Digitalizing river aquatic ecosystems

被引:2
|
作者
Bai, Yaohui [1 ]
Lin, Hui [1 ,2 ]
Wang, Chenchen [1 ,3 ]
Wang, Qiaojuan [1 ,2 ]
Qu, Jiuhui [1 ,4 ]
机构
[1] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Key Lab Drinking Water Sci & Technol, Beijing 100085, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Tianjin Chengjian Univ, Sch Environm & Municipal Engn, Tianjin 300384, Peoples R China
[4] Tsinghua Univ, Ctr Water & Ecol, Beijing 100084, Peoples R China
来源
JOURNAL OF ENVIRONMENTAL SCIENCES | 2024年 / 137卷
基金
中国国家自然科学基金;
关键词
Digitalizing; River ecosystem health; Emerging pollutants; High throughput sequencing; Machine leaming; MACHINE LEARNING APPLICATIONS; WASTE-WATER; INDEX; DNA;
D O I
10.1016/j.jes.2023.03.012
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Traditional river health assessment relies on limited water quality indices and representative organism activity, but does not comprehensively obtain biotic and abiotic information of the ecosystem. Here, we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems. First, detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination (especially emerging pollutants) and DNA/RNA sequencing. Second, supervised machine leaming can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health. Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.(c) 2023 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
引用
收藏
页码:677 / 680
页数:4
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