Search Results (3,940)

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) provides an advanced proxy for global vegetation productivity. Recently, new high-quality remote sensing SIF datasets and reanalysis products have significantly advanced the application of SIF. However, the lack of long-term, daily resolution datasets continues to limit the precise exploration of vegetation dynamics, primarily due to challenges in daily modeling accuracy, substantial data volume, and computational demands. In this study, supported by the Google Earth Engine (GEE) platform, we developed a data-driven approach based on the Moving Spatial–Temporal Window Sampling (MSTWS) strategy for reconstructing long-term daily SIF. By learning the relationship between high-spatial-resolution Orbiting Carbon Observatory (OCO)-3 SIF and MODIS surface reflectance, we established a spatially and temporally specific daily prediction model for each day of the year (DOY), reconstructing the long-term daily OCO-3 SIF (DOSIF) from 2001 to the present with a global contiguous distribution. The prediction framework demonstrated robust performance with an R² of 0.92 on the training set and 0.81 on the validation set, indicating strong predictive ability and resistance to overfitting. Systematic evaluation of the dataset showed that DOSIF accurately captures the expected spatiotemporal distribution patterns. Cross-sensor validation with independent airborne SIF measurements further enhanced the reliability of the DOSIF dataset. Full article

Monitoring air quality is crucial on a global level. However, it is important to acknowledge the limitations of satellite-derived data in measuring gas concentrations. The Sentinel-5 satellite estimates pollutant density within an atmospheric column by measuring both the reflected solar radiation and the emitted radiation from the top of the Earth’s atmosphere. In other words, it assesses the presence of pollutants within the atmospheric column, but it cannot generate a method to isolate the amount of pollutants near the Earth’s surface from the total measured by the satellite. The authors completely ignored the methodology for converting the pollutant’s gas density within the atmospheric column into parts per million. In this commentary, we aim to clarify that it is neither practically nor operationally feasible to perform what the authors claimed as an evaluation of the accuracy of Sentinel-5p measurements from the ground stations they mentioned. Full article

This article presents the accumulated technical and scientific knowledge from energy performance upgrade work in emblematic and essential municipal and public buildings in Crete and the Greek islands, such as the Venetian historical building Loggia, which is used as the Heraklion City Hall, the Natural History Museum of Crete, Pancretan Stadium, the municipal swimming pool of the municipality of Minoa Pediadas, the indoor sports hall in Leros, primary schools, high schools and a cultural center. Each one of the aforementioned buildings has a distinct use, thus covering almost all different categories of municipal or public buildings and facilities. The applied energy performance upgrade process in general terms is: (1) Mapping of the current situation, regarding the existing infrastructure and final energy consumption. (2) Formulation and sizing of the proposed passive measures and calculation of the new indoor heating and cooling loads. (3) Selection, sizing and siting of the proposed active measures and calculation of the new expecting energy sources consumption. (4) Sizing and siting of power and heat production systems from renewable energy sources (RES). Through the work accomplished and presented in this article, practically all the most technically and economically feasible passive and active measures were studied: insulation of opaque surfaces, opening overhangs, natural ventilation, replacement of openings, daylighting solar tubes, open-loop geo-exchange plants, refrigerant or water distribution networks, air-to-water heat pumps, solar thermal collectors, lighting systems, automation systems, photovoltaics etc. The main results of the research showed energy savings through passive and active systems that can exceed 70%, depending mainly on the existing energy performance of the facility. By introducing photovoltaic plants operating under the net-metering mode, energy performance upgrades up to zero-energy facilities can be achieved. The payback periods range from 12 to 45 years. The setup budgets of the presented projects range from a few hundred thousand euros to 7 million euros. Full article

The synthesis of thin films in multilayer structures on different textiles is of interest due to their potential use in flexible solar absorber coatings and thin-film solar cells. The aim of the study was to deposit bismuth(III) sulphide and copper(II) sulphide thin films on various textiles at the same time. This was achieved using the sustainable and cost-effective successive ionic layer adsorption and reaction (SILAR) method. The study examined how the elemental distribution, phase composition, crystallinity, surface morphology, and optical features of the resulting films are determined by the intrinsic structure and material makeup of structural textiles. The analysis used data from scanning electron microscopy (SEM), energy dispersive X-ray (EDX) spectroscopy and X-ray diffraction (XRD), as well as ultraviolet-visible (UV-Vis) diffuse reflectance spectroscopy. Depending on the textiles used, the formed films were polycrystalline and rich in copper. According to the findings, the normalised atomic percentages were as follows: Cu, 57.66–68.75%; Bi, 1.19–5.26%; S, 30.06–38.63%. The direct transition optical energy gap values varied from 1.3 to 2.88 eV, while the indirect varied from 0.9 to 2.25 eV, and the refractive index from 1.3 to 1.8. These properties were influenced by the composition of the textiles and the films themselves. These properties directly impact the films’ applications. Full article

This paper investigates the integration of hydropower and solar energy within the Lower Olt River cascade as a pathway toward sustainable energy generation in Romania. The study focuses on the conceptual design of future hybrid power plants consisting of existing hydropower facilities where floating photovoltaic panels are proposed to be installed on the reservoir’s surfaces. An estimation of electricity production from both sources was performed, followed by the formulation of a trading strategy for the July–September 2025 period. The paper also explores the interaction between tactical and strategic management in hydropower operation and planning, describing how forecasting and decision-making processes are structured within the institutional framework. Finally, results for the selected hydropower plants demonstrate the positive influence of floating photovoltaic deployment on company performance, the national energy mix, and the overall sustainability of energy generation in Romania. Full article

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu₂O, g-C₃N₄/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies. Full article

The manufacturing techniques, structural features, and optical attributes of titanium dioxide (TiO₂) nanoparticles are highlighted in this study. These nanoparticles are notable for their remarkable photocatalytic activity, cheap cost, chemical stability, and biocompatibility. TiO₂ consists of three polymorph structures: anatase, rutile, and brookite. Because of its electrical characteristics and large surface area, anatase is the most efficient for photocatalysis when exposed to UV light. The crystallinity, size, and shape of titania nanoparticles (NPs) are influenced by diverse production techniques. Sol-gel, hydrothermal, solvothermal, microwave-assisted, and green synthesis with plant extracts are examples of common methods. Different degrees of control over morphology and surface properties are possible with each approach, and these factors ultimately affect functioning. For example, microwave synthesis provides quick reaction rates, whereas sol-gel enables the creation of homogeneous nanoparticles. XRD and SEM structural investigations validate nanostructures with crystallite sizes between 15 and 70 nm. Particle size, synthesis technique, and annealing temperature all affect optical characteristics such as bandgap (3.0–3.3 eV), fluorescence emission, and UV-visible absorbance. Generally speaking, anatase has a smaller crystallite size and a greater bandgap than rutile. TiO₂ nanoparticles are used in gas sensing, food packaging, biomedical coatings, dye-sensitized solar cells (DSSCs), photocatalysis for wastewater treatment, and agriculture. Researchers are actively exploring methods like adding metals or non-metals, making new composite materials, and changing the surface to improve how well they absorb visible light. Full article

Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering. Full article

We present two case studies of tropospheric aerosol transport observed over the high-altitude Helmos observatory (1800–2300 m a.s.l.) in Greece during September 2021. Two cases were linked to Saharan dust intrusions, of which one was additionally linked to a mixture of biomass-burning and continental aerosols. Aerosol vertical profiles from the AIAS mobile backscatter/depolarization lidar (532 nm, NTUA) revealed distinct aerosol layers between 2 and 6 km a.s.l., with particle linear depolarization ratio values of up to 0.30–0.40, indicative of mineral dust. The elevated location of Helmos allows lidar measurements in the free troposphere, minimizing planetary boundary layer influence and improving the attribution of long-range transported aerosols. Radiative impacts were quantified using the LibRadtran model. For the 27 September dust outbreak, simulations showed strong shortwave absorption within 3–7 km, peaking at 5–6 km, with surface forcing reaching −25 W m⁻² and TOA forcing around −12 W m⁻², thus, implying a net cooling by 13 W m⁻² on the Earth’s atmosphere system. In contrast, the 30 September mixed aerosol case produced substantial solar attenuation, a surface heating rate of 2.57 K day⁻¹, and a small positive forcing aloft (~0.05 K day⁻¹). These results emphasize the contrasting radiative roles of dust and smoke over the Mediterranean and the importance of high-altitude observatories for constraining aerosol–radiation interactions. Full article

This study presents the design, modeling, and multi-platform simulation of NEPTUNE-R, a solar-powered autonomous hydro-robot developed for sustainable water purification and oxygenation. Mechanical design was performed in Fusion 360, trajectory optimization in MATLAB R2024a, and dynamic motion analysis in Roblox Studio, creating a reproducible digital twin environment. The proposed path-planning strategies—Boustrophedon and Archimedean spiral—achieved full surface coverage across various lake geometries, with an average efficiency of 97.4% ± 1.2% and a 12% reduction in energy consumption compared to conventional linear patterns. The integrated Euler-based force model ensured stability and maneuverability under ideal hydrodynamic conditions. The modular architecture of NEPTUNE-R enables scalable implementation of photovoltaic panels and microbubble-based oxygenation systems. The results confirm the feasibility of an accessible, zero-emission platform for aquatic ecosystem restoration and contribute directly to Sustainable Development Goals (SDGs) 6, 7, and 14 by promoting clean water, renewable energy, and life below water. Future work will involve prototype testing and experimental calibration to validate the numerical findings under real environmental conditions. Full article

Dust plays a significant role in the atmospheric radiative balance by altering both shortwave and longwave radiation fluxes. While deserts are the primary sources of dust emissions, atmospheric circulation can transport dust over long distances, impacting air quality and climate in remote regions. These transport episodes, commonly known as dust events, vary in intensity and effects. Despite extensive research, uncertainties persist regarding their precise radiative impacts. This study examines the direct radiative effects of dust events in 2024 (a year marked by heightened dust activity) over Limassol, Cyprus. A comprehensive approach is employed, integrating radiative transfer modelling, ground-based solar radiation measurements, and dust optical property analysis. The LibRadtran radiative transfer package is used to simulate atmospheric radiative transfer under dust-laden conditions, incorporating key dust optical properties such as Aerosol Optical Depth, Single Scattering Albedo, and the Asymmetry Parameter retrieved from the Limassol’s AERONET station. Observations from solar radiation station at the ERATOSTHENES Centre of Excellence serve as validation for the model. This study quantifies the radiative impact of dust by evaluating changes in surface irradiance, providing valuable insights into the role of dust in atmospheric energy balance. Full article

The swift advancement of renewable energy technology has highlighted the need for effective photovoltaic (PV) solar energy tracking systems. Deep learning (DL) has surfaced as a promising method to improve the precision and efficacy of photovoltaic (PV) solar tracking by utilising complicated patterns in meteorological and PV system data. This systematic literature review (SLR) seeks to offer a thorough examination of the progress in deep learning architectures for photovoltaic solar energy tracking over the last decade (2016–2025). The review was structured around four research questions (RQs) aimed at identifying prevalent deep learning architectures, datasets, performance metrics, and issues within the context of deep learning-based PV solar tracking systems. The present research utilised SLR methodology to analyse 64 high-quality publications from reputed academic databases like IEEE Xplore, Science Direct, Springer, and MDPI. The results indicated that deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models, are extensively employed to improve the accuracy and efficiency of photovoltaic solar tracking systems. Widely utilised datasets comprised meteorological data, photovoltaic system data, time series data, temperature data, and image data. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), were employed to assess model efficacy. Identified significant challenges encompass inadequate data quality, restricted availability, high computing complexity, and issues in model generalisation. Future research should concentrate on enhancing data quality and accessibility, creating generalised models, minimising computational complexity, and integrating deep learning with real-time photovoltaic systems. Resolving these challenges would facilitate advancements in efficient, reliable, and sustainable photovoltaic solar tracking systems, hence promoting the wider adoption of renewable energy technology. This review emphasises the capability of deep learning to transform photovoltaic solar tracking and stresses the necessity for interdisciplinary collaboration to address current limitations. Full article

In recent years, the widespread adoption of photovoltaic (PV) panels for electricity generation has provided significant momentum toward sustainable energy goals. However, it has been observed that the accumulation of dust and contaminants on panel surfaces markedly reduces efficiency by blocking solar radiation from reaching the surface. Consequently, dust detection has become a critical area of research into the energy efficiency of PV systems. This study proposes SolPowNet, a novel Convolutional Neural Network (CNN) model based on deep learning with a lightweight architecture that is capable of reliably distinguishing between images of clean and dusty panels. The performance of the proposed model was evaluated by testing it on a dataset containing images of 502 clean panels and 340 dusty panels and comprehensively comparing it with state-of-the-art CNN-based approaches. The experimental results demonstrate that SolPowNet achieves an accuracy of 98.82%, providing 5.88%, 3.57%, 4.7%, 18.82%, and 0.02% higher accuracy than the AlexNet, VGG16, VGG19, ResNet50, and Inception V3 models, respectively. These experimental results reveal that the proposed architecture exhibits more effective classification performance than other CNN models. In conclusion, SolPowNet, with its low computational cost and lightweight structure, enables integration into embedded and real-time applications. Thus, it offers a practical solution for optimizing maintenance planning in photovoltaic systems, managing panel cleaning intervals based on data, and minimizing energy production losses. Full article

Parabolic trough collectors (PTCs) are effective solar thermal systems, but their performance can be significantly enhanced through surface treatments. This research investigates the enhancement of thermal performance in parabolic trough collectors (PTCs) by experimentally evaluating the results of surface coating on the absorber tube surface. To achieve this objective, a closed-loop PTC system was fabricated to conduct an experimental comparison between a conventional simple copper tube and a black-painted copper tube. The experimental setup was placed in Islamabad, Pakistan, operated under both laminar and turbulent flow conditions to measure key performance metrics, of temperature difference (ΔT) between the inlet and outlet. The results demonstrate a significant performance advantage for the black-painted tube. In laminar flow, the black-painted tube achieved an average ΔT of 3.54 °C, compared to 2.11 °C for the simple copper tube. Similarly, in turbulent flow, the black-painted tube’s ΔT was 2.1 °C, surpassing the simple copper tube’s 1.57 °C. This superior performance is primarily attributed to the black surface’s high solar absorptivity, which more effectively captures and converts solar radiation into thermal energy. The findings highlight the critical role of surface treatment in optimizing PTC efficiency and provide a practical method for improving solar thermal energy systems. Full article

This study investigates an integrated solar-driven single-effect H₂O–LiBr absorption chiller powered by low-grade thermal energy. A detailed thermodynamic model, comprising a solar collector, a thermal storage tank, and an absorption cycle, was developed using the Engineering Equation Solver (EES) software V10.561. A comprehensive parametric analysis and multi-objective optimization were then conducted to enhance both the energy and exergy performance of the system. The Response Surface Methodology (RSM), based on the Box–Behnken Design, was employed to develop regression models validated through analysis of variance (ANOVA). The generator temperature (78–86 °C), evaporator temperature (2.5–6.5 °C), and absorber/condenser temperature (30–40 °C) were selected as key variables. According to the results, the single-objective analyses revealed maximum values of COP = 0.8065, cooling capacity = 20.72 kW, and exergy efficiency = 39.29%. Subsequently, the multi-objective RSM optimization produced a balanced global optimum with COP = 0.797, cooling capacity = 20.68 kW, and exergy efficiency = 36.93%, achieved under optimal operating conditions of 78 °C generator temperature, 6.5 °C evaporator temperature, and 30 °C absorber/condenser temperature. The obtained results confirm the significance of the proposed low-grade solar absorption chiller, demonstrating comparable or superior performance to recent studies (e.g., COP ≈ 0.75–0.80 and ≈35–37%). This agreement validates the RSM-based optimization approach and confirms the system’s suitability for sustainable cooling applications in low-temperature solar environments. Full article