Soil properties play important roles in a lot of environmental issues like diffuse pollution, erosion hazards or precision agriculture. With the developments of soil process models and geographical information systems, the need for accurate knowledge about soil properties becomes more acute. However, while the sources of information become each year more numerous and diversified, they rarely provide us with data at the same time having the required level of spatial and attribute accuracy. An important challenge thus consists in combining those data sources at best so as to meet the high accuracy requirements. The Bayesian Maximum Entropy (BME) approach appears as a potential candidate for achieving this task: it is especially designed for managing simultaneously data of various nature and quality ("hard" and "soft" data, continuous or categorical). It relies on a two-steps procedure involving an objective way for obtaining a prior distribution in accordance with the general knowledge at hand (the ME part), and a Bayesian conditionalization step for updating this prior probability distribution function (pdf) with respect to the specific data collected on the study site. At each prediction location, an entire pdf is obtained, allowing subsequently the easy computation of elaborate statistics chosen for their adequacy with the objectives of the study. In this thesis, the theory of BME is explained in a simplified way using standard probabilistic notations. The recent developments towards categorical variables are incorporated and an attempt is made to formulate a unified framework for both categorical and continuous variables, thus emphasizing the generality and flexibility of the BME approach. The potential of the method for predicting continuous variables is then illustrated by a series of studies dealing with the soil texture fractions (sand, silt and clay). For the categorical variables, a case study focusing on the prediction of the status of the water table is presented. The use of multiple and sometimes contradictory data sources is also analyzed. Throughout the document, BME is compared to classic geostatistical techniques like simple, ordinary or indicator kriging. Thorough discussions point out the inconsistencies of those methods and explain how BME is solving the problems. Rather than being but another geostatistical technique, BME has to be considered as a knowledge processing approach. With BME, practitioners will find a valuable tool for analyzing their spatio-temporal data sets and for providing the stake-holders with accurate information about the environmental issues to which they are confronted. Read one of the articles extracted from Chapter V at : D'Or D., Bogaert P. and Christakos, G. (2001). Application of the BME Approach to Soil Texture Mapping. Stochastic Environmental Research and Risk Assessment 15(1): 87-100 ©Springer-2001. http://springerlink.metapress.com/app/home/contribution.asp?wasp=cbttlcpaeg1rqmdb4xv2&referrer=parent&backto=issue,6,6;journal,13,29;linkingpublicationresults,1,1

Various environmental standards have been established for the sake of public health and ecosystem diversity since environmental awareness was awakened in the late 1960s. However, the results were often unsatisfactory. Either environmental goals achieved were far from desired, or regional development was hampered due to some unpractical high environmental standards. The failure of these environmental standards resulted in innovations of environmental policy instruments to find practical environmental goals and methods approaching them scientifically. Another class of environmental policy instruments, so called economic incentive policies, is established based on environmental economics theory. A neo-classical economics framework is founded for setting appropriate environmental goals and assessing efficiency of environmental policies in reaching these goals. This thesis summarizes rationales and factors affecting the performance for environmental policy instruments under the neo-classical economic framework. Since the acid rain program, the first large-scale implementation of the emissions trading policy, has achieved great success in reducing SO₂ emissions from the electricity generators in the United States, the emission trading policy attracted many interests in this kind of environmental policy instrument. Many countries, such as China, plan to adopt the emissions trading policy to address various environmental problems. Hence, factors leading to the success of this program should be identified. Potential risks and problems must be addressed as well lest the emissions trading policy causes some problem during implementation. Feasibility of implementing an emissions trading policy will be discussed based on these results. Three kinds of geographic analyses, change detection, network analysis, and hot spots identification, are conducted in this thesis to study the effectiveness and efficiency of the acid rain program. It is found that the acid rain program is successful in improving the sustainability of the economic development in the United States. But the effectiveness is not as great as the high emissions cutting rate achieved in this program. In addition, the acid rain program lowers the compliance costs of achieving the environmental goal since the radius of the high quality coal service area doubles. Lastly, hot spots are found around the Ohio River valley and Los Angeles. Suggestions on integrating geographic factors into the economic framework are presented in order to eliminate the risk of causing severe environmental problems. Finally, the feasibility of migrating the emissions trading policy to China is discussed. Further work can be conducted in this direction to realize sustainable development quicker with lower costs.

Various environmental standards have been established for the sake of public health and ecosystem diversity since environmental awareness was awakened in the late 1960s. However, the results were often unsatisfactory. Either environmental goals achieved were far from desired, or regional development was hampered due to some unpractical high environmental standards. The failure of these environmental standards resulted in innovations of environmental policy instruments to find practical environmental goals and methods approaching them scientifically. Another class of environmental policy instruments, so called economic incentive policies, is established based on environmental economics theory. A neo-classical economics framework is founded for setting appropriate environmental goals and assessing efficiency of environmental policies in reaching these goals. This thesis summarizes rationales and factors affecting the performance for environmental policy instruments under the neo-classical economic framework. Since the acid rain program, the first large-scale implementation of the emissions trading policy, has achieved great success in reducing SO₂ emissions from the electricity generators in the United States, the emission trading policy attracted many interests in this kind of environmental policy instrument. Many countries, such as China, plan to adopt the emissions trading policy to address various environmental problems. Hence, factors leading to the success of this program should be identified. Potential risks and problems must be addressed as well lest the emissions trading policy causes some problem during implementation. Feasibility of implementing an emissions trading policy will be discussed based on these results. Three kinds of geographic analyses, change detection, network analysis, and hot spots identification, are conducted in this thesis to study the effectiveness and efficiency of the acid rain program. It is found that the acid rain program is successful in improving the sustainability of the economic development in the United States. But the effectiveness is not as great as the high emissions cutting rate achieved in this program. In addition, the acid rain program lowers the compliance costs of achieving the environmental goal since the radius of the high quality coal service area doubles. Lastly, hot spots are found around the Ohio River valley and Los Angeles. Suggestions on integrating geographic factors into the economic framework are presented in order to eliminate the risk of causing severe environmental problems. Finally, the feasibility of migrating the emissions trading policy to China is discussed. Further work can be conducted in this direction to realize sustainable development quicker with lower costs.

When modelling pollutants in the atmosphere, it is nearly impossible to get perfect results as the chemical and mechanical processes that govern pollutant concentrations are complex. Results are dependent on the quality of the meteorological input as well as the emissions inventory used to run the model. Also, models cannot currently take every process into consideration. Therefore, the model may get results that are close to, or show the general trend of the observed values, but are not perfect. However, due to the lack of observation stations, the resolution of the observational data is poor. Furthermore, the chemistry over large bodies of water is different from land chemistry, and in North America, there are no stations located over the great lakes or the ocean. Consequently, the observed values cannot accurately cover these regions. Therefore, we have combined model output and observational data when studying ozone concentrations in north eastern North America. We did this by correcting model output at observational sites with local data. We then interpolated those corrections across the model grid, using a Kriging procedure, to produce results that have the resolution of model results with the local accuracy of the observed values. Results showed that the corrected model output is much improved over either model results or observed values alone. This improvement was observed both for sites that were used in the correction process as well as sites that were omitted from the correction process.

Yield analysis is a critical step in memory designs considering a variety of performance constraints. Traditional circuit level Monte-Carlo simulations for yield estimation of Static Random Access Memory (SRAM) cell is quite time consuming due to their characteristic of low failure rate, while statistical method of yield sensitivity analysis is meaningful for its high efficiency. This thesis proposes a novel statistical model to conduct yield sensitivity prediction on SRAM cells at the simulation level, which excels regular circuit simulations in a significant runtime speedup. Based on the theory of Kriging method that is widely used in geostatistics, we develop a series of statistical model building and updating strategies to obtain satisfactory accuracy and efficiency in SRAM yield sensitivity analysis. Generally, this model applies to the yield and sensitivity evaluation with varying design parameters, under the constraints of most SRAM performance metric. Moreover, it is potentially suitable for any designated distribution of the process variation regardless of the sampling method.

In recent years, the application of resampling methods to dependent data, such as time series or spatial data, has been a growing field in the study of statistics. In this dissertation, we discuss two such applications. In spatial statistics, the reliability of Kriging prediction methods relies on the observations coming from an underlying Gaussian process. When the observed data set is not from a multivariate Gaussian distribution, but rather is a transformation of Gaussian data, Kriging methods can produce biased predictions. Bootstrap resampling methods present a potential bias correction. We propose a parametric bootstrap methodology for the calculation of either a multiplicative or additive bias correction factor when dealing with Trans-Gaussian data. Furthermore, we investigate the asymptotic properties of the new bootstrap based predictors. Finally, we present the results for both simulated and real world data. In time series analysis, the estimation of covariance parameters is often of utmost importance. Furthermore, the understanding of the distributional behavior of parameter estimates, particularly the variance, is useful but often difficult. Block bootstrap methods have been particularly useful in such analyses. We introduce a new procedure for the estimation of covariance parameters for replicated time series data.

Bei der Entwicklung neuer Produkte nehmen numerische Simulationen eine immer größere Rolle ein. Dadurch entsteht die Möglichkeit, relativ kostengünstig das neue Produkt zu testen, noch bevor ein teurer Prototyp angefertigt werden muss. Diese Möglichkeit weckt das Verlangen, Teile des Designprozesses zu automatisieren. Aber selbst mit den modernsten Algorithmen und Rechnern sind einige dieser Simulationen sehr zeitaufwändig, d.h. im Bereich von Minuten bis Stunden. Beispiele aus dem Automobilbereich dafür sind Kettentriebssimulationen, Strömungssimulationen oder Crashsimulationen. Mathematisch stehen dafür das Lösen von Differential-Algebraischen Gleichungen und partiellen Differentialgleichungen. Ziele des teilweise automatischen Designprozesses sind die Funktionsfähigkeit und möglichst optimale weitere Eigenschaften wie beispielsweise Leistung oder Kosten. In dieser Arbeit werden Optimierungsprobleme betrachtet, bei denen die Auswertung der Zielfunktion eine numerische Simulation erfordert. Um solche Probleme in annehmbarer Zeit lösen zu können, braucht man also Optimierungsverfahren, die mit wenigen Funktionsauswertungen schon gute Näherungen des globalen Optimums finden können. In dieser Arbeit werden Ersatzmodell-gestützte Optimierungsverfahren, die eine Kriging-Approximation benutzen, betrachtet. Diese Verfahren besitzen die oben genannten Anforderungen, sind aber nur eingeschränkt parallelisierbar. Die Arbeit gliedert sich wie folgt. Die für diese Arbeit benötigten Grundlagen der Optimierung werden im zweiten Kapitel vorgestellt. Das dritte Kapitel beschäftigt sich mit der Theorie der Kriging- Approximation. Die Verwendung eines Ersatzmodells zur Optimierung und die Parallelisierung der entstehenden Verfahren sind das Thema des vierten Kapitels. Im fünften Kapitel werden die vorgestellten Verfahren numerisch verifiziert und es werden Vorschläge für die Anwendung gegeben. Das sechste Kapitel gibt einen Überblick über die Kettentriebskonstruktion und die Verwendung der vorgestellten Algorithmen. Das letzte Kapitel fasst die erreichten Ziele zusammen und gibt Vorschläge für weitere Verbesserungen und Forschungsthemen.

Measurement error is an important issue in studies of environmental epidemiology. We considered the effects of measurement error in environmental covariates in several important settings affecting current public health research. Throughout this dissertation, we investigate the impacts of measurement error and consider statistical methodology to fix that error.

During the past two decades, University of Kentucky researchers have been acquiring seismic refraction/reflection data, as well as seismic downhole data, for characterizing the seismic velocity models of the soil/sediment overburden in the central United States. The dataset includes densely spaced measurements for urban microzonation studies and coarsely spaced measurements for regional assessments. The 519 measurements and their derivative products often were not in an organized electronic form, however, limiting their accessibility for use by other researchers. In order to make these data more accessible, this project constructed a database using the ArcGIS 9.1 software. The data have been formatted and integrated into a system serving a wider array of users. The seismic shear-wave velocity models collected at various locations are archived with corresponding x-, y-, and z-coordinate information. Flexibility has been included to allow input of additional data in the future (e.g., seismograms, strong ground-motion parameters and time histories, weak-motion waveform data, etc.). Using the completed database, maps of the region showing derivative dynamic site period (DSP) and weighted shear-wave velocity of the upper 30 m of soil (V30) were created using the ArcGIS 9.1 Geostatistical Analyst extension for examination of the distribution of pertinent dynamic properties for seismic hazard assessments. Both geostatistical and deterministic techniques were employed. Interpolation of V30 data yielded inaccurate predictions because of the high lateral variation in soil layer lithology in the Jackson Purchase Region. As a result of the relatively uniform distribution of depths to bedrock, the predictions of DSP values suggested a high degree of accuracy.

Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.