Holistochastic approach to human exposure assessment

在超级基金项目的前几年，很明显需要一个严格的建模框架，以便对环境命运、接触污染物以及由此产生的健康影响（对个体受体和人口水平）进行综合分析。事实上，如何有效地开发和应用这样的建模框架是当前人体暴露分析和风险评估中最重要的突出问题之一。在这个项目中，我们提出了一个整体贝叶斯最大熵（BME）框架来模拟环境命运和由此产生的污染物的时空分布，并调查对暴露人群的影响(术语“整体”意味着人类暴露整体的现实大于其部分的总和环境命运，暴露，健康影响等；术语“随机”指的是由于每个阶段涉及的物理变化和生物不确定性，我们需要采用人类暴露的概率特征)。我们打算开发一个非常通用和灵活的建模框架，涵盖各种各样的污染物分布，并涉及一个反馈过程，以促进必要的变化，因为更多的涉及到未来的人类暴露。BME框架允许与人类暴露问题相关的科学之间的横向整合，从而导致暴露和影响分布的准确和信息丰富的时空地图，并对整个风险案例进行综合分析。通过处理各种知识库，BME可以将与所研究的人类暴露现实方面相关的几门科学结合起来。风险评估问题将在BME框架内以综合和互动的方式解决。在开发BME建模框架时，将特别强调建立一个工作环境，使人体暴露建模和实验室（或现场）研究以一种有意义的方式相互结合。因此，BME是跨学科努力的核心组成部分，以模拟涉及自然变量、暴露机制、生物过程、生理参数和流行病学指标的环境卫生系统。在这种情况下，人体暴露分析和科学风险评估的一个重要组成部分是整体的BME。

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.