Digitalizing river aquatic ecosystems

作     者：Yaohui Bai Hui Lin Chenchen Wang Qiaojuan Wang Jiuhui Qu Yaohui Bai;Hui Lin;Chenchen Wang;Qiaojuan Wang;Jiuhui Qu

作者机构：Key Laboratory of Drinking Water Science and TechnologyResearch Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China University of Chinese Academy of SciencesBeijing 100049China School of Environmental and Municipal EngineeringTianjin Chengjian UniversityTianjin 300384China Center for Water and EcologyTsinghua UniversityBeijing 100084China 

出 版 物：《Journal of Environmental Sciences》 (环境科学学报（英文版）)

年 卷 期：2024年第137卷第3期

页      面：677-680页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 07[理学] 08[工学] 09[农学] 0815[工学-水利工程] 0903[农学-农业资源与环境] 0713[理学-生态学] 

基　　金：supported by the NationalNatural Science Foundation of China (No.52293442) the Special Fund from the State Key Joint Laboratory of Environment Simulation and Pollution Control (No.22Z01ESPCR) 

主　　题：Digitalizing River ecosystem health Emerging pollutants High throughput sequencing Machine learning 

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 ***,we propose a new approach to evaluate the ecological and health risks of river aquatic ***,detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination(especially emerging pollutants)and DNA/RNA ***,supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and *** proposed methodology transforms river ecosystem health assessment and can be applied in river management.