bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cooperative Programme for Monitoring and Evaluation of Long Range Air Pollution in
E
urope (
EMEP
)
Authors: El‐Shaarawi Abdel H. 
Published: January 2013

Abstract
EMEP is a long‐range monitoring program for air pollution in Europe. Stations used for monitoring are operated by the country they are located within. The operation of the monitoring follows standardized procedures with an associated data exchange mechanism.
Data Mining
Authors: Hand David J. 
Published: January 2013

AbstractData mining is a new discipline that has sprung up at the confluence of several other disciplines, stimulated chiefly by the growth of large databases. The basic motivating stimulus behind data mining is that these large databases contain information that is of value to the database owners, but this information is concealed within the mass of uninteresting data and remains to be uncovered. That is, one is seeking surprising, novel, unexpected, or valuable information, and the aim is to extract this information. This means that the subject is closely allied to exploratory data analysis. However, issues arising from the sizes of the databases, as well as ideas and tools imported from other areas, mean that there is more to data mining than merely exploratory data analysis.
Data Monitoring
Authors: Rodríguez José E. 
Published: January 2013

Abstract
The monitoring air, water, soil, or biota is the practice of sampling systematically these environmental elements. Thus, in the Environmental Sciences, the data monitoring denotes the data obtained by sampling systematically the environment. The primary purposes of data monitoring are, among others, the detection of levels of air pollutants, the detection of trends, and the elaboration of spatial and temporal summaries. Additional purposes include the estimation of the indexes of air quality or the support of a database for the application of environmental policies and regulations.
E
conomics,
E
nvironmental
Authors: Hanley Nick 
Published: January 2013

Abstract
Environmental economics is concerned with the management of environmental resources and the application of welfare economics (cost‐benefit analysis) to the environment. Historically, key areas of focus have been on open‐access resources and pollution. Unrestricted access to a fishery can lead to overfishing and depletion or even extinction of the fish stocks, while open access also leads to loss of net economic benefits to society. Burning fossil fuels results in more air pollution than would be the case if polluters had to pay a pollution tax or purchase emission permits. Conventionally, a distinction is drawn between environmental economics and resource economics. Environmental economics studies pollution and the application of cost‐benefit analysis, including environmental valuation. Resource economics studies the quantity and optimal use of renewable and exhaustible resources. The fields share many theoretical models and the boundary between them is permeable. This article focuses on environmental economics. Within environmental economics, two principal topics are studied: identifying the optimal environmental quality and developing institutional mechanisms to prevent excessive degradation.
E
mission
I
nventory
Authors: Fujita Eric M. 
Published: January 2013

Abstract
An emission inventory is a comprehensive listing of air pollutant emissions within a specific geographic location and time interval. Emission inventories provide the technical foundation for air quality management plans designed to improve or maintain ambient air quality. Air pollution control agencies use emission inventories to identify potential control measures and sources that are subject to controls, determine control program effectiveness, and predict future air quality through air quality simulation models.
Global Environment Monitoring System, Water
Authors: El‐shaarawi Abdel H. 
Published: January 2013

Abstract
The Global Environment Monitoring System (GEMS) grew out of the 1972 Stockholm Conference on the Environment. At that world conference, governments requested the establishment of a global monitoring program that could determine the status and trends of key environmental issues. The GEMS/Water Program was initiated in 1976 by the United Nations Environment Program (UNEP) and the World Health Organization (WHO), with the assistance of the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the World Meteorological Organization (WMO). The program had the twin objectives of improving water quality monitoring and assessment capabilities in participating countries, and determining the status and trends of regional and global water quality.
EpidemicModels
Authors: Isham Valerie 
Published: January 2013

AbstractMathematical modeling of infectious diseases has a long and distinguished history and is one of the most successsful areas of mathematical biology. Understanding the dynamics of the spread of an infection brings possibilities for its control. Human infections such as influenza, malaria, and HIV are still major worldwide causes of morbidity and mortality. In 2003, a new infection, SARS, spread rapidly around the world, with considerable transmission taking place before the infection was even recognized. In such situations, a large‐scale epidemic can only be avoided if strict control strategies are imposed quickly. A clear understanding of the basic theory of epidemic models is a prerequisite for this and, equally, for informing the development of the successful vaccination policies against a wide range of infections in routine use around the world. This article provides a brief introduction to that theory and to some natural extensions of the simple models and the practical concerns that they are designed to address.
E
xposure
A
ssessment
Authors: Brown Kathleen Ward , Levy Jonathan 
Published: January 2013

Abstract
Human exposure to an agent has been defined as an event consisting of contact with a specific concentration of the agent at a boundary between the human and the environment (e.g., lung or skin) for a specific amount of time. Exposure therefore refers to the intensity, frequency, and duration of contact with an agent, which is distinct from the actual amount of the agent absorbed or received by target organs (the dose). This generally entails identification of population(s) exposed as well as the sources of the agent, pathways for the agent to contact the target, exposure routes, and the uncertainties in the assessment. The goal of exposure assessment is often to determine the distribution of exposures to a specific agent across individuals, which provides both the population mean exposure (relevant for quantifying population risks) and the exposure levels for more highly exposed or susceptible individuals.
Forest‐Fire Models
Authors: Preisler Haiganoush K. , Ager Alan A. 
Published: January 2013

AbstractFor applied mathematicians forest‐fire models refer mainly to a nonlinear dynamic system often used to simulate spread of fire. For forest managers forest‐fire models may pertain to any of the three phases of fire management: prefire planning (fire risk models), fire suppression (fire behavior models), and postfire evaluation (fire effects models). In this context forest‐fire models may be statistical, rule‐based, process‐based, or a combination of all three.
Forest Health Monitoring
Authors: Augustin Nicole H. 
Published: January 2013

AbstractForest health monitoring (FHM) focuses on the health of the forest ecosystem. Recognition of the importance of forest health and, especially, the role of monitoring, increased with the acceptance of the Santiago Declaration. The declaration was accompanied by a set of criteria and indicators for the conservation and sustainable management of temperate and boreal forests. The criteria focus on conservation of biological diversity, maintenance of productive capacity, maintenance of forest ecosystem health, conservation of soil and water resources within forests, maintenance of forest contribution to global carbon cycles, and enhancement of long‐term multiple socioeconomic benefits to meet the needs of societies. The declaration calls for quantitative measurements of indicators associated with the criteria. The periodic and systematic measurement of the indicators requires nations to have FHM programs.
Generalized Linear Mixed Models
Authors: Mcculloch Charles E. , Neuhaus John M. 
Published: January 2013

AbstractGeneralized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.
Heritability
Authors: Fry James D. 
Published: January 2013

Abstract
The heritability of a trait is the proportion of variation in a population that can be attributed to genetic differences among individuals. Although usually applied to continuously varying traits, such as milk yield in dairy cows or blood pressure in humans, the concept of heritability can also be applied to discontinuous traits that have complex patterns of inheritance, such as the presence or the absence of medical conditions such as diabetes. The heritability can be estimated by measuring the degree of resemblance, quantified by the covariance, between relatives of various types, although special care must be taken when estimating heritability in humans.
Kriging
Authors: Anderes Ethan B. 
Published: January 2013

AbstractKriging, at its most fundamental level, is an interpolation method used to convert partial observations of a spatial field to predictions of that field at unobserved locations. In geostatistics, the field may represent permeability of soil and the observations from physical measurements in a well‐bore. In another example, of particular importance for environmental problems, the field may represent the concentration of pollutants in a contaminated site and the observations from remote sensing equipment. In this article, we give a short account of kriging under some basic modeling assumptions and observation scenarios. We give a number of equivalent derivations of the kriging prediction, each shedding light on a particular aspect of kriging. Taken as a whole, these derivations provide a broader perspective than any single derivation of kriging can provide.
Law and Environmental Statistics
Authors: Esterby Sylvia R. 
Published: January 2013

Abstract
Many environmental regulations and standards are based on statistical data and inferences drawn from them. Often the statistical analysis used by the US Environmental Protection Agency (EPA) or other governmental body, in developing an exposure or effluent limit or a monitoring program, is challenged by the industry or public interest group that is most affected by its decision. Then, the courts need to examine the record on which the EPA or other agency relied in setting the standard. Because there are several statutes regulating the environment in the United States as well as in Europe, in assessing the adequacy of the underlying scientific support of a regulation or protocol, one needs to be aware of the relevant statute. This article discusses several important statistical issues that have arisen in actual cases and briefly describe some other topics that arise in this area.
Morphometrics
Authors: Reyment Richard A. 
Published: January 2013

Abstract
Morphometrics is defined as the application of multivariate statistical procedures to the study of variation in morphological characteristics of organisms. As originally implemented, the analyses are based on standard measures (“distances”) spanning critical sites. A more recent introduction is concerned with quantifying outlines of organisms, the data then being a suite of arbitrary coordinate‐pairs around the circumference of the object, obtained by some automatically operating digitization procedure (e.g., the Zahn‐Rosskies method). Geometric morphometrics is the latest class to appear. It relies on diagnostically located “landmarks” that form the basis for superimposition of coordinate systems, visualizations by deformation grids. The first and third of these categories can, without particular difficulty, be allied with environmental indicators by appropriate techniques.
Mutagenesis, Environmental
Authors: Piegorsch Walter W. 
Published: January 2013

AbstractEnvironmetric methods are discussed for analyzing data from mutagenicity bioassays. Emphasis is placed on the “gold‐standard” microbial test system in environmental mutagenesis: the Ames/Salmonellamicrosome assay. The assay produces count data to quantify the mutational response; statistical issues of interest when analyzing such data include testing for extra‐Poisson variability, trend testing for a dose response, and trend testing under a nonmonotone dose response.
Parallel Computing: Statistical and Environmetric Uses
Authors: Kulperger Reg , Yu Hao 
Published: January 2013

Abstract
The common use of parallel computing has greatly evolved since the original encyclopedia article of 2001. Multicore processors are now quite common, so many computer users have this readily available on their desktop computers and laptops.
Now increasingly large datasets and simulation of complex statistical models are important in the study of many physical systems. Models in various areas, such as environment, biology, and other physical sciences, play an important role in prediction or detection of changes. All these require lots of computing power. Many of these computations can take advantage of parallel or distributed computing. This article discusses some of these ideas and then discusses how these are implemented in one specific language, R. In the present time, one generally no longer has to work at a low‐level programming language, as was the case a decade or two ago, but now certain types of parallel computations can be implemented at a relatively higher user‐friendly level, even with desktop computing.
Parallel computing consists of a computing environment connecting many processors. Instead of the previous generation where dedicated computer architecture was required, a more loose structure of distributed computing is now more common. This article is intended to give the reader an overview of the parallel computing environment, focusing on the statistical uses that can be made as opposed to a more detailed computing or engineering description.
Point Processes, Dynamic
Authors: Ensor Katherine Bennett 
Published: January 2013

Abstract
This article deals with the specification and estimation of the intensity rate of point processes. The dynamics can include an arbitrary number of changes of level or modeling the intensity rate as a continuous function of exogenous variables. For discrete change points, the change points can be specified online as the process is observed or in advance with techniques that allow for the estimation of number and time location of changes. The classes of conditional duration models or autoregressive conditional duration models allow direct modeling of the intensity rate of a point process. In spatial dynamic point processes, changes may be governed by topology of the region.
Ranks
Authors: Hallin Marc 
Published: January 2013

Abstract
Rank tests were introduced as a reaction against the traditional assumption of Gaussian observations. The most attractive feature of ranks and rank statistics is that they are distribution‐free, and thus, in principle, allow for exact critical values, irrespective of the underlying distribution of the observation. Their domain of application is, mainly but not exclusively, the class of semiparametric models obtained from the traditional parametric ones (location, scale, regression, autoregression, etc.) by treating the density of the underlying error or noise as an unspecified nuisance. Their popularity is explained by their success in a variety of practical situations, and their robustness against deviations from standard distributional assumptions.
Remote Sensing
Authors: Shao Guofan 
Published: January 2013

Abstract
Remote sensing is both a science and an art. Remote sensing has been involved from airborne to spaceborne applications since the 1960s. A variety of sensors are used to record both the visible and the invisible, such as infrared (IR), parts of the spectrum in the field of remote sensing. Depending on the number of spectral bands used, remote sensing includes panchromatic (one band), multispectral (several bands), and hyperspectral (dozens to hundreds of bands) remote sensing; depending on the source of energy/radiation, remote sensing can be passive or active. The ultimate goal of remote sensing is to extract information from remotely sensed data about the material properties of the earth's surface and of the atmosphere, together with their geographical relationships. Numerous approaches are available to extract information from remotely sensed data/imagery. Thematic maps derived with remote sensing methods contain errors, which can be assessed with reference data. It is important to use thematic maps with relatively high classification accuracy to avoid wrong conclusions or decision making based on the maps.
Risk assessment, ecological
Authors: Fox David R. , Halbrook Richard S. 
Published: January 2013

Abstract
Ecological Risk Assessment is a process for identifying, estimating, assessing and communicating both the likelihood and consequences of adverse outcomes on the biotic environment. These adverse outcomes can be associated with both natural (eg. earthquakes, floods) and human (eg. chemical pollution, mining, logging) activities or events. The scale and complexity of ecological risk assessments can vary from recommendations based on simple descriptive studies to regulatory frameworks arising from the application of detailed scientific investigations and advanced statistical analyses. While the methods and procedures have evolved constantly over the last 80 years or so, there remain gaps in key understanding of ecosystem processes and parameterisation of models to represent them. This ‘model incertitude’ represents a significant source of uncertainty in the utility of ERA outputs and is the focus of more contemporary methods. This article briefly traces the origins or ERA, highlighting similarities and differences with other variants of risk assessment before discussing common paradigms and structured approaches to the conduct of an ERA.
Water Bodies
Authors: Caissie Daniel , Chassé Joël , Rao Yerubandi R. 
Published: January 2013

AbstractWater bodies play a vital role in our daily lives from water supplies to recreational activities including many ecological benefits. This article provides an overview of most important aspects and processes of lakes, rivers, and estuaries. Lake processes are initially described in terms of morphological, light, and thermal characteristics. Then the heat balance and water movement are presented as they influence mixing and other important lake processes. Rivers and streams are described in terms of catchment hydrology as well as water movements (river hydraulics). As such, water availability and losses are described as well as the type of flow within channels that affects all aspects of the river environment. Estuaries are where both freshwater and saltwater meet. Accordingly, they are also influenced by many factors such as the freshwater runoff, Coriolis force, entrainment, and gravity currents among others, and these factors play a role in the mixing and stratification of estuaries. Therefore, the type of estuaries and related processes are briefly described.
Sensitivity Analysis of Computer Models
Authors: Fassò Alessandro 
Published: January 2013

Abstract
Sensitivity analysis of computer models has been developed over the last decades paralleling the development of massive computer simulations of engineering, chemical, and environmental systems.
Through extensive simulations, global sensitivity analysis is aimed at assessing the importance of each input to the model outputs by means of variance decomposition.
Depending on model complexity and simulation costs, various techniques are available ranging from design of experiments (DOE) to intensive Monte Carlo, metamodeling, and Bayesian inference.
Soil Erosion and Conservation
Authors: Li Sheng , Lobb David A. , Tiessen Kevin H.D. 
Published: January 2013

AbstractSoil erosion is one of the most serious environmental concerns worldwide. Continued soil erosion can result in degraded soil quality and ultimately reduced crop yields and profitability. In addition, eroded soil particles and attached nutrients, pesticides, pathogens, and toxins, transported off‐site, may contaminate surface water and air quality. In cultivated fields, soil erosion can be caused by water, wind, or directly by tillage. Individual soil erosion processes have characteristic signatures on different landform elements across the landscapes. Observed soil erosion in a field is the integrated result of all forms of soil erosion processes and their interactions. Agronomic measures, soil management, mechanical measures, and other soil conservation practices have been developed and used to control the adverse effects of soil erosion – influencing either the erosivity of the erosion forces or the erodibility of the soil landscape, or both. However, soil conservation practices developed for one erosion process do not necessarily work well on other erosion processes. The selection and implementation of soil conservation practices need to account for all erosion processes if we are to design environmentally sustainable systems.
Space‐Time Covariance Models
Authors: Gneiting Tilmann , Schlather Martin 
Published: January 2013

Abstract
Geostatistical approaches to modeling spatiotemporal data rely on flexible space–time covariance models. The area has been in vigorous development recently, and we review constructions of nonseparable spatiotemporal covariance functions that are based on geometric anisotropy, Fourier inversion, completely monotone functions, and dynamic physical models.
Splines in Nonparametric Regression
Authors: Helen Zhang Hao 
Published: January 2013

AbstractThis article is interested in splines as tools for visualizing and analyzing noisy observational data, and so restricts itself to smoothing splines and regression splines. The article first describes the univariate polynomial smoothing spline, which may be thought of as the forerunner of spline functions used in data analysis. It then describes cross‐validation and generalized cross‐validation (GCV) for choosing the smoothing parameter. After briefly describing regression splines, this entry then describes a number of generalizations of the univariate smoothing spline to various domains, which are obtained via the solution of a variational problem. These include the thin plate spline, the histospline, splines on the sphere, vector splines on the sphere, hybrid splines, partial splines, and smoothing spline analysis of variance (ANOVA) models on complex domains. Computational issues are also discussed. The last two sections focus on the topics of variable selection and model selection in nonparametric regression. Various penalized regression with penalty for function shrinkage and selection are described. Publicly available software is mentioned along the way.