Journal article
Modeling Ground Water Quality Sampling Decisions
Ground Water Monitoring & Remediation, Vol.8(4), pp.121-134
1988
DOI: 10.1111/j.1745-6592.1988.tb01112.x
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.
Details
- Title: Subtitle
- Modeling Ground Water Quality Sampling Decisions
- Creators
- Ya-Wen HsuehR. Rajagopal - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Ground Water Monitoring & Remediation, Vol.8(4), pp.121-134
- DOI
- 10.1111/j.1745-6592.1988.tb01112.x
- ISSN
- 1745-6592
- Language
- English
- Date published
- 1988
- Academic Unit
- Civil and Environmental Engineering; Geographical and Sustainability Sciences
- Record Identifier
- 9983557268602771
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