Variability conceptual model

FIGURE 2.3 A simple approach to identifying variables, uncertainties, and dependencies in a conceptual model diagram. For key, see Table 2.2. 

A critical extra phase to be included when planning probabilistic assessments is the selection and parameterization of distributions, to represent the sources of variability and uncertainty that have been identified in the conceptual model. The issues and approaches involved are discussed elsewhere in this book. 

Exposure assessment, however, is a highly complex process having different levels of uncertainties, with qualitative and quantitative consequences. Exposure assessors must consider many different types of sources of exposures, the physical, chemical and biological characteristics of substances that influence their fate and transport in the environment and their uptake, individual mobility and behaviours, and different exposure routes and pathways, among others. These complexities make it important to begin with a clear definition of the conceptual model and a focus on how uncertainty and variability play out as one builds from the conceptual model towards the mathematical/statistical model. 

Pore Size Distribution. The pore size distribution is a measure of the average size of the pores and the variability of pore sizes. It is usually determined by mercury porosimetry. This technique is based on a simple conceptual model of the pores that treats the pores as capillary tubes. The pressure required to force mercury into a pore (assuming that the pore behaves like a circular capillary) can be related to the radius of the pore by... 

The conceptual model of the risk assessment is the framework into which the data are placed. Like the selection of endpoints, the selection of a useful conceptual model is crucial to the success or failure of the risk assessment process. In some cases a simple single species model may be appropriate. Typically, models in ecological risk assessment are comprised of many parts and attempt to deal with the variability and plasticity of natural systems. Exposure to the system may come from many different sources. The consideration of organisms at risk depends upon the migratory and breeding habits of numerous organisms, many rare and specialized. 

Wilson, I.B. Cleary, P.D. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA, J. Am. Med. Assoc. 1995, 273, 59-65. 

Common conceptual models for liquid distribution and transport in variably saturated porous media often rely on oversimplified representation of media pore space geometry as a bundle of cylindrical capillaries, and on incomplete thermodynamic account of pore scale processes. For example, liquid adsorption due to surface forces and flow in thin films are often ignored. In this study we provide a review of recent progress in modeling liquid retention and interfacial configurations in variably saturated porous media and application of pore scale hydrodynamic considerations for prediction of hydraulic conductivity of unsaturated porous media. 

The selection of sites for monitoring must take into account the three-dimensional nature of the groundwater body, flow characteristics, variability of land use, ground-water vulnerability and the potential receptors. All these should have been identified in file conceptual model. An effective network of monitoring sites will be one that is able to detect tlie impacts from pressures and the evolution in groundwater quality along flow paths within the groundwater body. 

The conceptual model in Figure 2, combined with a continuum model approach, is shown to be appropriate for the analysis of THM processes at the DST because the rock mass is highly fractured, forming a dense, wellfracture network for fluid flow. This differs from many other fractured rock sites in Canada, Europe, and Asia, where underground tests have been conducted in sparsely fractured crystalline rocks (Rutqvist and Stephansson, 2003). In those formations, fluid flow is dominated by a few widely spaced fractures, which means that a continuum approach may not apply on the drift scale. In relation to other fractured rock sites, the rock mass at Yucca Mountain is relatively homogenous (ubiquitously fractured), with much less variability in rock-mass mechanical and hydrological properties. 

Coupled THMC modelling has to incorporate uncertainties. These uncertainties mainly concern uncertainties in the conceptual model and uncertainty in data, where the latter is related to fact that geologic media usually show strong spatial variability. 

The relative effect of the uncertainties above on the performance of the mathematical model depends on the system but the uncertainty in the conceptual model dominates in many cases. The effect of parameter uncertainty and variability depends on the system, and is to some extent possible to quantify by sensitivity and imcertainty analysis. However, uncertainty in the conceptual model is difficult to assess. A qualitative systematic description of the physical and chemical processes, their importance and interactions, and boundary conditions is necessary in order to minimize the uncertainty in the conceptual model. 

Conceptual models. This category is for macro-level. It is flexible and based on qualitative variables and hypothesis approach (She-rif and Kolarik 1981). 

It must be reemphasized that hybridization is only a conceptual and a mathematical model that allows us to calculate molecular parameters. Changing hybridization is simply modifying that original model to suit a current need, just as the concept of h)4)ridization represents only a change to the model of atomic energy levels. Variable hybridization should not be considered more fundamental than the VSEPR model, just more mathematical. The ability to make quantitative predictions of molecular geometry and physical properties makes the variable hybridization model quite useful for some problems. On the other hand, the VSEPR model is also valuable as an intuitive basis for qualitatively correct predictions. As is so often the case, we need not decide which of two complementary models to adopt for all situations we need only to determine which best serves our purposes in a particular case. 

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