Stressing a basic knowledge of statistics and statistical analysis for making sense of data in environmental engineering and sciences
For the environmental professional, accurate data collection, analysis, and assessment is crucial for the protection of public health and ecological well-being. Statistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary. Featuring numerous worked examples using actual data and "ready-made" software scripts, Statistical Applications for Environmental Analysis and Risk Assessment also includes:

• Descriptions of basic statistical concepts and principles in an informal style and no presumption of prior familiarity with the subject
• Detailed illustrations of applications in familiar practice areas in the environmental and related water resources fields using real-world data as would typically be encountered by practitioners
• Software scripts using the high-powered statistical software system, R, and supplemented by USEPA's ProUCL and USDOE's VSP software packages, which are all freely available
• Coverage of frequent data sample issues such as non-detects, outliers, skewness, sustained and cyclical trend that habitually plague environmental data samples
• Clear demonstrations of the crucial, but often overlooked, role of statistics in environmental sampling design and subsequent exposure risk assessment

Statistical Applications for Environmental Analysis and Risk Assessment is an excellent book for upper-undergraduate and graduate-level courses on environmental statistics for students in environmental engineering, geological and environmental science programs. The book is also a valuable reference for practicing environmental professionals, such as earth scientists, geologists, and hydrologists, who have to routinely analyze and interpret data.