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Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions.

The numerical study was conducted to compare process performance depending on the pump type and process configuration. The daily monitoring data of seawater temperature and salinity offshore from Daesan, Republic of Korea was used to reflect the site-specific seawater conditions. An algorithm for reverse osmosis in constant permeate mode was developed to simulate the process in time-variant conditions. Two types of pumps with different maximum leachable efficiencies were employed to organize pump-train configuration: separated feed lines and common pressure center design. The results showed pump type and design configuration did not have a significant effect on process performance. The annual means of specific energy consumption (SEC) for every design configuration were under 2 kWh/m3, except for a worst-case. The worst-case was decided when the pump was operated out of the best operation range. The two operation strategies were evaluated to determine the optimal configuration. The permeate flow rate was reduced to 80% of the designed permeate flow rate with two approaches: feed flow rate reduction in every train and pump shutdown in a specific train. The operation mode with feed flow rate reduction was more efficient than the other. The operating pressure reduction led to a decrease in SEC.

The potassium (K) sorption characteristics with three adsorbents, natural zeolite, ammonium acetate-treated zeolite, and manganese nodule, were studied and compared to see the potential use of manganese nodule as an alternative K adsorbent. In general, the Langmuir isotherm could fit the K sorption in the KCl solutions at different pH conditions better than the Freundlich isotherm. Based on the Langmuir parameters, the maximum K sorption was greater for the zeolite-based adsorbents (i.e., 40–42 mg g−1) than the manganese nodule (i.e., 2.0 mg g−1) at acidic conditions, while the manganese nodule (i.e., 9.7 mg g−1) showed better K sorption at neutral conditions. With the seawater samples, the zeolite-based adsorbents showed higher K recovery (4–14%) than the manganese nodule (0–8.8%). The K sorption on the zeolite-based adsorbents followed the pseudo-second-order kinetics and the K sorption rates were higher for the treated zeolite than the natural zeolite. The repeated sorption tests showed that the natural zeolite could potentially be reused up to three times without any significant loss of K sorption capacity, while the ammonium acetate-treated zeolite lost its K sorption capacity after the single sorption test. Overall, the results show that the manganese nodule may potentially be the alternative to zeolite for K recovery under certain conditions, yet the zeolite-based adsorbents are generally better than the manganese nodule. Thus, more studies to enhance the K recovery using zeolite, including surface modified zeolite, are recommended.

Osmotic and hydraulic pressures are both indispensable for operating membrane-based desalting processes, such as forward osmosis (FO), pressure-retarded osmosis (PRO), and reverse osmosis (RO). However, a clear relation between these driving pressures has not thus far been identified; hence, the effect of change in driving pressures on systems has not yet been sufficiently analyzed. In this context, this study formulates an actual mathematical relation between the driving pressures of membrane-based desalting processes by taking into consideration the presence of energy loss in each driving pressure. To do so, this study defines the pseudo-driving pressures representing the water transport direction of a system and the similarity coefficients that quantify the energy conservation rule. Consequently, this study finds three other theoretical constraints that are required to operate membrane-based desalting processes. Furthermore, along with the features of the similarity coefficients, this study diagnoses the commercial advantage of RO over FO/PRO and suggests desirable optimization sequences applicable to each process. Since this study provides researchers with guidelines regarding optimization sequences between membrane parameters and operational parameters for membrane-based desalting processes, it is expected that detailed optimization strategies for the processes could be established.

The Chao Phraya River in Thailand has been greatly affected by climate change and the occurrence of extreme flood events, hindering its economic development. This study assessed the hydrological responses of the Chao Phraya River basin under several climate sensitivity and greenhouse gas emission scenarios. The Soil and Water Assessment Tool (SWAT) model was applied to simulate the streamflow using meteorological and observed data over a nine-year period from 2003 to 2011. The SWAT model produced an acceptable performance for calibration and validation, yielding Nash-Sutcliffe efficiency (NSE) values greater than 0.5. Precipitation scenarios yielded streamflow variations that corresponded to the change of rainfall intensity and amount of rainfall, while scenarios with increased air temperatures predicted future water shortages. High CO2 concentration scenarios incorporated plant responses that led to a dramatic increase in streamflow. The greenhouse gas emission scenarios increased the streamflow variations to 6.8%, 41.9%, and 38.4% from the reference period (2003–2011). This study also provided a framework upon which the peak flow can be managed to control the nonpoint sources during wet season. We hope that the future climate scenarios presented in this study could provide predictive information for the river basin.

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

Global energy consumption has been highly dependent on fossil fuels which cause severe climate change and, therefore, the exploration of new technologies to produce effective renewable energy plays an important role in the world. Pressure-retarded osmosis (PRO) is one of the promising candidates to reduce the reliance on fossil fuels by harnessing energy from the salinity gradient between seawater and fresh water. In PRO, water is transported though a semi-permeable membrane from a low-concentrated feed solution to a high-concentrated draw solution. The increased volumetric water flow then runs a hydro-turbine to generate power. PRO technology has rapidly improved in recent years; however, the commercial-scale PRO plant is yet to be developed. In this context, recent developments on the PRO process are reviewed in terms of mathematical models, membrane modules, process designs, numerical works, and fouling and cleaning. In addition, the research requirements to accelerate PRO commercialization are discussed. It is expected that this article can help comprehensively understand the PRO process and thereby provide essential information to activate further research and development.

Pressure-retarded osmosis (PRO) is viewed as a highly promising renewable energy process that generates energy without carbon emissions in the age of the climate change regime. While many experimental studies have contributed to the quest for an efficiency that would make the PRO process commercially viable, computational modeling and simulation studies have played crucial roles in investigating the efficiency of PRO, particularly the concept of hybridizing the PRO process with reverse osmosis (RO). It is crucial for researchers to understand the implications of the simulation and modeling works in order to promote the further development of PRO. To that end, the authors collected many relevant papers and reorganized their important methodologies and results. This review, first of all, presents the mathematical derivation of the fundamental modeling theories regarding PRO including water flux and concentration polarization equations. After that, those theories and thermodynamic theories are then applied to depict the limitations of a stand-alone PRO process and the effectiveness of an RO-PRO hybridized process. Lastly, the review diagnoses the challenges facing PRO-basis processes which are insufficiently resolved by conventional engineering approaches and, in response, presents alternative modeling and simulation approaches as well as novel technologies.