The Journal of
the Korean Society on Water Environment

Bimonthly
  • ISSN : 2289-0971 (Print)
  • ISSN : 2289-098X (Online)
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Microhabitat Overlap and Mesohabitat Specificity of Benthic Macroinvertebrates Based on Quadrat Sampling in the Jojong Stream

공동수(Dongsoo Kong) ; 권용주(Yongju Kwon) ; 정찬영(Chanyoung Jeong)

https://doi.org/10.15681/KSWE.2025.41.6.439

This study investigated the mesohabitat preferences of benthic macroinvertebrates in the Jojong stream, Kyeonggi-do, South Korea, utilizing 160 quadrats across four sites and four habitat types: riffles, runs, pools, and riparians. The results indicated that broader stream channels support greater overall species richness and abundance; however, species values per unit area may decline. Further research is needed to determine whether this pattern arises from habitat aggregation, dispersal, or other factors. Notably, over half of the species were found outside of riffles, highlighting the necessity for multi-habitat approaches. Based on the Levins’ breadth index values and the significance of the permutation test, the generalist species identified in the Jojong stream include Dugesia sp. and Rhyacophila lata. Among the 157 identified species, the pairs with the highest Pianka niche overlap were all from different genera, suggesting that spatial isolation may occur during speciation. This finding warrants experimental verification. Non-metric multidimensional scaling (NMS) ordination distinctly separated riffles and runs, while pools and riparians functionally clustered together. Habitat-specific species included eight riffle specialists (e.g., Rhyacophila brevicephala, Hydropsyche orientalis), one run-specific species (Onychogomphus ringens), and six associated with pools and riparians (e.g., Siphlonurus chankae, Davidius lunatus). While site-specific, this study emphasizes the importance of mesohabitat diversity and microhabitat heterogeneity in assessing national stream health and biodiversity, advocating for long-term research on niche dynamics, competition, and dispersal.

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Efficiency Analysis of Coagulation-Based Hybrid Processes for NOM Removal in Low-SUVA Waters

정다희(Jeong Dahui) ; 황예진(Hwang Yejin) ; 김명호(Kim Myeongho) ; 이준호(Lee Junho)

https://doi.org/10.15681/KSWE.2025.41.6.455

This study examined the effectiveness of coagulation-based treatment combinations for removing natural organic matter (NOM) in waters with low Specific UV Absorbance (SUVA ≤ 2.0 L/mg?m). Four treatment processes were evaluated: coagulation alone (C), coagulation followed by ion exchange (C?IEX), coagulation with powdered activated carbon (C+P), and coagulation with PAC followed by ion exchange (C+P?IEX). These processes were tested using synthetic water containing humic acid (HA) and fulvic acid (FA). The results indicated that NOM characteristics significantly affected treatment performance, even when SUVA levels w ere similar. T he C+P p rocess demonstrated the h ighest r emoval r ates f or U V254 a nd d issolved organic carbon (DOC) and exhibited the greatest adsorption capacity (qe) in HA water. In contrast, the C+P? IEX process proved most effective for FA water, highlighting differences in molecular structure and hydrophobicity. However, the pH levels in IEX-related processes rose to between 9 and 11 due to the use of OH?-form resins, which introduced variability in process comparisons. Additionally, because the experiments utilized synthetic waters, interactions with other natural water constituents and the potential formation of disinfection by-products (DBPs) were not directly evaluated. These limitations underscore the necessity for further research in real water conditions with controlled pH levels. The findings suggest that relying solely on SUVA for process design may result in insufficient NOM removal, as it does not account for compositional differences. This study highlights the importance of NOM-specific characterization and customized process combinations to ensure effective management of NOM in low SUVA source waters.

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A Spatially Differentiated System Dynamics Analysis of Urban Flooding and Citizen Safety

이가영(Lee Gayoung) ; 김여원(Kim Yeowon)

https://doi.org/10.15681/KSWE.2025.41.6.463

Urban expansion and climate change have heightened flood risks by increasing impervious surfaces and altering rainfall patterns. Many previous studies, however, have overlooked spatial heterogeneity and feedback dynamics across urban zones. This study employs a system dynamics (SD) model to analyze the interactions between urban flooding and citizen safety in Sejong City, South Korea. Five policy scenarios were evaluated: (1) a baseline scenario with current drainage and green space, allowing for approximately 1% annual urbanization (“Undesirable”); (2) intensified precipitation (“Potential Risk”); (3) expansion of the drainage system (“Strategic”); (4) expansion of green space (“Adaptive”); and (5) a combination of gray and green infrastructure expansion (“Transformative”). The city was divided into core and peripheral zones to capture variations in land use. Results indicate that the Transformative scenario was the most effective, increasing citizen safety by 80% in core zones and 60% in peripheral zones. In core zones, the Strategic scenario provided immediate benefits, but long-term resilience required an integrated approach combining gray and green measures. In peripheral zones, green space expansion was generally sufficient, except in low-lying or newly urbanized areas, where infrastructure upgrades were necessary. By providing scenario-based simulations that incorporate feedbacks within urban systems, this study offers a framework for spatially differentiated flood risk management. The findings highlight the need to strengthen engineered systems in core zones while implementing nature-based or hybrid approaches in peripheral zones to enhance adaptive capacity amid increasing hydrological uncertainty.

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Mitigation of Green Algal (Chlorella spp.) Blooms Via Quorum Sensing Inhibition

문태웅(Moon Taeung) ; 홍서영(Hong Seoyoung) ; 김한신(Kim Han-Shin)

https://doi.org/10.15681/KSWE.2025.41.6.477

Harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems by degrading water quality and releasing toxins, which adversely affect water resource utilization and economic activities. Traditional physical methods, such as clay flocculation, offer only short-term solutions and carry the risk of secondary pollution. These limitations have spurred interest in developing biological controls, which promise long-term and environmentally friendly mitigation strategies. This study investigates quorum sensing (QS) disruption as a viable biological method for managing HABs. We hypothesized that lactonase, an enzyme capable of degrading bacterial signaling molecules, could inhibit algal growth. To test this, we focused on two bacterial strains, Enterococcus durans HEMM-1 and Listeria grayi HEMM-2, both isolated from a water treatment system and known to produce lactonase, a QS-quenching enzyme. To confirm their potential, we demonstrated that the supernatants from the HEMM-1 and HEMM-2 strains inhibited biofilm formation in P. aeruginosa by 50.78% and 33.55%, respectively. Expanding on this principle, we developed a practical application by immobilizing the QS-quenching bacteria in sodium alginate carriers. The results showed that the immobilized HEMM-1 and HEMM-2 carriers inhibited biofilm formation by 59.23% and 32.42%, respectively. Additionally, these carriers reduced the growth of Chlorella by 41.26% and 32.82%, respectively. Our findings suggest that this enzyme-based approach could effectively suppress algal growth, highlighting its potential as a sustainable and eco-friendly alternative for managing HABs.

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Assessing Climate Change Vulnerability of Three Hydropsyche Species Using Probability-Based Habitat Suitability and Random Forest Models

최형식(Choi Hyeongsik) ; 정찬영(Jeong Chanyoung) ; 강보미(Kang Bomi) ; 강성룡(Kang Sung-Ryong) ; 공동수(Kong Dongsoo)

https://doi.org/10.15681/KSWE.2025.41.6.484

Climate change significantly affects freshwater ecosystems by raising air and water temperatures, which in turn influences the physiology and distribution of aquatic organisms. This study evaluated the current and future habitat suitability for three species of Hydropsyche (H. kozhantschikovi, H. valvata, H. formosana) in South Korea, using these species as bioindicators of river health. We estimated equilibrium water temperature through a regression model that accounted for latitude, altitude, and coastal distance, allowing for spatial predictions at a 90-meter resolution. Species occurrence data were categorized into temperature intervals to create probability mass functions, which were fitted using four distribution models. The best model was selected based on normalized root mean square error. We developed habitat suitability index (HSI) curves from these models, and Random Forest classifiers, using elevation and temperature as predictors, achieved high accuracy (ROC?AUC > 0.80). Future distributions were projected under RCP 8.5, which assumes a 2.45 °C increase in water temperature by 2100. The species responded differently: H. kozhantschikovi displayed broad thermal tolerance with moderate habitat loss, H. valvata had narrow tolerance and experienced significant range contraction, while H. formosana showed an extremely limited distribution with slight thermal niche expansion. These findings underscore the species-specific vulnerabilities to warming and emphasize the need to conserve climate refugia in mid- and high-altitude streams.

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Prediction of Water Quality and Trophic State Index Changes in the Gangjeong-Goryeong Weir under SSP Scenarios: A Comparison of COD- and TOC-Based Indicators

정다현(Chung DaHyun) ; 정찬영(Jeong ChanYoung) ; 장유진(Jang Yujin) ; 임나연(Lim NaYeon) ; 이윤경(Lee YunKyung)

https://doi.org/10.15681/KSWE.2025.41.6.495

Climate change is accelerating eutrophication, which significantly contributes to water quality deterioration and ecosystem imbalance in lentic and regulated river systems. This study quantifies the impact of climate variability on water quality indicators related to eutrophication and projects their long-term trends under future climate scenarios. Focusing on the Gangjeong?Goryeong Weir section of the Nakdong River, we analyzed 14 years (2012?2025) of monthly water quality data, including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC), and chlorophyll-a (Chl-a), alongside climate variables such as precipitation, water temperature, and solar radiation. Using multiple linear regression (MLR), we identified key climatic drivers, revealing moderate explanatory power for COD, TN, TP, and TOC (R² = 0.33?0.40), but low for Chl-a (R² = 0.03), indicating that algal biomass is heavily influenced by non-climatic factors. We employed downscaled SSP1-2.6 and SSP5-8.5 projections for the years 2025?2100 to estimate future trophic states using COD-based (TSIKO) and TOC-based (TSIKO_TOC) indices. Both indices suggested sustained eutrophic to hypereutrophic conditions, with TSIKO_TOC consistently showing higher values, indicating a greater sensitivity of TOC to climate changes. The minimal differences between the scenarios highlight the limitations of linear models for long-term predictions. These findings emphasize the importance of incorporating climate-responsive indicators, such as TOC-based indices, into the assessment and management of eutrophication, providing a basis for adaptive policies in regulated river-reservoir systems.

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Optimization of Low Impact Development(LID) Design for Reducing Runoff and Suspended Solids Reduction Using Storm Water Management Model(SWMM)

김사욱(Sawook Kim) ; 이태호(Taeho Lee) ; 최지호(Jiho Choe) ; 김민석(Minseok Kim) ; 송정훈(Jeonghoon Song) ; 장지현(Jihyun Jang) ; 백상수(Sangsoo Baek)

https://doi.org/10.15681/KSWE.2025.41.6.510

Rapid urbanization and the extensive growth of impervious surfaces, particularly from asphalt pavement, have heightened the challenges of managing non-point source (NPS) pollution. Coupled with the increasing frequency and unpredictability of extreme rainfall events due to climate change, cities with high levels of imperviousness are becoming more susceptible to flooding and pluvial inundation. Low Impact Development (LID) practices have emerged as effective strategies for stormwater management, helping to mitigate runoff, reduce pollutant loads, and restore vital urban hydrological functions. Therefore, integrating LID techniques is crucial for urban flood management, controlling NPS pollution, and sustainably restoring the urban water cycle. However, thorough experimental validation and advanced modeling analyses are essential to comprehensively evaluate LID performance and develop reliable design guidelines for practical application. This study simulated stormwater runoff and pollutant transport under various rainfall scenarios using the EPA’s Storm Water Management Model (SWMM), applied to the storm sewer network of the Jungbang New Town District in Gyeongsan City, South Korea. Different LID configurations, scales, and implementation ratios were systematically tested to identify optimal combinations for effective runoff reduction and water quality improvement. The findings provide a solid scientific foundation for sustainable urban water cycle management and long-term water quality enhancement, offering valuable insights for policymakers, engineers, and urban planners.

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Estimation of Discharge Flow and Pollutant Load After Using Water Curtain Water in Greenhouse Complexes During Winter Season

이서영(Seo Young Lee) ; 김상민(Sang Min Kim)

https://doi.org/10.15681/KSWE.2025.41.6.524

In Korea, large greenhouse complexes are commonly established near rivers to ensure stable access to groundwater, which is intensively used during winter water-curtain cultivation to protect crops from low nighttime temperatures. However, most of the pumped groundwater is discharged directly into adjacent streams, raising concerns regarding both groundwater depletion and water-quality deterioration. This study investigated these issues in a major greenhouse cluster located in Sangnam-myeon, Miryang-si, Gyeongsangnam-do, by quantifying return flows and evaluating associated pollutant loads. From November 2024 to March 2025, four monitoring sites were equipped with water-level loggers, velocity sensors, time-lapse cameras, and automatic sampling instruments to capture hydrological and water-quality dynamics. Stage-discharge relationships were developed for each site and applied to construct continuous return-flow time series. The average return flow per greenhouse unit was 15.72 m3/day (range: 9.33-20.97), with peak discharges observed in December and January when heating demand was highest. Water-quality analysis revealed substantial pollutant loads originating from return flows, including SS (110.36 kg/km2 ?day), COD (29.16), TN (18.83), and NO3-N (6.38), whereas TP remained consistently low. These findings demonstrate that water-curtain return flows exert strong influences on both stream hydrology and non-point source pollution, underscoring the need for integrated management strategies that jointly consider groundwater use, winter agricultural practices, and downstream water-quality protection.

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The Effects of UV-C Irradiation on Cell Death and Growth Suppression of Microcystis aeruginosa (Cyanobacteria)

최하성(Choi Ha-Seong) ; 이정호(Lee Jung-Ho)

https://doi.org/10.15681/KSWE.2025.41.6.532

Microcystis aeruginosa, a cyanobacterium, creates harmful algal blooms in lakes and rivers during periods of high water temperatures and produces microcystin, a hepatotoxin that poses risks to human health. While UV irradiation has been proposed as a potential method for mitigating algal growth, its specific effects on the suppression of growth and reduction of biomass in cyanobacteria remain underexplored. This study aimed to assess the direct lethal effects and long-term growth suppression of UV-C irradiation on M. aeruginosa. Exposure to UV-C for 2 minutes resulted in a cell death ratio of 3.5%, while extending the exposure to 5 minutes increased the death ratio to 58.8%, with complete cell mortality achieved after 9 minutes. Regarding growth suppression, cultures exposed to UV-C for 2 minutes maintained a cell density below 500,000 cells mL?1 from days 5 to 19. In contrast, cultures exposed for 5 minutes sustained a density below 100,000 cells mL?1 from days 2 to 19, indicating a prolonged inhibitory effect. These results suggest that while 2 minutes of low-dose UV-C irradiation is insufficient for immediate cell death, it effectively induces long-term growth suppression. This mechanism underscores the potential of UV-C as a strategy for controlling harmful algal blooms and provides valuable insights for its practical application in water treatment and management of these blooms.

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Performance Prediction of Forward Osmosis Membrane Module Using Multiple Linear Regression and Artificial Neural Network Models

이해룡(Haelyong Lee) ; 미타 누르하야티(Mita Nurhayati) ; 이승윤(Sungyun Lee)

https://doi.org/10.15681/KSWE.2025.41.6.539

Sustainable desalination technologies are gaining attention, with forward osmosis (FO) emerging as a promising alternative to reverse osmosis due to its low energy consumption and reversible fouling. However, accurately predicting FO module performance remains a challenge. This study developed and compared multiple linear regression (MLR) and artificial neural network (ANN) models to predict the performance of FO membrane modules using 69 datasets from pilot-scale plate-and-frame systems operating under varied conditions (membrane areas: 7?63 m2; feed concentrations: 10?30 g/L; draw concentrations: 70?150 g/L; flow rates: 5?20 L/min). Variable importance analysis revealed that membrane area and feed concentration are the primary factors affecting water flux. Both models exhibited high predictive accuracy (R2 > 0 .95). The MLR model demonstrated an R² of 0.9577 and a root mean square error (RMSE) of 0.6550 L m-2 h-1, with statistical validation (F = 228.74, p < 10-32) and clear interpretability of variables. The ANN model achieved a slightly higher accuracy with an R2 of 0.9886 and an RMSE of 0.3498 L m-2 h-1, along with improved generalization stability. For predicting recovery rates, both models reached an R2 greater than 0.95, with the ANN model (0.9928) performing marginally better than the MLR model (0.9525). These results indicate that both methodologies provide reliable frameworks for predicting FO performance, with MLR offering interpretability and ANN delivering greater accuracy, making them suitable for different aspects of FO process design and scale-up.

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Evaluating Process Performance and Stability of Anaerobic Co-digestion of Food Waste and Sewage Sludge Infull-scale Anaerobic Sequencing Batch Reactor (ASBR)

김세영(Se Yeong Kim) ; 양현명(Hyeon Myeong Yang) ; 최영환(Young Hwan Choi) ; 권진홍(Jin Hing Kwon) ; 전항배(Hang Bae Jun)

https://doi.org/10.15681/KSWE.2025.41.6.549

This study assessed the performance and stability of an anaerobic sequencing batch reactor (ASBR) that processes food waste and sewage sludge through integrated anaerobic digestion. The physical stability was evaluated by monitoring temperature variations and water level changes during intermittent mixing operations. The temperature differences between the upper and lower sections remained stable, within 0.3±0.2 °C, while water levels exhibited consistent variations of 0.4 m, indicating effective mixing without scum formation or dead zones. Biological stability was analyzed through microbial community assessments under various stress conditions. Ammonia inhibition experiments (4 g/L addition) led to volatile fatty acids (VFAs) accumulating beyond 10 g/L and significant shifts in methanogenic communities. Acetoclastic methanogens (Methanosarcina and Methanosaeta) decreased markedly, while hydrogenotrophic methanogens (Methanoculleus and Methanobacterium) increased to help maintain system stability. Variations in organic loading rates from 3.3 to 6.6 kgVS/m³·d elicited sensitive responses from Methanosarcina populations, highlighting the vulnerability of acetoclastic pathways to operational stress. Performance evaluations across differing hydraulic retention times (HRTs of 95.4 to 31.5 days) showed an increase in biogas production (330 to 1,975 m³/d) and methane yield (0.291 to 0.509 m³CH₄/kgVS) as HRT decreased. The anaerobic biodegradability reached 79.3%, consistent with previous studies. Stability assessments using alkalinity fractions (IA/TA, IA/PA) proved to be more sensitive indicators than conventional VFAs/alkalinity ratios. Additionally, the correlation between alkalinity fractions and archaeal community structure indicated that the proportion of hydrogenotrophic methanogens increased with intermediate alkalinity, emphasizing their vital role in maintaining system stability under stress conditions.

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Monitoring of Polycyclic Aromatic Hydrocarbons in Sediments of Multipurpose Artificial Lakes and Environmental Risk Assessment Based on the STE Method

양윤모(Yang, Yunmo) ; 최명길(Choi, Myounggil) ; 최혜선(Choi, Hyeseon) ; 최희락(Choi, Heelak) ; 어성욱(Oa, Seongwook)

https://doi.org/10.15681/KSWE.2025.41.6.562

This study assessed the occurrence and environmental risk of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of four multipurpose reservoirs (Daecheong, Yongdam, Buan, and Boryeong) in the Geum River basin, Korea, from 2015 to 2024. Sediment samples were analyzed for grain size, total organic carbon (TOC), loss on ignition (LOI), and 16 priority PAHs designated by the U.S. EPA. The mean concentrations of Σ16PAHs ranged from 110 to 186 μg/kg, with localized hot spots occasionally exceeding 500 μg/kg. High molecular weight PAHs accounted for more than 75% of the total, indicating dominant pyrogenic origins such as fossil fuel and biomass combustion. However, the correlation between PAHs and TOC, LOI, or grain size was generally weak, reflecting limited anthropogenic inputs in protected water source areas and possible dilution by agricultural organic matter. Using the STE (Spatial, Temporal, and Extent factor) method, eight PAHs showed meaningful risk scores. Naphthalene presented a “very high” risk, while Benzo[k]fluoranthene, Anthracene, and Benzo[a]pyrene indicated “high” risk. Some compounds not currently listed as national priority substances, such as Naphthalene and Fluorene, also exceeded PNEC thresholds, suggesting the need for re-evaluation of management priorities. These findings highlight that although overall PAH levels in drinking-water reservoirs are moderate, sporadic events and specific compounds pose potential ecological risks, underscoring the importance of long-term monitoring and refined risk-based management.

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Teal Carbon Potential of the Upo Wetland in Korea: Evidence from Diatom and Organic Carbon Distributions

(Sang Deuk Lee) ; (Min Hwa Gu) ; (Mirye Park) ; (Kyung-Hoon Shin) ; (Hoil Lee) ; (Chae Hong Park)

https://doi.org/10.15681/KSWE.2025.41.6.576

This study investigates the relationship between total organic carbon (TOC) content and diatom abundance in sediment cores from the Upo Wetland, the largest natural inland wetland in Korea. Four sediment cores (UPW02?UPW05) were collected from shallow marginal and central basin areas to analyze the spatial and vertical patterns of organic carbon storage and microalgal productivity. The results revealed distinct depth profiles for both TOC and diatom abundance, with the marginal core (UPW02) showing the highest TOC levels (up to 5%) and diatom densities (up to 3 × 107 cells g-1) within the upper 10 cm. In the central basin cores, TOC and diatom peaks were less pronounced and primarily concentrated in the upper 0?5 cm. Throughout all sites, intervals of elevated diatom abundance consistently coincided with TOC maxima. A significant positive correlation between TOC and diatom abundance (Spearman ρ = 0.46, p = 0.001) suggests that diatom productivity has contributed to recent sedimentary carbon accumulation. Statistical analyses (ANOVA, ANCOVA, LMM) indicated that both site and depth significantly impacted the TOC? diatom relationship, reflecting interactions among hydrology, geomorphology, and light availability. The spatial differences between shallow and deep zones imply that biogenic carbon accumulation is more effective in areas with higher nutrient input and hydrological variability. These findings underscore the role of microalgae, particularly diatoms, as critical biological agents in carbon sequestration within freshwater wetlands. By integrating high-resolution TOC and diatom profiles, this study provides a quantitative foundation for incorporating microalgal productivity into national greenhouse gas inventories and for developing carbon assessment frameworks for Korean inland wetlands.

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The Impact of Data Anomalies on the Performance of Machine Learning Models for Algae Bloom Prediction

이은지(Eunji Lee) ; 박정수(Jungsu Park)

https://doi.org/10.15681/KSWE.2025.41.5.313

Field data often contain various anomalies due to natural variability and errors from sensors and experimental procedures. Since these anomalies can negatively affect model performance, it is crucial to detect and handle them. This study developed machine learning models to predict chlorophyll-a, a quantitative indicator of algal blooms, using water quality data collected in the field from 2015 to 2024 as independent variables. It also analyzed the impact of anomaly removal through an anomaly detection algorithm on model performance. First, datasets were constructed by randomly introducing anomalies into 5%, 10%, 15%, and 20% of the original data. Then, the Isolation Forest (IForest), an anomaly detection algorithm, was employed to detect and remove these anomalies. The effect of anomaly removal was assessed by applying the cleaned data to Extreme Gradient Boosting (XGBoost), an ensemble machine learning algorithm. The model trained on the original data achieved a root mean squared error (RMSE) of 7.541, while the RMSE of models trained on data with anomalies ranged from 8.777 to 17.503. Models trained on datasets with lower anomaly ratios demonstrated better performance. In contrast, models trained on data from which anomalies had been removed using IForest showed RMSE values ranging from 7.645 to 8.067. Similarly, better performance was observed in models trained on data with lower anomaly ratios prior to removal, although the performance differences based on the proportion of anomalies were relatively small. The results of this study demonstrate that anomaly removal can enhance the performance of machine learning models.

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Development of a Pollutant Load Delivery Model for Stream Water Quality: A Case Study of the Kyeongan TMDL Subwatershed

공동수(Dongsoo Kong)

https://doi.org/10.15681/KSWE.2025.41.5.321

This study introduces the Delivery Load?Flowrate?Discharge Load?Seasonality (LQLS) model, designed to assess pollutant dynamics in the Kyeongan Stream watershed, a key tributary of the Paldang Reservoir that supplies drinking water to Seoul. Utilizing monitoring data from 2021 to 2023 collected at two TMDL-designated terminal points, we categorized pollutant sources into discharges from sewage treatment plants (STPs), individual point sources, and non-point sources. The LQLS model, which integrates flow-dependent and seasonal functions, outperformed traditional L?Q and LQL models in predictive accuracy. The model performed well for BOD5 and total nitrogen across all statistical measures; however, it faced limitations in estimating total phosphorus, particularly in terms of Nash-Sutcliffe Efficiency (NSE) and RSR, despite showing acceptable bias and agreement metrics. Seasonal analysis indicated that BOD5 and phosphorus accumulated during the dry season, followed by gradual dilution during the monsoon, while total nitrogen exhibited a significant first-flush effect. The peak runoff depths for non-point source pollutant concentrations varied by parameter, with phosphorus showing sharp increases at high flow levels. Although non-point sources contributed the largest portion of total pollutant loads (BOD5: 63?74%, TN: 52?57%, TP: 61?68%), their impact on the annual mean delivered concentration was relatively low (BOD5: 13?20%, TN: 34?38%, TP: 18?26%). This discrepancy is attributed to the episodic nature of non-point source pollution and the exclusion of high-flow data from this study. Consequently, reductions in pollutant loads from STPs had the most significant effect on improving delivered concentrations, while reductions from non-point sources had the least impact.

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Evaluation of Optical Tracers for Quantifying TOC Contributions from Non-Point Source in Baseflow of Agricultural Watersheds

김정훈(Jeong-Hoon Kim) ; 박태준(Tae Jun Park) ; 김규범(Gyoo-Bum Kim) ; 허진(Jin Hur)

https://doi.org/10.15681/KSWE.2025.41.5.337

In agricultural watersheds, dissolved organic matter (DOM) from both natural and human-induced sources is transported to streams via baseflow after soil infiltration. Baseflow, a key hydrological component during low-flow periods, significantly contributes to the total organic carbon loads in receiving waters. However, no research has traced DOM sources through baseflow pathways. This study aimed to assess the effectiveness of optical indicators in identifying DOM sources in baseflow using end-member mixing analysis. We selected litter- and compost-derived DOM as contrasting end-members of terrestrial organic matter and compared their spectroscopic characteristics before and after soil interaction through batch adsorption and soil column infiltration experiments. Both end-members showed decreased specific UV absorbance (SUVA) and increased humification index (HIX) and biological index (BIX) after adsorption, indicating a preferential removal of smaller-sized aromatic compounds. Parallel factor analysis (PARAFAC) consistently revealed an increase in humic-like components (C1) and a decrease in protein-/polyphenol-like components (C2). Among the various spectroscopic indices, BIX and the fluorescence index (FI) demonstrated strong linearity (R²: BIX=0.99, FI=0.95) and high source sensitivity (slope-to-standard deviation ratio, S/SD: BIX=0.06, FI=0.05) across different mixing ratios and conditions. These findings suggest that BIX and FI are reliable tracers for quantifying DOM source contributions via baseflow, even after substantial soil interaction. In contrast, PARAFAC-derived components (%C1 and %C2) showed limited applicability under column conditions. This study underscores the value of fluorescence indices?particularly BIX and FI?as effective tools for tracking DOM sources in agricultural landscapes and provides a scientific foundation for managing non-point source pollution through baseflow pathways.

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Coastal Sediment Pollution by Heavy Metals in Ulsan: A Comparative Analysis with Incheon and Yeosu

김수진(Sujin Kim) ; 조민기(Minkee Cho) ; 김동우(Dongwoo Kim) ; 고수근(Sugeun Go) ; 배효관(Hyokwan Bae)

https://doi.org/10.15681/KSWE.2025.41.5.350

This study analyzed national monitoring data from 2015 to 2024 to evaluate patterns of heavy metal contamination in sediments and seawater along the industrialized coasts of Incheon, Ulsan, and Yeosu in Korea. Ulsan exhibited notably higher levels of heavy metal contamination in sediment, with median concentrations of Zn (80.4 mg/kg), Hg (0.155 mg/kg), and As (20.4 mg/kg) frequently exceeding the NOAA threshold effect levels (TEL), indicating persistent high-risk conditions. The ecological hazard quotients (HQ_EC50) were significantly higher than those derived from chemical assessments, particularly for Hg (8.00) and As (10.20), underscoring the substantial underestimation of ecological risks when relying solely on chemical criteria. Time-series analysis indicated a significant decline in the concentrations of regulated metals, especially Cu in Ulsan, following targeted pollution control measures, with an annual rate of decrease of -6.21% (p = 0.015). Conversely, concentrations of non-regulated metals such as As (annual increase of 3.92%, p = 0.010) and Cr (annual increase of 1.96%, p = 0.006) continued to rise. Incheon frequently exceeded long-term ecological criteria for Cu in seawater, although Hg concentrations significantly decreased at an annual rate of -8.26% (p = 0.012). Yeosu maintained consistently low and stable levels of heavy metals in both sediment and seawater, reflecting the effectiveness of environmental management efforts implemented in 2012. These findings highlight the critical need for multi-criteria ecological risk assessment frameworks and strengthened management strategies for heavy metals in industrial coastal areas.

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Performance Characteristics of Deep Learning Models for Algal Bloom Prediction in Rivers Using Transfer Learning

박정수(Jungsu Park)

https://doi.org/10.15681/KSWE.2025.41.5.365

Acquiring sufficient high-quality data is essential for developing machine learning models. However, collecting water quality data in river environments can be both costly and time-consuming, and the availability of adequate data is not always guaranteed. Transfer learning provides a promising solution by enabling the application of pre-trained models, developed using data from different locations, to a target site. In this study, we employed two deep learning models commonly used for time series forecasting: long short-term memory (LSTM) and one-dimensional convolutional neural network (1D CNN), to predict chlorophyll-a concentrations, a key indicator of algal blooms. For the model input data, we tested various sequence lengths (Seq): 1, 3, 5, 7, and 9 for the LSTM model, and 3, 5, 7, and 9 for the 1D CNN model. The results indicated that the LSTM model utilizing transfer learning achieved the best performance at sequence lengths of 7 and 9, with a Nash?Sutcliffe efficiency (NSE) of 0.843. In contrast, the corresponding models without transfer learning yielded significantly lower NSE values of 0.349 and ?0.014, respectively. For the 1D CNN model, the highest performance using transfer learning was observed with an NSE of 0.814 at Seq 5, while the model without transfer learning had an NSE of 0.608. Although the degree of improvement varied by model type and sequence length, the results clearly demonstrate that transfer learning has the potential to enhance the performance of algal bloom predictions.

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Functional Resonance Analysis Method (FRAM) for Structural Diagnosis and Resonance Scenario Analysis in Domestic Sewage Treatment Operations

최영환(Choi, Younghwan) ; 김세영(Kim, Seyeong) ; 권진홍(Kwon, Jinhong) ; 양현명(Yang, Hyeonmyeong) ; 전항배(Jun, Hangbae)

https://doi.org/10.15681/KSWE.2025.41.5.375

Domestic sewage treatment plants are critical for environmental protection and public health. Ensuring their operational efficiency and stability requires a systematic analysis of the complex interactions between various processes. Current technical diagnoses have largely focused on quantitative assessments of individual processes or equipment-centered evaluations, which fall short in revealing structural relationships and root causes of complex operational issues arising from cascading functional interactions. This study introduces the Functional Resonance Analysis Method (FRAM) as an innovative approach to diagnosing complex systems, applying it to domestic sewage treatment plants. We developed a systematic functional network modeling framework and identified key resonance pathways for a structural interpretation of diagnostic results. Utilizing the “Domestic Sewage Treatment Facility Technical Diagnosis Case Study” (2018-2019) published by the Korea Environment Corporation, we systematically analyzed 368 major cases from 54 facilities with processing capacities exceeding 3,000 m³/day. A comprehensive analytical template was created for systematically mapping problems and improvements, resulting in the identification of 16 major technical operational functions. Each function was structured according to FRAM's six essential elements, and the functional interconnection pathways were visualized using the FRAM Model Visualizer program. Quantitative analysis of 22 cases during winter’s low temperatures revealed that the aeration tank (F07) serves as a hub, demonstrating dominant cascade patterns from effluent to aeration to secondary clarification. Based on this hub-and-spoke structure, we developed a five-stage integrated response strategy aimed at fundamentally preventing cascade resonance, showcasing structural advantages over traditional individual response methods. This study establishes a new diagnostic paradigm for addressing complex issues in sewage treatment systems and lays the groundwork for future expansion into socio-technical system analysis.

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Analysis on the Mesohabitat Specificity of Benthic Macroinvertebrates Community Indices in the Jojong Stream

공동수(Dongsoo Kong) ; 권용주(Yongju Kwon) ; 김예지(Ye Ji Kim)

https://doi.org/10.15681/KSWE.2025.41.5.386

This study examined the relationship between community indices and area for benthic macroinvertebrates across different mesohabitats (riffle, run, pool, riparian) in the Jojong Stream, Gyeonggi Province, Korea, utilizing four non-probabilistic and fifteen probabilistic distribution models. Rarefaction-based estimates of expected species richness revealed significant discrepancies from observed values in smaller survey areas, indicating potential distortions in ecological interpretation. Most community indices?including species richness, abundance, diversity, dominance, evenness, and the benthic macroinvertebrate (BMI) saprobic index?varied with sampling area. Specifically, species richness and abundance consistently increased with area, while diversity and dominance tended to align even at smaller scales. In riffle habitats, BMI appeared relatively unaffected by area. Although the four-parameter generalized logistic power function showed a high overall fit, its complexity and risk of overfitting limit its practical application. In contrast, three-parameter models such as lognormal, Weibull, inverse Weibull, gamma, and generalized exponential distributions provided comparable accuracy with greater efficiency. The estimated habitat carrying capacity for species diversity was highest in main channel flows and riffles. Additionally, differential entropy indicated high heterogeneity in main channels and riparian zones, attributed to complex environmental factors such as substrate variability and vegetation. To accurately assess species-area relationships and habitat capacity at the site level, increased sampling effort is necessary. The lognormal model exhibited the highest accuracy, suggesting that larger stream systems support greater biodiversity, particularly where spatial heterogeneity enhances ecological richness.

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Sampling Method for Community Structure of Benthic Macroinvertebrates in Freshwater Sediment

송재하(Jea-ha Song) ; 곽인실(Ihn-Sil Kwak) ; 공동수(Dong-Soo Kong)

https://doi.org/10.15681/KSWE.2025.41.5.403

Effective biomonitoring of freshwater ecosystems necessitates the implementation of standardized and efficient methodologies for the sampling of benthic macroinvertebrates. This study investigated four critical methodological factors-core sampler diameter, number of replicates, sampling depth, and sieve mesh size-to optimize sediment sampling in Korean rivers. Field surveys were conducted at 15 sites across major river systems in South Korea from June 2022 to October 2023. Three core sizes (Φ5, 7.5, and 10 cm) were compared using 15 replicates each to assess collection efficiency. The optimal number of replicates was determined through the application of the Weibull model to evaluate species accumulation curves and stabilization of community indices. To explore vertical macroinvertebrate distribution patterns, sediment samples were stratified into six depth layers. Three sieve sizes (1.0, 0.5, and 0.2 mm) were assessed to examine their impact on the recovery of small-bodied taxa. The results indicated that the Φ7.5 cm core sampler provided an optimal balance between efficiency and representativeness. A sampling effort of six replicates (0.026 m²) was found to be sufficient to stabilize most community indices. The majority of taxa were concentrated in the upper 0-6 cm layer, while extending sampling to depths of 10-20 cm increased coverage to over 90% and revealed additional deep-burrowing species, suggesting that a depth of 15-20 cm is appropriate for comprehensive assessments. The 0.2 mm sieve was critical for capturing small-bodied taxa, which were significantly underestimated when utilizing 0.5 or 1.0 mm sieves. These findings provide evidence-based guidelines for standardized benthic macroinvertebrate sampling in Korean freshwater ecosystems, thereby enhancing the reliability of ecological assessments and long-term biomonitoring efforts.

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