Australian Rivers Institute, Griffith School of Environment, Griffith University, Southport, QLD, 4222, Australia; The University of Queensland, National Research Centre for Environmental Toxicology (Entox), Brisbane, QLD, 4108, Australia.
UFZ - Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany.
Water Res. 2017 Oct 15;123:734-750. doi: 10.1016/j.watres.2017.07.016. Epub 2017 Jul 9.
Surface waters can contain a diverse range of organic pollutants, including pesticides, pharmaceuticals and industrial compounds. While bioassays have been used for water quality monitoring, there is limited knowledge regarding the effects of individual micropollutants and their relationship to the overall mixture effect in water samples. In this study, a battery of in vitro bioassays based on human and fish cell lines and whole organism assays using bacteria, algae, daphnids and fish embryos was assembled for use in water quality monitoring. The selection of bioassays was guided by the principles of adverse outcome pathways in order to cover relevant steps in toxicity pathways known to be triggered by environmental water samples. The effects of 34 water pollutants, which were selected based on hazard quotients, available environmental quality standards and mode of action information, were fingerprinted in the bioassay test battery. There was a relatively good agreement between the experimental results and available literature effect data. The majority of the chemicals were active in the assays indicative of apical effects, while fewer chemicals had a response in the specific reporter gene assays, but these effects were typically triggered at lower concentrations. The single chemical effect data were used to improve published mixture toxicity modeling of water samples from the Danube River. While there was a slight increase in the fraction of the bioanalytical equivalents explained for the Danube River samples, for some endpoints less than 1% of the observed effect could be explained by the studied chemicals. The new mixture models essentially confirmed previous findings from many studies monitoring water quality using both chemical analysis and bioanalytical tools. In short, our results indicate that many more chemicals contribute to the biological effect than those that are typically quantified by chemical monitoring programs or those regulated by environmental quality standards. This study not only demonstrates the utility of fingerprinting single chemicals for an improved understanding of the biological effect of pollutants, but also highlights the need to apply bioassays for water quality monitoring in order to prevent underestimation of the overall biological effect.
地表水中可能含有多种有机污染物，包括农药、药品和工业化合物。虽然生物测定法已被用于水质监测，但对于单个微污染物的影响及其与水样中整体混合物效应的关系，我们的了解有限。在这项研究中，我们组装了一系列基于人类和鱼类细胞系的体外生物测定法和使用细菌、藻类、水蚤和鱼类胚胎的整体生物测定法，用于水质监测。生物测定法的选择是根据不良结局途径的原则进行的，以便涵盖已知由环境水样触发的毒性途径中的相关步骤。根据危害系数、现有环境质量标准和作用模式信息选择了 34 种水污染物，在生物测定试验组中对其进行了“指纹”分析。实验结果与现有文献效应数据之间存在相对较好的一致性。大多数化学物质在测定中具有活性，表明存在顶端效应，而较少的化学物质在特定的报告基因测定中具有反应，但这些效应通常在较低浓度下触发。单个化学物质效应数据用于改进多瑙河水样的已发表混合物毒性模型。虽然对于多瑙河水样，生物分析等效物的解释比例略有增加，但对于一些终点，只有不到 1%的观察到的效应可以用研究的化学物质来解释。新的混合物模型基本上证实了之前使用化学分析和生物分析工具监测水质的许多研究的发现。简而言之，我们的研究结果表明，对于污染物的生物效应，有更多的化学物质起作用，而不是那些通常通过化学监测计划或环境质量标准来量化的化学物质。这项研究不仅表明，对单个化学物质进行“指纹”分析有助于更好地了解污染物的生物效应，而且还强调需要应用生物测定法进行水质监测，以防止低估整体生物效应。
