Both status and progress in smart water metering (SWM) implementations in five selected countries (i.e., United States of America, United Kingdom, Australia, Israel, and South Korea) are investigated in this study. Despite the countless benefits of SWM implementation, the diffusion of the SWM technologies has been slow due to various challenges, including the absence of compulsory water policies, the lack of support from customers and expertise, and weak cost–benefit analysis. Over the past 30 years, the aforementioned countries have transitioned from a fixed charging to a volumetric charging regime composed of traditional water meters and SWM. Both the status and progress of SWM implementation are quite different among countries, although governments across the world have been applying water policies responding to water scarcity, population growth, and water demand management. However, the absence of strong water policies and political support for SWM implementation resulted in the slow and retarded spread of SWM implementation. Although several changes in water policies have occurred since 1990, there is no compulsory law for SWM implementation. Between 1995 and 2010, pilot/trial cases for SWM were dominant. After 2010, the number of SWM implementation kept increasing and all countries experienced more concentrated SWM implementation, despite the variances in both endpoints and completion of SWM implementation depending on water policies (i.e., acts and regulations) encouraging SWM implementations. The global market for SWM has consistently grown to USD 5.92 billion in 2020. Finally, the application of favourable water policies to optimize the use of water resources and to promote sustainable development is expected to drive the SWM market further.

The feasibility of applying nanoscale TiO₂/coconut shell powder (TCNSP) composite to remove HA in aqueous solution was evaluated, and the optimization of the photocatalytic systems using newly developed TCNSP composite was performed. The developed TCNSP composite has high specific surface area (i.e., 454 m²/g) and great porosity (i.e., 66.9%) with pore size of less than 5 μm. High removal efficiencies (≥95%) of HA were observed due to the significant synergistic effects by coupling adsorption and photocatalytic reaction of TCNSP composite. As the initial concentration of HA increased, the degradation rate (Kapp) decreased due to HA sorption saturation to the surface of TCNSP composite and the photon interception by HA molecules in aqueous solution. Since the increased loading amount of TCNSP composite enhanced the number of active sites,Kappvalues increased until the optimum loading amount of TCNSP composite. As pH values increased, HA removal efficiency decreased due to increasing electrostatic repulsion between HA and TCNSP composite. Based on the response surface methodology, higher HA removal efficiencies were obtained with acidic condition, longer reaction time, and appropriated loading amount of TCNSP. Further pilot-scale study is in progress using TCNSP composite combined with UVC to remove HA from large amounts of surface water (i.e., 200 m³/d).

The quantitative analysis of the disaster effect on water supply systems can provide useful information for water supply system management. In this study, a total disaster index (TDI) was developed using open-source public data in 419 water treatment plants in Korea with 23 input variables. The TDI quantifies the possible effects or damage caused by three major disasters (typhoons, heavy rain, and earthquakes) on water supply systems. The four components (regional factor, risk factor, urgency factor, and response and recovery factor) were calculated using input variables to determine the disaster index (DI) of each disaster. The weight of the input variables was determined using principal component analysis (PCA), and the weights of the DI of three natural disasters and four components used to calculate the TDI were determined by the analytical hierarchy process (AHP). Specifically, two ensemble machine learning models, random forest (RF) and XGBoost (XGB), were used to develop models to predict the TDI. Both models predicted the TDI with the coefficient of determination and root-mean-square error-observations standard deviation ratio of 0.8435 and 0.3957 for the RF model and 0.8629 and 0.3703 for the XGB model, respectively. The relative importance analysis suggests that the number of input variables can be minimized, which improves the models’ practical applicability.