Nonparametric Ranked-set Sampling Confidence Intervals for Quantiles of a Finite Population

Ranked-set sampling from a finite population is considered in this paper. Three sampling protocols are described, and procedures for constructing nonparametric confidence intervals for a population quantile are developed. Algorithms for computing coverage probabilities for these confidence intervals are presented, and the use of interpolated confidence intervals is recommended as a means to approximately achieve coverage probabilities that cannot be achieved exactly. A simulation study based on finite populations of sizes 20, 30, 40, and 50 shows that the three sampling protocols follow a strict ordering in terms of the average lengths of the confidence intervals they produce. This study also shows that all three ranked-set sampling protocols tend to produce confidence intervals shorter than those produced by simple random sampling, with the difference being substantial for two of the protocols. The interpolated confidence intervals are shown to achieve coverage probabilities quite close to their nominal levels. Rankings done according to a highly correlated concomitant variable are shown to reduce the level of the confidence intervals only minimally. An example to illustrate the construction of confidence intervals according to this methodology is provided.     

An improved formal approach to demographic loop analysis

Loop analysis is introduced to demographic analysis as a tool to compare relative contributions of different life-history types to population growth rate. In 1998, G. M. Wardle brought in basic concepts of the graph theory to demographic loop analysis and proposed a methodology to determine the loops from any life-cycle graph based on these concepts. However, the mathematics behind Wardle's methodology cannot readily be used by most population ecologists. A new methodology that is also based on graph theory concepts but both makes ecological sense in its application and is simpler to implement is proposed. Three rules of thumb serve as the basis of the proposed methodology that brings a more systematic approach to loop selection: it identifies only those loops that are ecologically meaningful (i.e., loops that are forward-flowing and with positive elasticity values). Thus, it produces a loop set that is more amenable to answer questions on comparison of different lifehistory types. It is tested on several life-cycle graphs from the literature. Three of these are presented: Vouacapoua americana, Dipsacus sylvestris, and Alcyonium sp. In each case, the methodology successfully produced a loop set that makes sense in terms of the ecology of the species. The methodology is also implemented as a couple of open-source computer codes. It is hoped that the proposed methodology will lead to wider use of loop analysis in demographic population studies.     

Use of post-stratification in composite sampling for estimating mean

We show with the results of a study conducted in the Hamadan Province, Iran as to how the use of composite sampling for estimating mean zinc concentration in the soil can help reduce analytical costs by reducing the number of analysis required. We also introduce post-stratification methodology in the composition step to take advantage of possible spatial dispersion. We speculate that the zinc concentration value depends on the sample location, we first stratified the sample set and then composite units from different strata randomly. The results of a simulation study show that the use of this approach not only reduces the total costs but also increases the precision of the estimator.     

Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM<Subscript>10</Subscript> concentrations in Rio de Janeiro

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM₁₀ levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003).     

Anwendung energetischer und nicht-energetischer Ressourcen in Energiesystemen: Vergleichende Bewertung

Goal and Scope

Which impact does the use of non-energetic abiotic resources (ores, minerals, etc.) have in life cycles of energy systems based on biogenic and fossil fuels? Is this kind of resource use less or more environmentally harmful than the utilisation of energetic abiotic resources (mineral oil, natural gas, etc.) in the same life cycles? This paper aims at answering these questions. In Part 1, a methodology is presented and applied to the life cycles of selected energy systems. Part 2 presents and discusses the results.

Method

This study looks at the complete life cycles of selected energy systems. The methodology used bases on the state-of-the-art of life cycle assessment (LCA) methodology. For the assessment of energetic abiotic resource use, a widely recognised method can be used. For the assessment of non-energetic abiotic resource use, no overall recognised methodological approach exists. That is why for this aspect two different methods are exemplarily applied and compared with each other.

Results and Conclusion

Results will be presented and discussed in Part 2.

Recommendation and Perspective

Recommendation and conclusions will be derived from study results in Part 2.     

Ein Indikator für den Einsatz gefährlicher Stoffe in Produkten und Prozessen: Monoethylenglykol-Äquivalente

In order to quantify hazardous substance use in production processes, a special methodology has been designed within the context of the EcoGrade integrated environmental assessment method developed by the Öko-Institut, Institute for Applied Ecology. This methodology uses monoethylene glycol (MEG) equivalents as an indicator value for hazardous substance use. MEG equivalents permit direct, noxious-substance-focussed comparison of processes and products (Bunke 2001). The assessment is based upon the standardized risk phrases assigned to the component substances. The MEG equivalent methodology is a refinement and application of the potency factor model (Wirkfaktorenmodell) of the German Technical Rule for Hazardous Substances (Technische Regel für Gefahrstoffe, TRGS) 440 (AGS 2001). The data required for the assessment procedure are available within companies (safety data sheets) or are readily accessible publicly (hazardous substance databanks). A further benefit is that inventory analysis of hazardous substances using the method presented here makes it possible to take hazardous substance use into account in a systematic manner within life-cycle assessment (LCA) studies. The methodology has been tested for the example of residential buildings. Note: The terms ‘hazardous substance’, ‘noxious substance’ and ‘hazardous constituent’ are used in this paper in the sense of substances that have one of the hazard attributes set out in Article 3 of the German Chemicals Act (Chemikaliengesetz).     

The Precautionary Principle and the Prevention of Marine Pollution

This paper argues that the environmental changes witnessed in the past decade call for a new approach to environmental management; an approach based not on the principle of the assimilative capacity of the environment but on the precautionary principle, and the emerging preventive environmental paradigm. Uncertainties in scientific knowledge and complexities in ecological systems have presented specific failures of the assimilative capacity methodology. It is argued that these failures are not circumstantial in nature, nor are they the result of misapplication of science by scientists. Rather, they represent inherent problems in the use of the assimilative capacity concept in environmental management. The emergence of the precautionary principle is discussed and a formulation of the principle is presented. In conjunction with the operational approach of clean production, we believe that this principle offers a sounder basis for the prevention of marine pollution in the next decade.     

The Precautionary Principle and the Prevention of Marine Pollution

This paper argues that the environmental changes witnessed in the past decade call for a new approach to environmental management; an approach based not on the principle of the assimilative capacity of the environment but on the precautionary principle, and the emerging preventive environmental paradigm. Uncertainties in scientific knowledge and complexities in ecological systems have presented specific failures of the assimilative capacity methodology. It is argued that these failures are not circumstantial in nature, nor are they the result of misapplication of science by scientists. Rather, they represent inherent problems in the use of the assimilative capacity concept in environmental management. The emergence of the precautionary principle is discussed and a formulation of the principle is presented. In conjunction with the operational approach of clean production, we believe that this principle offers a sounder basis for the prevention of marine pollution in the next decade.     

Sampling variability and estimates of density dependence: a composite-likelihood approach

It is well known that sampling variability, if not properly taken into account, affects various ecologically important analyses. Statistical inference for stochastic population dynamics models is difficult when, in addition to the process error, there is also sampling error. The standard maximum-likelihood approach suffers from large computational burden. In this paper, I discuss an application of the composite-likelihood method for estimation of the parameters of the Gompertz model in the presence of sampling variability. The main advantage of the method of composite likelihood is that it reduces the computational burden substantially with little loss of statistical efficiency. Missing observations are a common problem with many ecological time series. The method of composite likelihood can accommodate missing observations in a straightforward fashion. Environmental conditions also affect the parameters of stochastic population dynamics models. This method is shown to handle such nonstationary population dynamics processes as well. Many ecological time series are short, and statistical inferences based on such short time series tend to be less precise. However, spatial replications of short time series provide an opportunity to increase the effective sample size. Application of likelihood-based methods for spatial time-series data for population dynamics models is computationally prohibitive. The method of composite likelihood is shown to have significantly less computational burden, making it possible to analyze large spatial time-series data. After discussing the methodology in general terms, I illustrate its use by analyzing a time series of counts of American Redstart (Setophaga ruticilla) from the Breeding Bird Survey data, San Joaquin kit fox (Vulpes macrotis mutica) population abundance data, and spatial time series of Bull trout (Salvelinus confluentus) redds count data.     

Including model uncertainty in estimating variances in multiple capture studies

We present bootstrap-based methods which incorporate model uncertainty in estimating variances in multiple capture studies. Each of our three methods has a specific set of properties, and we discuss when each method should be used. Our first method can be used in any multiple capture setting, but it gives an estimate of the variance conditional on the number of observed animals. Our other two methods yield estimates of the unconditional variance; they require good estimates of part or all of the specific probability model, respectively. Smoothed estimated cell probabilities are utilized by the latter method. We contrast the three methods on a real-life data set, and then conduct simulations for a simple setting. Finally, we detail the use of our methodology for specific settings and discuss adaptations for tag-return studies.     

Anwendung energetischer und nicht-energetischer Ressourcen in Energiesystemen: Vergleichende Bewertung

Goal and Scope

Which impact does the use of non-energetic abiotic resources (ores, minerals, etc.) have in life cycles of energy systems based on biogenic and fossil fuels? Is this kind of resource use less or more environmentally harmful than the utilisation of energetic abiotic resources (mineral oil, natural gas, etc.) in the same life cycles? This paper aims at answering these questions. In Part 1, a methodology is presented and applied to the life cycles of selected energy systems. Part 2 presents and discusses the results.

Methods

The applied methodology has been explained in the Part 1. For the assessment of energetic abiotic resource use, a widely recognised method is used. For the assessment of nonenergetic abiotic resource use, no overall recognised methodological approach exists. That is why for this aspect two different methods are exemplarily applied and compared with each other.

Results and Conclusion

Results show that the two assessment approaches for non-energetic resource utilisation lead to qualitatively equal results. Nevertheless they differ considerably from each other in their absolute values. This makes obvious that there is still a need for further methodological research work on that issue. Nevertheless, both methodologies yield that the accumulated effect of ore and mineral use is considerably lower than the accumulated effect of fossil primary energy utilisation in all life cycles analysed. With the assumptions made, the use of non-energetic abiotic resources only plays a comparatively subordinate role in the environmental life cycle assessment of energy systems based on biogenic and fossil fuels.

Recommendation and Perspective

Results suggest that an important resource-related impact of biomass and fossil fuel powered energy systems is caused by their consumption of fossil primary energy resources. The impact of non-energetic resource use can be neglected in comparison to that. At the same time, results also make clear that there is still a considerable need for further methodical research aiming at a standardised assessment methodology for the use of non-energetic abiotic resources.     

A linear program model for point-nonpoint source control decisions: Theoretical development

A linear programming model for point-nonpoint pollutant source control decisions has been developed. Total phosphorus was highlighted as the water quality substance of concern, although the methodology presented is general enough to permit its use for other substances.The linear program was formulated to minimize the cost of meeting established constraints. Methods to achieve removal of pollutant sources included point and nonpoint (urban and rural) techniques. Constraints were established on the basis of available removal technologies and water quality considerations. The final model is presented in a form such that use of existing linear and separable program software is possible.     

Modeling grassland primary productivity using piecewise stationary,piecewise linear mathematics

A methodology for characterizing a complex ecological process — primary production — using linear mathematics is presented. The resulting model uses a limiting factor approach for calculating realized productivity based on prevailing conditions of moisture, temperature, incident radiation and available nutrients. Processes such as leaching and respiration are keyed to environmental variables. Simulation values are presented and compared to measured data.     

Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites

Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.     

Activated carbon adsorption,activated sludge and ozonation treatments evaluated by impact intensity based on acute toxicity test

A Toxicity Reduction Evaluation (TRE) conducted on various water treatments was successful in optimizing the acute toxicity of the final effluent. In particular, we found that the acute toxicity of the sample water treated by a water treatment process should not be correlated with the concentrations of the parent compounds still remaining in the water. Therefore, it was concluded that an acute toxicity test based on a bioassay must be performed in order to evaluate the efficiency of the water treatment process for various effluents containing environmental pollutants. In addition, the treatability and the resultant toxicity was evaluated as a simple scoring system. The scoring system ranked the treated water in three classes of toxicity with the calculation of several factors for the concerned toxicity. Using an impact evaluation based on the score of the characteristics, different water treatment processes could be directly compared. The new methodology presented here is specifically effective in listing possible unit operations for a water treatment process based on the impact, and thereby enables targeted and cost-effective water treatment.     

Modelling the distribution of plant species using the autologistic regression model

For modeling the distribution of plant species in terms of climate covariates, we consider an autologistic regression model for spatial binary data on a regularly spaced lattice. This model belongs to the class of autologistic models introduced by Besag (1974). Three estimation methods, the coding method, maximum pseudolikelihood method and Markov chain Monte Carlo method are studied and comparedvia simulation and real data examples. As examples, we use the proposed methodology to model the distributions of two plant species in the state of Florida.     

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.     

The impact of coastal flooding on conservation areas: A study of the Clyde Estuary, Scotland

This study examines the impact of flooding on land of high conservation value located along part of the southern shoreline of the River Clyde Estuary in western Scotland. This paper hypothesizes that, over the next 50 years, the frequency and extent of coastal flooding will increase due to the gradual effect of global warming and the consequent rise in sea-level and increase in storminess. It is argued that because of the great cost of constructing new flood defence systems it will not be possible to protect all land areas to an equal extent from flooding. A means of ranking different land use will be necessary so that society can make a rational judgement concerning which sections of coastline will be worth protecting. This study provides a methodology that combines an objective ranking of conservation areas using non-economic indicators with a GIS model of flood potential, and permits accurate forecasts of flood losses to conservation areas of different ecological value. The conservation case study used in this paper proposes the use of an ecological weighting value based on five ecological variables each of 10 categories. Tables and maps identify the sites that have been highlighted as consisting of the most ‘valuable’ conservation sites. The methodology makes extensive use of geographical information systems (GIS) to model the predicted areas of flooding and to calculate conservation weighting values of the land areas.     

The impact of coastal flooding on conservation areas: A study of the Clyde Estuary, Scotland

This study examines the impact of flooding on land of high conservation value located along part of the southern shoreline of the River Clyde Estuary in western Scotland. This paper hypothesizes that, over the next 50 years, the frequency and extent of coastal flooding will increase due to the gradual effect of global warming and the consequent rise in sea-level and increase in storminess. It is argued that because of the great cost of constructing new flood defence systems it will not be possible to protect all land areas to an equal extent from flooding. A means of ranking different land use will be necessary so that society can make a rational judgement concerning which sections of coastline will be worth protecting. This study provides a methodology that combines an objective ranking of conservation areas using non-economic indicators with a GIS model of flood potential, and permits accurate forecasts of flood losses to conservation areas of different ecological value. The conservation case study used in this paper proposes the use of an ecological weighting value based on five ecological variables each of 10 categories. Tables and maps identify the sites that have been highlighted as consisting of the most ‘valuable’ conservation sites. The methodology makes extensive use of geographical information systems (GIS) to model the predicted areas of flooding and to calculate conservation weighting values of the land areas.