Prudent interventions for reducing selenium (Se) in groundwater and streams within an irrigated river valley must be guided by a sound understanding of current field conditions. An emerging picture of the nature of Se contamination within the Lower Arkansas River Valley in Colorado is provided by data from a large number of groundwater and surface water sampling locations within two study regions along the river. Measurements show that dissolved Se concentrations in the river are about double the current Colorado Department of Public Health and Environment (CDPHE) chronic standard of 4.6 microg L(-1) for aquatic habitat in the upstream region and exceed the standard by a factor of 2 to 4 in the downstream region. Groundwater concentrations average about 57.7 microg L(-1) upstream and 33.0 microg L(-1) downstream, indicating a large subsurface source for irrigation-induced dissolution and mobilization of Se loads to the river and its tributaries. Inverse correlation was found between Se concentration and the distance to the closest identified shale in the direction upstream along the principal groundwater flow gradient. The data also exhibited, among other relationships, a moderate to strong correlation between dissolved Se and total dissolved solids in groundwater and surface water, a strong correlation with uranium in groundwater, and power relationships with nitrate in groundwater. The relationship to nitrate, derived primarily from N fertilizers, reveals the degree to which dissolved Se depends on oxidation and inhibited reduction due to denitrification and suggests that there are prospects for reducing dissolved Se through nitrate control. Current and future results from these ongoing studies will help provide a foundation for modeling and for the discovery of best management practices (BMPs) in irrigated agriculture that can diminish Se contamination.

Geological carbon storage (GCS) has been proposed as a favorable technology to reduce carbon dioxide (CO 2 ) emissions to the atmosphere. One of the main concerns about GCS is the risk of CO 2 escape from the storage formation through leakage pathways in the sealing layer. This study aims at understanding the main sources of uncertainty affecting the upward migration of CO 2 through preexisting "passive" wells and the risk of fissuring of target formation during GCS operations, which may create pathways for CO 2 escape. The analysis focuses on a potential GCS site located within the Michigan Basin, a geologic basin situated on the Lower Peninsula of the state of Michigan. For this purpose, we perform a stochastic analysis (SA) and a global sensitivity analysis (GSA) to investigate the influence of uncertain parameters such as: permeability and porosity of the injection formation, passive well permeability, system compressibility, brine residual saturation and CO 2 end-point relative permeability.For the GSA, we apply the extended Fourier Amplitude Sensitivity Test (FAST), which can rank parameters based on their direct impact on the output, or first-order effect, and capture the interaction effect of one parameter with the others, or higher-order effect. To simulate GCS, we use an efficient semianalytical multiphase flow model, which makes the application of the SA and the GSA computationally affordable. Results show that, among model parameters, the most influential on both fluid overpressure and CO 2 mass leakage is the injection formation permeability. Brine residual saturation also has a significant impact on fluid overpressure. While influence of permeability on fluid overpressure is mostly first-order, brine residual saturation's influence is mostly higher-order. CO 2 mass leakage is also affected by passive well permeability, followed by porosity and system compressibility through higher order effects.

Successful large-scale implementation of geological CO 2 sequestration (GCS) will require the preliminary assessment of multiple potential injection sites. Risk assessment and optimization tools used in this effort typically require large numbers of simulations. This makes it important to choose the appropriate level of complexity when selecting the type of simulation model. A promising multiphase semi-analytical method proposed by [40] to estimate key system attributes (i.e. pressure distribution, CO 2 plume extent, and fluid migration) has been found to reduce computational run times by three orders of magnitude when compared to other standard numerical techniques. The premise of the work presented herein is that the existing semi-analytically leakage algorithm proposed by [40] may be further improved in computational efficiency by applying a fixed point type iterative global pressure solution to eliminate the need to solve large sets of linear equations at each time step. Results show that significant gains in computational efficiency are obtained with this new methodology. In addition, this modification provides the same enhancement to similar semi-analytical algorithms that simulate singe-phase injection into multi-layer domains.

Geological carbon sequestration (GCS) has been identified as having the potential to reduce increasing atmospheric concentrations of carbon dioxide (CO 2 ). However, a global impact will only be achieved if GCS is cost-effectively and safely implemented on a massive scale. This work presents a computationally efficient methodology for identifying optimal injection strategies at candidate GCS sites having uncertainty associated with caprock permeability, effective compressibility, and aquifer permeability. A multi-objective evolutionary optimization algorithm is used to heuristically determine non-dominated solutions between the following two competing objectives: 1) maximize mass of CO 2 sequestered and 2) minimize project cost. A semi-analytical algorithm is used to estimate CO 2 leakage mass rather than a numerical model, enabling the study of GCS sites having vastly different domain characteristics. The stochastic optimization framework presented herein is applied to a feasibility study of GCS in a brine aquifer in the Michigan Basin (MB), USA. Eight optimization test cases are performed to investigate the impact of decision-maker (DM) preferences on Pareto-optimal objective-function values and carbon-injection strategies. This analysis shows that the feasibility of GCS at the MB test site is highly dependent upon the DM's risk-adversity preference and degree of uncertainty associated with caprock integrity. Finally, large gains in computational efficiency achieved using parallel processing and archiving are discussed.

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