In this paper, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isolation Methods (BattLeDIM). The method is based on the application of the hydraulic model of the water distribution network and the comparison of simulated pressures against the corresponding values observed in field. In particular, it consists of two phases: (1) calibration of the hydraulic model of the network; and (2) detection and localization of the leakages affecting the water distribution network through the application of engineering judgment and the adoption of an enumerative procedure. The method was applied to the case-study network of L-Town, enabling 16 of 23 leakages to be efficiently detected and 11 of these also accurately localized. The proposed method is simple and transparent and can aid water utilities in water leakage management.

The spread of the COVID-19 pandemic induced many countries, including Italy, to implement social distancing measures and to suspend the majority of educational and working activities, which significantly impacted peoples’ lifestyles. To support water utilities in understanding the impacts of the COVID-19 pandemic on water consumption and improving water distribution system resilience, the effects of the lockdown were investigated with reference to a residential district metered area (DMA) in the city of Rovigo (northern Italy), in which smart monitoring of water consumption at the level of individual users started in 2019. The water consumption recorded during the lockdown period was analyzed at different levels of temporal and spatial aggregation and compared with the consumption recorded in the same period of the previous year. The results show that, during the lockdown period, the overall water consumption in this mainly residential area increased by 18%. Moreover, water consumption was observed to be more spread out over the day, with a decrease (and a delay) in peak morning consumption, which was particularly evident on weekdays.

Recent studies point out that water distribution networks can be affected by long- and short-term pressure oscillations due to the users' activity. However, these transients, generated at the household level, before reaching the water distribution network pass through, and thus affect, the water service line and can contribute to its deterioration. Despite the role of user-induced transients in stressing service lines, few studies in the literature explored the topic, exclusively by means of laboratory tests. The current study is aimed at exploring the effects of user's activity on a real service line starting from the field monitoring of pressure data at 500-Hz temporal resolution. Pressure signals are collected both when activating single water devices of the user supplied by the service line and during the ordinary use of domestic devices. The analyses of the acquired data highlight that the domestic service line is subjected to significant pressure variations (which can reach extreme values of −15 and +65 m) based on the device type and distance between the device and the service line and that the use of these devices can continuously stress the water service line.

Application of smart meters to the residential sector can provide insight into where and when water is used, thereby enabling utilities to achieve an efficient management of water distribution systems. Moreover, detailed information about domestic water use can be obtained by disaggregating smart meter data collected at the household inlet point. In this paper, a rule-based, automated methodology for disaggregating household water-use data into end uses is presented. The methodology is applicable to 1-min temporal resolution data, whose granularity is slightly lower than the one generally used in other methodologies, potentially allowing it to be applied to several contexts in the field of water-use monitoring. The methodology was set up and validated with data collected for 2 months through intrusive monitoring of four households in Bologna, Italy, and represents a pioneering case in which disaggregation performance is directly assessed by the comparison against data collected at each end use. The results obtained showed that the methodology enables household water use to be efficiently disaggregated even if detailed information about end-use features is not available.