• Journal of Korean Society of Environmental Engineers
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• v.36 no.5
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• pp.378-385
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• 2014

The chlorophyll-a concentration in algae bloom warning system of Hangang basin did not predict the cyanobacteria dominance. In this study, suggest the predicting indicator of cyanobacteria dominance through analyzing the environmental factors affecting on the cell count of cyanobacteria. Firstly, the dominance of algae was analyzed with seasonal variation during Jan. 2012~Sep. 2013. The diatom dominated phytoplankton communities during the period of January~April. In the May~June, the green algae dominated. And, the dominance of algae was changed to cyanobacteria in the July~August. Also, the environmental factors affecting to cyanobacteria blooms ; nutrients (TN, TP), temperature, precipitation, dam-discharge were evaluated during the study period. Rather than temperature factor, relatively low dam discharge causes cyanobacteria to grow rapidly and create a blooms. The low dam-discharge may increase the water retention time. Finally, it is proved that a low ratio of TN to TP (<29:1) can favour the development of cyanobacteria blooms. Thus, the predicting indicator (TN:TP) have need to apply to the alarm bloom warning system of Hangang basin.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.101-119
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• 2022

Existing algal bloom monitoring is based on field sampling, and there is a limit to understanding the spatial distribution of algal blooms, such as the occurrence and spread of algae, due to local investigations. In this study, algal bloom monitoring was performed using an unmanned aerial vehicle and multispectral sensor, and data on the distribution of algae were provided. For the algal bloom monitoring site, data were acquired from the Mulgeum·Mae-ri site located in the lower part of the Nakdong River, which is the areas with frequent algal bloom. The Chlorophyll-a(Chl-a) value of field-collected samples and the Chl-a estimation formula derived from the correlation between the spectral indices were comparatively analyzed. As a result, among the spectral indices, Maximum Chlorophyll Index (MCI) showed the highest statistical significance(R²=0.91, RMSE=8.1mg/m³). As a result of mapping the distribution of algae by applying MCI to the image of August 05, 2021 with the highest Chl-a concentration, the river area was 1.7km², the Warning area among the indicators of the algal bloom warning system was 1.03km²(60.56%) and the Algal Bloom area occupied 0.67km²(39.43%). In addition, as a result of calculating the number of occurrence days in the area corresponding to the "Warning" in the images during the study period (July 01, 2021~November 01, 2021), the Chl-a concentration above the "Warning" level was observed in the entire river section from 12 to 19 times. The algal bloom monitoring method proposed in this study can supplement the limitations of the existing algal bloom warning system and can be used to provide information on a point-by-point basis as well as information on a spatial range of the algal bloom warning area.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.312-320
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• 2019

The occurrence of cyanobacteria causes problems such as oxygen depletion and increase of organic matter in the water body due to mass prosperity and death. Each year, Algae bloom warning System is issued due to the effects of summer heat and drought. It is necessary to quantitatively characterize the occurrence of cyanobacteria for proactive green algae management in the main Nakdong river. In this study, we analyzed the major influencing factors on cyanobacteria bloom using visualization and correlation analysis. A decision tree, a machine learning method, was used to quantitatively analyze the conditions of cyanobacteria according to the influence factors. In all the weirs, meteorological factors, temperature and SPI drought index, were significantly correlated with cyanobacterial cell number. Increasing the number of days of heat wave and drought block the mixing of water in the water body and the stratification phenomenon to promote the development of cyanobacteria. In the long term, it is necessary to proactively manage cyanobacteria considering the meteorological impacts.