This paper presents a performance evaluation and economic analysis of a reverse osmosis (RO) and pressure retarded osmosis (PRO) hybrid process to propose guidelines for its economic competitiveness use in the field. A model to predict the performance of a hybrid process using RO and PRO was developed based on a solution-diffusion model modified with film theory. The effects of external and internal concentration polarization (ICP) on PRO efficiency were considered in the model. Moreover, a simple cost model was applied to analyze the effects of seawater TDS and feedwater for the PRO process, water and salt permeability of PRO membrane, and membrane and energy cost on the RO-PRO hybrid process. The results show that the water transport coefficient, ICP, and seawater and feedwater TDS are important factors affecting the performance of the PRO process. On the other hand, the effect of the salt transport coefficient is not substantial. The RO-PRO hybrid process can be economically competitive with the RO process when electricity is expensive, the PRO membrane cost is cheap, and the power density and PRO recovery process are high.

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.

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.

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.

To overcome the limitations of traditional Reverse Osmosis (RO) desalination, Membrane Distillation (MD) has gained attention as an effective solution for improving the treatment of seawater and RO brine. Despite its potential, the formation of inorganic scales, particularly calcium sulfate (CaSO₄), continues to pose a major challenge. This research aims to explore the scaling mechanisms in MD systems through a combination of experimental analysis and dynamic modeling. Using real seawater and RO brine as feed sources, the scaling behavior was examined under various operational conditions, such as temperature and feed concentration. Optical Coherence Tomography (OCT) was utilized to monitor the real-time development of fouling layers, offering valuable insights into surface crystal formation processes. A System Dynamics Model (SDM) was created based on the experimental data to predict flux decline trends with precision. The model correlated well with experimental observations, highlighting key factors that drive scaling severity. This integrated approach deepens our understanding of scaling dynamics and provides actionable strategies to mitigate fouling in MD systems, thereby enhancing the efficiency and stability of MD desalination operations. Ultimately, this study underscores the potential of combining OCT with system dynamics modeling as a powerful approach for visualizing and validating scaling processes, offering a practical framework for optimizing MD performance and contributing to more sustainable desalination practices.

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.