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In Italy, the use of chainsaws for field operations such as Felling (FE), Delimbing (DE), and Bucking (BU) is widespread due to the topography, the medium–small size of farms, and the predominant presence of broad-leaved forests managed through coppicing. However, this has led to an increase in injuries and illnesses due to exposure to physical factors (e.g., noise, dust, and vibrations) and chemical agents (e.g., various volatile compounds). Occupational health and safety legislation in Italy has undergone several phases, including the approval of U.T. 81/2008. The present study aims to evaluate the noise generated by chainsaws and the concentration of pollutants (CO, VOC, and C6H6) present in chainsaw exhaust gases during interventions in a chestnut coppice in relation to the limits set by current legislation. The analysis of the noise generated by chainsaws during chestnut cutting operations showed that it exceeded the legal noise limits during all chainsaw activities, with peak levels of about 110 dB. The detected noise could cause important critical issues in relation to the health and safety of specialized operators. Furthermore, the correlation between the specific work (FE, DE, and BU) and the ratio between maximum and average values of CO and VOC emissions was evaluated. Notably, comparable levels of maximum VOC emissions were observed during the FE and BU phases. However, the average emission values during these phases exhibited significant differences, suggesting higher VOC production when the engine was running but not actively engaged in cutting. The highest emissions were recorded during the FE phase (CO = 135 ppm, VOC = 17.28 ppm, and C6H6 = 2.13 ppm).

Regeneration of existing buildings and abandoned areas is a governmental and European key objective. This research focuses on Poveglia: an abandoned island in the Venetian lagoon, Italy. The underpinning idea concerns the upgrade of Poveglia to a zero energy Cognitive University Campus, connected to other educational institutions in the lagoon. The vision aims at reusing abandoned islands combining architecture, learning environments and digital networks, to demonstrate the potential of new technologies for the transformation of abandoned areas. The case study demonstrates the effectiveness of employing improved and revised systems and tools, developed in eLUX lab at the University of Brescia, for the regeneration of the island. The inclusion of the island in a smart campus network results in triggering positive synergies both enhancing energy efficiency and the learning environment. A further development concerns the setup of strategies for historical preservation, energy efficiency and renewable energy production.

Citizens in dense built environments are susceptible to the simultaneous occurrence of Slow Onset Disaster (SLOD) events, being particularly prone to increasing temperatures and air pollution. Previous research works have assessed these events’ arousal separately and have identified when their intensity is critical. However, few have integrated their analysis, possibly limited by the quality and granularity of available data, the accessibility and distribution of sensors, and measurements not emulating the surroundings of a pedestrian. Thus, this work performed an outdoor meso-scale multi-hazard-based risk analysis to study the aggregated effects of the SLODs mentioned above. The study was carried out to narrow down the time-frames within 2019 in which these two events could have affected citizens’ health the most. A weighted fuzzy logic was applied to superimpose climatic (temperature, humidity, wind speed, and solar irradiance) and air quality (particulate matter, ozone, and ammonium) distress (true risk) on an hourly basis, allocated using set healthy and comfortable ranges for a specific dense urban climate context within Milan (Italy), processing data from Milano via Juvara station. The findings show that sensitive groups were at risk of high temperature and pollution separately during 26% and 29% of summer and mid-season hours, respectively; while multi-hazard risk would arise during 10.93% of summer and mid-season hours, concentrated mainly between 14:00 and 20:00.

Digital Twins (DT) are powerful tools to support asset managers in the operation and maintenance of cognitive buildings. Building Information Models (BIM) are critical for Asset Management (AM), especially when used in conjunction with Internet of Things (IoT) and other asset data collected throughout a building’s lifecycle. However, information contained within BIM models is usually outdated, inaccurate, and incomplete as a result of unclear geometric and semantic data modelling procedures during the building life cycle. The aim of this paper is to develop an openBIM methodology to support dynamic AM applications with limited as-built information availability. The workflow is based on the use of the IfcSharedFacilitiesElements schema for processing the geometric and semantic information of both existing and newly created Industry Foundation Classes (IFC) objects, supporting real-time data integration. The methodology is validated using the West Cambridge DT Research Facility data, demonstrating good potential in supporting an asset anomaly detection application. The proposed workflow increases the automation of the digital AM processes, thanks to the adoption of BIM-IoT integration tools and methods within the context of the development of a building DT.

In accordance with European objectives, the Basilicata region intends to promote the use of energy systems and heat generators powered by lignocellulosic biomass, so the present study aimed to investigate the availability of logging residues and most suitable areas for the construction of bioenergy production plants. The life cycle assessment (LCA) methodology was employed to conduct an environmental impact assessment of the biomass distribution and its transport, and spatial LCA was used to evaluate the impact of regional transport. One cubic meter kilometer (m3 km−1) was used as the functional unit and a small lorry was considered for the transport. The results showed that the available harvesting residues amounted to 36,000 m3 and their loading environmental impact accounted for 349 mPt m−3. The impacts of transport (4.01 mPt m−3) ranged from 3.4 to 144,400 mPt km−1 forest parcel−1, mainly affecting human health (95%) and, second, the ecosystem quality (5%). Three possible sites for bioenergy plant location were identified considering the environmental impact distribution due to feedstock transport. Findings from this research show the importance of considering the LCA of biomass acquisition in site selection and can fill the knowledge gaps in the available literature about spatial LCA.

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities’ lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema.