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Hydrothermal carbonization (HTC) provides an attractive alternative method for the treatment of high-moisture waste and, in particular, digested sludge. HTC could reduce the costs and environmental risks associated with sludge handling and management. Although it is recognized that the dewaterability of hydrochars produced from digested sludge, even at mild temperatures (180–190 °C), is highly improved with respect to the starting material, the filterability of HTC slurries for the recovery of the solid material (hydrochar) still represents a challenge. This study presents the results of an investigation into the filterability of agro-industrial digested sludge HTC slurries produced by a C-700 CarboremTM HTC industrial-scale plant. The filterability of HTC slurries, produced at 190 °C for 1 h, with the use of acid solutions of hydrochloric acid, sulfuric acid or citric acids, was investigated by using a semi-industrial filter press. The use of sulfuric acid or citric acid solutions, in particular, significantly improved the filterability of HTC slurries, reducing the time of filtration and residual moisture content. The acid treatment also promoted the migration of heavy metals and phosphorus (P) in the HTC filtrate solution. This study demonstrates that P can be recovered via the precipitation of struvite in high yields, recovering up to 85 wt% by mass of its initial P content.

Dark fermentation (DF) is a simple method for hydrogen (H2) production through the valorization of various organic wastes that can be used as feedstock. In particular, an organic fraction of municipal solid waste (OFMSW) is a fermentation substrate that can easily be gathered and provides high yields in biogas and value-added organic compounds such as volatile fatty acids (VFAs). DF is coupled with a methanogenic reactor to enhance biogas production from the OFMSW. In this study, a two-stage reactor was conducted and monitored to optimize the methane yield by reducing the HRT at the DF reactor. A focus of the functional inference based on a next-generation sequence (NGS) metabarcoding analysis and comparison of microbial communities that populate each reactor stage was performed. Concerning gas quality, the two-stage system observed a hydrogen-rich biogas in the first fermentative reactor (on average 20.2%) and an improvement in the methane content in the second methanogenic digester, which shifted from 61.2% obtained for the one-stage experiment to 73.5%. Such increases were due to the improvement in substrate hydrolysis. As for the specific biogas production, the results showed an overall increase of 50%.

An original methodology for the Water Footprint Assessment (WFA) of a Product for the wine-making industry sector is presented, with a particular focus on the evaluation procedure of the grey water. Results obtained with the proposed methodology are also presented for an Italian case study. The product was analyzed using a life-cycle approach, with the aim of studying the water volumes of each phase according to the newly-released ISO 14046 international standard. The functional unit chosen in this study is the common 0.75 liter wine bottle. An in-house software (V.I.V.A.) was implemented with the goal of accounting for all the contributions in a cradle-to-grave approach. At this stage, however, minor water volumes associated with some foreground and background processes are not assessed. The evaluation procedure was applied to a case study and green, blue, and grey water volumes were computed. Primary data were collected for a red wine produced by an Umbrian wine-making company. Results are in accordance with global average water footprint values from literature, showing a total WF of 632.2 L/bottle, with the major contribution (98.3%) given by green water, and minor contributions (1.2% and 0.5%) given by grey and blue water, respectively. A particular effort was dedicated to the definition of an improved methodology for the assessment of the virtual water volume required to dilute the load of pollutants on the environment below some reference level (Grey WF). The improved methodology was elaborated to assure the completeness of the water footprint assessment and to overcome some limitations of the reference approach. As a result, the overall WF can increase up to 3% in the most conservative hypotheses.

This study will review the environmental implications of dynamic policy objectives and instruments outlined in the European Union 7th Framework Programme (EU-FP7) Project DYNAmic policy MIXes for absolute decoupling of EU resource use from economic growth (DYNAMIX) to address reductions in food consumption, food waste and a change in waste handling systems. The environmental implications of reductions in protein intake, food waste reductions, food waste management and donations are addressed using a life cycle approach to find the greenhouse gas (GHG) emissions, land use and water consumption. Data are provided from the Statistics Division of the Food and Agriculture Organization (FAOSTAT) food balance sheets for the European Union (EU) with a base year of 2010 and life cycle inventory (LCI) data from a meta-study of available GHG, land use and water consumption data for major food products. The implications are reviewed using a number of scenarios for the years 2030 and 2050 assuming policy instruments are fully effective. Results indicate that reductions in animal-based protein consumption significantly reduce environmental impacts, followed thereafter by reductions in food waste (assuming this also reduces food consumption). Despite the positive implications the policy mixes may have for targets for decoupling, they are not enough to meet GHG emissions targets for the EU outlined in the DYNAMIX project, although land and water use have no significant change compared to 2010 levels.

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

This paper presents the results of multidisciplinary research which addresses the issue of sustainability within the design of urban agricultural landscapes through the joint solution of architecture and hydro-geomorphology for the management, accessibility and use of urban agricultural landscapes. If the traditional separation between urban and productive land has now been overcome by the inclusion of agricultural areas within the cities, what is still under discussion is how to make its planning sustainable, especially in densely urbanized contexts. This research was developed with a methodology able to combine different types of data in order to satisfy needs relating to the availability of water for land supply and to make this landscape—both rural and urban—accessible to the city. Historical traces, interpretative insights, and hydro-geomorphological reading models of the territory were analysed. Their interweaving has made it possible to develop a prototype of a water and landscape infrastructure that combines a system for subsurface water, cycle–pedestrian paths and small-scale architecture features around the water tanks. The research has been tested in the city of Pozzuoli (NA) in Southern Italy, within the Monteruscello neighbourhood. The result led to a design solution of an integrated system which contributes to implementing the green transition within cities.

As a consequence of climate change and urbanization, many cities will have to deal with more flooding and extreme heat stress. This paper presents a framework to maximize the effectiveness of Nature-Based Solutions (NBS) for flood risk reduction and thermal comfort enhancement. The framework involves an assessment of hazards with the use of models and field measurements. It also detects suitable implementation sites for NBS and quantifies their effectiveness for thermal comfort enhancement and flood risk reduction. The framework was applied in a densely urbanized study area, for which different small-scale urban NBS and their potential locations for implementation were assessed. The overall results show that the most effective performance in terms of flood mitigation and thermal comfort enhancement is likely achieved by applying a range of different measures at different locations. Therefore, the work presented here shows the potential of the framework to achieve an effective combination of measures and their locations, which was demonstrated on the case of the Sukhumvit area in Bangkok (Thailand). This can be particularly suitable for assessing and planning flood mitigation measures in combination with heat stress reduction.