bluuebunny/crossref_metadata_2025_split huggingface dataset:
Matching sampling designs and significance tests in environmental studies
Authors: Evans J. C. , Coote B. G. 
Published: February 2007

AbstractTraditional ANOVA models are often successfully employed in environmental fields. However, there is a need to carefully examine the basic design, the assumptions and the form of the analysis so that valid tests of significance and standard errors are used. Examples will show that wrong conclusions can be drawn if this is not done. Environmental studies usually deal with mixed models that contain some factors that are fixed (i.e. particular levels or effects are of interest) and others that are random (i.e. the levels of these factors are sampled from a larger population). The type of replication, and consequent choice of error, applied to testing any fixed effect depend on the underlying assumptions made. Examples illustrate that correct matching of designs, tests and standard erors will allow these traditional methods to be applied, using mixed‐models software, to a wide range of studies where standard textbook replication cannot be used.
A discrete time logistic regression model for analyzing censored survival data
Authors: Maul A. 
Published: February 2007

AbstractConsideration is given to survival data analysis by modelling the hazard as a discrete function of time. This is done for each individual who is examined independently from the other individuals of the sample observed. Assuming time has been divided into intervals of the same length, the hazard associated with any specific time interval is taken to be of the form of a logistic function including a number of time‐dependent covariates which serve to characterize the individual under consideration. Asymptotic maximum likelihood results are given for the estimation of both the regression coefficients in the hazard function and the survivor function corresponding to a given profile, i.e. the successive values of the different covariates. The likelihood ratio statistic for testing the effects of the various covariates in order to compare several survival curves with respect to longevity is also derived. The process of model fitting is illustrated by two examples referring to clinical trails on leukaemia and advanced lung cancer patients, respectively.
Data‐based mechanistic modelling and the rainfall‐flow non‐linearity
Authors: Young Peter C. , Beven Keith J. 
Published: February 2007

AbstractAlthough rainfall‐flow processes have received much attention in the hydrological literature, the nature of the non‐linear processes involved in the relationship between rainfall and river flow still remains rather unclear. This paper outlines the first author's data‐based mechanistic (DBM) approach to model structure identification and parameter estimation for linear and non‐linear dynamic systems and uses it to explore afresh the non‐linear relationship between measured rainfall and flow in two typical catchments. Exploiting the power of recursive estimation, state dependent non‐linearities are identified objectively from the time series data and used as the basis for the estimation of non‐linear transfer function models of the rainfall—flow dynamics. These objectively identified models not only explain the data in a parametrically efficient manner but also reveal the possible parallel nature of the underlying physical processes within the catchments. The DBM modelling approach provides a useful tool for the further investigation of rainfall‐flow processes, as well as other linear and non‐linear environmental systems. Moreover, because DBM modelling uses recursive estimation, it provides a powerful vehicle for the design of real‐time, self‐adaptive environmental management systems. Finally, the paper points out how DBM models can often be interpreted directly in terms of dynamic conservation equations (mass, energy or momentum) associated with environmental flow processes and stresses the importance of parallel processes in this connection.
Survival analyses of radiated animals incorporating competing risks and covariates
Authors: Gupta Ramesh C. , Albanese Richard A. 
Published: February 2007

AbstractThe United States Air Force has been interested in studying the effect of different types of radiation encountered by its personnel in space. A study was, therefore, conducted at the USAF School of Aerospace Medicine, Brooks Air Force, Texas with rhesus monkeys as experimental subjects. These subjects were exposed to different types of radiation such as: (1) electromagnetic radiation; (2) electrons; (3) protons; and (4) nuclei of elements of higher numbers with different amount of radiation. These subjects were followed over a period of 338 months. In this paper an interesting problem related to health and radiation has been addressed. The effects of radiation, taking into account the cause of death (cancer or heart disease) along with the covariates such as sex, age, type of exposure, dose, are examined. A general log‐linear hazard model approach is studied. The model estimates the cause specific hazard rates, assuming piecewise exponential distribution, and exhibits the survival function for each of the covariate groups and the probability of death due to each cause. A data set called ‘Delayed Bio‐Effects Colony’, of radiated animals, is analysed and some conclusions are drawn. Overall the study has brought out the effect of high and low doses of radiation on both the male and female groups. The procedure presented in the paper distinguishes between time varying hazards. Thus the methodology may be useful for other survival related environmental problems, be this with regard to animal or human survival. Thus, the paper could contribute to its more widespread use.
Empirical bayes estimators for spatially correlated incidence rates
Authors: Devine Owen J. , Louis Thomas A. , Halloran M. Elizabeth 
Published: February 2007

AbstractAssessments of the potential health impacts of contaminants and other environmental risk factors are often based on comparisons of disease rates among collections of spatially aligned areas. These comparisons are valid only if the observed rates adequately reflect the true underlying area‐specific risk. In areas with small populations, observed incidence values can be highly unstable and true risk differences among areas can be masked by spurious fluctuations in the observed rates. We examine the use of Bayes and empirical Bayes methods for stabilizing incidence rates observed in geographically aligned areas. While these methods improve stability, both the Bayes and empirical Bayes approaches produce a histogram of the estimates that is too narrow when compared to the true distribution of risk. Constrained empirical Bayes estimators have been developed that provide improved estimation of the variance of the true rates. We use simulations to compare the performance of Bayes, empirical Bayes, and constrained empirical Bayes approaches for estimating incidence rates in a variety of multivariate Gaussian scenarios with differing levels of spatial dependence. The mean squared error of estimation associated with the simulated observed rates was, on average, five times greater than that of the Bayes and empirical Bayes estimates. The sample variance of the standard Bayes and empirical Bayes estimates was consistently smaller than the variance of the simulated rates. The constrained estimators produced collections of rate estimates that dramatically improved estimation of the true dispersion of risk. In addition, the mean square error of the constrained empirical Bayes estimates was only slightly greater than that of the unconstrained rate estimates. We illustrate the use of empirical and constrained empirical Bayes estimators in an analysis of lung cancer mortality rates in Ohio.
Parameter and quantile estimation for the generalized extreme‐value distribution
Authors: Castillo Enrique , Hadi Ali S. 
Published: February 2007

AbstractThe generalized extreme‐value distribution (GEVD) was introduced by Jenkinson (1955). It is now widely used to model extremes of natural and environmental data. The GEVD has three parameters: a location parameter (−∞ < Λ < ∞), a scale parameter (α > 0) and a shape parameter (−∞ < k < ∞). The traditional methods of estimation (e.g., the maximum likelihood and the moments‐based methods) have problems either because the range of the distribution depends on the parameters, or because the mean and higher moments do not exist when k ≤ − 1. The currently favoured estimators are those obtained by the method of probability‐weighted moments (PWM). The PWM estimators are good for cases where −1/2 < k < 1/2. Outside this range of k, the PWM estimates may not exist and if they do exist they cannot be recommended because their performance worsens as k increases. In this paper, we propose a method for estimating the parameters and quantiles of the GEVD. The estimators are well‐defined for all possible combinations of parameter and sample values. They are also easy to compute as they are based on equations which involve only one variable (rather than three). A simulation study is implemented to evaluate the performance of the proposed method and to compare it with the PWM. The simulation results seem to indicate that the proposed method is comparable to the PWM for −1/2 < k < 1/2 but outside this range it gives a better performance. Two real‐life environmental data sets are used to illustrate the methodology.
Optimal sampling designs for dependent spatial units
Authors: Benedetti Roberto , Palma Daniela 
Published: February 2007

AbstractA geographical domain is partitioned into a set, with cardinalityN, of areal units (i.e. census tracts), each of them having an attribute variablez.Observations are often to be recorded for a subsetSof areal units whose cardinality isn.Under the hypothesis of dependence of the underlying data generating processZ, the following questions are considered: which is the best linear unbiased estimator (BLUE) of the mean of the processZ, and which is the subsetSthat minimizes the variance of this estimator? A weighted average estimator is used and the performances of some combinatorial optimization algorithms are tested to solve this problem. The simulated annealing algorithm is shown to be a suitable solution even when dealing with large data sets. Moreover, numerical comparisons are made between sampling designs obtained by using simulated annealing and the classical simple random and systematic sampling criteria.
Factors regulating residual aluminium concentrations in treated waters
Authors: Driscoll Charles T. , Letterman Raymond D. 
Published: February 2007

AbstractElevated concentrations of residual aluminium (Al) is an important issue for water treatment facilities. Concerns associated with high concentrations of residual aluminium include increased turbidity in drinking water, reduced effectiveness of disinfection, decreased carrying capacity of the water distribution system as well as potential effects on human health. The objectives of this study were to review the literature on mechanisms for controlling residual aluminium concentrations and to conduct a field study to evaluate the forms of residual aluminium in three water treatment plants. Both the literature review and results of the field study suggest that elevated concentrations of residual aluminium appear to occur largely in either of two forms; particulate aluminium or dissolved (monomeric) aluminium. To decrease concentrations of residual aluminium which largely occur as monomeric aluminium, it is necessary to maintain the pH of treated water near the pH of minimum Al(OH)3 solubility (6.5–7.0), remembering that this value varies with temperature. Elevated concentrations of residual aluminium associated with particulate aluminium may be due to influent suspensions with a high aluminium content or relatively large amounts of aluminium coagulant per amount of particulate matter in raw water. The results of this study suggest that filtered water turbidity should be less than 0.1 NTU to minimize particulate aluminium concentrations that contribute to residual aluminium.
Statistical evaluation of selected air pollutants in Athens, Greece
Authors: Kambezidis H. D. , Tulleken R. , Amanatidis G. T. , Paliatsos A. G. , Asimakopoulos D. N. 
Published: February 2007

AbstractA statistical evaluation of the air pollution situation in Athens for the year 1982 is presented. This analysis refers to NO2, NO, O3 and SO2 concentration levels measured at the National Observatory of Athens (NOA) with a fully automatic monitoring station. The seasonal, weekly and daily variations of the above pollutants are in agreement with the corresponding activities in the city which are responsible for these emissions. Auto‐ and cross‐correlation techniques are applied to the pollutants for January and July. It is found that all pollutants exhibit a well defined oscillatory behaviour because of their diurnal peaks in both months, except for NO2 in January whose auto‐correlation function shows no regular shape. For the same reason the January NO‐NO2 cross‐correlation function behaves in the same way; the pairs NO‐O3 and NO2‐O3 exhibit a clear oscillatory pattern. The spectral estimate method is also employed to the above pollutants for the two months in question. The energy content of O3, for instance, in July shows to be concentrated around a frequency corresponding to a periodicity of 24 hours.
Extension of spatial information, bayesian kriging and updating of prior variogram parameters
Authors: Cui Haiyan , Stein Alfred , Myers Don E. 
Published: February 2007

AbstractVariograms are used to describe the spatial variability of environmental variables. In this study, the parameters that characterize the variogram are obtained from a variogram in a different but comparably polluted area. A procedure is presented for improving the variogram modelling when data become available from the area of interest. Interpolation is carried out by means of a Bayesian form of kriging, where prior distributions of the variogram parameters are used. This procedure differs from current procedures, since commonly applied least squares estimation for the variogram is avoided. The study is illustrated with data from a cadmium pollution in the Netherlands, where this form of extrapolation was compared with ordinary kriging. When sufficient data are available (more than 140), ordinary kriging gave the most precise predictions. When the number of data was small (i.e. less than 60), predictions obtained with Bayesian kriging were more precise as compared to those obtained with ordinary kriging. This leads to a considerable reduction of costs, without loss of information.
Case‐control designs for the detection of spatial clusters of diseases
Authors: Biggeri Annibale , Marchi Marco 
Published: February 2007

AbstractTo detect spatial clusters of rare diseases, an extension of the method proposed by Besag and Newell to the context of case‐control sampling is presented. For each case location a circle containing at leastkother disease cases is drawn. For each circle, the probability of type I error, under the null hypothesis of no clustering, is computed with the Poisson formula with parameter given by the product of the case‐control ratio times the numbercout ofNcontrols classified within the circle. In the paper a hierarchical Bayesian formulation of the problem is used to cope with the variability in the numbercof sampled controls. The observed number of cases is assumed to follow the Poisson‐binomial distribution with hyperparameterpmodelled as a beta (c+ 1,N‐c+ 1) random variate. A case‐control study on lung cancer in the River Serchio Valley 1987–90 (Tuscany, Italy) exemplifies the method. Statistically significant clusters of cases were found in the vicinity of a copper foundry.
Effect of atmospheric stability on concentration fluctuations and wake retention times for dispersion in the vicinity of an isolated building
Authors: Higson H. L. , Griffiths R. F. , Jones C. D. , Biltoft C. 
Published: February 2007

AbstractThis study is concerned with the effect of changes in atmospheric stability on concentration fluctuations near an isolated building. Field experiments were conducted to measure concentration fluctuations at several locations close to a model building, for continuous point sources upwind of the building. Experiments were conducted in flat terrain using a 2 m cube as the model. Concentrations were measured at a frequency of 200 Hz using a detector system with a response time of 1/50 second. Meteorological conditions varied from stable to unstable. Analysis of the time series of concentration produced by these experiments has shown that there are several periods when the gas behaves as if it were switched off at the source (due to shifts in the wind direction). Comparisons were made in such cases of the time taken for the source gas to evacuate the wake recirculation region. It was found that this period was several times longer in stable meteorological conditions than in unstable conditions. This finding has important implications in relation to hazard assessment near buildings.
Fixed‐point values of contaminant cloud dilution
Authors: Heagy W. K. , Sullivan P. J. 
Published: February 2007

AbstractThe contaminant concentration values that are experienced by a fixed observer are represented by an expected‐mass‐fraction function. It is shown that the α‐β prescription of moments enable the expected‐mass‐fraction to be approximated. The release of a puff of contaminant in the neutral atmospheric boundary layer is used as an illustrative example.
A contaminant‐dilution heuristic model
Authors: Labropulu F. , Sullivan P. J. , Ye H. 
Published: February 2007

AbstractThe sheet‐strand texture of a contaminant concentration field in turbulent flows is the basis of a simple model of the dilution process in contaminant clouds and plumes. The model is used to explain qualitative observations on kurtosis, skewness and probability density functions near the source in continuous line and point source plumes in grid turbulence.
Penalized regression in action: Estimating pollution roses from daily averages
Authors: Eilers Paul H. C. 
Published: February 2007

AbstractA pollution rose consists of selective averages of concentrations of an air pollutant for all sectors of the wind rose. Generally the sectors are 10 or 20 degrees wide and one‐hour averages of concentrations and wind directions are used. Complications arise when daily averages of concentrations have to be used. The number of data is much smaller and the variability of the wind direction over 24 hours is large. In this paper, a linear regression model is presented and applied to a real set of data. The standard solution of the regression problem is shown to give useless results; this is a consequence of the ill‐conditioning (multicollinearity) of the data. Large improvements are achieved with ridge regression and other forms of penalized least squares estimation. Cross‐validation is used for obtaining good values of the penalty parameter automatically.
An approximation to the likelihood function with application to environmental data
Authors: El‐Shaarawi A. H. , Naderi A. 
Published: February 2007

AbstractThe likelihood function for the location and scale family of distributions is approximated so that closed form estimates for the location and scale parameters can be obtained. The results are then extended to the particular case oft‐distributions with unknown degrees of freedom. The data are transformed so that the transformed data nearly follow a location and scale family of thet‐distribution. Simulation studies are done to examine the performance of the estimators and an application using data on the concentrations of α‐BHC in the Niagara River is presented.
A network design criterion for estimating selected attributes of the semivariogram
Authors: Zimmerman Dale L. , Homer Kenneth E. 
Published: February 2007

AbstractThe semivariogram plays a central role in a geostatistical analysis of spatially correlated environmental data. A valid semivariogram model is selected, and the parameters of that model are estimated, before kriging (spatial prediction) of environmental variables is performed. These inference procedures are generally based upon examination of the empirical semivariogram, which consists of average squared differences of data taken at sites lagged the same distance apart in the same direction. In some situations, the investigator may wish to estimate some attributes of the semivariogram more precisely than others. The efficient estimation of such attributes is affected significantly by the spatial configuration of the network of sites where measurements are taken. In this paper, an approach to network design is proposed that emphasizes the utility of the network for estimating selected attributes of the semivariogram. Details are given as to how the approach can be applied to precisely estimate (a) the ratio of nugget effect to sill, (b) a set of compatibility‐determining attributes, and (c) a geometric anisotropy. Acid deposition data taken from a sampling network in the eastern United States are used to further illustrate the approach.
Process understanding of a wastewater batch reactor with block‐wise PLS
Authors: Aguado D. , Zarzo M. , Seco A. , Ferrer A. 
Published: January 2007

AbstractIn this work a systematic methodology ‘block‐wise PLS’ has been applied to thoroughly analyse data from a sequencing batch reactor (SBR) operated for biological phosphorus removal from wastewater. The aim of this study was to diagnose process variables (collected by the inexpensive and low‐maintenance sensors installed in the SBR) likely related to the main key indicator of process performance: the phosphorus removal efficiency (PRE), determined off‐line in the quality control laboratory. In this way, it is intended to aid the process operators in the detection of abnormal values of these critical variables which would indicate undesirable process performance, so that, they could act on the process to avoid the negative consequences of the disturbance as soon as possible. The results of this research clearly show that the applied methodology can be considered as an effective tool to diagnose the process and can contribute to achieve a better process understanding as well as to optimise the process. Copyright © 2007 John Wiley & Sons, Ltd.
Environmental review strategy for R&D activities
Authors: Rice Steven C. 
Published: February 2007

AbstractAn organized environmental review strategy can assist an R&D facility or organization in ensuring that their activities not only comply with applicable site and operational requirements, but also address and resolve environmental issues and costs during the process development phase of their work. It is at this stage of R&D where efforts toward minimizing generation of hazardous waste and reducing “end‐of‐the‐pipe” control in potential commercial applications can be most effective. This article presents and describes five basic concepts which, when combined, can create a strong, purposeful environmental review program. Exxon Research and Engineering Company's review process is described as a specific example.