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Theses and Dissertations

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Browsing Theses and Dissertations by Author "Bell, Harry F., committee member"

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    Framework for development of data analysis protocols for groundwater quality monitoring systems
    (Colorado State University. Libraries, 1992) Adkins, Nadine C., author; Ward, Robert C., advisor; Loftis, Jim C., committee member; Iyer, Hariharan, committee member; Bell, Harry F., committee member
    Protocols for field sampling and laboratory sampling are used on a routine basis to produce accurate and precise water quality data. Efforts are now being focused on providing decision makers with the information they need from that data. Statistics is one method of extracting information from data. There are no widely accepted protocols for statistically analyzing groundwater quality data. Due to the wide variety of field conditions encountered in groundwater monitoring, a general protocol would be of limited use. What is needed is a set of guidelines for writing site specific data analysis protocols. A framework for developing data analysis protocols (DAPs) is presented in this thesis. The framework is essentially a "how-to" manual for protocol writers. It is designed to be concise, easy to use, and based on the current state-of-the-art. The focus of the framework is the analysis of groundwater quality data at hazardous waste facilities. Detailed background information is presented for the framework. The four main issues that are addressed include: information goals, data record attributes, and choice and interpretation of statistical results. There is a great deal of confusion in the water quality community regarding these issues. This thesis does not attempt to resolve that confusion. Instead, the goal was to sort out the areas of conflict and uncertainty, and present them in a clear manner. Recommendations are provided where possible. The framework was used to write a data analysis protocol for an IBM semiconductor manufacturing plant in Hopewell Junction, New York. The combination of flexibility in the basic framework and the availability of detailed background information was quite effective. It allowed the data analysis protocol to be site specific and scientifically defensible.
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    Statistical analysis of water quality data affected by limits of detection
    (Colorado State University. Libraries, 1986) Porter, Paul Steven, author; Ward, Robert C., advisor; Bell, Harry F., committee member; Loftis, Jim C., committee member; Skogerboe, Rod, committee member
    Many water quality problems are related to substances which are present at concentrations too low to be measured precisely. Obtaining information from a monitoring system which produces many results near the fringes of analytical capabilities is not straightforward. This thesis is a discussion of the concerns one should have when statistically analyzing water quality data from such a system. Two general approaches are discussed. The traditional approach is to regard all measurements as precise or imprecise. Precise results are simply numerical responses, for which statistical analysis may lead to valid and sound monitoring information. Imprecise results are reported as "ND", or not detected, with criteria for reporting based on categories of measurement precision. Measurement error which leads to censoring is described. The impact of this error on the statistical characteristics of water quality data is illustrated using a model appropriate for analyte concentrations near the limit of detection. It is shown that the statistical properties of a set of measurements may not resemble the population from which samples were taken. This suggests the use of statistical methods which acknowledge observation error. Loss of information due to censoring is demonstrated and it is proposed that a numerical result be reported for all measurements. It is also suggested that an estimate of data precision accompany all results. This would permit the data user to censor at levels of uncertainty chosen by the user, rather than having information censored by the measurement process. When the number of results with significant observation error is small, or when data has been censored and no information is available regarding such error is available, statistical methods intended for censored data may appropriately be used. Such methods covering a variety of water quality problems are reviewed. Numerical examples of many methods are provided.