Holistochastic approach to human exposure assessment

在实施超级基金项目的前几年中，显然需要一个严格的模型框架，以便能够综合分析环境命运、接触污染物以及由此产生的健康影响（对个人受体和人口水平）。事实上，如何有效地开发和应用这样一个建模框架的问题是当前人类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随机贝叶斯最大熵（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;“随机”一词是指由于每个阶段都涉及物理变化和生物不确定性，我们需要对人体接触进行概率表征）。我们的目的是开发一个非常通用和灵活的建模框架，涵盖各种污染物分布，并涉及一个反馈过程，以促进必要的变化，因为更多的是在未来的人类暴露。BME框架允许与人类暴露问题相关的科学之间的横向整合，导致准确和翔实的暴露和影响分布的时空图和整个风险案例的综合分析。通过处理各种知识库，BME可以汇集与所研究的人类暴露现实方面相关的几门科学。风险评估问题将在生物多样性监测和评价框架内以综合和互动方式加以处理。在开发BME建模框架的同时，将特别强调建立一个工作环境，使人类暴露建模和实验室（或现场）研究以有意义的方式相互结合。因此，生物监测是一个跨学科的努力，模拟环境卫生系统，其中涉及自然变量，接触机制，生物过程，生理参数和流行病学指标的一个核心组成部分。在这种情况下，全随机BME是人类接触分析和科学风险评估的重要组成部分。

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.