장가연 Gayeon Jang , 조민경 Minkyoung Jo , 김자연 Jayun Kim , 김상준 Sangjun Kim , 박힘찬 Himchan Park , 박준홍 Joonhong Park

DOI:10.15681/KSWE.2024.40.3.121 Vol.40(No.3) 121-129, 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

Climate change scenarios, Cyanobacteria, Harmful algal bloom (HAB), Machine learning, Nakdong river

박윤경 Yoonkyung Park , 안창혁 Chang Hyuk Ahn

DOI:10.15681/KSWE.2024.40.3.131 Vol.40(No.3) 131-139, 2024

This study investigated the chemical properties of Organic Soil Amendments (OSAs) made from organic waste. It also assessed the effectiveness of using these OSAs in the soil layer of Green Infrastructure (GI) to reduce stormwater runoff and non-point source pollutants. The goal was to improve the national environmental value through resource recycling and contribute to the circular economy transformation and carbon neutrality of urban GI. The OSAs used in this study consisted of spent coffee grounds and food waste compost. They were found to be nutrient-rich and stable as artificial soils, indicating their potential use in the soil layer of GI facilities. Applying OSAs to bio-retention cells and permeable pavement resulted in a reduction of approximately 11-17% in stormwater runoff and a decrease of about 16-18% in Total Phosphorus (TP) discharge in the target area. Increasing the proportion of food waste compost in the OSAs had a positive impact on reducing stormwater runoff and pollutant emissions. This study highlights the importance of utilizing recycled resources and can serve as a foundation for future research, such as establishing parameters for assessing the effectiveness of GI facilities through experiments. To enable more accurate analysis, it is recommended to conduct studies that consider both the chemical and biological aspects of substance transfer in OSAs.

Green infrastructure, Non-point sources pollution, Organic soil ameliorant, Recycled resources, Stormwater runoff