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Zhou J, Pang Y, Wang H, Li W, Liu J, Luo Z, Shao W, Zhang H. Sewage network operational risks based on InfoWorks ICM with nodal flow diurnal patterns under NPIs for COVID-19. WATER RESEARCH 2023; 246:120708. [PMID: 37827041 DOI: 10.1016/j.watres.2023.120708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 09/18/2023] [Accepted: 10/06/2023] [Indexed: 10/14/2023]
Abstract
Non-Pharmaceutical Interventions (NPIs) have been widely employed globally over the past three years to control the rapid spread of coronavirus disease 2019 (COVID-19). These measures have imposed restrictions on urban residents' activities and significantly influenced sewage discharge characteristics within sewage network, particularly in densely populated cities in China. This study focused on the nodal flow diurnal patterns and sewage network operational risks before and after epidemic lockdown in Beijing from March to May in 2022. Nodal flow diurnal patterns on weekdays and weekends before and after NPIs were analyzed using measured data through statistical and mathematical methods. A sewage network model was established to simulate and analyze the operational risks based on InfoWorks ICM before and after epidemic lockdown. The main conclusions were as follows: (1) In predominantly residential areas, the total wastewater volume increased by approximately 28.76 % to 33.52 % after the implementation of strict NPIs. The morning and midday "M" peaks on normalized weekdays transformed into "N" peaks, and the morning peak time was delayed by 0.5 to 1 hour after the lockdown; (2) Following NPIs, More than 90 % of manholes' average water levels rose to varying degrees, approximately 50 % of pipe lengths exhibited a full flow state; (3) When the lockdown was in place during a hot summer day, sewage overflow phenomena were observed in 4.6 % and 9.6 % of manholes, respectively, with per capita daily drainage equivalent reaching 40-50 %. These findings hold significant implications for the proactive planning and operational management of water industry infrastructure during major emergencies.
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Affiliation(s)
- Jinjun Zhou
- Faculty of architecture, civil and transportation engineering, Beijing University of Technology, Beijing 100124, China
| | - Yali Pang
- Faculty of architecture, civil and transportation engineering, Beijing University of Technology, Beijing 100124, China
| | - Hao Wang
- Faculty of architecture, civil and transportation engineering, Beijing University of Technology, Beijing 100124, China.
| | - Wentao Li
- Faculty of architecture, civil and transportation engineering, Beijing University of Technology, Beijing 100124, China
| | - Jiahong Liu
- China Institute of Water Resources and Hydropower Research State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
| | - Zhuoran Luo
- School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China
| | - Weiwei Shao
- China Institute of Water Resources and Hydropower Research State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
| | - Haijia Zhang
- Faculty of architecture, civil and transportation engineering, Beijing University of Technology, Beijing 100124, China
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Steenbeek R, Timmers PHA, van der Linde D, Hup K, Hornstra L, Been F. Monitoring the exposure and emissions of antibiotic resistance: Co-occurrence of antibiotics and resistance genes in wastewater treatment plants. JOURNAL OF WATER AND HEALTH 2022; 20:1157-1170. [PMID: 36044186 DOI: 10.2166/wh.2022.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic has brought new momentum to 'wastewater-based epidemiology' (WBE). This approach can be applied to monitor the levels of antibiotic-resistant genes (ARGs), which in terms are used to make inferences about the burden of antimicrobial resistance (AMR) in human settlements. However, there is still little information about temporal variability in ARG levels measured in wastewater streams and how these influence the inferences made about the occurrence of AMR in communities. The goal of this study was hence to gain insights into the variability in ARG levels measured in the influent and effluent of two wastewater treatment plants in The Netherlands and link these to levels of antibiotic residues measured in the same samples. Eleven antibiotics were detected, together with all selected ARGs, except for VanB. Among the measured antibiotics, significant positive correlations (p > 0.70) with the corresponding resistance genes and some non-corresponding ARGs were found. Mass loads varied up to a factor of 35 between days and in concomitance with rainfall. Adequate sampling schemes need to be designed to ensure that conclusions are drawn from valid and representative data. Additionally, we advocate for the use of mass loads to interpret levels of AMR measured in wastewater.
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Jia Y, Zheng F, Maier HR, Ostfeld A, Creaco E, Savic D, Langeveld J, Kapelan Z. Water quality modeling in sewer networks: Review and future research directions. WATER RESEARCH 2021; 202:117419. [PMID: 34274902 DOI: 10.1016/j.watres.2021.117419] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/20/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.
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Affiliation(s)
- Yueyi Jia
- College of Civil Engineering and Architecture, Zhejiang University, China.
| | - Feifei Zheng
- College of Civil Engineering and Architecture, Anzhong Building, Zijingang Campus, Zhejiang University, Zhejiang University, A501, , 866 Yuhangtang Rd, Hangzhou 310058, China.
| | - Holger R Maier
- School of Civil, Environmental and Mining Engineering, The University of Adelaide, Australia.
| | - Avi Ostfeld
- Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel.
| | - Enrico Creaco
- Dipartimento di Ingegneria Civile e Architettura, University of Pavia, Via Ferrata 3 Pavia 27100, Italy; School of Civil, Environmental and Mining Engineering, The University of Adelaide, Australia.
| | - Dragan Savic
- KWR Water Research Institute, the Netherlands; Centre for Water Systems, University of Exeter, United Kingdom; Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia.
| | - Jeroen Langeveld
- Faculty of Civil Engineering and Geosciences, Delft University of Technology, the Netherlands.
| | - Zoran Kapelan
- Faculty of Civil Engineering and Geosciences, Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands; Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, United Kingdom.
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Abstract
The EU Directive 2018/2001 recognized wastewater as a renewable heat source. Wastewater from domestic, industrial and commercial developments maintains considerable amounts of thermal energy after discharging into the sewer system. It is possible to recover this heat by using technologies like heat exchangers and heat pumps; and to reuse it to satisfy heating demands. This paper presents a review of the literature on wastewater heat recovery (WWHR) and its potential at different scales within the sewer system, including the component level, building level, sewer pipe network level, and wastewater treatment plant (WWTP) level. A systematic review is provided of the benefits and challenges of WWHR across each of these levels taking into consideration technical, economic and environmental aspects. This study analyzes important attributes of WWHR such as temperature and flow dynamics of the sewer system, impacts of WWHR on the environment, and legal regulations involved. Existing gaps in the WWHR field are also identified. It is concluded that WWHR has a significant potential to supply clean energy at a scale ranging from buildings to large communities and districts. Further attention to WWHR is needed from the research community, policymakers and other stakeholders to realize the full potential of this valuable renewable heat source.
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Kusumawardhana A, Zlatanovic L, Bosch A, van der Hoek JP. Microbiological Health Risk Assessment of Water Conservation Strategies: A Case Study in Amsterdam. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:2595. [PMID: 33807661 PMCID: PMC7967349 DOI: 10.3390/ijerph18052595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 02/24/2021] [Accepted: 03/03/2021] [Indexed: 11/24/2022]
Abstract
The aim of this study was to assess the health risks that may arise from the implementation of greywater reuse and rainwater harvesting for household use, especially for toilet flushing. In addition, the risk of cross connections between these systems and the drinking water system was considered. Quantitative microbial risk assessment (QMRA) is a method that uses mathematical modelling to estimate the risk of infection when exposure to pathogens happens and was used in this study to assess the health risks. The results showed that using rainwater without prior treatment for toilet flushing poses an annual infection risk from L. pneumophila at 0.64 per-person-per-year (pppy) which exceeds the Dutch standard of 10-4 pppy. The use of untreated greywater showed a risk that is below the standard. However, treatment is recommended due to the ability of P. aeruginosa to grow in the reuse system. Moreover, showering and drinking with cross-connected water has a high annual infection risk that exceeds the standard due to contact with Staphylococcus aureus and E. coli O157:H7. Several measures can be implemented to mitigate the risks such as treating the greywater and rainwater with a minimum of 5-log removal, closing the toilet lid while flushing, good design of greywater and rainwater collection systems, and rigorous plumbing installation procedures.
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Affiliation(s)
- Agung Kusumawardhana
- Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5, 2600 AA Delft, The Netherlands; (A.K.); (L.Z.)
| | - Ljiljana Zlatanovic
- Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5, 2600 AA Delft, The Netherlands; (A.K.); (L.Z.)
- Amsterdam Institute for Advanced Metropolitan Solutions, Kattenburgerstraat 5, 1018 JA Amsterdam, The Netherlands
- Water Supply Company Noord-Holland PWN, Rijksweg 501, 1991 AS Velserbroek, The Netherlands
| | - Arne Bosch
- Waternet, P.O. Box 94370, 1090 GJ Amsterdam, The Netherlands;
| | - Jan Peter van der Hoek
- Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5, 2600 AA Delft, The Netherlands; (A.K.); (L.Z.)
- Amsterdam Institute for Advanced Metropolitan Solutions, Kattenburgerstraat 5, 1018 JA Amsterdam, The Netherlands
- Waternet, P.O. Box 94370, 1090 GJ Amsterdam, The Netherlands;
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Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand. WATER 2020. [DOI: 10.3390/w12102692] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.
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