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Hydrothermal liquefaction (HTL) is an effective way to treat solid wastes with high moisture content. The co-hydrothermal liquefaction (co-HTL) experiments of oily scum and poplar sawdust biochar at the different hydrothermal temperatures were performed in this work. The changes of the appearance and components of the liquid products were comprehensively studied. The results showed that the addition of biochar into oily scum significantly reduced the moisture content of the residue hydrochars obtained after co-HTL. As the hydrothermal temperature increased, the liquid products obtained from co-HTL turned clearer and lighter in color, and the recovery rate of the liquid products significantly increased. The co-HTL of bi-ochar and oily scum could effectively improve the liquid quality and enhance the recovery rate of hydrochars. The carbon numbers of the liquid products obtained from co-HTL were concentrated in C5-C11, which were main compositions of gas-oline. This work can provide basic data and theoretical reference for oily scum efficient treatment and engineering practice.

Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction in PM2.5 concentrations in recent years, the health burden caused by PM2.5 pollution has not decreased as expected. Therefore, a comprehensive analysis of the health burden caused by PM2.5 is necessary for more effective air quality management. This study makes an innovative contribution by integrating the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI), providing a comprehensive framework to assess the health impacts of green space coverage, promoting healthy urban environments and sustainable development. Using Nanjing, China, as a case study, we constructed a health impact assessment system based on PM2.5 concentrations and quantitatively analyzed the spatiotemporal evolution of premature deaths caused by PM2.5 from 2000 to 2020. Using Multiscale Geographically Weighted Regression (MGWR), we explored the impact of greening improvement on premature deaths attributed to PM2.5 and proposed relevant sustainable governance strategies. The results showed that (1) premature deaths caused by PM2.5 in Nanjing could be divided into two stages: 2000–2015 and 2015–2020. During the second stage, deaths due to respiratory and cardiovascular diseases decreased by 3105 and 1714, respectively. (2) The spatial variation process was slow, with the overall evolution direction predominantly from the southeast to northwest, and the spatial distribution center gradually shifted southward. On a global scale, the Moran’s I index increased from 0.247251 and 0.240792 in 2000 to 0.472201 and 0.468193 in 2020. The hotspot analysis revealed that high–high correlations slowly gathered toward central Nanjing, while the proportion of cold spots increased. (3) The MGWR results indicated a significant negative correlation between changes in green spaces and PM2.5-related premature deaths, especially in densely vegetated areas. This study comprehensively considered the spatiotemporal changes in PM2.5-related premature deaths and examined the health benefits of green space improvement, providing valuable references for promoting healthy and sustainable urban environmental governance and air quality management.

The pine caterpillar (Dendrolimus spectabilis Bulter, Lepidoptera: Lasiocampidae) is a destructive insect threatening forest communities across Eurasia. The pest is polyvoltine, and under global warming, more favorable temperatures can lead to additional generations. Here, we simulated the pine caterpillar voltinism under current and future climatic scenarios based on insect thermal physiology and cumulative growing degree day (CGDD) model. Subsequently, we revealed the future change patterns of the voltinism along elevational and latitudinal gradients. The results showed that both CGDD and pine caterpillar voltinism are increasing. The current voltinism of pine caterpillar ranges from 1.26 to 1.56 generations (1.40 ± 0.07), with an increasing trend of 0.04/10a. Similar trends are expected to continue under the future climate scenarios, with values of 0.01/10a, 0.05/10a, 0.07/10a, and 0.09/10a for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. At the elevation and latitudinal gradients, voltinism increases across all ranges, peaking at 500–1000 m and latitudes of 34–34.5° N. This study highlights that the increase in voltinism is not limited to low-elevation and -latitude regions but is predicted across various elevations and latitudes. These findings can enhance our understanding of how climate change affects pine caterpillar voltinism and contribute to forest pest management strategies, although this study assumes a linear relationship between temperature and voltinism, without considering other ecological factors.

Power generation is an important part of air vehicle energy management when developing long-endurance and reusable hypersonic aircraft. In order to utilize an air turbine power generation system on board, fuel-based rotating cooling has been researched to cool the turbine’s rotor blades. For fuel-cooling air turbines, each blade corresponds to a separate cooling channel. All the separate cooling channels cross together and form a distributary cavity and a confluence cavity in the center of the disk. In order to determine the flow characteristics in the distributary and confluence cavities, computational fluid dynamics (CFD) simulations using the shear–stress–transport turbulence model were carried out under the conditions of different rotating speeds and different mass flow rates. The results showed great differences between non-rotating flow and rotating flow conditions in the distributary and confluence cavities. The flow in the distributary and confluence cavities has rotational velocity, with obvious layering distribution regularity. Moreover, a high-speed rotational flow surface is formed in the confluence cavity of the original structure, due to the combined functions of centrifugal force, inertia, and the Coriolis force. Great pressure loss occurs when fluid passes through the high-speed rotational flow surface. This pressure loss increases with the increase in rotating speed and mass flow rate. Finally, four structures were compared, and an optimal structure with a separated outlet channel was identified as the best structure to eliminate this great pressure loss.

In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.

Iron-based oxygen carriers (OCs) have received much attention due to their low costs, high mechanical strengths and high-temperature stabilities in the chemical looping gasification (CLG) of biomass, but their chemical reactivity is very ordinary. Converter steel slags (CSSs) are steelmaking wastes and rich in Fe2O3, CaO and MgO, which have good oxidative ability and good stability as well as catalytic effects on biomass gasification. Therefore, the composite OCs prepared by mechanically mixing CSSs with iron-based OCs are expected to be used to increase the hydrogen production in the CLG of biomass. In this study, the catalytic performance of CSS/Fe2O3 composite OCs prepared by mechanically mixing CSSs with iron-based OCs on the gasification of brewers’ spent grains (BSGs) were investigated in a tubular furnace experimental apparatus. The results showed that when the weight ratio of the CSSs in composite OCs was 0.5, the relative volume fraction of hydrogen reached the maximum value of 49.1%, the product gas yield was 0.85 Nm3/kg and the gasification efficiency was 64.05%. It could be found by X-ray diffraction patterns and scanning electron microscope characterizations that the addition of CSSs helped to form MgFe2O4, which are efficient catalysts for H2 production. Owing to the large and widely distributed surface pores of CSSs, mixing them with iron-based OCs was beneficial for catalytic steam reforming to produce hydrogen.

Climate change poses a significant global challenge that necessitates concerted efforts toward carbon neutrality. Circulating fluidized bed (CFB) boilers have gained prominence in various industries due to their adaptability and reduced emissions. However, many current control systems rely heavily on manual operator intervention and lack advanced automation, which constrains the operational efficiency. This study addressed the need for dynamic models capable of monitoring and optimizing NOx emissions in CFB boilers, especially under fluctuating loads and strict regulatory standards. We introduced the TimesNet model, which utilizes fast Fourier transform (FFT) to extract key frequency components, transforming 1D time series data into 2D tensors for enhanced feature representation. The model employs Inception blocks for multi-scale feature extraction and incorporates residual connections with amplitude-weighted aggregation to mitigate catastrophic forgetting during training. The results indicated that TimesNet achieved R2 values of 0.98, 0.97, and 0.95 across training, validation, and testing datasets, respectively, surpassing conventional models with a reduced MAE of 1.63 mg/m3 and RMSE of 3.35 mg/m3. Additionally, it excelled in multi-step predictions and effectively managed long-term dependencies. In conclusion, TimesNet provides an innovative solution for the precise monitoring of NOx emissions in CFB boilers by enhancing predictive stability and robustness and addressing salient limitations in existing models to optimize combustion efficiency and regulatory compliance.

Alkali metal-ion capacitors (AMICs) combine the advantages of the high specific energy of alkali metal-ion batteries (AMIBs) and the high power output of supercapacitors (SCs), which are considered highly promising and efficient energy storage devices. It is found that carbon has been the most widely used electrode material of AMICs due to its advantages of low cost, a large specific surface area, and excellent electrical conductivity. However, the application of carbon is limited by its low specific capacity, finite kinetic performance, and few active sites. Doping heteroatoms in carbon materials is an effective strategy to adjust their microstructures and improve their electrochemical storage performance, which effectively helps to increase the pseudo-capacitance, enhance the wettability, and increase the ionic migration rate. Moreover, an appropriate heteroatom doping strategy can purposefully guide the design of advanced AMICs. Herein, a systematic review of advanced heteroatom (N, S, P, and B)-doped carbon, which has acted as a positrode and negatrode in AMICs (M = Li, Na, and K) in recent years, has been summarized. Moreover, emphasis is placed on the mechanism of single-element doping versus two-element doping for the enhancement in the performance of carbon positrodes and negatrodes, and an introduction to the use of doped carbon in dual-carbon alkali metal-ion capacitors (DC-AMICs) is discussed. Finally, an outlook is given to solve the problems arising when using doped carbon materials in practical applications and future development directions are presented.

Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a massive amount of data, which is disadvantageous for the online monitoring of batteries. This paper proposes a lithium-ion battery degradation research method based on the CNN-Transformer model. By leveraging the efficiency of the CNN model in feature extraction, it reduces the dependency of the Transformer model on data volume, thereby ensuring faster overall model training without a significant loss in model accuracy. This facilitates the online monitoring of battery degradation. The dataset used for training and validation consists of charge–discharge data from 124 lithium iron phosphate batteries. The experimental results include an analysis of the model training results for both single-battery and multiple-battery data, compared with commonly used models such as LSTM and Transformer. Regarding the instability of single-battery data in the CNN-Transformer model, statistical analysis is conducted to analyze the experimental results. The final model results indicate that the root mean square error (RMSE) of capacity predictions for the majority of batteries among the 124 batteries is within 3% of the actual values.