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A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions

机译：基于物种敏感度分布的鲁棒风险评估方法

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摘要

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real-world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation.
机译：由于对概率性程序需求的普遍认识不断提高，因此制定环境质量标准的指南越来越多地基于概率性风险评估。概率风险评估中最常用的工具之一是物种敏感度分布（SSD），它表示受某种特定毒物影响的属于生物集合的受影响物种的比例。我们的研究重点是反演SSD曲线，目的是估算对研究中的p％的生物群落有害的有毒化合物的HCp浓度。为此，我们建议使用可靠的统计方法，以考虑到数据中存在异常值或明显偏斜的情况，这些异常情况可能在没有任何生态基础的情况下发生。一种可靠的方法可以利用参数模型的整个邻域，从而使分析人员能够解决与理想模型之间的典型实际偏差。我们研究了两种经典的HCp估算方法，并考虑了这些估算器的可靠版本。此外，在异方差情况下，我们还将数据转换与鲁棒的估计方法结合使用。为了说明和比较所提出的方法，提出了使用真实数据集和模拟数据的不同方案。这些场景说明，使用可靠的估算方法可以增强HCp估算。

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