Species sensitivity distributions (SSDs) are used in chemical safety assessments to derive predicted-no-effect-concentrations (PNECs) for substances with a sufficient amount of relevant and reliable ecotoxicity data available. For engineered nanomaterials (ENMs), ecotoxicity data are often compromised by poor reproducibility and the lack of nano-specific characterization needed describe an ENM under test exposure conditions. This may influence the outcome of SSD modelling and hence the regulatory decision-making. This study investigates how the outcome of SSD modelling is influenced by: 1) Selecting input data based on the nano-specific “nanoCRED” reliability criteria, 2) Direct SSD modelling avoiding extrapolation of data by including long-term/chronic NOECs only, and 3) Weighting data according to their nano-specific quality, the number of data available for each species, and the trophic level abundance in the ecosystem. Endpoints from freshwater ecotoxicity studies were collected for the representative nanomaterials NM-300 K (silver) and NM-105 (titanium dioxide), evaluated for regulatory reliability and scored according to the level of nano-specific characterization conducted. The compiled datasets are unique in exclusively dealing with representative ENMs showing minimal batch-to-batch variation. The majority of studies were evaluated as regulatory reliable, while the degree of nano-specific characterization varied greatly. The datasets for NM-300 K and NM-105 were used as input to the nano-weighted n-SSWD model, the probabilistic PSSD+, and the conventional SSD Generator by the US EPA. The conventional SSD generally yielded the most conservative, but least precise HC₅ values, with 95 % confidence intervals up to 100-fold wider than the other models. The inclusion of regulatory reliable data only, had little effect on the HC₅ generated by the conventional SSD and the PSSD+, whereas the n-SSWD estimated different HC₅ values based on data segregated according to reliability, especially for NM-105. The n-SSWD weighting of data significantly affected the estimated HC₅ values, however in different ways for the sub-datasets of NM-300 K and NM-105. For NM-300 K, the inclusion of NOECs only in the weighted n-SSWD yielded the most conservative HC₅ of all datasets and models (a HC₅ based on NOECs only could not be estimated for NM-105, due to limited number of data). Overall, the estimated HC₅ values of all models are within a relatively limited concentration range of 25−100 ng Ag/L for NM-300 K and 1−15 μgTiO₂/L for NM-105.

物种敏感度分布（SSD）用于化学安全评估，以得出具有足够数量的相关和可靠的生态毒性数据的物质的预测无效应浓度（PNEC）。对于工程纳米材料（ENM），可再生性差通常会损害生态毒性数据，并且缺乏在测试暴露条件下描述ENM所需的纳米特异性表征的缺乏。这可能会影响SSD建模的结果，从而影响监管决策。这项研究调查了SSD建模结果如何受到以下因素的影响：1）基于纳米特定的“ nanoCRED”可靠性标准选择输入数据，2）直接SSD建模通过仅包括长期/长期NOEC来避免数据外推，以及3）根据纳米特有的质量加权数据，每个物种可获得的数据数量，以及生态系统中的营养级丰度。从淡水生态毒性研究的终点收集了代表性的纳米材料NM-300 K（银）和NM-105（二氧化钛），评估了调节可靠性，并根据所进行的纳米特异性表征水平进行了评分。编译后的数据集在专门处理代表性ENM时表现出独特性，显示出最小的批次间差异。大多数研究被评价为监管可靠，而纳米特异性表征的程度差异很大。NM-300 K和NM-105的数据集被用作美国EPA的纳米加权n-SSWD模型，概率PSSD +和常规SSD发生器的输入。传统的SSD通常是最保守的，_5个值，具有95％的置信区间，比其他模型宽100倍。仅包含监管可靠的数据，对传统SSD和PSSD +生成的HC ₅几乎没有影响，而n-SSWD根据根据可靠性分离的数据估算了不同的HC ₅值，尤其是对于NM-105。数据的n-SSWD加权显着影响估计的HC ₅值，但是对于NM-300 K和NM-105的子数据集以不同的方式。对于NM-300 K，仅在加权n-SSWD中包含NOEC会产生所有数据集和模型中最保守的HC ₅（HC ₅由于数据数量有限，无法仅针对NM-105估算基于NOEC的数据）。总体而言，所估计的HC ₅所有型号的值是纳克的Ag / L为NM-300 K和1-15μgTiO25-100相对有限的浓度范围内₂ / L为NM-105。