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| environmental data analysis course: Environmental Data Analysis with MatLab William Menke, Joshua Menke, 2011-09-02 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. |
| environmental data analysis course: Data Analysis and Statistics for Geography, Environmental Science, and Engineering Miguel F. Acevedo, 2012-12-07 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 course: Health, Safety, and Environmental Data Analysis Anthony J. Joseph, 2020-07-09 Professionals in environmental health and safety (EHS) management use statistics every day in making decisions. This book was created to provide the quantitative tools and techniques necessary to make important EHS assessments. Readers need not be statistically or mathematically inclined to make the most of this book-mathematical derivations are kept to a minimum and subjects are approached in a simple and factual manner, complemented with plenty of real-world examples. Chapters 1-3 cover knowledge of basic statistical concepts such as presentation of data, measurements of location and dispersion, and elementary probability and distributions. Data gathering and analysis topics including sampling methods, sampling theory, testing, and interference as well as skills for critically evaluating published numerical material is presented in Chapters 4-6. Chapters 7-11 discuss information generation topics-regression and correlation analysis, time series, linear programming, network and Gnatt charting, and decision analysis-tools that can be used to convert data into meaningful information. Chapter 12 features six examples of projects made successful through statistical approaches being applied. Readers can use these approaches to solve their own unique problems. Whether you are a EHS professional, manager, or student, Health, Safety, and Environmental Data Analysis: A Business Approach will help you communicate statistical data effectively. |
| environmental data analysis course: Analyzing Environmental Data Walter W. Piegorsch, A. John Bailer, 2005-03-04 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. |
| environmental data analysis course: Modeling and Data Analysis: An Introduction with Environmental Applications John B. Little, 2019-03-28 Can we coexist with the other life forms that have evolved on this planet? Are there realistic alternatives to fossil fuels that would sustainably provide for human society's energy needs and have fewer harmful effects? How do we deal with threats such as emergent diseases? Mathematical models—equations of various sorts capturing relationships between variables involved in a complex situation—are fundamental for understanding the potential consequences of choices we make. Extracting insights from the vast amounts of data we are able to collect requires analysis methods and statistical reasoning. This book on elementary topics in mathematical modeling and data analysis is intended for an undergraduate “liberal arts mathematics”-type course but with a specific focus on environmental applications. It is suitable for introductory courses with no prerequisites beyond high school mathematics. A great variety of exercises extends the discussions of the main text to new situations and/or introduces new real-world examples. Every chapter ends with a section of problems, as well as with an extended chapter project which often involves substantial computing work either in spreadsheet software or in the R statistical package. |
| environmental data analysis course: Statistical Methods in Water Resources D.R. Helsel, R.M. Hirsch, 1993-03-03 Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences. |
| environmental data analysis course: Statistics for Censored Environmental Data Using Minitab and R Dennis R. Helsel, 2012-02-01 Praise for the First Edition . . . an excellent addition to an upper-level undergraduate course on environmental statistics, and . . . a 'must-have' desk reference for environmental practitioners dealing with censored datasets. —Vadose Zone Journal Statistics for Censored Environmental Data Using Minitab® and R, Second Edition introduces and explains methods for analyzing and interpreting censored data in the environmental sciences. Adapting survival analysis techniques from other fields, the book translates well-established methods from other disciplines into new solutions for environmental studies. This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health. Now incorporating the freely available R software as well as Minitab® into the discussed analyses, the book features newly developed and updated material including: A new chapter on multivariate methods for censored data Use of interval-censored methods for treating true nondetects as lower than and separate from values between the detection and quantitation limits (remarked data) A section on summing data with nondetects A newly written introduction that discusses invasive data, showing why substitution methods fail Expanded coverage of graphical methods for censored data The author writes in a style that focuses on applications rather than derivations, with chapters organized by key objectives such as computing intervals, comparing groups, and correlation. Examples accompany each procedure, utilizing real-world data that can be analyzed using the Minitab® and R software macros available on the book's related website, and extensive references direct readers to authoritative literature from the environmental sciences. Statistics for Censored Environmental Data Using Minitab® and R, Second Edition is an excellent book for courses on environmental statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for??environmental professionals, biologists, and ecologists who focus on the water sciences, air quality, and soil science. |
| environmental data analysis course: Statistical Data Analysis Explained Clemens Reimann, Peter Filzmoser, Robert Garrett, Rudolf Dutter, 2011-08-31 Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book. |
| environmental data analysis course: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 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 |
| environmental data analysis course: Nondetects and Data Analysis Dennis R. Helsel, 2005 STATISTICS IN PRACTICE Statistical methods for interpreting and analyzing censored environmental data Nondetects And Data Analysis: Statistics for Censored Environmental Data provides solutions for environmental scientists and professionals who need to interpret and analyze data that fall below the laboratory detection limit. Adapting survival analysis methods that have been successfully used in medical and industrial research, the author demonstrates, for the first time, their practical applications for studies of trace chemicals in air, water, soils, and biota. Readers quickly become proficient in these methods through the use of real-world examples that are solved using MINITAB® Release 14, a popular statistical software package, as well as other commonly used software packages. Everything needed to master these innovative statistical methods is provided, including: Accompanying Web site featuring answers to book exercises and datasets, as well as MINITAB® macros to perform methods, which are not available in the commercial version Methods for data with multiple detection limits Solutions for research studies in which all data are below detection limits Techniques for constructing confidence, prediction, and tolerance intervals for data with nond-tects Methods for data with multiple detection limits Chapters are organized by objective, such as computing intervals, comparing groups, and correlations, which enables readers to more easily apply the text to their particular research and goals. Extensive references to the literature for more in-depth research are provided; however, the text itself avoids complex math and calculus making it accessible to anyone in the environmental sciences. Environmental scientists and professionals will find the hands-on guidance and practical examples invaluable. |
| environmental data analysis course: Statistics for Ecologists Using R and Excel Mark Gardener, 2017-01-16 This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference – t-test and U-test; correlation – Spearman’s rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal–Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. – Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel – Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. – Amazon 5-star review It has been very easy to follow and will be perfect for anyone. – Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. – Goodreads, 4-star review |
| environmental data analysis course: Basic Environmental Data Analysis for Scientists and Engineers Ralph R.B. Von Frese, 2019-11-22 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 |
| environmental data analysis course: Data Analysis and Statistics for Geography, Environmental Science, and Engineering Miguel F. Acevedo, 2012-12-07 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 sustainability, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applicable across a variety of science and engineering disciplines. Learn How to Use a Variety of Data Analysis and Statistics Methods Based on the author’s many years of teaching graduate and undergraduate students, this textbook emphasizes hands-on learning. Organized into two parts, it allows greater flexibility using the material in various countries and types of curricula. The first part covers probability, random variables and inferential statistics, applications of regression, time series analysis, and analysis of spatial point patterns. The second part uses matrix algebra to address multidimensional problems. After a review of matrices, it delves into multiple regression, dependent random processes and autoregressive time series, spatial analysis using geostatistics and spatial regression, discriminant analysis, and a variety of multivariate analyses based on eigenvector methods. Build from Fundamental Concepts to Effective Problem Solving Each chapter starts with conceptual and theoretical material to give a firm foundation in how the methods work. Examples and exercises illustrate the applications and demonstrate how to go from concepts to problem solving. Hands-on computer sessions allow students to grasp the practical implications and learn by doing. Throughout, the computer examples and exercises use seeg and RcmdrPlugin.seeg, open-source R packages developed by the author, which help students acquire the skills to implement and conduct analysis and to analyze the results. This self-contained book offers a unified presentation of data analysis methods for more effective problem solving. With clear, easy-to-follow explanations, the book helps students to develop a solid understanding of basic statistical analysis and prepares them for learning the more advanced and specialized methods they will need in their work. |
| environmental data analysis course: Advanced Mapping of Environmental Data Mikhail Kanevski, 2013-05-10 This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more. |
| environmental data analysis course: Analysis and Modelling of Spatial Environmental Data Mikhail Kanevski, Michel Maignan, 2004-03-30 Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local probability distribution function estimation, and select aspects of Geographical Information Systems. It includes real case studies using Geostat Office software tools under MS Windows and also provides tools and methods to solve problems in prediction, characterization, optimization, and density estimation. The author describes fundamental methodological aspects of the analysis and modelling of spatially distributed data and the application by way of a specific and user-friendly software, GSO Geostat Office. Presenting complete coverage of geostatistics and machine learning algorithms, the book explores the relationships and complementary nature of both approaches and illustrates them with environmental and pollution data. The book includes introductory chapters on machine learning, artificial neural networks of different architectures, and support vector machines algorithms. Several chapters cover monitoring network analysis, artificial neural networks, support vector machines, and simulations. The book demonstrates thepromising results of the application of SVM to environmental and pollution data. |
| environmental data analysis course: Environmental Systems Analysis with MATLAB® Stefano Marsili-Libelli, 2016-04-21 Explore the inner workings of environmental processes using a mathematical approach. Environmental Systems Analysis with MATLAB® combines environmental science concepts and system theory with numerical techniques to provide a better understanding of how our environment works. The book focuses on building mathematical models of environmental systems, and using these models to analyze their behaviors. Designed with the environmental professional in mind, it offers a practical introduction to developing the skills required for managing environmental modeling and data handling. The book follows a logical sequence from the basic steps of model building and data analysis to implementing these concepts into working computer codes, and then on to assessing their results. It describes data processing (rarely considered in environmental analysis); outlines the tools needed to successfully analyze data and develop models, and moves on to real-world problems. The author illustrates in the first four chapters the methodological aspects of environmental systems analysis, and in subsequent chapters applies them to specific environmental concerns. The accompanying software bundle is freely downloadable from the book web site. It follows the chapters sequence and provides a hands-on experience, allowing the reader to reproduce the figures in the text and experiment by varying the problem setting. A basic MATLAB literacy is required to get the most out of the software. Ideal for coursework and self-study, this offering: Deals with the basic concepts of environmental modeling and identification, both from the mechanistic and the data-driven viewpoint Provides a unifying methodological approach to deal with specific aspects of environmental modeling: population dynamics, flow systems, and environmental microbiology Assesses the similarities and the differences of microbial processes in natural and man-made environments Analyzes several aquatic ecosystems’ case studies Presents an application of an extended Streeter & Phelps (S&P) model Describes an ecological method to estimate the bioavailable nutrients in natural waters Considers a lagoon ecosystem from several viewpoints, including modeling and management, and more |
| environmental data analysis course: Machine Learning for Spatial Environmental Data Mikhail Kanevski, Vadim Timonin, Alexi Pozdnukhov, 2009-06-09 This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine lea |
| environmental data analysis course: Environmental Data Analysis Carsten Dormann, 2020-12-20 Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg. |
| environmental data analysis course: Environmental Statistics Vic Barnett, 2005-12-13 In modern society, we are ever more aware of the environmental issues we face, whether these relate to global warming, depletion of rivers and oceans, despoliation of forests, pollution of land, poor air quality, environmental health issues, etc. At the most fundamental level it is necessary to monitor what is happening in the environment – collecting data to describe the changing scene. More importantly, it is crucial to formally describe the environment with sound and validated models, and to analyse and interpret the data we obtain in order to take action. Environmental Statistics provides a broad overview of the statistical methodology used in the study of the environment, written in an accessible style by a leading authority on the subject. It serves as both a textbook for students of environmental statistics, as well as a comprehensive source of reference for anyone working in statistical investigation of environmental issues. Provides broad coverage of the methodology used in the statistical investigation of environmental issues. Covers a wide range of key topics, including sampling, methods for extreme data, outliers and robustness, relationship models and methods, time series, spatial analysis, and environmental standards. Includes many detailed practical and worked examples that illustrate the applications of statistical methods in environmental issues. Authored by a leading authority on environmental statistics. |
| environmental data analysis course: Statistical Analysis of Environmental Space-Time Processes Nhu D. Le, James V. Zidek, 2006-09-13 This book provides a broad introduction to the subject of environmental space-time processes, addressing the role of uncertainty. It covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. In particular, with members of their research group the authors developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development. |
| environmental data analysis course: Environmental Policy Analysis and Practice Michael R Greenberg, 2008-03-13 Pressing environmental challenges are frequently surrounded with stakeholders on all sides of the issues. Opinions expressed by government agencies, the private sector, special interests, nonprofit communities, and the media, among others can quickly cloud the dialogue, leaving one to wonder how policy decisions actually come about. In Environmental Policy Analysis and Practice, Michael R. Greenberg cuts through the complicated layers of bureaucracy, science, and the public interest to show how all policy considerations can be broken down according to six specific factors: 1) the reaction of elected government officials, 2) the reactions of the public and special interests, 3) knowledge developed by scientists and engineers, 4) economics, 5) ethical imperatives, and 6) time pressure to make a decision. The book is organized into two parts, with the first part defining and illustrating each one of these criteria. Greenberg draws on examples such as nuclear power, pesticides, brownfield redevelopment, gasoline additives, and environmental cancer, but focuses on how these subjects can be analyzed rather than exclusively on the issues themselves. Part two goes on to describe a set of over twenty tools that are used widely in policy analysis, including risk assessment, environmental impact analysis, public opinion surveys, cost-benefit analysis, and others. These tools are described and then illustrated with examples from part one. Weaving together an impressive combination of practical advice and engaging first person accounts from government officials, administrators, and leaders in the fields of public health and medicine, this clearly written volume is poised to become a leading text in environmental policy. |
| environmental data analysis course: Extraction Techniques for Environmental Analysis John R. Dean, 2022-03-01 Extraction Techniques for Environmental Analysis Explore the analytical approach to extraction techniques In Extraction Techniques for Environmental Analysis, accomplished environmental scientist and researcher John R. Dean delivers a comprehensive discussion of the extraction techniques used for organic compounds relevant to environmental analysis. In the book, extraction techniques for aqueous, air, and solid environmental matrices are explored and case studies that highlight those techniques are included. Readers will find in-depth treatments of specific extraction techniques suitable for adoption in their own laboratories, as well as reviews of relevant analytical techniques used for the analysis of organic compound extracts (with a focus on chromatographic separation and detection). Extraction Techniques for Environmental Analysis also includes a chapter that extensively covers the requirements for an analytical laboratory, including health and safety standards, as well as: A thorough introduction to pre-sampling, as well as the extraction of aqueous samples, including the classical approach for aqueous extraction and solid phase extraction Comprehensive explorations of the extraction of gaseous samples, including air sampling Practical discussions of the extraction of solid samples, including pressurized fluid extraction and microwave-assisted extraction In-depth examinations of post-extraction procedures, including pre-concentration using solvent evaporation Extraction Techniques for Environmental Analysis is a must-read resource for undergraduate students of applied chemistry, as well as postgraduates taking analytical chemistry courses or courses in related disciplines, like forensic or environmental science. |
| environmental data analysis course: Statistical Methods for Environmental Pollution Monitoring Richard O. Gilbert, 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. |
| environmental data analysis course: Understanding by Design Grant P. Wiggins, Jay McTighe, 2005 What is understanding and how does it differ from knowledge? How can we determine the big ideas worth understanding? Why is understanding an important teaching goal, and how do we know when students have attained it? How can we create a rigorous and engaging curriculum that focuses on understanding and leads to improved student performance in today's high-stakes, standards-based environment? Authors Grant Wiggins and Jay McTighe answer these and many other questions in this second edition of Understanding by Design. Drawing on feedback from thousands of educators around the world who have used the UbD framework since its introduction in 1998, the authors have greatly revised and expanded their original work to guide educators across the K-16 spectrum in the design of curriculum, assessment, and instruction. With an improved UbD Template at its core, the book explains the rationale of backward design and explores in greater depth the meaning of such key ideas as essential questions and transfer tasks. Readers will learn why the familiar coverage- and activity-based approaches to curriculum design fall short, and how a focus on the six facets of understanding can enrich student learning. With an expanded array of practical strategies, tools, and examples from all subject areas, the book demonstrates how the research-based principles of Understanding by Design apply to district frameworks as well as to individual units of curriculum. Combining provocative ideas, thoughtful analysis, and tested approaches, this new edition of Understanding by Design offers teacher-designers a clear path to the creation of curriculum that ensures better learning and a more stimulating experience for students and teachers alike. |
| environmental data analysis course: Introduction to Environmental Data Science William W. Hsieh, 2022-12-31 Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data. |
| environmental data analysis course: Fundamentals of Environmental Sampling and Analysis Chunlong Zhang, 2007-02-26 An integrated approach to understanding the principles of sampling, chemical analysis, and instrumentation This unique reference focuses on the overall framework and why various methodologies are used in environmental sampling and analysis. An understanding of the underlying theories and principles empowers environmental professionals to select and adapt the proper sampling and analytical protocols for specific contaminants as well as for specific project applications. Covering both field sampling and laboratory analysis, Fundamentals of Environmental Sampling and Analysis includes: A review of the basic analytical and organic chemistry, statistics, hydrogeology, and environmental regulations relevant to sampling and analysis An overview of the fundamentals of environmental sampling design, sampling techniques, and quality assurance/quality control (QA/QC) essential to acquire quality environmental data A detailed discussion of: the theories of absorption spectroscopy for qualitative and quantitative environmental analysis; metal analysis using various atomic absorption and emission spectrometric methods; and the instrumental principles of common chromatographic and electrochemical methods An introduction to advanced analytical techniques, including various hyphenated mass spectrometries and nuclear magnetic resonance spectroscopy With real-life case studies that illustrate the principles plus problems and questions at the end of each chapter to solidify understanding, this is a practical, hands-on reference for practitioners and a great textbook for upper-level undergraduates and graduate students in environmental science and engineering. |
| environmental data analysis course: Urban Analytics Alex D. Singleton, Seth Spielman, David Folch, 2017-11-27 The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically. Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Remote Sensing, Big Data and Geodemographics. Written in an accessible style and packed with illustrations and interviews from key urban analysts, this is a groundbreaking new textbook for students of urban planning, urban design, geography, and the information sciences. |
| environmental data analysis course: Environmental Economics and Policy Lynne Lewis, Thomas H. Tietenberg, 2019-11-26 Environmental Economics and Policy is a best-selling text for environmental economics courses. Offering a policy-oriented approach, it introduces economic theory, empirical fieldwork, and case studies that show how underlying economic principles provided the foundation for environmental policies. Key features include: Introductions to the theory and method of environmental economics, including externalities, benefit-cost analysis, valuation methods, and ecosystem goods and services. Extensive coverage of the major issues including climate change mitigation and adaptation, air and water pollution, and environmental justice. Boxed Examples and Debates throughout the text, which highlight global examples and major talking points. This text will be of use to undergraduate students of economics. Students will leave the course with a global perspective of how environmental economics has played and can continue to play a role in promoting fair and efficient environmental management. The text is fully supported with end-of-chapter summaries, discussion questions, and self-test exercises in the book. Additional online resources include references, as well as PowerPoint slides for each chapter. |
| environmental data analysis course: Ecology, Genetics and Evolution of Metapopulations Ilkka A. Hanski, Oscar E. Gaggiotti, 2004-05-17 Ecology, Genetics and Evolution of Metapopulations is acollection of specially commissioned articles that looks at fragmented habitats, bringing together recent theoretical advances and empirical studies applying the metapopulation approach. Several chapters closely integrate ecology with genetics and evolutionary biology, and others illustrate how metapopulation concepts and models can be applied to answer questions about conservation, epidemiology, and speciation. The extensive coverage of theory from highly regarded scientists and the many substantive applications in this one-of-a-kind work make it invaluable to graduate students and researchers in a wide range of disciplines. - Provides a comprehensive and authoritative account of all aspects of metapopulation biology, integrating ecology, genetics, and evolution - Developed by recognized experts, including Hanski who won the Balzan Prize for Ecological Sciences - Covers novel applications of the metapopulation approach to conservation |
| environmental data analysis course: Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt, Antonello Pasini, Caren Marzban, 2008-11-28 How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods. |
| environmental data analysis course: Executive Data Science Roger Peng, 2016-08-03 In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You'll get a crash course in data science so that you'll be conversant in the field and understand your role as a leader. You'll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You'll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects. |
| environmental data analysis course: Business Analytics S. Christian Albright, Wayne L. Winston, 2017 |
| environmental data analysis course: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
| environmental data analysis course: Experimental Design and Data Analysis for Biologists Gerald Peter Quinn, Michael J. Keough, 2002-03-21 Regression, analysis of variance, correlation, graphical. |
| environmental data analysis course: Vis Enviro Science EPUB High School 6 Year Access David M. Hassenzahl, Linda R. Berg, Mary Catherine Hager, 2017-11-06 |
| environmental data analysis course: Meta-Analysis in Environmental Economics J.C. van den Bergh, Kenneth J. Button, Peter Nijkamp, G.C. Pepping, 2013-03-09 Meta-analysis is a formal synthesis of results and findings of scientific studies, which can assist in gaining new insights, explaining differences between results of similar studies, or determine useful directions of research. In this book we focus on the use of meta-analysis in environmental economics and related fields of study. The first part of the book covers the overall meta-approach methodology for social sciences and economics in particular. This is followed by technical and non-technical discussions of statistical and rough-set techniques for analysis. At appropriate places this is supplemented with reviews of applications in environmental economics and related fields. In the second part of the book a number of case studies show different aspects of the application of meta-analysis. The research areas considered include, among others, tourism multipliers, air pollution valuation, risk and value of life, pesticide price policy, travel time savings, and transport externality and policy issues. The benefits of the appropriate application of meta-analysis in environmental economics are a better use of existing information and knowledge, removal of some of the subjectivity from analysis and forecasting, and greater clarity as to where future efforts in environmental economic analysis can most gainfully be deployed. |
| environmental data analysis course: New Developments in Classification and Data Analysis Maurizio Vichi, 2005-02-22 The volume presents new developments in data analysis and classification. Particular attention is devoted to clustering, discrimination, data analysis and statistics, as well as applications in biology, finance and social sciences. The reader will find theory and algorithms on recent technical and methodological developments and many application papers showing the empirical usefulness of the newly developed solutions. |
| environmental data analysis course: Exercises in Environmental Science Michael SLATTERY, Kristi ARGENBRIGHT, 2018-12-03 |
| environmental data analysis course: A Primer for Computational Biology Shawn T. O'Neil, 2017-12-21 A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the natural environment of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful pipe operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2. |
| environmental data analysis course: EDS. Environmental Data Service United States. Environmental Data Service, 1976 |
ESM 566/CE 566 Environmental Data Analysis - Portland State …
The main purpose of this course is to help students think statistically and use statistics effectively in framing, conducting, and reporting their researches.
EAS 4480: Environmental Data Analysis - gatech.edu
Through practical examples and MATLAB demonstrations, students gain hands-on experience in analyzing research data from atmospheric and earth sciences.
TEACHING GUIDE - uie.edu
This course introduces the principles and practices of Environmental Data Analytics, structured into four key units that progressively build the skills and knowledge necessary to tackle …
Course: Introduction to Environmental Data Analysis
Course Description: This course focuses on the basic training and practical skill in analyzing environmental data using the R programming language. The focus is on the principles and …
Environmental Data Analysis Course Syllabus & Schedule …
Course Overview: In this course, we will explore the fundamentals of environmental data analysis focusing on practical applications using real data and addressing real-world problems. Topics …
ENV 170 – ENVIRONMENTAL DATA ANALYSIS AND …
This course is designed to develop skills and knowledge needed to assemble, manage, visualize, analyze, and communicate about environmental data. Students will learn fundamental concepts …
ENVS 6450 Multivariate Environmental Data Analysis
Course Description: This course will examine the application of statistical techniques to analyzing multivariate environmental data. Methods will include Ordination (e.g., Principal Components …
CE5015 - Environmental Monitoring and Data Analysis
Course Type: Theory Description: To introduce the students to the basic principles, methods and instrumentation used in air, water Wastewater, soil, sludge and solid waste monitoring. To …
Data Analysis in the Environmental Sciences
You will learn how to think about data, its uncertainty, how models and data are related and depend on underlying assumptions, and how to synthesize information contained in data. The class will …
Advanced Environmental Data Analysis for Environmental Finance
This course will familiarize students with environmental data types, sources, access, analysis, and modeling using standard and emerging tools. Emphasis will be on environmental risk evaluation …
Course number and name: EOC 4631C Ocean and …
Laboratory work involving analysis of ocean engineering-related signals using modern data acquisition systems. (b) Prerequisites: EGN4323 (Vibration Synthesis and Analysis) and …
EAEE E4257: Environmental Data Analysis and Modeling – …
This course will provide students with an understanding of fundamental statistical concepts for understanding and modeling environmental data. In this class we will focus on analyzing and …
Environmental Data Analysis in R - assallab.org
With the knowledge gained in this course, students will be ready to undertake their own data analysis in R and increase their efficiency through reproducible workflows. The course assumes …
Data Analysis in Environmental Engineering - Ohio State …
Application of programming and statistical methods for engineering data analysis. Will explore distribution, variance, and multivariate methods. Will provide a deeper understanding of analysis …
Environmental Science Research Methods and Data Analysis
Students will begin to develop this predictive framework in Environmental Science Research Methods and Data Analysis. After completing this course students should: be familiar with …
GEOG/EOS 230 Introduction to Environmental Data Analysis …
This course provides students with the fundamental skills and knowledge required to analyze environmental data. Students will learn statistical methods, data visualization techniques, and …
Syllabus ES 207 Environmental Data Analysis University of …
environmental data. This class emphasizes both theoretical and applied aspects of data analysis methods. Weekly lab exercises are from environmental applications. Topics include: distribution, …
ENVS*3340 Environmental Data Analysis - University of Guelph
This course is focused on finding, collecting and interpreting data of the physical environment. Students will access various online databases, such as meteorological and hydrological time …
1. Course number and name: EOC 4631C Ocean and …
An ability to make such environmental measurements as ambient acoustic noise, surface waves or sound velocity profiles, followed by a thorough data analysis. (a,e,k/1,2,6)
Course title: Environmental Statistics Course code: NRE 115 …
The course has been designed and intended to help budding environmental scientists/managers to answer these questions in order better to understand and design systems for environmental …
ESM 566/CE 566 Environmental Data Analysis - Portland …
The main purpose of this course is to help students think statistically and use statistics effectively in framing, conducting, and reporting their researches.
EAS 4480: Environmental Data Analysis - gatech.edu
Through practical examples and MATLAB demonstrations, students gain hands-on experience in analyzing research data from atmospheric and earth sciences.
TEACHING GUIDE - uie.edu
This course introduces the principles and practices of Environmental Data Analytics, structured into four key units that progressively build the skills and knowledge necessary to tackle …
Course: Introduction to Environmental Data Analysis
Course Description: This course focuses on the basic training and practical skill in analyzing environmental data using the R programming language. The focus is on the principles and …
Environmental Data Analysis Course Syllabus & Schedule …
Course Overview: In this course, we will explore the fundamentals of environmental data analysis focusing on practical applications using real data and addressing real-world problems. Topics …
ENV 170 – ENVIRONMENTAL DATA ANALYSIS AND …
This course is designed to develop skills and knowledge needed to assemble, manage, visualize, analyze, and communicate about environmental data. Students will learn fundamental concepts …
ENVS 6450 Multivariate Environmental Data Analysis
Course Description: This course will examine the application of statistical techniques to analyzing multivariate environmental data. Methods will include Ordination (e.g., Principal Components …
CE5015 - Environmental Monitoring and Data Analysis
Course Type: Theory Description: To introduce the students to the basic principles, methods and instrumentation used in air, water Wastewater, soil, sludge and solid waste monitoring. To …
Data Analysis in the Environmental Sciences
You will learn how to think about data, its uncertainty, how models and data are related and depend on underlying assumptions, and how to synthesize information contained in data. The class will …
Advanced Environmental Data Analysis for Environmental …
This course will familiarize students with environmental data types, sources, access, analysis, and modeling using standard and emerging tools. Emphasis will be on environmental risk evaluation …
Course number and name: EOC 4631C Ocean and …
Laboratory work involving analysis of ocean engineering-related signals using modern data acquisition systems. (b) Prerequisites: EGN4323 (Vibration Synthesis and Analysis) and …
EAEE E4257: Environmental Data Analysis and Modeling – …
This course will provide students with an understanding of fundamental statistical concepts for understanding and modeling environmental data. In this class we will focus on analyzing and …
Environmental Data Analysis in R - assallab.org
With the knowledge gained in this course, students will be ready to undertake their own data analysis in R and increase their efficiency through reproducible workflows. The course assumes …
Data Analysis in Environmental Engineering - Ohio State …
Application of programming and statistical methods for engineering data analysis. Will explore distribution, variance, and multivariate methods. Will provide a deeper understanding of analysis …
Environmental Science Research Methods and Data Analysis
Students will begin to develop this predictive framework in Environmental Science Research Methods and Data Analysis. After completing this course students should: be familiar with …
GEOG/EOS 230 Introduction to Environmental Data Analysis …
This course provides students with the fundamental skills and knowledge required to analyze environmental data. Students will learn statistical methods, data visualization techniques, and …
Syllabus ES 207 Environmental Data Analysis University of …
environmental data. This class emphasizes both theoretical and applied aspects of data analysis methods. Weekly lab exercises are from environmental applications. Topics include: distribution, …
ENVS*3340 Environmental Data Analysis - University of …
This course is focused on finding, collecting and interpreting data of the physical environment. Students will access various online databases, such as meteorological and hydrological time …
1. Course number and name: EOC 4631C Ocean and …
An ability to make such environmental measurements as ambient acoustic noise, surface waves or sound velocity profiles, followed by a thorough data analysis. (a,e,k/1,2,6)
Course title: Environmental Statistics Course code: NRE …
The course has been designed and intended to help budding environmental scientists/managers to answer these questions in order better to understand and design systems for environmental …
