Search Results (4,347)

Soil salinity stress, intensified by extreme weather patterns, significantly threatens global watermelon [Citrullus lanatus (Thunb.) Matsum & Nakai] production. Watermelon, a moderately salt-sensitive crop, exhibits reduced germination, stunted growth, and impaired fruit yield and quality under saline conditions. As freshwater resources decline and agriculture’s dependency on irrigation leads to soil salinization, we need sustainable mitigation strategies for food security. Recent advances highlight the potential of using salt-tolerant rootstocks and breeding salt-resistant watermelon varieties as long-term genetic solutions for salinity. Conversely, agronomic interventions such as drip irrigation and soil amendments provide practical, short-term strategies to mitigate the impact of salt stress. Biostimulants represent another tool that imparts salinity tolerance in watermelon. Plant growth-promoting microbes (PGPMs) have emerged as promising biological tools to enhance watermelon tolerance to salt stress. PGPMs are an emerging tool for mitigating salinity stress; however, their potential in watermelon has not been fully explored. Nanobiochar and nanoparticles are another unexplored tool for addressing salinity stress. This review highlights the intricate relationship between soil salinity and watermelon production in a unique manner. It explores the various mitigation strategies, emphasizing the potential of PGPM as eco-friendly bio-inoculants for sustainable watermelon management in salt-affected soils. Full article

The online coupling of aerosols and clouds and its effect on surface global horizontal irradiance (GHI) has not yet been thoroughly investigated in the Weather Research and Forecasting Model with Solar extensions (WRF-Solar), despite its potential significance for solar energy applications. This study addresses this critical gap by implementing a computationally efficient, coupled aerosol–cloud scheme and evaluating its impacts on GHI predictability. Simulations with online aerosol–cloud coupling are systematically compared to uncoupled simulations during March 2021, a period marked by two distinct pollution episodes over north China. The online coupling enhances aerosol optical depth (AOD) simulations, increasing the correlation coefficient from 0.19 to 0.51 while reducing the absolute bias from 0.54 to 0.48 and root mean square error from 0.82 to 0.72, compared to uncoupled simulations. Enhanced cloud microphysics (droplet concentration, water path) yields better cloud optical depth estimates, reducing all-sky GHI bias by 14.5% (63.5 W/m² for the uncoupled scenario and 54.3 W/m² for the coupled scenario) through improved aerosol–cloud–meteorology interactions. Notably, the simultaneous spatiotemporal improvement of both AOD and GHI suggests enhanced internal consistency in aerosol–cloud–radiation interactions, which is crucial for operational solar irradiance forecasting in pollution-prone regions. The results also highlight the practical value of incorporating online aerosol coupling in solar forecasting models. Full article

The complex lagoon system of Carmen, Pajonal, and Machona in the Southern Gulf of Mexico is characterized by highly active sedimentary dynamics. To reproduce the sedimentary dynamics processes, the MOHID model, coupled with the SWAN wave model, was applied to different scenarios through a climatic analysis of winds. Historical wind data indicate that the region has experienced a significant shift in the principal wind component over the last two decades. Furthermore, hurricanes have impacted the lagoon system on multiple occasions in recent decades. Five numerical experiments were conducted, considering both historical and present-day wind conditions, the impact of Hurricane Larry, and engineering works such as breakwaters, to better understand the sedimentary dynamics of the lagoon system. Model results revealed intense and variable sediment transport depending on the intensity and direction of the prevailing winds, waves, extreme weather events, and breakwater locations. Full article

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development. Full article

Climate change has intensified extreme weather events and accelerated soil salinization, posing serious threats to crop yield and quality. Salinity stress, now affecting about 20% of irrigated lands, is expected to worsen due to rising temperatures and sea levels. At the same time, the global population is projected to exceed 9 billion by 2050, demanding a 70% increase in food production (UN, 2019; FAO). Agriculture, responsible for 34% of global greenhouse gas emissions, urgently needs sustainable solutions. Microbial inoculants, known as “plant probiotics,” offer a promising eco-friendly alternative by enhancing crop resilience and reducing environmental impact. In this study, we evaluated the plant growth-promoting (PGP) traits and melatonin-producing capacity of Bacillus aerius EH2-5. To assess its efficacy under salt stress, soybean seedlings at the VC stage were inoculated with EH2-5 and subsequently subjected to salinity stress using 150 mM and 100 mM NaCl treatments. Plant growth parameters, the expression levels of salinity-related genes, and the activities of antioxidant enzymes were measured to determine the microbe’s role in promoting plant growth and mitigating salt-induced oxidative stress. Here, our study shows that the melatonin-synthesizing Bacillus aerius EH2-5 (7.48 ng/mL at 24 h after inoculation in Trp spiked LB media) significantly improved host plant (Glycine max L.) growth, biomass, and photosynthesis and reduced oxidative stress during salinity stress conditions than the non-inculcated control. Whole genome sequencing of Bacillus aerius EH2-5 identified key plant growth-promoting and salinity stress-related genes, including znuA, znuB, znuC, and zur (zinc uptake); ptsN, aspA, and nrgB (nitrogen metabolism); and phoH and pstS (phosphate transport). Genes involved in tryptophan biosynthesis and transport, such as trpA, trpB, trpP, and tspO, along with siderophore-related genes yusV, yfhA, and yfiY, were also detected. The presence of multiple stress-responsive genes, including dnaK, dps, treA, cspB, srkA, and copZ, suggests EH2-5′s genomic potential to enhance plant tolerance to salinity and other abiotic stresses. Inoculation with Bacillus aerius EH2-5 significantly enhanced soybean growth and reduced salt-induced damage, as evidenced by increased shoot biomass (29%, 41%), leaf numbers (12% and 13%), and chlorophyll content (40%, 21%) under 100 mM and 150 mM NaCl compared to non-inoculated plants. These results indicate EH2-5′s strong potential as a plant growth-promoting and salinity stress-alleviating rhizobacterium. The EH2-5 symbiosis significantly enhanced a key ABA biosynthesis enzyme-related gene NCED3, dehydration responsive transcription factors DREB2A and NAC29 salinity stresses (100 mM and 150 mM). Moreover, the reduced expression of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) by 16%, 29%, and 24%, respectively, and decreased levels of malondialdehyde (MDA) and hydroxy peroxidase (H₂O₂) by 12% and 23% were observed under 100 mM NaCl compared to non-inoculated plants. This study demonstrated that Bacillus aerius EH2-5, a melatonin-producing strain, not only functions effectively as a biofertilizer but also alleviates plant stress in a manner comparable to the application of exogenous melatonin. These findings highlight the potential of utilizing melatonin-producing microbes as a viable alternative to chemical treatments. Therefore, further research should focus on enhancing the melatonin biosynthetic capacity of EH2-5, improving its colonization efficiency in plants, and developing synergistic microbial consortia (SynComs) with melatonin-producing capabilities. Such efforts will contribute to the development and field application of EH2-5 as a promising plant biostimulant for sustainable agriculture. Full article

Temperate Highland Peat Swamps on Sandstone (THPSS) are wetlands in the Blue Mountains, south-eastern Australia. The wetlands have legislative protection as endangered ecological communities. They have long-standing cultural significance for Gundungurra Traditional Custodians. Previous studies document their degradation by urban development and vulnerability to extreme weather. Water quality in our study was assessed at wetlands in protected areas and compared with others exposed to urban development. We derived water quality guidelines that are intended to help future water quality assessment at THPSS and, in particular, to detect any impact from urban development on these wetland systems. Water quality in urban swamps was consistent with the freshwater salinisation syndrome despite all the swamps having relatively low electrical conductance (<140 µS cm⁻¹). Urban swamp water had salinity (mean 87.3 µS cm⁻¹) three times that of non-urban swamps (mean 28 µS cm⁻¹). The ionic composition of urban swamp water was dominated by calcium and bicarbonate, consistent with urban alkalisation syndrome. Our guidelines instead recommend limits for pH, salinity, turbidity, dissolved oxygen, and metals detected in greater concentrations that were found in urban swamps (iron, manganese, barium, and strontium). Our results support the theory that the dissolution of urban concrete materials is a degradation process that contributes to the impairment of urban swamp water quality. Full article

Soil moisture serves as a critical factor in the hydrological cycle, affecting plant growth, ecosystem health, and groundwater reserves. Current methods for monitoring and predicting it fail to account for the complexities introduced by climatic variations and other influencing factors, such as the effects of atmospheric interference and data gaps, leading to reduced prediction accuracy. To address these challenges, this study introduces a novel soil moisture prediction model based on remote sensing and deep learning, utilizing the Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) optimized by the Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and incremental learning (IL) techniques. The proposed method for monitoring soil moisture utilizes hyperspectral and soil moisture data from a 2017 campaign in Karlsruhe, encompassing variables such as datetime, soil moisture percentage, soil temperature, and remote sensing spectral bands. The proposed methodology begins with comprehensive preprocessing of historical remote sensing data to fill gaps, reduce noise, and correct atmospheric disturbances. It then employs a unique seasonal mapping and grouping technique, enhanced by the AdaK-MCC method, to analyze the impact of climatic changes on soil moisture patterns. The model’s innovative feature selection approach, using HCSWO, identifies the most significant predictors, ensuring optimal data input for the AGRL-RBFN model. The model achieves an impressive accuracy of 98.09%, a precision of 98.17%, a recall of 97.24%, and an F1-score of 98.95%, outperforming existing methods. Furthermore, it attains a mean absolute error (MAE) of 0.047 in gap filling and a Dunn Index of 4.897 for clustering. Although successful in many aspects, the study did not investigate the relationship between soil moisture levels and specific crops, which presents an opportunity for future research aimed at enhancing smart agricultural practices. Furthermore, the model can be refined by integrating a wider range of datasets and improving its resilience to extreme weather conditions, thereby providing a reliable tool for climate-responsive agricultural management and water conservation strategies. Full article

The direct relationship between aerosols and clouds strongly influences the effects of clouds on the global climate. Aerosol particles act as cloud condensation nuclei (CCN) and ice nuclei (IN), affecting cloud formation, microphysics, and precipitation, as well as increasing the reflection of solar radiation at the cloud tops. Processes such as gas-to-particle conversion and new particle formation (NPF) control aerosol properties that, together with meteorological conditions, regulate cloud droplet nucleation through Köhler theory and related effects. The indirect aerosol effects described by Twomey and Albrecht demonstrate how changes in aerosols impact droplet number, cloud lifetime, and precipitation efficiency. Cloud microphysical processes, including droplet growth, collision-coalescence, and solid-phase mechanisms such as riming, vapor diffusion, and aggregation, shape precipitation development in warm, cold, and mixed-phase clouds. Ice nucleation remains a significant uncertainty due to the diversity of aerosol types and nucleation modes. This work synthesizes these physical interactions to better understand how the chemical and physical properties of aerosols influence cloud and precipitation processes, supporting improvements in weather and climate prediction models despite numerical challenges arising from the complexity of aerosol–cloud interactions. Full article

The Karnak Temples are considered one of Egypt’s most significant archaeological sites, dating back to the Middle Kingdom (c. 2000–1700 BC) and were continuously expanded until the Ptolemaic period (305–30 BC). As the second most visited UNESCO World Heritage archaeological site in Egypt after the Giza Pyramids, Karnak faces severe deterioration processes due to prolonged exposure to environmental impacts, mechanical damage, and historical interventions. This study employs a multidisciplinary approach integrating non-destructive testing (NDT) methods to assess the physical and mechanical condition and degradation mechanisms of scattered sandstone blocks at the site. Advanced documentation techniques, including Reflectance Transformation Imaging (RTI), photogrammetry, and Infrared Thermography (IRT), were used to analyze surface morphology, thermal stress effects, and weathering patterns. Ultrasonic Pulse Velocity (UPV) testing provided internal structural assessments, while spectral and gloss analysis quantified chromatic alterations and surface roughness. Additionally, the Karsten Tube test determined the water absorption behavior of the sandstone, highlighting variations in porosity and susceptibility to salt crystallization. In this sense, the results indicate that climatic factors such as extreme temperature fluctuations, wind erosion, and groundwater infiltration contributed to sandstone deterioration. Thermal cycling leads to microcracking and granular disintegration, while high capillary water absorption accelerates chemical weathering processes. UPV analyses showed substantial internal decay, with low-velocity zones correlating with fractures and differential cementation loss. Finally, an interventive conservation plan was proposed. Full article

The Australian alpaca industry is continuing to develop as an alternative fibre industry to the traditional merino or angora industries. This study aimed to investigate herd behaviour in an extensive system in south eastern Australia. Healthy adult female alpacas (Huacaya n = 32, Suri n = 32) over two years old were inducted into the trial and kept together across a 10 month period. A total of 5 animals were removed during the study due to illthrift or death unrelated to the study. GoPro cameras were set up at 5 locations in the paddock for 3 days in the middle of every season (Summer, Autumn, Winter, Spring) to record alpaca behaviour without a human observer present. Visual observations were taken at 0800, 1000, 1100, 1300, 1500 for 60 min. Behaviour observations were taken every 5 min from the videos according to a prepared ethogram. A count of animals exhibiting each behaviour was recorded at each time point within each of the designated 60-minute periods. A generalised linear mixed-effects model (GLMM) was run on binary data for each behaviour. Behaviours that returned a predicted proportion of over 0.10 for all seasons were used in an ordinal logistic regression that was then utilised to determine the effect of the season, time of day, and weather conditions on the number of animals. Season significantly impacted the number of alpacas grazing, resting, and standing (p < 0.0001). Alpacas were more likely to be grazing throughout the day in cooler seasons (autumn, winter) and resting in the warmer parts of the day in summer and spring. The time of day impacted the proportion of alpacas resting and grazing (p < 0.05) but not standing (p = 0.4432). This study highlights that alpacas spend the majority of the daylight hours grazing, with some variability across different seasons, which may impact ideal management practices to optimise production in an extensive system. Full article

Rice (Oryza sativa L.) plays a pivotal role in the Brazilian economy, serving as a staple food for more than half of the world’s population and thereby contributing to global food security. Projections of future climate change scenarios indicate an increase in extreme weather events. Among climate variables that impact the development and productivity of irrigated rice, solar radiation is one of the most important in defining productive potential. Understanding the risks imposed on agricultural production by the occurrence of days with reduced luminosity availability is crucial for guiding adequate responses that mitigate the negative impacts of climate variability. Therefore, this study aimed to investigate the effect of shade on the metabolism and productivity of irrigated rice plants, with a specific focus on the synthesis of photosynthetic pigments, carbohydrate accumulation, invertase activity, and the nutritional status and grain yield of rice. For this, the study was conducted on the field rice cultivars IRGA 424 RI, BRS PAMPA, and BRS PAMPEIRA, which were subjected to 35% shading using black nylon netting installed when the plants reached the reproductive stage (R0). The restriction was maintained until the R4 stage, and later, from the R4 stage until the R9 stage. After the imposition of treatments, evaluations took place at the phenological stages R2, R4, R6, and R8. In shaded plants, a higher content of photosynthetic pigments and a lower accumulation of carbohydrates were observed, which was reflected in an increase in the activity of invertase enzymes. These conditions were able to potentiate effects on the nutritional status of the plants, in addition to reducing productivity and 1000-grain weight and increasing spikelet sterility, due to changes in the source–sink relationship, with effects more pronounced in cultivars BRS PAMPA and BRS PAMPEIRA during the R4–R9 period. Full article

The industrial sector is a major contributor to energy-related CO₂ emissions in Europe, making the transition to renewable energy solutions essential. Decarbonization strategies integrate renewable energy sources, power-to-heat technologies, and energy storage systems into existing production sites to enhance sustainability and flexibility. However, a key challenge lies in designing energy systems that remain robust under long-term operational uncertainties. Usually the design of each energy system component is discrete, as it is manufactured in a predetermined size. Classical state-of-the-art coupled design and operational optimization methods are based on continuous design variables, which might give sub-optimal solutions. This study overcomes this limitation by employing novel, computationally efficient robust quantum-classical discrete-design methods. Traditional approaches often optimize operations for a single year due to the computational limitations of operational optimization algorithms, leading to designs that lack robustness. By incorporating long-term operational uncertainties, this approach ensures that selected energy-system configurations minimize both CO₂ emissions and costs while maintaining resilience to variations in weather conditions and demand fluctuations. Robust discrete designs which consider operational uncertainties show 12% less global warming impact (GWI) with 27% higher total annualized cost (TAC) compared to designs based on operational optimization without uncertainty. A novel quantum-assisted non-dominated sorting genetic algorithm (QANSGA-II) shows accuracy up to 90%, which leads to 27% less computational effort than the NSGA-II algorithm. This novel method can help industries to search larger and more optimal robust discrete-design spaces for making decarbonization decisions. Full article

One type of pore fundamental to water dynamics is the intra-aggregate pore, which holds water vital for plant and root system development, mainly in finer-textured soils such as clays. The distribution of intra-aggregate pores also influences the redistribution of water. Thus, it is important to study the dynamics of the intra-aggregate pore network under processes such as wetting and drying cycles (WDC). Changes in these pore types can play essential roles in organic matter protection, water movement, microbial activity, and aggregate stability. To date, there are few studies analyzing the impact of WDC on intra-aggregate pore dynamics. This study aims to provide results in this regard, analyzing changes in the pore architecture of a subtropical Oxisol under no-tillage (NT), conventional tillage (CT), and forest (F) after WDC application. Three-dimensional X-Ray microtomography images of soil aggregate samples (2–4 mm) subjected to 0 and 12 WDC were analyzed. The results showed that WDC did not affect (p > 0.05) the imaged porosity, number of pores, fractal dimension, tortuosity, and pore connectivity for the different soil management types. To analyze the permeability and hydraulic conductivity of the soil pore system, the most voluminous pore (MVP) was examined. No differences were observed in the imaged porosity, fraction of aggregate occupied by the MVP, connectivity, tortuosity, hydraulic radius, permeability, and hydraulic conductivity between 0 and 12 WDC for the MVP. Comparing soil management types after 12 WDCs, for example, F samples became more porous than CT and NT samples. In contrast, the pore system of NT had a lower fractal dimension and was more tortuous than that of CT and F samples. Our results show that for highly weathered soils such as the Brazilian Oxisol studied, the intra-aggregate pore network proved resistant to changes with WDC, regardless of the type of management adopted. Full article

The rapid development of intelligent high-speed railways (HSRs) has significantly improved the transportation efficiency of modern transit systems, while also imposing higher bandwidth demands on mobile communication systems. Free-space optical (FSO) communication technology, as a promising solution, can effectively meet the high-speed data transmission requirements in intelligent HSR scenarios. In this paper, we consider an intelligent HSR system based on beamwidth-adaptive FSO communication and investigate the coverage performance of the system. Different from the circular cells used in traditional radio frequency wireless communication systems, this paper focuses on the coverage problem of narrow-strip-shaped cells in HSR systems based on FSO communication. When the transmitter emits a wide beam, the channel gain includes geometric loss, atmospheric attenuation, and atmospheric turbulence. When the transmitter emits a narrow beam, the channel gain includes pointing error, atmospheric attenuation, and atmospheric turbulence. To adapt the width of the transmitter’s beam, we propose a beamwidth-adaptive HSR system and a beamwidth-adaptive method. Furthermore, we derive closed-form expressions of the edge coverage probability (ECP) and the percentage of cell coverage area (CCA), where the ECP is the probability that the received signal-to-noise ratio at the cell edge is greater than or equal to a given threshold, and the percentage of CCA dictates the percentage of locations within a cell that are not in outage. The accuracy of the derived theoretical expressions is validated through Monte-Carlo simulations. The average relative error of the ECP between theoretical and simulation results is only 0.035%, and the corresponding error of the percentage of CCA is 0.087%. In addition, the impacts of factors such as cell diameter, transmission power, signal-to-noise ratio threshold, and weather visibility on coverage performance are also discussed. Full article

Climate change has magnified health disparities across the Southern African Development Community (SADC) region by destabilizing the critical natural systems, which include water security, food production, and disease ecology. The IPCC (2007) underscores the disproportionate impact on low-income populations characterized by limited adaptive capacity, exacerbating existing vulnerabilities. Rising temperatures, erratic precipitation patterns, and increased frequency of extreme weather events ranging from prolonged droughts to catastrophic floods have created favourable conditions for the spread of waterborne diseases such as cholera, dysentery, and typhoid, as well as the expansion of vector-borne diseases zone also characterized by warmer and wetter conditions where diseases like malaria thrives. This study employed a comparative analysis of climate and health data across Malawi, Zimbabwe, Mozambique, and South Africa examining the interplay between climatic shifts and disease patterns. Through reviews of national surveillance reports, adaptation policies, and outbreak records, the analysis reveals the existence of critical gaps in preparedness and response. Zimbabwe’s Matabeleland region experienced a doubling of diarrheal diseases in 2019 due to drought-driven water shortages, forcing communities to rely on unsafe alternatives. Mozambique faced a similar crisis following Cyclone Idai in 2019, where floodwaters precipitated a threefold surge in cholera cases, predominantly affecting children under five. In Malawi, Cyclone Ana’s catastrophic flooding in 2022 contaminated water sources, leading to a devastating cholera outbreak that claimed over 1200 lives. Meanwhile, in South Africa, inadequate sanitation in KwaZulu-Natal’s informal settlements amplified cholera transmission during the 2023 rainy season. Malaria incidence has also risen in these regions, with warmer temperatures extending the geographic range of Anopheles mosquitoes and lengthening the transmission seasons. The findings underscore an urgent need for integrated, multisectoral interventions. Strengthening disease surveillance systems to incorporate climate data could enhance early warning capabilities, while national adaptation plans must prioritize health resilience by bridging gaps between water, agriculture, and infrastructure policies. Community-level interventions, such as water purification programs and targeted vector control, are essential to reduce outbreaks in high-risk areas. Beyond these findings, there is a critical need to invest in longitudinal research so as to elucidate the causal pathways between climate change and disease burden, particularly for understudied linkages like malaria expansion and urbanization. Without coordinated action, climate-related health inequalities will continue to widen, leaving marginalized populations increasingly vulnerable to preventable diseases. The SADC region must adopt evidence-based, equity-centred strategies to mitigate these growing threats and safeguard public health in a warming world. Full article