bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental Forensics
Authors: Johnson Glenn W.
Published: January 2013

Environmental forensics is the scientific evaluation of environmental data for the purposes of evaluation of the history and sources of environmental contamination. Forensics investigations are often conducted in legal rather than purely academic context. Scientific methods that may be used in such studies are large and diverse: limited only by relevance to the problem at hand, and the degree to which the method has proved reliable and has been accepted within the scientific community. Several common data analysis methods that have seen extensive use in forensic studies are principal components analysis, receptor models, and simple graphical displays such as concentration maps.
Frequency Curves
Authors: Hosking J.R.M.
Published: January 2013

The fundamental property of a random quantityXis the frequency with which its possible values occur. A frequency curve is the graphical representation of this distribution of frequencies.
Frontier Model
Authors: Ley Eduardo
Published: January 2013

Frontier models in econometrics are used to construct reference technologies to make inferences about production activities. A similar frontier specification is proposed to make inferences in the context of landscape ecology–when studying the tolerance of wild animals to human settlements.
Gene‐Environment Interaction
Authors: Ryan Louise
Published: January 2013

Gene‐environment interaction refers to the fact that response to an environmental exposure may differ according to an individual's genotype. It represents a major component of genetic epidemiology, the discipline focused on investigating the role of genetic factors, as well as their interaction with environmental factors, in the occurrence of human disease.
Generalized Linear Models
Authors: Jørgensen Bent
Published: January 2013

Generalized linear models provide a general framework for handling regression modeling for normal and non‐normal data, including multiple linear regression, ANOVA, logistic regression, Poisson regression and log‐linear models for contingency tables. All the major statistical packages include facilities for fitting generalized linear models. A generalized linear model is defined by choosing a link function and a variance function, along with choosing a response variable and a set of explanatory variables. The link function transforms the mean of the response variable to a scale where the model is linear. The variance function describes how the variance behaves as a function of the mean. Each choice of variance function corresponds to a certain deviance function, and model fitting is accomplished by minimizing the deviance, generalizing least squares fitting. Inference on parameters and hypothesis testing is performed by means of analysis of deviance, generalizing the classical ANOVA method. Estimation and analysis of deviance are based on quasi‐likelihood methods, requiring only second‐moment assumptions, thereby providing a certain robustness against misspecification of the probability model. The choice of link and variance functions may be checked by means of residual analysis.
Geographic Information Systems (GIS), Spatial Statistics In
Authors: Cressie Noel , Liu Desheng
Published: January 2013

A Geographic Information System (GIS) is a networked collection of hardware and software tools that recognizes georeferencing in the data and features the georeferencing in database management, data analysis, and visualization of results (principally maps) from the analysis. This combination of geography and computer science has been known as geographic information science. Spatial statistics in GIS has grown in importance, however, spatial databases in a GIS can be very large. It may be that, without some form of dimension reduction, only the most simple statistical methodologies will scale up.
Global Ecology
Authors: Lerdau Manuel
Published: January 2013

Global ecology began with Joseph Priestley's experiments in the eighteenth century demonstrating that plants in effect purified air and thus allowed animals to survive. These results provided the first hard evidence that the activities of some organisms had impacts on the functioning of other organisms at spatial scales greater than that of the individual. More recently, Alfred Redfield demonstrated in the middle of the last century that the structure and behavior of marine organisms played a major role in determining the chemistry of the oceans. In the last 25 years, global ecology has expanded to include a wide range of organismal activities that affect continental and larger spatial scales.
Global Warming
Authors: North Gerald R.
Published: January 2013

Global warming refers to observation that the earth has been warming over the past century and the related theory that humans are responsible for the changes in global climate. The estimates of the warming are based on a global network of instrumental observations that the global average temperature of the planet has been increasing at an average rate of about 0.7 ± 0.2 °C per century over the last century. Indicators based upon paleoclimatic information stretching over the last several hundred thousand years suggest that this rate of warming is highly unusual, and not likely to be part of a pattern of natural variability internal to the system. Besides the temperature record, there are numerous other indicators of secular change in the planetary climate. A few of these include retreat of mountain glaciers, rise of sea level, melting of permafrost, reduction of sea ice, increases in heavy precipitation events, changes in growing season, as well as many others.

The cause of such a warming is now rather well established. While we do not have complete enough information on all aspects of the climate system, we do have excellent coverage of a number of the relevant factors since the 1970s – the beginning of the satellite era. During this last few decades, we have not only accurate estimates of surface temperatures, but good observations of many aspects of the system including potential perturbing causes of change to the system such as the brightness of the sun and the concentrations of trace gases that can cause warming. In this article, the case will be made that the warming is real and that we have a good theory for why it is happening.
Habitat Survey and Sampling Designs
Authors: Gould William R.
Published: January 2013

Habitat measurements will always be an important component of ecological studies. Traditionally, researchers have used methods applied to relatively small areas using either plot‐based or plotless methods. Variables such as percent cover, density, and biomass are commonly used together as a means of quantifying habitat in a particular area. The efficacy of habitat measurements depends on observer effects, vegetation form, and spatial pattern, and the scale at which measurements are made. In all cases, choice of variables and measurement techniques directly impact accuracy of results and must be carefully considered prior to data collection. Regardless of technique, random sampling strategies such as simple or stratified designs are essential for valid statistical inference. Recent advances in satellite and radar technology have resulted in a myriad of additional methods for collecting and analyzing habitat data. Such technology has also altered the scale and types of questions being asked. However, these technological advances are not without error and still require careful use and in many cases, ground‐truthing.
Healthy Worker Effect
Authors: Eisen Ellen A. , Picciotto Sally , Robins James M.
Published: January 2013

Epidemiologic studies of occupationally exposed cohorts are useful in identifying and quantifying environmental risks because workers are generally exposed to higher levels of toxic materials than the general population. Although higher exposures make it easier to detect and characterize modest elevations in risk, such studies are plagued by a particularly pervasive form of selection bias, referred to as the healthy worker effect (HWE). The HWE is reflected in better health status of exposed workers relative to the general population. This bias arises from two sources: (i) workers were initially healthy enough to be hired, whereas the general population includes persons unfit for work, and (ii) workers who stay healthy (survivors) remain exposed, whereas less healthy coworkers reduce exposure via time off work, job transfer, or early termination. Unlike standard methods, G‐estimation has been shown to reduce healthy worker survivor bias.
Hierarchy Theory, Ecological
Authors: Allen Timothy F.H.
Published: January 2013

Hierarchy theory looks at and announces the decisions of the observer of complex systems. It deals with the first compression down to the universe in which is the second compression that translates experience into models. It does not predict, but rather organizes the arena in which normal science does predict. Bio‐social hierarchies are richer than physical hierarchies because coding stabilizes more levels. Hierarchies put part/whole dual structures, holons, at particular levels that are ordered on five principles, in three basic hierarchical types. Hierarchies describe emergence. Hierarchy theory is robust to realism which looks for ontologically real hierarchies, or constuctivist philosophies that are agnostic as to a real material external world. It can also tolerate anti‐realist philosophies that assert that hierarchies are only human constructions.
Hormesis
Authors: Calabrese Edward J.
Published: January 2013

This article provides a general description of the hormesis dose–response model. Hormetic dose–responses have become reported more frequently in the literature with advances in measuring chemicals at progressively lower doses and with the extensive use of
in vitro
methods, which permits an evaluation of more doses/concentrations that has been typically used in
in vivo
studies. It is expected that the hormesis concept will play a progressively more significant role in the hazard assessment process and in drug discovery and development in forthcoming years.
Image Analysis
Authors: Horgan Graham , Glasbey Chris
Published: January 2013

Image analysis is the process of extracting information from digital images. We outline some of the main ideas and illustrate with an application in mapping forestry using satellite images.
Impact Assessment: Assessing a Local Biological Effect With Before and After Data
Authors: Stewart‐oaten Allan
Published: January 2013

The design and analysis of monitoring studies to assess chronic biological effects of anthropogenic environmental change when data can be gathered before the change are discussed. Intervention Analysis compares Before and After time series at an Impact site. A special case, sometimes useful when the anticipated effects are “local,” is Before‐After, Control‐Impact (BACI), using comparison sites as covariates to reduce extraneous variance and serial correlation. Both sampling error and natural temporal processes cause uncertainty in inferences: both must be modeled. The models are themselves uncertain. The Impact site is not randomly selected, so these are observational studies, not experiments. Whether and how the change caused the “effects” must be based on their timing and on studies of mechanisms.
Inhalation Toxicology
Authors: Sen Pranab K.
Published: January 2013

Inhalation toxicology relates to the composite assemblage of ambient toxins with respect to their prevalence, uptake sites, and intake process by inhalation, mode of biological, molecular, and carcinogenic reactions, and consequential health hazards. From a statistical perspective, the major task is to arouse our awareness of potential and asserted toxic substances, to gather scientific evidence of their impact on human life and on our ecosystem as a whole, and to appraise (statistically as well as epidemiologically) the risk due to the aggregation of such toxic and carcinogenic (overt or not) effects of our own bioenvironment. This risk assessment has emerged as an interdisciplinary one, where toxicology, molecular biology, pharmacology, epidemiology, and environmental health sciences all have major shares; nevertheless, statistics plays a key role in this crucial assessment task.
I
nternational
B
iometric
S
ociety
Authors: Billard Lynne
Published: January 2013

The International Biometric Society (IBS) plays a leading role in the advancement of biometry, biostatistics, and statistics at the international level by fostering the exchange of new ideas and relevant methodologies for a wide range of separate disciplines, including the important areas of environmental biology and ecology. This article briefly reviews the formation and structure of the IBS, and provides an historical record of its international leadership and editors of its journals
Biometrics
and
JABES
.
Landscape Pattern Metrics
Authors: McGarigal Kevin
Published: January 2013

Real landscapes contain complex spatial patterns in the distribution of resources that vary over time; quantifying these patterns and their dynamics is the purview of landscape pattern analysis. Landscape pattern analysis involves quantifying the spatial heterogeneity of point patterns, linear networks, continuous surface patterns, and categorical patch mosaics, and sometimes custom hybridizations of these basic models, although the focus in landscape ecology and of this article has been on patch mosaics. In this context, landscape metrics are algorithms that quantify specific spatial characteristics of patches, classes of patches, or entire landscape mosaics, or of the patch mosaic in the neighborhood of each focal cell. These metrics fall into two categories: those that quantify the composition of the map without reference to spatial attributes (i.e., the variety and abundance of patch types), and those that quantify the spatial configuration of the map (i.e., the spatial character and arrangement, position, or orientation of patches within the class or landscape). Metrics are further categorized as structural metrics that measure physical structure of the mosaic without explicit reference to a particular phenomenon, or functional metrics that require additional parameterization and measure the structure of the mosaic as it relates to a specific organism or ecological process. Not surprisingly, landscape metrics are highly sensitive to landscape definition with respect to the thematic content and resolution, spatial grain and extent, and the landscape boundary. The current use and interpretation of landscape metrics is constrained by the lack of a proper theoretical understanding of metric behavior across a wide range of landscape structure gradients, the lack of a proper spatial and/or temporal reference framework for ecologically interpreting the computed value of each metric, and the challenges of choosing a parsimonious suite of metrics for a particular application given the plethora of existing metrics. Ultimately, the definition of the landscape, the establishment of a meaningful reference framework, and the choice of metrics should be based on some hypothesis about the observed landscape pattern and what processes or constraints might be responsible for that pattern. Fortunately, programs like FRAGSTATS make the calculation of landscape metrics practical and accessible to all landscape ecologists.
Longitudinal Studies
Authors: Dean C. B. , Hu X. J.
Published: January 2013

A longitudinal study observes its subjects repeatedly over time. The subjects can be individuals or pre existing groups of interest, such as species and regions, and the time refers to the metameter for the occasions of observation. Longitudinal data allow one to separate cohort and time effects. This article summarizes the special features of longitudinal studies, and overviews statistical study design and data analysis in longitudinal settings.
Long‐Range Dependence
Authors: Robinson Peter. M.
Published: January 2013

Developments in research on stationary and nonstationary time series with long range dependence are reviewed.
Long‐Term Environmental Monitoring
Authors: Van Der Maarel Eddy
Published: January 2013

Monitoring (from the Latin monere = to warn) is the act of following or controlling a process through a monitor and being warned if the process deviates from expectation. Environmental monitoring is the controlling of an environmental process, or ‘the intermittent surveillance to ascertain the extent of compliance with a predetermined standard or degree of deviation from an expected norm’. This means in practice a series of carefully planned, repeated observations of parameters indicating changes in a process we do not want to change. Monitoring ‘provides the essential data on how systems are changing and how fast’.
Measurement Error
Authors: Stefanski L.A.
Published: January 2013

Recognition that measurement error in the independent (predictor) variable of data used for regression modeling necessitates special methods of model fitting dates back to the late 1800s. In the ensuing 100 years attention was devoted almost entirely to measurement error in linear regression models, at which time advances in computing opened the door to the nonlinear statistical modeling generally, and to the study of measurement error in nonlinear regression modeling in particular. This article contains an introduction to the problem of measurement error in regression using linear regression measurement error models to illustrate key ideas. It includes a discussion of the types of measurement error commonly studied (classical and Berkson, differential and nondifferential) and how those types arise in practice. A summary of the consequences of ignoring measurement error when analyzing data measured with error is presented, with detailed results given for simple and multiple linear measurement error models. Pointers to the literature on both linear and nonlinear measurement error models are provided.
Meta‐Analysis
Authors: Weerasinghe Swarna
Published: January 2013

Meta‐analysis is an analytical method of synthesizing, systematically combining, and integrating results across different studies with similar research focuses. While limiting the analytical methods applicable to environmental studies, this article presents a step‐by‐step approach to meta‐analysis. Brief descriptions presented in each section of this article cover the initial steps of meta‐analysis such as formulation of a research question, development of a literature search strategy, and assessment of quality of the published literature by reviewing possible biases. Next steps provide essentials of data extraction, methods of meta‐data analysis as well as interpretation of results. An attempt is made to provide hypothetical examples to illustrate each meta‐analytical step. The goal of this article is to provide an overview of the fundamentals of meta‐analysis of environmental studies.
Meteorological Extremes
Authors: Lombardo Franklin T. , Pintar Adam L. , Possolo Antonio , Simiu Emil , Yeo Donghun
Published: January 2013

This article reviews basic principles of the statistical analysis of large extreme values (EVs), the role of simulations in the development of relevant meteorological data sets, the interaction between the EV modeling process and its application to engineering, and specific issues pertaining to wind, snow, ice, storm surge, and ocean waves. Classical approaches and the peak over threshold approach (POT) to the estimation of univariate extreme value distributions are briefly described. The distributions include: the Gumbel (Type I), Fréchet (Type II), reverse Weibull (Type III) extreme value distributions. One version of the POT approach relevant for meteorological extremes describes the joint observation of the time and magnitude of the threshold exceedance as a two‐dimensional nonhomogeneous Poisson process. It is noted that the dependence on direction of the extreme meteorological quantities of interest plays an important role in the estimation of extreme effects of these quantities on engineering structures. Also noted are the relative paucity of data on freezing rain and associated glaze ice accretion, and difficulties encountered in the estimation of tornado wind speeds. The article is concluded with some brief comments on multivariate extremes.
Mixed Outcomes
Authors: Molenberghs Geert , Geys Helena
Published: January 2013

Measurements of both continuous and discrete outcomes are encountered in many statistical problems. A common example is a developmental toxicity study, which typically involves exposing timepregnant animals (rats, mice) to an environmental agent during major organogenesis. The dams are sacrificed prior to normal delivery, at which time the uterus is removed and thoroughly examined. Among viable fetuses, the incidence of any malformation (binary data) and reductions in fetal weight (continuous data) are typically of primary concern, since both have been found to be sensitive indicators of a toxic effect. This motivates the formulation of a joint distribution with mixed continuous and discrete outcomes.
Mixing Distribution
Authors: Leroux Brian
Published: January 2013

A random variableYis said to have a mixture distribution if its probability density (or mass) function can be written as an average over a parametric family of distributions. An example is the Poisson‐gamma distribution, in which conditional on a gamma‐distributed variableM, the variableYhas a Poisson distribution with meanM. The probability distribution assigned to the conditioning parameter (e.g., gamma) is called the mixing distribution. Mixture distributions are particularly useful in environmental and other sciences for modeling overdispersion in discrete data. An extension called the hidden Markov model is available to also account for autocorrelation in discrete data.
Multivariate Kriging
Authors: Wackernagel Hans
Published: January 2013

Kriging is a special type of optimal linear prediction applied to random functions, with the particular requirement that the covariance structure of the random function should be known. Kriging may be extended to several variables by specifying a corresponding system of direct and cross covariance functions to be used in cokriging. Alternatively, secondary variables known at all sample and prediction locations can be included into (co‐)kriging as external drift.
National Institute of Statistical Science (
NISS
),
US
Authors: Karr Alan F.
Published: January 2013

The National Institute of Statistical Sciences (NISS) is a research institute, established in 1990 and located in Research Triangle Park, North Carolina, whose mission is to identify, catalyze and foster high‐impact cross disciplinary research involving the statistical sciences.
National Resources Inventory (
NRI
),
US
Authors: Nusser Sarah M.
Published: January 2013

The US National Resources Inventory (NRI) is a longitudinal survey conducted by the US Department of Agriculture's Natural Resources Conservation Service in cooperation with the Iowa State University Center for Survey Statistics and Methodology. The NRI is designed to periodically assess conditions and trends for soil, water, and related natural resources on nonfederal lands of the United States, with major releases in years ending in two and seven. The survey has evolved over a period of several decades, with the goal of providing information to support agricultural and environmental policy development and program implementation. The current design involves annual remotely sensed data collection on a large area sample, with smaller onsite surveys to collect more intensive data on specific natural resources.
Nelder Plots
Authors: Barry Ronald P.
Published: January 2013

A Nelder plot, also called a Nelder Wheel or Nelder Fan, is a systematic planting design in which plants or trees are planted at the intersection of circular arcs and linear spokes. In general, Nelder plots allow many different planting densities to be examined in a single plot. This is frequently more efficient than planting a different plot for each planting density. Nelder plots can be constructed that allow the effect of different planting geometries to be examined in a single plot. This article discusses the construction of Nelder plots and the analysis of data derived from Nelder plots.
Neurotoxicology, Environmental
Authors: Catalano Paul J.
Published: January 2013

Neurotoxicity testing is an important area of noncancer toxicity evaluation. This article provides a description of the functional observational battery (FOB), a frequently employed neurobehavioral assessment, and discusses some of the statistical and data analytical issues surrounding its analysis. Emphasis is given to describing the multivariate nature of the FOB data.
Occupancy Problem
Authors: Mcdonald Trent L.
Published: January 2013

The “occupancy problem” has also been called the “sequential occupancy problem” and the “coupon‐collector's problem”. Although there exist many variations, one popular statement of the problem asks, “What is the probability that a set ofnindependent multinomial trials yield at least one member ofyclasses?” The answer to this question has direct application in many sampling situations, particularly when sampling for members of a population with certain characteristics (such as species members, members with defects, etc.). An answer to this question, under general probabilities of class membership, is given in this article through the occupancy probability mass function and cumulative mass function. This article also mentions the corresponding waiting‐time question that asks how many multinomial trials need to be conducted (i.e. how large shouldnbe) in order to have a certain chance of obtaining at least one member ofyclasses.
Official Statistics, Environmental
Authors: Ross N. Phillip , Kollander Mel
Published: January 2013

The generation of
official
statistics on the environment is an evolving activity as nations begin to understand better the impact of anthropogenic environmental pollution on the state of the world's environment. Countries are experiencing increasing pressure to compete globally and to provide for the health and welfare of their human populations. These countries are realizing that proper measures of the environment are necessary for their ability to compete in the global market place and participate as members in geographic trading blocs. This pressure will have a positive effect on the production of
official
environmental statistics worldwide. In this article, we provide a brief overview of
official
statistics activities of a number of countries and international organizations that provide
official
statistics on the environment. Emphasis is on North American and European
official
statistics activities. We provide references to several web sites that provide broader information on environmental statistics for other areas of the world.
Ornithological Data
Authors: Morgan Byron J. T. , Viallefont Anne
Published: January 2013

This article introduces the different types of ornithological data, obtained either at the population level (count data, occupancy data) or at the individual level (capture–mark–recapture data, radiotracking data, and geolocation data), as well as references to the modeling/analysis techniques appropriate for each type of data. It also gives a list of organizations and web sites through which such data can be made available, as well as an extensive list of references to up‐to‐date articles and books.
Outcome‐Dependent Selection Models
Authors: Ding Jieli , Eva Lu Tsui‐shan , Zhou Haibo
Published: January 2013

An outcome‐dependent selection (ODS) is a retrospective sampling scheme where one observes the environmental exposure/covariates with a probability that depends on the observed outcome values. By allowing the selection probability of each individual to depend on the outcome variable, ODS design can reduce the cost of the study and improve the efficiency. The case‐control study and case‐cohort study in epidemiology are well‐known examples of ODS schemes. We review recent advances in research on ODS designs with a continuous response. This includes inferences on more sophisticated designs with survival data, clustered/longitudinal data, two‐stage ODS design, and outcome and auxiliary‐dependent (OADS) schemes.
Outliers
Authors: Van Aelst Stefan
Published: January 2013

Datasets frequently contain outliers which are observations that strongly deviate from the pattern in the majority of the data. Detecting outliers and measuring their influence on an estimation procedure is not trivial. In one or two dimensions graphical inspection of the data may be helpful, but in higher dimensions more reliable methods are needed. We discuss robust estimation to obtain estimates that can resist the influence of outliers and related outlier diagnostics to identify the outliers in a dataset.
Ozone
Authors: Cocchi Daniela , Trivisano Carlo
Published: January 2013

After illustrating the differences between tropospheric and stratospheric ozone, ozone production from photochemical reactions is described. Remarks follow on potential damages on human health and vegetation due to ozone toxicity and the need of its control via air quality standards and guidelines. Emphasis is given to monitoring, data collection and statistical analysis of ozone concentrations: statistical models are becoming important for ozone analysis and can be associated to deterministic chemistry transport and meteorological models. They are able to summarize large amounts of data collected in time and space, to account for confounders, like meteorological variables, and to evaluate uncertainties due to measurement errors. Global, regional and local scales are different contexts for analysis. Bayesian methods are very flexible tools for solving non standard problems, like missing values, change of support and misalignment.

A variety of modern statistical methods useful for the analysis of ozone concentrations is reviewed: model fitting and extreme value analysis, spatio and spatio‐temporal models, methods for long‐term trends estimation, models for the analysis of the relationship between ozone concentrations and human health.
Point Processes, Spatial‐Temporal
Authors: Schoenberg Frederic Paik , Brillinger David R. , Guttorp Peter
Published: January 2013

A spatial–temporal point process (also called space–time or spatio‐temporal point process) is a random collection of points, where each point represents the time and location of an event. Examples of events include incidence of disease, sightings or births of a species, or the occurrences of fires, earthquakes, lightning strikes, tsunamis, or volcanic eruptions. Typically the spatial locations are recorded in three spatial coordinates, e.g. longitude, latitude, and height or depth, though sometimes only one or two spatial coordinates are available or of interest.
Population Dynamics
Authors: Quinn Terrance J.
Published: January 2013

Population dynamics is the study of change in an animal or fish population. Studies range from field work to collect biological data to complex mathematical and statistical models. Contemporary models attempt to achieve biological realism by containing components representing key factors affecting the population and uncertainty in those processes by adding stochastic effects. Estimation of animal abundance is an important subject and includes mark‐recapture, line‐transect, and several other methods. The simplest useful population model is the logistic, in which a population has sigmoidal growth to the carrying capacity. More complex models may include age and/or length structure, natural and harvesting mortality, recruitment of new individuals into the population, individual growth, reproductive parameters, species interactions, spatial structure, and movement. A common feature of realistic models that have an equilibrium carrying capacity is that there is density‐dependence in some population process that causes a reduction in the rate of increase as the population gets large. For example, many studies have found density‐dependence in early life survival. An important use of population dynamics models is stock assessment of fisheries. Scientists collect fisheries and biological data to estimate population parameters in assessment models that are then used to develop a sustainable harvest strategy.
Population Health Surveillance
Authors: Lawson Andrew B.
Published: January 2013

This article described the basic issues and principles underlying Population health surveillance. First, temporal and then spatial and spatio‐temporal aspects of the problem are considered. The emphasis is on change‐point methods and jump detection methods in time and clustering in space. The intersection of space and time leads to the concept of persistence of features.
Demographic Stochastic Models
Authors: Ver Hoef Jay M.
Published: January 2013

Population viability analysis is the application and study of extinction effects in ecological population modeling. Population viability analysis has important applications for studying the global and local extinction of populations. It attempts to identify threats to populations and then evaluate the probability that the population will persist to some future time.
Pseudoreplication
Authors: Stewart‐Oaten Allan
Published: January 2013

Hurlbert defines pseudo‐replication as “testing for treatment effects with an error term inappropriate to the hypothesis being considered”. An alternative definition is “estimating the size of chance variation without accounting for all its likely sources”.
Radiation and Radiative Transfer
Authors: Essex Christopher
Published: January 2013

The physics and mathematics of the the transport of energy by means of radiation is presented and put into the context of the Earth's atmosphere. The resulting mathematical structures correct simplistic popular misconceptions and language about radiative processes in the environment.
Radioactivity
Authors: Scott E. Marian
Published: January 2013

A diverse set of statistical tools are needed to quantify, model, and handle data on radioactivity in the environment. The behavior of radioactive elements is naturally stochastic, and in this article some of the statistical aspects are covered.
Radon
Authors: Ginevan Michael E. , Watkins Deborah K.
Published: January 2013

This article discusses radon, the second leading cause of lung cancer after cigarette smoking. Statistical issues discussed include estimation of the form of the dose‐response function relating lung cancer risk to radon exposure, the radiation dose that one might receive from a given level of radon exposure, and appropriate strategies for reduction of radon related risk.
Random Fields
Authors: Ensor Katherine Bennett
Published: January 2013

Random fields provide a framework to address both spatial and spatial‐temporal random structure and have served as a key modeling tool since the 1950s. Adler's text in 1981 on the geometry of random fields served as a foundational piece [1] and currently has well over 1500 citations at the time of this article. This article is adapted from Adler's original submission [2] with updated references.
Ranked set sampling
Authors: Patil G.P. , Surucu Baris , Egemen Didem
Published: January 2013

Environmental monitoring and assessment typically requires observational data as opposed to data obtained from controlled experiments. Obtaining such data requires identification of sample units to represent the population of concern, followed by selection of particular units to quantify the characteristic(s) of interest. Sample units are generally the smallest possible units of measurement, such as plots, soil cores, and individuals, while typical characteristics of interest include biomass, chemical concentrations, or “head counts.” Typically the most expensive part of this process is laboratory analysis, while identification of potential sample units is comparatively simple. One can therefore achieve great observational economy if one can identify a large number of sample units to represent the population of interest, yet only have to quantify a carefully selected subsample. This potential for observational economy was recognized for estimating mean pasture and forage yields in the early 1950s when McIntyre proposed a method, later coined ranked set sampling (RSS) by Halls and Dell, and currently under active investigation in various quarters.
Receptor and Hybrid Modeling Tools
Authors: Vedantham Ram , Norris Gary , Duvall Rachelle
Published: January 2013

Practical and economical plans to reduce air pollution require models that relate the sources of airborne pollution to concentrations at sampling sites. In receptor models, samples of airborne pollution are subjected to extensive chemical analysis. Contributing sources leave chemical fingerprints in the sample. The amount of pollution coming from each source can be estimated if the chemical fingerprints of the sources are known. Thus, receptor models do not require any knowledge of emission rates or meteorological observations that are very important to atmospheric dispersion models. However, receptor models do rely on the observed concentrations of chemical species in the atmosphere. These species must be conserved during transport between the source and the receptor. Chemical elements are good candidates for receptor modeling since these are often immutable under ordinary conditions and relatively easily measured.
Ripley'sKFunction
Authors: Dixon Philip M.
Published: January 2013

Ripley's K function summarizes spatial point process data. It can be used to describe a set of locations, test hypotheses about patterns, and estimate parameters in a spatial point process model. For a stationary point process,K(t) is the expected number of additional points within distancetof a focal point divided by the intensity of the process. A univariate version is used for one set of locations and a multivariate version is used when points can be labeled by a small number of groups. This article reviews the properties of Ripley's K function and two related functions, then illustrates the computation and interpretation using data on the locations of trees in a swamp hardwood forest.
Risk Assessment, Probabilistic
Authors: Thompson Kimberly M.
Published: January 2013

A primary criticism of environmental risk assessment as it has been practiced in the last 20 years has been the failure of analysts to properly account for issues of variability and uncertainty. Many risk assessments focused on producing ‘point estimates’ of risks have provided risk managers and decision makers with information that is at best incomplete and at worst dangerously misleading. As the field of risk assessment has matured, techniques for better treatment of variability and uncertainty have been developed and incorporated into what is known as ‘probabilistic’ risk assessments. In contrast to the point estimate approach, where single values are assumed for each model input, the probabilistic approach quantifies the possible range of values that each input may take, as well as the probability associated with each value. Modern computational power then allows the analyst to generate a multidimensional distribution of risk that reflects both the variability of risk across different individuals in the population as well as the uncertainty associated with each estimate.
Risk Assessment, Quantitative
Authors: Stern Alan
Published: January 2013

Two different forms of risk assessment are commonly in practice. They have somewhat different orientations and address different regulatory and communications goals. One form of risk assessment can be referred to as safety assessment. This type of assessment seeks to identify the largest level of exposure or dose that can reasonably be expected to be without adverse effect. It does not necessarily provide quantitative information on the risk associated with exposure above this “safe” level. The other type of risk assessment seeks to quantify the risk associated with any exposure or dose of an environmental agent. This latter form of risk assessment is the focus of this article.
SAS
Authors: Littell Ramon C.
Published: January 2013

The SAS® System is an integrated system of software for data management, analysis, and presentation.
Small area estimation
Authors: Lahiri Partha
Published: January 2013

Many programs that monitor the environment provide nationwide environmental statistics for large geographical areas. There is, however, a growing need to obtain these statistics for small geographical areas where the data are too sparse to produce reliable estimates. This article reviews how empirical Bayes and hierarchical Bayes methods can be implemented using both area level and unit level hierarchical models that combine information from different sources to produce reliable environment‐related statistics for small areas. This article also discusses methodologies for estimating the uncertainty of the derived estimates and the related issues concerning model building.
Smoothing
Authors: Kafadar Karen
Published: January 2013

This article describes smoothing as an exploratory tool in data analysis, including an underlying model that motivates the need for smoothing, the goals and characteristics of good smoothers, general classes of smoothers (linear and nonlinear), and illustrations of the value of smoothing. Smoothing is related to the concepts of general fitting, parametric regression, and nonparametric regression, and the smoothers are illustrated as both one‐ and two‐dimensional data sets. Some insight is offered on choosing smoothing parameters, and some resources for computing smoothers and further references are also provided.
Soil Surveys
Authors: Brus Dick J. , Lark Richard Murray
Published: January 2013

The aim of a soil survey is to predict soil conditions at unvisited sites. In traditional soil survey, the use of statistics is principally for
post hoc
evaluation of the quality of the choropleth map in terms of purity and within‐ and between‐map unit variance of soil properties. Probability sampling designs have been proposed for this. Pedometrical soil surveys treat all phases of the soil survey explicitly as a statistical problem. This had led to the adoption and development of, amongst other techniques, optimized sampling for mapping purposes, automated fuzzy classification of soil profiles, and geostatistical interpolation. Typically covariates related to the soil‐forming factors and derived from remotely sensed images or digital elevation models are integrated into the geostatistical prediction.
Source Apportionment
Authors: Watson John G. , Chow Judith C.
Published: January 2013

Source apportionment models use variations in air pollutant composition, time, and space to infer source types and quantify their contributions. Marker species are sought in source emissions that minimize collinearity among the pollutant patterns found in many source types. Engine exhaust, biomass burning, geological material, sea salt, and secondary sulfates and nitrates can be separated and quantified with commonly measured elements, ions, and carbon fractions. More detailed organic compounds, isotopic abundances, and particle morphology are usually needed to distinguish source subtypes, such as diesel and gasoline engine exhaust. The data sets are noisy, and uncertainties in source contribution estimates must be assessed. At a minimum, variability in the chemical patterns in emissions and ambient concentrations should be quantified and propagated. Sample sizes should encompass a range of source contributions, which can be accomplished by locating sampling sites and selecting measurement periods where large contributions are expected. A “weight of evidence” approach assesses uncertainties by the application of several source apportionment techniques with reconciliation of discrepancies for the source contribution estimates.
Spatial Covariance
Authors: Sampson Paul D.
Published: January 2013

Modeling of the spatial dependence structure of environmental processes is fundamental to almost all statistical analyses of data that are sampled spatially. Such analyses address tasks such as spatial estimation (Kriging), monitoring network design (Optimal spatial design), as well as the basic scientific characterization of the second‐order properties of these processes. This article reviews the literature on nonstationary spatial covariance modeling primarily under the headings of smoothing and kernel methods, basis function models, process convolution models, and spatial deformation models. This discussion raises some general issues and comments on new and forthcoming developments.
Species Richness
Authors: Gerow Kenneth
Published: January 2013

Species richness (i.e., number of species present) is a tantalizingly simple parameter that is exceedingly difficult to estimate well, primarily because of the bedeviling role of rare and hard to detect species. The collection of estimation approaches is rich, eclectic, and diverse, with no single method standing out as always best.
Standards, environmental
Authors: Esterby Sylvia
Published: January 2013

This article describes the past, present, and future techniques by which environmental standards are set. Concern regarding the lack of acknowledgment of uncertainty and variability in many standards, and thus in reported results on compliance, has lead to an emphasis on the role of statistical methodology in environmental standard setting. Research into the feasibility, advantages, and disadvantages of such methodology is ongoing, but much remains to be done. Encompassing within its line of investigation areas, such as toxicology, environmental sampling methodology, and spatiotemporal modeling, and affecting the fundamental procedures of industry, traffic management, medical and environmental agencies, to name just a few of many, this area of the rapidly growing field of environmetrics may prove to be one of the most far‐reaching and important statistical applications in the coming years.
Survival Analysis
Authors: Andersen Per Kragh
Published: January 2013

Survival analysis deals with analysis of time‐to‐event data and right‐censoring will often be present. A brief review of methods dealing with censored data, including textbooks references, is provided.
Toxic Substances Control Act, US
Authors: Margosches Elizabeth H.
Published: January 2013

The US Toxic Substances Control Act (TSCA) is aimed at overseeing new chemical development, testing and information gathering for existing chemicals, and risk management efforts. It provides for the development of regulations and collection of data.
Uncertainty Analysis
Authors: Smith Eric P.
Published: January 2013

Mathematical, statistical, and other models are often developed to aid researchers and management in making decisions about environmental problems and the associated risk. Uncertainty analysis is associated with a set of methods to aid in understanding the limitations in the decision process and predictions from these models. Uncertainty is typically associated with deterministic (model, parameter) and stochastic (variation) components. Several methods for estimating the uncertainty such as Monte Carlo methods, frequentist and Bayesian analysis, and sensitivity analysis are described. Common problems in uncertainty such as safety and extrapolation factors, decision making under uncertainty, and estimation of uncertainty are discussed.
Up‐and‐Down Designs
Authors: Flournoy Nancy , P. Oron Assaf
Published: January 2013

Up‐and‐down designs are useful for evaluating and comparing the toxicity of pesticides and other chemical compounds. In particular, the primary goal of an up‐and‐down design in toxicity studies is to estimate the dose having a prespecified toxicity rate Γ,0 < Γ < 1. A secondary goal is to limit the experimental subjects that are given highly toxic exposure. Up‐and‐down designs can be sequential or group sequential.
Weibull Distribution
Authors: Mateu Jorge
Published: January 2013

The article brings together various results on the Weibull distribution in a unified manner. The introduction contains numerous references regarding applications of the Weibull distribution and the subsequent sections address a variety of theoretical developments. Some of the theoretical developments covered include characterization, extreme value theory, parameter estimation, and goodness‐of‐fit.