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Basic Environmental Data Analysis for Scientists and Engineers

Author: Ralph R.B. Von Frese

Publisher: CRC Press

Published: 2019-11-22

Total Pages: 282

ISBN-13: 1000725618

Classroom tested and the result of over 30 years of teaching and research, this textbook is an invaluable tool for undergraduate and graduate data analysis courses in environmental sciences and engineering. It is also a useful reference on modern digital data analysis for the extensive and growing community of Earth scientists and engineers. Basic Environmental Data Analysis for Scientists and Engineers introduces practical concepts of modern digital data analysis and graphics, including numerical/graphical calculus, measurement units and dimensional analysis, error propagation and statistics, and least squares data modeling. It emphasizes array-based or matrix inversion and spectral analysis using the fast Fourier transform (FFT) that dominates modern data analysis. Divided into two parts, this comprehensive hands-on textbook is excellent for exploring data analysis principles and practice using MATLAB®, Mathematica, Mathcad, and other modern equation solving software. Part I, for beginning undergraduate students, introduces the basic approaches for quantifying data variations in terms of environmental parameters. These approaches emphasize uses of the data array or matrix, which is the fundamental data and mathematical processing format of modern electronic computing. Part II, for advanced undergraduate and beginning graduate students, extends the inverse problem to least squares solutions involving more than two unknowns. Features: Offers a uniquely practical guide for making students proficient in modern electronic data analysis and graphics Includes topics that are not explained in any existing textbook on environmental data analysis Data analysis topics are very well organized into a two-semester course that meets general education curriculum requirements in science and engineering Facilitates learning by beginning each chapter with an ‘Overview’ section highlighting the topics covered, and ending it with a ‘Key Concepts’ section summarizing the main technical details that the reader should have acquired Indexes many numerical examples for ready access in the classroom or other venues serviced by electronic equation solvers like MATLAB®, Mathematica, Mathcad, etc. Offers supplemental exercises and materials to enhance understanding the principles and practice of modern data analysis

Data Analysis and Statistics for Geography, Environmental Science, and Engineering

Author: Miguel F. Acevedo

Publisher: CRC Press

Published: 2012-12-07

Total Pages: 549

ISBN-13: 1466592214

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Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustain

Environmental Data Analysis

Author: Zhihua Zhang

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2016-11-21

Total Pages: 334

ISBN-13: 3110424908

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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

Statistics for Environmental Engineers, Second Edition

Author: Linfield C. Brown

Publisher: CRC Press

Published: 2002-01-29

Total Pages: 584

ISBN-13: 9781420056631

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Two critical questions arise when one is confronted with a new problem that involves the collection and analysis of data. How will the use of statistics help solve this problem? Which techniques should be used? Statistics for Environmental Engineers, Second Edition helps environmental science and engineering students answer these questions when the goal is to understand and design systems for environmental protection. The second edition of this bestseller is a solutions-oriented text that encourages students to view statistics as a problem-solving tool. Written in an easy-to-understand style, Statistics for Environmental Engineers, Second Edition consists of 54 short, "stand-alone" chapters. All chapters address a particular environmental problem or statistical technique and are written in a manner that permits each chapter to be studied independently and in any order. Chapters are organized around specific case studies, beginning with brief discussions of the appropriate methodologies, followed by analysis of the case study examples, and ending with comments on the strengths and weaknesses of the approaches. New to this edition: Thirteen new chapters dealing with topics such as experimental design, sizing experiments, tolerance and prediction intervals, time-series modeling and forecasting, transfer function models, weighted least squares, laboratory quality assurance, and specialized control charts Exercises for classroom use or self-study in each chapter Improved graphics Revisions to all chapters Whether the topic is displaying data, t-tests, mechanistic model building, nonlinear least squares, confidence intervals, regression, or experimental design, the context is always familiar to environmental scientists and engineers. Case studies are drawn from censored data, detection limits, regulatory standards, treatment plant performance, sampling and measurement errors, hazardous waste, and much more. This revision of a classic text serves as an ideal textbook for students and a valuable reference for any environmental professional working with numbers.

Environmental Data Analysis with MatLab

Author: William Menke

Publisher: Elsevier

Published: 2009-10-13

Total Pages: 288

ISBN-13: 0123918871

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Environmental Data Analysis with MatLab is a reference work designed to teach students and researchers the basics of data analysis in the environmental sciences using MatLab, and more specifically how to analyze data sets in carefully chosen, realistic scenarios. Although written in a self-contained way, the text is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial, available at the author's website: http://www.ldeo.columbia.edu/users/menke/edawm/index.htm. This book is organized into 12 chapters. After introducing the reader to the basics of data analysis with MatLab, the discussion turns to the power of linear models; quantifying preconceptions; detecting periodicities; patterns suggested by data; detecting correlations among the data; filling in missing data; and determining whether your results are significant. Homework problems help users follow up upon case studies. This text will appeal to environmental scientists, specialists, researchers, analysts, and undergraduate and graduate students in Environmental Engineering, Environmental Biology and Earth Science courses, who are working to analyze data and communicate results. Well written and outlines a clear learning path for researchers and students Uses real world environmental examples and case studies MatLab software for application in a readily-available software environment Homework problems help user follow up upon case studies with homework that expands them

Applied Data Analysis and Modeling for Energy Engineers and Scientists

Author: T. Agami Reddy

Publisher: Springer Science & Business Media

Published: 2011-08-09

Total Pages: 446

ISBN-13: 1441996133

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Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.

Quantitative Analysis and Modeling of Earth and Environmental Data

Author: Jiaping Wu

Publisher: Elsevier

Published: 2021-12-04

Total Pages: 504

ISBN-13: 0128163429

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Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offers a systematic, quantitative analysis of multi-sourced data and provides information about the spatial distribution and temporal dynamics of natural attributes (physical, biological, health, social). It includes models and techniques for handling data that may vary by space and/or time, and aims to improve understanding of the physical laws of change underlying the available numerical datasets, while taking into consideration the in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). It considers the synthesis of scientific theory-based methods (stochastic modeling, modern geostatistics) and data-driven techniques (machine learning, artificial neural networks) so that their individual strengths are combined by acting symbiotically and complementing each other. The notions and methods presented in Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Including real-world practical applications as well as practice exercises, this book is a comprehensive step-by-step tutorial of theory-based and data-driven techniques that will help students and researchers master data analysis and modeling in earth and environmental sciences (including environmental health and human exposure applications). Explores the analysis and processing of chronotopologic (i.e., space-time and spacetime) data that varies spatially and/or temporally, which is the case with the majority of data in scientific and engineering disciplines Studies the synthesis of scientific theory and empirical evidence (in its various forms) that offers a mathematically rigorous and physically meaningful assessment of real-world phenomena Covers a wide range of data describing a variety of attributes characterizing physical phenomena and systems including earth, ocean and atmospheric variables, environmental and ecological parameters, population health states, disease indicators, and social and economic characteristics Includes case studies and practice exercises at the end of each chapter for both real-world applications and deeper understanding of the concepts presented

Analyzing Environmental Data

Author: Walter W. Piegorsch

Publisher: John Wiley & Sons

Published: 2005-03-04

Total Pages: 520

ISBN-13: 9780470848364

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Environmental statistics is a rapidly growing field, supported by advances in digital computing power, automated data collection systems, and interactive, linkable Internet software. Concerns over public and ecological health and the continuing need to support environmental policy-making and regulation have driven a concurrent explosion in environmental data analysis. This textbook is designed to address the need for trained professionals in this area. The book is based on a course which the authors have taught for many years, and prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of data evaluation. The text extends beyond the introductory level, allowing students and environmental science practitioners to develop the expertise to design and perform sophisticated environmental data analyses. In particular, it: Provides a coherent introduction to intermediate and advanced methods for modeling and analyzing environmental data. Takes a data-oriented approach to describing the various methods. Illustrates the methods with real-world examples Features extensive exercises, enabling use as a course text. Includes examples of SAS computer code for implementation of the statistical methods. Connects to a Web site featuring solutions to exercises, extra computer code, and additional material. Serves as an overview of methods for analyzing environmental data, enabling use as a reference text for environmental science professionals. Graduate students of statistics studying environmental data analysis will find this invaluable as will practicing data analysts and environmental scientists including specialists in atmospheric science, biology and biomedicine, chemistry, ecology, environmental health, geography, and geology.

Applied Statistics for Environmental Science with R

Author: Abbas F. M. Al-Karkhi

Publisher: Elsevier

Published: 2019-09-13

Total Pages: 240

ISBN-13: 0128186232

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Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems. Includes step-by-step tutorials to aid in understanding the process and implementation of unique data Presents statistical theory in a simple way without complex mathematical proofs Shows how to analyze data using R software and provides R scripts for all examples and figures

Data Science Applied to Sustainability Analysis

Author: Jennifer Dunn

Publisher: Elsevier

Published: 2021-05-11

Total Pages: 312

ISBN-13: 0128179775

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Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery Includes considerations sustainability analysts must evaluate when applying big data Features case studies illustrating the application of data science in sustainability analyses