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基于暴露的化学优先排序的决策分析方法。

A decision analytic approach to exposure-based chemical prioritization.

机构信息

Biosystems & Agricultural Engineering, Michigan State University, East Lansing, Michigan, United States of America.

出版信息

PLoS One. 2013 Aug 5;8(8):e70911. doi: 10.1371/journal.pone.0070911. Print 2013.

Abstract

The manufacture of novel synthetic chemicals has increased in volume and variety, but often the environmental and health risks are not fully understood in terms of toxicity and, in particular, exposure. While efforts to assess risks have generally been effective when sufficient data are available, the hazard and exposure data necessary to assess risks adequately are unavailable for the vast majority of chemicals in commerce. The US Environmental Protection Agency has initiated the ExpoCast Program to develop tools for rapid chemical evaluation based on potential for exposure. In this context, a model is presented in which chemicals are evaluated based on inherent chemical properties and behaviorally-based usage characteristics over the chemical's life cycle. These criteria are assessed and integrated within a decision analytic framework, facilitating rapid assessment and prioritization for future targeted testing and systems modeling. A case study outlines the prioritization process using 51 chemicals. The results show a preliminary relative ranking of chemicals based on exposure potential. The strength of this approach is the ability to integrate relevant statistical and mechanistic data with expert judgment, allowing for an initial tier assessment that can further inform targeted testing and risk management strategies.

摘要

新型合成化学品的制造在数量和种类上都有所增加,但就毒性而言,其环境和健康风险通常还不完全了解,尤其是接触风险。虽然在有足够数据的情况下,评估风险的工作通常是有效的,但对于商业中绝大多数的化学品来说,评估风险所需的危害和接触数据还没有。美国环境保护署已经启动了 ExpoCast 计划,以开发基于暴露风险的快速化学评估工具。在这种情况下,提出了一种模型,根据化学物质在生命周期内的固有化学特性和基于行为的使用特征来评估化学物质。这些标准在决策分析框架内进行评估和整合,便于对未来有针对性的测试和系统建模进行快速评估和优先级排序。一项案例研究概述了使用 51 种化学品的优先级排序过程。结果显示了基于暴露潜力的化学品初步相对排名。这种方法的优势在于能够将相关的统计和机制数据与专家判断相结合,从而进行初步的分层评估,进一步为有针对性的测试和风险管理策略提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec33/3733911/8144dba7d30c/pone.0070911.g001.jpg

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