Integrated Technology for Environmental Monitoring and Information Production (NATO Science Series: IV: Earth and Environmental Sciences)

Integrated Technology for Environmental Monitoring and Information Production (NATO Science Series: IV: Earth and Environmental Sciences) Review

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Integrated Technology for Environmental Monitoring and Information Production (NATO Science Series: IV: Earth and Environmental Sciences) Feature

This book focuses on the current issue of environmental data management in view of the difficulties associated with various aspects of data management, i.e., objectives and constraints, design of data collection networks, statistical and physical sampling, remote sensing and GIS, databases, reliability of data, data analysis, and transformation of data into information. The subject has an international, interdisciplinary nature and constitutes the basis for all environmental management activities. The book presents an integrated approach to the problem, consisting of: -integration of different levels and aspects of environmental data management; -state-of-the-art evaluation of problems and solutions relevant to each aspect; -integration of views and expertise of scientists from different disciplines and different countries; -integration of interdisciplinary approaches to data collection and information retrieval.

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The evolution of environmental legislation: a strategic transaction approach.(Environmental policy): An article from: Journal of Economic Issues

The evolution of environmental legislation: a strategic transaction approach.(Environmental policy): An article from: Journal of Economic Issues Review

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The evolution of environmental legislation: a strategic transaction approach.(Environmental policy): An article from: Journal of Economic Issues Feature

This digital document is an article from Journal of Economic Issues, published by Association for Evolutionary Economics on June 1, 2004. The length of the article is 3548 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: The evolution of environmental legislation: a strategic transaction approach.(Environmental policy)
Author: Paul J. Thomassin
Publication: Journal of Economic Issues (Refereed)
Date: June 1, 2004
Publisher: Association for Evolutionary Economics
Volume: 38 Issue: 2 Page: 493(10)

Distributed by Thomson Gale

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Green IT Strategies and Applications: Using Environmental Intelligence (Advanced & Emerging Communications Technologies)

Green IT Strategies and Applications: Using Environmental Intelligence (Advanced & Emerging Communications Technologies) Review

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Green IT Strategies and Applications: Using Environmental Intelligence (Advanced & Emerging Communications Technologies) Feature

Bhuvan Unhelkar takes you on an all-encompassing voyage of environmental sustainability and Green IT. Sharing invaluable insights gained during two battle-tested decades in the information and communication technologies industry, he provides a comprehensive examination of the wide-ranging aspects of Green IT—from switching-off monitors, virtualizing data centers, and optimizing processes to bringing attitude change through training and the use of green metrics for reporting.

Combining extensive research, literature review experimentation, and decades of practical consulting experience, Green IT Strategies and Applications: Using Environmental Intelligence is your complete reference for undertaking a successful Green IT transformation. The environmentally responsible business strategies described in this book include motivators and drivers, transformation phases, management of risks, measuring and reporting of carbon, compliance with the ISO14000 family of standards, and the crucial nexus between Lean and green—resulting in what can be called Environmental Intelligence.

This environmentally conscious IT reference delves beyond the corporate responsibilities of organizations in a market-driven economy to demonstrate the importance of carbon management as an integral part of good business management. Increasing profits, reducing costs, applying innovations in business, adhering to government standards, process management, and the socio-cultural aspects of business are all masterfully intertwined with Green IT issues.

This book is equipped with case studies from different industrial sectors, including hospital (service), packaging (product), and telecom (infrastructure). It provides a complete suite of strategies, applications, tools, and techniques that will enable you to establish company-wide environmental strategies, a green value system, and the forward thinking required to properly position your organization for the low-carbon economy on the horizon.

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Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice)

Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice) Review

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Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice) Feature

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.

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Maple for Environmental Sciences: a Helping Hand

Maple for Environmental Sciences: a Helping Hand Review

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Maple for Environmental Sciences: a Helping Hand Feature

A presentation of what Maple can do and how it does it in the context of environmental sciences. The text includes introductory tutorials in each chapter combined with extensive marginal comments which are followed by a complete application. These include the contouring of water table data, the physical chemistry of kidney stones, and acid rain. The book also provides a special application to enable students to use "self help" in the case that Maple seem unable to do the simplest things.

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Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe [An article from: Environmental Modelling and Software]

Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe [An article from: Environmental Modelling and Software] Review

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Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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Most data mining techniques have rarely been used in ecology. To address the specific needs of scientists analysing data from a plant trait database developed during the LEDA project, a web-based data mining tool has been developed. This paper presents the DIONE data miner and the project it has been developed in. It addresses the nature of plant trait data from a data mining perspective and points out problems that arise when preparing data for this process. The availability of a large amount of high quality data is an essential prerequisite for successful data mining. To ensure this, a software aided reviewing process has been integrated into the LEDA Traitbase system. The process enables third-party contributors to easily commit their data to LEDA, while assuring adherence of the data to the LEDA standard. The paper concludes with information about data mining results on plant trait data achieved so far and gives an outlook on the applicability of data mining to the fields of ecology.

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A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model [An article from: Environmental Modelling and Software]

A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model [An article from: Environmental Modelling and Software] Review

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A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This paper illustrates the application of both local and global sensitivity analysis techniques to an estimation of the uncertainty in the output of a 3D reaction-diffusion ecological model; the model describes the seasonal dynamics of dissolved Nitrogen and Phosphorous, and those of the phytoplanktonic and zooplanktonic communities in the lagoon of Venice. Two sources of uncertainty were taken into account and compared: (1) uncertainty concerning the parameters of the governing equation; (2) uncertainty concerning the forcing functions. The mean annual concentrations of Dissolved Inorganic Nitrogen (DIN) was regarded as the model output, as it represents the largest fraction of the Total Dissolved Nitrogen, TDN, for which the current Italian legislation sets a quality target in the lagoon of Venice. A local sensitivity analysis was initially used, so as to rank the parameters and provide an initial estimation of the uncertainty, which is a result of an imperfect knowledge of the dynamic of the system. This uncertainty was compared with that induced by an imperfect knowledge of the loads of Nitrogen, which represent the main forcing functions. On the basis of the results of the local analysis, the most important parameters and loads were then taken as the sources of uncertainty, in an attempt to assess their relative contributions. The global uncertainty and sensitivity analyses were carried out by means of a sampling-based Monte Carlo method. The results of the subsequent input-output regression analysis suggest that the variance in the model output could be partitioned among the sources of uncertainty, in accordance with a linear model. Based on this model, 79% of the variance in the mean annual concentration of DIN was accounted for by the uncertainty in the parameters which specify the dynamics of the phytoplankton and zooplankton, and only 5% by the uncertainties in the three main Nitrogen sources.

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A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software]

A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software] Review

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A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This paper presents the methodology for the spatial and temporal interpolation of air quality data. As a practical example, the methodology is applied to the daily nitric oxide NO concentrations measured at 23 stations around Paris. Analysis of the temporal and spatial variability of observations of NO in the Paris area is divided into: (i) time series analysis of AirParif data; and (ii) development of combined spatial and temporal analysis techniques using NO observations from 19 stations. The first part of the paper shows how advanced methods of nonstationary time series analysis can be used to interpolate the data sets of NO concentrations over periods where measurements are missing and to decompose the time series into trend and harmonic components. The results of this analysis applied to 19 stations around Paris are then used in further spatio-temporal analysis of the data. This consists of two steps: (i) preliminary analysis of spatial relations within the data sets; and (ii) the development of a spatio-temporal model for log-transformed NO measurements. The results of the analysis indicate that the simple spatio-temporal model consisting of trend and noise efficiently represents the spatio-temporal variations in the data and it can be applied to predict air pollution variations in time and space at un-sampled locations.

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Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns [An article from: Environmental Modelling and Software]

Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns [An article from: Environmental Modelling and Software] Review

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Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
To evaluate the future state of river water in view of actual pollution loading or different management options, water quality models are a useful tool. However, the uncertainty on the model predictions is sometimes too high to draw proper conclusions. Because of the complexity of process based river water quality models, it is best to investigate this problem according to the origin of the uncertainty. If the uncertainty stems from input data or parameter uncertainty, more reliable results are obtained by performing specific measurement campaigns. The aim of the research reported in this paper is to guide these measurement campaigns based on an uncertainty analysis. The practical case study is the river Dender in Flanders, Belgium. First an overview of different techniques that give valuable information for the reduction of input and parameter uncertainty is given. A global sensitivity analysis shows the importance of the different uncertainty sources. Further an analysis of the uncertainty bands is performed to find differences in uncertainty between certain periods or locations. This shows that the link between periods with high uncertainty and specific circumstances (climatological, eco-regional, etc.) can help in gathering data for the calibration of submodels (e.g. diffuse pollution vs. point pollution).

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Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan [An article from: Environmental Modelling and Software]

Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan [An article from: Environmental Modelling and Software] Review

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Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995-2002) monitoring of 12 parameters at 13 different sites (14@?976 observations). Hierarchical cluster analysis grouped 13 sampling sites into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, based on the similarity of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

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