基于荧光传感方法的藻类在线监测

 湖泊水库严重的水质富营养化导致水华大面积暴发，已成为目前主要环境问题之一。实现水华暴发前后浮游藻类种群结构变化过程（主要包括浮游藻类种群组成、数量变化过程）的有效监测是分析水华发生的生态学机制、掌握水华的生消过程和实现水华预警的基础。本文基于藻类活体荧光光谱技术、嵌入式微控制技术和实时采集与处理技术，研发出具有实时在线监测能力的藻类在线荧光仪，可实现不同光谱组藻类（蓝藻、绿藻和褐藻）的分类测量。通过对比实验分析发现，藻类在线荧光仪对蓝藻、绿藻和褐藻的分类测量误差均小于17.88%。同时，巢湖外场实验表明藻类在线荧光仪具有长期稳定的运行能力。具有以上指标的藻类在线荧光仪在水环境监测、水华监测预警和水质富营养化现状评估等领域具有广阔的应用前景。

Online Monitoring Technology for Algae by Fluorescent Sensing Method

Water quality eutrophication and breakout of water bloom in lakes and reservoirs have already become a major environmental concern. To analyze the ecological mechanism of breakout of water bloom, to understand the generating and vanishing process of water bloom and to achieve an early warning of water bloom, the effective monitoring of the population structure of planktonic algae during breakout of water bloom is essential, which mainly includes species composition and quantitative variation of planktonic algae. Based on sensing method of fluorescence in vivo, embedded control technology and real-time data acquisition technology, this paper proposes a design of online fluorometer for algae. The fluorometer for algae can be used to measure the content of different spectral groups of algae (blue algae, green algae and brown algae) in vivo and in situ. The results of comparative experiments show that the measurement errors of blue algae, green algae and brown algae are less than 17.88%. Field experiments in Caohu show that fluorometer has a good stability. The fluorometer with a good performance would have further potential applications in environment monitoring, water bloom early-warning and evaluation of lake eutrophication.