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Study region: Chao Phraya River Basin—a major river with unique characteristics located in Thailand. Study focus: This study sought to simulate the flow rates in the Chao Phraya River Basin, which is a tidal river that poses challenges to traditional modeling approaches. The soil and water assessment tool (SWAT) is a hydrological model extensively employed for simulating flow rates. However, limitations arise in applying the SWAT model to the Chao Phraya River Basin due to its tidal nature, resulting in an unsatisfactory model performance. To address this, a long short-term memory (LSTM) model, i.e., the SWAT–LSTM model, was introduced to complement the SWAT model. New hydrological insights for the Region: The collaborative coupling of hydrological information derived from the SWAT and LSTM notably enhanced the model performance. This improvement was assessed using the Nash-Sutcliffe efficiency (NSE), demonstrating an increase from 0.13 to 0.72. The incorporation of topographic static data in the coupling model was also investigated to provide the basic characteristics of the basin to the model. The results yielded an NSE exceeding 0.79. The shoreline water level was identified as a crucial input feature for indicating tidal patterns. The findings highlight the effectiveness of coupling the SWAT with LSTM for predicting tidal river flow rates, implying their applicability in similar scenarios across different basins.

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships. Here, we propose a DL modeling framework, incorporating convolutional neural networks (CNNs) to efficiently replicate physics-based model outputs at high spatial resolution. The goal was to estimate groundwater head and surface water depth in the Sabgyo Stream Watershed, South Korea. The model datasets consisted of input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, and initial hydrological conditions. The initial conditions and target data were obtained from the fully distributed hydrological model HydroGeoSphere (HGS), whereas the other inputs were actual measurements in the field. By optimizing the training sample size, input design, CNN structure, and hyperparameters, we found that CNNs with residual architectures (ResNets) yielded superior performance. The optimal DL model reduces computation time by 45 times compared to the HGS model for monthly hydrological estimations over five years (RMSE 2.35 and 0.29 m for groundwater and surface water, respectively). In addition, our DL framework explored the predictive capabilities of hydrological responses to future climate scenarios. Although the proposed model is cost-effective for hydrological simulations, further enhancements are needed to improve the accuracy of long-term predictions. Ultimately, the proposed DL framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds.

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.

Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.

Although remote sensing techniques have been used to monitor toxic cyanobacteria with hyperspectral data in inland water, it is difficult to optimize conventional bio-optical algorithms for individual water bodies because of the complex optical properties of various water components. Therefore, this study adopted a spatial attention convolutional neural network (spatial attention CNN) to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in the Geum, Nakdong, and Yeongsan rivers in South Korea in order to evaluate cyanobacteria using remote sensing reflectance data. The CNN model utilized a spatial attention module to analyze the importance of the bands in the reflectance data. Then, the spatial attention CNN model was compared with different bio-optical algorithms for each study area. The spatial attention CNN model was generalized to estimate the pigment concentrations in the target rivers, and the model performance was evaluated by correlation coefficient (R) and root mean squared error (RMSE) between the observed and estimated concentrations of the algal pigments. The spatial attention CNN model, which was generalized to estimate the pigment concentrations in the target rivers, had R values above 0.87 and 0.88 for Chl-a and PC, respectively. However, the optimized band ratio algorithms for Chl-a and PC had R values above 0.83 and 0.70, respectively. Hence, it showed better performance than the conventional bio-optical algorithms. The spatial attention module provided attention weights for visualizing important features in the reflectance data. Specifically, the 600 nm, 650 nm, and near-infrared regions had high attention weights for estimating the concentrations of Chl-a and PC. Based on these findings, this study demonstrated that the spatial attention CNN model has a high potential for good application performance in various water bodies.

Intensive algal blooms increasingly degrade the inland water quality. Hence, this study aimed to analyze the algal phenomena quantitatively and qualitatively using synoptic monitoring, algal pigment analysis, and a deep learning model. Water surface reflectance was measured using field monitoring and drone hyperspectral image sensing. The algal experiment conducted on the water samples provided data on major pigments including chlorophyll-a and phycocyanin, accessory pigments including lutein, fucoxanthin, and zeaxanthin, and absorption coefficients. Based on the reflectance and absorption coefficient spectral inputs, a one-dimensional convolutional neural network (1D-CNN) was developed to estimate the concentrations of the major and minor pigments. The 1D-CNN could model periodic trends of chlorophyll-a, phycocyanin, lutein, fucoxanthin, and zeaxanthin compared to the observed ones, with R2 values of 0.87, 0.71, 0.76, 0.78, and 0.74, respectively. In addition, major and secondary pigment maps developed by applying the trained 1D-CNN model to the processed drone hyperspectral image inputs successfully provided spatial information regarding the spots of interest. The model provided explicit algal biomass information using the estimated major pigments and implicit taxonomical information using accessory pigments such as green algae, diatoms, and cyanobacteria. Therefore, we provide strong evidence of the extendibility of deep learning models for analyzing various algal pigments to gain a better understanding of algal blooms.

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model’s performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption coefficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016–2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption coefficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir.

Oral cancer: Stopping the spread Resveratrol, a naturally occurring compound present in grape skins, peanuts, and blueberries, reduces the ability of oral squamous cell carcinoma (OSCC) cells to spread to other parts of the body. Over half a million new cases of OSCC are diagnosed each year, and spread beyond the mouth is the most common cause of death. Hoi Young Lee at Konyang University, Daejon, Republic of Korea, and co-workers investigated whether a protein called RCP, which increases the invasiveness of many but not all types of cancer, is implicated in aggressive spread of OSCC. They found that RCP does increase the invasiveness of OSCC. Resveratrol is known to suppress the spread of many cancers, and strongly curtailed the spread of OSCC by blocking RCP activity. These results shed light on OSCC invasiveness, and offer a potential new treatment.

In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L–1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.

Abstract Rechargeable seawater battery (SWB) is a unique energy storage system that can directly transform seawater into renewable energy. Placing a desalination compartment between SWB anode and cathode (denoted as seawater battery desalination; SWB‐D) enables seawater desalination while charging SWB. Since seawater desalination is a mature technology, primarily occupied by membrane‐based processes such as reverse osmosis (RO), the energy cost has to be considered for alternative desalination technologies. So far, the feasibility of the SWB‐D system based on the unit cost per desalinated water ($ m−3) has been insufficiently discussed. Therefore, this perspective aims to provide this information and offer future research directions based on the detailed cost analysis. Based on the calculations, the current SWB‐D system is expected to have an equipment cost of ≈1.02 $ m−3 (lower than 0.60–1.20 $ m−3 of RO), when 96% of the energy is recovered and stable performance for 1000 cycles is achieved. The anion exchange membrane (AEM) and separator contributes greatly to the material cost occupying 50% and 41% of the total cost, respectively. Therefore, future studies focusing on creating low cost AEMs and separators will pave the way for the large‐scale application of SWB‐D.

Antarctic shelf systems play an important role in organic matter circulation on Earth; hence, identifying the characteristics of dissolved organic matter (DOM) can be a good indicator for understanding its origin, as well as climate change. In this study, to identify the characteristics of DOM in the ice shelf systems, surface water was collected from the open sea (OS) and Marian cove (fjord, FJ). Although there were no differences in DOM characteristics between sampling sites in the quantitative analyses, the DOM in the surface water of each region seemed to be more affected by terrestrial than marine biological sources in optical and molecular properties. This finding indicates that the terrestrial DOM related to mosses based on the results of molecular properties: high levels of lipid-like (35–39%) and unsaturated hydrocarbon-like (UH; 27–34%) in both the OS and the FJ regions and significantly higher tannin-like substance and condensed aromatic structures in the FJ than in the OS region. When comparing the FJ transect samples, those nearest to a glacier (FJ1; 0.93 km from the glacier) showed relatively low salinity, high dissolved organic carbon, and high chromophoric DOM, indicating that terrestrial DOM (possibly produced by moss) inflow occurred with the runoff from the freshly melting land ice and glacier. However, no significant differences in molecular composition were detected, suggesting that terrestrial DOM is introduced into the ice shelf systems by melting land ice, and glacier runoff could be a major source of DOM-rich seawater during austral fall when low marine biological activity occurs. This study has a great significance as background data for DOM characteristics in the ice shelf systems due to the enhanced biological activity during the austral summer.

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)—which has high temporal, spatial, and spectral resolutions—is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7–589.6, 603.6–631.8, 641.2–655.35, 664.8–679.0, 698.0–712.3, and 731.4–784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments.

Ovarian cancer: Natural compound suppresses cancer cell invasiveness Rab coupling protein (RCP)-induced tumor cell migration has been implicated in tumor pathophysiology and patient outcomes. Hoi Young Lee and colleagues at Konyang University in Daejeon, South Korea, have previously shown that RCP promotes ovarian cancer cell invasiveness by stabilizing cell adhesion receptors. In their latest study they find that RCP also increases the levels of two of its protein-binding partners and activates an important mediator of growth factor signaling, Focal Adhesion Kinase (FAK). Interestingly, treating ovarian cancer cells with curcumin, a natural compound extracted from the spice turmeric, not only blocked the effects of RCP on cell adhesion and FAK activation, it also potentiated the inhibitory effects of the chemotherapeutic agent doxorubicin on cell invasiveness. Further research will determine whether curcumin could be used to halt ovarian cancer progression.

Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R2 values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified.

In this study, the adsorption of herbicides using ground coffee residue biochars without (GCRB) and with NaOH activation (GCRB-N) was compared to provide deeper insights into their adsorption behaviors and mechanisms. The physicochemical characteristics of GCRB and GCRB-N were analyzed using Brunauer–Emmett–Teller surface area, Fourier transform infrared spectroscopy, scanning electron microscopy, and X-ray diffraction and the effects of pH, temperature, ionic strength, and humic acids on the adsorption of herbicides were identified. Moreover, the adsorption kinetics and isotherms were studied. The specific surface area and total pore volume of GCRB-N (405.33 m2/g and 0.293 cm3/g) were greater than those of GCRB (3.83 m2/g and 0.014 cm3/g). The GCBR-N could more effectively remove the herbicides (Qe,exp of Alachlor = 122.71 μmol/g, Qe,exp of Diuron = 166.42 μmol/g, and Qe,exp of Simazine = 99.16 μmol/g) than GCRB (Qe,exp of Alachlor = 11.74 μmol/g, Qe,exp of Diuron = 9.95 μmol/g, and Qe,exp of Simazine = 6.53 μmol/g). These results suggested that chemical activation with NaOH might be a promising option to make the GCRB more practical and effective for removing herbicides in the aqueous solutions.

In the past few decades, membrane-based processes have become mainstream in water desalination because of their relatively high water flux, salt rejection, and reasonable operating cost over thermal-based desalination processes. The energy consumption of the membrane process has been continuously lowered (from >10 kWh m−3 to ~3 kWh m−3) over the past decades but remains higher than the theoretical minimum value (~0.8 kWh m−3) for seawater desalination. Thus, the high energy consumption of membrane processes has led to the development of alternative processes, such as the electrochemical, that use relatively less energy. Decades of research have revealed that the low energy consumption of the electrochemical process is closely coupled with a relatively low extent of desalination. Recent studies indicate that electrochemical process must overcome efficiency rather than energy consumption hurdles. This short perspective aims to provide platforms to compare the energy efficiency of the representative membrane and electrochemical processes based on the working principle of each process. Future water desalination methods and the potential role of nanotechnology as an efficient tool to overcome current limitations are also discussed.

This study investigated the influence of combined organic fouling and inorganic scaling on the flux and fouling behaviors of thin-film composite (TFC) forward osmosis (FO) membranes. Two organic macromolecules, namely, bovine serum albumin (BSA) and sodium alginate (SA), and gypsum (GS), as an inorganic scaling agent, were selected as model foulants. It was found that GS scaling alone caused the most severe flux decline. When a mixture of organic and inorganic foulants was employed, the flux decline was retarded, compared with when the filtration was performed with only the inorganic scaling agent (GS). The early onset of the conditioning layer formation, which was due to the organics, was probably the underlying mechanism for this inhibitory phenomenon, which had suppressed the deposition and growth of the GS crystals. Although the combined fouling resulted in less flux decline, compared with GS scaling alone, the concoction of SA and GS resulted in more fouling and flux decline, compared with the mixture of BSA and GS. This was because of the carboxyl acidity of the alginate, which attracted calcium ions and formed an intermolecular bridge.

Blooms of the ichthyotoxic dinoflagellate Cochlodinium polykrikoides are responsible for massive fish mortality events in Korean coastal waters (KCW). They have been consistently present in southern KCW over the last two decades, but they were not observed in 2016, unlike in the previous years. Despite extensive studies, the cause of this absence of this dinoflagellate bloom remains largely unknown. Thus, we compared physico-chemical and biological data from along the Tongyeong coast between 2016 and the previous four years (2012–2015). The averages of water temperature and salinity in August, 2016 were significantly (p < 0.001) different from those in the previous years. The amount of Changjiang River discharge, which can affect the environmental conditions in the southern Korean coastal area via ocean currents, was larger than in the previous years, resulting in a reduction in the salinity level in August when blooms of C. polykrikoides usually occurred. Moreover, compared to previous years, in 2016, there was a weak expansion of C. polykrikoides blooms in the Goheung-Oenarodo area where C. polykrikoides blooms were annually initiated in KCW. Lastly, the strong winds from the typhoon Lionrock may also have contributed to the early termination of this dinoflagellate bloom. Together with these findings, the combination of these environmental conditions in 2016, unlike in previous years, may have inhibited the formation of C. polykrikoides blooms along the Tongyeong coast.

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash–Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.

This study explored the possibility of using reverse osmosis (RO) reject water as a mixing water for producing cementitious bricks using calcium sulfoaluminate (CSA) cement along with gypsum, and it investigated the changes in the properties of CSA cement pastes when RO reject water was used. The results were compared with those obtained using purified water and seawater. Overall, the use of RO reject water improved the cement paste’s strength. Given that the use of RO reject water very slightly affected ettringite formation but more significantly influenced the Al2O3-Fe2O3-mono (AFm) phases (i.e., monosulfate, kuzelite, and Friedel’s salt) and amorphous aluminum hydroxide (AH3), the strength improvement was likely mainly due to the formation of Friedel’s salt rather than ettringite formation. This study also demonstrated that the use of RO reject water for brick production satisfied the Korean Standards (KS) F 4004 and toxicity characteristic leaching procedure (TCLP); thus, it is recommended to use RO reject water as a mixing water to produce CSA cement bricks for use in construction.

Existing buildings are likely to consume more energy and emit more greenhouse gases than new buildings because of inevitable deteriorations in physical performance. Accordingly, retrofitting of existing buildings is considered essential to reduce energy consumption and greenhouse gas emissions from the building sector. However, assessing the energy performance of existing buildings accurately has limitations because building materials undergo physical deterioration and the actual operational conditions differ from as-built documentation. There is also a difference in the level of data acquisition required for building energy performance assessment depending on the conditions of the building. The aim of this paper is to present types of methods for energy performance assessment of existing buildings considering this data acquisition level. We analyzed various assessment methods, which were classified into three prototypes of methods according to the required level of data acquisition. Type 1 assessed the target building based on literature sources. Type 2 conducted on-site audit and assessed the target building based on additional collected data. Type 3 assessed the target building by further estimating the building properties through analysis of the measured energy data. The applicability of the proposed methods were demonstrated using case studies of three buildings located in Seoul, South Korea.

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.

Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned.

This study assessed the U.S. Environmental Protection Agency's Storm Water Management Model (SWMM) for urban water management challenges. This study conducted a sensitivity analysis to identify the most influential factors in the SWMM. Moreover, the performance of SWMM was evaluated with the HYDRUS-1D module in simulating infiltration rates. The sensitivity results showed field capacity as the most significant factor, highlighting the need for advanced modeling techniques to consider factors like field capacity. The SWMM was evaluated by the HYDRUS-1D that SWMM consistently underestimated peak infiltration rates and commenced infiltration calculations only when soil moisture exceeded field capacity. It reveals its limitations in handling unsaturated soil conditions and highlights the consideration of the matric head of the soil during the infiltration calculation in soil media. Moreover, the evaluation of bioretention areas showed larger areas resulting in more substantial flow reductions but with significant variability under different rainfall conditions. Accordingly, this result emphasizes the importance of careful consideration for environmental factors in bioretention design. This study contributes by enhancing understanding of SWMM's limitations in simulating urban water management challenges. Thus, this research will offer technical assistance to stakeholders focused on challenges such as runoff, hydrologic cycle, and urban flooding in urban areas. [ABSTRACT FROM AUTHOR]