• Energy Research
• 2016-2025close
• 6. Clean water close

Abstract Levee systems are an important part of California’s water infrastructure, engineered to provide resilience against flooding and reduce flood losses. The growth in California is partly associated with costly infrastructure developments that led to population expansion in the levee protected areas. Therefore, potential changes in the flood hazard could have significant socioeconomic consequences over levee protected areas, especially in the face of a changing climate. In this study, we examine the possible impacts of a warming climate on flood hazard over levee protected land in California. We use gridded maximum daily runoff from global circulation models (GCMs) that represent a wide range of variability among the climate projections, and are recommended by the California’s Fourth Climate Change Assessment Report, to investigate possible climate-induced changes. We also quantify the exposure of several critical infrastructure protected by the levee systems (e.g. roads, electric power transmission lines, natural gas pipelines, petroleum pipelines, and railroads) to flooding. Our results provide a detailed picture of change in flood risk for different levees and the potential societal consequences (e.g. exposure of people and critical infrastructure). Levee systems in the northern part of the Central Valley and coastal counties of Southern California are likely to observe the highest increase in flood hazard relative to the past. The most evident change is projected for the northern region of the Central Valley, including Butte, Glenn, Yuba, Sutter, Sacramento, and San Joaquin counties. In the leveed regions of these counties, based on the model simulations of the future, the historical 100-year runoff can potentially increase up to threefold under RCP8.5. We argue that levee operation and maintenance along with emergency preparation plans should take into account the changes in frequencies and intensities of flood hazard in a changing climate to ensure safety of levee systems and their protected infrastructure.

In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

In this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.

The influence of ethanol on the degradation kinetics of linear alkyl benzene sulfonate (LAS) and organic matter was investigated using batch experiments with different initial LAS concentrations (8.3 mg L-1 to 66.9 mg L-1) and biomass immobilized on sand. Data were fitted with a substrate inhibition model. Concentrations of 2.4 mg LAS L-1 and 18.9 mg LAS L-1 (without and with ethanol) provided the maximum LAS utilization rate by the biomass (Sbm). For LAS degradation, ethanol addition favored a lower decrease in the specific substrate utilization rate (robs), even at the LAS concentration usually reported as inhibitory (> 14.4 mg L-1). For organic matter degradation, robs was higher with ethanol. Higher biomass differentiation was observed at higher LAS concentrations. With ethanol, microbial selection occurred at LAS concentrations near Sbm. At higher LAS concentrations, the dominance and diversity values did not change significantly with ethanol, whereas without ethanol, their behaviors were irregular.

In this paper, we propose and investigate an original approach to energy conversion based on polyzwitterionic hydrogels, which exhibit an antipolyelectrolyte effect that enables them to swell in salt water and shrink in water of a different (i.e., desalinated water) salinity. The swelling and shrinking processes run cyclically and can move a piston up or down reversibly, thus transforming the antipolyelectrolyte effect into a mechanical force based on the salinity gradient. This phenomenon makes polyzwitterionic hydrogels suitable for use in a smart, polymeric engine. We apply this approach to investigate energy recovery from a polysulfobetaine-based hydrogel. The cross-linking density, external load, particle size, and repeatability of energy recoverability of hydrogels are examined. The maximum energy recovery from 0.4 g of hydrogel in feed (calculated based on dry form) of 102 mJ/kg was obtained by a hydrogel with a 3% cross-linking density, a 200-300 μm particle size, and 100 g external load. Excellent reproducibility of engine cycles was achieved over 10 cycles. This concept is complementary to the osmotic engine concept based on a polyelectrolyte hydrogel. In addition, polyzwitterionic materials have become a benchmark material for preventing biofouling, and the swelling properties of such materials can be further modulated and tuned.

Scarcity of water resources in the entire country is more serious in the northern provinces like Golestan. Therefore, farmers have to use saline water or waste waters. One way to adapt to this condition is to use unconventional water such as Caspian Sea water, with lower salinity as compared with water from free seas, for common crops like soybean. Thus feasibility of using Caspian Sea water that has integrated with narrow common water and the calibration of Aquacrop model under the effect of salinity and water stress for soybean crop in Golestan province are more important. The Aquacrop model assumes a linear relationship between the biomass yield (BY) and crop transpiration one one hand and water productivity (WP) value on the other (Steduto and Albrizio, 2005). This model, as compared to other crop models, requires minimal input data and its new version 4.0 (June, 2012) has a salinity module which was used in this research to simulate the grain yield (GY) and WP of soybean under deficit and saline water irrigation. Furthermore, the AquaCrop model has not often been tested to simulate the yield of soybean under saline conditions in the semi-arid climate of Golestan province. This study aims at evaluating the efficiency of AquaCrop model in soybean yield simulation. The results are to be used for optimizing water consumption under water and saline stress. To this end, an experiment was carried out at the research farm of Gorgan University of Agricultural Sciences and Natural Resources.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Abstract This paper aims to investigate sustainable waste management solutions for the city of Rio de Janeiro in Brazil, based on a life cycle approach. The Life Cycle Assessment (LCA) was performed by using the LCA-IWM methodology. The model was adapted to the Brazilian solid waste context and to the local characteristics, including waste composition, electricity mix and regulations. The annual amount of waste generated was the functional unit adopted. Eight municipal solid waste management strategies were evaluated. Scenarios including mixed waste collection and source separated collection as well as materials recovery and energy from waste were investigated. Scenarios were ranked based on the LCA results. Sensitivity analysis was carried out by varying the electricity mix. The results indicate that the current situation of municipal solid waste (MSW) in Rio de Janeiro presents the worst performance in terms of aggregated environmental burdens, indicating the urgency for implement new strategies toward a more environmentally friendly and sustainable MSW management system. It is noted that the better LCA performances were obtained in scenarios with high separately collection rates. Among them, the scenario based on recyclables recovery and anaerobic digestion shows to be a promising strategy to improve environmental sustainability. Sensitivity analysis demonstrates that the ranking of scenarios was not affected by changes in the electricity mix. From the results, the investment in source separated waste collection and materials recovery is the most environmentally friendly MSW strategy for Rio de Janeiro. In addition, scenarios with an emphasis in materials recovery presented higher environmental benefits than the alternatives focused on energy generation.