| 引用本文: | 褚乔,张壹萱,张玉超,马荣华,胡旻琪.基于水华蓝藻固有光学特性的主要类群定量识别方法.湖泊科学,2021,33(1):74-85. DOI:10.18307/2021.0122 |
| Chu Qiao,Zhang Yixuan,Zhang Yuchao,Ma Ronghua,Hu Minqi.Quantitative identification methods of bloom-forming cyanobacterial groups of inland lakes based on inherent optical properties. J. Lake Sci.2021,33(1):74-85. DOI:10.18307/2021.0122 |
|
| |
|
|
| 本文已被：浏览 4483次 下载 3217次 |
码上扫一扫！ |
|
|
| 基于水华蓝藻固有光学特性的主要类群定量识别方法 |
|
褚乔1,2, 张壹萱1,2, 张玉超1,3, 马荣华1,3, 胡旻琪1,2
|
|
1.中国科学院南京地理与湖泊研究所中国科学院流域地理学重点实验室, 南京 210008;2.中国科学院大学, 北京 100049;3.淮阴师范学院, 江苏区域现代农业与环境保护协同创新中心, 淮安 223300
|
|
| 摘要: |
| 蓝藻水华是湖泊水体富营养化的重要特征之一，不同水华蓝藻类群形成的水华特征、危害及其治理方法差异显著.因此，如何快速、准确地掌握不同蓝藻类群的时空分布特征成为实施富营养化湖泊污染治理与生态恢复、蓝藻生态灾害预测预警中一个亟待解决的科学问题.本研究基于纯藻种实验室培养和室内光学控制实验，在微囊藻（Microcystis）、鱼腥藻（Dolichospermum）、束丝藻（Aphanizomenon）3种主要水华蓝藻固有光学特性的基础上，通过甄别不同水华蓝藻的吸收、散射和后向散射光谱的特征波段，构建了基于吸收和散射特性的5种水华蓝藻类群的非线性最优化定量识别模型，其中，基于440、620和675 nm 3个波段吸收的a-CIM440，620，675具有较为稳定的定量识别能力；并基于野外实测光学特性数据，实现了巢湖主要水华蓝藻类群的定量监测，初步分析了巢湖主要水华蓝藻类群的时空分布特性.研究表明，巢湖的水华蓝藻以鱼腥藻、微囊藻为主，束丝藻较少，鱼腥藻主要出现在温度较低的季节，微囊藻在夏季的西部湖区占优势；巢湖水华主要为微囊藻藻华和鱼腥藻藻华，且浓度较高的蓝藻主要存在于水体表面以下20 cm范围内；微囊藻和鱼腥藻在非藻华断面垂向上均匀分布.本研究可为富营养化湖泊蓝藻水华预测预警以及相关管理部门决策提供重要的理论依据和科学支撑. |
| 关键词: 富营养化湖泊 水华蓝藻类群 固有光学特性 非线性最优化模型 巢湖 |
| DOI：10.18307/2021.0122 |
| 分类号: |
| 基金项目:国家自然科学基金项目（41671371）和江苏省科技厅社会发展面上项目（BE2019774）联合资助. |
|
| Quantitative identification methods of bloom-forming cyanobacterial groups of inland lakes based on inherent optical properties |
|
Chu Qiao1,2, Zhang Yixuan1,2, Zhang Yuchao1,3, Ma Ronghua1,3, Hu Minqi1,2
|
|
1.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.University of Chinese Academy of Sciences, Beijing 100049, P. R. China;3.Jiangsu Collaborative Innovation Center of Regional Modern Agriculture&Environmental Protection, Huaiyin Normal University, Huai'an 223300, P. R. China
|
| Abstract: |
| Cyanobacteria bloom is one of the important characteristics of eutrophication in lake waters. The characteristics, hazards, and treatment methods of different bloom-forming cyanobacterial groups are significantly different. Therefore, in the process of implementing eutrophic lake pollution control, ecological restoration, and cyanobacteria ecological disaster prediction and early warning, how to quickly and accurately grasp the spatiotemporal distribution characteristics of different cyanobacteria groups have become an urgent scientific question. This study is based on the laboratory cultivation of pure algae species and indoor optical control experiments. Based on the inherent optical characteristics of the three main bloom-forming cyanobacterial groups: Microcystis, Dolichospermum, and Aphanizomenon, we screened the characteristic bands of the absorption and scattering spectra of different cyanobacterial groups and five nonlinear optimization quantitative identification models were constructed respectively. The model a-CIM440,620,675 based on the absorption characteristic bands of 440, 620 and 675 nm shows the best performances. By applying field measured optical characteristics data to this model, quantitative monitoring of the main cyanobacterial group in Lake Chaohu was achieved, and the temporal and spatial distribution of the main bloom-forming cyanobacterial group in Lake Chaohu was analyzed. The results show that the cyanobacterial groups in Lake Chaohu are dominated by species of Microcystis and Dolichospermum. Microcystis generally occupies the western region of the lake in summer, whereas Dolichospermum dominated in cold seasons. In Lake Chaohu both of Microcystis bloom and Dolichospermum bloom are found in May. The non-algal bloom sections are mainly Microcystis and Dolichospermum, and they are both vertically and uniformly distributed. There are Microcystis and Dolichospermum in Lake Chaohu, and the higher concentration of cyanobacteria mainly exists below the water surface within 20 cm. This study can provide important theoretical basis and scientific support for the prediction and early warning of cyanobacteria blooms in eutrophic lakes and help with the decisions of relevant management departments. |
| Key words: Eutrophic lakes bloom-forming cyanobacteria inherent optical properties nonlinear optimization model Lake Chaohu |
|
|
|
|
