Search Results (4,454)

Solar radiation is crucial for intercropping, while partial shading can protect intercropped soybean leaves from irradiation damage during the pod-ripening period under high solar radiation. This study explored the leaf dynamics and soybean quality for the maize–soybean system, for monoculture soybean (MS), monoculture maize (MM), two-row maize + three-row soybean (IS2-3), and four-row maize + four-row soybean (IS4-4). The results revealed that soybean leaves under IS2-3 and IS4-4 treatments showed increases in Rubisco activity of 59.8% and 12.4% compared with MS, respectively. The antioxidant capacity in soybean leaves in MS was higher than that under intercropping treatments. Soybean leaves under IS2-3 and IS4-4 exhibited higher alpha and beta diversities in their endophytes compared with MS. The relative abundance of pathotrophs under IS2-3 was reduced by 19.1% and 22.6% compared to that of those under MS and IS4-4, respectively. The total land equivalent ratio (LER) under IS2-3 was more than 1.00, and increased by 6.4% and 15.7% compared with IS4-4 in 2023 and 2024, respectively. Soybean seeds under IS2-3 and IS4-4 showed 4.1% and 4.2% increases in crude protein content compared to those of MS, respectively. Among various biosynthesis and metabolism processes, flavone and flavonol biosynthesis exerted a stronger influence on soybean seeds in MS, IS2-3, and IS4-4. Soybean seeds under IS2-3 showed elevated genistein content and reduced daidzein content compared with those of MS. Intercropping soybean treatments, especially IS2-3, maintained leaf health during the pod-ripening period and enhanced the crude protein content compared with sole soybean treatment, thus guiding the design of intercropping in areas with high solar radiation. Full article

PM₂.₅ plays a significant role in urban climate, especially as urban development accelerates. In this study, surface PM₂.₅, skin temperature, surface air temperature, net longwave radiation, net shortwave radiation, sensible heat flux, and latent heat flux were directly analyzed in Nanchang from 2020 to 2022. The results indicate that PM₂.₅ in Nanchang is highest during winter and lowest in summer. On an annual scale, surface PM₂.₅ reduces skin and surface air temperatures at a rate of 0.75 °C/(μg m⁻³) by decreasing net solar radiation and increasing net longwave radiation at night. Conversely, it increases air temperature by absorbing radiation, leading to a surface inversion. Furthermore, surface PM₂.₅ influences surface air and skin temperatures by modulating the latent heat fluxes. Full article

Free-range egg production plays a key role in the global food system, and current market trends suggest that consumer demand for free-range eggs will continue to rise. Free-range egg production is susceptible to a wide range of factors, including climatic conditions, management practices, and disease presence. These factors can cause variability in the laying rate of a flock over time, leading to fluctuations in egg production. The main purpose of this study was to investigate the risk of short-term free-range egg production losses using data derived from a combination of sensing technologies and management activities. Production and environmental data were collected from a commercial farm comprising seven flocks of laying hens. The variables studied included laying rate, feed intake, water intake, solar radiation, humidity, precipitation, and indoor/outdoor temperature. These were processed into a set of aggregate features calculated across a 14-day moving window. Generalized estimating equations were used to analyze the association between the derived production and environmental features and the probability of a short-term drop in egg production, expressed through deviations in the laying rate on the day immediately following the data window. Odds ratios were used to express the relative risk of a production drop by comparing the features for window periods where production drops occur to the window periods where production drops did not occur. The results demonstrated that a range of data features based on the laying rate, feed intake, water intake, and indoor/outdoor temperatures all had significant associations with the odds of a production drop. Key findings from the study show that an increase in feed intake and laying rate measured across the 14-day data window were correlated with a lower risk of a sudden drop in egg production. Conversely, a low mean indoor temperature (x < 16.1 °C group), measured through environmental sensing data, was correlated with a higher risk of a sudden drop in egg production. This study quantifies the link between data features derived from production and environmental monitoring and egg production issues, thereby providing useful insights on the most important data items captured through day-to-day monitoring, which can be used for proactive management. Further research should be carried out to investigate how technologies such as machine learning and analytics platforms can be applied for the task of forecasting production interruptions using the data features explored in this study. Full article

This study investigates magnetohydrodynamic (MHD) heat and mass transport in a water-based ternary hybrid nanofluid flowing past an exponentially accelerated vertical porous plate. Two critical scenarios are analyzed: (i) uniform heat flux with variable mass diffusion and (ii) varying heat source with constant species diffusion. The model integrates thermal radiation, heat sink/source, thermal diffusion, and chemical reaction effects to assess flow stability and thermal performance. Governing equations are non-dimensionalized and solved analytically using the Laplace transform method, with results validated against published data and finite difference method outcomes. Ternary hybrid nanofluids exhibit a significantly higher Nusselt number compared to hybrid and conventional nanofluids, demonstrating superior heat transfer capabilities. Magnetic field intensity reduces fluid velocity, while porosity enhances momentum transfer. Thermal radiation amplifies temperature profiles, critical for energy systems. Concentration boundary layer thickness decreases with higher chemical reaction rates, optimizing species diffusion. These findings contribute to the development of advanced thermal management systems, such as solar energy collectors and nuclear reactors, enhance energy-efficient industrial processes, and support biomedical technologies that require precise heat and mass control. This study positions ternary hybrid nanofluids as a transformative solution for optimizing high-performance thermal systems. Full article

Solar radiation is one of the most abundant energy sources in the world and is a crucial parameter that must be researched and developed for the sustainable projects of future generations. This study evaluates the performance of different machine learning methods for solar radiation prediction in Konya, Turkey, a region with high solar energy potential. The analysis is based on hydro-meteorological data collected from NASA/POWER, covering the period from 1 January 1984 to 31 December 2022. The study compares the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), and Artificial Neural Networks (MLANN, RBANN). The hydro-meteorological variables used include temperature, relative humidity, precipitation, and wind speed, while the target variable is solar radiation. The dataset was divided into 75% for training and 25% for testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that LSTM and Bi-LSTM models performed best in the test phase, demonstrating the superiority of deep learning-based approaches for solar radiation prediction. Full article

This extensive study examines the solar rooftop photovoltaic potential (RTP) over polluted cities in major geographic and economic zones of India. The study examines the climatology of solar radiation attenuation due to aerosol, clouds, architectural effects, etc. The study exploits earth observations from ground, satellite, and radiative transfer modeling (RTM) in conjunction with geographic information systems tools. The study exploits long-term observations of cloud properties from the Meteosat Second Generation (MSG) satellites operated by EUMETSAT and aerosol properties data gathered from ground-based measurements provided by AERONET. The innovation in the study is defined in two steps. Firstly, we estimated the RTP using the current state of the art in the field, which involved using suitability factors and energy output based on the PVGIS simulations and extrapolating these for effective rooftop areas of the cities. Secondly, we advanced beyond the current state of the art by incorporating roof morphological characteristics and various area share factors to assess the RTP in more realistic terms. These two steps were applied under two different scenarios. The study determined that the optimum tilt angle is equal to the cities’ latitude for installing solar PV systems. In addition, the research emphasizes the advantages for the environment while offering energy and economic losses. According to our findings, the RTP in the rural city examined in this study is 31% greater than the urban city of India under both scenarios. The research has found that the metropolitan city, which boasts a maximum rooftop area of approximately 167 km², could host a significant RTP of around 13,005 ± 1210.71 (6970 ± 751.38) MWh per year under scenario 1 (scenario 2). Overall, solar radiation losses due to aerosol effects dominate radiation losses due to cloud effects on the city scale. Amongst all polluted cities, estimated financial losses due to aerosols, clouds, and shadows are 11,241.70 million, 4439 million, and 1167.65 million rupees, respectively. Our findings emphasize the necessity of accounting for air pollution for accurate solar potential assessments in thoughtful city planning. The creative approach that utilizes publicly available data establishes a strong foundation for penetrating solar photovoltaic (PV) technology into society. This integration could significantly contribute to climate change mitigation and adaptation efforts, promoting environmentally sustainable urban development and prevention strategies. Full article

Urban green spaces play a crucial role in mitigating the effects of urban microclimates. This study quantitatively explored how the spatial structural parameters of plant communities regulate microclimates during the hot summer in Zhuji City, Zhejiang Province. Field measurements and ENVI-met simulations were conducted to evaluate the microclimatic effects of different plant communities, including broadleaf and coniferous tree communities. Microclimatic variables, such as air temperature, relative humidity, and solar radiation, were analyzed. The results revealed that spatial structural parameters, such as Acanopy/H, sky view factor (SVF), and canopy density, significantly affected temperature reduction and humidity increase. Among these, the canopy-to-height ratio (Acanopy/H) was a promising potential factor influencing cooling. Simulations revealed that with a constant tree height, cooling and humidification benefits increased as Acanopy/H increased. However, with a constant canopy area, these benefits were greater when Acanopy/H ratio decreased. This study emphasizes the importance of spatial structural parameters in optimizing summer microclimatic regulation, providing key insights into urban green space design to enhance thermal comfort. These findings can guide the planning of climate-resilient plant landscapes in subtropical cities. Full article

The impact of urban morphology characteristics on regional thermal environments is a crucial topic in urban planning and climate adaptation research. However, existing studies are often limited to a single dimension and fail to fully reveal the spatiotemporal impact mechanisms of multi-dimensional urban morphology on thermal environments and their connection to regional planning policies. This study focuses on the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), combining quantitative data from landscape pattern indices, land use expansion patterns, and local climate zones (LCZs) derived from 2000 to 2020. By using geographically weighted regression and spatial autocorrelation analysis, we systematically explore the spatiotemporal effects and mechanisms of multi-dimensional urban morphology characteristics on regional thermal effects. We found the following points. (1) Built-up land patch density is significantly positively correlated with LST, with the urban heat island (UHI) effect spreading from core areas to the periphery; this corroborates the thermal environment differentiation features under the “multi-center, networked” spatial planning pattern of the GBA. (2) Outlying expansion mitigates local LST rise through an ecological isolation effect, and infill expansion significantly exacerbates the UHI effect due to high-intensity development, reflecting the differentiated impacts of various expansion patterns on the thermal environment. (3) LCZ spatial distribution aligns closely with regional planning, with the solar radiation shading effect of high-rise buildings significantly cooling daytime LSTs, whereas the thermal storage properties of traditional building materials and human heat sources cause nighttime LST increases; this reveals the deep influence of urban morphology mechanisms, building materials, and human activities on thermal environments. The findings provide scientific support for achieving a win–win goal of high-quality development and ecological security in the GBA while also offering a theoretical basis and practical insights for thermal environment regulation in high-density urban clusters worldwide. Full article

This study investigates the anatomical adaptations and ecological plasticity of C. ambiguus in extreme environmental conditions by analyzing the structural characteristics of its leaves and annual shoots collected from 12 populations in the arid regions of Mangystau, including Western Karatau, Northern Aktau, and the Tyubkaragan Peninsula. Microscopic and statistical analyses revealed significant variability in key anatomical traits, including epidermal thickness, collenchyma, primary cortex, and vascular bundle area, highlighting the species’ adaptive responses to drought, high solar radiation, and limited water availability. The epidermal thickness ranged from 14.85 µm (Pop_12 Botakan) to 22.51 µm (Pop_6 Samal), demonstrating xeromorphic adaptations for reducing transpiration. At the same time, the vascular bundle area varied from 286.06 × 10⁻³ mm² (Pop_3 Emdikorgan) to 528.51 × 10⁻³ mm² (Samal), indicating differences in water transport efficiency across populations. Despite substantial anatomical variation, the low coefficients of variation (0.31%–6.31%) suggested structural stability, reinforcing C. ambigua’s ability to maintain functional integrity under environmental stress. Canonical Correlation Analysis (CCA) confirmed that environmental factors such as soil type, elevation, and water availability significantly influenced anatomical traits. Floristic analysis revealed distinct patterns of species richness, with the highest diversity recorded in Pop_4 and Pop_7, while Pop_12 and Pop_9 exhibited lower diversity, indicating potential vulnerability. Furthermore, the identified anatomical traits could serve as key markers for selecting drought-resistant genotypes in afforestation and restoration programs. This study also highlighted the need for the long-term monitoring of C. ambigua populations to assess the impact of climate change on structural adaptations. These findings offer a framework for integrating ecological and genetic studies to refine conservation strategies for xerophytic species. Full article

Light curve analysis of defunct satellites is critical for characterizing their rotational motion. An accurate understanding of this aspect will benefit active debris removal and on-orbit servicing missions as part of the solution to the space debris issue. In this study, we explored the attitude behavior of inactive GLONASS satellites, specifically a repeating pattern observed in their spin period evolution. We utilized a large amount of data available in the light curve database maintained by the Astronomical Institute of the University of Bern (AIUB). The morphology of the inactive GLONASS light curves typically features four peaks in two pairs and is presumably attributed to the presence of four evenly distributed thermal control flaps or radiators on the satellite bus. The analysis of the periods extracted from the light curves shows that nearly all of the inactive GLONASS satellites are rotating and exhibit a periodic oscillating pattern in their spin period evolution with an increasing or decreasing secular trend. Through modeling and simulation, we found that the periodic pattern is likely a result of canted solar panels that provide an asymmetry in the satellite model and enable a wind wheel or fan-like mechanism to operate. The secular trend is a consequence of differing values of the specular reflection coefficients of the front and back sides of the solar panels. Assuming an empirical model describing the spin period evolution of 18 selected objects, we found significant variations in the average spin period and amplitude of the oscillations, which range from 8.11 s to 469.58 s and 1.10 s to 513.24 s, respectively. However, the average oscillation period remains relatively constant at around 1 year. Notably, the average spin period correlates well with the average amplitude. The empirical model can be used to extrapolate the spin period in the future, assuming that the oscillating pattern is preserved and roughly shows a linear trend. Full article

Arsenic contamination poses a severe health risk in regions with high water vulnerability and limited treatment infrastructure. This study evaluates a photovoltaic-powered water treatment system for arsenic removal in La Yarada Los Palos District, Tacna, Peru, where arsenic concentrations reached up to 0.0417 mg/L, significantly surpassing the World Health Organization (WHO) limit of 10 µg/L (0.01 mg/L) for drinking water. The system integrates a natural sedimentation pretreatment stage in a geomembrane-lined reservoir, followed by oxidation with sodium hypochlorite, coagulation, and adsorption. Arsenic removal efficiencies ranged from 99.72% to 99.85%, reducing residual concentrations below WHO guidelines. Pretreatment significantly improved performance, reducing turbidity by up to 66.67% and TSS by up to 70.37%, optimizing subsequent treatment stages. Operationally, pretreatment decreased cleaning frequency from six to four cleanings per month, while backwashing energy consumption dropped by 33% (from 45.72 kWh to 30.48 kWh). The photovoltaic system leveraged the region’s high solar radiation, achieving an average daily generation of 20.31 kWh and an energy surplus of 33.08%. The system’s performance was evaluated within the context of existing arsenic removal technologies, demonstrating that the integration of natural sedimentation and renewable energy constitutes a viable operational alternative. Given the regulatory framework in Peru, where arsenic limits align with WHO standards, conventional water treatment systems are normatively and technically unfeasible under national legislation. Furthermore, La Yarada Los Palos District faces challenges due to its limited infrastructure for conventional electrification via power grid, as identified in national reports on rural electrification and gaps in access to basic services. Beyond its performance in the study area, the system’s modular design allows adaptation to diverse water sources with varying arsenic concentrations, turbidity levels, and other physicochemical characteristics. In remote regions with limited access to the power grid, such as the study site, photovoltaic energy provides a self-sustaining and replicable alternative, particularly in arid and semi-arid areas with high solar radiation. These conditions are not exclusive to Latin America but are also prevalent in remote regions of Africa, the Middle East, Asia, and Oceania, where groundwater arsenic contamination is a significant issue and renewable energy availability can enhance water treatment sustainability. These findings underscore the potential of using sustainable energy solutions to address water contamination challenges in remote areas. The modular and scalable design of this system enables its replication in regions with adverse hydrogeological conditions, integrating renewable energy and pretreatment strategies to enhance water treatment performance. The framework presented in this study offers a replicable and efficient approach for implementing eco-friendly water treatment systems in regions with similar environmental and resource constraints. Full article

Process-based crop growth models, as an important analytical tool in agricultural production, face the problem of calibrating many parameters during the application process, and sensitivity analysis (SA) can quantify the effects of the model input parameters on the model output and provide an important basis for parameter calibration. In this study, we combined the good performance of the Agricultural Production Systems sIMulator Next-Generation (APSIM NG) model in simulating crop growth, soil carbon and nitrogen cycles, and soil N₂O emissions with the efficient computational efficiency of the extended Fourier amplitude test (EFAST) method. The sensitivity of the APSIM NG model to the simulation of soil N₂O emissions was systematically investigated using the EFAST method in a dry-crop wheat field in the semi-arid region of the Loess Plateau in Longzhong, China, where 28 crop cultivar parameters, 15 soil parameters, 4 meteorological parameters, and 4 field management parameters were selected. The parameters were selected based on the existing literature and the official documents of the model, and the parameter boundaries were determined based on the initial values of the APSIM NG model and the measured data and adjusted upward and downward by the standard normal distribution. In this study, parameters with a first-order sensitivity index ($S_i$) > 0.05 and a total sensitivity index ($S_{Ti}$) > 0.10 were identified as having a significant influence on the model outputs. The results of this study demonstrated that soil N₂O emission modeling in dry-crop wheat fields showed high sensitivity to the following parameters: (1) Among the crop cultivar parameters, the sensitivity from high to low was the leaf appearance rate, maximum leaf area, maximum nitrogen concentration of the grain, and thermal time from the starting grain-fill stage to end grain-fill stage. (2) Among the soil parameters, the sensitivity from high to low was a lower effective moisture limit, wilting coefficient, and ammonium nitrogen content. (3) Among the meteorological parameters, precipitation and solar radiation showed high sensitivity. (4) Among the field management parameters, the nitrogen application rate exhibited the most significant sensitivity. For this reason, we believe that by prioritizing the calibration of the most sensitive parameters through the results of the sensitivity analysis in this study, the workload of the APSIM NG model in the calibration process can be effectively reduced, which is conducive to the rapid localization and application of the model. Full article

Solar steam generation (SSG) has garnered significant attention for its potential in water purification applications. While composites with physically combined structures based on semiconductors or biomass have been developed for SSG, there remains a critical need for low-cost, high-efficiency devices. In this study, TiO₂ composites exhibiting excellent stability, high solar absorption, porous microstructure, and hydrophilic surfaces were identified as effective materials for SSG and water purification for the first time. A novel SSG device was designed by decorating TiO₂ onto three-dimensional carbonized Osmanthus fragrans leaves (TiO₂/carbonized OFL). Compared to directly carbonized OFL (without TiO₂) and Osmanthus fragrans leaves with templated TiO₂ (OFL-templated TiO₂), the TiO₂/carbonized OFL carbon composites demonstrated enhanced solar absorption, achieving over 99% in the visible region and more than 80% in the near-infrared region. Under solar illumination of 1 kW·m⁻², the TiO₂/carbonized OFL device achieved a high water evaporation rate of 2.31 kg·m⁻²·h⁻¹, which is 1.6 times higher than that of carbonized OFL and 3.45 times higher than OFL-templated TiO₂. Additionally, the TiO₂/carbonized OFL system exhibited remarkable efficiency in treating pharmaceutical wastewater, with a chemical oxygen demand (COD) removal efficiency of 98.9% and an ammonia nitrogen removal efficiency of 90.8% under solar radiation. Full article

There is a difficulty in survey sampling when non-response (NR) occurs in the process of estimating the population parameters. This study examines the effectiveness of combined and separate log-type estimators when using bivariate auxiliary information when NR occurs in data. In this study, we propose families of novel log-type estimators under various scenarios. We performed an analysis on the reliability and efficiency of our proposed estimators in situations when NR occurs in both study and auxiliary variables and when NR occurs only in study variables. In this study, we have concentrated on certain issues like how the non-response effects the estimators’ efficiency, how different NR rates effect the precision of estimators, and how the combined and separate types of estimators handle the problem of NR. We proved the efficiency of our proposed estimators by using the bias and mean square error (MSE) metrics under different NR rates, illustrating the positive correlation between higher NR rates and increased errors. To evaluate the impact of NR on MSE values, we took four real datasets, which included a cost of living index dataset for 121 nations and another dataset which is essential for forecasting solar UV radiation hazards influenced by environmental factors, thus enhancing public health awareness and preventive strategies. Additionally, a simulation study comprising 10,000 iterations was also performed. This study provides survey practitioners with valuable guidance on selecting strong estimation methods to enhance the accuracy and efficiency of survey estimates in the context of non-response. This investigation contributes to the domain of survey sampling by demonstrating the robustness and effectiveness of log-type estimators. These estimators enhance survey findings by effectively addressing NR issues. Full article

The Amazon has undergone significant changes in the landscape with the expansion of human activities. The objective of this study was to characterize the relationship between soil temperature (ST) and moisture (SM) with meteorological data and soil attributes in pasture, forest, and transition areas in the Itacaiúnas River Watershed (IRW), Eastern Amazon. Soil samples were analyzed to determine chemical and granulometric attributes. SM and ST were measured up to 40 cm deep using sensors, and the meteorological variables were quantified by hydrometeorological stations. The chemical characteristics and granulometry indicated greater limitations in the Forest soil, with lower levels of organic carbon and higher contents of sand. In Pasture A, Pasture B, and Transition areas, with some exceptions, there was a progressive increase in ST from July to September. In general, SM was positively correlated with rainfall and negatively correlated with ST, air temperature, wind speed, and solar radiation. Linear models for ST (10–20 cm depth) in Pasture B and Forest areas indicate positive relationships with air temperature and wind speed and negative relationships with solar radiation. The findings of this study can be useful in decision-making regarding the management of ecosystems in the IRW. Full article