Department of Materials Science and Engineering, University of California, Irvine, California, USA.
School of Law, University of California, Los Angeles, California, USA.
Integr Environ Assess Manag. 2019 Nov;15(6):895-908. doi: 10.1002/ieam.4182. Epub 2019 Sep 13.
Chemical hazard assessment (CHA), which aims to investigate the inherent hazard potential of chemicals, has been developed with the purpose of promoting safer consumer products. Despite the increasing use of CHA in recent years, finding adequate and reliable toxicity data required for CHA is still challenging due to issues regarding data completeness and data quality. Also, collecting data from primary toxicity reports or literature can be time consuming, which promotes the use of secondary data sources instead. In this study, we evaluate and characterize numerous secondary data sources on the basis of 5 performance attributes: reliability, adequacy, transparency, volume, and ease of use. We use GreenScreen for Safer Chemicals v1.4 as the CHA framework, which defines the endpoints of interest used in this analysis. We focused upon 34 data sources that reflect 3 types of secondary data: chemical-oriented data sources, hazard-trait-oriented data sources, and predictive data sources. To integrate and analyze the evaluation results, we applied 2 multicriteria decision analysis (MCDA) methodologies: multiattribute utility theory (MAUT) and stochastic multiobjective acceptability analysis (SMAA). Overall, the findings in this research program allow us to explore the relative importance of performance criteria and the data source quality for effectively conducting CHA. Integr Environ Assess Manag 2019;00:1-14. © 2019 SETAC.
化学危害评估（CHA）旨在研究化学品固有的危害潜力，目的是促进更安全的消费产品。尽管近年来 CHA 的使用越来越多，但由于数据完整性和数据质量等问题，仍然难以找到用于 CHA 的足够和可靠的毒性数据。此外，从原始毒性报告或文献中收集数据可能很耗时，因此转而使用二手数据来源。在这项研究中，我们根据 5 个性能属性对大量二手数据来源进行评估和描述：可靠性、充分性、透明度、容量和易用性。我们使用 GreenScreen for Safer Chemicals v1.4 作为 CHA 框架，该框架定义了本分析中使用的感兴趣的终点。我们重点关注 34 种反映 3 种类型二手数据的数据源：面向化学的数据源、面向危害特征的数据源和预测性数据源。为了整合和分析评估结果，我们应用了 2 种多准则决策分析（MCDA）方法：多属性效用理论（MAUT）和随机多目标可接受性分析（SMAA）。总的来说，该研究项目的结果使我们能够探索性能标准和数据源质量对于有效进行 CHA 的相对重要性。《综合环境评估与管理》2019 年 00 期：1-14。©2019 SETAC。
