Sook-Hyun Nam

This study presents a gas chromatography-based electronic nose (GC-E-nose) approach for screening and classifying the response patterns of nine predefined odorants in drinking water. The PLSR models provided compound-level concentration prediction, with R² values ranging from 0.86 to 0.99 and RMSE values below 27 µg/L. LDA effectively visualized compound- and concentration-dependent response patterns. Canonical discriminant analysis (CDA) showed moderate discrimination among the response classes of representative standards; preparation-level leave-one-out cross-validation (LOOCV) of the 225 valid standard-solution observations yielded an overall sample-level accuracy of 74.7% and a macro-averaged sensitivity of 77.6% across the six predefined response classes. Because none of the target ODCs was detected in the field samples, CDA could not be externally validated. Instead, matrix-matched spiked samples were used to evaluate PLSR, supporting the proposed method as a rapid screening tool prior to confirmatory GC-MS analysis.

Taste is a critical yet under-monitored parameter influencing consumer trust in drinking water. Despite its importance, conventional systems rarely quantify taste objectively for operational management. This study introduces a novel sensor-based Taste Index (TI), developed using a potentiometric electronic tongue (E-tongue) with seven ion-selective electrodes, to enable continuous, quantitative evaluation of taste stability across treatment and distribution systems. Multivariate analyses, including principal component analysis and partial least squares discriminant analysis, characterized treatment-dependent variations and spatial heterogeneity. The TI was defined as the normalized Euclidean distance from the final treated water reference (TI = 0.00). Results showed raw water at TI = 1.00, while a temporary increase to TI = 0.38 post-ozonation indicated the formation of taste-active byproducts. Notably, distribution samples with TI > 0.4 precisely corresponded to areas with documented aesthetic complaints. This research presents the first application of a sensor-derived TI for proactive taste monitoring. By enabling early anomaly detection and process tracking, the TI supports data-driven, consumer-centered water management. Its scalability and real-time applicability position it as a practical tool for smart water infrastructure and enhanced operational control.

Pharmaceuticals and personal care products (PPCPs) are recognized as emerging contaminants of concern, even at ultra-trace concentrations. However, the current detection systems are prohibitively expensive and typically rely on labor-intensive, lab-based workflows that lack automation in sample pretreatment. In this study, we developed a robotic and on-flow solid-phase extraction (ROF-SPE) system, fully integrated with online liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the on-site and real-time monitoring of 16 PPCPs in wastewater effluent. The system automates the entire pretreatment workflow—including sample collection, filtration, pH adjustment, solid-phase extraction, and injection—prior to seamless coupling with LC–MS/MS analysis. The optimized pretreatment parameters (pH 7 and 10, 12 mL wash volume, 9 mL elution volume) were selected for analytical efficiency and cost-effectiveness. Compared with conventional offline SPE methods (~370 min), the total analysis time was reduced to 80 min (78.4% reduction), and parallel automation significantly enhanced the throughput. The system was capable of quantifying target analytes at concentrations as low as 0.1 ng/L. Among the 16 PPCPs monitored at a municipal wastewater treatment plant in South Korea, only sulfamethazine and ranitidine were not detected. Compounds such as iopromide, caffeine, and paraxanthine were detected at high concentrations, and seasonal variation patterns were also observed This study demonstrates the feasibility of a fully automated and on-site SPE pretreatment system for ultra-trace environmental analysis and presents a practical solution for the real-time monitoring of contaminants in remote areas.

The consistent quality control of ultrapure water (UPW) in semiconductor manufacturing depends on removing trace organonitrogen compounds such as urea. Due to its high solubility, chemical stability, and neutral polarity, urea is inadequately removed by conventional processes. Even at low concentrations, it elevates total organic carbon (TOC) and reduces electrical resistivity. The use of reclaimed water as a sustainable feed stream amplifies this challenge because its nitrogen content is variable and persistent. Conventional methods such as reverse osmosis, ultraviolet oxidation, and ion exchange remain limited in treating urea due to its uncharged, low-molecular-weight nature. This review examines the performance and limitations of these processes and explores electrochemical oxidation (EO) as an alternative. Advances in EO are analyzed with attention to degradation pathways, electrode design, reaction selectivity, and operational parameters. Integrated systems combining EO with membrane filtration, adsorption, or chemical oxidation are also reviewed. Although EO shows promise for selectively degrading urea, its application in UPW production is still in its early stages. Challenges such as low conductivity, byproduct formation, and energy efficiency must be addressed. The paper first discusses urea in reclaimed water and associated removal challenges, then examines both conventional and emerging treatment technologies. Subsequent sections delve into the mechanisms and optimization of EO, including electrode materials and operational parameters. The review concludes with a summary of main findings and a discussion of future research directions, aiming to provide a comprehensive foundation for validating EO as a viable technology for producing UPW from reclaimed water.

Urban water systems are essential infrastructure but significantly contribute to greenhouse gas emissions through their operation. This study analyzed the greenhouse gas emissions of Incheon’s water system and proposed effective reduction strategies. In 2021, total greenhouse gas emissions from Incheon’s water system are 410,407 tCO₂eq, with the sanitation sector accounting for 82.1% and water supply for 17.9%. N₂O from wastewater treatment contributes 59.2% of total emissions, followed by CO₂ (36.6%) and CH₄ (4.2%). Sensitivity analysis using system dynamics identified per capita water consumption (LPCD) reduction as the most impactful mitigation strategy, surpassing widely adopted strategies such as renewable energy adoption. Scenario analysis showed that an aggressive policy could reduce emissions by 28.8% by 2050 compared to the baseline scenario. These findings provide a decision-making policy for carbon-neutral urban water management, emphasizing the need for integrated approaches to water management, emphasizing water demand reduction, energy efficiency, and sludge management.

The rapid expansion of lithium-ion battery (LIB) markets for electric vehicles and renewable energy storage has exponentially increased lithium demand, driving research into sustainable extraction methods. Traditional lithium recovery from brine using evaporation ponds is resource intensive, consuming vast amounts of water and causing severe environmental issues. In response, Direct Lithium Extraction (DLE) technologies have emerged as more efficient, eco-friendly alternatives. This review explores two promising electrochemical DLE methods: Electrodialysis (ED) and Capacitive Deionization (CDI). ED employs ion-exchange membranes (IEMs), such as cation exchange membranes, to selectively transport lithium ions from sources like brine and seawater and achieves high recovery rates. IEMs utilize chemical and structural properties to enhance the selectivity of Li⁺ over competing ions like Mg²⁺ and Na⁺. However, ED faces challenges such as high energy consumption, membrane fouling, and reduced efficiency in ion-rich solutions. CDI uses electrostatic forces to adsorb lithium ions onto electrodes, offering low energy consumption and adaptability to varying lithium concentrations. Advanced variants, such as Membrane Capacitive Deionization (MCDI) and Flow Capacitive Deionization (FCDI), enhance ion selectivity and enable continuous operation. MCDI incorporates IEMs to reduce co-ion interference effects, while FCDI utilizes liquid electrodes to enhance scalability and operational flexibility. Advancements in electrode materials remain crucial to enhance selectivity and efficiency. Validating these methods at the pilot scale is crucial for assessing performance, scalability, and economic feasibility under real-world conditions. Future research should focus on reducing operational costs, developing more durable and selective electrodes, and creating integrated systems to enhance overall efficiency. By addressing these challenges, DLE technologies can provide sustainable solutions for lithium resource management, minimize environmental impact, and support a low-carbon future.

Tryptophan-like fluorescence (TLF) is a key indicator of water contamination, particularly of microbial origin and biodegradable organic compounds. This study introduces an Internet of Things (IoT)-enabled portable device (IoT-TLF-PD) for real-time monitoring of microbial quality and biochemical oxygen demand (BOD₅). The device was tested using surface water (S1), secondary wastewater (S2), and final wastewater effluents (S3). Results showed significant correlations between TLF intensity, Escherichia coli (E. coli) counts, and BOD₅, with R² values of 0.77 (S1), 0.61 (S2), and 0.76 (S3) for BOD₅, and 0.60 (S2) to 0.68 (S3) for E. coli. Considering various water samples, a strong correlation was found between E. coli and BOD₅ with TLF intensity normalized by total organic carbon (TOC) concentration (TLF intensity/TOC). The R² value for E. coli was 0.92, and for BOD₅, it was 0.77. This indicates the necessity of accounting for organic matter concentration when interpreting TLF intensity in water quality assessments. The study confirmed the potential of the IoT-TLF-PD to serve as a cost-effective, real-time indicator for assessing water quality, especially for detecting microbial contamination. This technology offers a valuable tool for environmental monitoring and water management.

Trace organic contaminants (TrOCs), including pharmaceutically active compounds (PhACs), present significant challenges for conventional water treatment processes and pose potential risks to environmental and human health. To address these issues, nanofiltration (NF) and reverse osmosis (RO) membrane technologies have gained attention. This study aims to evaluate the performance of NF and RO membranes in removing TrOCs from wastewater and develop a predictive model using the Solution Diffusion Model. Experiments were conducted using a stirred cell setup at various target concentrations, stirring speeds, and operating pressures, with acetaminophen and caffeine selected as representative pharmaceutical compounds. The results demonstrated that most of the pharmaceutical compounds were effectively removed, showing excellent performance. NF membranes exhibited high permeate flux with somewhat lower removal efficiency (average 84.17%), while RO membranes demonstrated high removal efficiency (average 99.21%), highlighting their importance in trace pharmaceutical treatment. The predictive model based on the solution diffusion model correlated well with the experimental data, suggesting its potential utility for large-scale system applications. This study confirms that NF and RO membranes are effective technologies for the removal of TrOCs from wastewater, offering a promising solution to the challenges posed by trace pharmaceutical contaminants.

Small islands are supplied with water from underground sources, simple seawater desalination facilities, or water supply shipment. However, this water supply can be interrupted because of the sudden depletion of groundwater, as groundwater level prediction is inaccurate. Additionally, seawater desalination facilities are difficult to maintain, resulting in frequent breakdowns. When the water tank capacity is below a certain level, island residents contact the water supply shipment manager to request a shipment from land. In Korea, a seawater desalination plant project using ships was newly attempted to solve the water supply problem for island regions. Through this project, an attempt was made to supply water to many island areas suffering water supply disruptions due to drought. The purpose of this study is to compare water supply routes to multiple island regions using existing water supply shipment with desalination plants on ships through network analysis based on a geographic information system. To optimize sailing route, length (m), road connection type, and name of each road section, actual operation data, distance, etc., were set up on a network dataset and analyzed. In addition, the operational model predicted the stability of water supply using the GoldSim simulator. As a result, when sailing on the optimal route based on network analysis, the existing water supply routes could be reduced (2153 km -> 968 km) by more than 55%, and operational costs can be verified to be reduced. Additionally, the validity of the network analysis results was confirmed through actual travel of the representative route.