山仔水库环境因子与藻类群落生长的相互作用机制探究及应对建议  

The Interplay between Environmental Factors and Algal Community Growth in Shanzai Reservoir

作　　者：郑震 冷东梅 郑莺 姚鹏峰 魏雪霞 庞维海[2] 谢丽[2] 李惠平 ZHENG Zhen;LENG Dongmei;ZHENG Ying;YAO Pengfeng;WEI Xuexia;PANG Weihai;XIE Li;LI Huiping(Fuzhou Academy of Environmental Sciences,Fuzhou 350000,China;The Yangtze River Water Environment Key Laboratory of the Ministry of Education,College of Environmental Science and Engineering,Tongji University,Shanghai 200092,China)

机构地区：[1]福州市环境科学研究院,福州350000 [2]长江水环境教育部重点实验室,同济大学环境科学与工程学院,上海200092

出　　处：《灌溉排水学报》2023年第11期131-139,共9页Journal of Irrigation and Drainage

基　　金：福建省环保科技计划项目(2021R015)。

摘　　要：【目的】深入研究藻类群落与环境因子的演替关系及相关性,揭示山仔水库水华爆发机制。【方法】采用RDA和相关性分析的方法,对山仔水库2020—2021年的水温、pH值、DO、透明度和优势藻类等指标进行对应分析,探究该水库藻类群落季节性演替及垂向分布特点。同时,采用机器学习的方法建立相关环境因子与叶绿素a之间的关系。【结果】①山仔水库全年TN和TP营养盐平均质量浓度分别为(0.675±0.137)mg/L和(0.021±0.006)mg/L,长期处于Ⅲ类水体。②水库优势藻类以硅藻和蓝藻为主,硅藻爆发期为5—7月,主要在中温、弱光环境下;蓝藻爆发期在7—9月,造成水体pH值、DO和透明度下降;蓝藻与水温、pH值、浊度、TP显著正相关,R^(2)分别为0.71、0.77、0.65、0.74。③硅藻则与水温、pH值、浊度、TP等指标负相关,R^(2)介于-0.43~-0.37;GBDT机器学习算法拟合效果最优,R^(2)可达0.85。【结论】山仔水库不同种类藻类的爆发时期及爆发机制存在差异,根据其相关环境因子建立的机器学习模型适用性较好。【Objective】This study delves into the successional interrelationship between algal communities and environmental factors to uncover the mechanisms behind water bloom outbreaks in Shanzai Reservoir.【Method】Utilizing redundancy analysis and correlation analysis,we investigated the temporal variations in water temperature,pH,dissolved oxygen(DO),transparency,and dominant algal species in Shanzai Reservoir from 2020 to 2021.We explored the seasonal succession and vertical distribution of the algal community.A machine learning approach was used to establish the relationship between pertinent environmental factors and chlorophyll a.【Result】The average total nitrogen(TN)and total phosphorus(TP)concentrations in Shanzai Reservoir measured in the studied period were(0.675±0.137)mg/L and(0.021±0.006)mg/L,respectively.Water quality in the reservoir was Class III grade.The dominant algal species in the reservoir are diatoms and cyanobacteria.Diatom blooms were prevalent from May to July,particularly under moderate temperature and reduced light conditions.In contrast,cyanobacteria proliferated from July to September,leading to a reduction in pH,DO and water quality.The cyanobacterial outbreaks had a significant positive correlation with water temperature,pH,water turbidity,and TP,with their R^(2)being 0.71,0.77,0.65,and 0.74,respectively.Diatoms were negatively correlated to water temperature,pH,turbidity,and TP,with their R^(2)ranging from-0.43 to-0.37.Using machine learning algorithms can improve the R^(2)to 0.8526.【Conclusion】There were differences in outbreak timings between different algae species in Shanzai Reservoir due to the difference in their underlying mechanism.Machine learning model has a good applicability and can be used for accurate analysis of algal blooms in Shanzai Reservoir.

关 键 词：水库 藻类群落 环境因子 水华 RDA分析 

分 类 号：S274.3[农业科学—农业水土工程]