[1] During in situ remediation, a treatment solution is often injected into a contaminated aquifer to degrade the groundwater contaminant. Since contaminant degradation reactions occur only at locations where the treatment solution and groundwater contaminant overlap, mixing of the treatment solution and the contaminated groundwater is necessary for reaction to occur. Mixing results from molecular diffusion and pore-scale dispersion, which operate over small length scales; thus, mixing during in situ remediation can only occur where the separation distance between the treatment solution and contaminated groundwater is small. To promote mixing, advection can be used to spread the treatment solution into the contaminated groundwater to increase the extent of the region where the two solutions coexist. A certain degree of passive spreading is the natural consequence of aquifer heterogeneity, which is manifested as macrodispersion. An alternative mechanism is active spreading, in which unsteady flows lead to stretching and folding of plumes. Active spreading can be accomplished by engineered injection and extraction (EIE), in which clean water is injected and extracted at wells surrounding a contaminant plume to create unsteady flow fields that stretch and fold the treatment solution and contaminant plumes. For a model system in which nested plumes of two reactants undergo scalar transport and instantaneous reaction, the simulation results reported here indicate that EIE enhances degradation of groundwater contamination in homogeneous and heterogeneous aquifers compared to baseline models without EIE. Furthermore, this study shows that the amount of reaction provided by the spreading due to EIE is greater than the amount of reaction due to spreading from heterogeneity alone.Citation: Piscopo, A. N., R. M. Neupauer, and D. C. Mays (2013), Engineered injection and extraction to enhance reaction for improved in situ remediation, Water Resour. Res., 49,[3618][3619][3620][3621][3622][3623][3624][3625]
Understanding the effects of environmental management strategies on society and the environment is critical for evaluating their effectiveness, but is often impeded by limited data availability. In this article, we present a method that can help scientists to support resource managers' thinking about social-ecological relationships in coupled human and natural systems. Our method aims to model qualitative cause-effect relationships between management strategies and ecosystem services, using information provided by knowledgeable participants, and the tradeoffs between strategies. Social, environmental, and cultural indicators are organized using the Driver-Pressure-State-Impact-Response, or DPSIR, framework. The relationships between indicators are evaluated using a decision tree and numerical representations of interaction strength. We use a matrix multiplication procedure to model direct and indirect interaction effects, and we provide guidelines for combining effects. Results include several data tables from which information can be visualized to understand the plausible interaction effects of implementing management strategies on ecosystem services. We illustrate our method with a water quality management case study on Cape Cod, Massachusetts.
Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders-a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss *
Policies and regulations designed to address nutrient pollution in coastal waters are often complicated by delays in environmental and social systems. Social and political inertia may delay the implementation of cleanup projects, and even after the best nutrient pollution management practices are developed and implemented, long groundwater travel times may delay the impact of inland or upstream interventions. These delays and the varying costs of nutrient removal alternatives used to meet water quality goals combine to create a complex dynamic decision problem with tradeoffs about when, where, and how to intervene. We use multi‐objective optimization to quantify the tradeoffs between costs and minimizing the time to meet in‐bay nutrient reduction goals represented as a Total Maximum Daily Load (TMDL). We calculate the impact of using in‐bay (in situ) nutrient removal through shellfish aquaculture relative to waiting for traditional source control to be implemented. We apply these methods to the Three Bays Watershed in Cape Cod, Massachusetts. In gross benefit terms, not accounting for any social costs, this equates to an average value of 37¢ (2035 TMDL target date) and 11¢ (2060 TMDL target date) per animal harvested over the plan implementation period. Our results encourage the consideration of alternative and in situ approaches to tackle coastal pollution while traditional source control is implemented and its effects realized over time.
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