Über die Autorin bzw. den Autor

ROBERT D. GIBBONS, PhD, is Professor of Biostatistics and Director of the Center for Health Statistics at the University of Illinois at Chicago

DAVID E. COLEMAN, MS, is Senior Technical Specialist-Statistics at the Alcoa Technical Center in Alcoa Center, Pennsylvania.

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Comprehensive coverage, state-of-the-art methods

This groundbreaking volume describes the statistical theory that underlies the detection and quantification of environmental pollution both in the laboratory and in the field. It presents the foundation of relating measured concentrations to true concentrations and the development of intervals of uncertainty for true concentrations, and it presents a comprehensive review of the problem of estimating thresholds at which detection and quantification decisions can be made reliably.

The authors demonstrate the use of analytical measurements in making environmental impact decisions and in comparing environmental data to regulatory standards and naturally occurring background concentrations. Taking the next step in a major evolution in the way environmental impact decisions are made, Statistical Methods for Detection and Quantification of Environmental Contamination:

• Presents statistical methods that allow the earliest possible detection and quantification of contaminants
• Describes procedures applicable to all environmental constituents
• Covers numerous state-of-the-art approaches
• Includes case studies demonstrating practical applications of these approaches

An indispensable handbook for scientists and engineers involved in environmental monitoring programs, this book is also an important resource for public health officials, waste facility managers, regulators, statisticians, and analytical chemists.

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Detection and Quantification of Environmental Contamination was among those chosen.

Aus dem Klappentext

Comprehensive coverage, state-of-the-art methods

This groundbreaking volume describes the statistical theory that underlies the detection and quantification of environmental pollution both in the laboratory and in the field. It presents the foundation of relating measured concentrations to true concentrations and the development of intervals of uncertainty for true concentrations, and it presents a comprehensive review of the problem of estimating thresholds at which detection and quantification decisions can be made reliably.

The authors demonstrate the use of analytical measurements in making environmental impact decisions and in comparing environmental data to regulatory standards and naturally occurring background concentrations. Taking the next step in a major evolution in the way environmental impact decisions are made, Statistical Methods for Detection and Quantification of Environmental Contamination:

• Presents statistical methods that allow the earliest possible detection and quantification of contaminants
• Describes procedures applicable to all environmental constituents
• Covers numerous state-of-the-art approaches
• Includes case studies demonstrating practical applications of these approaches

An indispensable handbook for scientists and engineers involved in environmental monitoring programs, this book is also an important resource for public health officials, waste facility managers, regulators, statisticians, and analytical chemists.

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Detection and Quantification of Environmental Contamination was among those chosen.

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Statistical Methods for Detection and Quantification of Environmental Contamination

John Wiley & Sons

Chapter One

In recent years, statistical methods have played a major role in environmental monitoring programs. With the development of a modern statistical approach to environmental regulatory statistics (Davis, 1994; Davis and McNichols, 1987; Gibbons, 1987a,b, 1994, 1996; Gilbert, 1987) there has been a major evolution in the way in which environmental impact decisions are made. This early work has focused largely on the earliest possible detection of a release, often termed environmental detection monitoring. As these new methods have become incorporated into state and federal regulation and guidance (U.S. EPA, 1987, 1988, 1989, 1992), the need for improved statistical approaches to related problems of assessment, compliance, and corrective-action monitoring has grown as well. Unfortunately, far less statistical work has been done in this area, and corresponding environmental impact decisions are still often based on a comparison of individual measurements to fixed standards, or at best, simple normal confidence bounds. Often, a facility or property is declared as environmentally impacted if a single measured concentration exceeds an environmental standard. First, we should be concerned about such a practice because we should be interested in comparison of the true concentration to the standard, not simply the measured concentration. Of course, without infinite sampling we can never really know the true concentration; however, statistical analysis provides a means of drawing inference to the true concentration distribution from a series of measured concentrations. Second, we should be concerned about such a practice because it treats all environmental problems as being equal. For example, exceeding an environmental standard in 1 of 5 samples is a very different problem than exceeding an environmental standard in 1 of 500 samples. Again, the statistical approach to this problem incorporates our uncertainty in the true concentration distribution rather than simply assuming that the measurements are made without error and represent truth in and of themselves. In fact, nothing could be further from the truth.

Since much of the threat of pollution comes from human beings and manufactured products, the constituents of concern are often anthropogenic and do not exist naturally in the environment. Here, the variability in the system is based in large part on the practice of the laboratory and the preparation and analysis of individual samples. In almost all cases, analytical measurements are accepted as true concentrations without regard to their uncertainty. Confidence bounds on measured concentrations are rarely reported, despite the availability of historical data that could routinely be used for this purpose. In the absence of such uncertainty estimates, laboratories most often rely on limits of detection and quantification to screen analytical measurements. The limit of detection, which we denote as [L.sub.D] following the pioneering work of Currie (1968), allows us to make the binary decision of "detected" with specified levels of confidence for errors of both the first (false positive) and second (false negative) kinds. The limit of quantification, [L.sub.Q] (Currie, 1968), is the concentration at which the true concentration can reliably be measured. In many ways, these two types of limits are simply points along a continuum that describes the relationship between true concentration and uncertainty. As we will see, the statistical modeling of such a relationship is complicated by the fact that uncertainty is rarely, if ever, constant, even for small intervals on this continuum. Of course, the role of this uncertainty must be incorporated in making environmental monitoring decisions, although in practice, it rarely is.

The purpose of this book is to describe the statistical theory that underlies the detection and quantification of environmental pollution in both the laboratory and the field. In the laboratory, we present the foundation of relating measured concentrations to true concentrations and the development of intervals of uncertainty for true concentrations given a new measured concentration. Related to this problem is the problem of estimating thresholds on this curve that define concentrations at which detection and quantification decisions can reliably be made. In this book we present a comprehensive review of this topic with directions for future research.

In the field, we discuss how analytical measurements can be used in making environmental impact decisions and more broadly, how environmental data can be compared to regulatory standards, naturally occurring background concentrations, or both. Again, we present a comprehensive review of this problem and directions for future research.

Overview of the Book In most chapters a general introduction to the problem is presented, followed by increasingly complex solutions. In some cases, statistical theory is presented that may not be accessible to all readers; however, it is included for completeness and the hope that this book may provide a foundation for further statistical research in this area. Despite complexity, for each solution or statistical approach to a particular problem, a relevant example is provided with computational details and/or tables that can be used for routine application of the statistical results. Attention is paid to statistical properties of alternative approaches, including false positive and false negative rates associated with each test and factors related to these error rates where possible. Recommendations are provided for specific problems based on characteristics such as number of monitoring wells, number of constituents, distributional form of measurements, and detection frequency. The reader may use the book to help craft an assessment, compliance, or corrective-action monitoring program for most environmental media that have been or potentially have been affected by one or more pollutants. Although discussed to some degree for completeness, the reader interested in routine environmental detection monitoring is referred to the previous book by Gibbons (1994), where this topic is dealt with in considerable detail. Similarly, the reader interested in statistical aspects of environmental sampling is referred to the excellent book by Gilbert (1987).

Part I contains 10 chapters in which conceptual and statistical issues of detection and quantification in the laboratory are discussed. Although much of the work and illustrations involve problems in the environmental sciences, the problems and solutions apply to all aspects of analytical chemistry and to calibration problems in other fields as well. For example, much of the work done in analysis of the contents of the foods that we eat are directly amenable to solution using the methods described in Part I. Chapter 2 begins with a discussion of the conceptual foundations underlying the chemical measurement process, calibration function, sensitivity, precision, and accuracy. In addition, in Chapter 2 we discuss the conceptual basis for detection and quantification decisions both within and across laboratories.

In Chapter 3 we review the statistical foundations that underlie the methods described in the book. They are divided into the areas of hypothesis...