Current Air Quality Issues

Air pollution is thus far one of the key environmental issues in urban areas. Comprehensive air quality plans are required to manage air pollution for a particular area. Consequently, air should be continuously sampled, monitored, and modeled to examine different action plans. Reviews and research papers describe air pollution in five main contexts: Monitoring, Modeling, Risk Assessment, Health, and Indoor Air Pollution. The book is recommended to experts interested in health and air pollution issues

Analyse de sensibilité des incertitudes paramétriques dans les évaluations d’aléas géotechniques

Epistemic uncertainty can be reduced via additional lab or in site measurements or additional numerical simulations. We focused here on parameter uncertainty: this corresponds to the incomplete knowledge of the correct setting of the input parameters (like values of soil properties) of the model supporting the geo-hazard assessment. A possible option tomanage it is via sensitivity analysis, which aims at identifying the contribution (i.e. the importance) of the different input parameters in the uncertainty on the final hazard outcome. For this purpose, advanced techniques exist, namely variance-based global sensitivity analysis. Yet, their practical implementation faces three major limitations related to the specificities of the geo-hazard domain: 1. the large computation time cost (several hours if not days) of numerical models; 2. the parameters are complex functions of time and space; 3. data are often scarce, limited if not vague. In the present PhD thesis, statistical approaches were developed, tested and adapted to overcome those limits. A special attention was paid to test the feasibility of those statistical tools by confronting them to real cases (natural hazards related to earthquakes, cavities and landslides).Les incertitudes épistémiques peuvent être réduites via des études supplémentaires (mesures labo, in situ, ou modélisations numériques, etc.). Nous nous concentrons ici sur celle "paramétrique" liée aux difficultés à évaluer quantitativement les paramètres d’entrée du modèle utilisé pour l’analysedes aléas géotechniques. Une stratégie de gestion possible est l’analyse de sensibilité, qui consiste à identifier la contribution (i.e. l’importance) des paramètres dans l’incertitude de l’évaluation de l’aléa. Des approches avancées existent pour conduire une telle analyse. Toutefois, leur applicationau domaine des aléas géotechniques se confronte à plusieurs contraintes : 1. le coût calculatoire des modèles numériques (plusieurs heures voire jours) ; 2. les paramètres sont souvent des fonctions complexes du temps et de l’espace ; 3. les données sont souvent limitées, imprécises voire vagues. Danscette thèse, nous avons testé et adapté des outils statistiques pour surmonter ces limites. Une attention toute particulière a été portée sur le test de faisabilité de ces procédures et sur la confrontation à des cas réels (aléas naturels liés aux séismes, cavités et glissements de terrain)

Quality control charts under random fuzzy measurements

Includes bibliographical references. .We consider statistical process control charts as tools that statistical process control utilizes for monitoring changes; identifying process variations and their causes in industrial processes (manufacturing processes) and which help manufacturers to take the appropriate action, rectify problems or improve manufacturing processes so as to produce good quality products. As an essential tool, researchers have always paid attention to the development of process control charts. Also, the sample sizes required for establishing control charts are often under discussion depending on the field of study. Of late, the problem of Fuzziness and Randomness often brought into modern manufacturing processes by the shortening product life cycles and diversification (in product designs, raw material supply etc) has compelled researchers to invoke quality control methodologies in their search for high customer satisfaction and better market shares (Guo et al 2006). We herein focus our attention on small sample sizes and focus on the development of quality control charts in terms of the Economic Design of Quality Control Charts; based on credibility measure theory under Random Fuzzy Measurements and Small Sample Asymptotic Distribution Theory. Economic process data will be collected from the study of Duncan (1956) in terms of these new developments as an illustrative example. or/Producer, otherwise they are undertaken with respect to the market as a whole. The techniques used for tackling the complex issues are diverse and wide-ranging as ascertained from the existing literature on the subject. The global ideology focuses on combining two streams of thought: the production optimisation and equilibrium techniques of the old monopolistic, cost-saving industry and; the new dynamic profit-maximising and risk-mitigating competitive industry. Financial engineering in a new and poorly understood market for electrical power must now take place in conjunction with - yet also constrained by - the physical production and distribution of the commodity

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Digital Soil Mapping Approaches for Assisting Site-Specific Soil Management in Sugarcane Growing Areas

The Australian sugarcane industry has developed the “Six Easy Steps” nutrient and ameliorant management guidelines with the aim of optimising productivity and profitability, without adversely influencing the soil condition and causing off-farm effects. This involves knowing the spatial variation of soil properties, such as; cation exchange capacity (CEC), exchangeable calcium (Exch. Ca) and magnesium (Mg) and exchangeable sodium percentage (ESP). One way to generate soil information is to use a digital soil mapping (DSM) approach. Specifically, combine limited soil data with easier to collect ancillary data via mathematical models. This thesis focusses on developing digital soil maps (DSM) in different Australia sugarcane growing districts. Chapter 1 describes the need for DSM while Chapter 2 describes the basic components of DSM, including proximal sources of ancillary data and mathematical models. Moreover, the literature is reviewed to provide demonstrated case studies of DSM of various soil properties (e.g. CEC, Exch. Ca, Exch. Mg and ESP), with gaps identified and research chapters presented to bridge these. In Chapter 3, the application of DSM to predict CEC is explored to assist with the quantification of uncertainty due to ancillary data. In Chapter 4, the aim was to determine optimal components for DSM of topsoil Exch. Ca and Mg. In Chapter 5, the potential of wavelet analysis was explored where there was complex variation in ancillary data relative to topsoil ESP. In Chapter 6, a comparison was made of DSM to account for topsoil (0 – 0.3 m) ESP using mathematical or numerical clustering (FKM) models to create soil classes with a conventional Soil Order map (e.g. soils and land suitability of Burdekin River Irrigation Area). The results showed DSM can be applied to a wide range of soil properties and classes, especially when all the available ancillary data was used in combination. Useful guidelines on operational aspects including transect spacing (7.5 – 30 m) and soil samples for calibration (1 per hectare) were described. Future research should explore other ancillary data sources (e.g. crop yield), mathematical models (e.g. machine learning) and follow up improvement in soil condition as a function of the application of nutrient and ameliorants in accordance with the “Six Easy Steps” guidelines in the various study areas

Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective

Uncertainty quantification (UQ) in its broadest sense aims at quantitatively studying all sources of uncertainty arising from both computational and real-world applications. Although many subtopics appear in the UQ field, there are typically two major types of UQ problems: forward and inverse uncertainty propagation. The present study focuses on the former, which involves assessing the effects of the input uncertainty in various forms on the output response of a computational model. In total, this thesis reports nine main developments in the context of forward uncertainty propagation, with special emphasis on a Bayesian active learning perspective. The first development is concerned with estimating the extreme value distribution and small first-passage probabilities of uncertain nonlinear structures under stochastic seismic excitations, where a moment-generating function-based mixture distribution approach (MGF-MD) is proposed. As the second development, a triple-engine parallel Bayesian global optimization (T-PBGO) method is presented for interval uncertainty propagation. The third contribution develops a parallel Bayesian quadrature optimization (PBQO) method for estimating the response expectation function, its variable importance and bounds when a computational model is subject to hybrid uncertainties in the form of random variables, parametric probability boxes (p-boxes) and interval models. In the fourth research, of interest is the failure probability function when the inputs of a performance function are characterized by parametric p-boxes. To do so, an active learning augmented probabilistic integration (ALAPI) method is proposed based on offering a partially Bayesian active learning perspective on failure probability estimation, as well as the use of high-dimensional model representation (HDMR) technique. Note that in this work we derive an upper-bound of the posterior variance of the failure probability, which bounds our epistemic uncertainty about the failure probability due to a kind of numerical uncertainty, i.e., discretization error. The fifth contribution further strengthens the previously developed active learning probabilistic integration (ALPI) method in two ways, i.e., enabling the use of parallel computing and enhancing the capability of assessing small failure probabilities. The resulting method is called parallel adaptive Bayesian quadrature (PABQ). The sixth research presents a principled Bayesian failure probability inference (BFPI) framework, where the posterior variance of the failure probability is derived (not in closed form). Besides, we also develop a parallel adaptive-Bayesian failure probability learning (PA-BFPI) method upon the BFPI framework. For the seventh development, we propose a partially Bayesian active learning line sampling (PBAL-LS) method for assessing extremely small failure probabilities, where a partially Bayesian active learning insight is offered for the classical LS method and an upper-bound for the posterior variance of the failure probability is deduced. Following the PBAL-LS method, the eighth contribution finally obtains the expression of the posterior variance of the failure probability in the LS framework, and a Bayesian active learning line sampling (BALLS) method is put forward. The ninth contribution provides another Bayesian active learning alternative, Bayesian active learning line sampling with log-normal process (BAL-LS-LP), to the traditional LS. In this method, the log-normal process prior, instead of a Gaussian process prior, is assumed for the beta function so as to account for the non-negativity constraint. Besides, the approximation error resulting from the root-finding procedure is also taken into consideration. In conclusion, this thesis presents a set of novel computational methods for forward UQ, especially from a Bayesian active learning perspective. The developed methods are expected to enrich our toolbox for forward UQ analysis, and the insights gained can stimulate further studies

Weight of evidence to assess sediment quality

Estuaries are perhaps the most threatened environments in the coastal fringe; the coincidence of high natural value and attractiveness for human use has led to conflicts between conservation and development. These conflicts occur in the Sado Estuary since its location is near the industrialised zone of Peninsula of Setúbal and at the same time, a great part of the Estuary is classified as a Natural Reserve due to its high biodiversity. These facts led us to the need of implementing a model of environmental management and quality assessment, based on methodologies that enable the assessment of the Sado Estuary quality and evaluation of the human pressures in the estuary. These methodologies are based on indicators that can better depict the state of the environment and not necessarily all that could be measured or analysed. Sediments have always been considered as an important temporary source of some compounds or a sink for other type of materials or an interface where a great diversity of biogeochemical transformations occur. For all this they are of great importance in the formulation of coastal management system. Many authors have been using sediments to monitor aquatic contamination, showing great advantages when compared to the sampling of the traditional water column. The main objective of this thesis was to develop an estuary environmental management framework applied to Sado Estuary using the DPSIR Model (EMMSado), including data collection, data processing and data analysis. The support infrastructure of EMMSado were a set of spatially contiguous and homogeneous regions of sediment structure (management units). The environmental quality of the estuary was assessed through the sediment quality assessment and integrated in a preliminary stage with the human pressure for development. Besides the earlier explained advantages, studying the quality of the estuary mainly based on the indicators and indexes of the sediment compartment also turns this methodology easier, faster and human and financial resource saving. These are essential factors to an efficient environmental management of coastal areas. Data management, visualization, processing and analysis was obtained through the combined use of indicators and indices, sampling optimization techniques, Geographical Information Systems, remote sensing, statistics for spatial data, Global Positioning Systems and best expert judgments. As a global conclusion, from the nineteen management units delineated and analyzed three showed no ecological risk (18.5 % of the study area). The areas of more concern (5.6 % of the study area) are located in the North Channel and are under strong human pressure mainly due to industrial activities. These areas have also low hydrodynamics and are, thus associated with high levels of deposition. In particular the areas near Lisnave and Eurominas industries can also accumulate the contamination coming from Águas de Moura Channel, since particles coming from that channel can settle down in that area due to residual flow. In these areas the contaminants of concern, from those analyzed, are the heavy metals and metalloids (Cd, Cu, Zn and As exceeded the PEL guidelines) and the pesticides BHC isomers, heptachlor, isodrin, DDT and metabolits, endosulfan and endrin. In the remain management units (76 % of the study area) there is a moderate impact potential of occurrence of adverse ecological effects and in some of these areas no stress agents could be identified. This emphasizes the need for further research, since unmeasured chemicals may be causing or contributing to these adverse effects. Special attention must be taken to the units with moderate impact potential of occurrence of adverse ecological effects, located inside the natural reserve. Non-point source pollution coming from agriculture and aquaculture activities also seem to contribute with important pollution load into the estuary entering from Águas de Moura Channel. This pressure is expressed in a moderate impact potential for ecological risk existent in the areas near the entrance of this Channel. Pressures may also came from Alcácer Channel although they were not quantified in this study. The management framework presented here, including all the methodological tools may be applied and tested in other estuarine ecosystems, which will also allow a comparison between estuarine ecosystems in other parts of the globe