通过集群来减少样本大小:一种使大组有机化合物风险评估可行的方法?
Renske P J Hoondert1, B A Wols2, Patrick Steven Bäuerlein3
1KWR Water Research, Groningenhaven 7, Nieuwegein 3433 PE, The Netherlands.
Journal of water and health
|August 30, 2024
概括
这项研究将900多种非常令人担忧的物质 (SVHCs) 分组为集群,以预测它们的毒性和评估饮用水风险. 这种方法有助于保护人类健康和环境.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 风险评估 风险评估
背景情况:
- 非常令人担忧的物质 (SVHC) 对饮用水部门构成风险.
- 评估危险需要了解排放途径,毒性,存在和SVHCs的去除.
- 由于大量的SVHCs,单个化合物评估通常是不可行的.
研究的目的:
- 开发一种方法来评估饮用水中SVHCs的风险.
- 通过聚类来减少需要单个测试的化合物数量.
- 预测SVHC的毒性,以告知风险评估.
主要方法:
- 根据化学特性和结构,将超过1000个SVHC分为51个组.
- 开发使用随机森林和多重线性回归的毒性预测模型.
- 应用模型来预测聚类化合物的毒性.
主要成果:
- 集群有效地减少了风险评估的样本大小.
- 毒性预测模型提供了对潜在人类健康风险的见解.
- 该方法证明了使用代表性化合物进行群体行为评估的可行性.
结论:
- 聚类是一种用于饮用水部门管理和评估SVHC的实用方法.
- 毒性模型提高了对与SVHCs相关的潜在健康风险的理解.
- 这项研究支持改善保护公众健康和环境的决策.
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