During the previous years of the Superfund project it has become obvious that there is a need for a rigorous modeling framework that it will permit the integrated analysis of environmental fate, exposure to pollutants and the resulting health impacts (on the individual receptor and the population levels). In fact, the issue of how to develop and apply efficiently such a modeling framework is one of the most important outstanding questions in current human exposure analysis and risk assessment. In this project we propose a holistochastic Bayesian maximum entropy (BME) framework to model the spatiotemporal distribution of environmental fate and resulting pollutants and to investigate the effects on the exposed population (the term """"""""holistic"""""""" implies a human exposure whole that has a reality greater than the sum of its parts environmental fate, exposure, health effects, etc.; the term """"""""stochastic"""""""" denotes that as a result of the physical variations and biological uncertainties involved at every stage, we need to employ a probablistic characterization of human exposure). It is our intention to develop a very general and flexible modeling framework that covers a wide variety of pollutant distributions and involves a feedback process to facilitate that necessary changes as more is covered about human exposure in the future. The BME framework allows the horizontal integration among sciences related to the human exposure problem that leads to accurate and informative spatiotemporal maps of exposure and effect distributions and an integrative analysis of the whole risk case. By processing a variety of knowledge bases, BME can bring together several sciences which are relevant to the aspect of human exposure reality that is examined. Risk assessment issues will be addressed integratively and interactively within the BME framework. While developing the BME modeling framework, particular emphasis will be given to establishing a working environment where human exposure modeling and laboratory (or field) research are integrate with each other in a meaningful way. BME is thus a central component of an interdisciplinary effort to model environmental health systems which involve natural variables, exposure mechanisms, biological processes, physiology parameters, and epidemiological indicators. In this context, holistochastic BME is a vital component of human exposure analysis and scientific risk assessment.
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