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Statistical Methods for Groundwater Monitoring, Second Edition
ISBN-13: 9780470164969 / Angielski / Twarda / 2009 / 400 str.
Statistical Methods for Groundwater Monitoring, Second Edition
ISBN-13: 9780470164969 / Angielski / Twarda / 2009 / 400 str.
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In order to assess the effectiveness of groundwater monitoring devices and their end results, statistical techniques must be employed. Thoroughly updated and expanded, the Second Edition examines the multiple problems inherent in the analysis of groundwater monitoring data and illustrates their application and interconnections.
"This book is an excellent supplementary text for courses on environmental statistics or reference for researchers and practitioners." ( Book News, December 2009)
Preface.
Acknowledgments.
Acronyms.
1 NORMAL PREDICTION INTERVALS.
1.1 Overview.
1.2 Prediction Intervals for the Next Single Measurement from a Normal Distribution.
1.3 Prediction Limits for the Next k Measurements from a Normal Distribution.
1.4 Normal Prediction Limits with Resampling.
1.5 Simultaneous Normal Prediction Limits for the Next k Samples.
1.6 Simultaneous Normal Prediction Limits for the Next r of m Measurements at Each of k Monitoring Wells.
1.7 Normal Prediction Limits for the Mean(s) of m > 1 Future Measurements at Each of k Monitoring Wells.
1.8 Summary.
2 NONPARAMETRIC PREDICTION INTERVALS.
2.1 Overview.
2.2 Pass 1 of m Samples.
2.3 Pass m – 1 of m Samples.
2.4 Pass First or all m – 1 Resamples.
2.5 Nonparametric Prediction Limits for the Median of m Future Measurements at each of k Locations.
2.6 Summary.
3 PREDICTION INTERVALS FOR OTHER DISTRIBUTIONS.
3.1 Overview.
3.2 Lognormal Distribution.
3.3 Lognormal Prediction Limits for the Median of m Future Measurements.
3.4 Lognormal Prediction Limits for the Mean of m Future Measurements.
3.5 Poisson Distribution.
3.6 Summary.
4 GAMMA PREDICTION INTERVALS AND SOME RELATED TOPICS.
4.1 Overview.
4.2 Gamma Distribution.
4.3 Comparison of Gamma mean to a Regulatory Standard.
4.4 Summary.
5 TOLERANCE INTERVALS.
5.1 Overview.
5.2 Normal Tolerance Limits.
5.3 Poisson Tolerance Limits.
5.4 Gamma Tolerance Limits.
5.5 Nonparametric Tolerance Limits.
5.6 Summary.
6 METHOD DETECTION LIMITS.
6.1 Overview.
6.2 Single Concentration Designs.
6.3 Calibration Designs.
6.4 Summary.
7 PRACTICAL QUANTITATION LIMITS.
7.1 Overview.
7.2 Operational Definition.
7.3 A Statistical Estimate of the PQL.
7.4 Derivation of the PQL.
7.5 A Simpler Alternative.
7.6 Uncertainty in Y ∗.
7.7 The Effect of the Transformation.
7.8 Selecting N.
7.9 Summary.
8 INTERLABORATORY CALIBRATION.
8.1 Overview.
8.2 General Random Effects Regression Model for the Case of Heteroscedastic Measurement Errors.
8.3 Estimation of Model Parameters.
8.4 Applications of the Derived Results.
8.5 Summary.
9 CONTAMINANT SOURCE ANALYSIS.
9.1 Overview.
9.2 Statistical Classification Problems.
9.3 Nonparametric Methods.
9.4 Summary.
10 INTRA–WELL COMPARISON.
10.1 Overview.
10.2 Shewart Control Charts.
10.3 (CUSUM) Control Charts.
10.4 Combined Shewart–CUSUM Control Charts.
10.5 Prediction Limits.
10.6 Pooling Variance Estimates.
10.7 Summary.
11 TREND ANALYSIS.
11.1 Overview.
11.2 Sen Test.
11.3 Mann–Kendall Test.
11.4 Seasonal Kendall Test.
11.5 Some Statistical Properties.
11.6 Summary.
12 CENSORED DATA.
12.1 Conceptual Foundation.
12.2 Simple Substitution Methods.
12.3 Maximum Likelihood Estimators.
12.4 Restricted Maximum Likelihood Estimators.
12.5 Linear Estimators.
12.6 Alternative Linear Estimators.
12.7 Delta Distributions.
12.8 Regression Methods.
12.9 Substitution of Expected Values of Order Statistics.
12.10 Comparison of Estimators.
12.11 Some Simulation Results.
12.12 Summary.
13 NORMAL PREDICTION LIMITS FOR LEFT–CENSORED DATA.
13.1 Prediction Limit for Left–Censored Normal Data.
13.2 Simulation Study.
13.3 Summary.
14 TESTS FOR DEPARTURE FROM NORMALITY.
14.1 Overview.
14.2 A Simple Graphical Approach.
14.3 The Shapiro–Wilk Test.
14.4 Shapiro–Francia Test.
14.5 D′Agostino Test.
14.6 Methods Based on Moments of a Normal Distribution.
14.7 Multiple Independent Samples.
14.8 Testing Normality in Censored Samples.
14.9 The Kolmogorov–Smirnov Test.
14.10 Summary.
15 VARIANCE COMPONENT MODELS.
15.1 Overview.
15.2 Least–Squares Estimators.
15.3 Maximum Likelihood Estimators.
15.4 Summary.
16 DETECTING OUTLIERS.
16.1 Overview.
16.2 Rosner Test.
16.3 Skewness Test.
16.4 Kurtosis Test.
16.5 Shapiro–Wilk Test.
16.6 Em statistic.
16.7 Dixon Test.
16.8 Summary.
17 SURFACE WATER ANALYSIS.
17.1 Overview.
17.2 Statistical Considerations.
17.3 Statistical Power.
17.4 Summary.
18 ASSESSMENT AND CORRECTIVE ACTION MONITORING.
18.1 Overview.
18.2 Strategy.
18.3 LCL or UCL?
18.4 Normal Confidence Limits for the Mean.
18.5 Lognormal Confidence Limits for the Median.
18.6 Lognormal Confidence Limits for the Mean.
18.7 Nonparametric Confidence Limits for the Median.
18.8 Confidence Limits for Other Percentiles of the Distribution.
18.9 Summary.
19 REGULATORY ISSUES.
19.1 Regulatory Statistics.
19.2 Methods to be Avoided.
19.3 Verification Resampling.
19.4 Interwell versus Intrawell Comparisons.
19.5 Computer Software.
19.6 More Recent Developments.
20 SUMMARY.
Topic Index.
Robert D. Gibbons, PhD, is Director of the Center for Health Statistics and Professor of Biostatistics and Psychiatry at the University of Illinois at Chicago. A Fellow of the American Statistical Association and member of the Institute of Medicine of the National Academy of Sciences, Dr. Gibbons has written more than 200 journal articles in the areas of statistics and psychometrics. He is the coauthor of Longitudinal Data Analysis and Statistical Methods for Detection and Quantification of Environmental Contamination, both published by Wiley.
DULAL K. BHAUMIK, PhD, is Professor of Biostatistics, Psychiatry, and Bioengineering at the University of Illinois at Chicago. A Fellow of the American Statistical Association, Dr. Bhaumik has published more than fifty journal articles in his areas of research interest, which include environmental statistics, statistical problems in psychiatry, biostatistics, design of experiments, and statistical inference.
SUBHASH ARYAL, PhD, is Assistant Professor of Biostatistics at the University of North Texas Health Science Center at Fort Worth. He has coauthored numerous published articles on statistics in the environmental sciences.
A new edition of the most comprehensive overview of statistical methods for environmental monitoring applications
Thoroughly updated to provide current research findings, Statistical Methods for Groundwater Monitoring, Second Edition continues to provide a comprehensive overview and accessible treatment of the statistical methods that are useful in the analysis of environmental data. This new edition expands focus on statistical comparison to regulatory standards that are a vital part of assessment, compliance, and corrective action monitoring in the environmental sciences.
The book explores quantitative concepts useful for surface water monitoring as well as soil and air monitoring applications while also maintaining a focus on the analysis of groundwater monitoring data in order to detect environmental impacts from a variety of sources, such as industrial activity and waste disposal. The authors introduce the statistical properties of alternative approaches, such as false positive and false negative rates, that are associated with each test and the factors related to these error rates. The Second Edition also features:
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An introduction to Intra–laboratory Calibration Curves and random–effects regression models for non–constant measurement variability
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Coverage of statistical prediction limits for a gamma–distributed random variable, with a focus on estimation and testing of parameters in environmental monitoring applications
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A unified treatment of censored data with the computation of statistical prediction, tolerance, and control limits
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Expanded coverage of statistical issues related to laboratory practice, such as detection and quantitation limits
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An updated chapter on regulatory issues that outlines common mistakes to avoid in groundwater monitoring applications as well as an introduction to the newest regulations for both hazardous and municipal solid waste facilities
Each chapter provides a general overview of a problem, followed by statistical derivation of the solution and a relevant example complete with computational details that allow readers to perform routine application of the statistical results. Relevant issues are highlighted throughout, and recommendations are also provided for specific problems based on characteristics such as number of monitoring wells, number of constituents, distributional form of measurements, and detection frequency.
Statistical Methods for Groundwater Monitoring, Second Edition is an excellent supplement to courses on environmental statistics at the upper–undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the fields of biostatistics, engineering, and the environmental sciences who work with statistical methods in their everyday work.
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