This Special Issue of Journal of Water Supply: Research and Technology – AQUA includes a collection of papers dealing with advanced approaches on urban water works and water cycle management. The majority of these papers were initially presented at the 3rd EWaS International Conference on ‘Insights on the Water-Energy-Food Nexus’ that was held on June 27–30, 2018, on Lefkada island, Greece (http://ewas3.civ.uth.gr/). Those papers have been extended, enriched and revised by at least 50% compared to the original ones presented at the aforementioned conference. All papers went through a standard peer-review process following the high-quality standards set by the Journal of Water Supply: Research and Technology – AQUA.
The topics of the papers included in the current Special Issue are briefly presented below.
The paper by Annus et al. (2020) aims at determining the influence of irregular pipe wall roughness on the flow velocity in a water distribution system (WDS) containing old pipes. It is shown that in old rough pipes, the mean velocities are higher than expected, indicating that in the modelling of the propagation of contamination in a WDS, actual pipe diameters with reasonable roughness need to be defined. For this reason, three types of pipe wall build-up were investigated using EPANET2 and computational fluid dynamics to estimate the velocity correction coefficients for EPANET2 calculations. The results showed that pipe wall build-up and sedimentation settling may reduce the pipe nominal diameter of more than 50%, leading to unrealistically large roughness values in WDS model calibration. Actual pipe diameters should be estimated and used in the WDS calibration, leading to more realistic roughness values and average flow velocities. This is very important in water quality modelling and risk assessment. In order to prevent carrying capacity failures, proper restoration actions like systematic pipe flushing and/or scraping have to be implemented to remove settled materials from the pipe walls.
The paper by Ioannidou & Arthur (2020) deals with experimental laboratory study on a permeable interlocking concrete block pavement rig, investigating the short-term hydrological performance of the pavement, and water quality aspects related to the retention capacity of suspended solids (SS) through the pavement structure. The experimental results showed that high stormwater attenuation ability of the pavement was demonstrated by the mean outflow duration of 10.5 h after the rainfall event. More than 50% of the total rainfall volume was retained within the permeable pavement structure, suggesting significant stormwater storage capacity. Results on water quality testing with emphasis on SS showed that there is an increased tendency in the sediment mass retention progressively after each rainfall event.
The paper by Janjua & Hassan (2020) describes a new methodology for the allocation of scarce water resources in a complex system using a stochastic game theory which is an extension of Bankruptcy theory. This approach can be a useful tool for decision making when it comes to the sharing of water resources under critical scarce conditions for complex water resource systems having competing water demands. The authors proposed also the ‘Weighted Bankruptcy’ approach that can be used under a stochastic setting. The Bankruptcy Rules have been applied in the water resource system in ‘four’ critical scarcity scenarios for distributing the water among the four provinces of Pakistan in the Indus River Basin. The available water is allocated under the Simple and Weighted Bankruptcy rules, and their results were compared, showing that under all four scenarios the Weighted Bankruptcy Rules favour the agents which have a high agricultural productivity. The Stochastic Bankruptcy approach under the Simple and the Weighted Bankruptcy Rules can provide important strategic information for the better management and sustainable sharing of water resources, particularly during water scarcity and under increasing water demand in future, helping policy makers to facilitate negotiation in managing conflict and dispute over water resources allocation problems.
The paper by Kändler et al. (2020) attempts to develop and evaluate a new concept of a smart in-line storage system on the background of traditional in-line and off-line detention solutions. The new decentralized real-time controlled in-line detention system will reduce peak runoff from urban catchment to the existing urban stormwater system (UDS) downstream. The system has independently operated control manholes that are capable of exchanging information between each other and pro-actively adapt to the changing stormwater runoff hydrograph. The system was compared with two traditional solutions – off-line storage tanks and in-line detention facilities, and the base scenario – a system with no runoff control. The concept has been successfully tested in a 12.5 ha urban development area in Tallinn, Estonia. The results showed that the in-line detention with real time control (RTC) has the highest cut in the peak flow, the lowest total cost and the lowest investment costs over a 50-year period. Additional benefits include better sedimentation removal and a smaller construction footprint, being very important in dense urban areas.
The paper by Kenda et al. (2020) deals with a thorough evaluation of the performance of different statistical modelling techniques in groundwater and surface water level prediction scenarios as well as some aspects of the application of data-driven modelling in practice (feature generation, feature selection, heterogeneous data fusion, hyperparameter tuning, and model evaluation). Standard batch and incremental machine learning techniques for regression and classification are included. The latter are used on binned target values of water levels. Twenty-one different regression and classification techniques were tested. The results reveal that batch regression techniques are superior to incremental techniques in terms of accuracy and that among them gradient boosting, random forest and linear regression perform best. An interpretation is that even though classification offers a wide range of machine learning techniques, the nature of the data is such that no binning is possible without worsening the results. The final performance depends heavily on how well the targets are binned, which is a complex task that must consider the density and distribution of values and is a matter of subjective interpretation. On the other hand, regression techniques can naturally handle the prediction of a target continuum of values, which leads to better results for certain large-scale applications. Automatic feature engineering and feature selection algorithms, that enrich the water level values with contextual information and later prune it in order to avoid overfitting of machine learning models are important contributions of the presented approach.
The paper by Liosis et al. (2020) focuses on the mixing mechanism of two water streams, one with magnetic particles and the other with wastewater, to investigate the possible capture of heavy metal ions from contaminated water in microfluidics. The study takes into consideration various external magnetic configurations. The optimum mixing is obtained when particles are uniformly distributed along the volume of water in the duct for the combined action of a permanent, spatially and temporally aligned magnetic field. The study results showed that mixing is enhanced as the frequency of the magnetic field decreases or its amplitude increases, while the magnetic gradient is found to play an insignificant role in the present configuration. Low frequency cases result in high mean concentration of particles, up to two times higher, compared to the cases with higher frequency. The optimum combination is found for a magnetic magnitude of 0.6 T, gradient of 8 T/m and frequency of 5 Hz where concentration reaches the optimal value of 0.77 mg/mL along the volume of the duct.
The paper by Retsinis et al. (2020) deals with time-dependent, unsteady flow studied in prismatic open channels with symmetric trapezoidal and triangular cross sections and small bottom slope. The results from three explicit numerical methods, namely, the Lax-Diffusive, MacCormack, and Lambda methods, and the implicit Preissmann scheme, are compared to those from the hydraulic Muskingum-Cunge method and the HEC-RAS commercial software program, being the innovative aspect of this work. All codes were implemented in Matlab environment. Coding all numerical schemes in suitable programming language revealed practical aspects and difficulties of code implementation useful for future works. The results show that explicit schemes produced satisfactory results if compared with the results of the more demanding implicit schemes. Numerical results compare well to the ones from the Muskingum-Cunge method and one-dimensional unsteady flow calculations software HEC-RAS, requiring more effort than the simpler Muskingum-Cunge method which can be used for the preliminary design of long prismatic open channels under unsteady flow conditions.
The paper by Vassiljev et al. (2020) focuses on modelling water quality in the watershed with a large share of drained peat soils, as eutrophication is one of the main environmental pressures on marine environments in the Baltic Sea region. In this work, water quantity and quality of the watershed with a high percentage of peat soils were modelled using field-scale use models. The analysis of CORINE and soil maps showed that four dominant areas prevail on the watershed: forest on peat, forest on clay loam, arable on clay loam and pastures on peat. Calculations were accomplished for five different soil profiles. Areas occupied by each profile were estimated by optimization to obtain a maximal value of sum Nash–Sutcliffe efficiency (NSE) coefficients for the water flow and NO3-N concentrations. Daily water flow and concentrations of nitrate nitrogen were modelled for 11 years. The reliability of modelling was estimated by the NSE. The investigation showed that a good fit between measured and modelled nitrate-nitrogen concentrations using MACRO and SOILN for MACRO models was found. The results also showed that even when low human activity exists, weather conditions may lead to a positive trend of nitrates in rivers. Models are applicable to the planning activities of River Basin Management, as they allow modelling of the water quantity and quality at the watersheds with a high content of peat soils. River Basin Management, with a focus on drained peat soils, needs additional solutions to improve the quality of water in rivers. Initial results showed that nitrate concentrations may be decreased by decreasing the depth of drains.
The paper by Yaranal et al. (2020) deals with the low-cost simplified method for implementation of pressure-assisted osmotic (PAO) backwash (BW) for spiral wound reverse osmosis (RO) membrane module. The effect of membrane design and an operating parameter concerning the efficiency of PAO membrane BW is analyzed. Design and operating parameters considered in this study include: (i) spacer thickness; (ii) dimension of the permeate channel; (iii) number of leaves; and (iv) BW water pressure. The experimental results revealed that spiral-wound module with less pressure drop in the permeate channel can provide efficient PAO membrane BW. Using a concentrated brine and using concentrated feed solution during PAO BW will improve the BW efficiency. The performance of PAO BW with respect to membrane cleaning efficiency is analyzed for three different high recovery RO systems by purifying 1,500 liters of water. The system with brine recirculation (CCRO) is observed to have less membrane fouling than the others, but as CCRO uses more equipment than the others, it leads to higher capital cost. A system without brine recycling (single stage RO) is found to be more energy efficient than the other systems reported in this work, and this has to be verified for higher feed concentration. The effectiveness of PAO BW in RO membrane is counterbalanced, to some extent, by the significantly higher permeate TDS when backwash is applied.
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