bluuebunny/crossref_metadata_2025 huggingface dataset:
ESTIMATING BIRD INJURY FROM THE M/T ATHOS/ INCIDENT
Authors: Nixon Zachary , Michel Jacqueline , Hoff James , Forsell Doug , Krest Sherry , Hossler Robert , Clark Kathy , Nichols Ted , Dunn John
Published: January 2013

On 26 November 2004, the MIT Athos 1 incident resulted in the release of an estimated 265,000 gallons of crude oil into the Delaware River System during a very dynamic period of bird migration in the region. The unique nature of the spill area, large numbers of birds, and complicating migratory activity required a novel approach. This effort, based on a large and high-quality field data set, represents one of the most complex risk-based damage assessments performed for birds injured during an oil spill to date. The approach estimates injury, both direct and indirect, while accounting for changing populations and oiling rates across time and in different areas for multiple guilds. During and after the spill, field teams conducted extensive aerial and ground surveys. Data from ground and aerial surveys were used in a rigorous risk-based assessment to estimate the extent of bird and wildlife losses resulting from the MIT Athos 1 incident. The total number of birds present in the area was estimated from detectability-adjusted aerial survey data for nine species guilds in three time periods. The number of birds in different oiling categories for each of these same guilds and time periods was estimated from ground survey data. This oiling information was then used to estimate the number of birds that were oiled and died in the field, or that survived with sublethal impacts. These estimates, combined with data from the wildlife rescue and recovery effort, were used to determine the total number of birds impacted (i.e. direct injury). Production foregone due to the loss of future generations (i.e. indirect injury), was included in the estimation of total injury as the discounted loss of production from dead individuals, and individuals that were oiled and survived, but then failed to reproduce. Demographic and reproductive statistics for model species from each guild were used to estimate this loss with age-structured population models.
The National Oil Spill Response and Renewable Energy Test Facility
Authors: DeVitis Dave , Schmidt William , Delgado Jane , Crickard Mike , Potter Steve
Published: January 2013

The American Society of Testing and Materials (ASTM) subcommittee on skimmers recently adopted a standard methodology for measuring I skimmer performance, F 2709 - Standard Test Method for Determining Nameplate Recovery Rate of Stationary Oil Skimmer Systems. Current industry practice allows manufacturers to label skimmers with a nameplate capacity based solely on the skimmer's offload pump capability without regard to the recovery rate as a system. Additionally there is no consideration given to the degradation in recovery performance when pumping fluids with viscosities higher than water. Typically the manufacturer's claimed value is unrealistic when estimating the oil recovery rate (ORR) of a skimming system. Integrating actual performance data into the planning and regulation process is prudent from all perspectives. In the absence of third party data, the United States Coast Guard (USCG) will de-rate a manufacturer's claimed nameplate capacity by 80% or more when calculating the Effective Daily Recovery Capacity (EDRC). The USCG uses EDRC as a key component in rating and regulating the oil spill response capability of responsible parties and oil spill response organizations (OSROs).

The ASTM's new skimmer protocol has been used recently at Ohmsett to evaluate four oleophilic skimmers as potential alternatives to the skimmers currently used in Alaska's Prince William Sound (PWS) oil spill response plan. The selected skimmer has undergone a number of modifications with improvements quantified over four additional tests series. This paper focuses on the most recent test of this skimmer, conducted in cold-water conditions using both fresh and weathered Alaska North Slope (ANS) crude oil. During the latest testing, two newly introduced tests were performed: a 24-hour endurance test and a qualitative recovery test in the presence of seaweed.
Anticipating California Levee Failure: The State of the Delta Levees and Government Preparation and Response Strategies for Protecting Natural Resources from Freshwater Oil Spills
Authors: Port Patricia Sanderson , Hoover Samuel A.
Published: January 2013

Seismic activity and other natural and human forces significantly threaten the structural integrity of Delta levee systems. Breached levees appreciably impact fresh water resources, damage crops and livestock, destroy homes, schools, public works, businesses and wild species habitat, among other vital, sensitive areas. Levee failure often leads to oil and petroleum contamination of freshwater resources, particularly in dense residential, agricultural and commercial areas protected by levee systems that depend upon such water resources for drinking, irrigation and industrial uses.

San Joaquin Delta to illustrate selected impacts to a freshwater region jeopardized by levee failure, this paper will: 1) discuss the current state of the Delta levee systems and detail the fragility of the system and the potential consequences of major impacts - earthquakes and floods likely to jeopardize this system; 2) review issues concerning levee breach preparedness, including practices to maintain levee structure and promote levee rehabilitation; and 3) suggest a coordinated federal, state and local response to contain damage caused by a levee breach, with particular focus on oil and hazardous materials spill impacts.

As with many levee systems, the issues presented by a levee breach to the Sacramento-San Joaquin Delta region are complex and require close, concerted federal, state and local participation, not only to rapidly and effectively respond to impacted areas, but to anticipate and avoid more massive levee failure. Employment of these levee protection and breach response measures will help defend, among other vital resources, domestic, agricultural and industrial water for use from oil, petroleum and other hazardous contaminants.
Industry Led Multi-Jurisdiction, Multi-Event Exercise
Authors: McCaffrey Patrick S.
Published: January 2013

Marathon Oil gets its Corporate Emergency Response Team (CERT) together once a year in a tabletop exercise. CERT is made up of volunteers from throughout the company and the exercise serves both to hone response skills and to build teamwork. The 2009 annual exercise was held in Huntington, West Virginia. The exercise involved a diesel leak in Ohio, a worst case discharge of crude oil in Kentucky and a barge/security incident in West Virginia. These incidents pulled in responders from 3 United States Environmental Protection Agency (USEPA) regions, the United States Coast Guard (USCG), West Virginia National Guard, 3 states, 4 counties and 3 business units. In all 280 people from 26 organizations were involved in the drill. The planning effort involved many of the organizations who played in the exercise and was modeled after the Spill of National Significance (SONS1) 2007 planning effort for the Evansville Venue which built from the bottom up.

Major goals of the drill were to: deeply integrate Unified Command, and stress the CERT with both traditional and nontraditional problems. The endgame was the delivery of an Incident Action Plan (IAP) which included the company's first attempt at an inland salvage plan. Unified Command was pushed down into Operations, Planning, Environmental and Joint Information. Integration in Unified Command was done in several ways: traditional jurisdictional decision making, position/task assignments, and resource pooling. A Joint Information Center (JIC) was setup for the exercise, and one of the JIC's tasks was to accommodate real media coverage. A group hotwash was held for all participants and even though 100 action items were identified, the group consensus was that the drill was valuable for both public and private responders, and their organizations.
New Tools Simplify Oil Spill Response Mapping in Oregon and Highlight the Need for National Standards
Authors: Pettit Don J. , Jett Steven M. , Christensen Jeff
Published: January 2013

The Oregon Department of Environmental Quality (DEQ) was an early leader in developing some of the first Oil Spill Response Plans in the Pacific Northwest, dating back to the mid-1970s. Early work culminated in the development of Geographic Response Plans (GRPs) for the Oregon Coast and major estuary systems. However, since their development in the mid-1990s, the GRPs have not been significantly updated due to a lack of resources to overcome technological hurdles and shortages of field and office personnel. Additionally, the collection of natural resource data used for plan development has lagged in Oregon. Improvements in GIS technology have made it possible for Oregon to create a state-wide platform for emergency response information (the Oregon Incident Response Information System or OR-IRIS), which can also serve as an information base for the development and revision of GRPs. With the ESRI file geodatabase as the repository for useful legacy data and newly collected geographic information, the process to create and maintain GRPs has been substantially simplified, and the required resources have been reduced. However, with these changes in the ability to store large amounts of data comes the need to organize and streamline output. The Deepwater Horizon Spill presented a unique challenge to quickly gather, process and utilize vast amounts of geographic data to satisfy response planning needs within the Gulf of Mexico response, and highlighted the need for coordinated and organized GIS services to responders. The time is now to develop regional or national GRP production templates utilizing a common spatial database structure and common map symbology, thus assuring that incident mapping needs are able to be met when a large spill such as a Spill of National Significance, or a spill which crosses state boundaries, draws multi-agency, multi-state or regional personnel to a response.
Age‐Growth Modeling
Authors: Sagary Nokoe K.
Published: January 2013

This section is on growth of nonhuman individuals. Growth of an individual or a group of organisms is generally defined as the change in dimensions over time. Growth rates vary between and within plant and animal species. They are generally influenced by both genetic and phenotypic factors, and may be negative or positive. Positive growth relates to increases in dimensions (weight, height, and number of component parts) over time, whereas decay or negative growth refers to the decline over time. Human interference, either intentionally or accidentally, environmental influences, and management practices can substantially influence the growth forms and patterns of individuals and population. Age‐growth functions constitute important analytical tools in the management of natural resources. These models are explicit expressions of both theory and empirical knowledge, and are useful for growth and yield prediction and evaluation of alternative management strategies, among others. The approach to age‐growth modeling may be based on either primary (single) unit or stand (aggregate of units). Irrespective of modeling philosophy, the goal is identification of the most parsimonious and biologically reasonable model that best describes the relationship between the response variable (which is a measure of growth) and time (age).
Agroforestry
Authors: Lund H. Gyde
Published: January 2013

Agroforestry is the management of lands for both trees and crops. In addition to providing some income to the land owner, the practice protects the soil, fosters biodiversity, sequesters carbon, and helps restore degraded lands. Because the responsibility for surveying croplands and forest land often falls to two different ministries (agriculture and forestry), information about the amount of these lands and their resources is often lacking at the national level. The Food and Agriculture Organization (FAO) of the United Nations is expanding its Global Forest Resource Assessments (FRA) to include trees outside forests (TOF), which would include those on agroforestry lands. This action will help fill the information gap for those who manage the countries' resources.
Hierarchical Bayesian Space‐Time Analysis
Authors: Berliner L. Mark
Published: January 2013

This article introduces the hierarchical Bayesian (HB) approach to modeling the spatial‐temporal structure and dependence as well as the model uncertainty. Many problems in the environmental sciences involve inferences for space‐time‐varying physical processes, which can be summarized in three stages: Data model, process model and prior on parameters. The HB approach allows for combining observations with physical models.
Bayesian Methods and Modeling
Authors: Gelfand Alan E.
Published: January 2013

In the past 20 years, we have seen a remarkable rejuvenation of the Bayesian inference paradigm. Novel computational tools in conjunction with inexpensive high‐speed computing have enabled the fitting of challenging Bayesian models. As a result, a more integrated view of inference, using observational data, synthesizing multiple sources of information, specifying multilevel models, has emerged to explain complex processes. Environmental processes provide an exceptionally rich source of application for this full and exact inference paradigm.
Biomarkers
Authors: Thurston Sally W.
Published: January 2013

Biomarkers are cellular, biochemical, or molecular changes in a biological system. Biomarkers are often broadly classified as markers of exposure (either of internal dose or of the biologically effective dose), markers of preclinical biological effects, markers of susceptibility, or markers of disease. Biomarkers of exposure that are known to be on the causal pathway to cancer have been used to help identify environmental carcinogens. Biomarkers of disease are often associated with a premalignant condition, but recently epigenetic changes such as DNA methylation have been proposed as disease biomarkers. Markers of susceptibility include genetic polymorphisms that affect the probability of proceeding from one disease state to the next. Challenges in using biomarkers in cancer research include the inability to measure biomarker levels in the target tissue of controls, and the possibility that the disease process may alter biomarker levels. Established and well‐validated biomarkers, as well as new biomarkers, hold promise for a better understanding of the cancer process and for identifying individuals at increased cancer risk.
Biomonitoring
Authors: Conquest Loveday L.
Published: January 2013

In its ecological sense, biomonitoring refers to the recording of observations of animal or plant populations, often over space and time. Owing to the possible presence of spatially and temporally correlated observations, the “what, where, when, and how” of biomonitoring studies must be carefully considered, beginning with the choice of appropriate response variables to measure. There are many available methods to analyze data from biomonitoring field studies or experiments. The standard toolbox of statistical techniques, particularly in the area of regression analysis, now includes generalized linear models and generalized additive models. These allow the incorporation of non‐Gaussian (non‐normal) error terms, and can describe nonlinear relationships using nonparametric smoothing functions. Analysis of a single dataset may include liberal use of graphics, handling of data with heterogeneous variances and below detection limits, and dealing with irregular coverage through space and time. A multipronged approach to analysis of complex datasets that often arise from biomonitoring studies is not unusual.
Three‐State Carcinogenicity Model
Authors: Dinse Gregg E.
Published: January 2013

A common stochastic model for animal carcinogenicity data has three states: N (alive with no tumor), T (alive with tumor), and D (dead from natural causes). The intensity rates for N → T, T → D, and N → D transitions at time
t
are denoted by λ(
t
), α(
t
), and β(
t
), respectively, which correspond to the hazard rates for times from birth to tumor onset, from tumor onset to natural death, and from birth to natural death without a tumor. Carcinogenesis refers to the development of new tumors; so analyses of carcinogenicity data should focus on the tumor incidence rate, λ(
t
). Three factors complicate tumor incidence analyses: all onset times are censored if tumors are occult; tumor lethality makes censoring informative; and differential mortality exacerbates biases arising from inadequate survival or lethality adjustment. The most appropriate analysis of tumor incidence data depends on what information is available and what assumptions are reasonable. This article describes a variety of scenarios and, in each case, outlines approaches for comparing tumor incidence rates across treatment groups.
Censored Data Analysis
Authors: Portnoy Stephen
Published: January 2013

Censoring is a special form of missing data that arises in a wide variety of situations. Censoring occurs whenever the measurement is restricted to some specific range, but the values being measured can lie outside the range. Proper statistical analysis of such data requires specific adjustments, some of which are described here. Serious errors in inference can be made if censoring is not taken into account properly.
Center for Global Environmental Research
Authors: El‐shaarawi Abdel H.
Published: January 2013

The Center for Global Environmental Research (CGER) was established to contribute to the solution of pressing environmental problems and has three major activities: integration of environmental research through enhanced communication and international cooperation; support to global environmental research; and long‐term and continuous monitoring relevant to global environmental issues.
Chemical Mass Balance
Authors: Hopke Philip K.
Published: January 2013

The chemical mass balance is a receptor model that uses known composition profiles of pollution sources to apportion ambient concentrations to those respective sources. The standard application of this approach uses an effective variance least‐squares method to fit a sample of ambient data to a matrix of source profiles.
Chemometrics
Authors: Hopke Philip K , Spiegelman Clifford H. , Park KWANG‐Su
Published: January 2013

Chemometrics is a chemical discipline that uses mathematics, statistics, and formal logic (i) to design or select optimal experimental procedures; (ii) to provide maximum relevant chemical information by analyzing chemical data; and (iii) to obtain knowledge about chemical systems. In applications in industry, (iii) can be changed as follows: to obtain knowledge about chemical systems for monitoring or controlling operation of systems. This article focuses on areas of chemometrics that are most relevant to environmetrics.
Clean Water Act,
US
Authors: Piegorsch Walter W.
Published: January 2013

The US Clean Water Act (CWA) was intended to direct environmental attention/protection to surface waters in the United States and specifically to “restore and maintain the chemical, physical, and biological integrity of the Nation's waters.” This article briefly reviews the CWA and selected, associated, environmetric issues.
Cooperative Programme for Monitoring and Evaluation of Long Range Air Pollution in
E
urope (
EMEP
)
Authors: El‐Shaarawi Abdel H.
Published: January 2013

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

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

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

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

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

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

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

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

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

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

Generalized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.
Generalized Regression Estimators
Authors: Park Mingue , Fuller Wayne A.
Published: January 2013

Regression and regression related procedures have been used often in survey estimation. We review the basic properties of regression estimators and investigate variance estimation for regression estimators. Some range restricted regression procedures are also explored.
Heritability
Authors: Fry James D.
Published: January 2013

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

Ahierarchical modelrefers to a statistical model specified in stages, often with the purpose of borrowing strength across the various experimental units.
Journal of Agricultural, Biological, and Environmental Statistics
Authors: Young Linda J.
Published: January 2013

JABES
strives to develop the interface between statistics and the biological sciences with an emphasis on agriculture, biotechnology, the environment, and natural resources.
Kriging
Authors: Anderes Ethan B.
Published: January 2013

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

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

Model‐assisted sampling is concerned with making design‐based inference using model‐based estimators to improve precision.
Morphometrics
Authors: Reyment Richard A.
Published: January 2013

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

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

The common use of parallel computing has greatly evolved since the original encyclopedia article of 2001. Multicore processors are now quite common, so many computer users have this readily available on their desktop computers and laptops.

Now increasingly large datasets and simulation of complex statistical models are important in the study of many physical systems. Models in various areas, such as environment, biology, and other physical sciences, play an important role in prediction or detection of changes. All these require lots of computing power. Many of these computations can take advantage of parallel or distributed computing. This article discusses some of these ideas and then discusses how these are implemented in one specific language, R. In the present time, one generally no longer has to work at a low‐level programming language, as was the case a decade or two ago, but now certain types of parallel computations can be implemented at a relatively higher user‐friendly level, even with desktop computing.

Parallel computing consists of a computing environment connecting many processors. Instead of the previous generation where dedicated computer architecture was required, a more loose structure of distributed computing is now more common. This article is intended to give the reader an overview of the parallel computing environment, focusing on the statistical uses that can be made as opposed to a more detailed computing or engineering description.
Point Processes, Dynamic
Authors: Ensor Katherine Bennett
Published: January 2013

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

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

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

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

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

Sensitivity analysis of computer models has been developed over the last decades paralleling the development of massive computer simulations of engineering, chemical, and environmental systems.

Through extensive simulations, global sensitivity analysis is aimed at assessing the importance of each input to the model outputs by means of variance decomposition.

Depending on model complexity and simulation costs, various techniques are available ranging from design of experiments (DOE) to intensive Monte Carlo, metamodeling, and Bayesian inference.
Sensitivity and Specificity
Authors: Piegorsch Walter W.
Published: January 2013

A brief introduction is given on the use of the terms
sensitivity
and
specificity
. Emphasis is directed at diagnostic testing/screening, where the terms delineate forms of true positive and true negative rates of the diagnostic outcomes.
Size‐Biased Sampling
Authors: Zimmerman Dale L.
Published: January 2013

Size biased sampling refers to a type of nonrandom sampling in which the likelihood of an object's inclusion in the sample is proportional to the size (or length) of the object.
Soil Erosion and Conservation
Authors: Li Sheng , Lobb David A. , Tiessen Kevin H.D.
Published: January 2013

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

Geostatistical approaches to modeling spatiotemporal data rely on flexible space–time covariance models. The area has been in vigorous development recently, and we review constructions of nonseparable spatiotemporal covariance functions that are based on geometric anisotropy, Fourier inversion, completely monotone functions, and dynamic physical models.
Spatially constrained sampling
Authors: Stein Alfred
Published: January 2013

This article describes sampling in the presence of spatial constraints. The technique has been developed on the basis of simulated annealing. It is applicable for a range of environmental sampling and monitoring techniques. After introducing the technique, two specific examples are given. An outlook toward further developments is given.
Splines in Nonparametric Regression
Authors: Helen Zhang Hao
Published: January 2013

This article is interested in splines as tools for visualizing and analyzing noisy observational data, and so restricts itself to smoothing splines and regression splines. The article first describes the univariate polynomial smoothing spline, which may be thought of as the forerunner of spline functions used in data analysis. It then describes cross‐validation and generalized cross‐validation (GCV) for choosing the smoothing parameter. After briefly describing regression splines, this entry then describes a number of generalizations of the univariate smoothing spline to various domains, which are obtained via the solution of a variational problem. These include the thin plate spline, the histospline, splines on the sphere, vector splines on the sphere, hybrid splines, partial splines, and smoothing spline analysis of variance (ANOVA) models on complex domains. Computational issues are also discussed. The last two sections focus on the topics of variable selection and model selection in nonparametric regression. Various penalized regression with penalty for function shrinkage and selection are described. Publicly available software is mentioned along the way.
Space‐Time Stochastic Modelling in Human Exposure
Authors: Christakos George
Published: January 2013

Perceptions regarding the meaning of the term stochastic modeling (SM) are not as uniform as one might think. This should not come as a surprise, in view of the multidisciplinary nature of environmental health science. Depending on the application considered, one may refer to stochastic models as environmental, ecologic, genetic, epidemiologic, medical, etc. A common factor in all these cases is that SM is concerned with the mathematically rigorous and scientifically meaningful representation, explanation, and prediction of natural phenomena (physical, biological, etc.) in uncertain environments. Uncertainty may be due to measurement errors, heterogeneous databases, erratic biologic fluctuations in space/time, insufficient knowledge, etc. Within such a framework, SM seeks to provide a realistic environmental situation with spatio‐temporal continuity and internal consistency.
Sustainability
Authors: Southam Colette , Southam Gordon
Published: January 2013

A relatively stable environment was vital for the advancement of human civilization over the past 12,000 years (the Holocene). During this time period, humans became experts in exploiting natural resources, allowing for an enormous population expansion as a technologically advanced society. Recognition that our very success is causing widespread damage to Earth's ecosystems is the current basis of sustainability initiatives. Life on this planet is endlessly interconnected. Scientists cannot approach sustainability issues in isolation and sustainable development management needs to reconcile social, economic, and environmental demands – the “three pillars” of sustainability. Collaborative and multidisciplinary academic research is required to understand, predict, and manage the effects humans are having on Earth in order to become sustainable.
Toxicology, Environmental
Authors: Bailer A. John , Oris James T.
Published: January 2013

Environmental toxicology addresses the potential adverse impacts of toxicants on the function of organisms in the environment. This impact could be evaluated ranging from the molecular level to species interactions at an ecosystem function level. A historic context for environmental toxicology is introduced with connections between toxicity tests and environmental regulations being drawn. Statistical dimensions of risk assessment components are presented. An illustration from toxicant impacts in receiving waters is used to describe hypothesis‐testing approaches for evaluating toxicity and is contrasted with potency estimates based on inverting regression relationships. The entry concludes with a consideration of other regulatory endpoints and future directions in methods development.
Trend Detecting
Authors: Woodward Wayne A.
Published: January 2013

This article discusses the problem of testing for trend in time series data, which is a difficult problem because, among other things, there is not a unified definition of trend. A variety of trend definitions are given and the distinction between deterministic and random trends is discussed. Simulated realizations with random trends are shown along with the global temperature data, and the issues related to deciding whether an apparent trend is deterministic or random are discussed. We examine classical tests for linear trend and discuss the inflated observed significance levels associated with many of these tests. Several tests are presented that have been developed to better control the observed significance level. Also discussed is the problem of using nonparametric methods to test for more general types of trend, such as monotonic trends, that may have no known functional form.