Environmental Stewardship and Sustainability, The Procter & Gamble Company, Mason, Ohio, USA.
Data & Modeling Sciences, The Procter & Gamble Company, Mason, Ohio, USA.
Environ Toxicol Chem. 2019 Jul;38(7):1526-1541. doi: 10.1002/etc.4444. Epub 2019 Jun 24.
Application factors are routinely applied in the extrapolation of laboratory aquatic toxicity data to ensure protection from exposure to chemicals in the natural environment. The magnitude of the application factor is both a scientific and a policy decision, but in any case, it should be rooted in scientific knowledge so as to not be arbitrary. Information-rich chemicals are often subjected to species sensitivity distribution (SSD) analysis to transparently describe certain aspects of assessment uncertainty and are normally subjected to much smaller application factors than screening information data sets. We describe a new set of tools useful to assess the quality of SSDs. Twenty-two data sets and 19 chemicals representing agrochemicals, biocides, surfactants, metals, and common wastewater contaminants were compiled to demonstrate how the tools can be used. "Add-one-in" and "leave-one-out" simulations were used to investigate SSD robustness and develop quantitative evidence for the use of application factors. Theoretical new toxicity data were identified for add-one-in simulations based on the expected probabilities necessary to lower the hazardous concentration to 5% of a species (HC5) by a factor of 2, 3, 5, or 10. Simulations demonstrate the basis for application factors in the range of 1 to 5 for well-studied chemicals with high-quality SSDs. Leave-one-out simulations identify the fact that the most influential values in the SSD come from the extremes of the sensitive and tolerant toxicity values. Mesocosm and field data consistently demonstrate that HC5s are conservative, further justifying the use of small application factors for high-quality SSDs. Environ Toxicol Chem 2019;38:1526-1541. © 2019 SETAC.
应用因子通常用于将实验室水生毒性数据外推，以确保免受自然环境中化学物质暴露的影响。应用因子的大小既是一个科学决策，也是一个政策决策，但在任何情况下，它都应该基于科学知识，以免任意行事。信息丰富的化学物质通常会进行物种敏感性分布（SSD）分析，以透明地描述评估不确定性的某些方面，并且通常比筛选信息数据集受到更小的应用因子。我们描述了一组有用的新工具，用于评估 SSD 的质量。为了演示如何使用这些工具，我们编译了 22 个数据集和 19 种化学物质，代表了农药、生物杀灭剂、表面活性剂、金属和常见废水污染物。“加一”和“留一”模拟用于调查 SSD 的稳健性，并为应用因子的使用提供定量证据。根据将物种危害浓度降低到 5%（HC5）的 2、3、5 或 10 倍所需的预期概率，对“加一”模拟中的理论新毒性数据进行了识别。模拟表明，对于具有高质量 SSD 的研究充分的化学物质，应用因子的基础在 1 到 5 之间。留一模拟确定了 SSD 中最具影响力的值来自敏感和耐受毒性值的极端。中尺度和现场数据一致表明，HC5 是保守的，这进一步证明了对于高质量 SSD 应使用较小的应用因子。环境毒理化学 2019；38:1526-1541。 © 2019 SETAC。
