bluuebunny/crossref_metadata_2025_split huggingface dataset:
DO BASINS AND BEAKERS NEGATIVELY BIAS DISPERSANT-EFFECTIVENESS TESTS?
Authors: Nedwed Tim , Coolbaugh Tom 
Published: January 2013

ABSTRACT
Beaker and basin dispersant-effectiveness tests are used to help determine if application of dispersants in a real oil-spill incident is worthwhile. This paper provides evidence indicating that these tests negatively bias expected dispersion at sea because beakers and basins do not allow the spreading of oil slicks that occurs after application of dispersant in the unbounded open ocean. One reason is that closed system walls and/or oil-slick containment methods impose physical boundaries that restrict oil spreading.
In addition to these physical constraints, surfactant films developed after applying dispersant form on the water surface surrounding an oil slick and act as chemical herders to keep slicks thick. This occurs in the field during a real incident and in beaker and basin dispersant-effectiveness tests. Surfactant films on the water surface, however, are fragile and can't persist in the open ocean but can persist throughout the short duration of standard dispersant-effectiveness tests.
This paper provides a background discussion of how surfactant films contaminate the water surface on the perimeter of oil slicks to restrict spreading in both open and closed systems and evidence that these fragile films don't persist in the open ocean. The discussion is followed by a description of lab tests that showed even minimal water-surface contamination from the surfactants in a widely available dispersant significantly restricted spreading to keep slicks thick.
Thick oil slicks, by their nature, will obviously require more turbulence to disperse than a thin film of the same oil. Thus, it is believed that the restricted slick spreading inherent in dispersant-effectiveness tests completed in basins and beakers results in significantly lower performance than would be expected at sea not only for heavier oils but for all oils in tests simulating low-energy conditions.
THE MODELING OF SPILLS RESULTING FROM THE CATASTROPHIC FAILURE OF ABOVE GROUND STORAGE TANKS AND THE DEVELOPMENT OF MITIGATION
Authors: Atherton W. , Ash J. W. , Alkhaddar R. M. 
Published: January 2013

ABSTRACT
The risk of accidents involving the catastrophic failure of storage tanks is estimated to be low, in the region of 5 × l0−6 per tank year. However, recent accidents involving major oil spills at storage facilities located in Belgium (2004) along with USA and England (2005) have shown that tank failures do nevertheless occur. Causalities of such events vary; the consequences however are ordinarily the same, incurring environmental, financial and infrastructure losses.
The normal mitigation technique employed to prevent such losses is secondary containment, usually in the form of a bund wall or earthen dyke. Researchers have investigated the reliability of such methods, examining the effects of tank failure, both theoretically and experimentally in terms of loss of containment.
A United Kingdom Health and Safety Executive (HSE) review conducted in 1997 concluded that the then available data was limited and focussed attention on the work of Greenspan and Johansson (1981) and the later work of Trobojevic and Slater (1989). This led to the HSE commissioning Liverpool John Moores University (LIMU) in 2003 to undertake a large-scale spill-modelling program with the aim of quantifying the level of overtopping and the magnitudes of the dynamic pressures on the bunds. The study examined the effect of axisymmetric releases on a total of 96 tank and bund arrangements.
Such losses have proven to be significant and in some cases the nature of the dynamic pressures has brought in to question the structural integrity of the bunds themselves. Research has since concentrated on modelling alternative modes of failure, such as directional releases, which could be considered to be the more common mode of failure likely to be encountered.
The conclusions to this work have generated additional research to investigate possible methods of mitigation that could be incorporated into the design of facilities with the ultimate aim of further reducing losses in the event of tank failure. Two promising methods have been identified, one involving modification to the primary containment (tank) with the other being a change to the design of the profile of the secondary containment (bund wall).
Government and Industry Cooperation in Practice: Azerbaijan's Experience
Authors: Hassanov Anar , Gandilov Namig , Jayson Daniel , Huseynov Tariel , Kangarli Ilkin , Ibrahimov Aghalar , Taylor Peter Mark 
Published: January 2013

ABSTRACT
In recent times, Azerbaijan has seen the development of a significant modern offshore industry. The oil and shipping industries in Azerbaijan bring economic benefits but also give rise to the need for robust oil spill prevention and preparedness measures.
Azerbaijan signed the International Convention on Oil Pollution Preparedness, Response and Cooperation (OPRC) in 2004 and continues to develop and refine its national system of oil spill response. This paper discusses how the Azerbaijan government and key players in the oil industry have successfully worked together to achieve alignment in prevention, protection and response to major incidents.
The implementation of the OPRC Convention is the responsibility of the Ministry of Emergency Situations (MES) of the Republic of Azerbaijan. Within the national response system a national oil spill contingency plan has been developed. The main offshore developments in the Caspian since the 1990s have been led by BP, as operator of a number of Production Sharing Agreements. BP has implemented comprehensive oil spill response plans and is working in partnership with MES to integrate this planning into the national framework. The oil spill management systems adopted by BP and the government are compatible and commensurate with guidance published by the International Maritime Organization in 2011. These aligned management systems allow for an effective Joint Command and coordination of resources in the case of a major incident. The key to building effective oil spill preparedness are a willing dialogue, integrated command structure, joint training and exercising and upgrade of hardware and information systems' software. The cooperation between government and BP relating to offshore risks strengthens the national capacity to deal with spills risks other than from offshore platforms, including the anticipated increase of oil shipments across the Caspian Sea. Furthermore, these efforts have been supported by international organizations and the regional industry initiative, OSPRI, of which BP is a member.
The experience of Azerbaijan provides a model demonstrating how partnership between government and industry can achieve synergy and it confirms the importance of signing and implementing the OPRC Convention.
Acid Rain
Authors: Butler T.J. , Likens Gene E. 
Published: January 2013

Abstract

Acid rain is a popular term that can include all forms of precipitation, as well as fog and cloudwater, that is more acidic than expected from natural causes. Natural background levels of precipitation acidity tend to be around pH of 5.1–5.2. This compares with present annual average values of pH 4.3–4.4 for parts of eastern North America, and pH values less than 4.5 for large areas of Europe and eastern Asia. As pH decreases, H
+
concentration or acidity increases exponentially. Individual storms in affected areas, especially in the summer, can often produce pH values below 3.5, and cloudwater in some areas can be below pH 3.0. A more appropriate term than acid rain is “acidic deposition,” which includes both wet and dry deposition (gases and particles) of acidic compounds on the earth's surface from the atmosphere. Accurate measurements of dry deposition are unfortunately much more difficult to obtain than wet deposition. However, dry deposition can account for 10–70% of the total amount of acidic deposition.
Adaptive Designs
Authors: Flournoy Nancy , Oron Assaf P. 
Published: January 2013

AbstractThe designs described in this article involve treatment allocation probabilities that change according to prior data. Such designs are useful to protect from extreme results that might result from unrestricted randomization, to improve power (e.g., when group variances are unknown and unequal), when prior information is too limited to fix the sampling space a priori (e.g., regions of desirable efficacy and unacceptable toxicity are unknown). Procedures are described for studying K‐groups, but emphasis is placed on dose‐finding experiments. Some procedures involve randomization and some are deterministic given past data. Some are model based and others are nonparametric. Some procedures have a specific optimization criterion as a goal (e.g., maximize power or some other utility) and others are heuristically formulated, with large variation in ease of application and subsequent data analysis.
Air Quality
Authors: Swithenbank Jim , Sharifi Vida N. , Finney Karen N. , Goh Y. R. 
Published: January 2013

Abstract
Both indoor and outdoor air quality can affect health. The World Health Organisation guidance provides relevant information and measurements are required to assess the local environment. A large number of parameters should be monitored by appropriate instrumentation and sources of significant pollutants can then be investigated. Pollutants such as submicron particles that can penetrate deep into the lungs are now receiving particular attention.
American Statistical Association, Section on Statistics and the Environment
Authors: Nussbaum Barry D. 
Published: January 2013

Abstract
The Section on Statistics and the Environment was founded in 1990 to reflect the usage of statistical methodology in analyzing important environmental problems. The Section serves as a focal point for developing enhancements to methodology and improving training in environmental statistics. This article describes the origination and ongoing activities of the Section, including its vision, mission, and strategic plan.
Bioindicators
Authors: Mcgeoch Melodie A. 
Published: January 2013

Abstract
A biological indicator is a species (or group of species) that readily reflects some aspect of the state of the environment within which it is found. Bioindicators are used to detect or demonstrate the existence of a particular environmental state, and to monitor changes in that state. In this way they are used as tools to extract abiotic or biotic information from the environment so that this information can be used for making scientifically based management and policy decisions.
Monitoring, Biological
Authors: Smith Eric P. 
Published: January 2013

AbstractIn the environmental context, biological monitoring refers to the gathering of information on biological organisms to assess the environmental impact or the biological health of the environment. In a strict sense, the term applies to a continuing collection of data used to establish whether explicitly stated quality control conditions are being met. Other terms that are related include biological surveys, assessment, and surveillance. Biological surveys are commonly carried out by national agencies to monitor birds, whales, fish, and other organisms. Volunteer‐based surveys are an integral part of biomonitoring.
Capture–Recapture Sampling Designs
Authors: Schwarz Carl 
Published: January 2013

Abstract
Good study design is necessary to obtain unbiased and efficient results from capture–recapture (CR) studies. We describe the considerations necessary for successful completion of CR studies. Rather than focusing on analytical issues, we discuss the stochastic processes that give rise to the data derived from such studies. We focus on the attributes of studies necessary to ensure that the assumptions of analyses associated with CR studies are met. We hope to expose investigators to data collection issues associated with classic CR designs, and also to point out novel applications of these designs.
Catch Statistics
Authors: Sampson David 
Published: January 2013

AbstractFisheries science uses a wide variety of quantitative methods ranging from simple data tabulations to complex statistical models with hundreds of estimated parameters. Unlike the other sciences, however, in which experiments are often used successfully to evaluate competing hypotheses, many issues in fisheries are not amenable to controlled experiments because the fish are long‐lived and the fish populations are widely dispersed and suffer highly variable environmental conditions. Because fishers tend to operate in areas of high fish density where they can obtain favorable catch rates, in general the characteristics of the harvested fish will not be representative of the fish population at large. Further, different fish species often are not comparable in value and larger individuals of a given species are normally preferred to smaller ones. Hence, many of the quantitative methods in fisheries science are related to two broad problems: collecting data on the characteristics of the harvested fish and the magnitude of the harvest, and relating the harvest to the dynamics of the fish population in its entirety.
Categorical Data
Authors: Imrey Peter B. , Simpson Douglas G. 
Published: January 2013

AbstractCategorical data refers to counts of events or individuals observed through some defined process and allocated to subgroups, or categories, corresponding to levels of one or more attributes. This article reviews methods for interpreting collections of such counts when they arise from apparently random environmental processes and may be treated as dependent variables relative to potentially explanatory factors or covariates. Introduction of basic terminology including measures of relative frequency and association is followed by review of the Poisson, binomial, multinomial and hypergeometric probability distributions and products thereoef, that result from conditioning upon sums of independent Poisson counts. These form the basis for modeling the random variation in observed categorical data. For modeling structural relationships, generalized linear models are first defined, and Poisson regression, logistic regression, and log‐linear models are each considered within that framework. Methods are summarized for analyzing correlated counts from observing a categorical dependent variable on the same observational units under several measurement conditions or at multiple observation times, or on multiple observational units within matched sets. These methods include weighted least‐squares functional regression, conditional logistic regression, Cochran–Mantel–Haenszel tests, generalized linear mixed models, and analyses using generalized estimating equations. Overdispersed, zero‐inflated, and ordinal data, Bayes and empirical Bayes methods, spatial modeling, exact methods and software in late 2011 are briefly discussed.
Change, Detecting
Authors: Esterby Sylvia R. 
Published: January 2013

AbstractFundamental to the assessment of the effect of human activities on the environment is the detection of changes in environmental systems. It may be of interest to detect changes occurring over time, over space, or over both time and space. Although closely linked to the determination of status and the estimation of the size and components of change, detection is the primary consideration here. Methods for detecting change include: (i) graphical methods, exploratory, and more formal; (ii) tests of hypotheses, where the hypothesis characterizes the original status; and (iii) diagnostic tests that detect departures from a specified model. Most methods considered in this article are for changes over a single dimension and will often have been developed for data collected over time. However, such methods will usually be applicable to data collected in one dimension over space. Datasets for which more than one dimension needs to be taken into account generally require more modeling and thus are less often investigated from the detection point of view.
Change‐in‐Ratio
Authors: Udevitz Mark S. 
Published: January 2013

Abstract
Change‐in‐ratio (CIR) methods are used to estimate parameters for ecological populations subject to differential removals from population subclasses. Subclasses can be defined according to criteria such as sex, age, or size of individuals. Removals are generally in the form of closely monitored sport or commercial harvests. Estimation is based on observed changes in subclass proportions caused by the removals.
Clustering
Authors: Chenouri Shojaeddin 
Published: January 2013

Abstract
Cluster analysis is concerned with the subdivision of individual objects into natural groups on the basis of collected data relating to these objects. The data may consist of observations on each object, or of perceived similarities or dissimilarities between pairs. As a statistical technique, cluster analysis is a form of “unsupervised classification,” as opposed to supervised classification, in which the groups and the group membership of the data are known, and are used to form rules to classify future observations. It is closely related to ordination, including principal component analysis and projection pursuit. These techniques are often used to justify the results of cluster analysis.
Coarse Woody Debris
Authors: Stahl Goran , Dolloff C. Andrew 
Published: January 2013

Abstract
Coarse woody debris (CWD), synonymous with large woody debris, is a term used to describe wood from dead trees. Although an exact definition is not available, many researchers include all pieces of wood that are at least 10 cm in diameter and 1 m in length. CWD can consist of any coarse part of a dead tree, such as trunks or parts of trunks, branches, stumps, and root wads. A distinction is often made between standing dead trees, snags, and dead trees on the ground, logs. Dead roots in the soil are generally not considered as CWD although there is no principal reason why they should not be. Challenges to the measurement and quantification of CWD range from sampling issues to multivariate statistics for studying species occurrences and communities on dead wood. A brief overview of some specific environmetrical challenges is given in this article.
Competing Risk Analysis
Authors: Gupta Ramesh C. 
Published: January 2013

AbstractIn studies of risk assessment due to environmental exposures, we are interested in the mortality due to a particular disease when several other diseases are acting on a person's life. The method of analyzing survival data in such a population is called “competing risk analysis.” We also have a fundamental assumption that each death takes place due to a single cause. When an individual is put on test, only his time of death and cause of death are recorded. This competing risk data provides insufficient information to determine the joint distribution of the survival times giving rise to an identifiability problem. In this article, the cause‐specific hazard functions are introduced and their estimation and testing, for modeling, both independent and dependent lifetimes are discussed. The identifiability problem is also addressed and some ways to deal with the competing risk data in the presence of association between the latent survival times are suggested.
Species Competition
Authors: Young Linda J. 
Published: January 2013

Abstract
Competition occurs when the interaction of two organisms has an adverse effect on both. The primary models for competition are based on the Lotka‐Volterra equations. Replacement series experiments are a standard approach to evaluating the competitive relationships of two or more species. Measures of niche overlap, such as similarity indices, are often used as initial indicators of competition although they are recognized not to be true measures of competition. The probability of a species being present may be related to environmental variables and the presence of other species using logistic regression.
Databases, Health Assessment
Authors: Darnell Ross 
Published: January 2013

Abstract
Environmental issues regarding possible environmental pollution links to human and ecological health are becoming more numerous and controversial. The ideal approach to assessing potential health risks from environmental exposure is to conduct well‐designed, prospective, environmental, epidemiological studies. However, prospective epidemiological studies are difficult and expensive to undertake, and even retrospective studies can be prohibitively complex. Reliable environmental data are not easy to find and often they may not have been collected in a manner that makes them easy to use. The availability of data increases the risk of identifying single units using increasingly powerful search engines by ill‐intentioned users. The increasing public availability of health and environmental data raises the risk of identifying individuals, forcing data owners to develop techniques to reduce this risk.
Decision Support Systems, Environmental
Authors: Paul Nathanail C. 
Published: January 2013

AbstractDecisions involving environmental aspects are complex. The underlying environmental processes are tackled by teams that are invariably multidisciplinary in nature and involve sundry specialists: earth, physical, chemical, geochemical, biological, benthic, and ecological sciences. Many decisions, such as water resources allocation, irrigation and agricultural practices, toxic waste management, industrial effluent regulation, acid rain mitigation, and climate change accommodation are interrelated. The solution or mitigation of one problem usually affects another. In addition, the constraints to these solutions are legion: economic considerations for remedial costs, social values of clean water and air, political and public opinions on biodiversity, and so on. Environmental decision makers and policy advisors need to consider all these complex factors in their decision‐making and policy‐building processes. The purpose of this article is to discuss the tools that an environmental manager may need to support decision‐making processes. With the advent of information technology, this article focuses on the possible use of computer systems to provide the support. Their design, implementation, advantages, and disadvantages are discussed and examples of applications and future directions are given. These systems are calledenvironmental decision support systems.
Defoliation
Authors: Candy Steven G. , Zalucki Myron P. 
Published: January 2013

AbstractFrom ancient times, mankind has attempted either to harness or to prevent defoliation of trees and plants. The positive and negative impact of defoliation by invertebrates (chiefly insects), vertebrates, plant diseases, or abiotic factors such as air pollution is an integral part of agriculture, forestry, and natural resource management. Defoliation is “an unseasonable reduction in the foliage cover of a plant due to attacks by insects or fungal disease, or as a result of other factors such as drought, storms, or chemicals in the atmosphere.” Defoliation encompasses partial or complete loss of the plant's foliage and individual leaves. The most significant impacts of natural defoliation are crop losses in agriculture and production forestry and, for native plant communities, possible changes in structure and species composition and loss of amenity values. It is not possible to review in this article the huge amount of information available on defoliation. In terms of measurement, estimation, or prediction, there are a number of commonalties across the range of activities in which quantification of defoliation is required.