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http://dx.doi.org/10.11626/KJEB.2019.37.3.433

Development of simple tools for algal bloom diagnosis in agricultural lakes  

Nam, Gui-Sook (Rural Research Institute, Korea Rural Community Corporation(KRC))
Lee, Seung-Heon (Rural Research Institute, Korea Rural Community Corporation(KRC))
Jo, Hyun-Jung (R&D Center, Dongmoonent Co., Ltd)
Park, Joo-Hyun (R&D Center, Dongmoonent Co., Ltd)
Cho, Young-Cheol (Department of Environmental Engineering, Chungbuk National University)
Publication Information
Korean Journal of Environmental Biology / v.37, no.3, 2019 , pp. 433-445 More about this Journal
Abstract
This study was designed to develop simple tools to easily and efficiently predict the occurrence of algal bloom in agricultural lakes. Physicochemical water quality parameters were examined to reflect the phytoplankton productivity in 182 samples collected from 15 agricultural lakes from April to October 2018. Total phytoplankton abundance was significantly correlated with chlorophyll-a (Chl-a) (r=0.666) and Secchi depth (SD) (r= -0.351). The abundances of cyanobacteria and harmful cyanobacteria were also correlated with Chl-a (r=0.664, r=0.353) and SD (r= -0.340, r= -0.338), respectively, but not with total nitrogen (TN) and total phosphorus (TP). The Chl-a concentration was correlated with SD (r= -0.434), showing a higher similarity than phytoplankton abundance. Therefore, Chl-a and SD were selected as diagnostic factors for algal bloom prediction, instead of analyzing the standing crop of harmful cyanobacteria used in algae alarm systems. Specifically, accurate diagnoses were made using realtime SD measurements. The algal bloom diagnostic tool is an inverse cone-shaped container with an algal bloom diagnosis chart that modified SD and turbidity measurement methods. Lake water was collected to observe the number of rings visible in the container or the number indicated in each ring, depending on the degree of algal bloom,and to determine the final stage of algal blooming by comparison to the colorimetric level on the diagnosis chart. For an accurate diagnosis, we presented 4-step diagnostic criteria based on the concentration of Chl-a and the number of rings and a fan-shaped algal bloom diagnosis chart with Hexa code names. This tool eliminated the variables and errors of previous methods and the results were easily interpreted. This study is expected to facilitate the diagnosis of algal bloom in agricultural lakes and the establishment of an efficient algal bloom management plan.
Keywords
agricultural lake; algal bloom; diagnostic tool; harmful cyanobacteria; phytoplankton;
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Times Cited By KSCI : 1  (Citation Analysis)
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