• 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.

• ALGAE
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• v.18 no.4
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• pp.361-363
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• 2003

This study was conducted at Seomjin River estuary to identify the causative species of algal bloom and their blooming cycles. Field surveys were conducted at 4 stations in every week from April to December of 1999. Thirty species were observed as the causative species of alga bloom. Skeletonema costatum, Thalassiosira sp., and microflagellate spp. (mixed red tide: Chroomonas sp. and two species of Prasinophycea) made algal blooms during the present study period. In addition, toxic algal species of diatom Pseudo-nitzschia multiseries and dinoflagellate Dinophysis acuminata were observed. The algal blooms were caused by microflagellate spp. in June, Thalassiosira sp. in July and Skeletonema costatum in August. Generally, the algal blooms persisted for about 5 days in this area.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.457-464
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• 2017

The large breeding of algae in rivers has caused the algal bloom and has becoming a serious national problem for the safety of water sources. Therefore, in order to supply stable water resources through securing clean water, it is necessary to develop technology for prevention of water pollution caused by algal bloom. The purpose of this study is to improve the water quality management ability of river by applying the algal bloom detection technique using UAV. Unmanned aerial images were acquired for the Dodong in the middle region of the Nakdong River where algal bloom are frequent. In addition, the phytoplankton concentration was acquired through the sampling of algal bloom and the examination of water quality. Correlation between phytoplankton concentrations and the results of applying the algal bloom index to the Unmanned aerial images showed a strong positive correlation. The remote sensing method suggested in this study is expected to improve the initial response capability of river water pollution.

• Environmental Engineering Research
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• v.24 no.3
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• ALGAE
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• v.21 no.3
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• pp.283-286
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• 2006

This study carried out phylogenetic analysis of dinoflagellate Gonyaulax polygramma which was responsible for a harmful algal bloom episode in Korea in 2004. Molecular phylogenetic tree inferred from the partial LSU rDNA data showed that G. polygramma came up among the monophyletic Gonyaulax clade, but did not have apparent genetic affiliation to other Gonyaulax species. This result appears to be consistent with characteristic morphological features of G. polygramma such as epitheca sharply tapering to the apex and thecal plates ornamented with numerous longitudinal striations.

• 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.

• Proceedings of the Korea Society of Environmental Biology Conference
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• 2004.05a
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• pp.25-25
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• 2004

We studied algal growth-related water quality of the Dongbok lake which is the drinking water reservoir for the Gwangju municipality. Peridinium cinctum and several diatomic algal species frequently caused water bloom throughout the lake from early spring to late autumn. With the heaviest predominance of Peridinium cintum in May 2003, COD was 13.7 mg/I in the surface layer. Highly turbid surface water with 46.8 mg/I of SS was also caused by Perdinium bloom. Peridinium bloom decisively eliminated cyanobacterial growth in the lake, otherwise cyanobacterial bloom resulted. Dense algal layer was confined in the upper several meters of the water column above the thermocline, which gives relatively algae-free water in deeper layer suitable for drinking source water supply. Upon collapse of thermocline, water quality of the surface layer was improved while deeper layer was deteriorated in terms of water quality. This paper deals with some details of water quality changes with algal growth in the Dongbok lake past two years.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.52-63
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• 2007

The objectives of this study were to setup a laterally-averaged two-dimensional eutrophication model in Daecheong Reservoir, and to validate the model under two different hydrological conditions; drought year (2001) and wet year (2004). The suggested modeling approach was found to be very effective to simulate the dynamic variations of water temperature, nutrients, dissolved oxygen, and algae in the reservoir. The model satisfactorily replicated the algal bloom that happened between Janggae (Sta.4) and Haenam (Sta.5) during summer of 2001, although the peak concentration was slightly underestimated due to the laterally averaged assumption. The allochthonous phosphorus and algae induced from upstream and So-oak stream during several rainfall events were found to be most significant sources of algal bloom in 2001. In contrast to draught year, the flood events happened during summer months of 2004 tended to remove the hypolimnetic anaerobic conditions and dilute the dissolved phosphorus in the upper reach of the reservoir, and in turn mitigated algal bloom. It implies that the impact of hydrological and hydrodynamic conditions on the reservoir water quality is highly significant, and a drought year may be more vulnerable to algal bloom in the reservoir.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.887-894
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• 2006

Water quality in the Daecheong reservoir has been deteriorated by algal bloom every year. Algal bloom is propagated from eutrophicated tributary into the main body of the reservoir during the wet season. Nutrients from diffuse sources trigger the propagation of the algal bloom. This study is aimed to analyze relationship between the water current by the simulation and algae transport from the main body in the Daecheong reservoir including tributary where algal bloom has occurred seriously every year. Water quality model CE-QUAL-W2 was applied to analyze water movement in draught season (2001) and flooding season (2003). The result of simulation corresponded with the observed water elevation level, 63~80 m and showed stratification of the Daecheong reservoir. In the draught season, as velocity and direction off low in the reservoir was estimated to affect algae transport including nutrient supply from small tributary, algal blooms occurred in the stagnate zone of middle stream of the reservoir. On the other hand, in the flooding season, it was resulted in nutrient transport from upstream of main reservoir and nutrients were delivered up to downstream by fast water velocity. In result, algal blooms occurred in stagnate zone of reservoir downstream as the current of downstream was retarded according to dam outflow control.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.915-925
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• 2011

Following the industrialization and urbanization in Korea, algal bloom causes aesthetic displeasure and many other problems such as taste and odor, coloration, scum, increase in pH, filter-bed blockage. There were some cases involving human death by microcystins during summertime in foreign countries. In Korea, Harmful cyanobacteria such as Microcystis and Anabaena develop in summer in the Daecheong reservoir, one of the main water resources, with the retention time of above 200 days. To better control algal bloom, the Ministry of Environment has been running algal bloom alert system from 1998 for the Daecheong reservoir, which needs to be improved to reflect the characteristics of river-type lakes. For this reason, we try to find new measures to improve an algal bloom alert system for each water zone considering the characteristics of harmful cyanobacteria in this study.