• Energy Research

The main goal of this paper is to study the effect of the spatio-temporal changes of Land Use/Land Cover (LULC) within the hydrologic regime of the Cervaro basin in Southern Italy. LANDSAT Thematic Mapper (TM) imagery acquisition dates from 1984, 2003, 2009, and 2011 were selected to produce LULC maps covering a time trend of 28 years. Nine synthetic bands were processed as input data identified as the most effective for the Artificial Neural Network (ANN) classification procedure implemented in this case study. To assess the possible hydrological effects of the detected changes during rainfall events, a physically-based lumped approach for infiltration contribution was adopted within each sub-basin. The results showed an increase in flood peak and a decrease of the rangelands, forests, and bare lands between 1984 and 2011, indicating a good correlation between flooding areas and land use changes, even if it can be considered negligible in basins of large dimensions. These results showed that the impact of land use on the hydrological response is closely related to watershed scale.

The research of a general methodology to predict the pump performance in a reverse mode, knowing those of a pump in a direct mode, is a question that is still open. The scientific research is making many efforts toward answering this question, but at present, there is still not much clarity. This consideration has been the starting point of this research that thanks to artificial neural networks and evolutionary polynomial regression methods have tried to investigate and define the real weight of every input parameter, representing the efficiency of a pump in a direct way, on the output parameters, and representing efficiency of a pump used like a turbine.

The COVID-19 pandemic affected the lives of millions of people, radically changing their habits in just a few days. In many countries, containment measures prescribed by national governments restricted the movements of entire communities, with the impossibility of attending schools, universities, workplaces, and no longer allowing for traveling or leading a normal social life. People were then compelled to revise their habits and lifestyles. In such a situation, the availability of drinking water plays a crucial role in ensuring adequate health conditions for people and tackling the spread of the pandemic. Lifestyle of the population, climate, water scarcity and water price are influent factors on water drinking demand and its daily pattern. To analyze the effect of restriction measures on water demand, the instantaneous flow data of five Apulian towns (Italy) during the lockdown have been analyzed highlighting the important role of users’ habits and the not negligible effect of commuters on the water demand pattern besides daily volume requested.

Recent statistics shows that CO2 emissions from road transport have been increasing. In addition, the paradigm of “more electric vehicles” does not seem to be positive from the environmental point of view. In fact, assuming the current energy mix system, studies focusing on Life Cycle Assessment and Well-To-Wheels analysis demonstrate that electric vehicles are less eco-friendly than traditional internal combustion-based engines. Then, it is mandatory to shift toward renewable energy sources to produce electricity with less CO2 emission. In this work, it is proposed to use a new e-vehicles charging system based on Pumps used as Turbine (PATs). This system uses the pressure in excess that could be available in a water distribution network (WDN). Such an excess of pressure is usually destroyed by pressure-reducing valves with the aim to reduce water leaks. PATs are also able to reduce water pressure and produce electrical energy that can be supplied to e-vehicles charging stations. Then, a bi-level methodology to design and optimize the e-charging stations system for (individual or shared) e-bikes and/or e-scooters is proposed. The method allows determining the optimal number of e-vehicles, charging stations docks, and PATs on the study area according to the WDN layout and hydraulic properties as well as the road network characteristics and demand of e-vehicles. The potential of the methodology is shown by an application to a real case study.

The aim of the present study is to evaluate the technical and economic feasibility of supplying recharge points for Electric Vehicles converting water pressure in excess into electric energy from a water distribution network. A real water distribution network was analyzed, by designing a power station consisting of three components: a Pump-as-Turbine, an ad hoc generator, and a control system. The last one controls the flow rate trend at the inlet of the Pump-as-Turbine to regulate the rotational speed corresponding to the optimal range of efficiency. Energy produced permits to supply a recharging point for Electric Vehicles on site with the aim to convert water pressure generally dissipated and, consequently, lost into a renewable energy and to reduce power grid congestion. The economic analysis confirms the convenience of the proposed power station, and overall, a comparable cost with pressure reduction valve devices normally adopted to control pressure pattern and leakages of a water distribution network.

Salento is a regional coastal karst aquifer located in Southern Italy with a highly complex geological, geomorphological, and hydrogeological structure. High and unruly exploitation of groundwater from licensed and unlicensed wells for irrigation and drinking purposes affects groundwater, with consequent degradation of its qualitative and quantitative status. The increased frequency of meteorological droughts and rising temperatures may only worsen the already compromised situation. The absence of complete and enduring monitoring of groundwater levels prevents the application of some methodologies, which require long time series. The analysis of climate indexes to describe the groundwater level variation is a possible approach under data scarcity. However, this approach may not be obvious for complex aquifers (in terms of scale, intrinsic properties, and boundary conditions) where the response of the groundwater to precipitation is not necessarily linear. Thus, the proposed research deals with the assessment of the response of the Salento aquifer to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) and groundwater levels for nine monitoring wells from July 2007 to December 2011. The study aims at evaluating the ability of the above indicators to explain the behavior of groundwater on complex aquifers. Moreover, it has the general aim to verify their more general reliable application. Results of three different correlation factors outline direct and statistically significant correlations between the time series. They describe the Salento aquifer as a slow filter, with a notable inertial behavior in response to meteorological events. The SPI 18-months demonstrates to be a viable candidate to predict the groundwater response to precipitation variability for the Salento aquifer.

The monitoring data of a micrometeorological station in Lecce (Southern Italy, Puglia region, Salento Peninsula) were used with the aim of interpreting the variations in the groundwater level in a shallow aquifer as a function of hydrometeorological forcing. For this scope, the aridity index (AI) was calculated to evaluate the surface-water stress. Although the results indicated a moderate drought during the 2019–2020 hydrological year, a critical situation for the surface hydrological balance in the first quarter of 2020 did not occur. Therefore, the reduction in the groundwater level measured in April can be ascribed to the excessive domestic water consumption that occurred during the lockdown for COVID-19.