Modeling Ground Water Quality Sampling Decisions |
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| Authors: | Ya-Wen Hsueh R Rajagopal |
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| Institution: | Ya-Wen Hsueh received undergraduate training in liberal arts at the National Taiwan University and a master's degree in geography with an emphasis in water resources from the University of Iowa. Her major research interests are in the fields of water resources Systems analysis, environmental impact assessment, and geographic information Systems. Her current affiliation at the National Taiwan University (Department of Environmental Science, Taiwan, Republic of China) includes research dealing with an assessment of state-of-the-art applications and management of Geographic Information Systems in the Department of Interior of the Government of Taiwan.;R. Rajagopal received undergraduate and graduate education in mathematics and statistics from the University of Bombay;a master's degree in operations research from the University of Florida;and a Ph.D. in resource management with an emphasis on ground water pollution control from the University of Michigan. He is a professor of geography and civil and environmental engineering at the University of Iowa. He is currently a visiting scientist in the Advanced Monitoring Systems Division, Environmental Monitoring Systems Laboratory, P.O. Box 93478, U.S. Environmental Protection Agency, Las Vegas, NV 89193. |
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| Abstract: | Questions such as what, where, when, and how often to sample play a central role in the development of monitoring strategies. Limited resources will not permit sampling for many contaminants at the same frequency at all well sites. Therefore, a resource allocation strategy is necessary to arrive at answers for the preceding types of questions. Such a strategy for a ground water quality monitoring program is formulated as an integer programming model (an optimization model). The model will be of use in the process of deciding what constituents to sample and where to sample them so as to maximize a given objective, subject to a set of budget, sampling, and regulatory constraints. The maximization objective in the model is defined as a weighted function of population exposure to a scaled measure of observed chemical concentrations. The sampling constraints are based on the observed variability of contaminants in the aquifer, needed precision in estimates, a chosen level of significance, the available budget for implementing the program, and selected regulatory constraints. The model is tested with field data obtained for 10 selected constituents from more than 650 wells in the Cambrian-Ordovician aquifer in Iowa. Results from two alternative formulations of the model are compared, analyzed, and discussed. Further avenues for research are briefly outlined. |
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