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:24
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
丝暮发布了新的文献求助10
刚刚
3秒前
3秒前
科目三应助Lucky采纳,获得10
5秒前
6秒前
7秒前
Liz1054完成签到,获得积分10
7秒前
FashionBoy应助如意的如雪采纳,获得10
7秒前
ma3501134992应助真没招了采纳,获得10
7秒前
Ava应助陈陈陈1采纳,获得50
8秒前
sholck完成签到,获得积分10
9秒前
li完成签到 ,获得积分10
9秒前
ruochenzu发布了新的文献求助10
9秒前
xy发布了新的文献求助10
9秒前
激动的一曲完成签到,获得积分10
10秒前
wanci应助Q华采纳,获得10
11秒前
12秒前
完美世界应助王林春采纳,获得10
12秒前
愉快向彤发布了新的文献求助10
14秒前
千束完成签到 ,获得积分10
15秒前
wanci应助丝暮采纳,获得10
15秒前
孙小雨发布了新的文献求助10
16秒前
风雨哈佛路完成签到,获得积分10
18秒前
18秒前
xy完成签到,获得积分10
19秒前
19秒前
20秒前
20秒前
Live完成签到,获得积分10
21秒前
我是老大应助lilyz615采纳,获得10
22秒前
上善若水完成签到,获得积分10
23秒前
季生发布了新的文献求助10
24秒前
仙兮熙完成签到 ,获得积分10
24秒前
临风完成签到,获得积分10
25秒前
迟迟发布了新的文献求助10
25秒前
mega白发布了新的文献求助10
25秒前
核桃发布了新的文献求助30
26秒前
28秒前
北风完成签到,获得积分10
28秒前
复杂平凡完成签到,获得积分10
29秒前
高分求助中
Malcolm Fraser : a biography 700
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
Climate change and sports: Statistics report on climate change and sports 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Organic Reactions Volume 118 400
A Foreign Missionary on the Long March: The Unpublished Memoirs of Arnolis Hayman of the China Inland Mission 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6461407
求助须知:如何正确求助?哪些是违规求助? 8269878
关于积分的说明 17629157
捐赠科研通 5532023
什么是DOI,文献DOI怎么找? 2906524
邀请新用户注册赠送积分活动 1883303
关于科研通互助平台的介绍 1729169