Search Results (9,152)

The term SHIP (solar heat for industrial processes) or SHIPs (solar heat for industrial plants) refers to the use of collected solar radiation for meeting industrial heat demands, rather than for electricity generation. The global thermal capacity of SHIP systems at the end of 2024 stood slightly above 1 GWth, which is comparable to the electric power capacity of a single power station. Despite this relatively small presence, SHIP systems play an important role in rendering industrial processes sustainable. There are two aims in the current study. The first aim is to cover various types of SHIP systems based on the variety of their collector designs, operational temperatures, applications, radiation concentration options, and solar tracking options. SHIP designs can be as simple as unglazed solar collectors (USCs), having a stationary structure without any radiation concentration. On the other hand, SHIP designs can be as complicated as solar power towers (SPTs), having a two-axis solar tracking mechanism with point-focused concentration of the solar radiation. The second aim is to shed some light on the status of SHIP deployment globally, particularly in 2024. This includes a drop during the COVID-19 pandemic. The findings of the current study show that more than 1300 SHIP systems were commissioned worldwide by the end of 2024 (cumulative number), constituting a cumulative thermal capacity of 1071.4 MWth, with a total collector area of 1,531,600 m². In 2024 alone, 120.3 MWth of thermal capacity was introduced in 106 SHIP systems having a total collector area of 171,874 m². In 2024, 65.9% of the installed global thermal capacity of SHIP systems belonged to the parabolic trough collectors (PTCs), and another 22% of this installed global thermal capacity was attributed to the unevacuated flat plate collectors (FPC-Us). Considering the 106 SHIP systems installed in 2024, the average collector area per system was 1621.4 m²/project. However, this area largely depends on the SHIP category, where it is much higher for parabolic trough collectors (37,740.5 m²/project) but lower for flat plate collectors (805.2 m²/project), and it is lowest for unglazed solar collectors (163.0 m²/project). The study anticipates large deployment in SHIP systems (particularly the PTC type) in 2026 in alignment with gigascale solar-steam utilization in alumina production. Several recommendations are provided with regard to the SHIP sector. Full article

Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for PV power forecasting. The ASSA framework employs a dual-branch attention structure that combines sparse and dense attention paths with adaptive weighting to effectively filter noise while preserving essential spatiotemporal features. This design addresses the critical issues of computational redundancy and noise amplification in standard self-attention by adaptively filtering irrelevant interactions while maintaining global dependencies in Transformer-based PV forecasting. In addition, a deep feedforward network and a feature refinement feedforward network (FRFN) adapted from the ASSA–Transformer are incorporated to further improve feature extraction. The proposed algorithms are evaluated using time-series data from the Desert Knowledge Australia Solar Centre (DKASC), with input features including temperature, relative humidity, and other environmental variables. Comprehensive experiments demonstrate that the ASSA models’ accuracy in short-term PV power forecasting increases with longer forecast horizons. For 1 h ahead forecasts, it achieves an R${}^2$ of 0.9115, outperforming all other models. Under challenging rainfall conditions, the model maintains a high prediction accuracy, with an R${}^2$ of 0.7463, a mean absolute error of 0.4416, and a root mean square error of 0.6767, surpassing all compared models. The ASSA attention mechanism enhances the accuracy and stability in short-term PV power forecasting with minimal computational overhead, increasing the training time by only 1.2% compared to that for the standard Transformer. Full article

Transpiration and canopy shading are the main ways that trees cool urban environments; this is crucial to human survival and improving urban livability in the context of global warming and rapid urbanization. So far, most studies focus on the combined cooling effect of transpiration and canopy shading, but their individual contributions have not been widely explored. Therefore, a quantitative framework was developed by carrying out a long-term field experiment and microenvironment simulations to investigate the cooling effect of a single Ficus concinna. The results show that the annual mean cooling effects of shading and transpiration are 0.17 ± 0.27 °C and 0.30 ± 0.13 °C, accounting for 21.2 ± 51.6% and 44.7 ± 26.3% of total cooling, respectively. Shade cooling demonstrates strong radiative dependence, reaching a peak of 0.63 °C with a cooling contribution of 77.1% during summer at noon due to solar radiation interception. In contrast, nighttime and winter conditions revealed shading-induced temperature increases up to 0.52 °C via longwave radiation reflection. By contrast, transpiration cooling demonstrated temperature dependence, which increased with air temperature and peaked at 1.03 °C (contributing 70.0% to the total cooling) before stomata closing. This mechanistic analysis quantitatively reveals that F. concinna provides cooling effects through a dynamic complementarity between transpiration and shading. These findings could offer a biophysically grounded basis for optimizing urban greening strategies and contribute to the theoretical advancement of nature-based urban climate solutions. Full article

Municipal solid waste incineration promotes sustainable development by reducing waste, recovering resources, and minimizing environmental impact, with furnace temperature control playing a key role in maximizing efficiency. Accurate real-time temperature prediction is crucial in developing countries to optimize incineration, re-duce emissions, and enhance energy recovery for global sustainability. To address this, we propose a method integrating an improved whale optimization algorithm (IWOA) with a self-attention gated recurrent unit (SAGRU). Using the maximal information coefficient (MIC) to identify key factors, we optimize SAGRU parameters with IWOA, enhancing prediction accuracy by capturing temporal dependencies. Experimental validation from an MSWI plant in China demonstrates that the proposed model significantly enhances prediction accuracy under complex conditions. When compared with the Elman and LSTM models, the error is reduced by 0.7146 and 0.4689, respectively, highlighting its strong potential for practical applications in waste incineration temperature control. Full article

Under the dual pressure of rapid urbanization and global warming, the urban heat island (UHI) effect has been intensifying, accompanied by a continuous increase in heat exposure. As a typical example of rapid urbanization in China, Xi’an is facing severe challenges. However, previous research on diurnal variations in long-term UHI effects and heat risks is insufficient. So, this study utilized the temperature level threshold method and the heat exposure risk assessment model to investigate the spatiotemporal evolution characteristics of diurnal variations in the UHI and population heat exposure risks in Xi’an from 2003 to 2023. The results indicate that (1) over the past two decades, both the summer UHI intensity and the population heat exposure risks in Xi’an exhibited an overall intensifying trend, (2) spatial expansion followed a radial diffusion pattern centered on the urban core, with heat risk levels decreasing outward, (3) the nighttime expansion of high-level UHI zones and risk areas was slightly less than during the daytime, and (4) changes in the thermal environment often preceded population aggregation, indicating a lag effect in the evolution of heat exposure risks. This study deepened the understanding of the UHI and heat exposure for governments and planners and can help propose scientific UHI mitigation measures. Full article

Terrestrial water storage anomaly (TWSA) plays a vital role in regulating the global water cycle and freshwater availability. Understanding the drivers behind long-term TWSA changes is critical, yet disentangling natural and anthropogenic influences remains challenging. This study employs the Geographical Detector method and multisource data to quantify the individual and interactive effects of multiple drivers on TWSA trends across the upper, middle, and lower reaches of the Yangtze River Basin (YRB). In the upper YRB, temperature, snow water equivalent, vegetation, precipitation, and reservoir storage are the primary contributors. In the middle YRB, precipitation, temperature, and soil moisture dominate. Although nighttime light (a proxy for urbanization) alone explains only 1.94% of the variation in this region, its interaction with precipitation increases explanatory power to 56.3%, highlighting a strong nonlinear effect. In the lower YRB, precipitation and runoff are the leading factors, while nighttime light again exhibits enhanced influence through interactions. These findings reveal the spatial heterogeneity and synergistic nature of TWSA drivers and underscore the need to consider both natural variability and human-induced processes when assessing long-term water storage dynamics. The results offer valuable insights for sustainable water resource management in the context of climate change and rapid urban development. Full article

To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term solar radiation, atmospheric substances, and air temperature at 29 representative stations of baseline surface radiation network (BSRN) in the world, the relationships and the mechanisms between air temperature and atmospheric substances were studied in more detail. A universal non-linear relationship between T and S/G was still found, which supported the previous relationship between T and S/G. This further revealed that a high (or low) air temperature is strongly associated with large (or small) amounts of atmospheric substances. The mechanism is that all kinds of atmospheric substances can keep and accumulate solar energy in the atmosphere and then heat the atmosphere, causing atmospheric warming at the regional and global scales. Therefore, it is suggested to reduce the direct emissions of all kinds of atmospheric substances (in terms gases, liquids and particles, and GLPs) from the natural and anthropogenic sources, and secondary formations produced from atmospheric compositions via chemical and photochemical reactions (CPRs) in the atmosphere, to slow down the regional and global warming through our collective efforts, by all mankind and all nations. Air temperature increased at most BSRN stations and many sites in China, and decreased at a small number of BSRN stations during long time scales, revealing that the mechanisms of air temperature change were very complex and varied with region, atmospheric substances, and the interactions between solar radiation, GLPs, and the land. Full article

Global climate change and rapid urbanization have intensified urban heat risks, particularly in cities such as Jinan that face pronounced heat-related environmental challenges. This study takes Jinan’s main urban area as a case example, integrating the Local Climate Zone (LCZ) framework with the Hazard–Exposure–Vulnerability–Adaptability (HEVA) model to develop multi-temporal heat risk maps. The results indicate the following: (1) High-risk zones are primarily concentrated in the densely built urban core, whereas low-risk areas are mostly located in peripheral green spaces, water bodies, and forested regions. (2) Heat risk shows clear diurnal patterns, peaking between noon and early afternoon and expanding outward from the city center. (3) LCZ6 (open low-rise), despite its theoretical advantage for ventilation, exhibits unexpectedly high levels of heat hazard, exposure, and vulnerability. (4) SHAP-based analysis identifies land surface temperature (LST), floor area ratio (FAR), impervious surface area ratio (ISA), housing value, building coverage ratio (BCR), and the distribution of cooling facilities as the most influential drivers of heat risk. These findings offer a scientific foundation for developing multi-scale, climate-resilient urban planning strategies in Jinan and hold significant practical value for improving urban resilience to extreme heat events. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

Forest management practices such as clearcutting, thinning, and prescribed burning are widely implemented to achieve various ecological and silvicultural objectives, yet their effects on soil carbon dynamics and belowground processes remain uncertain. We conducted a global meta-analysis of 414 observations from 110 studies to quantify the impacts of these practices on total soil respiration (SR), its autotrophic (Ra) and heterotrophic (Rh) components, and associated biophysical and soil variables. Clearcutting and prescribed burning both reduced SR by an average of 11%, driven largely by Ra declines following reductions in live biomass, forest floor inputs, and microbial biomass. Thinning caused no significant change in SR, likely due to the limited belowground disturbance and residual vegetation compensatory growth, although impacts intensified when combined with post-treatments (e.g., residue removal or site-preparation burns), resembling those of clearcutting or repeated burns. In contrast, post-burn treatments following clearcutting did not substantially alter biological factors or SR components. Across practices, soil temperature increased due to the opening of the canopy, middle- and understory vegetation, and forest floor disturbance, but this warming showed no consistent relationship with Rh or SR. Instead, responses were primarily governed by substrate availability, highlighting its central role in soil carbon fluxes under management disturbances. Full article

Raptors, being at top of the food chain, serve as important models to study the impact of changing climate, as they are more vulnerable due to their unique ecology. They are vulnerable to extinction, with 52% species declining population and 18% are threatened globally. The effect of climate change on raptors is poorly studied in the Eastern Himalayan region. The present study offers a complete investigation of climate change effects on the raptors in the northeast region of the Eastern Himalayas, employing ensemble species distribution modeling. The future predictions were employed to model the climate change across two socioeconomic pathways (SSP) i.e. SSP245 and SSP585 for the periods 2041–2060 and 2061–2080. Specifically, five algorithms were employed for the ensemble model, viz. boosted regression tree (BRT), generalized linear model (GLM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt) and random forest (RF). The study highlights worrying results, as only 10.5% area of the NE region is presently suitable for Falco severus, 11.4% for the critically endangered Gyps tenuirostris, and a mere 6.9% area is presently suitable for the endangered Haliaeetus leucoryphus. The most influential covariates were precipitation of the driest quarter, precipitation of the wettest month, and temperature seasonality. Future projection revealed reduction of 33–41% in suitable habitats for F. severus, G. tenuirostris is expected to lose 53–96% of its suitable habitats, and H. leucoryphus has lost nearly 94–99% of its suitable habitats. Such decline indicates apparent habitat fragmentation, with shrinking habitat patches. Full article

Rotating detonation engines (RDEs) are expected to have higher specific work and efficiency, but the high-temperature transonic flow delivered by the combustor poses relevant design and technological difficulties. This work proposes a 1D model for turbine internal cooling design which can be used to explore multiple design options during the preliminary design of the cooling system. Being based on an energy balance applied to an infinitesimal control volume, the model is general and can be adapted to other applications. The model is applied to design a cooling system for a pre-existing stator blade geometry. Both the inputs and the outputs of the 1D simulation are in good agreement with the values found in the literature. Subsequently, 1D results are compared to a full conjugate heat transfer (CHT) simulation. The agreement on the internal heat transfer coefficient is excellent and is entirely within the uncertainty of the correlation. Despite some criticality in finding agreement with the thermal power distribution, the Mach number, the total pressure drop, and the coolant temperature increase in the cooling channels are accurately predicted by the 1D code, thus confirming its value as a preliminary design tool. To guarantee the integrity of the blade at the extremities, a cooling solution with coolant injection at the leading and trailing edge is studied. A finite element analysis of the cooled blade ensures the structural feasibility of the cooling system. The computational economy of the 1D code is then exploited to perform a global sensitivity analysis using a polynomial chaos expansion (PCE) surrogate model to compute Sobol’ indices. Full article

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

Sphagneticola trilobata (L.) Pruski has been introduced to many countries due to its ornamental and economic value. However, it has been listed in the world’s 100 worst alien invasive species due to its invasive nature. This species easily escapes cultivation and forms dense ground covers. It reproduces asexually through ramet formation from stem fragments. It also produces a large number of viable seeds that establish extensive seed banks. The movement of stem fragments and the dispersal of seeds, coupled with human activity, contribute to its short- and long-distance distribution. S. trilobata grows rapidly due to its high nutrient absorption and photosynthetic abilities. It exhibits high genetic and epigenetic variation. It can adapt to the different habitats and tolerate various adverse environmental conditions, including cold and high temperatures, low and high light irradiation, low nutrient levels, waterlogging, drought, salinity and global warming. S. trilobata has powerful defense systems against herbivory and pathogen infection. These systems activate the jasmonic acid signaling pathway, producing several defensive compounds. This species may also acquire more resources through allelopathy, which suppresses the germination and growth of neighboring plants. These life history traits and defensive abilities likely contribute to its invasive nature. This is the first review to focus on the mechanisms of its invasiveness in terms of growth, and reproduction, as well as its ability to adapt to different environmental conditions and defend itself. Full article

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies. Full article