Real-time monitoring of energetic-environmental parameters in wastewater treatment plants enables big-data analysis for a true representation of the operating condition of a system, being still frequently mismanaged through policies based on the analysis of static data (energy billing, periodic chemical–physical analysis of wastewater). Here we discuss the results of monitoring activities based on both offline (“static”) data on the main process variables, and on-line (“dynamic”) data collected through a monitoring system for energetic-environmental parameters (dissolved oxygen, wastewater pH and temperature, TSS intake and output). Static-data analysis relied on a description model that employed statistical normalization techniques (KPIs, operational indicators). Dynamic data were statistically processed to explore possible correlations between energetic-environmental parameters, establishing comparisons with static data. Overall, the system efficiently fulfilled its functions, although it was undersized compared to the organic and hydraulic load it received. From the dynamic-data analysis, no correlation emerged between energy usage of the facility and dissolved oxygen content of the wastewater, whereas the TSS removal efficiency determined through static measurements was found to be underestimated. Finally, using probes allowed to characterize the pattern of pH and temperature values of the wastewater, which represent valuable physiological data for innovative and sustainable resource recovery technologies involving microorganisms.

In the era of digitalization, the process industry is one of the sectors most affected by the need for change. The adoption of IoT-based intelligent monitoring systems for the collection of real-time measurements of energy and other essential operational variables, on one hand, makes it possible to accumulate big data useful for the company management to monitor the stability of the production process over time, and on the other hand, helps to develop predictive models that enable more efficient work and production. The OTTORTO project stems from the need of the FARRIS company to adapt its production line to agriculture 4.0 policies, responding to the higher goals of digitization and technological transition imposed at the national and EU level. The objectives of the current study are (i) to present an “ad hoc” customized intelligent and multi-parameter monitoring system to derive real-time temperature and humidity measurements inside the company’s industrial drying kilns; and (ii) to show how it is possible to extract information from operational data and convert it into a decision support too and an effective knowledge medium to better understand the production process. Studying the correlations between temperature and humidity measurements showed that for most of the observation period, the system was thermodynamically quite stable in terms of major operational risks, such as humidity saturation inside the kilns causing condensation on the products to be dried. However, to remedy the occasional occurrence of such inefficiencies, implementing kilns with the introduction of forced air extraction systems could bring significant benefits in terms of improved energy-environmental performance.

A monitoring campaign performed on the Real Site of Carditello (found in the commune of San Tammaro, province of Caserta, Italy) was aimed to evaluate the parameters related to air quality and then to control and prevent the degradation risk. The analyses were carried out in the context of the project “Innovative methods and technologies for the conservation, valorisation and use of cultural heritage: environmental and archaeological analyses”, within the CULTURA CREA-MIBAC program, which aims to provide innovative methods for conservation and enhancement of cultural heritage through environmental and archaeometric analyses. Preliminarily, using field equipment, non-destructive investigations were carried out to identify constituent, restoration, and/or degradation materials belonging to the wall paintings on the main floor and to assess their conservation state under the current environmental conditions. Environmental monitoring was performed both outdoor (to assess the effects of atmospheric pollution and changes in the climate of the structure) and indoor the rooms of the site (to evaluate the conditions of conservation and fruition). The collected data are reported in a web platform, where one can check the real time monitoring data. The platform integrates both environmental data and information regarding the "health status" of site in order to provide a detailed analysis useful for its protection.

Promoting energetic and environmental sustainability in the naval sector requires a necessary understanding of the energy demand of vessels and of the factors affecting it. This article shows the results of a study conducted by the shipping company MedMar aimed at acquiring a detailed analysis of the energetic performances of its fleet. The study involved the analysis of fuel consumption and emissions of the fleet using a specific software and under different scenarios, assuming the navigation speed and the cargo level of the vessels as reference parameters. Simulations also provided a comparison, concerning emissions and externalities, between ships and two different means of transport. The purpose of this study was to identify potential areas of improvement, where ad hoc strategies could be used to further optimise the energetic and environmental performance of MedMar fleet and mitigate its impact on the delicate ecosystem of the gulf of Naples, where the fleet sails.