Methods Of Environmental Data Analysis
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Environmental Data Analysis
Author: Zhihua Zhang
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2016-11-21
Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: Preface Time series analysis Chaos and dynamical systems Approximation Interpolation Statistical methods Numerical methods Optimization Data envelopment analysis Risk assessments Life cycle assessments Index
Statistical Methods for Environmental Pollution Monitoring
Author: Richard O. Gilbert
language: en
Publisher: John Wiley & Sons
Release Date: 1987-02-15
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.
Numerical Methods in Environmental Data Analysis
Numerical Methods in Environmental Data Analysis introduces environmental scientists to the numerical methods available to help answer research questions through data analysis. One challenge in data analysis is misrepresentation of datasets that are relevant directly or indirectly to the research. This book illustrates new ways of screening dataset or images for maximum utilization, introducing environmental modeling, numerical methods, and computations techniques in data analysis. Throughout the book, the author includes case studies that provide guidance on how to translate research questions into appropriate models. Individuals working with data sets or images generated from environmental monitoring centers or satellites will find this book to be a concise guide for analyzing and interpreting their data. - Bridges the theoretical underpinnings of modeling to research - Illustrates the computational resolution of environmental issues alongside the use of open-source software - Provides information on the use of analogue versus digital data treatment processes