bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Assessment, Probabilistic
Authors: Thompson Kimberly M. 
Published: January 2013

Abstract
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

AbstractTwo 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.
Small area estimation
Authors: Lahiri Partha 
Published: January 2013

AbstractMany 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

AbstractThis 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

Abstract

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

Abstract
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

AbstractModeling 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.
Standards, environmental
Authors: Esterby Sylvia 
Published: January 2013

Abstract
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.
Uncertainty Analysis
Authors: Smith Eric P. 
Published: January 2013

Abstract
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

AbstractUp‐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.
Wildlife ecology
Authors: Bowyer R. Terry , Stewart Kelley M. , Monteith Kevin L. , Long Ryan A. 
Published: January 2013

AbstractStudies in wildlife ecology often involve species that are hunted, trapped, or are of additional economic or aesthetic importance to humans, including species that are rare, threatened, or endangered. The focus of the discipline is on the population rather than the individual; consequently, population ecology is central to wildlife conservation and management. Wildlife ecology has its philosophical and scientific beginnings in the early works of Aldo Leopold. Modern wildlife ecology often applies ecological theory to management and conservation of animals, but also includes theoretical studies on species that are considered wildlife. Wildlife ecology has become increasingly quantitative over time, and often employs capture‐recapture methods to estimate population size, survival, and other life‐history characteristics. Quantitative methods used to study wildlife include, but are not limited to, estimates of abundance, population viability analysis, home‐range estimation, movements, migration, spatial distributions, and landscape metrics. Radio telemetry is widely used to conduct research on wildlife. Habitat selection, stable isotope analysis, genetics, and behavioral sampling also play important roles in studies of wildlife.
Microbial Regulations
Authors: Wade Timothy J. , Dufour Alfred P. , Soller Jeffrey A. , Wymer Larry 
Published: January 2013

AbstractUntil relatively recently, exposure to fecally contaminated water caused massive morbidity and mortality due to diseases such as typhoid and cholera. While waterborne disease remains a major threat to public health in the developing world, the treatment and disinfection of public water and wastewater supplies have vastly reduced the transmission of these and other waterborne infectious diseases. The discovery and application of fecal indicator bacteria as a simple marker of feces resulted in simple methods to identify contaminated water sources and has been used worldwide. Despite these and other recent improvements in sanitation and hygiene, challenges remain. Even in developed countries, outbreaks of waterborne disease occur regularly affecting thousands each year. Outbreak surveillance, epidemiology studies, and quantitative microbial risk assessment can be applied to identify, prioritize, and quantify risks. Events such as the reemergence of cholera in Haiti emphasize the need for continued vigilance to maintain the microbial safety of water supplies.
The Canadian Environmental Sustainability Indicators: On Population‐Weighted Ground‐Level Ozone
Authors: Rastan Soheil , St. Lawrence Joe , Herod Dennis 
Published: January 2013

Abstract
This article extends the trend analysis and builds upon the Canadian Environmental Sustainability Indicators (ground‐level ozone indicator). It presents two additional population‐weighted ground‐level ozone concentration trends from 1990 to 2005. During this period, the estimated increase in population‐weighted concentration levels, based on the lower end of the annual concentration data, was statistically more significant than the estimated increase based on the middle range of the annual concentration data. However, no increase or decrease in population‐weighted concentration levels was detected in the upper end of the data. The results of this hypothesis‐generating study indicate that as far as the national estimate of population‐weighted eight‐hour maximum ground‐level ozone concentration is concerned, the good days are getting worse and the bad days remain the same.
Carbon Capture and Storage, Regulatory Framework
Authors: Torvanger Asbjørn 
Published: January 2013

Abstract
Carbon capture and storage (CCS) has potential to become a major option to mitigate emissions of greenhouse gases globally, but this requires a well‐functioning regulation system at both national and international levels. Owing to the long‐term perspective needed for geological storage of carbon dioxide and uncertainties with regard to characteristics and integrity of storage sites, storage is more challenging to regulate than capture of carbon dioxide from exhaust gases and transport of the compressed gas to a storage site. This article gives an overview of the status of CCS regulation at the international level and in some key countries, and discusses the main issues CCS regulation is facing as well as possible solutions.
Extremes: Spatial Parametric Statistical Modeling
Authors: Davison Anthony C. 
Published: January 2013

AbstractStatistics of spatial extremes is developing very rapidly, owing to the demands of applications in the environmental sciences and the insurance and risk industries. This article sketches the main ideas, based on classical extreme value statistics. The two main threads of work are the fitting of max‐stable models to spatial extremes, and the use of latent variable models, the latter typically in a Bayesian context.
Flood Risk Management, Quantitative Methods
Authors: van Gelder P. H. A. J. M 
Published: January 2013

AbstractIn this article, quantitative methods for flood risk management are discussed. Reliability analysis of flood defense systems and the probabilistic flood risk management approach are outlined. The application of quantitative methods offers the designer a way to unify the design of engineering structures, processes, and management systems. For this reason, there is a growing interest in the use of these methods in the design and safety assessment of flood defenses. This article describes the background of probabilistic analyses, uncertainties, and systems analysis for flood risk management.
Multivariate Extremes
Authors: Bortot Paola , Gaetan Carlo 
Published: January 2013

AbstractThis paper reviews the main probabilistic results on multivariate extremes. Historically, this branch of probability focused on the limiting distribution of the componentwise maximum of independent and identically distributed random vectors. A number of equivalent asymptotic characterizations are available and are summarized. Inferential issues arising from the statistical application of classical results are discussed. They motivate the development of alternative models for multivariate extremes, based on multivariate threshold exceedances, which have received increasing attention in the literature; these will also be reviewed.
Wildfire risk analysis
Authors: Rego Francisco C. , Colaço Maria Da Conceição A. 
Published: January 2013

AbstractWildfires are natural hazards that have both biological components (vegetation as fuel) and meteorological components (air temperature and humidity, wind). It can be defined as “any unplanned and uncontrolled vegetation fire which, regardless of the ignition source, may require suppression response or other actions according to agency policy.”
Probabilistic Geotechnical Hydraulics
Authors: van Gelder P.H.A.J.M. 
Published: January 2013

Abstract
In the field of hydraulic and geotechnical engineering and especially in the overlapping field, geotechnical hydraulic engineering, there are a large number of issues, which are probably solvable with a classical engineering approach, but which can be brought to a better financial and social optimum with a probabilistic approach. The reason for this is that in hydraulic engineering, the size and probability of exceedance of loading variables are only partially known and that in geotechnical engineering the strength variables of soils are also only partially known. The soil behavior depends strongly on its location and also on time because of the pore water pressures, for example, the softening of dikes at high water levels and the strength dissipation at excavations (building pits). In this short article, important research issues within geotechnical hydraulic engineering are discussed, followed by the modeling of failure mechanisms and random fields.
Thresholds, Ecological
Authors: Toms Judith D. 
Published: January 2013

Abstract
Ecological thresholds occur when the relationship between the response and explanatory variables is nonlinear, changing abruptly at one or more values. Such thresholds may be continuous or discontinuous, and different methods for estimating the location and number of thresholds have been developed for each type. For continuous thresholds, methods based on piecewise regression are preferred. Discontinuous thresholds can be transformed using the cumulative sum, resulting in transformed data with continuous thresholds, and piecewise regression techniques are then used for inference. Alternatively, Bayesian change‐point analysis can be used for inference. Special methods have been developed for detecting thresholds in spatial data.
Genetically Modified Organisms
Authors: Perry Joe N. 
Published: January 2013

AbstractGenetically modified organisms (GMOs) are those in which the genetic material (the DNA) has been altered in a way that does not occur naturally. In all countries, the release of GMOs into the environment is regulated and a risk assessment is required. GMOs have many medical applications, but this article focuses on agricultural and ecological uses. The principle of the comparative approach is explained and various statistical issues arising from it are discussed. These include the need for an equivalence or noninferiority test in addition to the traditional difference test; the need for power analysis; consumer and producer risk, false positives and false negatives; multiple testing and sample surveys. The importance of meta‐analyses, mathematical modeling, and uncertainty analysis is stressed. The issue of estimation for events that are deemed risks but which have never been observed is considered. Two case studies provide further background reading. The article finishes with some challenges for estimating the environmental impact of future GMOs.
Causal Assessment
Authors: Norton Susan B. , Suter Glenn W. 
Published: January 2013

AbstractCausal assessments bring together information to reach conclusions about how biological effects happened or can be produced. In environmental studies, a causal assessment may be prompted by observations of undesirable biological effects, concerns over a source or stressor, or questions about the efficacy of management actions. Observational studies are often the principal source of evidence available for causal assessments of environmental issues, but are problematic because causes are not manipulated, replicated, or randomly applied. Pragmatic strategies for interpreting observational study results for causal assessment include breaking up and analyzing causal pathways by mechanistic segment; comparing evidence for alternative causal explanations; using study design or statistical techniques to isolate the effects of individual variables; modeling the effects of multiple variables together; and supplementing observational results with evidence of sufficiency, exposure, and symptoms. Weight‐of‐evidence approaches are well suited to causal assessments because they can provide a common framework for synthesizing disparate types of information while retaining the evidence supporting or refuting conclusions.
Fisheries Stock Assessment
Authors: Jiao Yan 
Published: January 2013

AbstractNatural resource modeling of fisheries is called stock assessment and is crucial for proper fisheries management. This paper reviews the commonly used models, the data needed, and the parameter estimation methods for these models in fisheries stock assessment. Fisheries management strategy evaluation and decision analysis, which are based on the models developed and the estimated parameters, are discussed. Newly developed models and modeling techniques are also reviewed briefly.
Geostatistical Methods in Water Resources
Authors: Fasbender Dominique 
Published: January 2013

AbstractThis article intends to present, through various examples of applications, how geostatistical methods have contributed to a better understanding of water resources around the world. Each section is dedicated to a particular water resource and regroups the different applications in which geostatistical methods have been applied. These sections are not meant to be exhaustive but illustrative. A final section presenting a list of useful software and toolboxes has also been added.
Multivariate Analysis of Hydrological Variables
Authors: Chebana Fateh 
Published: January 2013

Abstract
Hydrological events are described through a number of dependent features. To be correctly treated, the latter should be considered jointly in a multivariate framework. The aims of the multivariate hydrological frequency analysis are given and the justifications of adopting the multivariate setting in hydrology are discussed. The main steps of the analysis are described. Some extensions and perspectives are presented.
Floods and Changing Climate: Seasonal Forecasts and Reconstruction
Authors: Sankarasubramanian A. , Lall Upmanu , Rajagopalan Balaji 
Published: January 2013

Abstract
It is widely acknowledged that both climate and land use changes modify flood frequency, thereby challenging the traditional assumption that the underlying stochastic process is stationary in time, and that the annual maximum flood corresponds to an independent identically distributed (iid) process. In this article, we employ a semiparametric approach to estimate flood quantiles conditional on selected “climate indices” that carry the signal of structured low‐frequency climate variation, and influence the atmospheric mechanisms that enhance or retard local precipitation and flood potential. The semiparametric approach that maximizes the local likelihood of the observed annual maximum peak in the climatic predictor state space is applied to estimate conditional flood quantiles for the Blacksmith Fork River near Hyrum (BFH), Utah. The estimated conditional flood quantiles correlate well with the observed annual maximum peaks, thus offering prospects for reconstructing past flood series as well as for short‐term forecasting.