Explore the vast range of titles available.

This paper compares and contrasts stochastic optimization and decision analysis as frameworks for the design of remedial pump-and-treat systems in contaminated aquifers. Both decision-making frameworks (1) seek a least-cost, low-risk remedial design; (2) consider uncertainty due to partial knowledge of field environments, which causes imperfect predictive capability of simulation; (3) target predictive uncertainty due to spatially variable hydraulic conductivities and handle it by invoking geostatistical uncertainty theory, and (4) deal with the design and economic impacts of uncertainty by employing the concept of reliability or its complement the probability of failure. The fundamental difference between the two approaches lies in the fact that decision analysis considers a broad suite of technological strategies from which one of many predetermined design alternatives is selected as the best, while stochastic optimization determines the optimal pump-and-treat design but considers only one technological strategy at a time. The early stochastic optimization formulations sought to quantify the cost of overdesign needed to achieve greater performance reliability. The procedure involved a cost minimization that led to the development of a trade-off curve of cost versus reliability. For each point on the trade-off curve a single-valued optimum was achieved by defining a preset level of desired reliability. Decision analysis has always involved a cost-risk minimization, in which a single-valued optimum is obtained by simultaneously accounting for all costs, including the risk costs associated with the probability of failure. Risk costs are assigned a dollar value based on the level of expected reliability; a trade-off curve is not needed. More-recent formulations using stochastic optimization follow the philosophy of the decision-analysis framework by accounting for risk costs through a penalty cost. Using the latter approach, we show that the objective functions in both frameworks are virtually identical. A decision maker should adopt a decision-analysis framework if he or she (1) wants to minimize total system cost by selecting the best design alternatives from among a specified set, (2) has a known risk-cost preference (utility function), (3) wants to consider a broad suite of technological alternatives, and (4) is willing to accept the numbers, locations, and pumping rates for wells that are the best of those under consideration but are not necessarily optimal. The advantages of decision analysis lie in the ease with which capital costs can be incorporated, and the ability to examine alternative designs that span multiple technologies. The disadvantages revolve around the difficulty in determining a decision maker's utility function, selecting a single-valued design as the best from the predefined set of design alternatives, and the inefficiencies introduced by the need for a full enumeration of the design alternatives. A decision maker should adopt optimization if he or she (1) is interested in a truly optimal selection of well locations and pumping rates, (2) has an unknown or uncertain risk-cost preference, and (3) is comfortable considering a single remedial technology at a time. The advantages of optimization lie in its clever and efficient methodologies for identifying a global optimum. The main disadvantages lie in the difficulties associated with a rigorous consideration of capital costs for nonlinear problems, and the fact that solutions do not typically span multiple technologies. The choice of whether to employ optimization or decision analysis as a design tool is not necessarily an either/or proposition, and we suggest possible avenues for their combined use.

Many emerging remediation technologies are designed to remove contaminant mass from source zones at DNAPL sites in response to regulatory requirements. There is often concern in the regulated community as to whether mass removal actually reduces risk, or whether the small risk reductions achieved warrant the large costs incurred. This paper sets out a proposed framework for quantifying the degree to which risk is reduced as mass is removed from DNAPL source areas in shallow, saturated, low permeability media. Risk is defined in terms of meeting an alternate concentration limit (ACL) at a compliance well in an aquifer underlying the source zone. The ACL is back-calculated from a carcinogenic health-risk characterization at a downgradient water-supply well. Source-zone mass-removal efficiencies are heavily dependent on the distribution of mass between media (fractures, matrix) and phase (aqueous, sorbed, NAPL). Due to the uncertainties in currently available technology performance data, the scope of the paper is limited to developing a framework for generic technologies rather than making specific risk-reduction calculations for individual technologies. Despite the qualitative nature of the exercise, results imply that very high total mass removal efficiencies are required to achieve significant long-term risk reduction with technology applications of finite duration. This paper is not an argument for no action at contaminated sites. Rather, it provides support for the conclusions of Cherry et al. (1992) that the primary goal of current remediation should be short-term risk reduction through containment, with the aim to pass on t future generations site conditions that are well-suited to the future applications of emerging technologies with improved mass removal capabilities

Paul Witherspoon has been an influential research leader in hydrogeology for over 50 years. He has made significant contributions to the understanding of the flow of fluids in fractured and porous rock formations, and has applied his findings to a diverse set of societally important issues, including the development of geothermal energy, the use of underground gas storage, and the siting and design of nuclear waste disposal facilities. Working with Paul Witherspoon was a life-altering experience for his many graduate students and colleagues. He was supportive, available, optimistic, and fun. All benefited from his ebullient life view, and the role model he provided in fostering a research environment that featured such a wonderful spirit of cooperation and camaraderie.

The life and career of Henry Darcy are intimately connected with the city of Dijon. He was born there; he died there; and it was there that he carried out the experiments that have brought him lasting fame. Darcy was not an obscure scientist; he was a well‐known and respected public figure. His efforts as an engineer and advocate put Dijon on the main line of the Paris‐Lyon railway, and brought a modern water‐distribution system to Dijon some 25 years before such a system was put in place in Paris. During his life he received great honors, suffered political persecution, and carried out his most productive research in his later years despite ill health. He died at the relatively young age of 55. The main square in the city of Dijon is named Place Darcy in his honor.

A Monte Carlo simulation of over 200,000 baseball games, using a programmed embodiment of the main features of the Sports Illustrated baseball game, shows that batting order exerts only a small influence on the outcomes of baseball games. The effect of using the best batting order rather than the worst is less than three extra wins per 162-game season. The traditional lineup, wherein a team's strongest batters hit in the third through fifth positions, is superior to a lineup in which batters are arranged in decreasing order of productivity.

Paul Witherspoon has been a seminal influence on the development of ideas and methodologies related to the hydrogeology of fractured rocks. His interest in the topic grew from his earliest studies on caprock integrity for underground gas storage, through his midcareer emphasis on the role of aquitards in hydrogeological systems, to his later work on thermohydrologic and hydromechanical couplings in geothermal systems and nuclear waste isolation.