Recently, there has been renewed interest in the development and use of empirical models to predict metal bioavailability and derive protective values for aquatic life. However, there is considerable variability in the conceptual and statistical approaches with which these models have been developed. In the present study, we review case studies of empirical bioavailability model development, evaluating and making recommendations on key issues, including species selection, identifying toxicity-modifying factors (TMFs) and the appropriate environmental range of these factors, use of existing toxicity data sets and experimental design for developing new data sets, statistical considerations in deriving species-specific and pooled bioavailability models, and normalization of species sensitivity distributions using these models. We recommend that TMFs be identified from a combination of available chemical speciation and toxicity data and statistical evaluations of their relationships to toxicity. Experimental designs for new toxicity data must be sufficiently robust to detect nonlinear responses to TMFs and should encompass a large fraction (e.g., 90%) of the TMF range. Model development should involve a rigorous use of both visual plotting and statistical techniques to evaluate data fit. When data allow, we recommend using a simple linear model structure and developing pooled models rather than retaining multiple taxa-specific models. We conclude that empirical bioavailability models often have similar predictive capabilities compared to mechanistic models and can provide a relatively simple, transparent tool for predicting the effects of TMFs on metal bioavailability to achieve desired environmental management goals. Environ Toxicol Chem 2019;39:85-100. © 2019 SETAC.

中文翻译：

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金属经验生物利用度模型的开发。

最近，人们对开发和使用经验模型来预测金属生物利用度并得出水生生物的保护价值重新产生了兴趣。然而，开发这些模型所采用的概念和统计方法存在相当大的差异。在本研究中，我们回顾了经验生物利用度模型开发的案例研究，对关键问题进行评估和提出建议，包括物种选择、确定毒性调节因素（TMF）和这些因素的适当环境范围、现有毒性数据集的使用开发新数据集的实验设计、导出物种特异性和汇总生物利用度模型的统计考虑因素，以及使用这些模型对物种敏感性分布进行标准化。我们建议结合现有的化学形态和毒性数据以及其与毒性关系的统计评估来识别TMF。新毒性数据的实验设计必须足够稳健，以检测对 TMF 的非线性响应，并且应涵盖 TMF 范围的大部分（例如 90%）。模型开发应严格使用可视化绘图和统计技术来评估数据拟合。当数据允许时，我们建议使用简单的线性模型结构并开发池模型，而不是保留多个特定分类群的模型。我们的结论是，与机械模型相比，经验生物利用度模型通常具有类似的预测能力，并且可以提供相对简单、透明的工具来预测 TMF 对金属生物利用度的影响，以实现所需的环境管理目标。环境毒理学2019；39:85-100。© 2019 SETAC。