Statistical Methods for Environmental Pollution Monitoring

by Gilbert, Richard O.

• ISBN13: 9780471288787
• ISBN10: 0471288780
• Edition: 1st
• Format: Hardcover
• Copyright: 1987-02-15
• Publisher: Wiley

Summary

This book discusses a broad range of statistical design and analysis methods that are particularly well suited to pollution data. It explains key statistical techniques in easy-to-comprehend terms and uses practical examples, exercises, and case studies to illustrate procedures. Dr. Gilbert begins by discussing a space-time framework for sampling pollutants. He then shows how to use statistical sample survey methods to estimate average and total amounts of pollutants in the environment, and how to determine the number of field samples and measurements to collect for this purpose. Then a broad range of statistical analysis methods are described and illustrated. These include: determining the number of samples needed to find hot spots analyzing pollution data that are lognormally distributed testing for trends over time or space estimating the magnitude of trends comparing pollution data from two or more populations New areas discussed in this sourcebook include statistical techniques for data that are correlated, reported as less than the measurement detection limit, or obtained from field-composited samples. Nonparametric statistical analysis methods are emphasized since parametric procedures are often not appropriate for pollution data. This book also provides an illustrated comprehensive computer code for nonparametric trend detection and estimation analyses as well as nineteen statistical tables to permit easy application of the discussed statistical techniques. In addition, many publications are cited that deal with the design of pollution studies and the statistical analysis of pollution data. This sourcebook will be a useful tool for applied statisticians, ecologists, radioecologists, hydrologists, biologists, environmental engineers, and other professionals who deal with the collection, analysis, and interpretation of pollution in air, water, and soil.

Author Biography

Richard O. Gilbert is the author of Statistical Methods for Environmental Pollution Monitoring, published by Wiley.

Table of Contents

Preface	ix
Introduction	1	(4)
Types and Objectives of Environmental Pollution Studies	1	(1)
Statistical Design and Analysis Problems	2	(1)
Overview of the Design and analysis Process	3	(1)
Summary	4	(1)
Sampling Environmental Populations	5	(12)
Sampling in Space and Time	5	(2)
Target and Sampled Populations	7	(2)
Representative Units	9	(1)
Choosing a Sampling Plan	10	(1)
Variability and Error in Environmental Studies	10	(3)
Case Study	13	(2)
Summary	15	(2)
Environmental Sampling Design	17	(9)
Introduction	17	(1)
Criteria for Choosing a Sampling Plan	17	(2)
Methods for Selecting Sampling Locations and Times	19	(5)
Summary	24	(2)
Simple Random Sampling	26	(19)
Basic Concepts	26	(1)
Estimating the Mean and Total Amount	27	(3)
Effect of Measurement Errors	30	(1)
Number of Measurement: Independent Data	30	(5)
Number of Measurement: Correlated Data	35	(7)
Estimating Var(x)	42	(1)
Summary	43	(2)
Stratified Random Sampling	45	(13)
Basic Concepts	45	(1)
Estimating the Mean	46	(1)
Estimating the Total Amount	47	(1)
Arbitrary Selection of Strata	48	(2)
Allocation of Samples of Strata	50	(1)
Number of Samples	51	(2)
Case Study	53	(1)
Summary	54	(4)
Two-Stage Sampling	58	(13)
Basic Concepts	58	(1)
Primary Units of Equal size	59	(5)
Primary Units of Unequal Size	64	(5)
Summary	69	(2)
Compositing and Three-Stage Sampling	71	(18)
Basic Concepts	72	(1)
Equal-Sized Unites	72	(7)
Unequal-Sized Units	79	(6)
Summary	85	(4)
Systematic Sampling	89	(17)
Sampling along a Line	90	(3)
Sampling over Space	93	(1)
Comparing Systematic with Random Sampling	94	(2)
Estimating the Mean and Variance	96	(3)
Population with Trends	99	(1)
Estimating Var(x) from a Single Systematic Sample	100	(2)
Estimating Spatial Distribution	102	(1)
Summary	103	(3)
Double Sampling	106	(13)
Linear Regression Double Sampling	106	(5)
Ratio Double Sampling	111	(1)
Case Study	112	(5)
Summary	117	(2)
Locating Hot Spots	119	(13)
Determining Grid Spacing	121	(4)
Size of Hot Spot Likely to be Hit	125	(1)
Probability of Not Hitting a Hot Spot	125	(2)
Taking Prior Information into Account	127	(1)
Probability that a Hot Spot Exists When None Has Been Found	128	(1)
Choosing the consumer's Risk	129	(2)
Summary	131	(1)
Quantiles, Proportions, and Means	132	(20)
Basic Concepts	132	(2)
Estimating Quantiles (Percentiles)	134	(2)
Confidence Limits for Quantiles	136	(1)
Estimating Proportions	136	(1)
Two-Sided Confidence Limits for the Mean	137	(1)
One-Sided confidence Limits for the Mean	138	(1)
Approximate Confidence Limits for the Mean	139	(1)
Alternative Estimators for the Mean and Standard Deviation	140	(1)
Nonparametric Estimators of Quantiles	141	(1)
Nonparametric Confidence Limits for Quantiles	141	(1)
Nonparametric Confidence Limits for Proportions	142	(2)
Confidence Limits When Data Are Correlated	144	(2)
Rank Von Neumann Test for Serial Correlation	146	(2)
Data Transformations	148	(1)
Summary	149	(3)
Skewed Distribution and Goodness-of-Fit Tests	152	(12)
Lognormal Distribution	152	(3)
Weibull, Gamma, and Beta Distribution	155	(2)
Goodness-of-Fit Tests	157	(5)
Summary	162	(2)
Characterizing Lognormal Populations	164	(13)
Estimating the Mean and Variance	164	(5)
Confidence Limits for the Mean	169	(2)
Estimating the Median	171	(2)
Confidence Limits for the Median	173	(1)
Choosing n for Estimating the Median	174	(1)
Estimating Quantiles	174	(1)
Summary	175	(2)
Estimating the Mean and Variance from Censored Data Sets	177	(9)
Data Near Detection Limits	177	(1)
Estimators of the Mean and Variance	178	(3)
Two-Parameter Lognormal Distribution	181	(3)
Three-Parameter Lognormal Distribution	184	(1)
Summary	184	(2)
Outlier Detection and Control Charts	186	(18)
Data Screening and Validation	186	(1)
Treatment of Outliers	187	(1)
Rosner's Test for Detecting up to k Outliers	188	(3)
Detecting Outliers in Correlated Variables	191	(1)
Other Outlier Tests	192	(1)
Control Charts	193	(9)
Summary	202	(2)
Detecting and Estimating Trends	204	(21)
Types of Trends	204	(1)
Statistical Complexities	205	(2)
Methods	207	(1)
Mann-Kendall Test	208	(9)
Sen's Nonparametric Estimator of Slope	217	(2)
Case Study	219	(4)
Summary	223	(2)
Trends and Seasonality	225	(16)
Seasonal Kendall Test	225	(2)
Seasonal Kendall Slope Estimator	227	(1)
Homogeneity of Trends in Different Seasons	228	(2)
Sen's Test for Trend	230	(1)
Testing for Global Trends	231	(5)
Summary	236	(5)
Comparing Populations	241	(13)
Tests Using Paired Data	241	(6)
Independent Data Sets	247	(5)
Summary	252	(2)
Appendix A: Statistical Tables	254	(20)
Appendix B: TREND	274	(22)
Symbols	296	(3)
Glossary	299	(2)
Bibliography	301	(14)
Index	315