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Identification of mutagenic and endocrine disrupting compounds in surface water and wastewater treatment plant effluents using high- resolution effect-directed analysis

机译:使用高分辨率效果导向分析鉴定地表水和废水处理厂废水中的致突变和内分泌干扰化合物

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Effect-directed analysis (EDA) has shown its added value for the detection and identification of compounds with varying toxicological properties in water quality research. However, for routine toxicity assessment of multiple toxicological endpoints, current EDA is considered labor intensive and time consuming. To achieve faster EDA and identification, a high-throughput (HT) EDA platform, coupling a downscaled luminescent Ames and cell-based reporter gene assays with a high-resolution fraction collector and UPLC-QTOF MS, was developed. The applicability of the HT-EDA platform in the analysis of aquatic samples was demonstrated by analysis of extracts from WWTP influent, effluent and surface water. Downscaled assays allowed detection of mutagenicity and androgen, estrogen and glucocorticoid agonism following high-resolution fractionation in 228 fractions. From 8 masses tentatively identified through non-target analysis, 2 masses were further investigated and chemically and biologically confirmed as the mutagen 1,2,3-benzotriazole and the androgen androstenedione. The compatibility of the high-throughput EDA platform with analysis of water samples and the incorporation of mutagenic and endocrine disruption endpoints allow for future application in routine monitoring in drinking water quality control and improved identification of (emerging) mutagens and endocrine disruptors. (C) 2019 The Authors. Published by Elsevier Ltd.
机译:效果导向分析(EDA)已显示出其在水质研究中检测和鉴定具有不同毒理学特性的化合物的附加价值。然而,对于多种毒理学终点的常规毒性评估,当前的EDA被认为是劳动密集型且耗时的。为了实现更快的EDA和鉴定,开发了一种高通量(HT)EDA平台,该平台将缩小的发光Ames和基于细胞的报告基因测定与高分辨率馏分收集器和UPLC-QTOF MS结合在一起。通过分析污水处理厂进水,出水和地表水的提取物,证明了HT-EDA平台在水生样品分析中的适用性。缩小比例的分析可在228个组分中进行高分辨率分离后检测诱变性和雄激素,雌激素和糖皮质激素激动剂。通过非目标分析初步确定了8种质量,进一步研究了2种质量,并通过化学和生物学方法确定为诱变剂1,2,3-苯并三唑和雄激素和雄烯二酮。高通量EDA平台与水样分析的兼容性以及诱变和内分泌干扰终点的结合,使得将来可用于饮用水质量控制的常规监测中,并改善(新兴)诱变剂和内分泌干扰物的识别。 (C)2019作者。由Elsevier Ltd.发布

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