• Energy Research
• 2021-2025close
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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.

AbstractElevated CO2 concentration has been reported to decrease grain nutrient concentrations and thus worsen nutritional deficiency and hidden hunger. One nutritional aspect is mineral content, yet mineral bioavailability can be limited by the presence of phytic acid. Given that future climate scenarios predict elevated global temperature driven by elevated atmospheric CO2 concentrations, we used Temperature by Free‐Air CO2 Enrichment (T‐FACE) field experiments to investigate whether elevated temperature alters the effects of elevated CO2 on grain mineral concentrations, grain mineral yields, and their bioavailability in a range of wheat and rice genotypes. We found that the negative effects of elevated CO2 were compensated for by positive effects of elevated temperature. As a result, the combined elevated CO2 and elevated temperature increased concentrations of some minerals by up to ~15% in both rice and wheat relative to control conditions. Moreover, the combined elevated CO2 and elevated temperature did not significantly change total yields of some minerals despite lower grain yields. The combined CO2 and temperature elevation increased phytic acid concentration in rice by 18.1% but decreased it in wheat by 3.5%. The mineral bioavailability, estimated as the mole ratio of phytic acid to minerals in rice and wheat grains, was limited by the combined CO2 and temperature elevation in only a few cases. Our results indicate that under future climate conditions of elevated temperature and CO2, the nutritional quality of rice and wheat with respect to minerals may remain unchanged.

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.

Yeast volatiles double starvation survival in Drosophila .

The solutions-based design framework of permaculture exhibits transformative potential, working to holistically integrate natural and human systems toward a more just society. The term can be defined and applied in a breadth of ways, contributing to both strengths and weaknesses for its capacity toward change. To explore the tension of breadth as strength and weakness, we interviewed 25 prominent permaculture teachers and practitioners across the United States (US) regarding how they define permaculture as a concept and perceive the term’s utility. We find that permaculture casts a wide net that participants grapple with in their own work. They engaged in a negotiation process of how they associate or disassociate themselves with the term, recognizing that it can be both unifying and polarizing. Further, there was noted concern of permaculture’s failure to cite and acknowledge its rootedness in Indigenous knowledge, as well as distinguish itself from Indigenous alternatives. We contextualize these findings within the resounding call for a decolonization of modern ways of living and the science of sustainability, of which permaculture can be critically part of. We conclude with recommended best practices for how to continuously (re-)define permaculture in an embodied and dynamic way to work toward these goals.

The energy crisis and increasing fossil fuel prices due to increasing demands, controlled supplies, and global political unrest have adversely affected agricultural productivity and farm profitability across the globe and Pakistan is not an exception. To cope with this issue of energy deficiency in agriculture, the best alternate strategy is to take advantage of biomass and solid waste potential. In low-income countries such as Pakistan, the greenhouse heating system mostly relies on fossil fuels such as diesel, gasoline, and LPG. Farmers are reluctant to adopt greenhouse farming due to the continuously rising prices of the fossil fuels. To reduce reliance on fossil fuel energy, the objective of this study was to utilize biomass from crop residues to develop an efficient and economical biomass furnace that could heat greenhouses to protect the crop from seasonal temperature effects. Modifications made to the biomass furnace, such as the incorporation of insulation around the walls of the furnace, providing turbulators in fire tubes, and a secondary heat exchanger (heat recovery system) in the chimney, have increased the thermal efficiency of the biomass furnace by about 21.7%. A drastic reduction in hazardous elements of flue gases was observed due to the addition of a water scrubber smoke filter in the exit line of the flue. The efficiency of the biomass furnace ranged from 50.42% to 54.18%, whereas the heating efficiency of the diesel-fired heater was 71.19%. On the basis of the equal heating value of the fuels, the unit material and operating costs of the biomass furnace for wood, cotton stalks, corn cobs, and cow dung were USD 2.04, 1.86, 1.78, and 2.00 respectively against USD 4.67/h for the diesel heater. The capital and operating costs of the biomass furnace were about 50% and 43.7% of the diesel heater respectively, resulting in a seasonal saving of about 1573 USD. The produced smoke was tested as environmental friendly under the prescribed limits of the National Environmental Quality Standards (NEQS), which shows potential for its large-scale adoption and wider applications.

Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.