Description
Book Synopsis: 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.
Details
Looking for a comprehensive guide to statistical methods for environmental pollution monitoring? Look no further! Our book, "Statistical Methods for Environmental Pollution Monitoring," is the ultimate resource you need. Packed with easy-to-comprehend explanations and practical examples, this book will equip you with the knowledge and skills to tackle pollution data with confidence.
Dr. Gilbert, a renowned expert in the field, takes you through a step-by-step journey, starting with a space-time framework for sampling pollutants. You'll learn how to estimate average and total amounts of pollutants in the environment using statistical sample survey methods. Need to find those hot spots? Discover how to determine the number of samples needed.
We understand that pollution data can be complex, which is why this book emphasizes nonparametric statistical analysis methods that are specifically tailored for pollution data. From analyzing lognormally distributed data to testing for trends over time or space, you'll unlock a wealth of statistical techniques that will enhance your understanding of environmental pollution. Don't worry about crunching the numbers - we provide a comprehensive computer code for nonparametric trend detection and estimation analyses, along with nineteen statistical tables for easy application.
But that's not all! This sourcebook also covers statistical techniques for correlated data, measurements below the detection limit, and field-composited samples - ensuring you are equipped to handle diverse scenarios in your work. With numerous citations to publications on pollution study design and statistical analysis, this book is an invaluable tool for statisticians, ecologists, environmental engineers, and other professionals dealing with pollution in air, water, and soil.
Don't miss this opportunity to enhance your skills and broaden your knowledge in environmental pollution monitoring. Get your copy of "Statistical Methods for Environmental Pollution Monitoring" today and unlock the secrets to effective statistical analysis in this critical field. Purchase now and embark on a journey towards becoming a pollution data expert.
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