| publications-1861 |
Peer reviewed articles |
2024 |
Rita Ribeiro, Maria Rosa |
The Role of Scenario-Building in Risk Assessment and Decision-Making on Urban Water Reuse |
Water |
10.3390/w16182674 |
Uncategorized |
Irrigation Systems |
|
Urban resilience and water resilience are both increasingly relying on urban non-potable water reuse under the context of the Climate Emergency, but sound risk assessment is lacking. Compared to the state of art, the proposed framework for health risk assessment and management of urban non-potable water reuse includes (i) an additional step for establishing the context and (ii) the risk identification step being extended to introduce a description of the activities from which the hazard exposure scenarios may be built. This novel scenario-building process allows for a clear and comprehensive risk description, assessment, and treatment. The model of risk management is structured around three primary components: the decision-makers, i.e., the municipal services and the population at risk (users and workers); data elements relevant for the risk management process (reclaimed water quality, hazards, hazardous events, sites where exposure can happen, exposure routes, and activities developed by the population at risk and their vulnerabilities); and the links between the decision-makers and these elements and between the elements themselves. Its application in a representative case study shows that the framework comprehensively guides decision-making and communication to relevant stakeholders. From this practical exercise, the main recommendations were derived for risk mitigation by the municipal risk manager and the park users. |
869171 |
|
|
|
| publications-1862 |
Peer reviewed articles |
2022 |
Konstantin W. Scheihing, Christine KĂĽbeck, Uwe SĂĽtering |
GIS-AHP Ensembles for Multi-Actor Multi-Criteria Site Selection Processes: Application to Groundwater Management under Climate Change |
Water 2022, 14, 1793 |
10.3390/w14111793 |
Predictive Analytics |
Irrigation Systems |
|
A possible adaptation pathway for water suppliers in Germany who face a climatically driven increase in water stress is the development of aquifers which are not used at their full potential. However, identifying suitable sites for aquifer development can go along with severe conflict potential due to the great variety of stakeholders who are involved in the decision-making process. We approach this multi-actor and multi-criteria decision-making problem by developing a geoinformation system-based analytic hierarchy process ensemble (GIS-AHP ensemble). As opposed to the classic GIS-AHP method that yields ratings of site suitability based on a single expert evaluation, the here proposed new GIS-AHP ensemble method respects multiple expert evaluations and allows for quantifying the robustness of yielded site ratings in multi-actor contexts, which helps to mitigate conflict potential. The respectively derived GIS-AHP ensemble site ratings for northwest Germany are successfully checked for plausibility in the framework of the study by using long-established groundwater abstraction areas as indicators for good site conditions. The GIS-AHP ensemble site ratings are further tested regarding their usability for long-term water supply planning by integrating a groundwater recharge scenario under climate change for the period 2020 to 2050. The proposed GIS-AHP ensemble methodology proves useful in the given case study for fostering integrated environmental decision-making and exhibits a high transferability to other, thematically differing site selection problems. |
869171 |
|
|
|
| publications-1863 |
Peer reviewed articles |
2021 |
Ravindra R. Patil, Saniya M. Ansari, Rajnish Kaur Calay, Mohamad Y. Mustafa |
Review of the State-of-the-art Sewer Monitoring and Maintenance Systems Pune, Municipal Corporation - A Case Study |
TEM Journal |
10.18421/tem104-02 |
Uncategorized |
Irrigation Systems |
|
There is an increasing trend of using automated and robotic systems for the tasks that are hazardous or inconvenient and dirty for humans. Sewers maintenance and cleaning is such a task where robots are already being used for inspection of underground pipes for blockages and damage. This paper reviews the existing robotic systems and various platforms and algorithms along with their capabilities and limitations being discussed. A typical mid-size city in a developing country, Pune, India is selected in order to understand the concerns and identify the requirements for developing robotic systems for the same. It is found that major concern of sewers are blockages but there is not enough information on both real-time detection and removal of it with robotic systems. On-board processing with computer vision algorithms has not been efficiently utilized in terms of performance and determinations for real-world implementations of sewer robotic systems. The review highlights the available methodologies that can be utilized in developing sewer inspection and cleaning robotic systems. |
821423 |
|
|
|
| publications-1864 |
Peer reviewed articles |
2023 |
György Schneider, Dorina Pásztor, Péter Szabó, László Kőrösi, Nandyala Siva Kishan, Penmetsa Appala Rama Krishna Raju, Rajnish Kaur Calay |
Isolation and Characterisation of Electrogenic Bacteria from Mud Samples |
Microorganisms |
10.3390/microorganisms11030781 |
Data Management & Analytics |
River Basins |
|
To develop efficient microbial fuel cell systems for green energy production using different waste products, establishing characterised bacterial consortia is necessary. In this study, bacteria with electrogenic potentials were isolated from mud samples and examined to determine biofilm-formation capacities and macromolecule degradation. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry identifications have revealed that isolates represented 18 known and 4 unknown genuses. They all had the capacities to reduce the Reactive Black 5 stain in the agar medium, and 48 of them were positive in the wolfram nanorod reduction assay. The isolates formed biofilm to different extents on the surfaces of both adhesive and non-adhesive 96-well polystyrene plates and glass. Scanning electron microscopy images revealed the different adhesion potentials of isolates to the surface of carbon tissue fibres. Eight of them (15%) were able to form massive amounts of biofilm in three days at 23 °C. A total of 70% of the isolates produced proteases, while lipase and amylase production was lower, at 38% and 27% respectively. All of the macromolecule-degrading enzymes were produced by 11 isolates, and two isolates of them had the capacity to form a strong biofilm on the carbon tissue one of the most used anodic materials in MFC systems. This study discusses the potential of the isolates for future MFC development applications. |
821423 |
|
|
|
| publications-1865 |
Peer reviewed articles |
2021 |
Eusun Han, Abraham George Smith, Roman Kemper, Rosemary White, John A Kirkegaard, Kristian Thorup-Kristensen, Miriam Athmann |
Digging roots is easier with AI |
Journal of Experimental Botany |
10.1093/jxb/erab174 |
Simulation & Modeling |
Precipitation & Ecological Systems |
|
Abstract The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1–5 cm cm–3) as well with low RLD (0.1–0.3 cm cm–3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations. |
884364 |
|
|
|
| publications-1866 |
Peer reviewed articles |
2022 |
Fatemeh Poureshghi Oskouei, Nga Phuong Dong, Subhashis Das, Chris Petrich, Rajnish Kaur Calay |
Enhanced Performance of Microbial Fuel Cells Using PVDF Activated Carbon Air Cathode and Electrochemically and Chemically Treated Carbon Felt Anode |
ECS Meeting Abstracts |
10.1149/ma2022-02542037mtgabs |
Uncategorized |
Hydropower Dams & reservoirs |
|
Microbial fuel cells (MFCs) harness the metabolism of microorganisms, converting chemical energy into electrical energy. Improving both Anode and Cathode design is thus of great significance to enhance the MFC performance and its commercial application. For the performance improvement of MFCs, the anode becomes a breakthrough point due to its influence on bacterial attachment and extracellular electron transfer (EET). On the other hand, air cathodes have considerable influence on the maximum power of air-driven MFCs. The cathodes used in MFCs need to have high catalytic activity for oxygen reduction, but they should be inexpensive watertight,and easy to manufacture. As the first part of this work, carbon felt was electrochemically and chemically treated by electrolyzing in nitric acid and phosphate buffer followed by soaking in aqueous ammonia. The treated and untreated carbon felts were utilized as anodes in MFCs, and current production was compared while the cathode was stainless steel mesh (SS-316L) in both cases. The treated carbon felt displays strong interaction with the microbial biofilm of Shewanella baltica 20 facilitating electron transfer from exoelectrogens to the anode. An MFC equipped with a treated carbon felt as anode has significantly lower charge-transfer resistance and achieves considerably better performance than one equipped with an untreated carbon felt anode. The enhanced electron transfer is attributed to newly generated carboxyl containing functional groups on the treated carbon felt. In the second part of the present study, SS-316L as a cathode was modified using phase inversion process to construct a poly vinylidenefluoride (PVDF) binder and an activated carbon catalyst according to the procedure reported previously. Finally, the MFC with treated carbon felt anode and PVDF air cathode was tested. The MFC with both modified anode and cathode achieves considerably better performance than one with a traditional carbon felt anode and SS-316L cathode. The maximum current density, power density, and energy recovery, and sensitivity of the biofilm to the heavy metals are significantly improved. |
821423 |
|
|
|
| publications-1867 |
Peer reviewed articles |
2024 |
Prasanna Mohan Doss, Marius Møller Rokstad, Franz Tscheikner-Gratl |
The performance of encoder–decoder neural networks for leak detection in water distribution networks |
Water Supply |
10.2166/ws.2024.174 |
Uncategorized |
River Basins |
|
ABSTRACT This work outlines the performance of three variants of deep neural networks for leak detection in water distribution networks, namely – autoencoders (AEs), variational autoencoders (VAEs), and long short-term memory autoencoders (LSTM-AEs). The multivariate pressure signals reconstructed from these models are analysed for leakage identification. The leak onset time is estimated using a fast approximation sliding window technique, which computes statistical discrepancies in prediction errors. The performance of all three variants is validated using the widely studied L-Town benchmark network. Furthermore, their feasibility for real-world application is studied by applying them to a real-world case study representing the data availability and network design often found in smaller- and medium-sized utilities in Norway. The results for the benchmark network showed that AE and LSTM-AE showed comparable detection performance for abrupt leaks with VAE performing the least. For incipient leaks, the LSTM-AE showed better detection performance with few false-positives. For the real-world dataset, the performance was significantly lower due to the quantity and quality of data available, and the contradiction of inherent requirements of data-driven models. In addition, the analysis revealed that the positioning of pressure sensors in the network is critical for the leak detection performance of these models. |
869171 |
|
|
|
| publications-1868 |
Peer reviewed articles |
2023 |
Costa, J.; Mesquita, E.; Ferreira, F.; Figueiredo, D.; Rosa, M.J.; Viegas, R.M.C. |
Modeling Chlorine Decay in Reclaimed Water Distribution Systems—A Lisbon Area Case Study |
Sustainability 2023 |
10.3390/su152316211 |
Uncategorized |
Hydropower Dams & reservoirs |
|
Climate change has emerged as a global challenge, with consequences for the environment and societies. To mitigate its impacts, reclaimed water (RW) offers potential by reducing water withdrawal and minimizing pollution discharges in the environment. Safe RW requires disinfection and a sound management of chlorine residuals throughout the RW distribution systems (RWDSs). This study focuses on implementing and calibrating a chlorine decay model using EPANET-MSX in a real RWDS, incorporating both bulk and wall decays. The bulk decay accounts for reactions of monochloramine formation, auto-decomposition, and depletion by a parallel second-order mechanism where monochloramine reacts both with fast and slow organic matter reactive fractions. Two wall decays were considered in the RWDS, one in the tank, modeled through an overall wall decay constant, and one in the pipes, modeled through a wall decay constant. Field experiments were conducted to calibrate the complete model. This model was used as a support tool to diagnose the RWDS status condition and cleaning needs, and to manage its operation. Through simulated scenarios considering monochloramine wall decays similar to those observed in drinking water distribution systems, the model allowed predicting adequate chlorine dosing in summer and winter scenarios, so as to guarantee monochloramine concentrations between 1 mg/L and 5 mg/L through the network. These results point to the potential use of much lower doses than the ones currently applied. |
869171 |
|
|
|
| publications-1869 |
Peer reviewed articles |
2024 |
Nga Phuong Dang, Chris Petrich, Dorina Pasztor, György Schneider, Rajnish Kaur Calay |
<i>Shewanella baltica</i> in a Microbial Fuel Cell for Sensing of Biological Oxygen Demand (BOD) of Wastewater |
Analytical Letters |
10.1080/00032719.2024.2341087 |
Data Management & Analytics |
River Basins |
|
No abstract available |
821423 |
|
|
|
| publications-1870 |
Peer reviewed articles |
2023 |
Ravindra R. Patil, Rajnish Kaur Calay, Mohamad Y. Mustafa, Saniya M. Ansari |
AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems |
Electronics |
10.3390/electronics12173606 |
Data Management & Analytics |
River Basins |
|
In artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems. |
821423 |
|
|
|
