Focused on the mechanics of managing environmental data, this book provides guidelines on how to evaluate data requirements, assess tools and techniques, and implement an effective system. Moving beyond the hypothetical, Gerald Burnette illustrates the decision-making processes and the compromises required when applying environmental principles and practices to actual data. Managing Environmental Data explains the basic principles of relational databases, discusses database design, explores user interface options, and examines the process of implementation. Best practices are identified during each portion of the process. The discussion is summarized via the development of a hypothetical environmental data management system. Details of the design help establish a common framework that bridges the gap between data managers, users, and software developers. It is an ideal text for environmental professionals and students. The growth in both volume and complexity of environmental data presents challenges to environmental professionals. Developing better data management skills offers an excellent opportunity to meet these challenges. Gaining knowledge of and experience with data management best practices complements students’ more traditional science education, providing them with the skills required to address complex data requirements.

Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization. - Introduces novel intelligent techniques needed to address environmental pollution for the well-being of the global environment - Offers perspectives on the design, development and commissioning of intelligent applications - Provides reviews on the latest intelligent technologies and algorithms related to state-of-the-art methodologies surrounding the monitoring and mitigation of environmental pollution - Puts forth insights on future generation intelligent pollution monitoring techniques

An integrated approach to environmental data management is necessitated by the complexity of the environmental problems that need to be addresses, coupled with the interdisciplinary approach that needs to be adopted to solve them. Agenda 21 of the Rio Environmental Conference mandated international programmes and organizations to take steps to develop common data and information management plans, and steps have been taken in this direction. The key word that defines the framework of the present book is `integration'. The book establishes the basics of integrated approaches and covers environmental data management systems within that framework, covering all aspects of data management, from objectives and constraints, design of data collection networks, statistical and physical sampling, remote sensing and GIS, databases, reliability of data, data analysis, and the transformation of data into information.

The diverse nature of environmental problems mankind has encountered within the last decade has developed a new understanding of the nature of environmental processes. Currently, the environment is considered as a continuum of air, soil and water as the vital components for sustaining life on earth. The interactive nature of these components requires that the environment is managed and protected as a cohesive whole. This can only be accomplished through an integrated approach to environmental management. Besides the concept of environmental continuum, prospects for sustainable development of natural resources and the recent recognition of global climate change impacts have also necessitated such an integrated approach to environmental management. Two basic tools for integrated management of the environment are modeling and environmental data. Both tools were available and valid in the past; however, the recent requirements for integrated environmental management have also led to a significant evolution of both modeling procedures and data management systems.

"Environmental Data Analysis with MatLab" is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. It is well written and outlines a clear learning path for researchers and students. It uses real world environmental examples and case studies. It has MatLab software for application in a readily-available software environment. Homework problems help user follow up upon case studies with homework that expands them.