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Water and energy are essential resources for human life, and carbon emissions (CEs) occur in tandem with their use. Thus, water, energy, and carbon are closely inter-related. Approximately 4% of the global energy is used in urban water sectors (UWSs), which encompass various processes such as water intake, treatment, and distribution and wastewater collection and treatment, all of which consume significant energy and emit CO2. Several countries are actively working toward achieving carbon neutrality by 2050–2060. Therefore, increasing energy efficiency and reducing CEs through comprehensive evaluations of UWSs is essential. This study aimed to quantify energy consumption and CEs in UWSs and proposed a methodology for analyzing water–energy–carbon (WEC) links at the city level. By applying it to Incheon Metropolitan City (IMC), we first identified the UWSs and established a WEC database. Based on this database, the WEC consumption and emissions were analyzed by process or administrative district, and visualizations using Sankey diagrams and Geographic Information System Mapping were created to enhance their understandability. In 2021, the UWSs in IMC consumed 308,496,107 kWh of energy, representing 32.7% of the public electricity consumption of IMC, with an average energy intensity of 0.46 and 0.38 kWh/m³ for water supply systems (WSSs) and sewerage systems (SSs), respectively. Their carbon emissions totaled 315,765,358 kg CO2, accounting for 2.7% of IMC's total carbon emissions, with an average carbon intensity of 0.21 and 0.58 kg CO2/m³ for WSSs and SSs, respectively. The proposed methodology was used to comprehensively evaluate WEC consumption and emissions in IMC. It is expected to enable relevant stakeholders to develop measures, such as water reuse and increasing renewable energy usage in water treatment and wastewater treatment plants, to build sustainable UWSs. [ABSTRACT FROM AUTHOR]

A model that computes the per-unit process energy consumption, energy intensity, CO2 emission, and CO2 intensity of water treatment plants is developed. This model is used to estimate the total energy consumption of six water treatment plants in Seoul Metropolitan City (SMC), which is comprised 80–85% for finished water pumping, 6–10% for ozone disinfection, 2–4% for rapid mixing, and 1–3% for non-process loads. The model results are validated against actual data for 2020 and 2021. The net energy consumption considering renewable energy production and use is then calculated, and the corresponding level of CO2 emissions is predicted. Four scenarios based on the projected water requirements for the year 2045 were evaluated as follows: increased energy efficiency in finished water pumping (Scenario 1), increased renewable energy production in water treatment plants (Scenario 2), increased energy efficiency in raw water pumping (Scenario 3), and reduced water supply per capita (Scenario 4). Compared to a baseline do-nothing scenario (Scenario 0), the net energy consumption is reduced by 3.57%, 2.61%, 3.42%, and 4.67% for Scenarios 1–4, respectively. Scenario 4, which is a water-driven approach, is best for reducing CO2 emissions, while Scenario 1 and 3, which are energy-driven approaches, are more effective at reducing CO2 intensity. [ABSTRACT FROM AUTHOR]

Demand estimation has been solved by using a weighted least-squares (WLS) estimator incorporating field measurements with system simulation model. WLS estimator results are sensitive to spurious measurements caused by supervisory control and data acquisition malfunctions. Estimates using the contaminated measurements are not reliable and bad data should be filtered prior to demand estimation. This study presents a series of statistical methods to detect bad data, identify their locations, and correct the data values. The proposed methods are based on a linear measurement model that linearly relates state variables (nodal demands) to the field measurements (pipe flow rates). Application to a simple hypothetical network using synthetically generated data shows that the method can be successfully used as a preprocessing for single and multiple noninteracting bad data for reliable demand estimation. DOI: 10.1061/(ASCE)WR.1943-5452.0000051 CE Database subject headings: Data analysis; Water distribution systems; Water demand. Author keywords: Bad data filtering; Demand estimation; Hypothesis test; SCADA systems; Water distribution.

Historically, a water distribution system's (WDS) hydraulic performance has been the primary operational concern. Over the past two decades, however, more attention has been paid to water quality behavior in WDS and today, water quality level is an equally important issue for many water utilities. In most cases, maintaining disinfectant levels is usually of interest to avoid the bacteria regrowth and to protect against the potential cross-contamination events. However, disinfectants, such as chlorine, decay over time and produce potentially harmful disinfectant by-products when they react with organic material in the water. Therefore, maintaining a minimum chlorine residual requirement throughout the WDS is a complex but important task. When online booster disinfection is combined with source disinfection, it has been shown that the total chlorine dosage can be reduced while maintaining minimum chlorine residuals across the system. Here, optimal valve operation has been combined with booster disinfection to improve the system water quality. Valves can be operated to alter the flow distribution in the network; prevent the isolation of water; and direct disinfectant laden water to locations where it is needed. A real-time optimal valve operation and booster disinfection problem is formulated as a single objective optimization model. The objective is to minimize chlorine injection mass at sources or to minimize excessive chlorine concentrations at withdrawal points while maintaining minimum chlorine concentrations and pressures throughout the system. The problem is solved using a genetic algorithm (GA). The application to a medium-sized WDS shows that optimal operation of existing valves combined with booster disinfection can improve water quality while requiring lower chlorine doses and resulting in little significant pressure reduction. Also, real-time operations can adapt to the temporal and spatial variations of system demands. DOI: 10.1061/(ASCE)WR.1943-5452.0000056 CE Database subject headings: Water quality; Algorithms; Water distribution systems. Author keywords: Optimal valve operation; Booster disinfection; Water quality; Genetic algorithm.

Real-time state estimates (SEs) of nodal demands in a water distribution system (WDS) can be developed using data from a supervisory control and data acquisition (SCADA) system. These estimates provide information for improved operations and customer service in terms of energy consumption and water quality. The SE results in a WDS are significantly affected by meastuement characteristics, i.e., meter types, numbers, and topological distributions. The number and type of meters are generally selected prior to a SCADA layout. Thus, selecting measurement locations is critical. The aim of this study is to develop a methodology that optimally locates field measurement sites and leads to more reliable SEs. An optimal meter placement (OMP) problem is posed as a multiobjective optimization form. Three distinctive objectives are formulated: (l) minimization of nodal demand estimation uncertainty; (2) minimization of nodal pressure prediction uncertainty; and (3) minimization of absolute error between demand estimates and their expected values. Objectives (1) and (2) represent the model precisions while Objective (3) describes the model accuracy. The OMP is solved using a multiobjective genetic algorithm (MOGA) based on Pareto-optimal solutions. The trade-off between model precision and accuracy is clearly observed in two case studies and it is recommended to use both criteria as objectives. It is also concluded that the proposed objectives are more appropriate for OMP purposes compared to calibration sampling design studies in which minimization of metering costs (i.e., number of meters) is used as one of the multiple objectives. The MOGA saves computational effort while providing optimal Pareto solutions compared to full enumeration for a small hypothetical network. For real networks, GA solutions, although not guaranteed to be globally optimal, are improvements over those obtained using less robust methods or designers' experienced judgment. DOI: 10.1061/(ASCE)WR.1943-5452.0000037 CE Database subject headings: Water meters; Water distribution systems; Algorithm. Author keywords: Optimal meter placement: State estimation; Water distribution; SCADA system; Multiple objective genetic algorithms.

A real-time estimation of water distribution system state variables such as nodal pressures and chlorine concentrations can lead to savings in time and money and provide better customer service. While a good knowledge of nodal demands is prerequisite for pressure and water quality prediction, little effort has been placed in real-time demand estimation. This study presents a real-time demand estimation method using field measurement provided by supervisory control and data acquisition systems. For real-time demand estimation, a recursive state estimator based on weighted least-squares scheme and Kalman filter are applied. Furthermore, based on estimated demands, real-time nodal pressures and chlorine concentrations are predicted. The uncertainties in demand estimates and predicted state variables are quantified in terms of confidence limits. The approximate methods such as first-order second-moment analysis and Latin hypercube sampling are used for uncertainty quantification and verified by Monte Carlo simulation. Application to a real network with synthetically generated data gives good demand estimations and reliable predictions of nodal pressure and chlorine concentration. Alternative measurement data sets are compared to assess the value of measurement types for demand estimation. With the defined measurement error magnitudes, pipe flow data are significantly more important than pressure head measurements in estimating demands with a high degree of confidence. DOI: 10.1061/(ASCE)HY.1943-7900.0000086 CE Database subject headings: Estimation; Least squares method; Water distribution systems; Uncertainty principles; Limit analysis.