Most of the major groundwater contamination in California, including that inundating the San Fernando, San Gabriel and San Bernadino Valleys, will be addressed using some variation of the pump-and-treat technology. The pump-and-treat strategy is often judged to be unsuccessful because of difficulties encountered in recovering the contaminants from relatively stagnant zones within stratified aquifer systems. These zones can exist at the particle scale, as intraparticle or intra-aggregate porosity, and at the larger scales, as low permeability layers or lenses interspersed in substantially more permeable layers. This work focuses first on achieving an efficient numerical solution to a system of groundwater flow and contaminant transport equations that sufficiently captures the dynamics of slow desorption in a two-dimensional porous medium. The upstreamweighted, multiple cell balance (UMCB) method is developed and verified here to provide such a solution. Next, this work focuses on coupling the simulation model with a management model to provide a design tool for pump-and-treat remediation of real aquifer systme. Zeroth, first and second moments are calculated for mobile and immobile aqueous concentration distributions, and tested as potential design objectives. In a departure from conventional approaches, spatial moment analysis is also applied to local differences between simulated mobile and immobile aqueous concentrations.

Results suggest that pump-and-treat systems in heterogeneous domains might best be designed as two phase operation. The first phase addresses the early time removal of mobile (i.e., readily accessible) phase contaminant, and suggests the conventional approach of placing extraction wells slightly downgradient of the plume centroid. The second attacks fractions of the contaminant plume that are either harbored within immobile porosity, or that have penetrated impermeable layers. The latter stage can be accomplished through maximizing the desorption driving force distribution, or be minimizing the spreading (variance) of this distribution. It is recommended that the techniques developed in this research be applied to one or more of California's on-going pump-and-treat systems.

Over-irrigation with reclaimed water may cause crop yield reduction and groundwater quality degradation. Continuous and automatic monitoring strategies are desirable as a means of guiding management schemes to avoid these problems. In this work, an optimal irrigation management scheme known as Receding Horizon Control (RHC) is proposed to balance water reuse and soil/groundwater quality. In this scheme, a wireless networked sensor array is deployed to provide on-line feedback to the simulators on which the management algorithm depends. A simulation model including a one- (vertical) dimensional form of the Richards equation coupled to energy and solute transport equations is automatically updated with real-time soil moisture, temperature, nitrate, and salinity sensor data on a regular basis. A genetic algorithm-based control scheme determines the optimal irrigation rate using current observations which continuously maximizes the reclaimed water usage while maintaining salinity and nitrate in soils at a certain level. Results from simulated soil moisture/nitrate control where maximum soil moisture/nitrate level throughout the soil depth is maintained are presented. On-site soil moisture control in Palmdale, CA, where reclaimed water is irrigated with center-pivot irrigation system at an agricultural site, is also demonstrated. An on-going field experiment in Merced, CA where automatic irrigation system is set up to control salinity level in soils is presented as well. The results demonstrate that coupling in situ observations with RHC process control algorithm is a viable strategy for achieving water reuse and agricultural objectives while minimizing negative impacts on environmental quality.

The contaminant assessment and management (or “Contam”) research area focuses on developing and implementing embedded networked sensing (ENS) technology to support this new observational strategy in the context of mass and energy distributions and fluxes across a range of temporal and synoptic scales. The specific areas of interest for Contam include soils, groundwater, and riparian systems. The Contam application domain is unique relative to the other three CENS applications in that it is often concerned with enabling adaptive management of environmental problems through engineered responses triggered by ENS observations. Example applications include improving our understanding of river metabolism in relation to adjacent and upstream land management practices, creating closed-loop feedback-control systems for conserving irrigation water and avoiding excessive nitrogen application in agricultural systems, delineating nutrient fluxes between groundwater and surface water, and rapid identification of coastal pollutants.