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The technological development of preparing bio-oil from low-temperature hydrothermal conversion of agricultural and forestry waste has positive significance for alleviating the shortage of oil energy supply and reducing environmental pollution. This paper selects typical oxides (Al2O3, CeO2, MgO, SiO2, TiO2, and ZnO) as catalysts to set up a low-temperature (220 °C) hydrothermal conversion process of cotton stalk containing pretreatment processes including chopping. For moderate amplification estimation, lab-scale experimental data is used as a benchmark for calculation, and the functional unit for this study is set to be a 1 kg bio-oil product. The results suggest that the cerium dioxide-involved process with the highest bio-oil yield and highest synthetic consumption, and the silica-involved process with the lowest bio-oil yield, caused the highest environmental impact, resulting in greenhouse gas (GHG) emissions of 67.729 kg CO2e/kg and 60.001 kg CO2e/kg, respectively. It indicates that catalysts need to consider the balance between synthetic consumption and catalytic performance. Magnifying lab-scale data to an industrial scale using scale-up frameworks introduces a low model uncertainty, as the practical value had little effect on the overall evaluation results. However, existing equipment data should be used to reduce the uncertainty of the model itself. The environmental sustainability of bio-oil production by low-temperature hydrothermal liquefaction still needs to be improved, especially by catalyst recovery and bio-oil yield improvement.

Since the industrial revolution, the financial sector has become a significant claimant toward the growth of human society. However, supporting the adverse environmental projects in financial terms has raised several queries about creating a direct linkage between financial market products and the environment.This research examines the nexus between sustainability, green innovations, financial technologies (FinTech), financial development, and natural resources for BRICS economies during 2000–2019. Using the Method of Moments Quantile Regression (MMQR), the results show that FinTech and natural resources adversely impact environmental sustainability across all three ranges of quantiles (0.10th-0.30th, 0.40th-0.60, and 0.70th-0.90th).Conversely, green innovations and financial development promote environmental sustainability across lower to higher-order quantiles (0.10th-0.90th), while economic growth contributes to higher emissions at major quantiles. Similar findings are endorsed using alternative estimators and suggest practical policy implications.

As the largest renewable electricity source, hydropower represents an alternative to fossil fuels to achieve a low-carbon future. However, increasing evidence suggests that hydropower reservoirs are an important source of biogenic greenhouse gases (GHGs), albeit with large uncertainties. Combining spatially resolved assessments of GHG fluxes and hydroelectric capacity databases, we assessed that global GHG emissions from reservoirs is 0.38 Pg CO2 eq.yr−1, accounting for 1.0% of global anthropogenic emissions. The median carbon intensity for hydropower is ∼63.0 kg CO2eq. MWh−1, which is lower than that for fossil fuels, but higher than that for other renewable energy sources. High carbon intensity is mostly linked to shallow (water storage depth <20 m) and eutrophic reservoirs. Furthermore, we found that the reservoir carbon intensity (CI) value would be markedly increased to 131.5 kg CO2eq. MWh−1 when considering the dams under construction and planning. A low-carbon future will benefit from optimal dam planning and management measures, i.e., applying sludge removal treatments, thereby reducing the proportion of shallow reservoirs and anthropogenic pollution.

Rotor imbalances present a serious problem for wind turbines. In particular, for offshore wind turbines, aerodynamic imbalance can have a severe impact because of the large rotor size. In this study, the impact of the aerodynamic imbalance is investigated. A novel framework for detecting aerodynamic imbalance is proposed. Firstly, a model of a 3MW direct-driven wind turbine was developed. The signals were acquired to test and verify the impact of aerodynamic imbalance. Secondly, a method based on optimized maximum correlated kurtosis deconvolution was proposed for the primary detection. The intrinsic mode functions of nacelle vibration were adopted as the input variable. The weak unbalanced signals could be discerned. Moreover, the azimuth of rotor allows the unbalanced blades to be obtained. Thirdly, a convolutional neural network with a new structure was used to determine the magnitudes of aerodynamic imbalances. The first layer of the convolutional neural network is sufficiently wide for improving feature extraction, it could make nacelle acceleration as the input. This structure exhibits accuracy and robustness satisfactorily. Finally, the framework was demonstrated in a high-fidelity simulation environment. Different scenarios of aerodynamic imbalance were tested, the results demonstrate the satisfactory performance of the proposed framework.

Using agricultural biomasses as solid carbon substrates in constructed wetlands (CWs) could be an effective way to achieve sustainable nitrogen removal for carbon-limited wastewater treatments. This study investigated the response of bacteria community in CWs to the addition of agricultural biomasses (wheat straw, walnut shell and apricot pit). Results indicated that the addition of different agricultural biomasses had distinct influence on bacterial communities in CWs. Both wheat straw and walnut shell increased the diversity of microbial communities and optimized the structure of microorganisms. The effect of apricot pit on the richness and evenness of microbial communities was not significant, but the composition of microorganisms was significantly affected at the phylum level, especially the relative abundance of phylum Saccharibacteria. Moreover, the addition of agricultural biomasses in CWs acclimatized more functional bacteria including nitrifier and denitrifier, which were proved to be positively correlated with the high-rate denitrification performance. The obtained results would be beneficial to understand the underlying microbial mechanism of nitrogen removal in CWs with agricultural biomass and provide some guidance on the practical application of CWs.

Abstract The narrow active temperature ranges of ectothermic tetrapods can be used as proxies for reconstructing paleoclimates. Here we deduce the climatic preferences of major Permo-Triassic tetrapod groups based on their known geographic distributions, the critical thermal limits of living tetrapods, and paleoclimate information from other sources. The resulting preferred temperature sequence of amniotes places most Triassic archosauromorphs at the high end of the spectrum, with preferred temperatures over 32 °C in some cases, followed by captorhinids, pareiasaurs, procolophonids, cynognathian cynodonts, dicynodonts (excluding Lystrosaurus), Proterosuchus fergusi, and finally Lystrosaurus at the lowest preferred temperature. The poleward distribution of Permian Lystrosaurus marks the border of cool temperate climates, whereas Triassic Lystrosaurus delineates the border of the arid zone. Most temnospondyls indicate the availability of perennial water sources. Captorhinids and pareiasaurs preferred dry climates, whereas dicynodonts preferred wetter conditions. Based on current evidence, central Pangea transitioned from an arid zone to a tropical zone during the late Olenekian.

Global climatic energy balance has been increasingly altered by massive emissions of greenhouse gases (GHGs), such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), leading to a variety of natural disturbances in terrestrial ecosystems. Further, the increasing use of fossil fuels and the looming climate crisis have created an unprecedented urgency for the development of a biobased circular economy. Therefore, production of biofuels from plant biomass is currently seen as a promising source of renewable energy, ensuring sustainable development with minimal carbon footprint. Soil acidification is considered one of the major obstacles to crop production and a significant source of GHGs emissions, especially N2O, because acidification changes the physicochemical and biochemical properties of the soil. Dolomite (DM) is the most widely used countermeasure to neutralize soil acidity to improve crop productivity and control net fluxes of GHGs. Nevertheless, the extent of GHG emissions following the application of DM under different environmental conditions is still unclear. Therefore, in this context, we conducted a meta-analysis using 32 peer-reviewed publications to determine the effects of DM, climate zones, and soil properties on GHGs emissions. The results of the current meta-analysis show that DM application significantly increased CO2 emissions (30.34 %) and CH4 emissions (4.91 %), but reduced N2O emissions by 54.88 %. A significant effect of DM (>10 t ha−1) on CO2, CH4, and N2O emissions was also observed. Increasing soil pH increased CO2 and N2O emissions by 188.34 % and 49.78 %, respectively, while reducing CH4 emissions by 81.94 %. Most importantly, WFPS, soil textural class, soil C:N ratio, and climate zones were identified as key edaphic factors affecting the GHG emissions following the application of DM. Overall, this meta-analysis fills in the gaps regarding the impact of the application of DM on GHGs emissions in different climates, soil properties, and experimental conditions. In ...