Modeling basin- and plume-scale processes of CO2 storage for full-scale deployment.

Integrated modeling of basin- and plume-scale processes induced by full-scale deployment of CO(2) storage was applied to the Mt. Simon Aquifer in the Illinois Basin. A three-dimensional mesh was generated with local refinement around 20 injection sites, with approximately 30 km spacing. A total annual injection rate of 100 Mt CO(2) over 50 years was used. The CO(2)-brine flow at the plume scale and the single-phase flow at the basin scale were simulated. Simulation results show the overall shape of a CO(2) plume consisting of a typical gravity-override subplume in the bottom injection zone of high injectivity and a pyramid-shaped subplume in the overlying multilayered Mt. Simon, indicating the important role of a secondary seal with relatively low-permeability and high-entry capillary pressure. The secondary-seal effect is manifested by retarded upward CO(2) migration as a result of multiple secondary seals, coupled with lateral preferential CO(2) viscous fingering through high-permeability layers. The plume width varies from 9.0 to 13.5 km at 200 years, indicating the slow CO(2) migration and no plume interference between storage sites. On the basin scale, pressure perturbations propagate quickly away from injection centers, interfere after less than 1 year, and eventually reach basin margins. The simulated pressure buildup of 35 bar in the injection area is not expected to affect caprock geomechanical integrity. Moderate pressure buildup is observed in Mt. Simon in northern Illinois. However, its impact on groundwater resources is less than the hydraulic drawdown induced by long-term extensive pumping from overlying freshwater aquifers.

Denitrification in the shallow ground water of a tile-drained, agricultural watershed.

Nonpoint-source pollution of surface water by N is considered a major cause of hypoxia. Because Corn Belt watersheds have been identified as major sources of N in the Mississippi River basin, the fate and transport of N from midwestern agricultural watersheds have received considerable interest. The fate and transport of N in the shallow ground water of these watersheds still needs additional research. Our purpose was to estimate denitrification in the shallow ground water of a tile-drained, Corn Belt watershed with fine-grained soils. Over a 3-yr period, N was monitored in the surface and ground water of an agricultural watershed in central Illinois. A significant amount of N was transported past the tile drains and into shallow ground water. The ground water nitrate was isotopically heavier than tile drain nitrate, which can be explained by denitrification in the subsurface. Denitrifying bacteria were found at depths to 10 m throughout the watershed. Laboratory and push-pull tests showed that a significant fraction of nitrate could be denitrified rapidly. We estimated that the N denitrified in shallow ground water was equivalent to 0.3 to 6.4% of the applied N or 9 to 27% of N exported via surface water. These estimates varied by water year and peaked in a year of normal precipitation after 2 yr of below average precipitation. Three years of monitoring data indicate that shallow ground water in watersheds with fine-grained soils may be a significant N sink compared with N exported via surface water.

Application of artificial neural networks to assess pesticide contamination in shallow groundwater.

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R=0.87) and pesticide detection efficiency (E=89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination.