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M32 Computer Modeling of Water Distribution Systems, Fourth Edition
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.
eBook
Coastal waters monitoring data: frequency distributions of the principal water quality variables
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.
Journal Article
The future of WRRF modelling – outlook and challenges
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.
Journal Article
Review of model-based and data-driven approaches for leak detection and location in water distribution systems
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.
Journal Article
Optimisation of Corrosion Control for Lead in Drinking Water Using Computational Modelling Techniques
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.
eBook
Examination of the spatial-temporal variations in terrestrial water reserves and green efficiency of water resources in China’s three northeastern provinces
Using technological advancements and analyzing urban water consumption patterns, this article employs GRACE satellite data and statistical records to conduct a comprehensive assessment and evaluation of water resource utilization efficiency across 34 prefecture-level cities in China’s three northeastern provinces—Liaoning, Jilin, and Heilongjiang—over the period spanning from 2003 to 2020. By utilizing the sophisticated Super-SBM model, the study delves into the spatial and temporal variations in terrestrial water reserves and green water usage efficiency. Additionally, the Tobit model is introduced to investigate the influencing factors of water resource utilization efficiency. The primary findings of the study are outlined below: The spatial distribution of terrestrial water resources in the three northeastern provinces reveals a clear north-south gradient, with abundant resources in the northern regions and scarcity in the southern parts. Seasonal fluctuations, albeit present, are relatively modest, with higher water storage levels typically observed in spring and summer, and lower levels in autumn and winter. Regarding the static water use efficiency among the 34 prefecture-level cities, Panjin stands out with the highest efficiency, whereas Qiqihar ranks lowest. Notably, 91.18% of the cities exhibit medium to high efficiency levels, reflecting commendable performance in water utilization throughout the region. Almost half of the cities have experienced an improvement in their water use efficiency compared to the previous year, signaling a gradual enhancement in water utilization capabilities. The average total factor productivity across the three northeastern provinces stands at 1.012, representing an annual growth rate of 1.2%. The efficiency of water resource utilization in these provinces is intricately linked to the technological progress index. To enhance water resource utilization efficiency, it is imperative to introduce advanced technologies, increase research investments, and foster technological advancements.
Journal Article
Edge Computing for Energy‐Efficient Sensor Scheduling in Water Distribution Systems
Water distribution systems (WDSs) utilize battery‐powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real‐time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing‐based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.
Journal Article
Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan
The northern sub-Himalayan region is the primary source of water for a large part of Pakistan. Changes in precipitation and precipitation extremes in the area may have severe impacts on water security and hydrology of Pakistan. The objective of the study is to evaluate the spatial characteristics of the trends in annual and seasonal precipitation and precipitation extremes in Gilgit Baltistan, the northern administrative territory of Pakistan surrounded by Hindu Kush, Karakoram, and the Himalayan regions. The daily gridded rainfall data (1951–2007) of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) at 0.25° spatial resolution was used for evaluating the trends. The novelty of the present study is the application of the modified Mann-Kendall (MMK) trend for the evaluation of the significance of precipitation trends in order to differentiate the secular trends from climate natural variability. Besides, cumulative distribution function (CDF) plots of daily rainfall for the early (1951–1980) and later (1981–2007) periods were used to show the changes in extremes. The results revealed no significant change in annual precipitation but increase in summer rainfall in the range of 0.25 to 1.25 mm/year in the upper part and decrease in winter precipitation from 0 to − 0.25 mm/year in the west part of the region. Annual number of rainy days was also found to decrease in winter up to − 1.33 days/decade where the region receives a major portion of total precipitation. The decrease in winter rainfall and rainy days caused an increase in continuous dry days (around 0.27 days/year) and decrease in continuous wet days (up to − 0.26 days/year). Trend analysis and CDF plot revealed that though the numbers of rainy days are decreasing, the numbers of extreme rainfall days are increasing, which indicates rainfall become more erratic and intense in the region. The increases in both continuous dry days and extreme rainfall days indicate more droughts and floods may have adverse impacts on the hydrology of Pakistan.
Journal Article
Using multivariate statistical analysis in assessment of surface water quality and identification of heavy metal pollution sources in Sarough watershed, NW of Iran
The Sarough watershed in NW Iran hosts a large amount of mineral occurrences and ore deposits which may be considered as the source of heavy metals in the region. The area has been studied previously; however, the methodology of this paper was less focused on previous studies. This study aimed to assess water quality, determine the spatial distribution pattern, and identify the sources of heavy metals in the main tributaries of Sarough watershed using pollution indexes, multivariate statistical methods, and processing data by geographic information system. Totally, 51 water samples were collected along the main rivers to determine the concentrations of heavy metals by ICP-MS. Regarding the drinking water, agriculture, and freshwater aquatic life guidelines, the rivers were assumed unsafe considering most of toxic elements’ content, especially As. The mean values for heavy metal pollution indexes (HPI: 237.32) and metal indexes (MI: 25.37) indicated the intensive heavy metal pollution. The cluster analysis categorized the 51 sampling sites into four clusters with respect to pollution level. The results obtained from the Kruskal–Wallis and multiple comparison tests had the harmony with the results of CA in introducing the most impacted sampling sites and the parameters responsible for water quality degradation. The results of PCA showed the maximum similarity between As, Sb, Se, Fe, and Mn as well as base metals which was attributed to anthropogenic input from mining and mineral processing wastes. Association of Cr and Ni may suggest a lithology source (weathering of metamorphosed ultramafic outcrops). The maps prepared in the GIS system showed the spatial distribution pattern of toxic elements with maximum values nearby mining sites which decreases gradually toward downstream areas. Finally, the results showed that the Sarough River and its tributaries are influenced by high concentrations of heavy metals from the drainages of mining and ore processing sites and naturally occurring metal loadings as well as the geogenic sources such as weathering of geologic formations and hot springs.
Journal Article