Accounting of the Water Footprint in Sewage Sludge Management Processes Using a Hybrid Approach

Accounting of the Water Footprint in Sewage Sludge Management Processes Using a Hybrid Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Accounting of the Water Footprint in Sewage Sludge Management Processes Using a Hybrid Approach Yi Zhang, Shufang Jin, Cao Yang, Long Chen, Cheng Li, Yijue Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8953814/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Water footprints have been widely used as a measure for efficient water management. This study innovatively applies water footprint assessment, completed by life cycle assessment(LCI), to sludge treatment technologies. An approach based on input–output life-cycle inventory analysis was applied to estimate water footprints, fresh water and gray water. Nine functional units and four typically sludge management scenarios in China were analyzed and compared. The fourscenarios are lime stabilization, composting, anaerobic digestion, and incineration. Results showed that gray water footprint is affected not only by technology but also by effluent standards and the background concentrations of pollutants in the receiving water. Among the four sludge management processes, lime stabilization required the largest amount of gray water, anaerobic digestion required the lowest. What’s more, anaerobic digestion is the best method for managing fresh water-saving because of its highly efficient energy recovery. If it is possible to further reduce the economic investment and operational energy consumption, anaerobic digestion may become the mainstream trend. The result also hightlingt the critical pollutants for gray water accounting differ in terms of effluent standards, and it significantly affects the amount of graywater footprint. Sludge management processes inevitably leading gray water footprint. This study can be used to calculate the grey water footprint of different sludge disposal processes, which can provide a reference for managers to select the process, with the specific requirements for water environmental protection, select the appropriate process and try to minimize “reasonable” gray water footprint. water footprint wastewater treatment sludge management input–output life-cycle inventory Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Water footprint was first introduced into water management by Arien Y. Hoekstra in 2002 to quantify both water consumption and pollution (Hoekstra et al., 2002; Hoekstra et al., 2011). In the calculation of water footprint, waters are categorized into blue, green, and gray waters. Blue water refers to surface water and groundwater; green water corresponds to the rainwater stored in soil, on the top of soil, or in vegetation temporarily; and gray water refers to the volume of fresh water needed to assimilate pollutant load and meet the national water standards (Hoekstra et al., 2011). The footprints for each of the three types of water can be quantified either separately or jointly. The green water footprint is mainly used in the agricultural field and. For industrial processes, is seldom use the green water which is refer to water stored in soil. Thus, the blue and green water footprints can be reasonably combined into as the fresh water footprint during industrial processes. The gray water footprint can be used as a quantitative index of water pollution to evaluate the appropriation of water resources. It is an indicator of the efficiency of wastewater treatment plants (WWTPs). For instance, if the gray water footprint exceeds the receiving river flow or the groundwater flow, the loading of the pollutants to the receiving body of water surpasses the environmental tolerance capacity. Thus, water quality deteriorates (Hoekstra et al., 2011). Reducing the gray water footprint generates certain environmental benefits for WWTPs (Shao et al., 2013). Theoretically, a zero gray water footprint can be achieved if the concentrations of the pollutants in effluents are lower than that set in the National Discharge Standards (Hoekstra et al., 2011). The sewage treatment process of a WWTP usually produces several by-products including sludge. Such by-products consume a certain amount of gray water and significantly affect the performance of a WWTP. Nonetheless, no previous study has examined gray water consumption in terms of valuing WWTP performance. This omission may have rendered incomplete the information provided to policymakers for consideration in environmental quality improvement. In China, a total of 14,637 sewage treatment plants were included in the national emission source statistics survey of year 2024. The total treated sewage volume was 93.97 billion tons, and the amount of sludge produced by these plants was 53.332 million tons. The disposal volume of the sludge was 53.166 million tons (ARCES,2023).. Thus, an efficient and environment-friendly sludge management scheme must be developed. Water footprint assessment (WFA) models have been developed and applied in a variety of fields since 2009. For example, the CROPWAT model was established to estimate green and blue water footprints during crop growth (Mekonnen et al., 2011; Hess et al., 2010; Chooyok et al., 2013 ). However, WFA has not been applied in sludge management because of the large amount of data required by this model (refer to SI). In addition, these data, which include the inventory of by-products from sludge management processes, are not always readily available. Therefore, an alternative approach known as life-cycle inventory (LCI) analysis is used to obtain data for this study. LCI estimates output inventories based on the input data for a given product system and provides comprehensive and representative information on the product system (ISO 14040, 1998 ; Boulay et al., 2013). The aim of this study is to evaluate the environmental influence of different sludge treatment processes from the perspective of water footprint, particularly focusing on their impact on the water environment. At the same time, the changes in the water footprint of sludge disposal under different emission conditions were also taken into consideration. A hybrid approach that combines water footprint estimation with LCI analysis was built to ensure that the data is sufficient. Four typical sludge management scenarios are applied in the case studies, namely, lime stabilization, composting, anaerobic digestion, and incineration. Their water footprints are estimated and compared. 2. Methodology and Materials The general approach used in this study is illustrated in the Fig. 1 . LCI was used to estimate the fresh and gray water footprints for each materials and energies ofsludge management scenarios. The amounts of materials energies involved were also calculated The total water footprints for these scenariosevaluated and compared. The details are provided in the Fig. 1 . The first phase involves data collection and inventory analysis. The key points for data collection are fresh water use, power consumption, chemical agent consumption, and the total amount of pollutants in each unit. The second phase focuses on the accounting of unit water footprints. The gray water footprint of a functional unit(1 t dry sludge) is determined by the pollutant that is associated with the largest gray water footprint. The third phase calculates the total water footprint of a scenario in which the water footprints of every unit that is relevant to sludge management are aggregated. 2.1. Data Source The data used for LCI analysis were obtained from previous studies, which used different processes to treat the sludge within the same period of time. The studies including fresh water consumed, power usage, chemical agent consumption, and pollutant discharge. Fresh water referred to the combination of blue and green waters. The analysis was based on the volume of fresh water per unit ton of disposed dry sludge (tDS). The details are provided in the Supporting Information (SI) section. This study estimates the amount of fresh water using China Life Cycle Database (CLCD) public version 0.8 (Liu et al., 2010 ). The fresh water defined in the CLCD consists of river, ground, and cooling water. The fresh water footprints of several relevant materials are listed in Table 1 along with energy consumption. Table 1 Fresh water footprints of the materials and the energy used in sludge management as retrieved from CLCD. Inventory Unit Fresh water footprint Coal 1 kg 2.4 L·kg − 1 Electric energy 1 kWh 7.6 L·kWh − 1 Lime 1 kg 8 L·kg − 1 High-density polyethylene 1 kg 198.56 L·kg − 1 Fuel 1 kg 230.4 L·kg − 1 FeSO 4 1 kg 1000 L·kg − 1 FeCl 3 1 kg 6072 a L·kg − 1 Pesticide 1 kg 120576 L·kg − 1 Note: Data regarding FeCl 3 are not provided in CLCD public version 0.8. Thus, relevant data are collected from the Ecoinvent database instead (EB/OL, 2010). In gray water calculation, the following equation is used (Hoekstra et al., 2011): \(W{F_{Grey}}=\frac{L}{{{C_{\hbox{max} }} - {C_{nat}}}}\) m 3 , (1) where WF gray = the gray water footprint of each gram of pollutant, m 3 L = the load of the pollutant, kg C max = the maximum acceptable concentration of a pollutant, kg/m 3 C nat = the background concentration of a pollutant, kg/m 3 The effluent standards of China for urban sewage treatment plant pollutants (GB18918-2002) is the current national standard. It categorizes the treated water quality into four levels, Level 3, Level 2, Level 1B, and Level 1A. Level 1A is the most stringent level. The pollutant concentrations under these four levels replace C max in sequence, so we can estimate and compare gray water footprints. The Environmental Quality Standards for the Surface Water of China (GB 3838 − 2002) categorizes river water quality into six levels, namely, I to V and –V. The water quality of Level –V is the worst. As reported in 2024 China Environmental Ecology Status Report, the percentages of river water in China were 92.4%, 7.3%, and 0.3% for Levels I to III, IV to V, and -V, respectively. In this study, the concentrations at Level III water quality were applied as C nat . The gray water footprints were estimated for several criteria pollutants, including, chemical oxygen demand (COD), biochemical oxygen demand (BOD 5 ), and NH 4 + -N. The gray water footprints of the main pollutants under different standards are presented in Table 2 . Table 2 Gray water footprints of main pollutants. Inventory c max / g·m − 3 c nat / g·m − 3 Gray water footprint/ (m 3 ) Level 3 Level 2 Level 1B Level 1A Standard III Level 3 Level 2 Level 1B Level 1A COD 120 100 60 50 20 0.010 0.013 0.025 0.033 BOD 5 60 30 20 10 4 0.017 0.038 0.063 0.167 NH 4 + -N 25 a 25 8 5 1 0.042 0.042 0.143 0.250 a. The concentration of NH 4 + -N is not clearly defined in Level 3; thus, Level 2 is adopted instead in consideration of severe eutrophication. 2.2. Water Footprints of Functional Units Depends on the final destination, Fig. 2 shows main types of sludge Functional units. When sorting out these units, we also referred to China's "Technical Policy for Sludge Treatment and Disposal as Well as Pollution Prevention in Urban Wastewater Treatment Plants",which is the current standard guideline in the Chinese sludge disposal industry. Pretreatment reduces the moisture content of the sludge in a procedure referred to as dewatering. Moisture content can be reduced to 80% by mechanical dewatering, whereas it can be lowered to 10% by thermal dewatering (Xie et al., 2010 ). Stabilization The techniques used for stabilization include lime stabilization, composting, incineration, and anaerobic digestion which aims to degrade organic matters, to sterilize pathogens, and to remove odors. (Brisolara et al., 2013). These approaches are energy-sensitive and are widely used; thus, they were selected for evaluation in this study (Wang et al., 2010a ; Fernandez et al., 2014 ; Chong et al., 2014). Landfills or Material reuse aims to facilitate the long-term stability of sludge via (Jain et al., 2005 ; Kelessidis et al., 2012). From the perspective of resource recycling, agricultural utilization is the more favorable treatment. The database for the sludge management process in China is not yet fully developed. Especially, it is necessary to compare several different processes simultaneously, this means that the nature and quantity of the disposed sludge must remain basically the same,which is even difficult. In order to address the issue of data scarcity, the quantitative data on the inputs and outputs of each functional obtained by LCI analysis. The fresh water usage of a functional unit is the sum of the fresh water in the materials involved. The gray water usage of the functional units is determined based on the critical pollutant refers to the pollutant among all the pollutants that has the highest water footprint of waste water (Hoekstra et al., 2011). 2.3. Case Study To evaluate the different sludge treatment processes’ impact on the water environment. Four widely used sludge treatment processeswere configured for evaluation as depicted in Fig. 3 . These configurations are referred to as Scenarios I—IV. Scenarios II—IV are energy-saving methods that recycle and reuse compost, biogas, and heat. Scenario I is the simplest way to treat sludge among the four and from the perspective of handling costs, it is also the most cost-effective option. Those scenarios are all useful technologies for treating sludge and reducing its impact on the environment compared to no treatment. A WWTP produces primary sludge from the primary sedimentation tank and biological sludge from the secondary sedimentation tank. The calculation was based on mixed sludge with 97.5% moisture content, due to the fact that the moisture content of the sludge will constantly change during the processing, the calculation basis of this studyis a per ton of dry sludge basis. During the sludge management process, the dry basis changes as auxiliary materials are added and as organic matters are degraded and burned. Dry basis increases by 52% by lime addition and by 135% with the addition of auxiliary materials during the composting process. The dry basis decreases by 32% due to anaerobic degeneration and by 70% because of incineration. Detailed information is provided in Section II of the SI. Thus, the change of dry basis should be considered in the calculation of the fresh and gray water footprints of each scenario. The water footprint of each scenario is the sum of those functionalunits. Bioenergy and heat energy recovery can offset part of the fresh water consumption in Scenario III and IV. These offsets were taken into account. 3. Results The analysis results presented in this section includes the following: (1) the water footprint of each functional unit considered in the sludge management process; (2) the evaluation of the case studies on the different sludge management scenarios; and (3) the effect of effluent levels on water footprint estimation. The details are provided in the succeeding sections. 3.1. Water Footprints of Functional Units Used in the Sludge Management Processes The functional unit water footprint used in the sludge management process was estimated by LCI analysis, as shown in Table 3. The details are provided in the SI section. Table 3. Accounting for the water footprints of functional units. Operational unit Fresh water footprint m 3 Gray water footprint m 3 Mixed-sludge dewatering 0.82 204 Digested-sludge dewatering 0.82 375.56 Thermal dewatering 6.6 3.89 Dewatering and lime stabilization 121.4 821.64 Composting and production 12.05 120 Incineration -1.32 40.46 Anaerobic digestion -10.08 0 Land application 0.61 0 Landfill 2.03 44 Note: The data in the table is based on each ton of dry sludge. In the mixed and digested-sludge dewatering processes, the chemical and electricity requirements are presumably similar. Thus, the fresh water estimations for both processes are identical. However, the results of grey water footprint vary greatly.because of the varying properties in the dehydration leachates of these two processes. The NH 4 + -N concentration in the leachates of the digested sludge is significantly higher than that in the mixed sludge, it increasing the amount of gray water. The fresh water in thermal dewatering units is mainly used as steam, which acted as the heat-transfer medium and the water for the heat-power production. The amount of gray water equals the volume evaporated in the dewatering unit, and the pollutant concentration in this evaporated water is presumably lower than the effluent standard. The use of lime as a chemical stabilizer in the treatment of sludge can induce synchronous dehydration and stabilization. Most fresh water is consumed in the production of dehydrating agents (FeCl 3 and CaO). The gray water is drawn from the high concentration of BOD 5 in the leachate. This high concentration is attributed to the high level of disrupted microorganism cells originating from lime-stabilized probes. Composting is performed in the sludge aerobic fermentation stabilization unit, where treated sludge can be used as agricultural fertilizer as it complies with relevant standards. Fresh water is primarily used to clean the compost site. The gray water footprint of composting is determined based on the pollutants in the compost filtrate (Huang et al., 2010,2014). The generated electricity in the incineration unit can not only meet the energy demanded by its own system under normal operating conditions, but it can also supply energy to other areas. Reusing electrical energy can offset fresh water consumption, thus inducing fresh water consumption. Data on the municipal solid waste incineration process are borrowed in the case study because information on the incineration unit is unavailable. Dry sludge is degraded into biogas in the mesophilic anaerobic digestion unit. This biogas can be converted into electrical power, and the amount of fresh water consumed is caused by the additional energy generated. Nonetheless, the gray water of anaerobic digestion is considered zero based on the assumption that all gray water is produced during the dewatering process performed later. The fresh water footprint of the land application unit is mainly derived from electrical and diesel production. The treated sludge can be used in land applications, and its properties should conform to the relevant standards. The assumption that the amount of gray water is zero is reasonable. In sludge landfill disposal, a large amount of the fresh water is consumed by the pesticides production process, pesticides are used for eliminating mosquitoes and flies in the sludge landfill site. The amount of gray water is calculated based on the high concentration of COD from the landfill leachate. 3.2. Water Footprints of Different Scenarios The water footprint of four scenarios are determined according to the least stringent effluent standard (Levels 3). The background concentrations (Levels I to III) are assumed to be the same of the four scenarios.. Therefore, the only influencing factor on the quantity of gray water is technologies applied. The fresh water and gray water footprints of Scenario I are significantly higher than those of other scenarios.(Fig.4) In this scenario, the sludge is subject to lime stabilization in a simple treatment method with low energy consumption. Sludge remains stable when the moisture content reaches the requirement of the landfill site (< 60%), but it is not treated fully. The lime stabilization process is a chemical treatment method that demands a lot of chemical agents. As a result, a large amount of fresh water is used. The pressure filtrate is the main secondary pollutant leading the large amounts of gray water. Anaerobic digestion is regarded as the best fresh water-saving technology because of its highly efficient energy recovery rate. In this process, methane can be employed to generate biomass energy and therefore offset parts of the fresh water footprint. The gray water is mainly produced by the significant amount of pressure filtrates containing high concentrations of NH 4 + -N. Aerobic composting is another resource-reuse method of treating sludge. Composting products generates economic benefits that can offset parts of the fresh water footprint in Scenario II, situation is similar to the anaerobic digestion method. The gray water in the incineration process is mainly derived from the evaporated water of the thermal dewatering and burning units. Moreover, thermal energy heat recovery can offset parts of fresh water consumption. 3.3. Effect of Effluent Standards on the Gray Water Footprint 3.3.1. Different Effluent Standards Affect the Gray Water Footprint of the functional units Dehydration units normally produce large amounts of leachate, and the concentration of pollutants in this leachate is significantly higher than those in other units. The change in effluent standards clearly influences gray water footprint. As indicated in Fig.5, the gray water estimates vary significantly with different pollutants and corresponding effluent standards. A stringent effluent standard typically increases the requirement for gray water. The variation range for each pollutant effluent is distinct under the different standards. For example, COD decreases by 40 mg/L when the standards shift from Level 2 to Level 1B, whereas BOD 5 decreases by 10 mg/L. These reductions alter the critical pollutants for gray water accounting. Fig. 5(a) displays the gray water of a mixed-sludge dewatering unit. The calculation of this water is based on a COD concentration at Level 1A standard. Under this strictest standard, BOD 5 becomes the dominant pollutant. In Fig.5(b), the amount of COD-based gray water is similar to that of the BOD-based one at Level 3 during the dewatering and lime stabilization process. However, BOD 5 becomes the critical pollutant when standards become strict. The choice of sewage treatment plant in relation to effluent standards does not follow a “the stricter the better” pattern. Furthermore, critical pollutants should be combined with local conditions. The choice of effluent standards should consider many other factors, such as operational cost and maximum resource-recovery benefits (Wang et al., 2010b; Wang et al., 2012). 3.3.2. Standard Effects on the Gray Water Footprints in Scenarios In the front accounting processes (3.1 and 3.2), the effluent standards at Level 3 refer to the highest effluent concentration The effluent standards may be increasingly strict in the future, and it will lower the maximum acceptable concentration leading to grey water footprint increase. Regardless of the standard chosen for evaluation, Scenario I (lime stabilization) produces the largest amount of gray water and is thus the least favored among the four scenarios.(Fig.6) By contrast, Scenario IV (incineration) is the most favorable approach. The amounts of gray water in the lime stabilization scenario and in the anaerobic digestion scenario change rapidly with different standards. For example, the amounts of gray water increased by 614.6% and 395.2% when the standard shifts from Level 3 to Level 1A due to the significant decreases in the concentration requirements for BOD 5 and NH 4 + -N, respectively. 4. Discussions This paper presented an input–output, LCI analysis-based hybrid method to account for water footprint in sludge management processes. Data from different sources were used to demonstrate this approach. Given a lack of data, some processes and materials were ignored, including sludge storage and transportation. In addition, the variation in and quality of data from different sources aggravated the uncertainties regarding the results. Nonetheless, these limitations did not weaken the effectiveness of the hybrid approach as a sludge management tool for a sustainable environment. Water footprint can be used to evaluate different technologies for secondary pollution. Furthermore, the gray water footprint ofsludge disposal process according an acceptable emission standard can be derived by accumulating data. Standards that change with region can measure if a sludge disposal technology meets the requirement of sustainable development. Therefore, the investigation of water footprint benefits the optimization of sludge disposal processes. The accounting of gray water footprint is based on the difference between the effluent standard and the natural background concentration (Cmax-Cnat). This footprint is not only closely related to the effluent standard but is also related to the surrounding environmental quality. If a sludge treatment plant is located in a seriously polluted area, then its gray water footprint is significantly greater than that of a similar plant located in a less-contaminated area given the increased background concentrations. Analogously, an increased amount of gray water is produced if a plant is located in an environmentally sensitive site and is under a strict effluent standard. The accounting of gray water accounting is also affected by hydrologic conditions. For instance, background concentration increases during the dry season and enhances the volume of gray water. Fresh water accounting in sludge management is less susceptible to surroundings. Moreover, fresh water consumption in sludge management is more stable than the consumption of crop growth and irrigation water. The latter varies significantly in different areas. The accounting of water footprint in this study does not take specific geographical and temporal information into consideration. The effluent standards and natural background concentrations used in gray water accounting are presumably similar across the different technologies. Thus, the accounting of gray water is technology-dependent, theoretically. However, water footprint is closely related to the surrounding environment. The data sources of the four technologies considered do not all originate from a specific area but are obtained from different sludge treatment plants all over China. Therefore, the comparison of these data may display some deviations. If accurate and comparable data can be collected, then the sustainability of water footprints can be assessed. This process is the next stage of water footprint accounting, and it depends on two criteria: geographical context and the characteristics of the process itself. The assessment is characterized by the capability to reduce or avoid water footprints at an acceptable cost. Sludge management processes inevitably produce gray water. Nonetheless, no global benchmark has been set regarding the “reasonable” maximum footprint of gray water for each technology. Therefore, the sustainability of a sludge management process is difficult to determine. The approach developed in this study can quantify the water footprints of different processes and provide a reference for deciding on this “reasonable” maximum footprint. Declarations Ethical Approval Not applicable Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Yi Zhang; Shufang Jin wrote the main manuscript text and Cheng Li; Yijue Wu prepared figures and tables. Cao Yang; Long Chen offer the funding. All authors reviewed the manuscript. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Any custom code, software, or materials developed for this study are available from the corresponding author upon reasonable request, unless restricted by intellectual property agreements or ethical considerations. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. References Boulay, A. M., Hoekstra, A. Y., Vionnet, S., 2013.Complementarities of Water-Focused Life Cycle Assessment and Water Footprint Assessment. Environ. Sci. Technol. 47, 11926-11927. Brisolara, K. F., Qi, Y. N., 2013. Biosolids and Sludge Management. Water Environ. Res. 85, 1283-1297. 2023 Annual Report on China's Environmental Statistics (ARCES), 2023. https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202412/t20241231_1099687.shtml. Chooyok, P., Pumijumnog N. and Ussawarujikulchai A., 2013. The Water Footprint Assessment of Ethanol Production from Molasses in Kanchanaburi and Supanburi Province of Thailand. 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Wang, X., Liu, J., Ren, N., Yu, H., Lee, D., Guo, X., 2012.Assessment of Multiple Sustainability Demands for Wastewater Treatment Alternatives: A Refined Evaluation Scheme and Case Study. Environ. Sci. Technol. 46, 5542-5549. Xie, X.Q., Huang, Z.Y., Dai, L. H., Xie, X. M., Liu, M.L., 2010. Study on Deep Dewatering and Recycling of Municipal Sludge in Xia men. China Water Wastewater. 26, 138-140. (In Chinese). Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3RvQrCMBDA8YhwLmddT5T4CoGAUwcf5UTIpODoqAjtYMU38Bl8hKrQqe4ODnVx1tHFT9wUUzeH/Ob8yV0ihOP8ISiNxvsjgoTS7JTxwLcnHq4TTXVPexhrlaXGnkgyTOjL9pxazeo+WOUYDFOlqNvUQMIMGGJRCSds2SVSWT81EmrDZMu4E5RuFvZbqlGiob4MtkwHoahnSairqHy5tgPqQJ/VOk/yWB/hnhgQzHmS5yMj6PuEReLYoHWXRvj6ykYYFU7niy8r4fR78gZ/O+44juN8dAPGrUZlArUuDwAAAABJRU5ErkJggg==","orcid":"","institution":"Shenzhen Academy of Environmental Science","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":626236968,"identity":"466f2c28-b67d-4bee-918c-cfd327aa204d","order_by":1,"name":"Shufang Jin","email":"","orcid":"","institution":"School of Eco-Environment, Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shufang","middleName":"","lastName":"Jin","suffix":""},{"id":626236971,"identity":"e708583f-4f9c-475e-8933-93984a112200","order_by":2,"name":"Cao Yang","email":"","orcid":"","institution":"School of Eco-Environment, Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Cao","middleName":"","lastName":"Yang","suffix":""},{"id":626236972,"identity":"846361e6-8064-419e-80d6-e9d882b50574","order_by":3,"name":"Long Chen","email":"","orcid":"","institution":"Shenzhen Academy of Environmental Science","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Chen","suffix":""},{"id":626236974,"identity":"7ede59c9-7084-4455-b7be-4538c60a0fc1","order_by":4,"name":"Cheng Li","email":"","orcid":"","institution":"Shenzhen Academy of Environmental Science","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Li","suffix":""},{"id":626236977,"identity":"74c663b2-40b1-40f7-8837-4b779d225983","order_by":5,"name":"Yijue Wu","email":"","orcid":"","institution":"Shenzhen Academy of Environmental Science","correspondingAuthor":false,"prefix":"","firstName":"Yijue","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-02-24 07:09:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8953814/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8953814/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107465464,"identity":"a9d4fac0-e14f-43df-b181-37f75a5458fd","added_by":"auto","created_at":"2026-04-21 18:18:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178011,"visible":true,"origin":"","legend":"\u003cp\u003eThe data sources and computational methods of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/455f289af120e43dd6100170.png"},{"id":107704491,"identity":"b0f0128f-7e2d-4bc7-b47f-753974bcdf41","added_by":"auto","created_at":"2026-04-24 08:45:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94820,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of functional units according to final destination.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/d973bfe670f812bad74ba1d9.png"},{"id":107465470,"identity":"7e774314-17ee-4b3f-bd54-2f7b5b7f1757","added_by":"auto","created_at":"2026-04-21 18:18:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":193441,"visible":true,"origin":"","legend":"\u003cp\u003eScenarios of sewage sludge management processes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/57f6146b09cf7471a38eb4f6.png"},{"id":107465467,"identity":"88d72034-b2a1-46f0-a508-b7606ab9723d","added_by":"auto","created_at":"2026-04-21 18:18:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":284242,"visible":true,"origin":"","legend":"\u003cp\u003eAmounts of fresh water and gray water of different scenarios.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/b860e1e4fd3d05a452bc4c6c.png"},{"id":107489707,"identity":"2a2e0774-9323-4243-b8a5-c878758b14c6","added_by":"auto","created_at":"2026-04-22 02:48:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":319312,"visible":true,"origin":"","legend":"\u003cp\u003eTwo functional gray water units based on three pollutants under different effluent standards.\u003c/p\u003e\n\u003cp\u003e(a) Gray water of the mixed-sludge dewatering unit (b) Gray water of the dewatering and lime stabilization unit.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/0b695028cbf1bef370e97869.png"},{"id":107465468,"identity":"9c3b6baa-06d4-4151-bc63-9fb2193f5c34","added_by":"auto","created_at":"2026-04-21 18:18:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":407505,"visible":true,"origin":"","legend":"\u003cp\u003eAmount of gray water under different standards.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/a24c3a865fc9f14f1b42c05d.png"},{"id":108976249,"identity":"32fda604-9f92-454d-a56b-3423e67e636b","added_by":"auto","created_at":"2026-05-11 11:01:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1510458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/e4f39d31-cda5-49d4-97f6-e5dd2524cc10.pdf"},{"id":107465465,"identity":"6e8ba081-18cb-4b1d-8f8d-b7d53f03ac7d","added_by":"auto","created_at":"2026-04-21 18:18:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":189724,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8953814/v1/131dff52d528ebb858e8fd7a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accounting of the Water Footprint in Sewage Sludge Management Processes Using a Hybrid Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWater footprint was first introduced into water management by Arien Y. Hoekstra in 2002 to quantify both water consumption and pollution (Hoekstra et al., 2002; Hoekstra et al., 2011). In the calculation of water footprint, waters are categorized into blue, green, and gray waters. Blue water refers to surface water and groundwater; green water corresponds to the rainwater stored in soil, on the top of soil, or in vegetation temporarily; and gray water refers to the volume of fresh water needed to assimilate pollutant load and meet the national water standards (Hoekstra et al., 2011). The footprints for each of the three types of water can be quantified either separately or jointly. The green water footprint is mainly used in the agricultural field and. For industrial processes, is seldom use the green water which is refer to water stored in soil. Thus, the blue and green water footprints can be reasonably combined into as the fresh water footprint during industrial processes. The gray water footprint can be used as a quantitative index of water pollution to evaluate the appropriation of water resources. It is an indicator of the efficiency of wastewater treatment plants (WWTPs). For instance, if the gray water footprint exceeds the receiving river flow or the groundwater flow, the loading of the pollutants to the receiving body of water surpasses the environmental tolerance capacity. Thus, water quality deteriorates (Hoekstra et al., 2011). Reducing the gray water footprint generates certain environmental benefits for WWTPs (Shao et al., 2013). Theoretically, a zero gray water footprint can be achieved if the concentrations of the pollutants in effluents are lower than that set in the National Discharge Standards (Hoekstra et al., 2011). The sewage treatment process of a WWTP usually produces several by-products including sludge. Such by-products consume a certain amount of gray water and significantly affect the performance of a WWTP. Nonetheless, no previous study has examined gray water consumption in terms of valuing WWTP performance. This omission may have rendered incomplete the information provided to policymakers for consideration in environmental quality improvement.\u003c/p\u003e \u003cp\u003eIn China, a total of 14,637 sewage treatment plants were included in the national emission source statistics survey of year 2024. The total treated sewage volume was 93.97\u0026nbsp;billion tons, and the amount of sludge produced by these plants was 53.332\u0026nbsp;million tons. The disposal volume of the sludge was 53.166\u0026nbsp;million tons (ARCES,2023).. Thus, an efficient and environment-friendly sludge management scheme must be developed.\u003c/p\u003e \u003cp\u003eWater footprint assessment (WFA) models have been developed and applied in a variety of fields since 2009. For example, the CROPWAT model was established to estimate green and blue water footprints during crop growth (Mekonnen et al., 2011; Hess et al., 2010; Chooyok et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, WFA has not been applied in sludge management because of the large amount of data required by this model (refer to SI). In addition, these data, which include the inventory of by-products from sludge management processes, are not always readily available. Therefore, an alternative approach known as life-cycle inventory (LCI) analysis is used to obtain data for this study. LCI estimates output inventories based on the input data for a given product system and provides comprehensive and representative information on the product system (ISO 14040, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Boulay et al., 2013).\u003c/p\u003e \u003cp\u003eThe aim of this study is to evaluate the environmental influence of different sludge treatment processes from the perspective of water footprint, particularly focusing on their impact on the water environment. At the same time, the changes in the water footprint of sludge disposal under different emission conditions were also taken into consideration. A hybrid approach that combines water footprint estimation with LCI analysis was built to ensure that the data is sufficient. Four typical sludge management scenarios are applied in the case studies, namely, lime stabilization, composting, anaerobic digestion, and incineration. Their water footprints are estimated and compared.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Methodology and Materials","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe general approach used in this study is illustrated in the Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. LCI was used to estimate the fresh and gray water footprints for each materials and energies ofsludge management scenarios. The amounts of materials energies involved were also calculated The total water footprints for these scenariosevaluated and compared. The details are provided in the Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe first phase involves data collection and inventory analysis. The key points for data collection are fresh water use, power consumption, chemical agent consumption, and the total amount of pollutants in each unit. The second phase focuses on the accounting of unit water footprints. The gray water footprint of a functional unit(1 t dry sludge) is determined by the pollutant that is associated with the largest gray water footprint. The third phase calculates the total water footprint of a scenario in which the water footprints of every unit that is relevant to sludge management are aggregated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Data Source\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe data used for LCI analysis were obtained from previous studies, which used different processes to treat the sludge within the same period of time. The studies including fresh water consumed, power usage, chemical agent consumption, and pollutant discharge. Fresh water referred to the combination of blue and green waters. The analysis was based on the volume of fresh water per unit ton of disposed dry sludge (tDS). The details are provided in the Supporting Information (SI) section.\u003c/p\u003e\n \u003cp\u003eThis study estimates the amount of fresh water using China Life Cycle Database (CLCD) public version 0.8 (Liu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The fresh water defined in the CLCD consists of river, ground, and cooling water. The fresh water footprints of several relevant materials are listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e along with energy consumption.\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFresh water footprints of the materials and the energy used in sludge management as retrieved from CLCD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInventory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFresh water footprint\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCoal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.4 L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eElectric energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kWh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.6 L\u0026middot;kWh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8 L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh-density polyethylene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e198.56 L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFuel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e230.4 L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFeSO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1000 L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFeCl\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6072\u003csup\u003ea\u003c/sup\u003e L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePesticide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1 kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e120576 L\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: Data regarding FeCl\u003csub\u003e3\u003c/sub\u003e are not provided in CLCD public version 0.8. Thus, relevant data are collected from the Ecoinvent database instead (EB/OL, 2010).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eIn gray water calculation, the following equation is used (Hoekstra et al., 2011):\u003c/p\u003e\n \u003cp\u003e\\(W{F_{Grey}}=\\frac{L}{{{C_{\\hbox{max} }} - {C_{nat}}}}\\) m\u003csup\u003e3\u003c/sup\u003e, (1)\u003c/p\u003e\n \u003cp\u003ewhere\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eWF\u003c/em\u003e\u003csub\u003egray\u003c/sub\u003e = the gray water footprint of each gram of pollutant, m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003eL\u003c/em\u003e\u0026thinsp;=\u0026thinsp;the load of the pollutant, kg\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e = the maximum acceptable concentration of a pollutant, kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u003csup\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003enat\u003c/sub\u003e = the background concentration of a pollutant, kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/sup\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe effluent standards of China for urban sewage treatment plant pollutants (GB18918-2002) is the current national standard. It categorizes the treated water quality into four levels, Level 3, Level 2, Level 1B, and Level 1A. Level 1A is the most stringent level. The pollutant concentrations under these four levels replace \u003cem\u003eC\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e in sequence, so we can estimate and compare gray water footprints.\u003c/p\u003e\n \u003cp\u003eThe Environmental Quality Standards for the Surface Water of China (GB 3838\u0026thinsp;\u0026minus;\u0026thinsp;2002) categorizes river water quality into six levels, namely, I to V and \u0026ndash;V. The water quality of Level \u0026ndash;V is the worst. As reported in 2024 China Environmental Ecology Status Report, the percentages of river water in China were 92.4%, 7.3%, and 0.3% for Levels I to III, IV to V, and -V, respectively. In this study, the concentrations at Level III water quality were applied as \u003cem\u003eC\u003c/em\u003e\u003csub\u003enat\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003eThe gray water footprints were estimated for several criteria pollutants, including, chemical oxygen demand (COD), biochemical oxygen demand (BOD\u003csub\u003e5\u003c/sub\u003e), and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N. The gray water footprints of the main pollutants under different standards are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGray water footprints of main pollutants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eInventory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e/ g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003csub\u003enat\u003c/sub\u003e/ g\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\n \u003cp\u003eGray water footprint/\u003c/p\u003e\n \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eLevel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLevel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eLevel 1B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eLevel 1A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eStandard III\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eLevel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eLevel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003eLevel 1B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ea. The concentration of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N is not clearly defined in Level 3; thus, Level 2 is adopted instead in consideration of severe eutrophication.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Water Footprints of Functional Units\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eDepends on the final destination, Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows main types of sludge Functional units. When sorting out these units, we also referred to China\u0026apos;s \u0026quot;Technical Policy for Sludge Treatment and Disposal as Well as Pollution Prevention in Urban Wastewater Treatment Plants\u0026quot;,which is the current standard guideline in the Chinese sludge disposal industry.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePretreatment reduces the moisture content of the sludge in a procedure referred to as dewatering. Moisture content can be reduced to 80% by mechanical dewatering, whereas it can be lowered to 10% by thermal dewatering (Xie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Stabilization The techniques used for stabilization include lime stabilization, composting, incineration, and anaerobic digestion which aims to degrade organic matters, to sterilize pathogens, and to remove odors. (Brisolara et al., 2013). These approaches are energy-sensitive and are widely used; thus, they were selected for evaluation in this study (Wang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e; Fernandez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chong et al., 2014).\u003c/p\u003e\n \u003cp\u003eLandfills or Material reuse aims to facilitate the long-term stability of sludge via (Jain et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kelessidis et al., 2012). From the perspective of resource recycling, agricultural utilization is the more favorable treatment.\u003c/p\u003e\n \u003cp\u003eThe database for the sludge management process in China is not yet fully developed. Especially, it is necessary to compare several different processes simultaneously, this means that the nature and quantity of the disposed sludge must remain basically the same,which is even difficult. In order to address the issue of data scarcity, the quantitative data on the inputs and outputs of each functional obtained by LCI analysis. The fresh water usage of a functional unit is the sum of the fresh water in the materials involved. The gray water usage of the functional units is determined based on the critical pollutant refers to the pollutant among all the pollutants that has the highest water footprint of waste water (Hoekstra et al., 2011).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Case Study\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eTo evaluate the different sludge treatment processes\u0026rsquo; impact on the water environment. Four widely used sludge treatment processeswere configured for evaluation as depicted in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These configurations are referred to as Scenarios I\u0026mdash;IV. Scenarios II\u0026mdash;IV are energy-saving methods that recycle and reuse compost, biogas, and heat. Scenario I is the simplest way to treat sludge among the four and from the perspective of handling costs, it is also the most cost-effective option. Those scenarios are all useful technologies for treating sludge and reducing its impact on the environment compared to no treatment.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eA WWTP produces primary sludge from the primary sedimentation tank and biological sludge from the secondary sedimentation tank. The calculation was based on mixed sludge with 97.5% moisture content, due to the fact that the moisture content of the sludge will constantly change during the processing, the calculation basis of this studyis a per ton of dry sludge basis. During the sludge management process, the dry basis changes as auxiliary materials are added and as organic matters are degraded and burned. Dry basis increases by 52% by lime addition and by 135% with the addition of auxiliary materials during the composting process. The dry basis decreases by 32% due to anaerobic degeneration and by 70% because of incineration. Detailed information is provided in Section II of the SI. Thus, the change of dry basis should be considered in the calculation of the fresh and gray water footprints of each scenario.\u003c/p\u003e\n \u003cp\u003eThe water footprint of each scenario is the sum of those functionalunits. Bioenergy and heat energy recovery can offset part of the fresh water consumption in Scenario III and IV. These offsets were taken into account.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe analysis results presented in this section includes the following: (1) the water footprint of each functional unit considered in the sludge management process; (2) the evaluation of the case studies on the different sludge management scenarios; and (3) the effect of effluent levels on water footprint estimation. The details are provided in the succeeding sections.\u003c/p\u003e\n \u003cp\u003e3.1. Water Footprints of Functional Units Used in the Sludge Management Processes\u003c/p\u003e\n \u003cp\u003eThe functional unit water footprint used in the sludge management process was estimated by LCI analysis, as shown in Table 3. The details are provided in the SI section.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAccounting for the water footprints of\u0026nbsp;functional units.\u003c/p\u003e\n \u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"545\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eOperational unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003eFresh water footprint m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003eGray water footprint\u0026nbsp;\u003c/p\u003e\n \u003cp\u003em\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eMixed-sludge dewatering\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eDigested-sludge dewatering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e375.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eThermal dewatering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eDewatering and lime stabilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e121.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e821.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eComposting and production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e12.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eIncineration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e40.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eAnaerobic digestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e-10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eLand application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42.0183%;\"\u003e\n \u003cp\u003eLandfill\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3394%;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30.6422%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: The data in the table is based on each ton of dry sludge.\u003c/p\u003e\n \u003cp\u003eIn the mixed and digested-sludge dewatering processes, the chemical and electricity requirements are presumably similar. Thus, the fresh water estimations for both processes are identical. However, the results of grey water footprint vary greatly.because of the varying properties in the dehydration leachates of these two processes. The NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentration in the leachates of the digested sludge is significantly higher than that in the mixed sludge, it increasing the amount of gray water.\u003c/p\u003e\n \u003cp\u003eThe fresh water in thermal dewatering units is mainly used as steam, which acted as the heat-transfer medium and the water for the heat-power production. The amount of gray water equals the volume evaporated in the dewatering unit, and the pollutant concentration in this evaporated water is presumably lower than the effluent standard.\u003c/p\u003e\n \u003cp\u003eThe use of lime as a chemical stabilizer in the treatment of sludge can induce synchronous dehydration and stabilization. Most fresh water is consumed in the production of dehydrating agents (FeCl\u003csub\u003e3\u003c/sub\u003e and CaO). The gray water is drawn from the high concentration of BOD\u003csub\u003e5\u003c/sub\u003e in the leachate. This high concentration is attributed to the high level of disrupted microorganism cells originating from lime-stabilized probes.\u003c/p\u003e\n \u003cp\u003eComposting is performed in the sludge aerobic fermentation stabilization unit, where treated sludge can be used as agricultural fertilizer as it complies with relevant standards. Fresh water is primarily used to clean the compost site. The gray water footprint of composting is determined based on the pollutants in the compost filtrate (Huang et al., 2010,2014).\u003c/p\u003e\n \u003cp\u003eThe generated electricity in the incineration unit can not only meet the energy demanded by its own system under normal operating conditions, but it can also supply energy to other areas. Reusing electrical energy can offset fresh water consumption, thus inducing fresh water consumption. Data on the municipal solid waste incineration process are borrowed in the case study because information on the incineration unit is unavailable.\u003c/p\u003e\n \u003cp\u003eDry sludge is degraded into biogas in the mesophilic anaerobic digestion unit. This biogas can be converted into electrical power, and the amount of fresh water consumed is caused by the additional energy generated. Nonetheless, the gray water of anaerobic digestion is considered zero based on the assumption that all gray water is produced during the dewatering process performed later.\u003c/p\u003e\n \u003cp\u003eThe fresh water footprint of the land application unit is mainly derived from electrical and diesel production. The treated sludge can be used in land applications, and its properties should conform to the relevant standards. The assumption that the amount of gray water is zero is reasonable.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIn sludge landfill disposal, a large amount of the fresh water is consumed by the pesticides production process, pesticides are used for eliminating mosquitoes and flies in the sludge landfill site. The amount of gray water is calculated based on the high concentration of COD from the landfill leachate.\u003c/p\u003e\n \u003cp\u003e3.2. Water Footprints of Different Scenarios\u003c/p\u003e\n \u003cp\u003eThe water footprint of four scenarios are determined according to the least stringent effluent standard (Levels 3). The background concentrations (Levels I to III) are assumed to be the same of the four scenarios.. Therefore, the only influencing factor on the quantity of gray water is technologies applied.\u003c/p\u003e\n \u003cp\u003eThe fresh water and gray water footprints of Scenario I are significantly higher than those of other scenarios.(Fig.4) In this scenario, the sludge is subject to lime stabilization in a simple treatment method with low energy consumption. Sludge remains stable when the moisture content reaches the requirement of the landfill site (\u0026lt; 60%), but it is not treated fully. The lime stabilization process is a chemical treatment method that demands a lot of chemical agents. As a result, a large amount of fresh water is used. The pressure filtrate is the main secondary pollutant leading the large amounts of gray water. Anaerobic digestion is regarded as the best fresh water-saving technology because of its highly efficient energy recovery rate. In this process, methane can be employed to generate biomass energy and therefore offset parts of the fresh water footprint. The gray water is mainly produced by the significant amount of pressure filtrates containing high concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N. Aerobic composting is another resource-reuse method of treating sludge. Composting products generates economic benefits that can offset parts of the fresh water footprint in Scenario II,\u0026nbsp;situation is\u0026nbsp;similar to the anaerobic digestion method. The gray water in the incineration process is mainly derived from the evaporated water of the thermal dewatering and burning units. Moreover, thermal energy heat recovery can offset parts of fresh water consumption.\u003c/p\u003e\n \u003cp\u003e3.3. Effect of Effluent Standards on the Gray Water Footprint\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.3.1. Different Effluent Standards Affect the Gray Water Footprint of the functional units\u003c/p\u003e\n \u003cp\u003eDehydration units normally produce large amounts of leachate, and the concentration of pollutants in this leachate is significantly higher than those in other units. The change in effluent standards clearly influences gray water footprint. As indicated in Fig.5, the gray water estimates vary significantly with different pollutants and corresponding effluent standards. A stringent effluent standard typically increases the requirement for gray water.\u003c/p\u003e\n \u003cp\u003eThe variation range for each pollutant effluent is distinct under the different standards. For example, COD decreases by 40 mg/L when the standards shift from Level 2 to Level 1B, whereas BOD\u003csub\u003e5\u0026nbsp;\u003c/sub\u003edecreases by 10 mg/L. These reductions alter the critical pollutants for gray water accounting. Fig. 5(a) displays the gray water of a mixed-sludge dewatering unit. The calculation of this water is based on a COD concentration at Level 1A standard. Under this strictest standard, BOD\u003csub\u003e5\u003c/sub\u003e becomes the dominant pollutant. In Fig.5(b), the amount of COD-based gray water is similar to that of the BOD-based one at Level 3 during the dewatering and lime stabilization process. However, BOD\u003csub\u003e5\u0026nbsp;\u003c/sub\u003ebecomes the critical pollutant when standards become strict. The choice of sewage treatment plant in relation to effluent standards does not follow a \u0026ldquo;the stricter the better\u0026rdquo; pattern. Furthermore, critical pollutants should be combined with local conditions. The choice of effluent standards should consider many other factors, such as operational cost and maximum resource-recovery benefits (Wang et al., 2010b; Wang et al., 2012).\u003c/p\u003e\n \u003cp\u003e3.3.2. Standard Effects on the Gray Water Footprints in Scenarios\u003c/p\u003e\n \u003cp\u003eIn the front accounting processes (3.1 and 3.2), the effluent standards at Level 3 refer to the highest effluent concentration The effluent standards may be increasingly strict in the future, and it will lower the maximum acceptable concentration leading to grey water footprint increase.\u003c/p\u003e\n \u003cp\u003eRegardless of the standard chosen for evaluation, Scenario I (lime stabilization) produces the largest amount of gray water and is thus the least favored among the four scenarios.(Fig.6) By contrast, Scenario IV (incineration) is the most favorable approach. The amounts of gray water in the lime stabilization scenario and in the anaerobic digestion scenario change rapidly with different standards. For example, the amounts of gray water increased by 614.6% and 395.2% when the standard shifts from Level 3 to Level 1A due to the significant decreases in the concentration requirements for BOD\u003csub\u003e5\u003c/sub\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, respectively.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"4. Discussions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis paper presented an input\u0026ndash;output, LCI analysis-based hybrid method to account for water footprint in sludge management processes. Data from different sources were used to demonstrate this approach. Given a lack of data, some processes and materials were ignored, including sludge storage and transportation. In addition, the variation in and quality of data from different sources aggravated the uncertainties regarding the results. Nonetheless, these limitations did not weaken the effectiveness of the hybrid approach as a sludge management tool for a sustainable environment. Water footprint can be used to evaluate different technologies for secondary pollution. Furthermore, the gray water footprint ofsludge disposal process according an acceptable emission standard can be derived by accumulating data. Standards that change with region can measure if a sludge disposal technology meets the requirement of sustainable development. Therefore, the investigation of water footprint benefits the optimization of sludge disposal processes.\u003c/p\u003e \u003cp\u003eThe accounting of gray water footprint is based on the difference between the effluent standard and the natural background concentration (Cmax-Cnat). This footprint is not only closely related to the effluent standard but is also related to the surrounding environmental quality. If a sludge treatment plant is located in a seriously polluted area, then its gray water footprint is significantly greater than that of a similar plant located in a less-contaminated area given the increased background concentrations. Analogously, an increased amount of gray water is produced if a plant is located in an environmentally sensitive site and is under a strict effluent standard. The accounting of gray water accounting is also affected by hydrologic conditions. For instance, background concentration increases during the dry season and enhances the volume of gray water. Fresh water accounting in sludge management is less susceptible to surroundings. Moreover, fresh water consumption in sludge management is more stable than the consumption of crop growth and irrigation water. The latter varies significantly in different areas.\u003c/p\u003e \u003cp\u003eThe accounting of water footprint in this study does not take specific geographical and temporal information into consideration. The effluent standards and natural background concentrations used in gray water accounting are presumably similar across the different technologies. Thus, the accounting of gray water is technology-dependent, theoretically. However, water footprint is closely related to the surrounding environment. The data sources of the four technologies considered do not all originate from a specific area but are obtained from different sludge treatment plants all over China. Therefore, the comparison of these data may display some deviations.\u003c/p\u003e \u003cp\u003eIf accurate and comparable data can be collected, then the sustainability of water footprints can be assessed. This process is the next stage of water footprint accounting, and it depends on two criteria: geographical context and the characteristics of the process itself. The assessment is characterized by the capability to reduce or avoid water footprints at an acceptable cost. Sludge management processes inevitably produce gray water. Nonetheless, no global benchmark has been set regarding the \u0026ldquo;reasonable\u0026rdquo; maximum footprint of gray water for each technology. Therefore, the sustainability of a sludge management process is difficult to determine. The approach developed in this study can quantify the water footprints of different processes and provide a reference for deciding on this \u0026ldquo;reasonable\u0026rdquo; maximum footprint.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003ch2\u003eEthical Approval\u003c/h2\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003ch2\u003eFunding\u003c/h2\u003e\n \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eYi Zhang; Shufang Jin wrote the main manuscript text and Cheng Li; Yijue Wu prepared figures and tables. Cao Yang; Long Chen offer the funding. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Any custom code, software, or materials developed for this study are available from the corresponding author upon reasonable request, unless restricted by intellectual property agreements or ethical considerations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDisclaimer/Publisher\u0026rsquo;s Note:\u003c/strong\u003e The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\u003c/p\u003e"},{"header":"References","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003col class=\"decimal_type\"\u003e\n \u003cli\u003eBoulay, A. M., Hoekstra, A. Y., Vionnet, S., 2013.Complementarities of Water-Focused Life Cycle Assessment and Water Footprint Assessment. Environ. Sci. Technol. 47, 11926-11927.\u003c/li\u003e\n \u003cli\u003eBrisolara, K. F., Qi, Y. N., 2013. Biosolids and Sludge Management. Water Environ. Res. 85, 1283-1297.\u003c/li\u003e\n \u003cli\u003e2023 Annual Report on China\u0026apos;s Environmental Statistics (ARCES),\u0026nbsp;2023. https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202412/t20241231_1099687.shtml.\u003c/li\u003e\n \u003cli\u003eChooyok, P., Pumijumnog N. and Ussawarujikulchai A., 2013. The Water Footprint Assessment of Ethanol Production from Molasses in Kanchanaburi and Supanburi Province of Thailand. APCBEE Procedia4th International Conference on Environmental Science and Development- ICESD, 5, 283-287.\u003c/li\u003e\n \u003cli\u003eFernandez, Y. B., Soares, A., Villa, R., Vale, P., Cartmell, E., 2014. Carbon capture and biogas enhancement by carbon dioxide enrichment of anaerobic digesters treating sewage sludge or food waste. Bioresource Technology. 159, 1-7.\u003c/li\u003e\n \u003cli\u003eHess, T., 2010. Estimating Green Water Footprints in a Temperate Environment. Water. 2, 351-362.\u003c/li\u003e\n \u003cli\u003eHoekstra, A. Y., 2002.Virtual water trade, Proceedings of the International Expert Meeting on Virtual Water Trade, Delft, Netherlands, Dec 12-13, 2002; Value of Water Research Report Series No.12, UNESCO-INE.\u003c/li\u003e\n \u003cli\u003eHoekstra, A. Y., Ashok, K. C., Maite, M. A., Mesfin, M. M., 2011.The water footprint assessment manual: setting the global standard.TJ International Ltd, Padstow, Cornwall. UK.\u003c/li\u003e\n \u003cli\u003eHuang, C.H., Zhang, Y., Luo, J.H., 2014. Rapid and High-Efficient Composting Process of Municipal Sewage Surplus Sludge. Applied Mechanics and Materials. 464, 184-188.\u003c/li\u003e\n \u003cli\u003eISO 14040,1998. Environmental management-Life cycle assessment-Principles and framework: International Organization for Standardization, Geneva, Switzerland.\u003c/li\u003e\n \u003cli\u003eJain, P., Jang, Y. C., Tolaymat, T., Witwer, M., Townsend, T., 2005. Recycling of water treatment plant sludge via land application: Assessment of risk. J. Residuals Sci. Tech. 2,13-23.\u003c/li\u003e\n \u003cli\u003eKelessidis, A., Stasinakis, A. S., 2012. Comparative study of the methods used for treatment and final disposal of sewage sludge in European countries. Waste Manage. 32,1186-1195.\u003c/li\u003e\n \u003cli\u003eLiu, X.L., Wang, H.T., Chen, J., He, Q., Zhang, H., Jiang, R., Chen, X.X.,Hou, P., 2010. Method and basic model for development of Chinese reference life cycle database. \u003cem\u003eActa Sci. Cir.\u003c/em\u003e 30,2136-2144.\u003c/li\u003e\n \u003cli\u003eMekonnen, M. M., Hoekstra, A. Y., 2011.The green, blue and gray water footprint of crops and derived crop products. Hydrol. Earth Sys.t Sc. 15, 1577-1600.\u003c/li\u003e\n \u003cli\u003eMinistry of Housing and Urban-Rural Development Organization (MHURDO), 2011. Notification of the urban sewage treatment facilities construction and operation in first quarter of 2013 in China.\u0026nbsp;Ministry of Housing and Urban-Rural Development of the People\u0026rsquo;s Republic of China, China.\u003c/li\u003e\n \u003cli\u003eShao, L., Chen, G. Q., 2013.Water Footprint Assessment for Wastewater Treatment: Method, Indicator, and Application. Environ. Sci. Technol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e47, 7787-7794.\u003c/li\u003e\n \u003cli\u003eSwiss center for Life Cycle Inventories. Ecoinvent database [EB/OL]. 2010. Swiss: Swiss center for Life Cycle Inventorieshttp://ecoinvent.ch.\u003c/li\u003e\n \u003cli\u003eWang, J. W., Zhang, T. Z. and Chen, J. N., 2010b.Operating costs for reducing total effluent loads of key pollutants in municipal wastewater treatment plants in China. Environ. Sci. Technol., 62, 995-1002.\u003c/li\u003e\n \u003cli\u003eWang, W., Luo, Y. X., Qiao, W., 2010a. Possible solutions for sludge dewatering in China. Frontiers Of Environ. Sci. \u0026amp; Eng. In China . 4, 102-107.\u003c/li\u003e\n \u003cli\u003eWang, X., Liu, J., Ren, N., Yu, H., Lee, D., Guo, X., 2012.Assessment of Multiple Sustainability Demands for Wastewater Treatment Alternatives: A Refined Evaluation Scheme and Case Study. Environ. Sci. Technol.\u0026nbsp;46, 5542-5549.\u003c/li\u003e\n \u003cli\u003eXie, X.Q., Huang, Z.Y., Dai, L. H., Xie, X. M., Liu, M.L., 2010. Study on Deep Dewatering and Recycling of Municipal Sludge in Xia men. China Water Wastewater. 26, 138-140. (In Chinese).\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"water footprint, wastewater treatment, sludge management, input–output life-cycle inventory","lastPublishedDoi":"10.21203/rs.3.rs-8953814/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8953814/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater footprints have been widely used as a measure for efficient water management. This study innovatively applies water footprint assessment, completed by life cycle assessment(LCI), to sludge treatment technologies. An approach based on input\u0026ndash;output life-cycle inventory analysis was applied to estimate water footprints, fresh water and gray water. Nine functional units and four typically sludge management scenarios in China were analyzed and compared. The fourscenarios are lime stabilization, composting, anaerobic digestion, and incineration. Results showed that gray water footprint is affected not only by technology but also by effluent standards and the background concentrations of pollutants in the receiving water. Among the four sludge management processes, lime stabilization required the largest amount of gray water, anaerobic digestion required the lowest. What\u0026rsquo;s more, anaerobic digestion is the best method for managing fresh water-saving because of its highly efficient energy recovery. If it is possible to further reduce the economic investment and operational energy consumption, anaerobic digestion may become the mainstream trend. The result also hightlingt the critical pollutants for gray water accounting differ in terms of effluent standards, and it significantly affects the amount of graywater footprint. Sludge management processes inevitably leading gray water footprint. This study can be used to calculate the grey water footprint of different sludge disposal processes, which can provide a reference for managers to select the process, with the specific requirements for water environmental protection, select the appropriate process and try to minimize \u0026ldquo;reasonable\u0026rdquo; gray water footprint.\u003c/p\u003e","manuscriptTitle":"Accounting of the Water Footprint in Sewage Sludge Management Processes Using a Hybrid Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 18:18:25","doi":"10.21203/rs.3.rs-8953814/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cfcfd1fa-ec49-48ce-8097-8f4c8df6ca05","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T18:18:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 18:18:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8953814","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8953814","identity":"rs-8953814","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}