https://doi.org/10.15681/KSWE.2026.42.2.139

이다훈(Dahoon Lee) ; 김태경(Taekyoung Kim) ; 이주성(Juseong Lee) ; 김민아(Minah Kim) ; 정한석(Hanseok Jeong)

Combined Sewer Overflows (CSOs) significantly contribute to nonpoint source pollution in urban watersheds. These overflows are characterized by rapid changes in flow and pollutant concentrations during the initial stages of rainfall when heavily polluted stormwater is released into receiving waters. To address these challenges, Best Management Practices (BMPs) have been implemented, with underground detention facilities recognized as effective structural BMPs in urban settings. This study utilized the Hydrological Simulation Program?FORTRAN (HSPF) model to quantitatively evaluate the water quality improvements associated with structural BMPs focusing on underground detention facilities within the Anyangcheon watershed. The model was calibrated and validated using observed data from 2017 to 2022, and four scenarios were developed: baseline, treatment efficiency, storage capacity, and facility relocation. Results indicated that doubling the storage capacity enhanced water quality in the upstream region, showing improvements of 0.49% for BOD, 0.25% for T-N, and 0.99% for T-P, while downstream improvements were more modest. Additionally, relocating facilities closer to wastewater treatment plants resulted in a maximum reduction of 0.75% for T-P, suggesting that spatial configuration had a greater impact on water quality improvement than treatment efficiency alone. This study provides a quantitative assessment of the performance of spatially dependent BMPs and establishes a scientific foundation for integrated water quality management in urban watersheds.

https://doi.org/10.15681/KSWE.2026.42.2.151

이은서(Eunseo Lee) ; 오승수(Seungsoo Oh) ; 김현우(Hyunwoo Kim) ; 김세윤(Seyoon Kim) ; 김나래(Narae Kim) ; 박정안(Jeong-Ann Park)

This study examined seasonal variations in water quality and algal communities in Uiam Lake and Gongji Stream. Water quality generally fell within Class II to III according to regional standards for rivers and lakes. Dissolved oxygen (DO) levels remained above 6.5 mg/L, and pH was stable and slightly alkaline. Electrical conductivity (EC) ranged from 125 to 321 μS/cm between April and August, while biochemical oxygen demand (BOD) tended to rise with increasing temperatures. Total nitrogen (T-N) levels consistently exceeded 2.0 mg/L, and total phosphorus (T-P) was below 0.066 mg/L in spring but increased during the summer months. Chlorophyll-a (Chl-a) concentrations were low, under 2.5 mg/m in March, but gradually increased with rising temperatures. Phycocyanin (PC) concentrations were low (≤0.77 μg/L) in June but rose significantly thereafter. Algal analyses indicated higher cell densities in Uiam Lake, classifying the overall algal assemblage as mesotrophic. Seasonal variations in algal diversity were noted, with greater diatom diversity in spring and increased green algal diversity in summer. Three of the four genera of harmful cyanobacteria were identified, including Microcystis sp.,Anabaena sp., and Oscillatoria sp. Algal-related variables (Chl-a, phycocyanin, and cell density) showed significant positive correlations with several water quality parameters (DO, pH, and BOD). The Uiam Lake water system was influenced by both biotic factors (algal and organic loading) and physicochemical factors. The calculated Trophic State Index ranged from 45 to 60, indicating conditions ranging from mesotrophic to eutrophic.

https://doi.org/10.15681/KSWE.2026.42.2.164

최종우(Jongwoo Choi) ; 강석(Seok Kang) ; 박준서(Junseo Park) ; 김시헌(Siheon Kim) ; 최진혁(Jin-Hyuk Choi) ; 박정안(Jeong-Ann Park)

The increasing consumption of wet wipes has led to significant waste management issues and environmental pollution. In particular, wipes composed of non-biodegradable and non-flushable materials cause frequent blockages in sewage systems. With the recent expansion of eco-friendly policies and heightened public awareness of sustainability, there is a growing effort to reduce environmental burdens by utilizing biodegradable raw materials. This study comparatively evaluated the environmental impacts of 15 commercially available wet wipes, comprising three products from each of five categories: cleaning, cosmetic, baby, general-purpose, and biodegradable types. To achieve this, several standardized tests were conducted: flushability was assessed according to ISO 12625-17; biodegradability was assessed through a 42-day soil burial test with weight loss, Scanning Electron Microscopy (SEM), and Fourier Transform Infrared Spectroscopy (FTIR); and acute aquatic toxicity was tested using Daphnia magna (OECD TG 202). The results indicated that cellulose-based biodegradable wipes generally exhibited superior flushability and soil biodegradability compared to synthetic fiber-based conventional wipes. FTIR analysis revealed amide bond peaks in cellulose-based samples, serving as evidence of microbial degradation, whereas synthetic fiber samples showed minimal chemical structural changes. SEM observations further confirmed surface degradation in cellulose-based samples, while synthetic polymer fibers remained largely unchanged. In the acute toxicity tests, extracts from certain wipes exhibited toxicity to Daphnia magna, confirming the potential environmental hazard of included chemical components. Furthermore, ECOSAR software analysis predicted high toxicity for limonene and orange oil, which were common ingredients in the tested samples, demonstrating consistency between experimental results and theoretical predictions.

https://doi.org/10.15681/KSWE.2026.42.2.177

강덕준(Dejun Jiang) ; 권혁구(Hyuk-Ku Kwon)

Global climate change and human-induced nutrient loading have intensified the eutrophication of aquatic ecosystems. This has led to frequent harmful algal blooms (HABs) in river systems, posing risks to water security and ecosystem health. To manage water quality proactively, accurate predictions of chlorophyll-a (chl-a) levels are essential. However, data-driven modeling encounters challenges such as sensor noise and the infrequent occurrence of high-concentration algal blooms compared to typical background conditions, a situation referred to as imbalanced regression. This study implemented a machine learning pipeline in the Miho River basin, utilizing hydro-chemical data from 2016 to 2025. The Isolation Forest (IForest) algorithm was employed to identify and remove multivariate outliers caused by sensor errors, thus ensuring the integrity of the training data. Two data augmentation strategies were assessed against a baseline Extreme Gradient Boosting (XGBoost) model to address distributional imbalance: Gaussian Noise (GN) injection and the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN). While GN augmentation yielded only marginal improvements, the SMOGN-augmented model demonstrated superior performance. The SMOGN-XGBoost model achieved the highest overall accuracy (R² = 0.81, RMSE = 4.49 μg/L). In the critical high-concentration range (top 25%), the SMOGN model enhanced explanatory power (R²) by 30.25% compared to the baseline. Feature importance analysis revealed that the balanced model exhibited increased sensitivity to dissolved oxygen (DO). Integrating anomaly detection with SMOGN-based data balancing presents a practical framework for early warning systems in river environments characterized by imbalanced data.