摘要
内分泌干扰效应化合物(EDCs)对生态系统的不利影响已引起广泛关注,识别环境中的EDCs是进行风险评估和管控的前提,但目前对该类物质的识别方法还不明确。介绍了从识别化合物结构到甄别EDCs的自上而下识别和从甄别内分泌干扰效应再进行化合物结构鉴定的自下而上识别这2种策略。自上而下策略中,可疑物筛查方法和非靶向筛查方法是识别未知化合物结构的主要方法,构建可疑物清单是该方法的关键,可通过综合利用各种非靶向筛查软件工具,实现对环境样品中未知结构化合物的识别。自下而上策略中,效应导向分析是较成熟的识别方法。另外,近年来发展的机器学习算法可以基于环境样品质谱谱图预测获得内分泌干扰效应活性结果,这极大地简化了效应导向分析流程。该综述为全面识别环境中EDCs并采取有效的控制策略提供了参考方法。
The adverse effects of endocrine-disrupting compounds(EDCs)in wastewater on ecosystems have garnered great concerns,necessitating stricter management to mitigate downstream ecological risks.The identification of EDCs in wastewater is a prerequisite for risk assessment and control.However,the current methods for addressing this issue are not well-established.This article presents a comprehensive overview of two strategies for EDC identification:a top-down approach that starts with compound structure identification and proceeds to evaluate their endocrine-disrupting effects,and a bottom-up approach that begins with effect-directed analysis and then determines the structure of compounds.For the top-down strategy,screening methods for suspected compounds and non-targeted screening techniques are the primary means to identify unknown compounds.Building a list of suspects is crucial,and the integration of various non-targeted screening software aids in recognizing unidentified compounds in environmental samples.The bottom-up approach relies on effect-directed analysis,which is a mature and widely used method.Recently,machine learning algorithms have advanced to directly predict EDC activity classifications from environmental sample mass spectrometry data,streamlining the effect-directed analysis processes.The present review provides a reference for the comprehensive identification of EDCs in wastewater,which is beneficial to effective control EDCs in wastewater.
作者
封书阳
吴刚
张徐祥
FENG Shuyang;WU Gang;ZHANG Xuxiang(State Key Laboratory of Pollution Control and Resource Reuse,School of the Environment,Nanjing University,Nanjing,Jiangsu 210023,China)
出处
《环境监控与预警》
2024年第4期1-8,共8页
Environmental Monitoring and Forewarning
基金
国家自然科学基金重点计划(52192682,52025102)
国家重点研发计划(2022YFC3901301)
中国博士后面上基金项目(2023M731595)
国家资助博士后研究员计划B项目(GZB20230300)
江苏省卓越博士后项目(2023ZB357)。
