Z. Kapelan
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
15 records found
1
Water supply networks represent key infrastructures to provide safe, reliable, drinking water with adequate pressure to communities, thus ensuring people’s health and well-being. These networks can be operated continuously or intermittently. Continuous water supply (CWS) is characterised by delivering permanently pressurised piped water to consumers with adequate pressure, meeting water quality standards and preventing potential contaminant intrusion. Intermittent water supply (IWS) also provides piped water, though only ensures delivery during limited periods of the day or the week, with interruptions from hours to days. This service is common in areas with limited water resources and with financial constraints. Despite the technological and management advancements in the water sector, most utilities with IWS have limited knowledge of the network performance due to unavailable or unreliable data or the lack of numerical models to better understand the systems’ operation. The development of numerical models to describe the phenomena in each IWS stage (filling, supplying and emptying) is important for design, diagnosis and management purposes. Most developed research focuses on the supply stage, using models with the assumption that the pipes are continuously pressurised. Since that is not the case, a model that allows simulating free-surface and pressurised flows is necessary to describe the other two IWS stages.
The thesis aims to develop and validate a new 1D model, based on the widely used SWMM solver, capable of describing the air-water interaction during pipe-filling events in IWS systems. The specific objectives of this research are: i) to identify, understand and characterise the most relevant air pocket related phenomena during pipe-filling events in single pipes and looped networks; ii) to learn how to incorporate the air pressurisation in SWMM solver as well as iii) the different mechanisms associated with the air pocket creation; iv) to understand the model's uncertainties related to these phenomena; and v) to test the developed model in a real-life network.
To accomplish objective i) and to contribute to objectives ii) - iv), an extensive experimental data collection program is developed to understand the phenomena related to the air pocket creation during the pipe-filling event. Collected data include time series of pressure and flow rate and video recordings of entrapped air pockets, for different pipe configurations and aeration conditions. Three pipe configurations are tested: a straight horizontal pipe, a single pipe with a high point and a single-loop pipe network. Three aeration conditions end are tested: no air release, restricted and unrestricted air release.
Several novel numerical developments are gradually implemented to fulfil key objectives ii) - iv). The first is the modification introduced in the existing SWMM hydraulic solver to incorporate the air phase. A conventional air accumulator model is implemented and coupled with SWMM flow calculations. Experimental data collected during the rapid filling of a single horizontal pipe for the three referred aeration conditions are used for model calibration and validation (fulfilling objective ii). Results show that the improved SWMM, AirSWMM(v1.0), describes better the effect of air behaviour during pipe-filling events than the original SWMM when using the EXTRAN surcharge method.
The AirSWMM(v1.0) model is improved to locate and quantify entrapped air pockets created during the pipe-filling events in single undulating pipe systems. Measurements are collected and video recordings are carried out to assess air pocket volumes for the three referred air release conditions. The stochastic nature of air pocket creation results in a range of air volumes predicted for the same aeration conditions. The new version of the model developed, AirSWMM(v2.0), is capable of simulating the air pocket creation, transport and entrainment (air and water mixing process). The stochastic nature of air pocket formation can be numerically simulated by conducting multiple runs of the new solver with different air entrainment ratios. The obtained numerical results show that AirSWMM(v2.0) can accurately locate and approximately quantify the entrapped air pocket volumes. These developments contribute to objective iii).
The AirSWMM(v2.0) model is further tested and validated using experimental data from a single-loop network laboratory setup. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapment for two high point locations and different nodal elevations, under three aeration conditions, are used. Experimental tests show that air entrapment occurs not only at the high point but along the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipe systems. AirSWWM(v2.0) model results for the looped pipe network demonstrate that this model can correctly locate large air pockets with a tendency to underestimate their volumes. These developments contribute to objective iv).
The AirSWWM(v2.0) model is also tested using a case study of a real-life network published in the literature to assess the accuracy of predicted locations and volumes of the air pockets created during a pipe-filling event. For this purpose, pressure-driven analysis is implemented to better simulate the nodal demands, leading to AirSWMM(v2.1), since this feature was not originally included in SWMM. Results show that pressure-heads predicted by AirSWMM(v2.1) compare well with field data when constant spatial discretisation is used, provided the Courant number is close to 0.15. The recommendations from international guidelines for the location of air release devices (from the American Water Works Association and Deltares) are compared to the predicted air pocket locations. The locations of the estimated air pockets agree with those from the international guidelines for air valve installation. However, these locations only represent part of the air valves needed, those that are necessary for releasing entrapped air during the pipe-filling events, not accounting for other air valves important for pipe failure or conservative design purposes. These developments contribute to objective v).
Further research on AirSWMM should focus on assessing the spatial discretisation that corresponds to the best compromise between accuracy and computational effort to describe the air pocket dynamics in real-life networks. Additional numerical analyses should assess if the developed methodologies can be incorporated into the Preissmann slot pressurisation scheme. More experimental tests are needed to better quantify the air entrainment in piped flows and to analyse the effect of two-phase flows on leakage rate. Further field tests, collecting high-frequency pressure head data, should be carried out during pipe-filling events to validate the developed models. ...
The thesis aims to develop and validate a new 1D model, based on the widely used SWMM solver, capable of describing the air-water interaction during pipe-filling events in IWS systems. The specific objectives of this research are: i) to identify, understand and characterise the most relevant air pocket related phenomena during pipe-filling events in single pipes and looped networks; ii) to learn how to incorporate the air pressurisation in SWMM solver as well as iii) the different mechanisms associated with the air pocket creation; iv) to understand the model's uncertainties related to these phenomena; and v) to test the developed model in a real-life network.
To accomplish objective i) and to contribute to objectives ii) - iv), an extensive experimental data collection program is developed to understand the phenomena related to the air pocket creation during the pipe-filling event. Collected data include time series of pressure and flow rate and video recordings of entrapped air pockets, for different pipe configurations and aeration conditions. Three pipe configurations are tested: a straight horizontal pipe, a single pipe with a high point and a single-loop pipe network. Three aeration conditions end are tested: no air release, restricted and unrestricted air release.
Several novel numerical developments are gradually implemented to fulfil key objectives ii) - iv). The first is the modification introduced in the existing SWMM hydraulic solver to incorporate the air phase. A conventional air accumulator model is implemented and coupled with SWMM flow calculations. Experimental data collected during the rapid filling of a single horizontal pipe for the three referred aeration conditions are used for model calibration and validation (fulfilling objective ii). Results show that the improved SWMM, AirSWMM(v1.0), describes better the effect of air behaviour during pipe-filling events than the original SWMM when using the EXTRAN surcharge method.
The AirSWMM(v1.0) model is improved to locate and quantify entrapped air pockets created during the pipe-filling events in single undulating pipe systems. Measurements are collected and video recordings are carried out to assess air pocket volumes for the three referred air release conditions. The stochastic nature of air pocket creation results in a range of air volumes predicted for the same aeration conditions. The new version of the model developed, AirSWMM(v2.0), is capable of simulating the air pocket creation, transport and entrainment (air and water mixing process). The stochastic nature of air pocket formation can be numerically simulated by conducting multiple runs of the new solver with different air entrainment ratios. The obtained numerical results show that AirSWMM(v2.0) can accurately locate and approximately quantify the entrapped air pocket volumes. These developments contribute to objective iii).
The AirSWMM(v2.0) model is further tested and validated using experimental data from a single-loop network laboratory setup. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapment for two high point locations and different nodal elevations, under three aeration conditions, are used. Experimental tests show that air entrapment occurs not only at the high point but along the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipe systems. AirSWWM(v2.0) model results for the looped pipe network demonstrate that this model can correctly locate large air pockets with a tendency to underestimate their volumes. These developments contribute to objective iv).
The AirSWWM(v2.0) model is also tested using a case study of a real-life network published in the literature to assess the accuracy of predicted locations and volumes of the air pockets created during a pipe-filling event. For this purpose, pressure-driven analysis is implemented to better simulate the nodal demands, leading to AirSWMM(v2.1), since this feature was not originally included in SWMM. Results show that pressure-heads predicted by AirSWMM(v2.1) compare well with field data when constant spatial discretisation is used, provided the Courant number is close to 0.15. The recommendations from international guidelines for the location of air release devices (from the American Water Works Association and Deltares) are compared to the predicted air pocket locations. The locations of the estimated air pockets agree with those from the international guidelines for air valve installation. However, these locations only represent part of the air valves needed, those that are necessary for releasing entrapped air during the pipe-filling events, not accounting for other air valves important for pipe failure or conservative design purposes. These developments contribute to objective v).
Further research on AirSWMM should focus on assessing the spatial discretisation that corresponds to the best compromise between accuracy and computational effort to describe the air pocket dynamics in real-life networks. Additional numerical analyses should assess if the developed methodologies can be incorporated into the Preissmann slot pressurisation scheme. More experimental tests are needed to better quantify the air entrainment in piped flows and to analyse the effect of two-phase flows on leakage rate. Further field tests, collecting high-frequency pressure head data, should be carried out during pipe-filling events to validate the developed models. ...
Water supply networks represent key infrastructures to provide safe, reliable, drinking water with adequate pressure to communities, thus ensuring people’s health and well-being. These networks can be operated continuously or intermittently. Continuous water supply (CWS) is characterised by delivering permanently pressurised piped water to consumers with adequate pressure, meeting water quality standards and preventing potential contaminant intrusion. Intermittent water supply (IWS) also provides piped water, though only ensures delivery during limited periods of the day or the week, with interruptions from hours to days. This service is common in areas with limited water resources and with financial constraints. Despite the technological and management advancements in the water sector, most utilities with IWS have limited knowledge of the network performance due to unavailable or unreliable data or the lack of numerical models to better understand the systems’ operation. The development of numerical models to describe the phenomena in each IWS stage (filling, supplying and emptying) is important for design, diagnosis and management purposes. Most developed research focuses on the supply stage, using models with the assumption that the pipes are continuously pressurised. Since that is not the case, a model that allows simulating free-surface and pressurised flows is necessary to describe the other two IWS stages.
The thesis aims to develop and validate a new 1D model, based on the widely used SWMM solver, capable of describing the air-water interaction during pipe-filling events in IWS systems. The specific objectives of this research are: i) to identify, understand and characterise the most relevant air pocket related phenomena during pipe-filling events in single pipes and looped networks; ii) to learn how to incorporate the air pressurisation in SWMM solver as well as iii) the different mechanisms associated with the air pocket creation; iv) to understand the model's uncertainties related to these phenomena; and v) to test the developed model in a real-life network.
To accomplish objective i) and to contribute to objectives ii) - iv), an extensive experimental data collection program is developed to understand the phenomena related to the air pocket creation during the pipe-filling event. Collected data include time series of pressure and flow rate and video recordings of entrapped air pockets, for different pipe configurations and aeration conditions. Three pipe configurations are tested: a straight horizontal pipe, a single pipe with a high point and a single-loop pipe network. Three aeration conditions end are tested: no air release, restricted and unrestricted air release.
Several novel numerical developments are gradually implemented to fulfil key objectives ii) - iv). The first is the modification introduced in the existing SWMM hydraulic solver to incorporate the air phase. A conventional air accumulator model is implemented and coupled with SWMM flow calculations. Experimental data collected during the rapid filling of a single horizontal pipe for the three referred aeration conditions are used for model calibration and validation (fulfilling objective ii). Results show that the improved SWMM, AirSWMM(v1.0), describes better the effect of air behaviour during pipe-filling events than the original SWMM when using the EXTRAN surcharge method.
The AirSWMM(v1.0) model is improved to locate and quantify entrapped air pockets created during the pipe-filling events in single undulating pipe systems. Measurements are collected and video recordings are carried out to assess air pocket volumes for the three referred air release conditions. The stochastic nature of air pocket creation results in a range of air volumes predicted for the same aeration conditions. The new version of the model developed, AirSWMM(v2.0), is capable of simulating the air pocket creation, transport and entrainment (air and water mixing process). The stochastic nature of air pocket formation can be numerically simulated by conducting multiple runs of the new solver with different air entrainment ratios. The obtained numerical results show that AirSWMM(v2.0) can accurately locate and approximately quantify the entrapped air pocket volumes. These developments contribute to objective iii).
The AirSWMM(v2.0) model is further tested and validated using experimental data from a single-loop network laboratory setup. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapment for two high point locations and different nodal elevations, under three aeration conditions, are used. Experimental tests show that air entrapment occurs not only at the high point but along the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipe systems. AirSWWM(v2.0) model results for the looped pipe network demonstrate that this model can correctly locate large air pockets with a tendency to underestimate their volumes. These developments contribute to objective iv).
The AirSWWM(v2.0) model is also tested using a case study of a real-life network published in the literature to assess the accuracy of predicted locations and volumes of the air pockets created during a pipe-filling event. For this purpose, pressure-driven analysis is implemented to better simulate the nodal demands, leading to AirSWMM(v2.1), since this feature was not originally included in SWMM. Results show that pressure-heads predicted by AirSWMM(v2.1) compare well with field data when constant spatial discretisation is used, provided the Courant number is close to 0.15. The recommendations from international guidelines for the location of air release devices (from the American Water Works Association and Deltares) are compared to the predicted air pocket locations. The locations of the estimated air pockets agree with those from the international guidelines for air valve installation. However, these locations only represent part of the air valves needed, those that are necessary for releasing entrapped air during the pipe-filling events, not accounting for other air valves important for pipe failure or conservative design purposes. These developments contribute to objective v).
Further research on AirSWMM should focus on assessing the spatial discretisation that corresponds to the best compromise between accuracy and computational effort to describe the air pocket dynamics in real-life networks. Additional numerical analyses should assess if the developed methodologies can be incorporated into the Preissmann slot pressurisation scheme. More experimental tests are needed to better quantify the air entrainment in piped flows and to analyse the effect of two-phase flows on leakage rate. Further field tests, collecting high-frequency pressure head data, should be carried out during pipe-filling events to validate the developed models.
The thesis aims to develop and validate a new 1D model, based on the widely used SWMM solver, capable of describing the air-water interaction during pipe-filling events in IWS systems. The specific objectives of this research are: i) to identify, understand and characterise the most relevant air pocket related phenomena during pipe-filling events in single pipes and looped networks; ii) to learn how to incorporate the air pressurisation in SWMM solver as well as iii) the different mechanisms associated with the air pocket creation; iv) to understand the model's uncertainties related to these phenomena; and v) to test the developed model in a real-life network.
To accomplish objective i) and to contribute to objectives ii) - iv), an extensive experimental data collection program is developed to understand the phenomena related to the air pocket creation during the pipe-filling event. Collected data include time series of pressure and flow rate and video recordings of entrapped air pockets, for different pipe configurations and aeration conditions. Three pipe configurations are tested: a straight horizontal pipe, a single pipe with a high point and a single-loop pipe network. Three aeration conditions end are tested: no air release, restricted and unrestricted air release.
Several novel numerical developments are gradually implemented to fulfil key objectives ii) - iv). The first is the modification introduced in the existing SWMM hydraulic solver to incorporate the air phase. A conventional air accumulator model is implemented and coupled with SWMM flow calculations. Experimental data collected during the rapid filling of a single horizontal pipe for the three referred aeration conditions are used for model calibration and validation (fulfilling objective ii). Results show that the improved SWMM, AirSWMM(v1.0), describes better the effect of air behaviour during pipe-filling events than the original SWMM when using the EXTRAN surcharge method.
The AirSWMM(v1.0) model is improved to locate and quantify entrapped air pockets created during the pipe-filling events in single undulating pipe systems. Measurements are collected and video recordings are carried out to assess air pocket volumes for the three referred air release conditions. The stochastic nature of air pocket creation results in a range of air volumes predicted for the same aeration conditions. The new version of the model developed, AirSWMM(v2.0), is capable of simulating the air pocket creation, transport and entrainment (air and water mixing process). The stochastic nature of air pocket formation can be numerically simulated by conducting multiple runs of the new solver with different air entrainment ratios. The obtained numerical results show that AirSWMM(v2.0) can accurately locate and approximately quantify the entrapped air pocket volumes. These developments contribute to objective iii).
The AirSWMM(v2.0) model is further tested and validated using experimental data from a single-loop network laboratory setup. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapment for two high point locations and different nodal elevations, under three aeration conditions, are used. Experimental tests show that air entrapment occurs not only at the high point but along the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipe systems. AirSWWM(v2.0) model results for the looped pipe network demonstrate that this model can correctly locate large air pockets with a tendency to underestimate their volumes. These developments contribute to objective iv).
The AirSWWM(v2.0) model is also tested using a case study of a real-life network published in the literature to assess the accuracy of predicted locations and volumes of the air pockets created during a pipe-filling event. For this purpose, pressure-driven analysis is implemented to better simulate the nodal demands, leading to AirSWMM(v2.1), since this feature was not originally included in SWMM. Results show that pressure-heads predicted by AirSWMM(v2.1) compare well with field data when constant spatial discretisation is used, provided the Courant number is close to 0.15. The recommendations from international guidelines for the location of air release devices (from the American Water Works Association and Deltares) are compared to the predicted air pocket locations. The locations of the estimated air pockets agree with those from the international guidelines for air valve installation. However, these locations only represent part of the air valves needed, those that are necessary for releasing entrapped air during the pipe-filling events, not accounting for other air valves important for pipe failure or conservative design purposes. These developments contribute to objective v).
Further research on AirSWMM should focus on assessing the spatial discretisation that corresponds to the best compromise between accuracy and computational effort to describe the air pocket dynamics in real-life networks. Additional numerical analyses should assess if the developed methodologies can be incorporated into the Preissmann slot pressurisation scheme. More experimental tests are needed to better quantify the air entrainment in piped flows and to analyse the effect of two-phase flows on leakage rate. Further field tests, collecting high-frequency pressure head data, should be carried out during pipe-filling events to validate the developed models.
The provision of safe drinking water is essential in every society since it determines people’s health and well-being. Drinking Water Distribution Networks (DWDN) are vital for this purpose but are susceptible to pathogen contamination and outbreaks due to cascading events after infrastructure failures, main repairs, human errors, or malicious attacks. When a contamination event occurs in the DWDN, the preservation of health of the public should be the top priority in every emergency response mechanism. Exposure to contaminated water can cause significant health risks by introducing pathogens such as enterovirus, Campylobacter, and Cryptosporidium. For this reason, DWDNs are nowadays considered critical infrastructures, recognized by USA's Presidential Policy Directive 21 and the European Union's Directive (EU) 2022/2557.
During contamination events in the DWDN, water utilities need to act quickly, make informed decisions, assess the threat, and effectively mitigate the event. The central objective of the study of this thesis was to generate knowledge to help address the growing challenge of waterborne pathogen contamination in DWDNs and develop applications that can enhance decision-making and immediate actions in such emergencies. Tools and methodologies were developed and evaluated focusing on two main pillars. The first pillar involves understanding the event based on historical knowledge. Innovative approaches were developed and assessed for Artificial Intelligence-based information extraction and question-answering using scientific publications, enabling rapid access to up-to-date pathogen characteristics, historical information on contamination events, and control actions. The second pillar focuses on predicting and managing the specific contamination event in real-time. Advanced modeling tools were created to simulate contamination events in DWDNs, providing realistic representations of hydraulics and water quality dynamics, predicted health impacts, and support for real-time decision-making during emergencies.
Chapter 2 describes the development of an Artificial Intelligence (AI)-based model that extracts specific pathogen information from the scientific literature. By leveraging Natural Language Processing (NLP) and Deep Learning (DL) techniques, the study evaluated the feasibility and performance of an Information Extraction model to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella. For the development of the model, a combination of supervised and rule-based techniques was adopted. The evaluation metrics showed a satisfactory performance for extraction of both qualitative and quantitative information with an overall F-score of 85% and 95% for the supervised and rule-based technique respectively. The model was also compared with a human extraction, returning similar results and indicating that the extracted information is of high quality. The results showed that the model can be used to rapidly extract critical information from text documents and be a useful tool for water utilities, enabling faster and more informed decision-making during the early stages of contamination.
Chapter 3 systematically assesses the performance of various open-source Large Language Models (LLM), including Llama 2, Mistral, and Gemma (and their variations) in a question-answering task related to pathogen contamination events of drinking water. The evaluation metrics included Precision, Recall, F1 score, Automated Accuracy, and Empty Score. The model with the highest performance on a set of 23 questions using 188 scientific publications was then manually evaluated by a human (Human Evaluation). The results showed that all models performed reasonably well with an average F1 score ranging from 81% to 87%. After considering all the evaluation metrics, the Llama 2 model was the most reliable model with an average Automated Accuracy of 86%. However, the hallucination effect of Llama 2 was evident. The Gemma model had a lower Automated Accuracy score but was less prone to hallucination. The Human Evaluation showed that the Llama 2 model delivered correct answers when the questions were clear and straightforward. However, when the question required further interpretation, the model often struggled. Overall, the study demonstrated that the use of LLMs in automated information extraction tasks show great potential for time-critical applications, such as processing large volumes of (historical) data in real-time thereby making it feasible to make historical information available in near rea-time in case of emergencies.
Building on the response to a pathogen contamination event in the DWDN, Chapter 4 presents the BeWaRE benchmark testbed, a comprehensive model. This testbed went beyond the state-of-the art and integrated all current relevant knowledge on pathogen transport and fate, bulk and wall chlorine decay, fast and slow chlorine reactions with TOC, TOC degradation, stochastic water demands, hydraulic uncertainty, and individual consumption patterns to calculate pathogen exposure and infection risk following the steps of Quantitative Microbial Risk Assessment (QMRA). A large wastewater contamination in different locations in a chlorinated and non-chlorinated network was simulated using three pathogens: Campylobacter, enterovirus, and Cryptosporidium. The results of this study showed that in non-chlorinated DWDNs, the modeled wastewater contamination event led to 11-46% infection risk in the total population, depending on the contamination location, but irrespective of the selected pathogen (due to the high pathogen concentration). On the other hand, in chlorinated DWDNs, the same scenarios resulted in lower infection risk for the pathogens that are susceptible to chlorine; 0.78-2.1 % for Campylobacter and 7.8-26.6 % for enterovirus. Moreover, the enterovirus infection risk was higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. While chlorination aids mitigation, large contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. Finally, the varying levels of pathogen susceptibility to chlorine, the contamination location and duration, influenced the infection risk, while the response window to reduce the health impact was short; in these scenarios 5-10 hours post-contamination. The study provided a novel approach to assessing health risks, offering critical insights for water utilities to optimize their response during emergencies.
Chapter 5 further explores the added value of using modeling tools to support decision-making during emergencies in the DWDN. This was demonstrated through PathoINVEST, an analytical tool that utilizes the BeWaRE benchmark methodology, which was presented in the previous Chapter, to support water utilities in modeling contamination events in the DWDN. A case study was conducted with the aim of comparing a traditional approach (representing the status quo of current practices of water utilities) with a model-based approach (use of real-time modeling tools) during an emergency response to a contamination event in the DWDN. The model-based approach was shown to be more efficient than the traditional approach in identifying the source of contamination (1.3 versus 3.7 hours), requiring fewer samples (4 versus 11) and resulting in lower infection risk by the time the source was identified (12% versus 20%) in this case study. Moreover, the model-based approach was more effective in finding the best valves to close in the network (as mitigation measures) since it resulted in a 3%-point infection risk reduction. However, some actions taken in the traditional approach, such as the rapid closure of valves (cutting the network in half and thus limiting further spreading) before the contamination source was identified, were critical in mitigating the contamination. Another key finding was the importance of having an up-to-date overview of valve settings in the DWDN schematization to provide reliable results on source identification since any discrepancies between the actual network and the model can lead to inaccurate infection risk estimates when using modeling tools to support decision-making. Overall, this case study showed that integrating modeling tools in the current practices of water utilities provides a robust framework for improving water contamination management and decision-making processes, thus safeguarding public health during emergencies.
A concluding viewpoint is offered in Chapter 6, which considers whether the initial research questions from Chapter 1 were successfully answered. The implications of this research for water utilities are examined, providing information on how the proposed methodologies can be (and have been) used in real-world scenarios, facilitating a faster decision-making and contributing to effective mitigation of emergencies. Finally, the perspectives and future research are discussed, emphasizing the role of AI and the advancements in modeling tools. AI has shown significant potential in enhancing situational awareness and rapid information extraction during emergencies. Water utilities should explore the integration of AI into their standard operating procedures to further enhance emergency responses and routine management. Regarding the use of modeling tools during emergencies, future research should address key gaps, such as the complex dynamics when wastewater interacts with chlorine, the competition between chlorine-reducing agents, and the validity of hydraulic modeling assumptions such as perfect mixing. Accounting for cumulative health risks (multiple pathogens) and refining dose-response models to differentiate between infection and illness probabilities can provide insights for effectively managing risks to vulnerable populations. Moreover, the incorporation of metrics like Disability-Adjusted Life Years (DALYs) into modeling efforts could enable better communication of health impacts and evaluation of mitigation strategies. Finally, Digital Twins and real-time microbial sensors are identified as transformative technologies that can provide real-time insights into network dynamics. These advancements can shift water utility management from reactive approaches to proactive, data-driven strategies, significantly enhancing public health protection, operational efficiency, and resilience.
...
During contamination events in the DWDN, water utilities need to act quickly, make informed decisions, assess the threat, and effectively mitigate the event. The central objective of the study of this thesis was to generate knowledge to help address the growing challenge of waterborne pathogen contamination in DWDNs and develop applications that can enhance decision-making and immediate actions in such emergencies. Tools and methodologies were developed and evaluated focusing on two main pillars. The first pillar involves understanding the event based on historical knowledge. Innovative approaches were developed and assessed for Artificial Intelligence-based information extraction and question-answering using scientific publications, enabling rapid access to up-to-date pathogen characteristics, historical information on contamination events, and control actions. The second pillar focuses on predicting and managing the specific contamination event in real-time. Advanced modeling tools were created to simulate contamination events in DWDNs, providing realistic representations of hydraulics and water quality dynamics, predicted health impacts, and support for real-time decision-making during emergencies.
Chapter 2 describes the development of an Artificial Intelligence (AI)-based model that extracts specific pathogen information from the scientific literature. By leveraging Natural Language Processing (NLP) and Deep Learning (DL) techniques, the study evaluated the feasibility and performance of an Information Extraction model to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella. For the development of the model, a combination of supervised and rule-based techniques was adopted. The evaluation metrics showed a satisfactory performance for extraction of both qualitative and quantitative information with an overall F-score of 85% and 95% for the supervised and rule-based technique respectively. The model was also compared with a human extraction, returning similar results and indicating that the extracted information is of high quality. The results showed that the model can be used to rapidly extract critical information from text documents and be a useful tool for water utilities, enabling faster and more informed decision-making during the early stages of contamination.
Chapter 3 systematically assesses the performance of various open-source Large Language Models (LLM), including Llama 2, Mistral, and Gemma (and their variations) in a question-answering task related to pathogen contamination events of drinking water. The evaluation metrics included Precision, Recall, F1 score, Automated Accuracy, and Empty Score. The model with the highest performance on a set of 23 questions using 188 scientific publications was then manually evaluated by a human (Human Evaluation). The results showed that all models performed reasonably well with an average F1 score ranging from 81% to 87%. After considering all the evaluation metrics, the Llama 2 model was the most reliable model with an average Automated Accuracy of 86%. However, the hallucination effect of Llama 2 was evident. The Gemma model had a lower Automated Accuracy score but was less prone to hallucination. The Human Evaluation showed that the Llama 2 model delivered correct answers when the questions were clear and straightforward. However, when the question required further interpretation, the model often struggled. Overall, the study demonstrated that the use of LLMs in automated information extraction tasks show great potential for time-critical applications, such as processing large volumes of (historical) data in real-time thereby making it feasible to make historical information available in near rea-time in case of emergencies.
Building on the response to a pathogen contamination event in the DWDN, Chapter 4 presents the BeWaRE benchmark testbed, a comprehensive model. This testbed went beyond the state-of-the art and integrated all current relevant knowledge on pathogen transport and fate, bulk and wall chlorine decay, fast and slow chlorine reactions with TOC, TOC degradation, stochastic water demands, hydraulic uncertainty, and individual consumption patterns to calculate pathogen exposure and infection risk following the steps of Quantitative Microbial Risk Assessment (QMRA). A large wastewater contamination in different locations in a chlorinated and non-chlorinated network was simulated using three pathogens: Campylobacter, enterovirus, and Cryptosporidium. The results of this study showed that in non-chlorinated DWDNs, the modeled wastewater contamination event led to 11-46% infection risk in the total population, depending on the contamination location, but irrespective of the selected pathogen (due to the high pathogen concentration). On the other hand, in chlorinated DWDNs, the same scenarios resulted in lower infection risk for the pathogens that are susceptible to chlorine; 0.78-2.1 % for Campylobacter and 7.8-26.6 % for enterovirus. Moreover, the enterovirus infection risk was higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. While chlorination aids mitigation, large contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. Finally, the varying levels of pathogen susceptibility to chlorine, the contamination location and duration, influenced the infection risk, while the response window to reduce the health impact was short; in these scenarios 5-10 hours post-contamination. The study provided a novel approach to assessing health risks, offering critical insights for water utilities to optimize their response during emergencies.
Chapter 5 further explores the added value of using modeling tools to support decision-making during emergencies in the DWDN. This was demonstrated through PathoINVEST, an analytical tool that utilizes the BeWaRE benchmark methodology, which was presented in the previous Chapter, to support water utilities in modeling contamination events in the DWDN. A case study was conducted with the aim of comparing a traditional approach (representing the status quo of current practices of water utilities) with a model-based approach (use of real-time modeling tools) during an emergency response to a contamination event in the DWDN. The model-based approach was shown to be more efficient than the traditional approach in identifying the source of contamination (1.3 versus 3.7 hours), requiring fewer samples (4 versus 11) and resulting in lower infection risk by the time the source was identified (12% versus 20%) in this case study. Moreover, the model-based approach was more effective in finding the best valves to close in the network (as mitigation measures) since it resulted in a 3%-point infection risk reduction. However, some actions taken in the traditional approach, such as the rapid closure of valves (cutting the network in half and thus limiting further spreading) before the contamination source was identified, were critical in mitigating the contamination. Another key finding was the importance of having an up-to-date overview of valve settings in the DWDN schematization to provide reliable results on source identification since any discrepancies between the actual network and the model can lead to inaccurate infection risk estimates when using modeling tools to support decision-making. Overall, this case study showed that integrating modeling tools in the current practices of water utilities provides a robust framework for improving water contamination management and decision-making processes, thus safeguarding public health during emergencies.
A concluding viewpoint is offered in Chapter 6, which considers whether the initial research questions from Chapter 1 were successfully answered. The implications of this research for water utilities are examined, providing information on how the proposed methodologies can be (and have been) used in real-world scenarios, facilitating a faster decision-making and contributing to effective mitigation of emergencies. Finally, the perspectives and future research are discussed, emphasizing the role of AI and the advancements in modeling tools. AI has shown significant potential in enhancing situational awareness and rapid information extraction during emergencies. Water utilities should explore the integration of AI into their standard operating procedures to further enhance emergency responses and routine management. Regarding the use of modeling tools during emergencies, future research should address key gaps, such as the complex dynamics when wastewater interacts with chlorine, the competition between chlorine-reducing agents, and the validity of hydraulic modeling assumptions such as perfect mixing. Accounting for cumulative health risks (multiple pathogens) and refining dose-response models to differentiate between infection and illness probabilities can provide insights for effectively managing risks to vulnerable populations. Moreover, the incorporation of metrics like Disability-Adjusted Life Years (DALYs) into modeling efforts could enable better communication of health impacts and evaluation of mitigation strategies. Finally, Digital Twins and real-time microbial sensors are identified as transformative technologies that can provide real-time insights into network dynamics. These advancements can shift water utility management from reactive approaches to proactive, data-driven strategies, significantly enhancing public health protection, operational efficiency, and resilience.
...
The provision of safe drinking water is essential in every society since it determines people’s health and well-being. Drinking Water Distribution Networks (DWDN) are vital for this purpose but are susceptible to pathogen contamination and outbreaks due to cascading events after infrastructure failures, main repairs, human errors, or malicious attacks. When a contamination event occurs in the DWDN, the preservation of health of the public should be the top priority in every emergency response mechanism. Exposure to contaminated water can cause significant health risks by introducing pathogens such as enterovirus, Campylobacter, and Cryptosporidium. For this reason, DWDNs are nowadays considered critical infrastructures, recognized by USA's Presidential Policy Directive 21 and the European Union's Directive (EU) 2022/2557.
During contamination events in the DWDN, water utilities need to act quickly, make informed decisions, assess the threat, and effectively mitigate the event. The central objective of the study of this thesis was to generate knowledge to help address the growing challenge of waterborne pathogen contamination in DWDNs and develop applications that can enhance decision-making and immediate actions in such emergencies. Tools and methodologies were developed and evaluated focusing on two main pillars. The first pillar involves understanding the event based on historical knowledge. Innovative approaches were developed and assessed for Artificial Intelligence-based information extraction and question-answering using scientific publications, enabling rapid access to up-to-date pathogen characteristics, historical information on contamination events, and control actions. The second pillar focuses on predicting and managing the specific contamination event in real-time. Advanced modeling tools were created to simulate contamination events in DWDNs, providing realistic representations of hydraulics and water quality dynamics, predicted health impacts, and support for real-time decision-making during emergencies.
Chapter 2 describes the development of an Artificial Intelligence (AI)-based model that extracts specific pathogen information from the scientific literature. By leveraging Natural Language Processing (NLP) and Deep Learning (DL) techniques, the study evaluated the feasibility and performance of an Information Extraction model to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella. For the development of the model, a combination of supervised and rule-based techniques was adopted. The evaluation metrics showed a satisfactory performance for extraction of both qualitative and quantitative information with an overall F-score of 85% and 95% for the supervised and rule-based technique respectively. The model was also compared with a human extraction, returning similar results and indicating that the extracted information is of high quality. The results showed that the model can be used to rapidly extract critical information from text documents and be a useful tool for water utilities, enabling faster and more informed decision-making during the early stages of contamination.
Chapter 3 systematically assesses the performance of various open-source Large Language Models (LLM), including Llama 2, Mistral, and Gemma (and their variations) in a question-answering task related to pathogen contamination events of drinking water. The evaluation metrics included Precision, Recall, F1 score, Automated Accuracy, and Empty Score. The model with the highest performance on a set of 23 questions using 188 scientific publications was then manually evaluated by a human (Human Evaluation). The results showed that all models performed reasonably well with an average F1 score ranging from 81% to 87%. After considering all the evaluation metrics, the Llama 2 model was the most reliable model with an average Automated Accuracy of 86%. However, the hallucination effect of Llama 2 was evident. The Gemma model had a lower Automated Accuracy score but was less prone to hallucination. The Human Evaluation showed that the Llama 2 model delivered correct answers when the questions were clear and straightforward. However, when the question required further interpretation, the model often struggled. Overall, the study demonstrated that the use of LLMs in automated information extraction tasks show great potential for time-critical applications, such as processing large volumes of (historical) data in real-time thereby making it feasible to make historical information available in near rea-time in case of emergencies.
Building on the response to a pathogen contamination event in the DWDN, Chapter 4 presents the BeWaRE benchmark testbed, a comprehensive model. This testbed went beyond the state-of-the art and integrated all current relevant knowledge on pathogen transport and fate, bulk and wall chlorine decay, fast and slow chlorine reactions with TOC, TOC degradation, stochastic water demands, hydraulic uncertainty, and individual consumption patterns to calculate pathogen exposure and infection risk following the steps of Quantitative Microbial Risk Assessment (QMRA). A large wastewater contamination in different locations in a chlorinated and non-chlorinated network was simulated using three pathogens: Campylobacter, enterovirus, and Cryptosporidium. The results of this study showed that in non-chlorinated DWDNs, the modeled wastewater contamination event led to 11-46% infection risk in the total population, depending on the contamination location, but irrespective of the selected pathogen (due to the high pathogen concentration). On the other hand, in chlorinated DWDNs, the same scenarios resulted in lower infection risk for the pathogens that are susceptible to chlorine; 0.78-2.1 % for Campylobacter and 7.8-26.6 % for enterovirus. Moreover, the enterovirus infection risk was higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. While chlorination aids mitigation, large contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. Finally, the varying levels of pathogen susceptibility to chlorine, the contamination location and duration, influenced the infection risk, while the response window to reduce the health impact was short; in these scenarios 5-10 hours post-contamination. The study provided a novel approach to assessing health risks, offering critical insights for water utilities to optimize their response during emergencies.
Chapter 5 further explores the added value of using modeling tools to support decision-making during emergencies in the DWDN. This was demonstrated through PathoINVEST, an analytical tool that utilizes the BeWaRE benchmark methodology, which was presented in the previous Chapter, to support water utilities in modeling contamination events in the DWDN. A case study was conducted with the aim of comparing a traditional approach (representing the status quo of current practices of water utilities) with a model-based approach (use of real-time modeling tools) during an emergency response to a contamination event in the DWDN. The model-based approach was shown to be more efficient than the traditional approach in identifying the source of contamination (1.3 versus 3.7 hours), requiring fewer samples (4 versus 11) and resulting in lower infection risk by the time the source was identified (12% versus 20%) in this case study. Moreover, the model-based approach was more effective in finding the best valves to close in the network (as mitigation measures) since it resulted in a 3%-point infection risk reduction. However, some actions taken in the traditional approach, such as the rapid closure of valves (cutting the network in half and thus limiting further spreading) before the contamination source was identified, were critical in mitigating the contamination. Another key finding was the importance of having an up-to-date overview of valve settings in the DWDN schematization to provide reliable results on source identification since any discrepancies between the actual network and the model can lead to inaccurate infection risk estimates when using modeling tools to support decision-making. Overall, this case study showed that integrating modeling tools in the current practices of water utilities provides a robust framework for improving water contamination management and decision-making processes, thus safeguarding public health during emergencies.
A concluding viewpoint is offered in Chapter 6, which considers whether the initial research questions from Chapter 1 were successfully answered. The implications of this research for water utilities are examined, providing information on how the proposed methodologies can be (and have been) used in real-world scenarios, facilitating a faster decision-making and contributing to effective mitigation of emergencies. Finally, the perspectives and future research are discussed, emphasizing the role of AI and the advancements in modeling tools. AI has shown significant potential in enhancing situational awareness and rapid information extraction during emergencies. Water utilities should explore the integration of AI into their standard operating procedures to further enhance emergency responses and routine management. Regarding the use of modeling tools during emergencies, future research should address key gaps, such as the complex dynamics when wastewater interacts with chlorine, the competition between chlorine-reducing agents, and the validity of hydraulic modeling assumptions such as perfect mixing. Accounting for cumulative health risks (multiple pathogens) and refining dose-response models to differentiate between infection and illness probabilities can provide insights for effectively managing risks to vulnerable populations. Moreover, the incorporation of metrics like Disability-Adjusted Life Years (DALYs) into modeling efforts could enable better communication of health impacts and evaluation of mitigation strategies. Finally, Digital Twins and real-time microbial sensors are identified as transformative technologies that can provide real-time insights into network dynamics. These advancements can shift water utility management from reactive approaches to proactive, data-driven strategies, significantly enhancing public health protection, operational efficiency, and resilience.
During contamination events in the DWDN, water utilities need to act quickly, make informed decisions, assess the threat, and effectively mitigate the event. The central objective of the study of this thesis was to generate knowledge to help address the growing challenge of waterborne pathogen contamination in DWDNs and develop applications that can enhance decision-making and immediate actions in such emergencies. Tools and methodologies were developed and evaluated focusing on two main pillars. The first pillar involves understanding the event based on historical knowledge. Innovative approaches were developed and assessed for Artificial Intelligence-based information extraction and question-answering using scientific publications, enabling rapid access to up-to-date pathogen characteristics, historical information on contamination events, and control actions. The second pillar focuses on predicting and managing the specific contamination event in real-time. Advanced modeling tools were created to simulate contamination events in DWDNs, providing realistic representations of hydraulics and water quality dynamics, predicted health impacts, and support for real-time decision-making during emergencies.
Chapter 2 describes the development of an Artificial Intelligence (AI)-based model that extracts specific pathogen information from the scientific literature. By leveraging Natural Language Processing (NLP) and Deep Learning (DL) techniques, the study evaluated the feasibility and performance of an Information Extraction model to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella. For the development of the model, a combination of supervised and rule-based techniques was adopted. The evaluation metrics showed a satisfactory performance for extraction of both qualitative and quantitative information with an overall F-score of 85% and 95% for the supervised and rule-based technique respectively. The model was also compared with a human extraction, returning similar results and indicating that the extracted information is of high quality. The results showed that the model can be used to rapidly extract critical information from text documents and be a useful tool for water utilities, enabling faster and more informed decision-making during the early stages of contamination.
Chapter 3 systematically assesses the performance of various open-source Large Language Models (LLM), including Llama 2, Mistral, and Gemma (and their variations) in a question-answering task related to pathogen contamination events of drinking water. The evaluation metrics included Precision, Recall, F1 score, Automated Accuracy, and Empty Score. The model with the highest performance on a set of 23 questions using 188 scientific publications was then manually evaluated by a human (Human Evaluation). The results showed that all models performed reasonably well with an average F1 score ranging from 81% to 87%. After considering all the evaluation metrics, the Llama 2 model was the most reliable model with an average Automated Accuracy of 86%. However, the hallucination effect of Llama 2 was evident. The Gemma model had a lower Automated Accuracy score but was less prone to hallucination. The Human Evaluation showed that the Llama 2 model delivered correct answers when the questions were clear and straightforward. However, when the question required further interpretation, the model often struggled. Overall, the study demonstrated that the use of LLMs in automated information extraction tasks show great potential for time-critical applications, such as processing large volumes of (historical) data in real-time thereby making it feasible to make historical information available in near rea-time in case of emergencies.
Building on the response to a pathogen contamination event in the DWDN, Chapter 4 presents the BeWaRE benchmark testbed, a comprehensive model. This testbed went beyond the state-of-the art and integrated all current relevant knowledge on pathogen transport and fate, bulk and wall chlorine decay, fast and slow chlorine reactions with TOC, TOC degradation, stochastic water demands, hydraulic uncertainty, and individual consumption patterns to calculate pathogen exposure and infection risk following the steps of Quantitative Microbial Risk Assessment (QMRA). A large wastewater contamination in different locations in a chlorinated and non-chlorinated network was simulated using three pathogens: Campylobacter, enterovirus, and Cryptosporidium. The results of this study showed that in non-chlorinated DWDNs, the modeled wastewater contamination event led to 11-46% infection risk in the total population, depending on the contamination location, but irrespective of the selected pathogen (due to the high pathogen concentration). On the other hand, in chlorinated DWDNs, the same scenarios resulted in lower infection risk for the pathogens that are susceptible to chlorine; 0.78-2.1 % for Campylobacter and 7.8-26.6 % for enterovirus. Moreover, the enterovirus infection risk was higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. While chlorination aids mitigation, large contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. Finally, the varying levels of pathogen susceptibility to chlorine, the contamination location and duration, influenced the infection risk, while the response window to reduce the health impact was short; in these scenarios 5-10 hours post-contamination. The study provided a novel approach to assessing health risks, offering critical insights for water utilities to optimize their response during emergencies.
Chapter 5 further explores the added value of using modeling tools to support decision-making during emergencies in the DWDN. This was demonstrated through PathoINVEST, an analytical tool that utilizes the BeWaRE benchmark methodology, which was presented in the previous Chapter, to support water utilities in modeling contamination events in the DWDN. A case study was conducted with the aim of comparing a traditional approach (representing the status quo of current practices of water utilities) with a model-based approach (use of real-time modeling tools) during an emergency response to a contamination event in the DWDN. The model-based approach was shown to be more efficient than the traditional approach in identifying the source of contamination (1.3 versus 3.7 hours), requiring fewer samples (4 versus 11) and resulting in lower infection risk by the time the source was identified (12% versus 20%) in this case study. Moreover, the model-based approach was more effective in finding the best valves to close in the network (as mitigation measures) since it resulted in a 3%-point infection risk reduction. However, some actions taken in the traditional approach, such as the rapid closure of valves (cutting the network in half and thus limiting further spreading) before the contamination source was identified, were critical in mitigating the contamination. Another key finding was the importance of having an up-to-date overview of valve settings in the DWDN schematization to provide reliable results on source identification since any discrepancies between the actual network and the model can lead to inaccurate infection risk estimates when using modeling tools to support decision-making. Overall, this case study showed that integrating modeling tools in the current practices of water utilities provides a robust framework for improving water contamination management and decision-making processes, thus safeguarding public health during emergencies.
A concluding viewpoint is offered in Chapter 6, which considers whether the initial research questions from Chapter 1 were successfully answered. The implications of this research for water utilities are examined, providing information on how the proposed methodologies can be (and have been) used in real-world scenarios, facilitating a faster decision-making and contributing to effective mitigation of emergencies. Finally, the perspectives and future research are discussed, emphasizing the role of AI and the advancements in modeling tools. AI has shown significant potential in enhancing situational awareness and rapid information extraction during emergencies. Water utilities should explore the integration of AI into their standard operating procedures to further enhance emergency responses and routine management. Regarding the use of modeling tools during emergencies, future research should address key gaps, such as the complex dynamics when wastewater interacts with chlorine, the competition between chlorine-reducing agents, and the validity of hydraulic modeling assumptions such as perfect mixing. Accounting for cumulative health risks (multiple pathogens) and refining dose-response models to differentiate between infection and illness probabilities can provide insights for effectively managing risks to vulnerable populations. Moreover, the incorporation of metrics like Disability-Adjusted Life Years (DALYs) into modeling efforts could enable better communication of health impacts and evaluation of mitigation strategies. Finally, Digital Twins and real-time microbial sensors are identified as transformative technologies that can provide real-time insights into network dynamics. These advancements can shift water utility management from reactive approaches to proactive, data-driven strategies, significantly enhancing public health protection, operational efficiency, and resilience.
The necessity for sustainable industrial processes and solutions has been intensified by climate change, which has led to an increased focus on enhancing resource efficiency and reducing greenhouse gas emissions. In accordance with the principles of the circular economy, the implementation of improved water-smart solutions and enhanced water management processes, including the reuse and recycling of wastewater and the recovery of resources such as water, energy and nutrients, represents a pivotal strategy for addressing challenges such as climate change and water pollution.
This dissertation examines novel bio-composite materials derived from resources recovered from the water sector. These materials incorporate natural fibres derived from untreated wastewater (i.e., cellulose fibres) or surface water management (i.e., reed and grass fibres), as well as fillers such as calcite derived from drinking water softening processes or agricultural waste (i.e., coconut shells, olive powder, and food residue). Bio-based resins, such as polyester with a reduced styrene content or furan resin, containing furfuryl alcohol, serve as binders.
The presence of a wide range of pollutants has a significant impact on water resources as a result of human activities. It is therefore imperative that comprehensive testing is conducted to ensure that the utilisation of recovered resources does not result in any adverse effects on human health or the environment. It is crucial to emphasise that, because of their derivation from recycled raw materials, the utilisation of the novel bio-composite materials should not be assumed to be intrinsically risk-free. It is therefore imperative that a comprehensive risk assessment of the environmental and human health risks associated with the production and application of the new bio-composite materials is conducted.
The overall aim of this research project is to develop an approach for the evaluation of potential risks to human health and the environment that may result from the production and application of the new bio-composite materials. In line with this, four research questions have been formulated to conduct this study:
- What are the main risks and related hazards associated with the production of new resource recovery-based bio-composite materials and their applications and how are these interlinked?What existing methods can be potentially used (and with what modifications) and which new ones need to be developed to assess these risks?
- What is the best approach to define and quantify the human health risks involved in the production of bio-composite materials?
- What is the environmental risk associated with the use of the new bio-composite materials in the aquatic environment? More specifically, what is the risk in case of canal bank protection elements made from these new materials?
- What is the environmental risk associated with the use of new bio-composite materials based building façade elements and how does the weathering of these elements affect this risk?
Above research questions have been addressed and answered in Chapters 2 – 5 of this dissertation. Below, a summary of the work done in order to address the formulated research questions is provided.
A comprehensive literature review, detailed in Chapter 2, was conducted at the outset of this work to identify the principal hazards and associated risks involved in drinking water and wastewater treatment plants, water reuse, and water-based resources recovery. The literature study identified potential microbial and chemical contaminants of the raw materials used to produce the new water-based resource recovery bio-composite materials. These contaminants may pose a risk to human health and the environment. Nevertheless, it was found that no risk assessment methodologies have yet been used to assess the potential human health and environmental risks associated with the production and application of the new bio-composite materials.
The novel human health risk assessment framework, which is described in Chapter 3, employed a qualitative risk analysis as the initial step, followed by a quantitative risk analysis. The Hazard and Operability (HAZOP) method identified the principal hazards during the production, and the qualitative Event Tree Analysis (ETA) methodology created a corresponding risk map. The results of the qualitative risk assessment indicated that the main risks of new bio-composite materials are caused by chemical and microbial contamination, which can have a negative impact on human health and the environment. A quantitative human health risk assessment was conducted on four alternative new bio-composite materials, employing both Quantitative Chemical Risk Assessment (QCRA) and Quantitative Microbial Risk Assessment (QMRA) methodologies, with deterministic and stochastic approaches. The results of the chemical risk assessment indicated that the cancer risk from styrene and furfuryl alcohol exceeded the established safety threshold. Similarly, the microbial risk assessment identified significant concerns with E. coli in cellulose fibres, with the risk exceeding safety limit. The assessments were conducted under the most unfavourable circumstances, without the use of personal protective equipment (PPE) or safety protocols. Furthermore, the assumption of maximum exposure to contaminants was made due to the limited availability of input data, which resulted in an overestimation of the overall risk.
The presence of chemical contamination in raw materials used for the production of new bio-composite materials gave rise to concerns not only for human health but also for potential negative environmental impact. In order to assess the environmental risks involved, two applications of these new materials were considered in this study: (a) canal bank protection elements, which prevents soil from collapsing into the water and (b) façade building elements as decorations panels.
To assess the environmental risks of chemical release, from the bio-composite materials used as canal bank protection, laboratory column leaching tests were conducted. This preliminary step provided data for an approximate environmental risk assessment in real-world conditions. The environmental risk assessment framework, developed in accordance with European guidelines, as detailed in Chapter 4, showed that the concentration of chemicals leached into surface water was within safety threshold. However, styrene and furfuryl alcohol contained in the resins may still pose a concern to environmental risk. It is crucial to acknowledge that the interpretation of these results should be approached with caution, given the absence of on-site data and the numerous assumptions made, including instantaneous mixing of the leaching chemicals and the absence of Brownian motion. Furthermore, the background concentrations in freshwater and the fate and degradation of chemicals in surface water were not considered. Also, the leaching process was evaluated over time, with the observation of a plateau indicating a significant slowdown in the leaching process accompanied by a reduction in the driving force, thereby providing a better understanding of the leaching behaviour.
Bio-composite materials utilised as façade construction elements are more susceptible to adverse weather conditions than those used for canal bank protection. Chapter 5 presents an analysis of potential leaching from bio-composites on a real-world building of a pumping station in the Netherlands. Two bio-composite alternatives were tested, and two samples per material were used: one new sample (as the initial application) and one UV-treated sample (as the long-term application after weathering) per material, for a total of four samples. The samples were subjected to leaching tests simulating two rainfall events of a duration of one hour. The risk assessment demonstrated that no leached chemicals exceeded the safety threshold, with no detection of styrene or furfuryl alcohol in the leaching effluent samples. However, these findings should be interpreted with caution due to the limited input data and the assumptions made, including the lack of on-site data and the focus on a single rain event rather than analysing leaching over a longer time period. The weathering treatments affected the materials in different ways based on their resin composition. Material M3 (made of polyester resin) exhibited aesthetic changes, while Material M4 (made of furan resin) demonstrated increased roughness, reduced water resistance and fibre detachment. Microscopic examination revealed significant wrinkling in M4, indicating that environmental exposure significantly affects these materials.
Overall, it can be concluded (Chapter 6) that both microbial and chemical risks are inherent in the production and applications of new bio-composite materials considered in this thesis. These risks originate from the utilization of specific raw materials, including calcite from drinking water, cellulose derived from wastewater, reed and grass sourced from surface water management conducted by water boards, as well as the resins and additives employed in new materials. The framework developed in this research, which includes laboratory testing, modelling and risk assessment methods, has been validated as applicable to the case studies used in this work. Being generic in nature, the framework also shows potential for human health and environmental risk assessments associated with different future applications of new bio-composite materials. ...
This dissertation examines novel bio-composite materials derived from resources recovered from the water sector. These materials incorporate natural fibres derived from untreated wastewater (i.e., cellulose fibres) or surface water management (i.e., reed and grass fibres), as well as fillers such as calcite derived from drinking water softening processes or agricultural waste (i.e., coconut shells, olive powder, and food residue). Bio-based resins, such as polyester with a reduced styrene content or furan resin, containing furfuryl alcohol, serve as binders.
The presence of a wide range of pollutants has a significant impact on water resources as a result of human activities. It is therefore imperative that comprehensive testing is conducted to ensure that the utilisation of recovered resources does not result in any adverse effects on human health or the environment. It is crucial to emphasise that, because of their derivation from recycled raw materials, the utilisation of the novel bio-composite materials should not be assumed to be intrinsically risk-free. It is therefore imperative that a comprehensive risk assessment of the environmental and human health risks associated with the production and application of the new bio-composite materials is conducted.
The overall aim of this research project is to develop an approach for the evaluation of potential risks to human health and the environment that may result from the production and application of the new bio-composite materials. In line with this, four research questions have been formulated to conduct this study:
- What are the main risks and related hazards associated with the production of new resource recovery-based bio-composite materials and their applications and how are these interlinked?What existing methods can be potentially used (and with what modifications) and which new ones need to be developed to assess these risks?
- What is the best approach to define and quantify the human health risks involved in the production of bio-composite materials?
- What is the environmental risk associated with the use of the new bio-composite materials in the aquatic environment? More specifically, what is the risk in case of canal bank protection elements made from these new materials?
- What is the environmental risk associated with the use of new bio-composite materials based building façade elements and how does the weathering of these elements affect this risk?
Above research questions have been addressed and answered in Chapters 2 – 5 of this dissertation. Below, a summary of the work done in order to address the formulated research questions is provided.
A comprehensive literature review, detailed in Chapter 2, was conducted at the outset of this work to identify the principal hazards and associated risks involved in drinking water and wastewater treatment plants, water reuse, and water-based resources recovery. The literature study identified potential microbial and chemical contaminants of the raw materials used to produce the new water-based resource recovery bio-composite materials. These contaminants may pose a risk to human health and the environment. Nevertheless, it was found that no risk assessment methodologies have yet been used to assess the potential human health and environmental risks associated with the production and application of the new bio-composite materials.
The novel human health risk assessment framework, which is described in Chapter 3, employed a qualitative risk analysis as the initial step, followed by a quantitative risk analysis. The Hazard and Operability (HAZOP) method identified the principal hazards during the production, and the qualitative Event Tree Analysis (ETA) methodology created a corresponding risk map. The results of the qualitative risk assessment indicated that the main risks of new bio-composite materials are caused by chemical and microbial contamination, which can have a negative impact on human health and the environment. A quantitative human health risk assessment was conducted on four alternative new bio-composite materials, employing both Quantitative Chemical Risk Assessment (QCRA) and Quantitative Microbial Risk Assessment (QMRA) methodologies, with deterministic and stochastic approaches. The results of the chemical risk assessment indicated that the cancer risk from styrene and furfuryl alcohol exceeded the established safety threshold. Similarly, the microbial risk assessment identified significant concerns with E. coli in cellulose fibres, with the risk exceeding safety limit. The assessments were conducted under the most unfavourable circumstances, without the use of personal protective equipment (PPE) or safety protocols. Furthermore, the assumption of maximum exposure to contaminants was made due to the limited availability of input data, which resulted in an overestimation of the overall risk.
The presence of chemical contamination in raw materials used for the production of new bio-composite materials gave rise to concerns not only for human health but also for potential negative environmental impact. In order to assess the environmental risks involved, two applications of these new materials were considered in this study: (a) canal bank protection elements, which prevents soil from collapsing into the water and (b) façade building elements as decorations panels.
To assess the environmental risks of chemical release, from the bio-composite materials used as canal bank protection, laboratory column leaching tests were conducted. This preliminary step provided data for an approximate environmental risk assessment in real-world conditions. The environmental risk assessment framework, developed in accordance with European guidelines, as detailed in Chapter 4, showed that the concentration of chemicals leached into surface water was within safety threshold. However, styrene and furfuryl alcohol contained in the resins may still pose a concern to environmental risk. It is crucial to acknowledge that the interpretation of these results should be approached with caution, given the absence of on-site data and the numerous assumptions made, including instantaneous mixing of the leaching chemicals and the absence of Brownian motion. Furthermore, the background concentrations in freshwater and the fate and degradation of chemicals in surface water were not considered. Also, the leaching process was evaluated over time, with the observation of a plateau indicating a significant slowdown in the leaching process accompanied by a reduction in the driving force, thereby providing a better understanding of the leaching behaviour.
Bio-composite materials utilised as façade construction elements are more susceptible to adverse weather conditions than those used for canal bank protection. Chapter 5 presents an analysis of potential leaching from bio-composites on a real-world building of a pumping station in the Netherlands. Two bio-composite alternatives were tested, and two samples per material were used: one new sample (as the initial application) and one UV-treated sample (as the long-term application after weathering) per material, for a total of four samples. The samples were subjected to leaching tests simulating two rainfall events of a duration of one hour. The risk assessment demonstrated that no leached chemicals exceeded the safety threshold, with no detection of styrene or furfuryl alcohol in the leaching effluent samples. However, these findings should be interpreted with caution due to the limited input data and the assumptions made, including the lack of on-site data and the focus on a single rain event rather than analysing leaching over a longer time period. The weathering treatments affected the materials in different ways based on their resin composition. Material M3 (made of polyester resin) exhibited aesthetic changes, while Material M4 (made of furan resin) demonstrated increased roughness, reduced water resistance and fibre detachment. Microscopic examination revealed significant wrinkling in M4, indicating that environmental exposure significantly affects these materials.
Overall, it can be concluded (Chapter 6) that both microbial and chemical risks are inherent in the production and applications of new bio-composite materials considered in this thesis. These risks originate from the utilization of specific raw materials, including calcite from drinking water, cellulose derived from wastewater, reed and grass sourced from surface water management conducted by water boards, as well as the resins and additives employed in new materials. The framework developed in this research, which includes laboratory testing, modelling and risk assessment methods, has been validated as applicable to the case studies used in this work. Being generic in nature, the framework also shows potential for human health and environmental risk assessments associated with different future applications of new bio-composite materials. ...
The necessity for sustainable industrial processes and solutions has been intensified by climate change, which has led to an increased focus on enhancing resource efficiency and reducing greenhouse gas emissions. In accordance with the principles of the circular economy, the implementation of improved water-smart solutions and enhanced water management processes, including the reuse and recycling of wastewater and the recovery of resources such as water, energy and nutrients, represents a pivotal strategy for addressing challenges such as climate change and water pollution.
This dissertation examines novel bio-composite materials derived from resources recovered from the water sector. These materials incorporate natural fibres derived from untreated wastewater (i.e., cellulose fibres) or surface water management (i.e., reed and grass fibres), as well as fillers such as calcite derived from drinking water softening processes or agricultural waste (i.e., coconut shells, olive powder, and food residue). Bio-based resins, such as polyester with a reduced styrene content or furan resin, containing furfuryl alcohol, serve as binders.
The presence of a wide range of pollutants has a significant impact on water resources as a result of human activities. It is therefore imperative that comprehensive testing is conducted to ensure that the utilisation of recovered resources does not result in any adverse effects on human health or the environment. It is crucial to emphasise that, because of their derivation from recycled raw materials, the utilisation of the novel bio-composite materials should not be assumed to be intrinsically risk-free. It is therefore imperative that a comprehensive risk assessment of the environmental and human health risks associated with the production and application of the new bio-composite materials is conducted.
The overall aim of this research project is to develop an approach for the evaluation of potential risks to human health and the environment that may result from the production and application of the new bio-composite materials. In line with this, four research questions have been formulated to conduct this study:
- What are the main risks and related hazards associated with the production of new resource recovery-based bio-composite materials and their applications and how are these interlinked?What existing methods can be potentially used (and with what modifications) and which new ones need to be developed to assess these risks?
- What is the best approach to define and quantify the human health risks involved in the production of bio-composite materials?
- What is the environmental risk associated with the use of the new bio-composite materials in the aquatic environment? More specifically, what is the risk in case of canal bank protection elements made from these new materials?
- What is the environmental risk associated with the use of new bio-composite materials based building façade elements and how does the weathering of these elements affect this risk?
Above research questions have been addressed and answered in Chapters 2 – 5 of this dissertation. Below, a summary of the work done in order to address the formulated research questions is provided.
A comprehensive literature review, detailed in Chapter 2, was conducted at the outset of this work to identify the principal hazards and associated risks involved in drinking water and wastewater treatment plants, water reuse, and water-based resources recovery. The literature study identified potential microbial and chemical contaminants of the raw materials used to produce the new water-based resource recovery bio-composite materials. These contaminants may pose a risk to human health and the environment. Nevertheless, it was found that no risk assessment methodologies have yet been used to assess the potential human health and environmental risks associated with the production and application of the new bio-composite materials.
The novel human health risk assessment framework, which is described in Chapter 3, employed a qualitative risk analysis as the initial step, followed by a quantitative risk analysis. The Hazard and Operability (HAZOP) method identified the principal hazards during the production, and the qualitative Event Tree Analysis (ETA) methodology created a corresponding risk map. The results of the qualitative risk assessment indicated that the main risks of new bio-composite materials are caused by chemical and microbial contamination, which can have a negative impact on human health and the environment. A quantitative human health risk assessment was conducted on four alternative new bio-composite materials, employing both Quantitative Chemical Risk Assessment (QCRA) and Quantitative Microbial Risk Assessment (QMRA) methodologies, with deterministic and stochastic approaches. The results of the chemical risk assessment indicated that the cancer risk from styrene and furfuryl alcohol exceeded the established safety threshold. Similarly, the microbial risk assessment identified significant concerns with E. coli in cellulose fibres, with the risk exceeding safety limit. The assessments were conducted under the most unfavourable circumstances, without the use of personal protective equipment (PPE) or safety protocols. Furthermore, the assumption of maximum exposure to contaminants was made due to the limited availability of input data, which resulted in an overestimation of the overall risk.
The presence of chemical contamination in raw materials used for the production of new bio-composite materials gave rise to concerns not only for human health but also for potential negative environmental impact. In order to assess the environmental risks involved, two applications of these new materials were considered in this study: (a) canal bank protection elements, which prevents soil from collapsing into the water and (b) façade building elements as decorations panels.
To assess the environmental risks of chemical release, from the bio-composite materials used as canal bank protection, laboratory column leaching tests were conducted. This preliminary step provided data for an approximate environmental risk assessment in real-world conditions. The environmental risk assessment framework, developed in accordance with European guidelines, as detailed in Chapter 4, showed that the concentration of chemicals leached into surface water was within safety threshold. However, styrene and furfuryl alcohol contained in the resins may still pose a concern to environmental risk. It is crucial to acknowledge that the interpretation of these results should be approached with caution, given the absence of on-site data and the numerous assumptions made, including instantaneous mixing of the leaching chemicals and the absence of Brownian motion. Furthermore, the background concentrations in freshwater and the fate and degradation of chemicals in surface water were not considered. Also, the leaching process was evaluated over time, with the observation of a plateau indicating a significant slowdown in the leaching process accompanied by a reduction in the driving force, thereby providing a better understanding of the leaching behaviour.
Bio-composite materials utilised as façade construction elements are more susceptible to adverse weather conditions than those used for canal bank protection. Chapter 5 presents an analysis of potential leaching from bio-composites on a real-world building of a pumping station in the Netherlands. Two bio-composite alternatives were tested, and two samples per material were used: one new sample (as the initial application) and one UV-treated sample (as the long-term application after weathering) per material, for a total of four samples. The samples were subjected to leaching tests simulating two rainfall events of a duration of one hour. The risk assessment demonstrated that no leached chemicals exceeded the safety threshold, with no detection of styrene or furfuryl alcohol in the leaching effluent samples. However, these findings should be interpreted with caution due to the limited input data and the assumptions made, including the lack of on-site data and the focus on a single rain event rather than analysing leaching over a longer time period. The weathering treatments affected the materials in different ways based on their resin composition. Material M3 (made of polyester resin) exhibited aesthetic changes, while Material M4 (made of furan resin) demonstrated increased roughness, reduced water resistance and fibre detachment. Microscopic examination revealed significant wrinkling in M4, indicating that environmental exposure significantly affects these materials.
Overall, it can be concluded (Chapter 6) that both microbial and chemical risks are inherent in the production and applications of new bio-composite materials considered in this thesis. These risks originate from the utilization of specific raw materials, including calcite from drinking water, cellulose derived from wastewater, reed and grass sourced from surface water management conducted by water boards, as well as the resins and additives employed in new materials. The framework developed in this research, which includes laboratory testing, modelling and risk assessment methods, has been validated as applicable to the case studies used in this work. Being generic in nature, the framework also shows potential for human health and environmental risk assessments associated with different future applications of new bio-composite materials.
This dissertation examines novel bio-composite materials derived from resources recovered from the water sector. These materials incorporate natural fibres derived from untreated wastewater (i.e., cellulose fibres) or surface water management (i.e., reed and grass fibres), as well as fillers such as calcite derived from drinking water softening processes or agricultural waste (i.e., coconut shells, olive powder, and food residue). Bio-based resins, such as polyester with a reduced styrene content or furan resin, containing furfuryl alcohol, serve as binders.
The presence of a wide range of pollutants has a significant impact on water resources as a result of human activities. It is therefore imperative that comprehensive testing is conducted to ensure that the utilisation of recovered resources does not result in any adverse effects on human health or the environment. It is crucial to emphasise that, because of their derivation from recycled raw materials, the utilisation of the novel bio-composite materials should not be assumed to be intrinsically risk-free. It is therefore imperative that a comprehensive risk assessment of the environmental and human health risks associated with the production and application of the new bio-composite materials is conducted.
The overall aim of this research project is to develop an approach for the evaluation of potential risks to human health and the environment that may result from the production and application of the new bio-composite materials. In line with this, four research questions have been formulated to conduct this study:
- What are the main risks and related hazards associated with the production of new resource recovery-based bio-composite materials and their applications and how are these interlinked?What existing methods can be potentially used (and with what modifications) and which new ones need to be developed to assess these risks?
- What is the best approach to define and quantify the human health risks involved in the production of bio-composite materials?
- What is the environmental risk associated with the use of the new bio-composite materials in the aquatic environment? More specifically, what is the risk in case of canal bank protection elements made from these new materials?
- What is the environmental risk associated with the use of new bio-composite materials based building façade elements and how does the weathering of these elements affect this risk?
Above research questions have been addressed and answered in Chapters 2 – 5 of this dissertation. Below, a summary of the work done in order to address the formulated research questions is provided.
A comprehensive literature review, detailed in Chapter 2, was conducted at the outset of this work to identify the principal hazards and associated risks involved in drinking water and wastewater treatment plants, water reuse, and water-based resources recovery. The literature study identified potential microbial and chemical contaminants of the raw materials used to produce the new water-based resource recovery bio-composite materials. These contaminants may pose a risk to human health and the environment. Nevertheless, it was found that no risk assessment methodologies have yet been used to assess the potential human health and environmental risks associated with the production and application of the new bio-composite materials.
The novel human health risk assessment framework, which is described in Chapter 3, employed a qualitative risk analysis as the initial step, followed by a quantitative risk analysis. The Hazard and Operability (HAZOP) method identified the principal hazards during the production, and the qualitative Event Tree Analysis (ETA) methodology created a corresponding risk map. The results of the qualitative risk assessment indicated that the main risks of new bio-composite materials are caused by chemical and microbial contamination, which can have a negative impact on human health and the environment. A quantitative human health risk assessment was conducted on four alternative new bio-composite materials, employing both Quantitative Chemical Risk Assessment (QCRA) and Quantitative Microbial Risk Assessment (QMRA) methodologies, with deterministic and stochastic approaches. The results of the chemical risk assessment indicated that the cancer risk from styrene and furfuryl alcohol exceeded the established safety threshold. Similarly, the microbial risk assessment identified significant concerns with E. coli in cellulose fibres, with the risk exceeding safety limit. The assessments were conducted under the most unfavourable circumstances, without the use of personal protective equipment (PPE) or safety protocols. Furthermore, the assumption of maximum exposure to contaminants was made due to the limited availability of input data, which resulted in an overestimation of the overall risk.
The presence of chemical contamination in raw materials used for the production of new bio-composite materials gave rise to concerns not only for human health but also for potential negative environmental impact. In order to assess the environmental risks involved, two applications of these new materials were considered in this study: (a) canal bank protection elements, which prevents soil from collapsing into the water and (b) façade building elements as decorations panels.
To assess the environmental risks of chemical release, from the bio-composite materials used as canal bank protection, laboratory column leaching tests were conducted. This preliminary step provided data for an approximate environmental risk assessment in real-world conditions. The environmental risk assessment framework, developed in accordance with European guidelines, as detailed in Chapter 4, showed that the concentration of chemicals leached into surface water was within safety threshold. However, styrene and furfuryl alcohol contained in the resins may still pose a concern to environmental risk. It is crucial to acknowledge that the interpretation of these results should be approached with caution, given the absence of on-site data and the numerous assumptions made, including instantaneous mixing of the leaching chemicals and the absence of Brownian motion. Furthermore, the background concentrations in freshwater and the fate and degradation of chemicals in surface water were not considered. Also, the leaching process was evaluated over time, with the observation of a plateau indicating a significant slowdown in the leaching process accompanied by a reduction in the driving force, thereby providing a better understanding of the leaching behaviour.
Bio-composite materials utilised as façade construction elements are more susceptible to adverse weather conditions than those used for canal bank protection. Chapter 5 presents an analysis of potential leaching from bio-composites on a real-world building of a pumping station in the Netherlands. Two bio-composite alternatives were tested, and two samples per material were used: one new sample (as the initial application) and one UV-treated sample (as the long-term application after weathering) per material, for a total of four samples. The samples were subjected to leaching tests simulating two rainfall events of a duration of one hour. The risk assessment demonstrated that no leached chemicals exceeded the safety threshold, with no detection of styrene or furfuryl alcohol in the leaching effluent samples. However, these findings should be interpreted with caution due to the limited input data and the assumptions made, including the lack of on-site data and the focus on a single rain event rather than analysing leaching over a longer time period. The weathering treatments affected the materials in different ways based on their resin composition. Material M3 (made of polyester resin) exhibited aesthetic changes, while Material M4 (made of furan resin) demonstrated increased roughness, reduced water resistance and fibre detachment. Microscopic examination revealed significant wrinkling in M4, indicating that environmental exposure significantly affects these materials.
Overall, it can be concluded (Chapter 6) that both microbial and chemical risks are inherent in the production and applications of new bio-composite materials considered in this thesis. These risks originate from the utilization of specific raw materials, including calcite from drinking water, cellulose derived from wastewater, reed and grass sourced from surface water management conducted by water boards, as well as the resins and additives employed in new materials. The framework developed in this research, which includes laboratory testing, modelling and risk assessment methods, has been validated as applicable to the case studies used in this work. Being generic in nature, the framework also shows potential for human health and environmental risk assessments associated with different future applications of new bio-composite materials.
This thesis presents a collection of novel methods for the circularity and efficiency assessment of resource recovery solutions related to the water treatment sector. The resource recovery solutions pertain to the drinking water and wastewater treatment plants and include the recovery of nutrients, cellulose, treated wastewater, energy, sewage sludge, and calcite from drinking water softeners. The thesis also contains a new water-food-energy nexus framework which is used to compare the conventional centralized approach to wastewater treatment with a decentralized source separation one.
...
This thesis presents a collection of novel methods for the circularity and efficiency assessment of resource recovery solutions related to the water treatment sector. The resource recovery solutions pertain to the drinking water and wastewater treatment plants and include the recovery of nutrients, cellulose, treated wastewater, energy, sewage sludge, and calcite from drinking water softeners. The thesis also contains a new water-food-energy nexus framework which is used to compare the conventional centralized approach to wastewater treatment with a decentralized source separation one.
This thesis investigates how serious games can be designed and used to improve decision-making for stormwater management in urban areas, which are increasingly vulnerable to intense rainfall due to high surface sealing, urbanization, and climate change. The challenges include limited public awareness of household measures to store and infiltrate stormwater, fragmented decision-making across public and private actors, and lack of collaborative planning platforms. Serious games—used to foster learning and cooperation—offer a promising but underexplored approach to address these barriers. The thesis makes four key contributions: (1) a review of existing serious games in urban water management, identifying gaps in current approaches; (2) a systems analysis methodology to identify relevant actors, elicit their perceptions, and develop a systems map that can be used as a foundation for game design; (3) the design and testing of SUDSbury, a board game to raise awareness among urban residents; and (4) the design and testing of Urban dRain, a case-specific game to support multi-actor collaboration for neighborhood-scale stormwater planning.
...
This thesis investigates how serious games can be designed and used to improve decision-making for stormwater management in urban areas, which are increasingly vulnerable to intense rainfall due to high surface sealing, urbanization, and climate change. The challenges include limited public awareness of household measures to store and infiltrate stormwater, fragmented decision-making across public and private actors, and lack of collaborative planning platforms. Serious games—used to foster learning and cooperation—offer a promising but underexplored approach to address these barriers. The thesis makes four key contributions: (1) a review of existing serious games in urban water management, identifying gaps in current approaches; (2) a systems analysis methodology to identify relevant actors, elicit their perceptions, and develop a systems map that can be used as a foundation for game design; (3) the design and testing of SUDSbury, a board game to raise awareness among urban residents; and (4) the design and testing of Urban dRain, a case-specific game to support multi-actor collaboration for neighborhood-scale stormwater planning.
Litter, particularly plastic, accumulating in water bodies is a challenging environmental issue that affects ecosystems, human health and the economy. Rivers are the main pathways of land-based plastic waste to the ocean, but they also act as potential temporary and long-termplastic sinks, where significant amounts of plastic waste accumulate, and even remain trapped for decades. The detection and quantification of floating litter in rivers and urban waterways is thus essential for evaluating pollution levels and informing mitigation actions. However, traditional monitoring methods, such as sampling with nets and booms, are not suitable for large-scale structured monitoring across multiple geographic locations in extensive river systems. Deep Learning (DL) methods have shown great promise in automatic detection and quantification of floating litter from images or videos. Given that this specific field is still in its early stages, this thesis aims to enhance the understanding of DL-based litter detection and quantification in riverine environments, identify key knowledge gaps, and explore methodologies to address these gaps and drive further advancements in this field....
...
Litter, particularly plastic, accumulating in water bodies is a challenging environmental issue that affects ecosystems, human health and the economy. Rivers are the main pathways of land-based plastic waste to the ocean, but they also act as potential temporary and long-termplastic sinks, where significant amounts of plastic waste accumulate, and even remain trapped for decades. The detection and quantification of floating litter in rivers and urban waterways is thus essential for evaluating pollution levels and informing mitigation actions. However, traditional monitoring methods, such as sampling with nets and booms, are not suitable for large-scale structured monitoring across multiple geographic locations in extensive river systems. Deep Learning (DL) methods have shown great promise in automatic detection and quantification of floating litter from images or videos. Given that this specific field is still in its early stages, this thesis aims to enhance the understanding of DL-based litter detection and quantification in riverine environments, identify key knowledge gaps, and explore methodologies to address these gaps and drive further advancements in this field....
Globally, there is an ongoing trend to improve the live-ability of cities. Furthermore, the importance of sufficient urban response systems for storm water is growing due to climate change and urbanization. Sustainable Urban Drainage Solutions (SUDSs) are seen as a key tool to tackle multiple urban challenges. This is as their design is multi-objective, focusing on water quantity, water quality, amenity, and biodiversity. However, currently knowledge is lacking amongst decision makers on how SUDS affect the urban environment. Therefore, this research aims to develop an assessment framework for SUDS-based storm water management in the urban landscape to help Dutch municipalities improve their decision making to improve the live-ability of the urban environment.
To evaluate the performance of SUDS, assessment frameworks include Key Performance Indicators (KPIs). KPIs are measurable indicators demonstrating how effective SUDS are in achieving their objectives. This research highlights that there is no universal or even country based standard assessment framework for SUDS. Furthermore, there are few examples of the translation of the scientific assessment methods of SUDS to engineering practice available. The assessment of the full effect of SUDS therefore remains unclear to practitioners.
This research proposes a new framework (”extended framework”) to assess the performance of SUDS, building on the existing framework (”conventional framework”) which assesses cost and water quantity currently used in engineering practice. The extended framework adds KPIs assessing the remaining three objectives of SUDS design. Firstly, water quality is assessed with the KPIs Site Pollution Index (SPI) and Pollutant Removal Capacity (PRC). Amenity with the KPIs Thermal Comfort Score (TCS) and impervious area. Thirdly, biodiversity is assessed with the KPI Biotope Area Factor (BAF). Furthermore, it improves the assessment of water quantity by replacing the currently used KPI with Expected Annual Damages (EAD). With the choice of KPIs in the extended framework, it is ensured that their assessment methodologies are suitable to engineering practice. Furthermore, by including KPIs that assess the multi-objectiveness of SUDS, the co-benefits are included in the extended framework.
With the application of both frameworks on the municipality of Alkmaar, this research substantiates the positive influence of SUDS on urban areas. The case study shows improving performance for water quantity, water quality, amenity and biodiversity if the number of SUDS increases.
To assess the effect of the extended framework on the decision making process, the MCDA type Compromise Programming (CP) is applied to the results of both frameworks. With the identification of the best choice of design based on the performance results of either the conventional or extended framework, the outcome of the CP method showed that using the extended framework as opposed to the conventional framework sometimes led to different design choices.
It is demonstrated that the extended framework indeed improves the decision making process to improve the live-ability of the urban environment. Even though the extended framework is more time consuming and may result in more costly designs, using this framework to base decision making on is likely to result in better quality of life for humans with a reduced negative impact on the associated natural environment. This framework better equips decision-makers to face
emerging urban challenges. However, both frameworks are of use in engineering practice. The ultimate choice of using either one of the frameworks is dependent on the goal of the project, the client, and the amount of time and money available. ...
To evaluate the performance of SUDS, assessment frameworks include Key Performance Indicators (KPIs). KPIs are measurable indicators demonstrating how effective SUDS are in achieving their objectives. This research highlights that there is no universal or even country based standard assessment framework for SUDS. Furthermore, there are few examples of the translation of the scientific assessment methods of SUDS to engineering practice available. The assessment of the full effect of SUDS therefore remains unclear to practitioners.
This research proposes a new framework (”extended framework”) to assess the performance of SUDS, building on the existing framework (”conventional framework”) which assesses cost and water quantity currently used in engineering practice. The extended framework adds KPIs assessing the remaining three objectives of SUDS design. Firstly, water quality is assessed with the KPIs Site Pollution Index (SPI) and Pollutant Removal Capacity (PRC). Amenity with the KPIs Thermal Comfort Score (TCS) and impervious area. Thirdly, biodiversity is assessed with the KPI Biotope Area Factor (BAF). Furthermore, it improves the assessment of water quantity by replacing the currently used KPI with Expected Annual Damages (EAD). With the choice of KPIs in the extended framework, it is ensured that their assessment methodologies are suitable to engineering practice. Furthermore, by including KPIs that assess the multi-objectiveness of SUDS, the co-benefits are included in the extended framework.
With the application of both frameworks on the municipality of Alkmaar, this research substantiates the positive influence of SUDS on urban areas. The case study shows improving performance for water quantity, water quality, amenity and biodiversity if the number of SUDS increases.
To assess the effect of the extended framework on the decision making process, the MCDA type Compromise Programming (CP) is applied to the results of both frameworks. With the identification of the best choice of design based on the performance results of either the conventional or extended framework, the outcome of the CP method showed that using the extended framework as opposed to the conventional framework sometimes led to different design choices.
It is demonstrated that the extended framework indeed improves the decision making process to improve the live-ability of the urban environment. Even though the extended framework is more time consuming and may result in more costly designs, using this framework to base decision making on is likely to result in better quality of life for humans with a reduced negative impact on the associated natural environment. This framework better equips decision-makers to face
emerging urban challenges. However, both frameworks are of use in engineering practice. The ultimate choice of using either one of the frameworks is dependent on the goal of the project, the client, and the amount of time and money available. ...
Globally, there is an ongoing trend to improve the live-ability of cities. Furthermore, the importance of sufficient urban response systems for storm water is growing due to climate change and urbanization. Sustainable Urban Drainage Solutions (SUDSs) are seen as a key tool to tackle multiple urban challenges. This is as their design is multi-objective, focusing on water quantity, water quality, amenity, and biodiversity. However, currently knowledge is lacking amongst decision makers on how SUDS affect the urban environment. Therefore, this research aims to develop an assessment framework for SUDS-based storm water management in the urban landscape to help Dutch municipalities improve their decision making to improve the live-ability of the urban environment.
To evaluate the performance of SUDS, assessment frameworks include Key Performance Indicators (KPIs). KPIs are measurable indicators demonstrating how effective SUDS are in achieving their objectives. This research highlights that there is no universal or even country based standard assessment framework for SUDS. Furthermore, there are few examples of the translation of the scientific assessment methods of SUDS to engineering practice available. The assessment of the full effect of SUDS therefore remains unclear to practitioners.
This research proposes a new framework (”extended framework”) to assess the performance of SUDS, building on the existing framework (”conventional framework”) which assesses cost and water quantity currently used in engineering practice. The extended framework adds KPIs assessing the remaining three objectives of SUDS design. Firstly, water quality is assessed with the KPIs Site Pollution Index (SPI) and Pollutant Removal Capacity (PRC). Amenity with the KPIs Thermal Comfort Score (TCS) and impervious area. Thirdly, biodiversity is assessed with the KPI Biotope Area Factor (BAF). Furthermore, it improves the assessment of water quantity by replacing the currently used KPI with Expected Annual Damages (EAD). With the choice of KPIs in the extended framework, it is ensured that their assessment methodologies are suitable to engineering practice. Furthermore, by including KPIs that assess the multi-objectiveness of SUDS, the co-benefits are included in the extended framework.
With the application of both frameworks on the municipality of Alkmaar, this research substantiates the positive influence of SUDS on urban areas. The case study shows improving performance for water quantity, water quality, amenity and biodiversity if the number of SUDS increases.
To assess the effect of the extended framework on the decision making process, the MCDA type Compromise Programming (CP) is applied to the results of both frameworks. With the identification of the best choice of design based on the performance results of either the conventional or extended framework, the outcome of the CP method showed that using the extended framework as opposed to the conventional framework sometimes led to different design choices.
It is demonstrated that the extended framework indeed improves the decision making process to improve the live-ability of the urban environment. Even though the extended framework is more time consuming and may result in more costly designs, using this framework to base decision making on is likely to result in better quality of life for humans with a reduced negative impact on the associated natural environment. This framework better equips decision-makers to face
emerging urban challenges. However, both frameworks are of use in engineering practice. The ultimate choice of using either one of the frameworks is dependent on the goal of the project, the client, and the amount of time and money available.
To evaluate the performance of SUDS, assessment frameworks include Key Performance Indicators (KPIs). KPIs are measurable indicators demonstrating how effective SUDS are in achieving their objectives. This research highlights that there is no universal or even country based standard assessment framework for SUDS. Furthermore, there are few examples of the translation of the scientific assessment methods of SUDS to engineering practice available. The assessment of the full effect of SUDS therefore remains unclear to practitioners.
This research proposes a new framework (”extended framework”) to assess the performance of SUDS, building on the existing framework (”conventional framework”) which assesses cost and water quantity currently used in engineering practice. The extended framework adds KPIs assessing the remaining three objectives of SUDS design. Firstly, water quality is assessed with the KPIs Site Pollution Index (SPI) and Pollutant Removal Capacity (PRC). Amenity with the KPIs Thermal Comfort Score (TCS) and impervious area. Thirdly, biodiversity is assessed with the KPI Biotope Area Factor (BAF). Furthermore, it improves the assessment of water quantity by replacing the currently used KPI with Expected Annual Damages (EAD). With the choice of KPIs in the extended framework, it is ensured that their assessment methodologies are suitable to engineering practice. Furthermore, by including KPIs that assess the multi-objectiveness of SUDS, the co-benefits are included in the extended framework.
With the application of both frameworks on the municipality of Alkmaar, this research substantiates the positive influence of SUDS on urban areas. The case study shows improving performance for water quantity, water quality, amenity and biodiversity if the number of SUDS increases.
To assess the effect of the extended framework on the decision making process, the MCDA type Compromise Programming (CP) is applied to the results of both frameworks. With the identification of the best choice of design based on the performance results of either the conventional or extended framework, the outcome of the CP method showed that using the extended framework as opposed to the conventional framework sometimes led to different design choices.
It is demonstrated that the extended framework indeed improves the decision making process to improve the live-ability of the urban environment. Even though the extended framework is more time consuming and may result in more costly designs, using this framework to base decision making on is likely to result in better quality of life for humans with a reduced negative impact on the associated natural environment. This framework better equips decision-makers to face
emerging urban challenges. However, both frameworks are of use in engineering practice. The ultimate choice of using either one of the frameworks is dependent on the goal of the project, the client, and the amount of time and money available.
Assessing the hydrologic performance of the Aquaflow in Rotterdam
A monitoring case study of the Agniesebuurt area of Rotterdam
A monitoring case study of the hydrologic performance of the Aquaflow infiltrating road foundation. The results show a good performance in peak delay of 4 - 8 hours, volume retention of 50% to 97% and peak reduction of 15% to 76% for varying rainfall events and initial soil conditions. The results are promising for future implementation to obtain more sustainable urban drainage systems in Rotterdam.
...
A monitoring case study of the hydrologic performance of the Aquaflow infiltrating road foundation. The results show a good performance in peak delay of 4 - 8 hours, volume retention of 50% to 97% and peak reduction of 15% to 76% for varying rainfall events and initial soil conditions. The results are promising for future implementation to obtain more sustainable urban drainage systems in Rotterdam.
Serious gaming to support the adoption of sustainable drainage solutions
A serious game to educate citizens about private household SuDS
There is an urgent need for urban environments to be more flood resilient. The public can participate in addressing this by adopting household sustainable drainage solutions (SuDS). However, their lack of knowledge and awareness is a barrier. This paper presents an educational serious game to overcome this barrier and explores whether it can educate citizens about private household SuDS (and to what degree). A serious game named Sudsbury is designed to educate the public on household SuDS and the urgency to adopt them in the context of climate change and urbanisation. A study of 14 urban inhabitants participated in three game sessions evaluated with pre and post-game surveys. The study found that Sudsbury is a fun and engaging public intervention method successful in educating and impacting personal norm attitudes, with the potential to create growth in support and adoption of household SuD. Sudsbury was most successful in improving knowledge acquisition of household SuDS but found comprehension of concepts was limited by deficiencies in the game realism.
...
There is an urgent need for urban environments to be more flood resilient. The public can participate in addressing this by adopting household sustainable drainage solutions (SuDS). However, their lack of knowledge and awareness is a barrier. This paper presents an educational serious game to overcome this barrier and explores whether it can educate citizens about private household SuDS (and to what degree). A serious game named Sudsbury is designed to educate the public on household SuDS and the urgency to adopt them in the context of climate change and urbanisation. A study of 14 urban inhabitants participated in three game sessions evaluated with pre and post-game surveys. The study found that Sudsbury is a fun and engaging public intervention method successful in educating and impacting personal norm attitudes, with the potential to create growth in support and adoption of household SuD. Sudsbury was most successful in improving knowledge acquisition of household SuDS but found comprehension of concepts was limited by deficiencies in the game realism.
Drinking water temperature is an essential parameter for water quality related to the physical, chemical and biological processes in water. However, in many countries, the drinking water temperature has not been taken seriously and was excluded from water quality standards. For example, in the Netherlands, the temperature of drinking water should not exceed 25℃ at customers’ taps, which is an advanced guide for ensuring water safety. Moreover, with the tendency of global warming, the drinking water temperature in the distribution system will become higher and higher without sufficient attention and practical solutions. Therefore, understanding the mechanism and cause of water temperature fluctuation is instrumental in finding appropriate measures to cope with it and improve the drinking water quality.
This thesis provides relatively comprehensive and overall ideas to research drinking water temperature. The objective of this thesis consists of three parts: i) Determining impact factors on water temperature; ii) Simulating water temperature in the distribution system; iii) Choosing measures to control water temperature. Firstly, data measurement and analytical methods were applied to determine impact factors on water temperature, and the influence level of each impact factor had been identified. Subsequently, implementing these impact factors to calibrate the water temperature simulation model to verify the model’s feasibility. Finally, the performance on reducing water temperature of three measures, porous asphalt, pervious interlocking concrete pavement, and grass cover, were compared to determine the most effective measure from the standpoint of pipe cover. The results show as following: i) Four impact factors are summarized as surface cover material, district heating pipe, shade effect and groundwater level based on the collected data; ii) It is feasible to simulate the water temperature in the water distribution system. For the model of city Almere, around 88% of simulation values had a difference smaller than 1℃ compared with measurement data; iii) Grass cover has a better performance than the porous asphalt and pervious interlocking cement pavement. Additionally, this thesis discusses limitations during the measurement and simulation process and more relative interventions to reduce water temperature.
In summary, this thesis further summarizes the various impact factors that affect the drinking water temperature and the measure to control drinking water temperature has launched from the point of pipe cover compared with previous references. These results provide guiding advice on the engineering projects of constructing and renovating drinking water distribution systems considering water temperature. ...
This thesis provides relatively comprehensive and overall ideas to research drinking water temperature. The objective of this thesis consists of three parts: i) Determining impact factors on water temperature; ii) Simulating water temperature in the distribution system; iii) Choosing measures to control water temperature. Firstly, data measurement and analytical methods were applied to determine impact factors on water temperature, and the influence level of each impact factor had been identified. Subsequently, implementing these impact factors to calibrate the water temperature simulation model to verify the model’s feasibility. Finally, the performance on reducing water temperature of three measures, porous asphalt, pervious interlocking concrete pavement, and grass cover, were compared to determine the most effective measure from the standpoint of pipe cover. The results show as following: i) Four impact factors are summarized as surface cover material, district heating pipe, shade effect and groundwater level based on the collected data; ii) It is feasible to simulate the water temperature in the water distribution system. For the model of city Almere, around 88% of simulation values had a difference smaller than 1℃ compared with measurement data; iii) Grass cover has a better performance than the porous asphalt and pervious interlocking cement pavement. Additionally, this thesis discusses limitations during the measurement and simulation process and more relative interventions to reduce water temperature.
In summary, this thesis further summarizes the various impact factors that affect the drinking water temperature and the measure to control drinking water temperature has launched from the point of pipe cover compared with previous references. These results provide guiding advice on the engineering projects of constructing and renovating drinking water distribution systems considering water temperature. ...
Drinking water temperature is an essential parameter for water quality related to the physical, chemical and biological processes in water. However, in many countries, the drinking water temperature has not been taken seriously and was excluded from water quality standards. For example, in the Netherlands, the temperature of drinking water should not exceed 25℃ at customers’ taps, which is an advanced guide for ensuring water safety. Moreover, with the tendency of global warming, the drinking water temperature in the distribution system will become higher and higher without sufficient attention and practical solutions. Therefore, understanding the mechanism and cause of water temperature fluctuation is instrumental in finding appropriate measures to cope with it and improve the drinking water quality.
This thesis provides relatively comprehensive and overall ideas to research drinking water temperature. The objective of this thesis consists of three parts: i) Determining impact factors on water temperature; ii) Simulating water temperature in the distribution system; iii) Choosing measures to control water temperature. Firstly, data measurement and analytical methods were applied to determine impact factors on water temperature, and the influence level of each impact factor had been identified. Subsequently, implementing these impact factors to calibrate the water temperature simulation model to verify the model’s feasibility. Finally, the performance on reducing water temperature of three measures, porous asphalt, pervious interlocking concrete pavement, and grass cover, were compared to determine the most effective measure from the standpoint of pipe cover. The results show as following: i) Four impact factors are summarized as surface cover material, district heating pipe, shade effect and groundwater level based on the collected data; ii) It is feasible to simulate the water temperature in the water distribution system. For the model of city Almere, around 88% of simulation values had a difference smaller than 1℃ compared with measurement data; iii) Grass cover has a better performance than the porous asphalt and pervious interlocking cement pavement. Additionally, this thesis discusses limitations during the measurement and simulation process and more relative interventions to reduce water temperature.
In summary, this thesis further summarizes the various impact factors that affect the drinking water temperature and the measure to control drinking water temperature has launched from the point of pipe cover compared with previous references. These results provide guiding advice on the engineering projects of constructing and renovating drinking water distribution systems considering water temperature.
This thesis provides relatively comprehensive and overall ideas to research drinking water temperature. The objective of this thesis consists of three parts: i) Determining impact factors on water temperature; ii) Simulating water temperature in the distribution system; iii) Choosing measures to control water temperature. Firstly, data measurement and analytical methods were applied to determine impact factors on water temperature, and the influence level of each impact factor had been identified. Subsequently, implementing these impact factors to calibrate the water temperature simulation model to verify the model’s feasibility. Finally, the performance on reducing water temperature of three measures, porous asphalt, pervious interlocking concrete pavement, and grass cover, were compared to determine the most effective measure from the standpoint of pipe cover. The results show as following: i) Four impact factors are summarized as surface cover material, district heating pipe, shade effect and groundwater level based on the collected data; ii) It is feasible to simulate the water temperature in the water distribution system. For the model of city Almere, around 88% of simulation values had a difference smaller than 1℃ compared with measurement data; iii) Grass cover has a better performance than the porous asphalt and pervious interlocking cement pavement. Additionally, this thesis discusses limitations during the measurement and simulation process and more relative interventions to reduce water temperature.
In summary, this thesis further summarizes the various impact factors that affect the drinking water temperature and the measure to control drinking water temperature has launched from the point of pipe cover compared with previous references. These results provide guiding advice on the engineering projects of constructing and renovating drinking water distribution systems considering water temperature.
This MSc thesis is a contribution to the African Water Corridor (AWC) project that gains insight into the future of water supply systems in the Sub-Saharan African small towns. The future water demand in these areas constitute a great challenge in the effort to provide safe water for everyone. Unfortunately, there are high uncertainties in the future posed by unforeseen changing factors such as global climate change and urbanization which makes it a challenge for decision-makers to develop strategies for these water supply systems in the long-term planning. The concept of resilience is introduced by many studies to address those uncertainties The objective of this thesis is to provide an approach for decision-makers to develop a resilience water supply system in small towns for long-term decision planning to answer the following research question: ‘How can a water supply system in a small African town be resilient and provide sufficient water in the future?’ This thesis presents a methodology that is resilience-based and develops reduced future supply and increased future demand scenarios for the small town in a period of 30 years. To analyze the water supply systems in small towns, Moamba has been used as a case study..
...
This MSc thesis is a contribution to the African Water Corridor (AWC) project that gains insight into the future of water supply systems in the Sub-Saharan African small towns. The future water demand in these areas constitute a great challenge in the effort to provide safe water for everyone. Unfortunately, there are high uncertainties in the future posed by unforeseen changing factors such as global climate change and urbanization which makes it a challenge for decision-makers to develop strategies for these water supply systems in the long-term planning. The concept of resilience is introduced by many studies to address those uncertainties The objective of this thesis is to provide an approach for decision-makers to develop a resilience water supply system in small towns for long-term decision planning to answer the following research question: ‘How can a water supply system in a small African town be resilient and provide sufficient water in the future?’ This thesis presents a methodology that is resilience-based and develops reduced future supply and increased future demand scenarios for the small town in a period of 30 years. To analyze the water supply systems in small towns, Moamba has been used as a case study..
Foul sewer networks face many challenges related to new pressures and ageing infrastructure. There is a need to be able to evaluate how networks will be able to adapt to varying population densities, urban development and ecologic changes. Some suggest the use of exploratory models to test large numbers of network configurations, intended as alternative responses to the driving pressures. However, in order to carry out exploratory modelling, a trade-off between computational time and accuracy must be achieved. An approach to generate and size foul sewer networks which allow computational savings was developed in a collaboration between Eawag, ETH Zurich and TU Delft.This approach was used in this study to evaluate the computational time savings and accuracy of a generated sewer network in hydraulic performance assessment. Two case studies of 7km2 (Port Phillip) and 57km2 (Melbourne), and different land uses were used to evaluate the ability of the generated network to represent a real network. It was found that computational time was reduced for both case studies, by a maximum factor of 10. Hydraulic performance was compared under high, typical and low flow conditions. It was found that high flow parameters are better represented in small case studies, where network capacity reduction and topology differences are less evident between real and generated networks. For low flow conditions, percentages of network length at risk of sedimentation were well represented for both case studies, with the larger case study showing slightly better performance. Under typical flow conditions, it was shown that topology simplification in the generated network leads to significant changes in times of concentration between networks, and hydrograph discrepancies, especially for the larger case study. However, general trends, including distribution of pipe cumulative flow percentage for pipes in sedimentation, or pipe diameter for pipe surcharge, are well represented by the generated network. Therefore, it is found that the generated network should be used to evaluate hydraulic performance trends in generated networks, as well as global values (eg. flood volume) if the network differences (eg. capacity, path length) are taken into account. Finally, future work needs are highlighted which could strengthen the findings of this study, including use of real network flow and hydraulic performance data. Moreover, the computational time savings for a full assessment (rather than just hydraulic evaluations) should be quantified and compared.
...
Foul sewer networks face many challenges related to new pressures and ageing infrastructure. There is a need to be able to evaluate how networks will be able to adapt to varying population densities, urban development and ecologic changes. Some suggest the use of exploratory models to test large numbers of network configurations, intended as alternative responses to the driving pressures. However, in order to carry out exploratory modelling, a trade-off between computational time and accuracy must be achieved. An approach to generate and size foul sewer networks which allow computational savings was developed in a collaboration between Eawag, ETH Zurich and TU Delft.This approach was used in this study to evaluate the computational time savings and accuracy of a generated sewer network in hydraulic performance assessment. Two case studies of 7km2 (Port Phillip) and 57km2 (Melbourne), and different land uses were used to evaluate the ability of the generated network to represent a real network. It was found that computational time was reduced for both case studies, by a maximum factor of 10. Hydraulic performance was compared under high, typical and low flow conditions. It was found that high flow parameters are better represented in small case studies, where network capacity reduction and topology differences are less evident between real and generated networks. For low flow conditions, percentages of network length at risk of sedimentation were well represented for both case studies, with the larger case study showing slightly better performance. Under typical flow conditions, it was shown that topology simplification in the generated network leads to significant changes in times of concentration between networks, and hydrograph discrepancies, especially for the larger case study. However, general trends, including distribution of pipe cumulative flow percentage for pipes in sedimentation, or pipe diameter for pipe surcharge, are well represented by the generated network. Therefore, it is found that the generated network should be used to evaluate hydraulic performance trends in generated networks, as well as global values (eg. flood volume) if the network differences (eg. capacity, path length) are taken into account. Finally, future work needs are highlighted which could strengthen the findings of this study, including use of real network flow and hydraulic performance data. Moreover, the computational time savings for a full assessment (rather than just hydraulic evaluations) should be quantified and compared.
Optimal Rehabilitation of Urban Drainage Systems
Application of single-objective optimisation for the implementation of Green-Blue-Grey Infrastructures in changing climate
Urban Drainage Systems (UDS) are one of the most vital yet, complex infrastructures that support people's livelihood in urban areas. However, due to their mainly underground infrastructure and complexity, the planning and management of UDS are usually associated with high investment, which stakeholders sometimes overlook. As long-lived infrastructures, UDS’s limited capability is being put under constantly increasing pressures. Amongst the pressures, the global effects of climate change on rainfall extremes is the most important. As climate change affects the rainfall extremes and the overall hourly and daily rainfall events, urban flooding issues are becoming more costly to manage. Several rehabilitation efforts have been made to address this issue with minimum cost and optimal performance in flood reduction by increasing the resiliency of UDS in order to minimise the duration and magnitude of urban flooding.
Rehabilitation of UDS can be done in several ways, including implementing Green-Blue-Grey Infrastructures (G-B-G measures). The combination of G-B-G measures can increase the resiliency of the UDS to withstand higher intensity rainfall by reducing both the peak flow and enlarging the capacity of the UDS system. Therefore, this thesis aims to develop a method to find the optimal way to rehabilitate an existing UDS to reduce the risk of flooding under the climate change rainfall scenarios.
The method developed coupled a hydrodynamic model, Storm Water Management Model (SWMM), and Genetic Algorithm (GA) to find the optimal solution to rehabilitate UDS. The effect of climate change was incorporated by simulating the solutions using composite design storms that represent the increase in hourly and daily rainfall extremes for 2030, 2050, and 2085. The objective function of this optimisation problem becomes the minimisation of the total cost to implement the measures for the rehabilitation of UDS, under the constraint that no flooding can happen on the system when tested against the climate change rainfall scenarios. Therefore, the decision variables of this optimisation are the size and location for each implemented measure, while the penalty cost is associated with the cost of each m3 of flooding.
Based on the analysis of the case study, the most appropriate Green-Blue measures to be implemented is Rain Barrels, Infiltration Trenches, and Pervious Pavements. Meanwhile, for grey measures, it is best to consider pipe and pump replacements and increasing the CSOs’ weirs. The optimisation was done using the developed formal method and manual trial-and-error. The results of the formal optimisation have been confirmed to outperform the result from manual optimisation using the traditional trial-and-error method. The optimal solutions proved that a combination of both grey and G-B measures produced the lowest cost to reduce flooding. Although the solutions can be adapted over time from 2030 until 2085, the results show that adaptive solutions might not be needed when the solution for 2085 is better implemented from the year 2030. Overall, it can be projected that in the future, the combination of G-B-G measures can produce an economically optimal solution to be implemented in order to achieve zero floodings in the case study location. ...
Rehabilitation of UDS can be done in several ways, including implementing Green-Blue-Grey Infrastructures (G-B-G measures). The combination of G-B-G measures can increase the resiliency of the UDS to withstand higher intensity rainfall by reducing both the peak flow and enlarging the capacity of the UDS system. Therefore, this thesis aims to develop a method to find the optimal way to rehabilitate an existing UDS to reduce the risk of flooding under the climate change rainfall scenarios.
The method developed coupled a hydrodynamic model, Storm Water Management Model (SWMM), and Genetic Algorithm (GA) to find the optimal solution to rehabilitate UDS. The effect of climate change was incorporated by simulating the solutions using composite design storms that represent the increase in hourly and daily rainfall extremes for 2030, 2050, and 2085. The objective function of this optimisation problem becomes the minimisation of the total cost to implement the measures for the rehabilitation of UDS, under the constraint that no flooding can happen on the system when tested against the climate change rainfall scenarios. Therefore, the decision variables of this optimisation are the size and location for each implemented measure, while the penalty cost is associated with the cost of each m3 of flooding.
Based on the analysis of the case study, the most appropriate Green-Blue measures to be implemented is Rain Barrels, Infiltration Trenches, and Pervious Pavements. Meanwhile, for grey measures, it is best to consider pipe and pump replacements and increasing the CSOs’ weirs. The optimisation was done using the developed formal method and manual trial-and-error. The results of the formal optimisation have been confirmed to outperform the result from manual optimisation using the traditional trial-and-error method. The optimal solutions proved that a combination of both grey and G-B measures produced the lowest cost to reduce flooding. Although the solutions can be adapted over time from 2030 until 2085, the results show that adaptive solutions might not be needed when the solution for 2085 is better implemented from the year 2030. Overall, it can be projected that in the future, the combination of G-B-G measures can produce an economically optimal solution to be implemented in order to achieve zero floodings in the case study location. ...
Urban Drainage Systems (UDS) are one of the most vital yet, complex infrastructures that support people's livelihood in urban areas. However, due to their mainly underground infrastructure and complexity, the planning and management of UDS are usually associated with high investment, which stakeholders sometimes overlook. As long-lived infrastructures, UDS’s limited capability is being put under constantly increasing pressures. Amongst the pressures, the global effects of climate change on rainfall extremes is the most important. As climate change affects the rainfall extremes and the overall hourly and daily rainfall events, urban flooding issues are becoming more costly to manage. Several rehabilitation efforts have been made to address this issue with minimum cost and optimal performance in flood reduction by increasing the resiliency of UDS in order to minimise the duration and magnitude of urban flooding.
Rehabilitation of UDS can be done in several ways, including implementing Green-Blue-Grey Infrastructures (G-B-G measures). The combination of G-B-G measures can increase the resiliency of the UDS to withstand higher intensity rainfall by reducing both the peak flow and enlarging the capacity of the UDS system. Therefore, this thesis aims to develop a method to find the optimal way to rehabilitate an existing UDS to reduce the risk of flooding under the climate change rainfall scenarios.
The method developed coupled a hydrodynamic model, Storm Water Management Model (SWMM), and Genetic Algorithm (GA) to find the optimal solution to rehabilitate UDS. The effect of climate change was incorporated by simulating the solutions using composite design storms that represent the increase in hourly and daily rainfall extremes for 2030, 2050, and 2085. The objective function of this optimisation problem becomes the minimisation of the total cost to implement the measures for the rehabilitation of UDS, under the constraint that no flooding can happen on the system when tested against the climate change rainfall scenarios. Therefore, the decision variables of this optimisation are the size and location for each implemented measure, while the penalty cost is associated with the cost of each m3 of flooding.
Based on the analysis of the case study, the most appropriate Green-Blue measures to be implemented is Rain Barrels, Infiltration Trenches, and Pervious Pavements. Meanwhile, for grey measures, it is best to consider pipe and pump replacements and increasing the CSOs’ weirs. The optimisation was done using the developed formal method and manual trial-and-error. The results of the formal optimisation have been confirmed to outperform the result from manual optimisation using the traditional trial-and-error method. The optimal solutions proved that a combination of both grey and G-B measures produced the lowest cost to reduce flooding. Although the solutions can be adapted over time from 2030 until 2085, the results show that adaptive solutions might not be needed when the solution for 2085 is better implemented from the year 2030. Overall, it can be projected that in the future, the combination of G-B-G measures can produce an economically optimal solution to be implemented in order to achieve zero floodings in the case study location.
Rehabilitation of UDS can be done in several ways, including implementing Green-Blue-Grey Infrastructures (G-B-G measures). The combination of G-B-G measures can increase the resiliency of the UDS to withstand higher intensity rainfall by reducing both the peak flow and enlarging the capacity of the UDS system. Therefore, this thesis aims to develop a method to find the optimal way to rehabilitate an existing UDS to reduce the risk of flooding under the climate change rainfall scenarios.
The method developed coupled a hydrodynamic model, Storm Water Management Model (SWMM), and Genetic Algorithm (GA) to find the optimal solution to rehabilitate UDS. The effect of climate change was incorporated by simulating the solutions using composite design storms that represent the increase in hourly and daily rainfall extremes for 2030, 2050, and 2085. The objective function of this optimisation problem becomes the minimisation of the total cost to implement the measures for the rehabilitation of UDS, under the constraint that no flooding can happen on the system when tested against the climate change rainfall scenarios. Therefore, the decision variables of this optimisation are the size and location for each implemented measure, while the penalty cost is associated with the cost of each m3 of flooding.
Based on the analysis of the case study, the most appropriate Green-Blue measures to be implemented is Rain Barrels, Infiltration Trenches, and Pervious Pavements. Meanwhile, for grey measures, it is best to consider pipe and pump replacements and increasing the CSOs’ weirs. The optimisation was done using the developed formal method and manual trial-and-error. The results of the formal optimisation have been confirmed to outperform the result from manual optimisation using the traditional trial-and-error method. The optimal solutions proved that a combination of both grey and G-B measures produced the lowest cost to reduce flooding. Although the solutions can be adapted over time from 2030 until 2085, the results show that adaptive solutions might not be needed when the solution for 2085 is better implemented from the year 2030. Overall, it can be projected that in the future, the combination of G-B-G measures can produce an economically optimal solution to be implemented in order to achieve zero floodings in the case study location.
Application of business intelligence as decision support systems in asset management of water connections
Case study in the Netherlands, in collaboration with water company “Evides”
When evaluating and applying asset management concepts, water companies can face challenges in enabling targeted recommendation-making due to difficulties in accessing and processing of large volumes of data. These factors can lead to entrance barriers in utilization of breakdown data when assessing network reliability and scope of attainable improvements in asset strategy and maintenance concepts. Proven solutions and contextualized research are not readily available for water companies which, as asset-intensive enterprise relying on physical assets to deliver water to its customers, have a big stake in optimizing its use of data. This project has set out to research whether aspects of recommendation making for asset management at water companies can be aided with application of commonly available and deployable business intelligence tools. To this end, a water company which faces similar challenges has been selected. Evides – a water provider in the region of Rijnmond seeks more data-driven approaches in asset management of water connections. This asset group can be characterized by high volume, high technological heterogeneity and high absolute number of breakdowns as compared to distribution pipes. Together with a vast volume of data, this combination of factors leads to challenges in maintaining a continuous oversight and transparent conversion of performance data into strategic goals and clear service level agreements. The case of Evides inspired a research approach in which application of a custom-made decision support system is evaluated for the process of recommendation making in asset management of water connections. Methodology for this research encompassed for semi-structured interviews with network specialists and managers to obtain information on current asset management goals and the corresponding recommendation-making process for water connections. Thereafter, a thematic analysis was conducted to distill the main themes depicting aspects of interests to network-specialists in charge of producing recommendations and to managers – the decision makers. The type and moments at which performance data is processed and consulted were described and positioned in a managerial decision-making model, together with aspects assessed at each stage. Simple performance indicators were selected to aid the assessments and to connect performance readings with company goals. Findings were thereafter embedded into a purpose-made prototype of a decision support system, utilizing capacity of business intelligence software in creating curated datasets and user-friendly front end. In the last phase of the research, network specialists participated in appraisal of the created tool by completing a series of tasks designed to assess performance of water connections. Surveys were then conducted among participants to evaluate the added value of the created tool in the context of recommendation-making for asset management of water connections at Evides. Results show that, for the case study company, the created tool allows for improvements in accessibility and connectivity of company performance data and can contribute towards greater transparency in goal setting and enabling data-driven recommendation making for asset management of water connections. Performance outliers and policy non-compliers can be localized easier and help company in localizing areas in need of attention. Display of simple performance indicators for connections as per user-selected criteria can in the long run enable more nuances in describing network performance, shifting away from binary descriptions of asset’s performance. In case of Evides, the performance management framework for water connections was discovered as insufficiently defined to allow for assessments of direct benefits as result of application of the designed decision support-system. It is therefore recommended for future research to apply similar methodology for asset groups with well defined performance management standards and to focus on experimental design with higher external validity.
...
When evaluating and applying asset management concepts, water companies can face challenges in enabling targeted recommendation-making due to difficulties in accessing and processing of large volumes of data. These factors can lead to entrance barriers in utilization of breakdown data when assessing network reliability and scope of attainable improvements in asset strategy and maintenance concepts. Proven solutions and contextualized research are not readily available for water companies which, as asset-intensive enterprise relying on physical assets to deliver water to its customers, have a big stake in optimizing its use of data. This project has set out to research whether aspects of recommendation making for asset management at water companies can be aided with application of commonly available and deployable business intelligence tools. To this end, a water company which faces similar challenges has been selected. Evides – a water provider in the region of Rijnmond seeks more data-driven approaches in asset management of water connections. This asset group can be characterized by high volume, high technological heterogeneity and high absolute number of breakdowns as compared to distribution pipes. Together with a vast volume of data, this combination of factors leads to challenges in maintaining a continuous oversight and transparent conversion of performance data into strategic goals and clear service level agreements. The case of Evides inspired a research approach in which application of a custom-made decision support system is evaluated for the process of recommendation making in asset management of water connections. Methodology for this research encompassed for semi-structured interviews with network specialists and managers to obtain information on current asset management goals and the corresponding recommendation-making process for water connections. Thereafter, a thematic analysis was conducted to distill the main themes depicting aspects of interests to network-specialists in charge of producing recommendations and to managers – the decision makers. The type and moments at which performance data is processed and consulted were described and positioned in a managerial decision-making model, together with aspects assessed at each stage. Simple performance indicators were selected to aid the assessments and to connect performance readings with company goals. Findings were thereafter embedded into a purpose-made prototype of a decision support system, utilizing capacity of business intelligence software in creating curated datasets and user-friendly front end. In the last phase of the research, network specialists participated in appraisal of the created tool by completing a series of tasks designed to assess performance of water connections. Surveys were then conducted among participants to evaluate the added value of the created tool in the context of recommendation-making for asset management of water connections at Evides. Results show that, for the case study company, the created tool allows for improvements in accessibility and connectivity of company performance data and can contribute towards greater transparency in goal setting and enabling data-driven recommendation making for asset management of water connections. Performance outliers and policy non-compliers can be localized easier and help company in localizing areas in need of attention. Display of simple performance indicators for connections as per user-selected criteria can in the long run enable more nuances in describing network performance, shifting away from binary descriptions of asset’s performance. In case of Evides, the performance management framework for water connections was discovered as insufficiently defined to allow for assessments of direct benefits as result of application of the designed decision support-system. It is therefore recommended for future research to apply similar methodology for asset groups with well defined performance management standards and to focus on experimental design with higher external validity.
Replacement optimisation for public infrastructure assets
Quantitative optimisation modelling taking typical public infrastructure related features into account
Ageing infrastructures and shortage of financing induce the need for optimising public infrastructure replacements. From an economic perspective, classical net present value comparison is traditionally the method of choice to decide on investments and replacements. The current research observes that typical infrastructure related features make the classical net present value comparison less suitable in its application for optimising infrastructure replacements. Especially the low discount rate of public sector organisations, price increases and price uncertainty contribute to this phenomenon in which the application of classical net present value comparison leads to suboptimal timing and costs. This observation led to the development of six dedicated replacement optimisation models for common types of infrastructure replacement challenges. A decision support guideline is provided to assist in selecting an appropriate model based on the sequence of intervention strategies, the development of forecasted cash flows and whether uncertainty is involved. The quantitative replacement optimisation models function as blueprints for similar challenges and support a wider decision-making context.
...
Ageing infrastructures and shortage of financing induce the need for optimising public infrastructure replacements. From an economic perspective, classical net present value comparison is traditionally the method of choice to decide on investments and replacements. The current research observes that typical infrastructure related features make the classical net present value comparison less suitable in its application for optimising infrastructure replacements. Especially the low discount rate of public sector organisations, price increases and price uncertainty contribute to this phenomenon in which the application of classical net present value comparison leads to suboptimal timing and costs. This observation led to the development of six dedicated replacement optimisation models for common types of infrastructure replacement challenges. A decision support guideline is provided to assist in selecting an appropriate model based on the sequence of intervention strategies, the development of forecasted cash flows and whether uncertainty is involved. The quantitative replacement optimisation models function as blueprints for similar challenges and support a wider decision-making context.