Tabulations of numerical concentration-based environmental benchmarks are commonly used to inform decisions on managing chemical exposures. Benchmarks are usually set at levels below which there is a low likelihood of adverse effects. Given the widespread use of tables of benchmarks, it is reasonable to expect that they are adequately reliable and fit for purpose. The degree to which a derived benchmark reflects an actual effect level or statistical randomness is critically important for the reliability of a numerical benchmark value. These expectations may not be met for commonly-used benchmarks examined in this study. Computer simulations of field sampling and toxicity testing reveal that small sample size and confounding from uncontrolled factors that affect the interpretation of toxic effects contribute to uncertainties that might go unrecognized when deriving benchmarks from data sets. The simulations of field data show that it is possible to derive a benchmark even when no toxicity is present. When toxicity is explicitly included in simulations, imposed effect threshold levels could not always be accurately determined. Simulations were also used to examine the influence of mixtures of chemicals on the determination of toxicity thresholds of chemicals within the mixtures. The simulations showed that data sets that appear large and robust can contain many smaller data sets associated with specific biota or chemicals. The sub-sets of data with small sample sizes can contribute to considerable statistical uncertainty in the determination of effects thresholds and can indicate that effects are present when they are absent. The simulations also show that less toxic chemicals may appear toxic when they are present in mixtures with more toxic chemicals. Because of confounding in the assignment of toxicity to individuals chemicals within mixtures, simulations showed that derived toxicity thresholds can be less than the actual toxicity thresholds. A set of best practices is put forward to guard against the potential problems identified by this work. These include conducting an adequate process of determining and implementing Data Quality Objectives (DQOs), evaluating implications of sample size, designing appropriate sampling and evaluation programs based on this information, using an appropriate tiered evaluation strategy that considers the uncertainties, and employing a weight of evidence approach to narrow the uncertainties to manageable and identified levels. The work underscores the importance of communicating the uncertainties associated with numerical values commonly included in tables for screening and risk assessment purposes to better inform decisions.

中文翻译：

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从野外和实验室数据得出环境基准和标准的样本量，稀有性和通用性的含义。

基于浓度数字的环境基准的表格通常用于指导管理化学品暴露的决策。基准通常设定在较低水平，在该水平下不良影响的可能性很小。鉴于基准表的广泛使用，可以合理地预期它们是足够可靠的并且适合于目的。导出基准反映实际效果水平或统计随机性的程度对于数字基准值的可靠性至关重要。对于本研究中检查的常用基准，可能无法满足这些期望。现场采样和毒性测试的计算机模拟表明，小样本量以及影响毒副作用解释的不受控制因素的混淆，会导致不确定性，当从数据集中导出基准时可能无法识别。现场数据的模拟表明，即使没有毒性，也有可能得出基准。当模拟中明确包括毒性时，施加的作用阈值水平可能无法始终准确确定。还使用模拟来检查化学品混合物对确定混合物中化学品毒性阈值的影响。模拟表明，看起来较大且健壮的数据集可以包含许多与特定生物群或化学物质相关的较小数据集。样本量较小的数据子集可在确定效应阈值时造成相当大的统计不确定性，并且可以指示不存在效应时存在效应。模拟还表明，毒性较低的化学物质与毒性较高的化学物质混合存在时，可能会显示毒性。由于混淆了混合物中单个化学物质的毒性分配，模拟显示派生的毒性阈值可能小于实际的毒性阈值。提出了一组最佳实践，以防止此项工作发现的潜在问题。其中包括进行适当的确定和实施数据质量目标（DQO）的过程，评估样本量的影响，根据此信息设计适当的采样和评估程序，使用考虑不确定性的适当分层评估策略，并采用证据权重方法将不确定性缩小到可管理和确定的水平。这项工作强调了传达与表格中通常包含的数值相关的不确定性以进行筛选和风险评估的目的的重要性，以便更好地为决策​​提供依据。