On the problem of model validation for predictive exposure assessments
On the problem of model validation for predictive exposure assessments
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DOI：
10.1007/bf02427917
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发表时间：
1997-06-01
期刊：
影响因子：
--
通讯作者：
Barnwell, TO
中科院分区：
文献类型：
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作者：
Beck, MB;Ravetz, JR;Barnwell, TO
开发和使用预测接触的模型越来越普遍，对美国环境保护署的许多风险评估至关重要。环境保护局为协助管理或政策决定而进行的暴露评估经常受到挑战，以证明其“科学有效性”。因此，模型验证已不可避免地成为EPA官员和监管社区的主要关注点，足以使EPA的风险评估论坛正在考虑模型验证的指导。本文件旨在编纂的问题和广泛的上述讨论的验证，特别是参考的发展和使用模型预测新的化学品对环境的影响。它的编写是为环境保护局风险评估论坛制定白色文件过程的一部分。其主题来自各个领域，包括生态系统分析、地表水质量管理、高放射性核废料对地下水的污染以及空气质量控制。模型验证的哲学和概念基础进行审查，从它是显而易见的，验证应被理解为一个任务的产品（或工具）的设计，最终将需要某种形式的质量保证协议。对模型验证的常用程序和方法进行了评述，包括不确定度分析。在对过去试图解决模型验证问题的尝试进行调查之后，我们通过引入与特定任务的性能具有最大相关性的模型的概念来结束，例如预测性暴露评估。
The development and use of models for predicting exposures are increasingly common and are essential for many risk assessments of the United States Environmental Protection Agency (EPA). Exposure assessments conducted by the EPA to assist regulatory or policy decisions are often challenged to demonstrate their ''scientific validity''. Model validation has thus inevitably become a major concern of both EPA officials and the regulated community, sufficiently so that the EPA's Risk Assessment Forum is considering guidance for model validation. The present paper seeks to codify the issues and extensive foregoing discussion of validation with special reference to the development and use of models for predicting the impact of novel chemicals on the environment. Its preparation has been part of the process in formulating a White Paper for the EPA's Risk Assessment Forum. Its subject matter has been drawn from a variety of fields, including ecosystem analysis, surface water quality management, the contamination of groundwaters from high-level nuclear waste, and the control of air quality. The philosophical and conceptual bases of model validation are reviewed, from which it is apparent that validation should be understood as a task of product (or tool) design, for which some form of protocol for quality assurance will ultimately be needed. The commonly used procedures and methods of model validation are also reviewed, including the analysis of uncertainty. Following a survey of past attempts at resolving the issue of model validation, we close by introducing the notion of a model having maximum relevance to the performance of a specific task, such as, for example, a predictive exposure assessment.