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Article

Estimation of Energy Consumption and CO2 Emissions of the Water Supply Sector: A Seoul Metropolitan City (SMC) Case Study

by
Li Li
1,
Gyumin Lee
2 and
Doosun Kang
2,*
1
Global Engineering Institute for Ultimate Society (GENIUS), Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea
2
Department of Civil Engineering, College of Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si 17104, Gyeonggi-do, Republic of Korea
*
Author to whom correspondence should be addressed.
Water 2024, 16(3), 479; https://doi.org/10.3390/w16030479
Submission received: 17 January 2024 / Revised: 29 January 2024 / Accepted: 30 January 2024 / Published: 31 January 2024
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Planning of Water-Energy Nexus under Climate Change)
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Abstract

:
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.

1. Introduction

Climate change adversely affects human life, infrastructure, and the ecosystem. Specifically, the increase in the Earth’s surface temperature is predicted to raise sea levels and the incidences of intense floods and droughts [1]. In 2021, South Korea emitted 616 million metric tons of CO2 and was ranked 10th among the countries in the world with the highest greenhouse gas (GHG) emissions [2]. South Korea has committed to net-zero CO2 emissions by 2050, with an interim goal of a 40% reduction in CO2 emissions by 2030 compared to the 2018 levels [3]. “Carbon neutrality”, the state of net-zero carbon dioxide emissions, has become an important goal in the sustainable development of essential distributed utilities like energy and water [4]. Consequently, numerous government organizations and utility companies are transitioning to low-carbon solutions [5,6].
Energy is critical for urban water supply. The United Nations Organization (UNO) reported that global energy consumption in relation to water pumping, treatment, and distribution accounts for approximately 8% of the total energy consumption [7]. Rothausen and Conwasy [8] reported that increasingly large amounts of energy would be required by water systems to meet the tightening regulatory requirements and counter the environmental effects of the significant rise in GHG emissions due to energy use. Water, energy, and carbon emissions are interconnected and critical for urban water sustainability and carbon emissions reduction [9]. Therefore, opportunities to improve energy and water use efficiency and recover and produce more energy should be identified to achieve the management of sustainable urban water supply [1].
Different aspects of energy consumption and CO2 emissions have been investigated in the previous literature on urban water systems. Horvath and Strokes [10] estimated and compared the energy consumption and CO2 emissions of California’s water supply alternatives, and Venkatesh and Brattebo [11] studied the energy consumption, cost, and environmental impacts of Oslo’s urban water cycle services. Research on South Korea’s domestic water supply includes studies by Chang et al. [12], who investigated the energy consumption and GHG emissions of water supply and reuse systems involving 42 extraction plants, 37 water treatment plants, and 23 distribution pumping stations. Kim and Chen [13] also studied the changes in energy and carbon intensities for the Seoul Metropolitan City (SMC) water supply sector. These studies quantified the total energy consumption and CO2 emissions for sub-systems of the water supply sector, such as raw water pumping and drinking water treatment. However, studies on the energy consumption of individual unit processes in the water supply system are lacking.
Raw water pumping and finished water distribution to end users require a large amount of energy and dominate the energy use of the water supply sector [14]. In a water treatment facility, large amounts of energy are required to remove sediments, contaminants, and chemicals so that the treated water meets drinking water standards. The accurate measurement of unit process-based energy consumption can identify which unit process accounts for the highest energy consumption and can help formulate comprehensive and targeted approaches for reducing energy consumption. This study quantified the amount of energy consumed by major water treatment processes and ranked the unit processes according to their energy consumption and CO2 emissions. Net energy consumption and CO2 emissions were also estimated after considering renewable energy production and use.
To estimate the energy consumption and CO2 emissions for the major unit processes in potable water treatment, we developed a model and calibrated it against empirical data. However, there remain data challenging for model calibration owing to the need for the precise and individualized empirical measurement of consumed energy by many unit processes involved in the water treatment. Nevertheless, the results of the model provide the first comprehensive estimates of energy consumption for individual unit processes of water treatment plants in the SMC. More importantly, the predictive performance of this model can be improved in the future as more data become available.
In addition, the effects of various efforts to reduce the net energy consumption and CO2 emissions in the water supply sector are then examined. This study assesses how energy needs might change after 20 years with the implementation of the following four improved scenarios: an improvement in energy efficiency for raw and finished water pumping, an increase in renewable energy production, and a reduction in water supply consumption per capita. The effects of these four scenarios were analyzed by calculating a reduction in energy consumption and carbon emissions in water supply facilities in the SMC and comparing the results to a do-nothing baseline scenario that maintained the characteristics of the 2020 water supply service. The key findings of this study can aid water utilities, city planners, engineers, and researchers in the field in estimating the energy consumption of typical unit processes associated with water treatment facilities where measured data are scarce or unavailable. Furthermore, the findings of this study can enable policymakers to make better-informed decisions regarding resources and energy management and climate change mitigation in the water supply sector.
The remainder of this paper is organized as follows. Section 2 describes the study area and target water supply facilities, the methodology used for energy consumption and CO2 production in the water treatment process, and applied future scenarios. Section 3 discusses the results of this study, and Section 4 presents conclusions and future research directions.

2. Materials and Methods

2.1. Study Area

The SMC is the capital of South Korea, and approximately 20 percent of the entire population of South Korea, which was reported to be 9,655,918 in 2023 [15], are SMC residents. The water supply services of SMC are operated by the Seoul Metropolitan Government (SMG) Office of Waterworks, and 100% of the population in the SMC is connected to the water supply system. This study investigated four water extraction and six water treatment facilities located in the SMC, as presented in Figure 1. The primary source of the city’s water supply is the Han River. In 2020, 1,063,300,876 m3 of raw water extracted from four extraction stations (Amsa, Jayang, Pungnab, and Gangbuk) was sent to five water treatment plants (Gangbuk, Ddukdo, Guui, Amsa, and Youngdengpo), and 83,181,840 m3 of raw water taken by the Korea Water Resources Corporation (K-water), which is a separate entity from SMG, was conveyed to the Gwangam treatment plant [16]. The raw water delivered to the six treatment plants undergoes an advanced treatment process, and purified water is distributed to the end users. Figure 2 shows these processes of the water supply system in the SMC.

2.2. Methodology and Data Sources

Owing to the scarcity of measured energy consumption data for unit operations of water systems in the SMC, the energy used by each unit process was estimated based on a report estimating the daily energy usage for common water treatment unit processes in the USA [18]. This report estimated the energy intensity for all unit processes in drinking water treatment as a function of the average flow rate, as shown in Table 1. To develop the energy intensity values, information from government entities, private research groups, the literature, and other sources were analyzed to characterize the water industries in terms of the number and type of facilities, processes, and electricity use. Each industry was then segmented based on parameters such as size, key process elements, and functions. A bottom-up approach based on available data was used to develop energy intensity values for various unit processes [18].
Based on tables containing small to large flow rates (i.e., 1–250 million gallons per day (MGD)), the composite energy usage for each of the six drinking water treatment plants made up of a series of combinations of unit processes was estimated using the model developed in this study. The six plants used advanced treatment technology involving the same unit processes. Compared to standard drinking water treatment, advanced treatment technologies additionally involve ozone treatment and biological-activated carbon (BAC) treatment [17], as shown in Figure 2. Unit processes, including rapid mixing, flocculation, sedimentation, chemical feed system, backwater pumping, residual pumping, thickened solid pumping, ozone disinfection, finished water pumping, and non-process load, were included in this model to compute the energy used by water treatment plants in the SMC. The process of applying this model is illustrated in Figure 3. The energy consumption of the finished water distribution is related to the energy efficiency of the finished water pumping process. The specific estimation of energy efficiency for the current finished water pumping process is beyond the scope of this study. Therefore, the energy consumption for the finished water pumping process was not estimated in this study. Instead, the approximate energy efficiency range for the finished water pumping process was obtained by comparing the measured energy consumption to the estimates in relation to the three energy efficiencies (i.e., low, medium, and high) from Table 1.
The energy used for buildings, such as office equipment, lighting, and air conditioning, is typically less than the process energy. However, this can account for a large portion of the total energy use (more than 30%) of smaller plants [18]. The energy consumed for the non-process load was reported to account for 2.88% of the total energy consumption for the “G” water treatment plant located in SMC, which is operated by K-water [19]. In this study, the percentage of energy used for the non-process load was calibrated as a parameter.
The developed model was calibrated by adjusting the percentage of the energy used for the non-process load to obtain the best fit between the calculated and observed daily energy consumption. Observed data were obtained from the South Korean Ministry of Environment (MOE) [16,20]. In this model, net energy consumption was calculated after including renewable energy production. Corresponding actual and net CO2 emissions were estimated using the appropriate CO2 emission factor. A screenshot of the graphical user interface (GUI) of the developed model is shown in Figure 4. The annual electricity consumption data for water extraction and treatment plants were obtained from the MOE [16,20], and the in situ renewable energy production data were obtained from the MOE [16] and SMG.

2.3. Scenario Development

The population of SMCs is projected to decrease from 9,911,000 in 2020 to 8,810,000 in 2045 [21]. The population across the water distribution districts of the six water treatment plants is assumed to decrease equally in this study. However, the daily water consumption per capita has increased over the last 10 years due to climate change and an increased number of households with one or two people [22], as shown in Figure 5. The Waterworks Office of SMG reported that, in the case of SMC, the daily water consumption per capita increases by 10 L when the temperature rises by an average of 10 °C [22]. Based on the trendline in Figure 5, the daily water supply per capita is expected to increase to 311 L by 2045. Overall, the required water supply is expected to increase from 1,134,613,170 m3/year in 2020 to 1,135,271,344 m3/year in 2045 despite the decreasing population. To achieve a reduction in net CO2 emissions in the SMC water supply sector, four plausible future scenarios were analyzed and compared with a baseline scenario. Scenarios 1–3 were energy-driven approaches, and Scenario 4 was a water-driven approach. Energy-driven approaches involve a direct reduction in consumed energy by improving energy efficiency or energy production in water supply facilities. In contrast, the water-driven approach indirectly reduces energy consumption by reducing water demand [13].

2.3.1. Scenario 0: Baseline Scenario

The baseline scenario assumes that the energy profile for water supply services in 2020 continues until 2045 (i.e., Scenario 0, the do-nothing scenario). The energy consumption and renewable energy production by type of the SMC water supply sector in 2020 are presented in Table 2. The energy intensities of raw water pumping and treatment for 2020 are 0.12 kWh/m3 and 0.24 kWh/m3, respectively (Table 3). Energy intensity was derived by dividing the energy consumed by the amount of processed water. The energy intensities of raw water extraction and water treatment observed in 2020 were applied to the baseline scenario up to 2045. The CO2 emissions were estimated by multiplying the emission factor, which was reported as 0.46625 kg CO2/kWh in 2020, by the energy consumption. Finally, CO2 intensities were estimated by dividing CO2 emissions by the amount of processed water. The energy intensity for the Gwangam plant was relatively low compared to that for other plants because finished water from this plant was distributed through the gravity flow.

2.3.2. Scenario 1: Increased Finished Water Pumping Efficiency

An et al. [19] reported that the “G” water treatment plant operated by K-water consumes 77.33% of the total energy for finished water pumping. Due to the significant impact of finished water pumping on the overall system’s energy use, the energy efficiency of the pumping system should be enhanced [18]. An approximate estimated range of current wire-to-water efficiencies for finished water pumping was obtained by comparing the measured energy consumption with reference estimates by Reekie et al. [18]. The energy use estimates for finished water pumping with wire-to-water efficiencies of 50% (low), 65% (medium), and 75% (high) are presented in Table 1. Scenario 1 assumes that the wire-to-water efficiencies of six water treatment plants are improved to be equal to or greater than 75% by the year 2045. Based on the energy use estimates for finished water pumping [18], the current ranges of energy efficiencies for the six water treatment plants were determined, as shown in Table 4. The wire-to-water efficiencies for Scenario 0 describe the current state of the finished water pumping process, and Scenario 1 assumes improved efficiencies (Table 4). As mentioned above, the efficiency of the Gwangam plant was not included in the analysis because its product water was distributed to customers via gravity flow. The improved efficiencies of finished water pumping were applied to the Gangbuk, Ddukdo, and Youngdengpo plants, and the consumed energy was computed using the developed model.

2.3.3. Scenario 2: Increased Renewable Energy Production

The One Less Nuclear Power Plant (OLNPP) was initiated by the SMG on 26 April 2012 to mitigate climate change and increase energy self-sufficiency [13]. The goal of OLNPP was to reduce energy usage by GJ 83.7 million by the end of 2014, which is equal to the amount of energy generated annually by a nuclear power plant [23]. As part of the OLNPP initiative, the SMG has introduced various technologies to recover energy, increase renewable energy use, and improve the energy efficiency of processes at water extraction and treatment facilities to achieve energy self-sufficiency in the water sector by 2030 [24]. The energy production efforts include 15.5 MW solar PVs installed at water treatment plants and wastewater treatment plants and 842 kW geothermal power stations installed at two water treatment plants [13]. These efforts to increase renewable energy production to help meet the energy requirements of these plants affect the net energy consumption and energy intensity in the water supply sector. Scenario 2 involves increasing renewable energy production by 50% compared to the energy produced in 2020 (as shown in Table 2).

2.3.4. Scenario 3: Increased Raw Water Pumping Efficiency

Water was pumped from four extraction plants (Amsa, Jayang, Pungnap, and Gangbuk) and sent to five water treatment plants (Amsa, Yeongdeungpo, Guui, Ddukdo, and Gangbuk). The related energy consumption for water supplied by the Gwangam treatment plant was not considered in this study. Table 5 presents the amount of water pumped annually, energy consumption, energy intensity, CO2 emissions, and CO2 intensity in 2020. Scenario 3 assumes that the energy intensity for water pumping is reduced by 10% due to improved water pumping efficiencies in the four water intake facilities.

2.3.5. Scenario 4: Saving Energy by Saving Water

According to the data provided by the Food and Agriculture Organization of the United Nations (FAO) in 2020 [25], Korea is a country with a high level of water stress at 85.22%. However, daily water use per capita in the SMC is approximately twice as high as that in major European cities. Although there are numerous technologies for improving the operational efficiency of water pumping and treatment, proactive actions to reduce water consumption by end users and throughout the water supply could have a significant effect on energy savings. In addition, water can be saved by water reuse, rainwater harvesting, and reducing water leaks [13]. The SMG has adopted rainwater collection as part of an urban regeneration project [26]. Several studies have assessed the effects of water-driven approaches [27,28]. In this study, a reduction in the per capita water usage by 5% compared with 2020 was analyzed in Scenario 4. For decreased water usage, we assumed that the energy intensity of the water treatment process remained constant. This assumption is based on the fact that the reduced daily water treatment volumes at the six water treatment plants continue to fall within the same interpolation ranges as the data presented in Table 1.

3. Results and Discussion

3.1. Energy Consumption in Water Treatment Process

The estimates of daily energy use for common water unit processes in the U.S. were applied to the main unit processes of SMC water treatment plants, except for finished water pumping and the non-process load. The percentages of the total energy used for the non-process load were calibrated for individual water treatment plants, and the estimated values are presented in Table 6. The percentage of total energy for the non-process load was estimated as 6.3% for smaller plants (up to 50 MGD), 2.9% for middle-size plants (up to 100 MGD), and 1.0% for large plants (greater than 180 MGD). The estimated percentage of the total energy used for the non-process load for medium-sized plants was found to be consistent with that estimated by An et al. [19], which was reported as 2.88%. Figure 6 shows the observed and computed daily energy consumption, and Table 7 presents the relative errors between the observed and computed values for the six water treatment plants. The annual average relative errors of these plants were less than 6%, except that for the Amsa plant, which was 12.34%.
Figure 7 presents the energy usage per-unit process in water treatment plants in 2020. The results show that the percentages of energy used for a range of different treatment processes were 80–85% for finished water pumping, 6–10% for ozone disinfection, 2–4% for rapid mixing, and 1–3% for non-process loads. The Gwangam plant is an exception because finished water pumping is not required.
Using the same CO2 emission factor (0.46625 kg CO2/kWh) as the MOE [16,20], the CO2 intensities of individual water treatment plants in 2020 and 2021 were computed and compared to the values provided by the MOE [16,20] (Figure 8).

3.2. Scenario Comparison

Most of the energy in the water supply sector is consumed in the form of electricity. Therefore, this study mainly considered CO2 emissions from electricity use. Figure 9 shows the annually averaged ratios of water production via the six water treatment plants over the last 10 years [16,29,30,31,32,33,34,35,36,37]. Based on these ratios, the amount of water required in 2045 is allocated in proportion to the six water treatment plants. Amsa and Gangbuk plants constituted 32.6% and 22.1% of the total amount of treated water, respectively.
CO2 emissions can be reported in terms of actual and net emissions. Net CO2 emissions represent the total emissions reduced by CO2 sequestration through carbon offsets, such as replacing electricity or fossil fuels with renewable energy [38]. Solar, wind, geothermal, and hydropower do not directly emit CO2 and are, therefore, considered carbon-free energies. It follows that the CO2 emissions factor for carbon-free energy is zero [39]. Figure 10 and Figure 11 graphs the energy intensity, net energy intensity, CO2 intensity, and net CO2 intensity of six water treatment plants for the three scenarios. This study assumes that renewable energy replaces electricity from non-carbon-free sources when calculating net energy and CO2 emissions. The net energy was obtained by subtracting the amount of energy produced from the amount of energy consumed. The actual energy intensities presented in Figure 10a show reduced energy consumption at the Gangbuk, Ddukdo, and Youngdengpo plants because of the improved efficiency of the finished water pumping process. Figure 10b shows that the net energy intensity of the Gangbuk plant decreased by 3.4% in Scenario 1, while it decreased by 5.8% in Scenario 2 compared to the baseline scenario. The Ddukdo and Youngdengpo plants revealed that Scenario 1 was more effective at reducing the net energy intensity and net CO2 intensity than Scenario 2 (Figure 10b and Figure 11b). There was no energy efficiency improvement for finished water pumping at the Gwangam, Guui, and Amsa plants, and no differences among the three scenarios were observed in terms of the actual energy intensity, as presented in Figure 10a. The actual energy intensity for the Gwangam plant is considerably smaller than that for the other plants because the energy consumption for finished water pumping, which accounts for the largest proportion of the total energy consumption, is zero for this plant because it distributes water using gravity. The greater actual energy intensity for the Ddukdo plant was due to the lower efficiency of the finished water pumping process, as shown in Table 4.
Figure 12 shows the potential energy savings after applying the four improved scenarios to the SMC water supply system. The total energy consumption in the baseline scenario (Scenario 0) was estimated to be 404,358,746 kWh/year. With the implementation of Scenario 4, the consumed energy and net energy were reduced by 4.44% and 4.67%, respectively, indicating that reducing per capita water consumption could be an important approach to saving energy. Improving the efficiency of finished water pumping to 75% (Scenario 1) and reducing the energy intensity of raw water pumping by 10% (Scenario 3) produced similar actual and net energy consumptions.
Figure 13 shows the actual and net CO2 emissions and CO2 intensities. The net CO2 emissions and net CO2 intensity were estimated considering the onsite-produced energy. Figure 12 and Figure 13a,b prove that Scenario 4 is the most effective approach for achieving considerable reductions in energy consumption and CO2 emissions. However, this water-driven approach does not effectively reduce CO2’s intensity, as shown in Figure 13c,d, because it does not involve energy efficiency improvements for raw water pumping and drinking water treatment processes or an increase in renewable energy production. Scenarios 1 and 3, which are energy-driven approaches, reduce 3.39% and 3.25% of actual CO2 intensities, respectively.
To investigate the impact of varying raw water pumping efficiencies in Scenario 3 and per capita water demand reductions in Scenario 4 on the CO2 emissions of the SMC, a comparative analysis was conducted. Specifically, we compared reductions in energy intensity of 5%, 10%, and 15%, and per capita water demand reductions of 1%, 3%, and 5%, with Scenarios 0–2, as depicted in Figure 14. The analysis revealed that a 15% reduction in the energy intensity of raw water pumping and a 5% decrease in per capita water usage result in a greater net reduction in CO2 emissions compared to Scenarios 1 and 2. However, if the reductions in Scenarios 3 and 4 are limited to 5% and 1%, respectively, these scenarios prove less effective than Scenarios 1 and 2 at reducing the SMC’s net CO2 emissions.

4. Conclusions

This study identified the major unit processes of the advanced water treatment process implemented in six water treatment plants in Seoul, South Korea, and developed a model to accurately compute the corresponding energy consumption. This model was calibrated using the reported energy consumption data from the MOE. For water treatment plants, the majority of energy use is associated with finished water pumping, accounting for 80–85% of the total energy consumption. To the best of our knowledge, no study has been conducted on this topic for SMC’s water supply sector. The results of this study can help water treatment facilities and electric utilities to better understand the link between water and energy.
The potential factors influencing the energy requirements for each water treatment process include topography, climate, operational efficiency, variability in treatment system design, and water use patterns [40]. Reekie et al. [18] presented energy intensity values for various unit processes commonly observed in water treatment facilities. In our study, we assumed that the six water treatment plants within the SMC employ standard advanced water treatment unit processes, except for finished water pumping. Consequently, applying the energy consumption estimates from U.S. water facilities to those in the SMC could introduce some estimation errors for water treatment energy use. However, our model, developed based on these initial assumptions, successfully predicted the per-unit process energy consumption for different SMC water treatment facilities, as validated with actual MOE data. The effective transferability of U.S. energy consumption data to the SMC was facilitated by combining Reekie et al.’s energy intensity data [18] with local empirical data, including the energy intensity of finished water pumping. Therefore, the model can enable water utilities, regulators, and policymakers to assess the energy use of specific processes and facilities and find opportunities to improve energy and CO2 management practices in the water supply sector in the SMC.
This study analyzed four plausible scenarios involving three energy-driven approaches and a water-driven approach and compared them to a do-nothing baseline scenario. The scenarios analyzed in this study quantified energy savings ranging from technically feasible to realistically achievable. The results of scenario analysis showed that the application of Scenarios 1–3, which are energy-driven approaches, reduces 3.57%, 2.61%, and 3.41% of net CO2 emissions, respectively, and the application of Scenario 4, which is a water-driven approach, reduced 4.44% of net CO2 emissions compared to the baseline scenario. Based on these results, reducing the per capita water consumption in combination with water reuse and conservation could be the most effective way to reduce the actual and net CO2 emissions of the SMC water supply sector. However, water operators need to continue to use more energy-efficient technologies to satisfy more stringent treatment requirements. Lee et al. [41] noted that energy intensity in the water sector is highly affected by the level of treatment and technology. The scenario of analysis in this study showed that improving the finished water pumping efficiency (Scenario 1) and raw water pumping efficiency (Scenario 3) was more efficient at decreasing actual and net CO2 intensities than the other two scenarios. Overall, we conclude that improving energy efficiency, increasing renewable energy production and use, and enhancing water conservation and reuse can help the potable water supply sector achieve carbon neutrality. The results of this study can be applied to sustainable water management and climate change mitigation for the water supply sector in urban areas.
Future studies should optimize and adapt this model to estimate the energy consumption and CO2 emissions of the potable water supply sector in other metropolitan areas. In addition, a similar analysis of wastewater treatment plants and sewer networks should be conducted to expand this study and include the entire urban water sector.

Author Contributions

Conceptualization, D.K.; methodology, L.L. and D.K.; software, G.L.; validation, L.L., G.L. and D.K.; formal analysis, L.L.; investigation, L.L., G.L. and D.K.; resources, L.L. and G.L.; data curation, L.L. and G.L.; writing—original draft preparation, L.L.; writing—review and editing, D.K.; visualization, L.L. and G.L.; supervision, D.K.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the (1) Korea Environment Industry & Technology Institute (KEITI) through the Water Management Program for Drought, funded by the Korean Ministry of Environment (MOE) (RS-2023-0023194) and (2) the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT—Ministry of Science and ICT) (grant number NRF-2020R1A2C1005554).

Data Availability Statement

Dataset available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 16 00479 g001
Figure 1. Location of SMC water supply facilities modelled in this study.
Figure 1. Location of SMC water supply facilities modelled in this study.
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Figure 2. Schematic of advanced water purification system implemented in SMC water treatment plants. (Modified from [17]).
Figure 2. Schematic of advanced water purification system implemented in SMC water treatment plants. (Modified from [17]).
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Figure 3. Process for computing energy consumption and CO2 emissions of water treatment plants.
Figure 3. Process for computing energy consumption and CO2 emissions of water treatment plants.
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Figure 4. Screenshot of user interface of developed model.
Figure 4. Screenshot of user interface of developed model.
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Figure 5. Annual SMC water supply requirement per capita for the last 10 years.
Figure 5. Annual SMC water supply requirement per capita for the last 10 years.
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Figure 6. Computed and observed energy consumptions for (a) 2020 and (b) 2021.
Figure 6. Computed and observed energy consumptions for (a) 2020 and (b) 2021.
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Figure 7. Energy use proportion per-unit process for (a) Gangbuk, (b) Guui, (c) Ddukdo, (d) Amsa, (e) Youngdengpo, and (f) Gwangam water treatment plants.
Figure 7. Energy use proportion per-unit process for (a) Gangbuk, (b) Guui, (c) Ddukdo, (d) Amsa, (e) Youngdengpo, and (f) Gwangam water treatment plants.
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Figure 8. Computed and observed CO2 intensity in (a) 2020 and (b) 2021.
Figure 8. Computed and observed CO2 intensity in (a) 2020 and (b) 2021.
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Figure 9. Annually averaged water production ratios of individual water treatment plants.
Figure 9. Annually averaged water production ratios of individual water treatment plants.
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Figure 10. Energy intensities for the six water treatment plants for Scenarios 0–2. (a) Actual energy intensity and (b) net energy intensity.
Figure 10. Energy intensities for the six water treatment plants for Scenarios 0–2. (a) Actual energy intensity and (b) net energy intensity.
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Figure 11. CO2 intensities for six water treatment plants for Scenarios 0–2. (a) Actual CO2 intensity and (b) net CO2 intensity.
Figure 11. CO2 intensities for six water treatment plants for Scenarios 0–2. (a) Actual CO2 intensity and (b) net CO2 intensity.
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Water 16 00479 g012
Figure 12. Energy consumption of the applied scenarios. (a) Actual energy consumption and (b) net energy consumption.
Figure 12. Energy consumption of the applied scenarios. (a) Actual energy consumption and (b) net energy consumption.
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Water 16 00479 g013
Figure 13. CO2 emissions and CO2 intensities of the applied scenarios. (a) Actual CO2 emissions, (b) net CO2 emissions, (c) actual CO2 intensity, and (d) net CO2 intensity.
Figure 13. CO2 emissions and CO2 intensities of the applied scenarios. (a) Actual CO2 emissions, (b) net CO2 emissions, (c) actual CO2 intensity, and (d) net CO2 intensity.
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Figure 14. Comparison of net CO2 emissions for different scenarios.
Figure 14. Comparison of net CO2 emissions for different scenarios.
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Table 1. Estimates of energy intensity of public water supply unit processes (in kWh/day) based on Reekie et al. [18].
Table 1. Estimates of energy intensity of public water supply unit processes (in kWh/day) based on Reekie et al. [18].
Unit ProcessEfficiencyPlant Production (MGD)
15102050100250
Source Water PumpingRaw surface
water pumping		High11858911772355588711,77429,435
Medium14572514492898724614,49136,228
Low18894218843768941918,83847,096
ClarificationRapid mixing 40175310620154030807700
Flocculation 1050901804509002260
Sedimentation 1545901754408752190
Chemical feed systems 65656565656565
Microfiltration
(in lieu of sedimentation)		 10050010002000500010,00025,000
Ultrafiltration (contaminant removal) 8004000800016,00040,00080,000200,000
Reverse osmosis (brackish water) 600029,80059,500119,000226,600453,200738,400
Reverse osmosis (ocean water) 12,00060,000120,000240,000600,0001,200,0003,000,000
Dissolved air flotation 11089517903600895017,90044,700
Air stripping 375185037407475N/AN/AN/A
Repumping within treatment plant ----195039009750
Filtration and Solid HandlingBackwash water pumps 156012525066012903220
Residuals pumping 42040802004001000
Thickened solid pumping ---1253106201540
Disinfection, Pumping and Non-process LoadsOnsite chlorine generation for disinfection 8542083016704160832520,820
Ozone disinfection 140560112515003840767019,175
UV disinfection 6231062512503120624015,600
Finished water pumpingHigh8454328896917,52039,62979,257198,143
Medium1040532711,038156348,77497,547243,868
Low1352692514,350803263,406126,811317,029
Non-process loads (buildings, HVAC, lighting, computers, etc.) 300120021003600900018,00045,000
Table 2. Energy consumption and renewable energy production of SMC water supply facilities in 2020.
Table 2. Energy consumption and renewable energy production of SMC water supply facilities in 2020.
Water
Supply		FacilitiesElectricity
Usage a
(kWh)		Energy Produced and Internally Consumed bEnergy Produced and Externally SuppliedTotal Energy Consumption c (kWh)Total Energy
Production d (kWh)		Self-Sufficiency a (%)
Solar Energy (kWh)Geothermal Energy (kWh)Solar Energy (kWh)
Raw
water
extraction		Amsa55,854,852---55,854,852--
Jayang24,024,44438,960--24,063,40438,9600.16
Pungnab19,963,26561,012--20,024,27761,0120.30
Gangbuk33,711,791---33,711,791--
Subtotal133,554,35299,972--133,654,32499,9720.07
Drinking
water
treatment		Gangbuk63,639,536217,87460,2466,369,31663,917,6566,647,43610.40
Gwangam6,559,274--1,843,1566,559,2741,843,15628.10
Guui29,160,838--758,18229,160,838758,1822.60
Ddukdo46,535,928--651,50346,535,928651,5031.40
Amsa65,292,441334,21769,9097,150,97965,696,5677,555,10511.50
Youngdenpo45,001,532--990,03445,001,532990,0342.20
Subtotal256,189,549552,091130,15517,763,170256,871,79518,445,4167.18
Note: a Data obtained from the 2021 MOE Report [16]. b Raw data provided by SMG. c Sum of “Electricity usage” and “Energy produced and internally consumed”. d Sum of “Energy produced and internally consumed” and “Energy produced and externally supplied”.
Table 3. Energy intensities for water extraction and treatment.
Table 3. Energy intensities for water extraction and treatment.
FacilityEnergy Intensity
(kWh/m3)
Raw water
extraction		Amsa0.15
Jayang0.11
Pungnab0.12
Gangbuk0.10
SMC average0.12
Drinking water treatmentGangbuk0.26
Gwangam0.09
Guui0.23
Ddukdo0.33
Amsa0.21
Youngdenpo0.28
SMC average0.24
Table 4. Wire-to-water efficiencies of finished water pumping at six water treatment plants.
Table 4. Wire-to-water efficiencies of finished water pumping at six water treatment plants.
Efficiency of Finished Water Pumping (%)
ScenarioGangbukGwangamGuuiDdukdoAmsaYoungdengpo
Scenario 065–75N/A7550–657565–75
Scenario 175N/A75757575
Table 5. Energy consumption and CO2 emissions for raw water pumping.
Table 5. Energy consumption and CO2 emissions for raw water pumping.
YearVolume of Water Withdrawals (m3/year)Energy Consumption (kWh/year)Energy Intensity (kWh/m3)CO2 Emission (t/year)CO2 Intensity (kg/m3)
20201,079,674,327133,654,3240.123862,2700.0577
Table 6. Calibrated percentage of total energy used for non-process load.
Table 6. Calibrated percentage of total energy used for non-process load.
GangbukGwangamDdukdoGuuiAmsaYoungdengpo
Plant capacity (MGD)1825710797256117
Percentage of total energy used for the non-process load1.0%6.3%2.9%2.9%1.0%2.9%
Table 7. Relative absolute errors (%) between observed and computed daily energy consumption.
Table 7. Relative absolute errors (%) between observed and computed daily energy consumption.
GangbukGwangamDdukdoGuuiAmsaYoungdengpo
20202.946.715.384.5712.090.61
20210.065.180.972.1812.594.32
Average1.505.953.183.3812.342.47
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Li, L.; Lee, G.; Kang, D. Estimation of Energy Consumption and CO2 Emissions of the Water Supply Sector: A Seoul Metropolitan City (SMC) Case Study. Water 2024, 16, 479. https://doi.org/10.3390/w16030479

AMA Style

Li L, Lee G, Kang D. Estimation of Energy Consumption and CO2 Emissions of the Water Supply Sector: A Seoul Metropolitan City (SMC) Case Study. Water. 2024; 16(3):479. https://doi.org/10.3390/w16030479

Chicago/Turabian Style

Li, Li, Gyumin Lee, and Doosun Kang. 2024. "Estimation of Energy Consumption and CO2 Emissions of the Water Supply Sector: A Seoul Metropolitan City (SMC) Case Study" Water 16, no. 3: 479. https://doi.org/10.3390/w16030479

APA Style

Li, L., Lee, G., & Kang, D. (2024). Estimation of Energy Consumption and CO2 Emissions of the Water Supply Sector: A Seoul Metropolitan City (SMC) Case Study. Water, 16(3), 479. https://doi.org/10.3390/w16030479

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