Digitalizing river aquatic ecosystems
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.