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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.

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.

The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years; physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site; the maximum weights of these two features reaches 0.304 and 0.288.

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Response of aquatic organisms to eutrophication have been well reported, while less studies are available for the recovery of eutrophic lakes following a reduction in the external loading, especially for systems where nitrogen is reduced but the phosphorus concentration is maintained high due to internal loading. Diatoms are nitrate (NO3-N) opportunists but can also use ammonium (NH4-N). They may, therefore, be more sensitive to nitrogen reduction than other algae that typically prefer NH4-N. We document the variations of nutrients and diatoms in subtropical, eutrophic Lake Taihu over 28 yr during which a reduction of the external loading resulted from lake management. According to the results of change point analysis, data on environmental variables were divided into two periods (P1: 1992-2006; P2: 2007-2019) with two different seasons (WS: Winter-Spring; SA: Summer-Autumn), respectively. Compared with P1-WS, the concentration of NH4-N decreased significantly whereas NO3-N showed no significant change in P2-WS. In contrast, NH4-N concentrations were low and showed no significant changes in P1-SA and P2-SA and NO3-N decreased significantly in the latter period. Accordingly, NO3-N: NH4-N mass ratios in P1-SA and P2-WS were all significantly higher than those in P2-SA and P1-WS, respectively. The biomass of WS diatom increased significantly and the timing of the peak biomass shifted from P1-SA to P2-WS since 2007. The SEM analysis showed that NO3-N was retained as a statistically significant predictor for diatom biomass in P1-SA and significant effects of windspeed, zooplankton and NH4-N on diatom biomass in P2-WS. Windspeed and zooplankton have further changed the biomass of diatoms in the case of declining inorganic nitrogen. We conclude that the magnitude of vernal suppression or stimulation of diatom assemblages has increased, concomitant with the variations of NH4-N and NO3-N: NH4-N mass ratios. Diatoms response to NH4-N or NO3-N is apparently changing in response to water temperature in this eutrophic shallow lake. Thus, parallel reductions in external nitrogen loading, along with variations in dominant inorganic nitrogen, will stimulate the growth of diatom and therefore increase the total biomass of phytoplankton in still high internal phosphorus loading, which is should be regarded as a good sign of restoration measures.

The performance evaluation and optimization of an energy conversion system design of an energy intensive drying system applied the method of combining exergy and economy is a theme of global concern. In this study, a gas-type industrial drying system of black tea with a capacity of 100 kg/h is used to investigate the exergetic and economic performance through the exergy and exergoeconomic methodology. The result shows that the drying rate of tea varies from the maximum value of 3.48 gwater/gdry matter h to the minimum 0.18 gwater/gdry matter h. The highest exergy destruction rate is found for the drying chamber (74.92 kW), followed by the combustion chamber (20.42 kW) in the initial drying system, and 51.83 kW and 21.15 kW in the redrying system. Similarly, the highest cost of the exergy destruction rate is found for the drying chamber (18.497 USD/h), followed by the combustion chamber (5.041 USD/h) in the initial drying system, and 12.796 USD/h and 5.222 USD/h in the redrying system. Furthermore, we analyzed the unit exergy rate consumed and the unit exergy cost of water removal in different drying sections of the drying system, and determined the optimal ordering of each component. These results mentioned above indicate that, whether from an energy or economic perspective, the component improvements should prioritize the drying chamber. Accordingly, minimizing exergy destruction and the cost of the exergy destruction rate can be considered as a strategy for improving the performance of energy and economy. Overall, the main results provide a more intuitive judgment for system improvement and optimization, and the exergy and exergoeconomic methodology can be commended as a method for agricultural product industrial drying from the perspective of exergoeconomics.

Sustainable food supply has gained considerable consumer concern due to the high percentage of spoilage microorganisms. Food industries need to expand advanced technologies that can maintain the nutritive content of foods, enhance the bio-availability of bioactive compounds, provide environmental and economic sustainability, and fulfill consumers’ requirements of sensory characteristics. Heat treatment negatively affects food samples’ nutritional and sensory properties as bioactives are sensitive to high-temperature processing. The need arises for non-thermal processes to reduce food losses, and sustainable developments in preservation, nutritional security, and food safety are crucial parameters for the upcoming era. Non-thermal processes have been successfully approved because they increase food quality, reduce water utilization, decrease emissions, improve energy efficiency, assure clean labeling, and utilize by-products from waste food. These processes include pulsed electric field (PEF), sonication, high-pressure processing (HPP), cold plasma, and pulsed light. This review describes the use of HPP in various processes for sustainable food processing. The influence of this technique on microbial, physicochemical, and nutritional properties of foods for sustainable food supply is discussed. This approach also emphasizes the limitations of this emerging technique. HPP has been successfully analyzed to meet the global requirements. A limited global food source must have a balanced approach to the raw content, water, energy, and nutrient content. HPP showed positive results in reducing microbial spoilage and, at the same time, retains the nutritional value. HPP technology meets the essential requirements for sustainable and clean labeled food production. It requires limited resources to produce nutritionally suitable foods for consumers’ health.