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Computer modeling is a water utility's most powerful tool for managing and operating a water distribution system. This 4th edition of M32 Computer Modeling of Water Distribution Systems, describes how to build accurate water distribution system models, and use models to manage assets and solve hydraulic and water quality problems.

Examining the results of the Italian national programme of marine coastal monitoring, the old problem has arisen about the choice of the most appropriate procedures and methods to validate data and screen preliminary data. Therefore, statistical distributions of water quality parameters have been taken into consideration, in order to assign appropriate frequency distributions to all the routinely measured variables. Each sample distribution has been analysed and defined by a probability density function (p.d.f.), by means of a powerful method of data analysis (Johnson 1949) that allows for the computation of statistical parameters of a wide variety of non-normal distributions. The resulting Johnson distributions are then classified depending on four fundamental categories of frequency distributions: normal, log-normal, bounded and unbounded. Theoretical aspects of the method are explained and discussed in an adequate way, so as to allow for practical applications. The shape and nature of these curves require further consideration, in order to understand the behaviour of water quality variables and to make comparison among different coastal zones. To this end, two coastal systems were considered in this work: the Emilia-Romagna coastal area of the NW Adriatic Sea and the Tuscany littoral of the Northern Tyrrhenian Sea. There are notable advantages to the adopted approach. First it offers the possibility to overcome severe constraints requested by the normality assumption, and avoids the troublesome search for the most appropriate transformation function (i.e. for ensuring normality). Second, it avoids searching for other kinds of theoretical distributions that are appropriate for the data. In our approach, the density functions are opportunely integrated, in such a way that, for whatever value assumed by a given variable, the corresponding expected percentage point value under the respective frequency curve, can be calculated, and vice versa. We believe that the Johnson method, although tested with coastal monitoring data, can be usefully adopted whenever we have to analyse environmental data and try to understand how an aquatic system works (e.g. large lakes). In the Appendix specific details about the Johnson classification criterion are reported and highlight the case of bimodal distributions. Finally, an example of data analysis is provided, by using the R (V. 2.11) software, with both graphical and numerical outputs.

Per- and polyfluoroalkyl substances (PFAS) are a class of fluorinated chemicals used widely in consumer and industrial products. Their human toxicity and ecosystem impacts have received extensive public, scientific and regulatory attention. Regulatory PFAS guidance is rapidly evolving, with the inclusion of a wider range of PFAS included in advisories and a continued decrease in what is deemed safe PFAS concentrations. In this study we collated PFAS concentration data for over 45,000 surface and groundwater samples from around the world to assess the global extent of PFAS contamination and their potential future environmental burden. Here we show that a substantial fraction of sampled waters exceeds PFAS drinking water guidance values, with the extent of exceedance depending on the jurisdiction and PFAS source. Additionally, current monitoring practices probably underestimate PFAS in the environment given the limited suite of PFAS that are typically quantified but deemed of regulatory concern. An improved understanding of the range of PFAS embodied in consumer and industrial products is required to assess the environmental burden and develop mitigation measures. While PFAS is the focus of this study, it also highlights society’s need to better understand the use, fate and impacts of anthropogenic chemicals. A global data analysis suggests that a large fraction of surface waters and groundwaters globally have concentrations of per- and polyfluoroalkyl substances (PFAS) that exceed international advisories or national regulations.

Karst regions offer a variety of natural resources such as freshwater and biodiversity, and many cultural resources. The World Karst Aquifer Map (WOKAM) is the first detailed and complete global geodatabase concerning the distribution of karstifiable rocks (carbonates and evaporites) representing potential karst aquifers. This study presents a statistical evaluation of WOKAM, focusing entirely on karst in carbonate rocks and addressing four main aspects: (1) global occurrence and geographic distribution of karst; (2) karst in various topographic settings and coastal areas; (3) karst in different climatic zones; and (4) populations living on karst. According to the analysis, 15.2% of the global ice-free continental surface is characterized by the presence of karstifiable carbonate rock. The largest percentage is in Europe (21.8%); the largest absolute area occurs in Asia (8.35 million km2). Globally, 31.1% of all surface exposures of carbonate rocks occur in plains, 28.1% in hills and 40.8% in mountains, and 151,400 km or 15.7% of marine coastlines are characterized by carbonate rocks. About 34.2% of all carbonate rocks occur in arid climates, followed by 28.2% in cold and 15.9% in temperate climates, whereas only 13.1 and 8.6% occur in tropical and polar climates, respectively. Globally, 1.18 billion people (16.5% of the global population) live on karst. The highest absolute number occurs in Asia (661.7 million), whereas the highest percentages are in Europe (25.3%) and North America (23.5%). These results demonstrate the global importance of karst and serve as a basis for further research and international water management strategies.

Karst aquifers contribute substantially to freshwater supplies in many regions of the world, but are vulnerable to contamination and difficult to manage because of their unique hydrogeological characteristics. Many karst systems are hydraulically connected over wide areas and require transboundary exploration, protection and management. In order to obtain a better global overview of karst aquifers, to create a basis for sustainable international water-resources management, and to increase the awareness in the public and among decision makers, the World Karst Aquifer Mapping (WOKAM) project was established. The goal is to create a world map and database of karst aquifers, as a further development of earlier maps. This paper presents the basic concepts and the detailed mapping procedure, using France as an example to illustrate the step-by-step workflow, which includes generalization, differentiation of continuous and discontinuous carbonate and evaporite rock areas, and the identification of non-exposed karst aquifers. The map also shows selected caves and karst springs, which are collected in an associated global database. The draft karst aquifer map of Europe shows that 21.6% of the European land surface is characterized by the presence of (continuous or discontinuous) carbonate rocks; about 13.8% of the land surface is carbonate rock outcrop.

The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like ‘black box’ models, computational fluid dynamics techniques, etc.? Can new data sources – e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis – keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.

Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands.

Pressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics‐based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data‐driven approaches have gained traction to overcome such limitations. In this work, we combine physics‐based modeling and graph neural networks (GNN), a data‐driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN‐based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real‐world scenarios. As a result, a new state‐of‐the‐art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average. Plain Language Summary Water management practitioners have resorted to mathematical simulation tools to reconstruct pressure, flow, and demand in order to improve their control operations. However, pure physics‐based methods need to deal with partially observable data, high uncertainty, and extensive manual calibration. We combine physics‐based modeling and graph neural networks, a data‐driven approach, to address the pressure estimation problem and overcome those limitations. Our work has two main contributions. First, a random sensor placement strategy makes our estimation model resilient to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties of real‐world scenarios. As a result, a new state‐of‐the‐art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several water distribution networks benchmarks, showing a reduction of absolute error of ≈40% on average. Key Points Mathematical Simulation Tools and graph neural networks were combined for pressure estimation in water distribution networks Random sensor placement during model training is a good strategy for robustness against unexpected sensors' location changes Time‐dependent patterns and Gaussian noise injection enable a realistic evaluation protocol for pressure estimation models

This book shows how compliance modelling has been used to very good effect in the optimisation of plumbosolvency control in the United Kingdom, particularly in the optimisation of orthophosphate dosing. Over 100 water supply systems have been modelled, involving 30% of the UKs water companies. This proof-of-concept project has the overall objective of demonstrating that these modelling techniques could also be applicable to the circumstances of Canada and the United States, via three case studies.