Space Habitat Data Centers—For Future Computing

Forest residue has been proposed as a feasible candidate for cellulosic biofuels. However, the number of studies assessing its water use remains limited. This work aims to analyze the impacts of forest-based biofuel on water resources and quality by using a water footprint approach. A method established here is tailored to the production system, which includes softwood, hardwood, and short-rotation woody crops. The method is then applied to selected areas in the southeastern region of the United States to quantify the county-level water footprint of the biofuel produced via a mixed alcohol gasification process, under several logistic systems, and at various refinery scales. The results indicate that the blue water sourced from surface or groundwater is minimal, at 2.4 liters per liter of biofuel (l/l). The regional-average green water (rainfall) footprint falls between 400 and 443 l/l. The biofuel pathway appears to have a low nitrogen grey water footprint averaging 25 l/l at the regional level, indicating minimal impacts on water quality. Feedstock mix plays a key role in determining the magnitude and the spatial distribution of the water footprint in these regions. Compared with other potential feedstock, forest wood residue shows promise with its low blue and grey water footprint.

Data storage optimizations (DS, e.g. low latency for data access) in data center networks(DCN) are difficult online-making problems. Previously, they are done with heuristics under static network models which highly rely on designers' understanding of the environment. Encouraged by recent successes in deep reinforcement learning techniques to solve intricate online assignment problems, we propose to use the Q-learning (QL) technique to train and learn from historical DS decisions, which can significantly reduce the data access delay. However, QL faces two challenges to be widely used in data centers. They are massive input data and the blindness on parameter settings which severely hamper the convergence of the learning process. To solve these two key problems, we develop an evolutionary QL scheme, named as LFDS (Low latency and Fast convergence Data Storage). In the initial stage of the LFDS, the input matrix of QL is sparse to shrink the dimensionality of the massive input data while retaining its information as much as possible. In the following training phase, a specialized neural network is adopted to achieves a quick approximation. To overcome the blindness during QL training, the two key parameters, learning rate, and discount rate are carefully tested with real data input and network architecture. The preferred range of learning rate and discount rate are recommended for the use of QL in data centers, which brings high training rewards and fast convergence. Extensive simulations with real-world data show that the data access latency is decreased by 23.5% and the convergence rate is increased by 15%.

System dynamics simulation and scenario optimization of China's water footprint under different SSP-RCP scenarios

Background and Objectives: One of the practical strategies for managing water resources is to estimate the plant's water needs and determine the amount of water used in the various stages of crop production. In order to calculate the amount of water consumed by agricultural products in the growth stages, the virtual water index and water footprint are used. Water footprint in a product is defined as the volume of fresh water used in it's production. Water footprint is defined as an indicator for the allocation of fresh water resources that provides valuable insights into the environmental impact of producing a product. Water footprint index shows actual consumption of water in three components: blue, green and gray, which is now considered in the direction of modern management of water resources with an integrated approach. In order to properly assess water consumption in the agricultural sector, it is necessary to study the water footprint index in different climates. Materials and Methods: To conduct the research, meteorological data were first obtained from the Meteorological Organization.Then the water needs of sugar beet, potato, forage corn and tomato plants in Hamedan, Ahvaz, Shahrekord, Mashhad and Isfahan climates were calculated from the CropWat software. The yield of the crop and the amount of nitrogen fertilizer used were obtained from the agricultural jihad of each province. crop yield and the parameters required in the calculations were calculated and aggregated through agricultural statistics Then, the average volume of water consumption, virtual water, water footprint and water use efficiency of these products were calculated for the statistical period of 2011-2017. Chapagin et al. (2006) Method was used to calculate water footprint. Results: In the present study, water consumption for agricultural products was investigated. The results showed that among the studied products, the potato plant with 2945.4 m3/ton in Ahvaz city with hot and humid climate has the highest water footprint and sugar beet with 906.1 m3/ton in climate cold and dry of Mashhad has the least water footprint. The highest amount of virtual water used for the studied plants is potato (0.84 m3/kg), tomato (0.51 m3/kg), sugar beet (0.46 m3/kg) and forage corn (0.39 m3/kg) was respectively to the climate of Ahwaz, Shahrekord, Ahwaz and Isfahan. The lowest amount of water consumption efficiency (1.2 kg/m3) also belonged to the potato in Ahvaz climate. The lowest virtual water and the highest water consumption efficiency for the studied plants were obtained in Hamedan climate. Conclusion: In the present study, water consumption for agricultural products was investigated. The results showed that the volume of water consumed in the studied products varies in different climates. According to the obtained results, it can be said that the climate of Hamedan due to the low amount of virtual water (0.29, 0.3, 0.19 and 0.14 m3/ kg) and high water consumption efficiency ( 3.43, 3.3, 5.19 and 7.46 kg/m3) for potatoes, tomatoes, sugar beets and fodder corn, respectively, compared to other climates studied for cultivation of all four the product is more suitable. Hence, according to recent droughs and water shortage, available water resources should be used accurately and decision must be made for cultivating the products with less water requirement and better performance.

Water and energy demands associated with bioenergy crop production on marginal lands are inextricably linked with land quality and land use history. To illustrate the effect of land marginality on bioenergy crop yield and associated water and energy footprints, we analyzed seven large-scale sites (9–21 ha) converted from either Conservation Reserve Program (CRP) or conventional agricultural land use to no-till soybean for biofuel production. Unmanaged CRP grassland at the same location was used as a reference site. Sites were rated using a land marginality index (LMI) based on land capability classes, slope, soil erodibility, soil hydraulic conductivity, and soil tolerance factors extracted from a soil survey (SSURGO) database. Principal components analysis was used to develop a soil quality index (SQI) for the study sites based on 12 soil physical and chemical properties. The water and energy footprints on these sites were estimated using eddy-covariance flux techniques. Aboveground net primary productivity was inversely related to LMI and positively related to SQI. Water and energy footprints increased with LMI and decreased with SQI. The water footprints for grain, biomass and energy production were higher on lands converted from agricultural land use compared with those converted from the CRP land. The sites which were previously in the CRP had higher SQI than those under agricultural land use, showing that land management affects water footprints through soil quality effects. The analysis of biophysical characteristics of the sites in relation to water and energy use suggests that crops and management systems similar to CRP grasslands may provide a potential strategy to grow biofuels that would minimize environmental degradation while improving the productivity of marginal lands.

Water scarcity has become the most significant limiting factor for sustainable economic and social development in the Yellow River Basin (YRB). Despite this, most current studies on water resources in the YRB from a water footprint (WF) perspective have focused on WF measurements and have explored the influencing factors of WF changes in certain industries, while the impact mechanisms driving regional WF changes remain unclear. To bridge this research gap, the WF of nine provinces in the YRB for 2012 and 2017 were quantified using an environmentally extended multi-regional input-output model (EE-MRIO), and the driving forces of regional WF changes were explored via structural decomposition analysis (SDA). The results showed that the WF of the YRB increased by 3.8% to 113.64 billion m3 between 2012 and 2017. With rapid economic development and enhanced inter-regional trade links, the external WF has played an important role in meeting local water demand. Technological advances and production structure adjustments contribute to the reduction of the WF, thus promoting the sustainable use and management of the YRB’s water resources. Both consumption patterns and final demand per capita have dominated the YRB’s WF growth, particularly in the economically developed middle and lower reaches, where urban household consumption drove the largest WF, accounting for over 40%. Therefore, in the future, continuous optimization of the consumption structure and guidance of green consumption awareness are expected to contribute more to the reduction in WF. The findings of this study reveal the primary causes of WF changes in the YRB and offer a theoretical justification for the formulation of water conservation and sustainable utilization policies.

Economic evaluation of the environmental impact of a dairy cattle intensive production cluster under arid lands conditions

Agriculture accounts for ~90% of India's fresh water use, and there are concerns that future food production will be threatened by insufficient water supply of adequate quality. This study aimed to quantify the water required in the production of diets in India using the water footprint (WF) assessment method. The socio-demographic associations of dietary WFs were explored using mixed effects regression models with a particular focus on blue (irrigation) WF given the importance for Indian agriculture. Dietary data from ~7000 adults living in India were matched to India-specific WF data for food groups to quantify the blue and green (rainfall) WF of typical diets. The mean blue and green WF of diets was 737l/capita/day and 2531l/capita/day, respectively. Vegetables had the lowest WFs per unit mass of product, while roots/tubers had the lowest WFs per unit dietary energy. Poultry products had the greatest blue WFs. Wheat and rice contributed 31% and 19% of the dietary blue WF respectively. Vegetable oils were the highest contributor to dietary green WF. Regional variation in dietary choices meant large differences in dietary blue WFs, whereby northern diets had nearly 1.5 times greater blue WFs than southern diets. Urban diets had a higher blue WF than rural diets, and a higher standard of living was associated with larger dietary blue WFs. This study provides a novel perspective on the WF of diets in India using individual-level dietary data, and demonstrates important variability in WFs due to different food consumption patterns and socio-demographic characteristics. Future dietary shifts towards patterns currently consumed by individuals in higher income groups, would likely increase irrigation requirements putting substantial pressure on India's water resources.

China's dietary changes would increase agricultural blue and green water footprint

In the Yellow River basin, China, ecosystems suffer from the overexploitation and utilization of water resources, resulting in various environmental impacts. Consideration must be given to both human and ecosystem water requirements in water resources management. A water footprint (WF) is a tool for estimating industrial, agricultural, commercial and household water requirements and for examining the impact of consumption on water resources. The study attempts to establish an approach to analyse the dynamic processes and driving forces that result in certain WFs. Using input–output tables for provinces and municipalities, we calculate water use coefficients, the total WF and the net external WF of consumption in China’s Yellow River Basin. A biproportional algorithm is employed to revise the input–output tables for analysing the temporal dynamics of the WF. The factor analysis and linear regression were used to analyse the main influencing factors of WF. Results indicate that the coefficient for water use by primary industries is highest and that coefficients for provincial water use differ significantly. Second, household consumption and residuals from capital accumulation constituted approximately half of the total WF of the Yellow River basin in 2002 and also differed significantly among provinces. Third, the ratio of the net external WF to the total WF increased, and the ratio of final consumption to the total WF declined during the period examined. Fourth, output by secondary industries correlated most strongly with the WF, followed by area under irrigation, per capita meat consumption, water consumption per 10,000-yuan increase in added value and population.

Evolution of Chinese urban household's water footprint

Water footprint and scenario analysis in the transformation of Chongming into an international eco-island

There is an increase in requirement and competition for water, while water resources are decreasing at an accelerating rate. Agriculture is the biggest consumer of water and therefore has the largest water footprint, which is not yet known. The largest portion is acknowledged to be for producing animal products. Water footprints account for the amounts of water used to produce a commodity for consumption, measured along the commodity life cycle. Water withdrawals from surface and groundwater are accounted for when assessing the water footprint. The three identified major determinants of a water footprint of meat include feed conversion efficiency (FCE), feed composition, and feed origin, with the first two being influenced greatly by the animal production system. In South Africa (SA), the two distinct production systems are the intensive and extensive production systems. Intensifying beef animals improves FCE due to faster growths per kg feed consumed, reduced activity, and therefore reduced water footprint. Beef cattle in the extensive system consume a large component of roughages, while the intensive system has a high concentrate to roughage ratio. This theoretically increases the water footprint in the intensive system. The literature indicates large amounts of volumetric water footprint indicators of boneless beef in SA. Water footprint assessment is critical for enabling consumers to make well-informed and sound decisions when considering changes in their behavior due to the effect this has on social, economic, and environmental wellbeing. This paper aims to postulate the various issues associated with water usage in beef production. These include factors affecting the water footprint of beef production and the effects it has on various aspects of both the environment and social wellbeing. It further explores the various methods to assess the water footprint of a product.

Extreme weather events have affected the environment and water resources in Taiwan for the last two decades. Heavy rainfall, typhoons, and rising sea levels have caused severe flooding along the Southwest Coast in Taiwan. Yunlin County, an important agricultural region, will be significantly affected by climate changes, especially in coastal areas with severe land subsidence. Therefore, using the concept of the water footprint and questionnaire surveys, this study examines personal water footprints in townships in Yunlin County to explore the effectiveness and sustainability of water management. The purpose of the water footprint concept is to quantify environmental burdens imposed by individuals’ demand for water. An individual water footprint involves direct and indirect water usage that is associated with personal habits. Analytical results show that the most individual water consumption is highest along coastal areas, such as Kouhu and Taixi, and mountainous areas, such as Gukeng, Douliu, and Linnei. Furthermore, one-way ANOVA of individuals’ daily water footprint reveals that individual water footprints vary significantly among Douliu, Gukeng, and Mailiao. The mean daily water footprint per capita in Douliu and Gukeng significantly exceeds that in Mailiao. This study considers the location quotients of industries in these three townships, which indicate that the location quotients of the accommodation and food and beverage industries in Douliu and Gukeng significantly exceed those of Mailiao. The individual virtual water use that is associated with the aforementioned industries is large. Clearly, individual water use habits in townships are related to the industry type. Douliu and Gukeng are major centers of the tertiary industry, which has a higher location quotient than in Mailiao. Mailiao is a major center of manufacturing as a secondary industry. Therefore, flourishing regions with tertiary industries have high virtual water consumption. These findings suggest that personal water use habits can explain why personal water footprints are larger in Yunlin County than in other counties in Taiwan.

Investigating agricultural water sustainability in arid regions with Bayesian network and water footprint theories