating these features is especially important for groundwater flow and transport modeling, and for design and operation of in situ remediation in complex hydrogeologic environments. We use transient hydraulic tomography to estimate 3D K in a volume of 15-m diameter by 20-m saturated thickness in a highly heterogeneous unconfined alluvial (clay to sand-and-gravel) aquifer with a K range of approximately seven orders of magnitude at an active industrial site in Assemini, Sardinia, Italy. A modified Levenberg-Marquardt algorithm was used for geostatistical inversion to deal with the nonlinear nature of the highly heterogeneous system. The imaging results are validated with pumping tests not used in the tomographic inversion. These tests were conducted from three of five clusters of continuous multichannel tubing (CMTs) installed for observation in the tomographic testing. Locations of high-K continuity and discontinuity, juxtaposition of very high-K and very low-K lenses, and low-K "plugs" are evident in regions of the investigated volume where they likely would not have been identified with interpolation from 1D profiles at the positions of the pumping well and five CMT clusters. Quality assessment methods identified a suspect high-K feature between the tested volume and a lateral boundary of the model.

Long-term mass transfer and mixing-controlled reactions of a DNAPL plume from persistent residuals.

Understanding and being able to predict the long-term behavior of DNAPL (i.e., PCE and TCE) residuals after active remediation has ceased have become increasingly important as attention at many sites turns from aggressive remediation to monitored natural attenuation and long-term stewardship. However, plume behavior due to mass loading and reactions during these later phases is less studied as they involve large spatial and temporal scales. We apply both theoretical analysis and pore-scale simulations to investigate mass transfer from DNAPL residuals and subsequent reactions within the generated plume, and, in particular, to show the differences between early- and late-time behaviors of the plume. In the zone of entry of the DNAPL entrapment zone where the concentration boundary layer in the flowing groundwater has not fully developed, the pore-scale simulations confirm the past findings based on laboratory studies that the mass transfer increases as a power-law function of the Peclét number, and is enhanced due to reactions in the plume. Away from the entry zone and further down gradient, the long-term reactions are limited by the available additive and mixing in the porous medium, thereby behave considerably differently from the entry zone. For the reaction between the contaminant and an additive with intrinsic second-order bimolecular kinetics, the late-time reaction demonstrates a first-order decay macroscopically with respect to the mass of the limiting additive, not with respect to that of the contaminant. The late-time decay rate only depends on the intrinsic reaction rate and the solubility of the entrapped DNAPL. At the intermediate time, the additive decays exponentially with the square of time (t(2)), instead of time (t). Moreover, the intermediate decay rate also depends on the initial conditions, the spatial distribution of DNAPL residuals, and the effective dispersion coefficient.

Stochastic modeling of short-term exposure close to an air pollution source in a naturally ventilated room: an autocorrelated random walk method.

For an actively emitting source such as cooking or smoking, indoor measurements have shown a strong "proximity effect" within 1 m. The significant increase in both the magnitude and variation of concentration near a source is attributable to transient high peaks that occur sporadically-and these "microplumes" cause great uncertainty in estimating personal exposure. Recent field studies in naturally ventilated rooms show that close-proximity concentrations are approximately lognormally distributed. We use the autocorrelated random walk method to represent the time-varying directionality of indoor emissions, thereby predicting the time series and frequency distributions of concentrations close to an actively emitting point source. The predicted 5-min concentrations show good agreement with measurements from a point source of CO in a naturally ventilated house-the measured and predicted frequency distributions at 0.5- and 1-m distances are similar and approximately lognormal over a concentration range spanning three orders of magnitude. By including the transient peak concentrations, this random airflow modeling method offers a way to more accurately assess acute exposure levels for cases where well-defined airflow patterns in an indoor space are not available.

Surge block method for controlling well clogging and sampling sediment during bioremediation.

A surge block treatment method (i.e. inserting a solid rod plunger with a flat seal that closely fits the casing interior into a well and stocking it up and down) was performed for the rehabilitation of wells clogged with biomass and for the collection of time series sediment samples during in situ bioremediation tests for U(VI) immobilization at a the U.S. Department of Energy site in Oak Ridge, TN. The clogging caused by biomass growth had been controlled by using routine surge block treatment for 18 times over a nearly four year test period. The treatment frequency was dependent of the dosage of electron donor injection and microbial community developed in the subsurface. Hydraulic tests showed that the apparent aquifer transmissivity at a clogged well with an inner diameter (ID) of 10.16 cm was increased by 8-13 times after the rehabilitation, indicating the effectiveness of the rehabilitation. Simultaneously with the rehabilitation, the surge block method was successfully used for collecting time series sediment samples composed of fine particles (clay and silt) from wells with ID 1.9-10.16 cm for the analysis of mineralogical and geochemical composition and microbial community during the same period. Our results demonstrated that the surge block method provided a cost-effective approach for both well rehabilitation and frequent solid sampling at the same location.

On the importance of diffusion and compound-specific mixing for groundwater transport: an investigation from pore to field scale.

Mixing processes significantly affect and limit contaminant transport and transformation rates in the subsurface. The correct quantification of mixing in groundwater systems must account for diffusion, local-scale dispersion and the flow variability in heterogeneous flow fields (e.g., flow-focusing in high-conductivity and de-focusing in low-conductivity zones). Recent results of multitracer laboratory experiments revealed the significant effect of compound-specific diffusive properties on the physical displacement of dissolved species across a representative range of groundwater flow velocities. The goal of this study is to investigate the role of diffusion and compound-specific mixing for solute transport across a range of scales including: (i) pore-scale (~10⁻² m), (ii) laboratory bench-scale (~10° m) and (iii) field-scale (~10² m). We investigate both conservative and mixing-controlled reactive transport using pore-scale modeling, flow-through laboratory experiments and simulations, and field-scale numerical modeling of complex heterogeneous hydraulic conductivity fields with statistical properties similar to the ones reported for the extensively investigated Borden aquifer (Ontario, Canada) and Columbus aquifer (Mississippi, USA, also known as MADE site). We consider different steady-state and transient transport scenarios. For the conservative cases we use as a metric of mixing the exponential of the Shannon entropy to quantify solute dilution either in a given volume (dilution index) or in a given solute flux (flux-related dilution index). The decrease in the mass and the mass-flux of the contaminant plumes is evaluated to quantify reactive mixing. The results show that diffusive processes, occurring at the small-scale of a pore channel, strongly affect conservative and reactive solute transport at larger macroscopic scales. The outcomes of our study illustrate the need to consider and properly account for compound-specific diffusion and mixing limitations in order to accurately describe and predict conservative and reactive transport in porous media.

Use of on-site bioreactors to estimate the biotransformation rate of N-ethyl perfluorooctane sulfonamidoethanol (N-EtFOSE) during activated sludge treatment.

Accurate rates are needed for models that predict the fate of xenobiotic chemicals and impact of inhibitors at full-scale wastewater treatment plants. On-site rates for aerobic biotransformation of N-ethyl perfluorooctane sulfonamidoethanol (N-EtFOSE), a fluorinated repellent, were determined by continuously pumping mixed liquor from an aeration basin into two well-mixed acrylic bioreactors (4-L) operated in parallel. Known masses of N-EtFOSE and bromide were continuously added to the reactors. Reactor effluents were then monitored for bromide, N-EtFOSE, and metabolites of N-EtFOSE. Of the six transformation products reported in batch studies, only N-ethyl perfluorooctane sulfonamido acetate (N-EtFOSAA) was detected in the effluents. Bromide addition to the reactors enabled rate estimates despite variations in flow rate. Pseudo-second order rate coefficients for the N-EtFOSE biotransformation to N-EtFOSAA, predicted using a dynamic model of the reactor system, were k=2.0 and 2.4Lg(-1)VSSd(-1) for the two reactors, which are slower than the rates previously obtained using batch reactors. Given the relatively slow rate of N-EtFOSE transformation, its sorption and volatilization may be important in wastewater processes. The methodology used in this study should be suitable for similar on-site rate assessments with other contaminants or inhibitors.

The behavior of effective rate constants for bimolecular reactions in an asymptotic transport regime.

Previous research has shown that rate constants measured in batch tests (κ) may over-predict the amount of product formation when used in continuum models, and that these rate constants are often much greater than effective ones (κ(eff)) determined from upscaling studies. However, there is evidence that mixing is more important than the rate constants when using upscaled models. We use a numerical two-dimensional pore-scale porous medium with an approach similar to an experimental column test, and focus on the scenario of the displacement and mixing of two solutions with irreversible bimolecular reactions. Break-through curves of multiple cross-sectional averaged concentrations are analyzed for conservative and reactive transport, as well as the segregation of reactant species along the cross-sections. We compute effective parameters for the continuum scale in order to better understand the impact of using intrinsic rate constants in upscaled models. For a range of Damköhler numbers (Da), we compute effective reaction rate parameters and a reaction effectiveness factor; the latter is described by an empirical formula that depends on the Damköhler number and captures the upscaled system behavior. Our pore-scale results also confirm the segregation concept advanced by Kapoor et al. (1997). We find that for Da>1, κ(eff)<<κ, and yet the relative difference in total mass transformation between the pore-scale simulation and what is predicted by the upscaled continuum model using κ is about 10%. The explanation for this paradox is that the early transition of the regime from rate-limited to mixing-limited results in a model that is relatively insensitive to the rate constant because mixing controls the availability of reactants. Thus, the reaction-rate parameter used in the model has limited influence on the rate of product computed.

Applicability of the dual-domain model to nonaggregated porous media.

More theoretical analysis is needed to investigate why a dual-domain model often works better than the classical advection-dispersion (AD) model in reproducing observed breakthrough curves for relatively homogeneous porous media, which do not contain distinct dual domains. Pore-scale numerical experiments presented here reveal that hydrodynamics create preferential flow paths that occupy a small part of the domain but where most of the flow takes place. This creates a flow-dependent configuration, where the total domain consists of a mobile and an immobile domain. Mass transfer limitations may result in nonequilibrium, or significant differences in concentration, between the apparent mobile and immobile zones. When the advection timescale is smaller than the diffusion timescale, the dual-domain mass transfer (DDMT) model better captures the tailing in the breakthrough curve. Moreover, the model parameters (mobile porosity, mean solute velocity, dispersivity, and mass transfer coefficient) demonstrate nonlinear dependency on mean fluid velocity. The studied case also shows that when the Peclet number, Pe, is large enough, the mobile porosity approaches a constant, and the mass transfer coefficient can be approximated as proportional to mean fluid velocity. Based on detailed analysis at the pore scale, this paper provides a physical explanation why these model parameters vary in certain ways with Pe. In addition, to improve prediction in practical applications, we recommend conducting experiments for parameterization of the DDMT model at a velocity close to that of the relevant field sites, or over a range of velocities that may allow a better parameterization.

Increasing confidence in mass discharge estimates using geostatistical methods.

Mass discharge is one metric rapidly gaining acceptance for assessing the performance of in situ groundwater remediation systems. Multilevel sampling transects provide the data necessary to make such estimates, often using the Thiessen Polygon method. This method, however, does not provide a direct estimate of uncertainty. We introduce a geostatistical mass discharge estimation approach that involves a rigorous analysis of data spatial variability and selection of an appropriate variogram model. High-resolution interpolation was applied to create a map of measurements across a transect, and the magnitude and uncertainty of mass discharge were quantified by conditional simulation. An important benefit of the approach is quantified uncertainty of the mass discharge estimate. We tested the approach on data from two sites monitored using multilevel transects. We also used the approach to explore the effect of lower spatial monitoring resolution on the accuracy and uncertainty of mass discharge estimates. This process revealed two important findings: (1) appropriate monitoring resolution is that which yielded an estimate comparable with the full dataset value, and (2) high-resolution sampling yields a more representative spatial data structure descriptor, which can then be used via conditional simulation to make subsequent mass discharge estimates from lower resolution sampling of the same transect. The implication of the latter is that a high-resolution multilevel transect needs to be sampled only once to obtain the necessary spatial data descriptor for a contaminant plume exhibiting minor temporal variability, and thereafter less spatially intensely to reduce costs.

Significant association between sulfate-reducing bacteria and uranium-reducing microbial communities as revealed by a combined massively parallel sequencing-indicator species approach.

Massively parallel sequencing has provided a more affordable and high-throughput method to study microbial communities, although it has mostly been used in an exploratory fashion. We combined pyrosequencing with a strict indicator species statistical analysis to test if bacteria specifically responded to ethanol injection that successfully promoted dissimilatory uranium(VI) reduction in the subsurface of a uranium contamination plume at the Oak Ridge Field Research Center in Tennessee. Remediation was achieved with a hydraulic flow control consisting of an inner loop, where ethanol was injected, and an outer loop for flow-field protection. This strategy reduced uranium concentrations in groundwater to levels below 0.126 µM and created geochemical gradients in electron donors from the inner-loop injection well toward the outer loop and downgradient flow path. Our analysis with 15 sediment samples from the entire test area found significant indicator species that showed a high degree of adaptation to the three different hydrochemical-created conditions. Castellaniella and Rhodanobacter characterized areas with low pH, heavy metals, and low bioactivity, while sulfate-, Fe(III)-, and U(VI)-reducing bacteria (Desulfovibrio, Anaeromyxobacter, and Desulfosporosinus) were indicators of areas where U(VI) reduction occurred. The abundance of these bacteria, as well as the Fe(III) and U(VI) reducer Geobacter, correlated with the hydraulic connectivity to the substrate injection site, suggesting that the selected populations were a direct response to electron donor addition by the groundwater flow path. A false-discovery-rate approach was implemented to discard false-positive results by chance, given the large amount of data compared.

Effects of nitrate on the stability of uranium in a bioreduced region of the subsurface.

The effects of nitrate on the stability of reduced, immobilized uranium were evaluated in field experiments at a U.S. Department of Energy site in Oak Ridge, TN. Nitrate (2.0 mM) was injected into a reduced region of the subsurface containing high levels of previously immobilized U(IV). The nitrate was reduced to nitrite, ammonium, and nitrogen gas; sulfide levels decreased; and Fe(II) levels increased then deceased. Uranium remobilization occurred concomitant with nitrite formation, suggesting nitrate-dependent, iron-accelerated oxidation of U(IV). Bromide tracer results indicated changes in subsurface flowpaths likely due to gas formation and/or precipitate. Desorption-adsorption of uranium by the iron-rich sediment impacted uranium mobilization and sequestration. After rereduction of the subsurface through ethanol additions, background groundwater containing high levels of nitrate was allowed to enter the reduced test zone. Aqueous uranium concentrations increased then decreased. Clone library analyses of sediment samples revealed the presence of denitrifying bacteria that can oxidize elemental sulfur, H(2)S, Fe(II), and U(IV) (e.g., Thiobacillus spp.), and a decrease in relative abundance of bacteria that can reduce Fe(III) and sulfate. XANES analyses of sediment samples confirmed changes in uranium oxidation state. Addition of ethanol restored reduced conditions and triggered a short-term increase in Fe(II) and aqueous uranium, likely due to reductive dissolution of Fe(III) oxides and release of sorbed U(VI). After two months of intermittent ethanol addition, sulfide levels increased, and aqueous uranium concentrations gradually decreased to <0.1 microM.

Cost optimization of DNAPL source and plume remediation under uncertainty using a semi-analytic model.

Dense non-aqueous phase liquid (DNAPL) spills represent a potential long-term source of aquifer contamination, and successful low-cost remediation may require a combination of both plume management and source treatment. In addition, substantial uncertainty exists in many of the parameters that control field-scale behavior of DNAPL sources and plumes. For these reasons, cost optimization of DNAPL cleanup needs to consider multiple treatment options and their associated costs while also gauging the influence of prediction uncertainty on expected costs. In this paper, we present a management methodology for field-scale DNAPL source and plume management under uncertainty. Using probabilistic methods, historical data and prior information are combined to produce a set of equally likely realizations of true field conditions (i.e., parameter sets). These parameter sets are then used in a simulation-optimization framework to produce DNAPL cleanup solutions that have the lowest possible expected net present value (ENPV) cost and that are suitably cautious in the presence of high uncertainty. For simulation, we utilize a fast-running semi-analytic field-scale model of DNAPL source and plume evolution that also approximates the effects of remedial actions. The degree of model prediction uncertainty is gauged using a restricted maximum likelihood method, which helps to produce suitably cautious remediation strategies. We test our methodology on a synthetic field-scale problem with multiple source architectures, for which source zone thermal treatment and electron donor injection are considered as remedial actions. The lowest cost solution found utilizes a combination of source and plume remediation methods, and is able to successfully meet remediation constraints for a majority of possible scenarios. Comparisons with deterministic optimization results show that not taking into account uncertainty can result in optimization strategies that are not aggressive enough and result in greater overall total cost.

Microbial communities in contaminated sediments, associated with bioremediation of uranium to submicromolar levels.

Microbial enumeration, 16S rRNA gene clone libraries, and chemical analysis were used to evaluate the in situ biological reduction and immobilization of uranium(VI) in a long-term experiment (more than 2 years) conducted at a highly uranium-contaminated site (up to 60 mg/liter and 800 mg/kg solids) of the U.S. Department of Energy in Oak Ridge, TN. Bioreduction was achieved by conditioning groundwater above ground and then stimulating growth of denitrifying, Fe(III)-reducing, and sulfate-reducing bacteria in situ through weekly injection of ethanol into the subsurface. After nearly 2 years of intermittent injection of ethanol, aqueous U levels fell below the U.S. Environmental Protection Agency maximum contaminant level for drinking water and groundwater (<30 microg/liter or 0.126 microM). Sediment microbial communities from the treatment zone were compared with those from a control well without biostimulation. Most-probable-number estimations indicated that microorganisms implicated in bioremediation accumulated in the sediments of the treatment zone but were either absent or in very low numbers in an untreated control area. Organisms belonging to genera known to include U(VI) reducers were detected, including Desulfovibrio, Geobacter, Anaeromyxobacter, Desulfosporosinus, and Acidovorax spp. The predominant sulfate-reducing bacterial species were Desulfovibrio spp., while the iron reducers were represented by Ferribacterium spp. and Geothrix spp. Diversity-based clustering revealed differences between treated and untreated zones and also within samples of the treated area. Spatial differences in community structure within the treatment zone were likely related to the hydraulic pathway and to electron donor metabolism during biostimulation.

Growth and cometabolic reduction kinetics of a uranium- and sulfate-reducing Desulfovibrio/Clostridia mixed culture: Temperature effects.

Bioremediation of contaminated soils and aquifers is subject to spatial and temporal temperature changes that can alter the kinetics of key microbial processes. This study quantifies temperature effects on the kinetics of an ethanol-fed sulfate-reducing mixed culture derived from a uranium-contaminated aquifer subject to seasonal temperature fluctuations. The mixed culture contains Desulfovibrio sp. and a Clostridia-like organism. Rates of growth, ethanol utilization, decay, and uranium reduction decreased with decreasing temperature. No significant uranium reduction was observed at 10 degrees C. While both Monod saturation kinetics and pseudo second-order kinetics adequately described the rates of growth and utilization of electron donor (ethanol), model parameters for the pseudo second-order expression had smaller uncertainties. Uranium reduction kinetics were best described by pseudo second-order kinetics modified to include a term for inactivation/death of cells.

Inhibition of a U(VI)- and sulfate-reducing consortia by U(VI).

The stimulation of microbial U(VI) reduction is currently being investigated as a means to reduce uranium's mobility in groundwater, but little is known about the concentration at which U(VI) might inhibit microbial activity, or the effect of U(VI) on bacterial community structure. We investigated these questions with an ethanol-fed U(VI)- and sulfate-reducing enrichment developed from sediment from the site of an ongoing field biostimulation experiment at Area 3 of the Oak Ridge Field Research Center (FRC). Sets of triplicate enrichments were spiked with increasing concentrations of U(VI) (from 49 microm to 9.2 mM). As the U(VI) concentration increased to 224 microM, the culture's production of acetate from ethanol slowed, and at or above 1.6 mM U(VI) little acetate was produced over the time frame of the experiment. An uncoupling inhibition model was applied to the data, and the inhibition coefficient for U(VI), Ku, was found to be approximately 100 microM U(VI), or 24 mg/L, indicating the inhibitory effect is relevant at highly contaminated sites. Microbial community structure at the conclusion of the experiment was analyzed with terminal restriction fragment length polymorphism (T-RFLP) analysis. T-RFs associated with Desulfovibrio-like organisms decreased in relative abundance with increasing U(VI) concentration, whereas Clostridia-like T-RFs increased.

In situ bioreduction of uranium (VI) to submicromolar levels and reoxidation by dissolved oxygen.

Groundwater within Area 3 of the U.S. Department of Energy (DOE) Environmental Remediation Sciences Program (ERSP) Field Research Center at Oak Ridge, TN (ORFRC) contains up to 135 microM uranium as U(VI). Through a series of experiments at a pilot scale test facility, we explored the lower limits of groundwater U(VI) that can be achieved by in-situ biostimulation and the effects of dissolved oxygen on immobilized uranium. Weekly 2 day additions of ethanol over a 2-year period stimulated growth of denitrifying, Fe(III)-reducing, and sulfate-reducing bacteria, and immobilization of uranium as U(IV), with dissolved uranium concentrations decreasing to low levels. Following sulfite addition to remove dissolved oxygen, aqueous U(VI) concentrations fell below the U.S. Environmental Protection Agengy maximum contaminant limit (MCL) for drinking water (< 30/microg L(-1) or 0.126 microM). Under anaerobic conditions, these low concentrations were stable, even in the absence of added ethanol. However, when sulfite additions stopped, and dissolved oxygen (4.0-5.5 mg L(-1)) entered the injection well, spatially variable changes in aqueous U(VI) occurred over a 60 day period, with concentrations increasing rapidly from < 0.13 to 2.0 microM at a multilevel sampling (MLS) well located close to the injection well, but changing little at an MLS well located further away. Resumption of ethanol addition restored reduction of Fe(III), sulfate, and U(VI) within 36 h. After 2 years of ethanol addition, X-ray absorption near-edge structure spectroscopy (XANES) analyses indicated that U(IV) comprised 60-80% of the total uranium in sediment samples. Atthe completion of the project (day 1260), U concentrations in MLS wells were less than 0.1 microM. The microbial community at MLS wells with low U(VI) contained bacteria that are known to reduce uranium, including Desulfovibrio spp. and Geobacter spp., in both sediment and groundwater. The dominant Fe(III)-reducing species were Geothrix spp.

Analyzing bank filtration by deconvoluting time series of electric conductivity.

Knowing the travel-time distributions from infiltrating rivers to pumping wells is important in the management of alluvial aquifers. Commonly, travel-time distributions are determined by releasing a tracer pulse into the river and measuring the breakthrough curve in the wells. As an alternative, one may measure signals of a time-varying natural tracer in the river and in adjacent wells and infer the travel-time distributions by deconvolution. Traditionally this is done by fitting a parametric function such as the solution of the one-dimensional advection-dispersion equation to the data. By choosing a certain parameterization, it is impossible to determine features of the travel-time distribution that do not follow the general shape of the parameterization, i.e., multiple peaks. We present a method to determine travel-time distributions by nonparametric deconvolution of electric-conductivity time series. Smoothness of the inferred transfer function is achieved by a geostatistical approach, in which the transfer function is assumed as a second-order intrinsic random time variable. Nonnegativity is enforced by the method of Lagrange multipliers. We present an approach to directly compute the best nonnegative estimate and to generate sets of plausible solutions. We show how the smoothness of the transfer function can be estimated from the data. The approach is applied to electric-conductivity measurements taken at River Thur, Switzerland, and five wells in the adjacent aquifer, but the method can also be applied to other time-varying natural tracers such as temperature. At our field site, electric-conductivity fluctuations appear to be an excellent natural tracer.