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Abstract：
OBJECTIVE: There is growing evidence that single substances present below their individual thresholds of effect may still contribute to combined effects. In component-based mixture risk assessment (MRA), the risks can be addressed using information on the mixture components. This is, however, often hampered by limited availability of ecotoxicity data. Here, the possible use of ecotoxicological threshold concentrations of no concern (i.e. 5th percentile of statistical distribution of ecotoxicological values) is investigated to fill data gaps in MRA.
METHODS: For chemicals without available aquatic toxicity data, ecotoxicological threshold concentrations of no concern have been derived from Predicted No Effect Concentration (PNEC) distributions and from chemical toxicity distributions, using the EnviroTox tool, with and without considering the chemical mode of action. For exposure, chemical monitoring data from European rivers have been used to illustrate four realistic co-exposure scenarios. Based on those monitoring data and available ecotoxicity data or threshold concentrations when no data were available, Risk Quotients for individual chemicals were calculated, to then derive a mixture Risk Quotient (RQmix).
RESULTS: A risk was identified in two of the four scenarios. Threshold concentrations contribute from 24 to 95% of the whole RQmix; thus they have a large impact on the predicted mixture risk. Therefore they could only be used for data gap filling for a limited number of chemicals in the mixture. The use of mode of action information to derive more specific threshold values could be a helpful refinement in some cases.
METHODS: For chemicals without available aquatic toxicity data, ecotoxicological threshold concentrations of no concern have been derived from Predicted No Effect Concentration (PNEC) distributions and from chemical toxicity distributions, using the EnviroTox tool, with and without considering the chemical mode of action. For exposure, chemical monitoring data from European rivers have been used to illustrate four realistic co-exposure scenarios. Based on those monitoring data and available ecotoxicity data or threshold concentrations when no data were available, Risk Quotients for individual chemicals were calculated, to then derive a mixture Risk Quotient (RQmix).
RESULTS: A risk was identified in two of the four scenarios. Threshold concentrations contribute from 24 to 95% of the whole RQmix; thus they have a large impact on the predicted mixture risk. Therefore they could only be used for data gap filling for a limited number of chemicals in the mixture. The use of mode of action information to derive more specific threshold values could be a helpful refinement in some cases.
摘要：
目的：越来越多的证据表明，低于其单独作用阈值的单一物质仍然可能导致联合作用。在基于成分的混合物风险评估(MRA)中，可以使用混合物成分的信息来解决风险。这是,然而,经常受到生态毒性数据可用性有限的阻碍。这里,为了填补MRA的数据空白,我们研究了无关注生态毒理学阈值浓度(即生态毒理学值统计分布的第5百分位数)的可能用途.
方法：对于没有可用水生毒性数据的化学品，从预测的无影响浓度（PNEC）分布和化学毒性分布中得出了无关的生态毒理学阈值浓度，使用EnviroTox工具，考虑和不考虑化学作用模式。为了暴露，来自欧洲河流的化学监测数据已被用来说明四种现实的共同暴露情景。根据这些监测数据和可用的生态毒性数据或没有数据时的阈值浓度，计算了单个化学品的风险商，然后得出混合风险商(RQmix)。
结果：在四种情况中的两种中确定了风险。阈值浓度占整个RQmix的24％至95％；因此，它们对预测的混合物风险有很大影响。因此，它们只能用于填充混合物中有限数量的化学物质的数据间隙。在一些情况下,使用动作模式信息来导出更具体的阈值可能是有帮助的细化。
方法：对于没有可用水生毒性数据的化学品，从预测的无影响浓度（PNEC）分布和化学毒性分布中得出了无关的生态毒理学阈值浓度，使用EnviroTox工具，考虑和不考虑化学作用模式。为了暴露，来自欧洲河流的化学监测数据已被用来说明四种现实的共同暴露情景。根据这些监测数据和可用的生态毒性数据或没有数据时的阈值浓度，计算了单个化学品的风险商，然后得出混合风险商(RQmix)。
结果：在四种情况中的两种中确定了风险。阈值浓度占整个RQmix的24％至95％；因此，它们对预测的混合物风险有很大影响。因此，它们只能用于填充混合物中有限数量的化学物质的数据间隙。在一些情况下,使用动作模式信息来导出更具体的阈值可能是有帮助的细化。
