ABSTRACTRecent emphasis on the need to protect ground‐water quality has resulted in an increased interest in ground‐water quality monitoring, particularly that monitoring performed in support of a regulatory ground‐water quality management program. Such monitoring must involve intensive surveys or special studies, as well as routine trend types of sampling. In both cases, adequate monitoring strategies require careful consideration of the statistical aspects of sampling theory.The purpose of this paper is to present statistical concepts that should be incorporated in the initial planning of a ground‐water quality monitoring program.The means of incorporating statistical theory into ground‐water quality monitoring suggested in this paper involves selecting the number of samples required based on a specified confidence interval about the mean of the variable under consideration. This approach requires that the variance of the sample mean be known. The expression for Var(x̄) will depend on the correlation structure of the population in question. If the observations taken can be assumed independent in both space and time (i.e., no spatial or serial correlation exists), then the number of samples required can be determined in a very straightforward manner. However, if the samples are correlated, part of the information contained in one observation will be contained in other observations as well. As a result, the sample size must be increased in order to achieve the same level of information that would be obtained in uncorrelated observations.Various sampling techniques can be employed in a ground‐water monitoring plan, including simple random sampling, systematic sampling, and stratified random sampling. Each technique has certain advantages and disadvantages with regard to ground‐water monitoring. An overall monitoring program should incorporate the most effective sampling techniques in order to achieve optimum information content from a minimum num
展开▼
