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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.

Abstract In the next 25 years an unprecedented number of new marine artificial structures, over 75,000 offshore wind turbines alone, are planned to meet global net zero targets. Structures are required to last for multiple decades in the hostile marine environment; where the largest cost across their whole lifecycle is on operations and maintenance dependent on accessibility in calm seas. However, the role of climate change on accessibility, and thus operational cost, has not been resolved. Here we provide the first study of future accessibility; evaluated from global climate model driven wave modelling, using the high emission scenario (RCP8.5). We found that climate change drives significant regional variation in accessibility, with the northern hemisphere benefiting from a 6% increase in operating windows whilst accessibility in parts of the southern hemisphere is reduced by 6-9%. These findings will help offshore developers and stakeholders incorporate adaptions to climate change as part of strategic planning practices.

Climate change is driving many species to shift their geographical ranges poleward to maintain their environmental niche. However, for endemic species with restricted ranges, like the Critically Endangered whitefin swellshark (Cephaloscyllium albipinnum), endemic to southeastern Australia, such dispersal may be limited. Nevertheless, there is a poor understanding of how C. albipinnum might spatially adjust its distribution in response to climate change or whether suitable refugia exist for this species in the future. Therefore, to address this gap, this study utilised maximum entropy (MaxEnt) modelling to determine the potential distribution of suitable habitat for C. albipinnum under present-day (2010–2020) climate conditions and for future conditions, under six shared socioeconomic pathways (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5) for the middle (2040–2050) and end (2090–2100) of the century. Under present-day conditions (2010–2020), our model predicted a core distribution of potentially suitable habitat for C. albipinnum within the Great Australian Bight (GAB), with benthic primary productivity and surface ocean temperature identified as key distribution drivers. However, under all SSP scenarios, future projections indicated an expected range shift of at least 72 km, up to 1,087 km in an east-southeast direction towards Tasmania (TAS). In all future climate scenarios (except SSP1-1.9 by 2100), suitable habitat is expected to decline, especially in the high-emission scenario (SSP5-8.5), which anticipates a loss of over 70% of suitable habitat. Consequently, all future climate scenarios (except SSP1-1.9 by 2100) projected a decrease in suitable habitat within a currently designated marine protected area (MPA). These losses ranged from 0.6% under SSP1-1.9 by 2050 to a substantial 89.7% loss in coverage under SSP5-8.5 by 2100, leaving just 2.5% of suitable habitat remaining within MPAs. With C. albipinnum already facing a high risk of extinction, these findings underscore its vulnerability to future climate change. Our results highlight the urgency of implementing adaptive conservation measures and management strategies that consider the impacts of climate change on this species.

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.

Slurry transportation is an essential process in numerous industrial applications, widely studied for its efficiency in material conveyance. Despite substantial research, the impact of pipe wall roughness on critical metrics such as pressure drop, specific energy consumption (SEC), and the Nusselt number remains relatively underexplored. This study provides a detailed analysis using a three-dimensional computational model of a slurry pipeline, with a 0.0549 m diameter and 3.8 m length. The model employs an Eulerian multiphase approach coupled with the RNG k-ε turbulence model, assessing slurry concentrations Cw = 40–60% (by weight). Simulations were conducted at flow velocities Vm = 1–5 m/s, with pipe roughness (Rh) ranging between 10 and 50 µm. Computational findings indicate that both pressure drop and SEC increase proportionally with roughness height, Vm, and Cw. Interestingly, the Nusselt number appears unaffected by roughness height, although it rises corresponds to Vm, and Cw. These insights offer a deeper understanding of slurry pipeline dynamics, informing strategies to enhance operational efficiency and performance across various industrial contexts.

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.

The present study investigates the enhancement of latent heat capacity and thermal stability in hybrid nano-enhanced solid–solid phase change materials (SS-PCMs) using Neopentyl Glycol (NPG) as the base material. The key contribution of this work lies in incorporating copper oxide (CuO) and titanium dioxide (TiO₂) nanoparticles to optimize thermal performance and ensure long-term stability. CuO (1 wt.%) and TiO₂ (0.1, 0.3, 0.5,0.7 wt%) were introduced into the matrix, and the thermal properties were systematically evaluated using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) before and after 500 thermal cycles. The optimal composition, consisting of 1 wt% CuO and 0.3 wt% TiO₂, demonstrated an initial latent heat capacity of 117 J/g, which increased to 123 J/g post-cycling, indicating exceptional thermal stability and phase retention. To further enhance predictive capabilities and reduce experimental costs, an artificial neural network (ANN) model was developed using the Keras API in Python to estimate thermal behaviour. The model achieved a high coefficient of determination (R2 = 0.9479) and a low root-mean-square error (RMSE = 2.0307), underscoring its accuracy and reliability. These findings establish the efficacy of hybrid nanoparticle incorporation in improving SS-PCMs’ thermal properties and emphasise the viability of machine learning as a robust predictive tool, mitigating the time and economic constraints associated with extensive experimental investigations.