Purpose The aim of the present paper is to describe the development of a life cycle assessment study of the service of potable water supply in Sicily, Italy. The analysis considers the stages of collection, treatment and distribution of potable water through the regional network, whilst the use stage of water is not included. Methods The selection of a methodological pattern coherently with the requirements of an environmental label such as the EPDs aims at allowing comparability among different studies. Results The analysis shows the shares of impacts along the life cycle chain, i.e. outputs by well fields and spring groups, purification and desalination plants, water losses in the waterworks, electrical consumption of waterworks systems and impacts of network maintenance. As concerns Global Warming Potential (GWP), the impact of purification plants represents a 6-7% share of the total, whilst desalination 74%. Water losses in the waterworks show an impact of 15-17%, the contribution owing to electrical consumption of waterworks systems and network maintenance results to be 3%. Desalination plants represents the major contribution to all the considered impact categories. Conclusions As concerns management issues, the most relevant impact categories resulted to be GWP, non-renewable energy resources and water consumption. Since the results for non-renewable energy resources are strictly connected to GWP emissions, , carbon footprint and water footprint can be profitably used as single issue indicators without the risk of burden shifting in studies aiming to evaluate the impact of potable water distribution
Evaluation of the sustainability of biomass pyrolysis requires a thorough assessment of the product yields and energy densities. With this purpose, a laboratory scale fixed bed reactor (FBR) was adapted from the standard Gray-King (GK) assay test on coal to conduct fixed bed pyrolysis experiments on agricultural and agro-industrial by-products. The present study provides results on the pyrolysis of two types of biomass: chipped olive tree trimmings (OT) and olive pomace (OP). Solid (char) and liquid (tar) product yields are reported. Mass yields are determined and compared with values obtained in similar works. Results indicate that char yield decreases from 49% (OT-db) and 50% (OP-db) at 325 °C to 26% (OT db) and 30% (OP-db) at 650 °C. Tar yield is almost constant (42%) at different reaction temperatures for OT, while it decreases slightly from 42% to 35% for OP. Energy density of the products at different peak temperatures is almost constant for OT (1.2), but slightly increases for OP (from a value of 1.3 to a value of 1.4).
This case study shows results of a calculation of carbon footprint (CFP) resulting from the production of nuts added value products for a large consumer market. Nuts consumption is increasing in the world and so is the consumer awareness of the environmental impact of goods, hence the calculation of greenhouse gas (GHG) emissions of food production is of growing importance for producers. Calculation of CO 2eq emissions was performed for all stages of the production chain to the final retail point for flour, grains, paste, chocolate covered nuts and spreadable cream produced from almonds, pistachios and hazelnuts grown and transformed in Italy and for peanuts grown in Argentina and transformed in Italy. Data from literature was used to evaluate CFP of raw materials, emissions from transport and packing were calculated using existing models, while emissions deriving from transformation were calculated empirically by multiplying the power of production lines (electrical and/or thermal) by its productivity. All values were reported in kg of CO 2 equivalent for each kg of packed product (net weight). Resulting values ranged between 1.2 g of CO 2 /kg for a 100 g bag of almond to 4.8 g of CO 2 /kg for the 100 g bag of chocolate covered almond. The calculation procedure can be well used for similar cases of large consumer food productions.
Wind speed forecasting is essential for effective planning of wind energy exploitation projects. The ability to predict short-term wind speed is a prerequisite for all the operators of the wind energy sector. Consequently it is essential to identify an efficient method for forecasts. In this paper, the wind speed in the province of Trapani (Sicily) is modeled by artificial neural network. Several model of neural network were generated and compared through error measures. Simulation results show that the estimated values of wind speed are in good agreement with the values measured by anemometers..
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