Journal
WATER
Volume 13, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/w13040439
Keywords
water quality modeling; harmful cyanobacteria; CyanoHABs; EFDC-NIER
Categories
Funding
- National Institute of Environmental Research (NIER) - Ministry of Environment (MOE) of the Republic of Korea [NIER-2020-01-01-012]
- Korea Environmental Industry & Technology Institute (KEITI) [NIER-2020-01-01-012] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study proposed a technique to predict harmful cyanobacteria concentrations using the source codes of the Environmental Fluid Dynamics Code from the National Institute of Environmental Research in South Korea. A graphical user interface was developed to generate information about water quality and algae for predictive modeling. Results showed a high prediction accuracy (62%) for harmful cyanobacteria in the Nakdong River in South Korea.
Cyanobacterial Harmful Algal Blooms (CyanoHABs) produce toxins and odors in public water bodies and drinking water. Current process-based models predict algal blooms by modeling chlorophyll-a concentrations. However, chlorophyll-a concentrations represent all algae and hence, a method for predicting the proportion of harmful cyanobacteria is required. We proposed a technique to predict harmful cyanobacteria concentrations based on the source codes of the Environmental Fluid Dynamics Code from the National Institute of Environmental Research. A graphical user interface was developed to generate information about general water quality and algae which was subsequently used in the model to predict harmful cyanobacteria concentrations. Predictive modeling was performed for the Hapcheon-Changnyeong Weir-Changnyeong-Haman Weir section of the Nakdong River, South Korea, from May to October 2019, the season in which CyanoHABs predominantly occur. To evaluate the success rate of the proposed model, a detailed five-step classification of harmful cyanobacteria levels was proposed. The modeling results demonstrated high prediction accuracy (62%) for harmful cyanobacteria. For the management of CyanoHABs, rather than chlorophyll-a, harmful cyanobacteria should be used as the index, to allow for a direct inference of their cell densities (cells/mL). The proposed method may help improve the existing Harmful Algae Alert System in South Korea.
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