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基于原位多传感器系统监测数据的中国太湖季节性水动力和藻类动力学的高时间分辨率分析。

Highly time-resolved analysis of seasonal water dynamics and algal kinetics based on in-situ multi-sensor-system monitoring data in Lake Taihu, China.

机构信息

Institute of Applied Geosciences, Working Group Environmental Mineralogy and Environmental System Analysis (ENMINSA) Karlsruhe Institute of Technology, Kaiserstraße 12, 76131 Karlsruhe, Germany.

Institute of Applied Geosciences, Working Group Environmental Mineralogy and Environmental System Analysis (ENMINSA) Karlsruhe Institute of Technology, Kaiserstraße 12, 76131 Karlsruhe, Germany.

出版信息

Sci Total Environ. 2019 Apr 10;660:329-339. doi: 10.1016/j.scitotenv.2019.01.044. Epub 2019 Jan 6.

Abstract

Predicting algal blooms is challenging due to rapid growth rates under suitable conditions and the complex physical, chemical, and biological processes involved. Physico-chemical parameters, monitored in this study by a high-resolution in-situ multi-sensor system and derived from lab-based water sample analyses, show the seasonal variation and have different degrees of vertical gradients across the water column. Through analyzing the changes and relations between multi-factors, we reveal pictures of water quality dynamics and algal kinetics. Nitrate has regular seasonal changes different to the seasonal patterns of total dissolved Phosphorus. Positive correlations are found between Chlorophyll a fluorescence and temperature, wind-induced resuspension and mixing promote the augment of Cyanobacteria fluorescence (Phycocyanin) signal. While the resuspension can also result in the increase of turbidity and affect the light environment for hydrophytes, the algal scums are the main reason for the high turbidity on the surface, which lower the illumination radiation in the water body. Those parameters are the primary dominants responsible for the change of algae from our monitoring data, which could be used as indicators for the dynamic changes of algae in the future.

摘要

由于在适宜条件下藻类的快速生长以及涉及的复杂物理、化学和生物过程,预测藻类水华具有挑战性。本研究通过高分辨率原位多传感器系统监测的理化参数,并从实验室水样分析中得出,这些参数显示了季节性变化,并在水柱中具有不同程度的垂直梯度。通过分析多因素之间的变化和关系,我们揭示了水质动力学和藻类动力学的图片。硝酸盐的季节性变化有规律,与总溶解磷的季节性模式不同。叶绿素 a 荧光与温度之间存在正相关关系,风引起的再悬浮和混合促进了蓝藻荧光(藻青蛋白)信号的增加。虽然再悬浮也会导致浊度增加,并影响水生植物的光照环境,但藻类浮沫是表面高浊度的主要原因,这会降低水体中的光照辐射。这些参数是我们监测数据中藻类变化的主要主导因素,可作为未来藻类动态变化的指标。

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