太湖北部湖区微囊藻生命周期和演替规律研究  

Life Cycle and Succession Patterns of Microcystis on the Northern Lakes of Taihu

作　　者：朱冰川 李国央 杨喆 宋挺 徐源 周文丽 华圣明 拓明香 孙蓓丽 张军毅 ZHU Bingchuan;LI Guoyang;YANG Zhe;SONG Ting;XU Yuan;ZHOU Wenli;HUA Shengming;TUO Mingxiang;SUN Beili;ZHANG Junyi(Jiangsu Wuxi Environmental Monitoring Center,Wuxi,Jiangsu 214121,China;Jiangsu Metabio Science&Technology Co.,Ltd.,Wuxi,Jiangsu 214028,China)

机构地区：[1]江苏省无锡环境监测中心,江苏无锡214121 [2]江苏宏众百德生物科技有限公司,江苏无锡214028

出　　处：《环境监控与预警》2024年第6期29-35,共7页Environmental Monitoring and Forewarning

基　　金：江苏省环境监测科研基金项目(2118);江苏省生态环境科研成果转化与推广项目(2022012)。

摘　　要：蓝藻水华暴发会使水质迅速恶化,并影响水生态系统的稳定性和饮用水安全。因此,对于蓝藻水华进行准确、快速、有效的监测预警至关重要。以太湖北部典型蓝藻水华发生区为研究区域,利用人工智能藻类自动识别技术分析了2020年3—10月共计501个样本,获取了藻类群落结构和藻类形态学的数据信息。研究结果表明:微囊藻属是调查期间的第一优势类群,优势度达57%;存在微囊藻的大周期规律,即微囊藻属下物种表现出明显的演替规律,尤其以细胞直径较大、夏秋季具有耐高温特征的惠氏微囊藻和铜绿微囊藻演替最为明显;存在微囊藻的小周期规律,即微囊藻细胞存在较为明显的日变化规律,其繁殖周期为2~5 d。通过建立准确、快速、有效的藻类人工智能监测技术,不仅可以快速分析水体中的藻类群落结构,还能够通过大数据挖掘分析微囊藻的演替和生长规律,对于开展更高精度的蓝藻水华预警工作具有重要意义。Cyanobacterial blooms rapidly degrade water quality,impacting aquatic ecosystem stability and drinking water safety.The accurate,rapid,and effective monitoring and early warning of cyanobacteria bloom are of utmost importance.To address this,our study selected a typical area in the northwest of Lake Taihu where Microcystis blooms occurred in 2020.Artificial intelligence techniques were used to analyze 501 samples collected from March to October,obtaining extensive statistical information on algal community structure and morphology.The research findings indicate the following:Microcystis spp.is the first dominant genus during the study period,accounting for 57%of the total;There is significant large-scale periodicity within the Microcystis genus,particularly evident in the succession of Microcystis species characterized by larger cell diameters and high-temperature resistance in the summer and autumn,such as Microcystis wesenbergii and Microcystis aeruginosa;A smaller periodicity in Microcystis cell density is observed,with a reproductive cycle of 2~5 days showing a distinct daily fluctuation pattern.By establishing accurate,rapid,and effective artificial intelligence-based monitoring techniques for algae,we can swiftly analyze algal community structure in water bodies and utilize big data mining to understand the succession and growth patterns of Microcystis.This is of great significance for the early warning of cyanobacterial bloom with higher precision.

关 键 词：蓝藻水华 微囊藻 细胞形态 繁殖演替 监测预警 太湖 

分 类 号：X835[环境科学与工程—环境工程]