School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 689-798, Republic of Korea.
School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, 130-743, Republic of Korea.
Water Res. 2017 Dec 1;126:319-328. doi: 10.1016/j.watres.2017.09.026. Epub 2017 Sep 18.
Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = -0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = -0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity.
了解有害藻类水华对于保护水生态系统和人类健康至关重要。本研究描述了蓝藻水华的时空分布，以确定在百济水库中蓝藻水华与环境因素之间的关系。在一个固定站点使用两年的蓝藻细胞数据和基于高光谱图像（HSI）的四个远程感测的藻蓝蛋白（PC）浓度分布，用于描述水华的时空变化与影响因素之间的关系。实施了人工神经网络模型和三维水动力模型，分别使用远程感测 HSI 来估计 PC 浓度和模拟水动力。统计测试结果表明，蓝藻生物量的变化与水温（斜率= 0.13，p 值<0.01）、总氮（斜率= -0.487，p 值<0.01）和总磷（斜率= 20.7，p 值<0.05）的变化显著相关，而生物量的变化与流出量的变化中度相关（斜率= -0.0097，p 值= 0.065）。水温是 8 月至 10 月三个月内影响 PC 浓度变化的主要因素，并且在这三个月中差异显著（p 值<0.01）。水动力参数对这三个月 PC 浓度的变化也有一定的影响。总的来说，本研究有助于描述蓝藻水华的时空变化，并确定影响水华变化的因素。本研究可能作为制定减少水华频率和严重程度的策略的基础发挥重要作用。
