Abstract

Ecological risk assessments of substances with limited ecotoxicity datasets typically employ an assessment factor (AF) method to determine a predicted no-effect concentration (PNEC) needed for risk characterization. A PNEC is usually derived by dividing the lowest toxicity value in the substance’s dataset by a certain assessment factor. The AF method, in its various iterations, has been used for decades, and has been criticized for its uncertainty, inaccuracy, over-conservatism, high variability, and lack of transparency. A novel assessment factor method is proposed as an alternative to traditional AF methods. The proposed method offers novel features attempting to address these criticisms: it breaks the factor down into three components - endpoint standardization, extrapolation for species variation, and mode-of-action consideration (each of which has sub-components). This method also accommodates the inclusion of read-across and modeled data to fill data gaps for the substance. In addition, it better manages atypical datasets and atypical endpoints. Finally, and paramount to this endeavor, this novel method improves consistency among risk assessors, and can increase transparency of the entire PNEC derivation process to all stakeholders.

摘要

具有有限生态毒性数据集的物质的生态风险评估通常采用评估因子 (AF) 方法来确定风险表征所需的预测无影响浓度 (PNEC)。PNEC 通常是通过将物质数据集中的最低毒性值除以某个评估因子得出的。AF 方法在其各种迭代中已经使用了几十年，并因其不确定性、不准确、过度保守、高可变性和缺乏透明度而受到批评。提出了一种新的评估因子方法作为传统 AF 方法的替代方法。所提出的方法提供了试图解决这些批评的新功能：它将因素分解为三个组成部分——终点标准化、物种变异的外推、和行动模式考虑（每个都有子组件）。该方法还适应包含交叉读取和建模数据以填补物质的数据空白。此外，它还可以更好地管理非典型数据集和非典型端点。最后，最重要的是，这种新方法提高了风险评估人员之间的一致性，并可以提高整个 PNEC 推导过程对所有利益相关者的透明度。