High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review

计算机科学 环境科学 生化工程 工程类
作者
Jifu Liu,Tongtong Xiang,Xue‐Chao Song,Shaoqing Zhang,Qi Wu,Jie Gao,Meilin Lv,Chunzhen Shi,Xiaoxi Yang,Yanna Liu,Jianjie Fu,Wei Shi,Mingliang Fang,Guangbo Qu,Hongxia Yu,Guibin Jiang
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:58 (23): 9925-9944 被引量:6
标识
DOI:10.1021/acs.est.3c10996
摘要

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.
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