Search Results (11)

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems. Full article

In recent years, biomonitoring has gained more attention, particularly when assessing the environmental health of significant areas, such as those near waste-to-energy facilities. These requirements coincide with the chance to detect environmental pollutants using sensitive organisms. Bees were shown to be quite effective in evaluating the presence of certain compounds by analyzing their associated matrices, such as pollen, honey, or wax. In our study, we employed the honey bee (Apis mellifera) as an indicator to initially monitor the vicinity of the waste-to-energy plant in Acerra, which is situated in the Campania region of Italy. The primary aim was to determine whether the facility was accountable for any environmental releases of dioxins or dioxin-like compounds. Then, we assessed the presence of additional pollutants in the same area, including trace elements, polycyclic aromatic hydrocarbons, and pesticides, released by human activities. To obtain further information about environmental quality, a second biomonitoring station was installed near the Caivano S.T.I.R. (Waste Shredding, Sifting, and Packaging Plant). The results showed the dioxin levels did not exceed predetermined limitations at the Acerra site, thus demonstrating the efficacy of the waste-to-energy facility and the bees’ ability to detect the presence of other pollutants. Additionally, this biomonitoring system exhibited sensitivity to environmental variations, thereby enabling the evaluation of xenobiotic flux between two proximate zones and across temporal scales. This pioneering study suggests the advantages of utilizing bees to detect a wide range of contaminants, thereby providing valuable insights into environmental quality and potential health risks for both ecosystems and human populations. Full article

Nowadays, designing and adopting sustainable and greener transport systems is of upmost interest. The European Commission and different EU countries are developing plans and programs—but also delivering resources—aimed at the decarbonization of cities and transport by 2030. In this paper, the case study of the city of Brescia, a city of about 200,000 inhabitants located in northern Italy, is addressed. Specifically, a preliminary operational and financial feasibility study is performed assuming the replacement of the entire compressed natural gas (CNG) powered bus fleet of a specific line; the two alternatives considered are battery electric buses (BEBs) and fuel cell electric buses (FCEBs). For the comparison and evaluation of the two alternatives, specific economic parameters of the three alternatives (BEB, FCEB and the current solution CNGB) were considered: CAPEX (CAPital EXpenditure) and OPEX (OPerational EXpenditure). This allowed us to determine the TCO (total cost of ownership) and TCRO (total cost and revenues of ownership) along three annuities (2022, 2025 and 2030). For the BEB alternative, the TCO and TCRO values are between EUR 0.58/km and EUR 0.91/km. In the case of the FCEB solution, the values of TCO and TCRO are between EUR 1.75/km and EUR 2.15/km. Considering the current CNGB solution, the TCO and TCRO values range between EUR 1.43/km and EUR 1.51/km. Full article

Wood decay fungi (WDF) are a well-known source of enzymes and metabolites which have applications in numerous fields, including myco-remediation. Pharmaceuticals are becoming more problematic as environmental water pollutants due to their widespread use. In this study, Bjerkandera adusta, Ganoderma resinaceum, Perenniporia fraxinea, Perenniporia meridionalis and Trametes gibbosa were chosen from WDF strains maintained in MicUNIPV (the fungal research collection of the University of Pavia) to test their potential to degrade pharmaceuticals. The degradation potential was tested in spiked culture medium on diclofenac, paracetamol and ketoprofen, three of the most common pharmaceuticals, and irbesartan, a particularly difficult molecule to degrade. G. resinaceum and P. fraxinea were found to be the most effective at degradation, achieving 38% and 52% (24 h) and 72% and 49% (7 d) degradations of diclofenac, 25% and 73% (24 h) and 100% (7 d) degradations of paracetamol and 19% and 31% (24 h) and 64% and 67% (7 d) degradations of ketoprofen, respectively. Irbesartan was not affected by fungal activity. The two most active fungi, G. resinaceum and P. fraxinea, were tested in a second experiment in discharge wastewater collected from two different wastewater treatment plants in northern Italy. A high degradation was found in azithromycin, clarithromycin and sulfametoxazole (from 70% up to 100% in 7 days). Full article

The evolution of district heating networks is moving toward low temperatures in heat distribution with so called 4th generation networks. However, the lowest heat transfer fluid temperatures in district heating are achieved through ultra-low temperature networks, referred to as 5th generation district heating networks (5GDHNs). Low temperatures in heat distribution results in an extremely different configuration of 5GDHN compared to traditional district heating network, especially in the grid substation due to the inability to directly couple the grid with the buildings. This paper presents a detailed design of a 5th generation substation prototype, which is carried out to verify the proper operation and monitor the performance of this type of substation in a real case study. The prototype is fed by low-temperature waste heat, currently dissipated through evaporative towers, and will be built in the city of Brescia, Italy. The layout of the substation prototype, consisting of a bidirectional pumping system, a reversible water-to-water heat pump, an inertial thermal energy storage and a heat exchanger, is presented. An analysis is performed to figure out which refrigerant offers the best performance of the heat pump. In addition, fixed the refrigerant, the performance of the grid connected heat pump is found to be increased from 29.5% to 55.5% for both heating and cooling compared with a stand-alone air-to-water heat pump solution. Finally, the process flow diagram and the piping and instrumentation diagram of the substation are presented and commented. Full article

As groundwater quality monitoring networks have been expanded over the last decades, significant time series are now available. Therefore, a scientific effort is needed to explore innovative techniques for groundwater quality time series exploitation. In this work, time series exploratory analysis and time series cluster analysis are applied to groundwater contamination data with the aim of developing data-driven monitoring strategies. The study area is an urban area characterized by several superimposing historical contamination sources and a complex hydrogeological setting. A multivariate time series cluster analysis was performed on PCE and TCE concentrations data over a 10 years time span. The time series clustering was performed based on the Dynamic Time Warping method. The results of the clustering identified 3 clusters associated with diffuse background contamination and 7 clusters associated with local hotspots, characterized by specific time profiles. Similarly, a univariate time series cluster analysis was applied to Cr(VI) data, identifying 3 background clusters and 7 hotspots, including 4 singletons. The clustering outputs provided the basis for the implementation of data-driven monitoring strategies and early warning systems. For the clusters associated with diffuse background contaminations and those with constant trends, trigger levels were calculated with the 95° percentile, constituting future threshold values for early warnings. For the clusters with pluriannual trends, either oscillatory or monotonous, specific monitoring strategies were proposed based on trends’ directions. Results show that the spatio-temporal overview of the data variability obtained from the time series cluster analysis helped to extract relevant information from the data while neglecting measurements noise and uncertainty, supporting the implementation of a more efficient groundwater quality monitoring. Full article

In urban wastewater treatment plants, bacteria lead the biological component of the depuration process, but the microbial community is also rich in fungi (mainly molds, yeasts and pseudo-yeasts), whose taxonomical diversity and relative frequency depend on several factors, e.g., quality of wastewater input, climate, seasonality, and depuration stage. By joining morphological and molecular identification, we investigated the fungal diversity in two different plants for the urban wastewater treatment in the suburbs of the two major cities in Lombardia, the core of industrial and commercial activities in Italy. This study presents a comparison of the fungal diversity across the depuration stages by applying the concepts of α-, β- and ζ-diversity. Eurotiales (mainly with Aspergillus and Penicillium), Trichosporonales (Trichosporon sensu lato), Saccharomycetales (mainly with Geotrichum) and Hypocreales (mainly with Fusarium and Trichoderma) are the most represented fungal orders and genera in all the stages and both the plants. The two plants show different trends in α-, β- and ζ-diversity, despite the fact that they all share a crash during the secondary sedimentation and turnover across the depuration stages. This study provides an insight on which taxa potentially contribute to each depuration stage and/or keep viable propagules in sludges after the collection from the external environment. Full article

The goal of this work is to apply the idea of the city in 15 min to railway stations that can become the starting point of the analysis as they represent the “gateway”, where users start their last mile of travel after getting off the train. Within the research, 11 railway stations located in the Lombardy Region in Italy were identified and analyzed. To perform the analysis, an analytical index was implemented and determined for each station: this index summarizes the main features of the station itself in relation to the territory in which it is located. The adopted approach is comparative: it is not important the absolute value of the index of each station, but the comparison between the different indices. In this way it is possible on the one hand to classify the stations and on the other hand to identify and propose possible interventions to improve the role of a railway station in a territory. The proposed model is expandable and replicable: it is possible to add other useful indicators for the calculation of the index of each station and it is also possible to perform the analysis in different territorial contexts. In fact, it is a decision support tool able to provide indications and information for the planning and programming of the railway system and of the city; among the potential users of the proposed model there are railway station managers and administrations. Full article

Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device’s error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method. Full article

This study presents an innovative stabilization method of fly ash derived from co-combustion of municipal solid waste and sewage sludge. Bottom ash, obtained from the same process, is used as a stabilizing agent. The stabilization method involved the use of two other components—flue gas desulfurization residues and coal fly ash. Leaching tests were performed on stabilized samples, aged in a laboratory at different times. The results reveal the reduction of the concentrations of heavy metals, particularly Zn and Pb about two orders of magnitude lower with respect to fly ash. The immobilization of heavy metals on the solid material mainly depends on three factors—the amount of used ash, the concentrations of Zn and Pb in as-received fly ash and the pH of the solution of the final materials. The inert powder, obtained after the stabilization, is a new eco-material, that is promising to be used as filler in new sustainable composite materials. Full article

Modern (Euro VI) heavy-duty vehicles have significantly lower pollutant emissions than older vehicles. However, there are still concerns regarding the emissions of refuse collection vehicles in cities, because in some cases they may use engines designed for long haulage trucks. For this reason, we tested a diesel Euro VI (step C) refuse collection heavy-duty vehicle, both in the laboratory on a chassis dynamometer and on the road, similar to the regulated in-service conformity cycle, but also with actual refuse collection cycles. Particle number (PN) and gaseous pollutants (NO_x, CO, HC) were measured using a Portable Emissions Measurement System (PEMS). Additionally, in the laboratory we used laboratory grade gaseous, particle number, and FTIR (Fourier-transform infrared spectroscopy) systems to assess the PEMS. For short periods, where the exhaust gas temperature was low for the aftertreatment devices (cold start, some city conditions), the NO_x emissions reached 2000 mg/km. Nevertheless, all pollutants were well below the applicable emissions limits expressed in mg/kWh for all cycles examined (in brackets the ratio to the laboratory limit): NO_x < 400 mg/kWh (0.87), CO < 850 mg/kWh (0.21), HC < 12 mg/kWh (0.08), PN < 2.4×10¹⁰ p/kWh (0.04). To make sure that this will always be the case, future heavy-duty type approval emissions regulations should specifically consider the urban conditions for municipality vehicles, such as refuse trucks. Full article