Search Results (509)

Apple (Malus domestica Borkh.) production is increasingly threatened by fungal diseases under conditions of intensive horticulture and ongoing climate change. In southeastern Kazakhstan, symptoms associated with Alternaria spp. have become more frequent in commercial orchards; however, molecularly confirmed data on the pathogen associated with these symptoms remain limited. This study aimed to identify, using multigene molecular phylogenetic analysis, an Alternaria isolate obtained from infected apple leaves and fruits, to confirm its pathogenicity experimentally, and to compare the susceptibility of apple cultivars to this pathogen. For molecular identification, nucleotide sequences of four genetic markers—the ITS region of rDNA, SSU, tef1-α, and RPB2—were obtained by PCR and sequencing. The sequences were compared with reference data from the GenBank database using BLASTn, and phylogenetic relationships were inferred using the maximum likelihood method based on a concatenated dataset of the four loci. The isolate KZ17 clustered within the A. alternata clade, with the broader node uniting this group with A. gossypina and A. longipes receiving a bootstrap value of 78%. A total of 20 fungal isolates were obtained from 112 symptomatic apple leaf and fruit samples. Among them, one representative isolate, KZ17, was selected for multilocus molecular identification and pathogenicity testing. The pathogenicity of isolate KZ17 was confirmed in inoculation experiments on apple microshoots under controlled conditions. Artificial inoculation of detached leaves and ripe fruits of 14 apple cultivars revealed significant cultivar-dependent differences in susceptibility. Lesion diameters ranged from 12.3 ± 0.20 to 19.2 ± 0.35 mm on detached leaves and from 15.0 ± 1.2 to 30.0 ± 2.4 mm on ripe fruits. The least susceptible cultivars were ‘Granny Smith’, ‘Zaman’, and ‘Maksat’, whereas ‘Voskhod’, ‘Saltanat’, and ‘Kamila’ showed the greatest susceptibility. These differences were statistically significant (p < 0.001). This study provides the first multigene-based confirmation of the association of A. alternata with apple leaf and fruit lesions in southeastern Kazakhstan and demonstrates cultivar-dependent differences in susceptibility to this pathogen. These findings contribute to improved pathogen diagnostics, germplasm screening for resistance, and the development of plant protection strategies for commercial apple orchards. Full article

Landfills represent a significant source of methane emissions, with important environmental, climatic and safety impacts due to the widespread and variable nature of these emissions. Traditional monitoring methods, such as flow chambers coupled with flame ionisation detectors (FIDs), provide high accuracy but are limited in terms of spatial representativeness, operational flexibility and cost, especially during large-scale or continuous monitoring campaigns. Within this context, the European ESCAPE project aims to develop a low-cost, portable and modular platform for the detection and quantification of low methane concentrations in ambient air at complex environmental sites. The system is based on commercial MOX and NDIR sensors integrated into portable toolboxes equipped with dedicated chambers, regulated suction systems and autonomous data acquisition units with real-time transmission. This work describes the development and testing of two identical toolboxes to assess system reproducibility and the transferability of predictive models between devices. Laboratory and field tests were carried out under controlled and real landfill conditions, with comparisons against portable FID measurements. Results showed good agreement between predicted methane concentrations and reference data, with correlation indexes up to 0.77. Moreover, transferring the machine learning model between toolboxes did not produce statistically significant performance reductions, demonstrating promising robustness and generalizability of the proposed calibration strategy. Full article

Background: Whether financial deepening and trade integration support or hinder environmental sustainability in hydrocarbon-dependent economies remains contested. Methods: This study examines the relationships among financial inclusion, energy intensity, trade openness, and CO₂ emissions per capita in Saudi Arabia for 1980–2020. The empirical strategy combines ARDL bounds testing, FMOLS, DOLS, CCR robustness, Toda–Yamamoto causality, and a battery of structural-break tests comprising Zivot–Andrews unit-root tests, Bai–Perron sup-F tests, and Chow tests. To address the mechanical correlation between carbon productivity and GDP, the per capita emissions specification (LNCP) is used as the primary outcome; carbon productivity (LNES) is reported for robustness. The small-sample sub-period results are stress-tested using ridge regression, residual-bootstrap confidence intervals, a GDP-augmented (scale-control) specification, and a break-date sensitivity analysis. Results: Cointegration is established. The Chow test identifies a significant break in the cointegrating relationship at 2001 (F = 7.36, p < 0.001 for LNCP), supported by the Zivot–Andrews endogenous-break dates for the financial-inclusion series (2000) and trade-openness series (2005), and by the Bai–Perron sup-F test (sup-F = 26.37 at 1990, exceeding the 1% Andrews critical value). Sub-sample re-estimation around 2001 shows that energy intensity, urbanisation, and trade openness are robust drivers of per capita emissions only after the break, while financial inclusion is statistically insignificant in both regimes once the GDP–carbon-productivity mechanical relationship is removed. Conclusions: The Saudi finance–environment relationship is structurally unstable, and policy assessments based on full-sample averages can be misleading. The evidence is best read as describing regime-dependent, conditional long-run associations rather than as identifying structural causal effects. By exposing the interactions, synergies, and trade-offs among financial deepening (SDG 8), energy efficiency (SDG 7), sustainable consumption and production (SDG 12), and climate action (SDG 13), the study shows how this descriptive quantitative evidence can inform—rather than directly identify—an instrument-level policy discussion. The findings are consistent with a Vision 2030 mix that prioritises energy efficiency and green-finance reform, with implications for SDG Targets 7.3, 8.10, 12.2, and 13.2 across oil-exporting economies. Full article

Accurate daily runoff prediction is essential for flood control, reservoir operation, and scientific water resources management. However, runoff processes are increasingly affected by climate change and human activities, leading to pronounced nonlinearity and nonstationarity that limit the performance of single data-driven models. This study aims to improve the reliability and hydrological credibility of daily runoff prediction by systematically evaluating recurrent neural network (RNN) structures and explicitly modeling prediction residuals. Three commonly used RNN architectures—long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM)—are systematically compared for daily runoff prediction in the Andi Reservoir watershed under identical hydrometeorological conditions. Based on the comparative results, BiLSTM is selected as the base model to capture dominant temporal dependencies. To further address systematic prediction errors under complex hydrological conditions, a residual-learning framework is constructed by integrating BiLSTM with extreme gradient boosting (XGBoost), in which XGBoost is employed to model and correct the nonlinear residuals of BiLSTM predictions. In addition, the Shapley Additive Explanations (SHAP) method is applied to interpret the contributions of input variables and to examine the learning mechanisms of both the base model and the residual-correction stage. Results indicate that BiLSTM performs better than LSTM and GRU for daily runoff prediction and that residual correction using XGBoost further enhances prediction accuracy and robustness, particularly under nonstationary conditions and peak-flow scenarios. The contribution of this study lies in providing a systematic modeling framework that combines model comparison, residual learning, and interpretability analysis to support more reliable daily runoff prediction in complex watersheds. Full article

Seasonal snowpack and snowmelt are critical regulators of forest ecosystem functioning in temperate, boreal, montane, and alpine regions. Snowpack acts as a temporary water and energy reservoir, while snowmelt determines the seasonal availability of water and influences ecosystem processes during the growing season. Climate change is altering snowfall patterns, snow accumulation, and melt timing, with consequences for forest productivity, resilience, and disturbance dynamics. This review synthesizes current knowledge on snow–forest interactions and identifies major research trends, methodological approaches, and remaining knowledge gaps. The study combines a bibliometric analysis and a qualitative literature review based on publications indexed in the Scopus and Web of Science databases. A total of 695 publications were included in the bibliometric dataset and analyzed to assess temporal trends, geographical patterns, research themes, and the ecological consequences of changing snow dynamics in forests. Representative studies from this dataset were subsequently synthesized to evaluate the influence of snowpack and snowmelt on forest ecosystem functioning, resilience, and sustainability. The reviewed literature shows that snowpack and snowmelt strongly regulate forest water availability, soil thermal conditions, nutrient cycling, vegetation responses, and carbon dynamics. Changes in snow regimes, particularly reduced snow accumulation and earlier melt, can increase the risk of soil freezing, modify moisture conditions, intensify water stress, and affect ecosystem carbon balance. However, the magnitude and direction of these effects depend on forest type, species composition, climate, and landscape characteristics. Forest structure also plays an important role in controlling snow interception, accumulation, persistence, and melt processes. The bibliometric analysis indicates a rapid increase in research interest in snow–forest interactions over the last two decades, with major contributions from the United States, Canada, China, and Northern Europe. Environmental sciences, hydrology, and ecology were the dominant research areas. Despite substantial progress, uncertainties remain regarding long-term ecosystem responses, species-specific vulnerabilities, and the interactions between declining snow cover and other climate-driven disturbances. This review emphasizes that understanding snowpack and snowmelt dynamics is essential for predicting forest ecosystem responses to climate change and for improving sustainable forest management and watershed conservation strategies in snow-dependent regions. Full article

An aeolian event and a fluvial episode affected Gale crater, Mars, prior to 3.6 billion years ago. Both were short-lived and catastrophic. The same two events also modified the Southern Highlands of the red planet during the same time interval. We show that events in Gale crater were a part of those that modified vast areas of the southern hemisphere of Mars. As such, the patterns documented in Gale crater are consistent with reshaping of large portions of Mars by short-lived catastrophic events by wind and water, although data from other regions are needed to establish this on a planetary scale. The study is based on data collected by the Curiosity rover during the past 14 years. The aeolian event that excavated Gale crater was lithologically controlled. It formed two distinct morphological provinces with two contrasting rock types. One was the cone-shaped ancestral Aeolis Mons, informally known as Mt. Sharp, that consists of sandstone, siltstone, and mudstone. The other was the nearly flat hollowed margin, the ancestral crater floor, that was initially covered by loose pebbles, cobbles, and boulders which were reworked and lithified to a conglomeratic rock unit later. Commonly reported Martian aeolian erosion rates cannot account for the abrasion and transport of 39,000 km³ of sediments out of Gale crater. This conclusion is supported by little modification of Gale crater during the past 3.6 billion years by ordinary winds. Our evaluation indicates that the excavation of Gale crater took place by a powerful aeolian perturbation that resembled a sand-blasting operation. It was short-lived, had extremely high erosion rates, and occurred during a cold and dry climate. The fluvial episode followed the aeolian event. The study of its sedimentary record indicates that it began with intense precipitation-driven great floods that eroded the ancestral Mt. Sharp, carved large canyons on its slope, and reworked gravels of the ancestral crater floor into giant bedforms. Flood waters also formed a deep lake that experienced one rise and one fall of lake-level and had a dynamic storm-driven sedimentation. The fluvial episode was also short-lived and indicates catastrophic actions of water during a warm and wet climate. As such, this study suggests that the extensive reshaping of the red planet during the Late Noachian period, including formation of valley networks, occurrence of hundreds of crater lakes, and excavation of numerous craters, were also due to short-lived, intense, climate-related perturbations by powerful wind and water rather than by ordinary, slow rate, long-duration processes. Another implication of the study is for the mineralogical evolution of Martian sedimentary rocks. It indicates that the Late Noachian period may have been mostly cold and dry, similar to the modern Mars. Its low water/rock ratio and cold temperatures halted chemical weathering that resulted in preservation of highly unstable minerals such as olivine and pyroxene. The fluvial perturbation with its high water/rock ratio was not long and/or warm enough to alter or significantly affect the mineralogy by weathering at the source region, or during the transport, or at the depositional site. Full article

Climate-change mitigation in resource-dependent economies requires lowering carbon intensity (CO₂ per unit of GDP) while sustaining growth. Using annual data for Saudi Arabia (1970–2022), we examine how growth composition—structural change, educational scale, and urban population scale—relates to carbon intensity, via quantile regression and Toda–Yamamoto Granger (predictive) causality. All relationships are short-run, estimated on first-differenced, stationary data without cointegration, with bootstrap inference (standard errors, confidence intervals, pseudo-R²). Energy intensity is the dominant predictor of carbon intensity (≈+1.0 at every quantile, p < 0.001), consistent with the Kaya identity. Among the composition variables, only urban population scale shows a robust, regime-dependent association, turning positive and significant at the 10% level at the upper-middle quantiles (τ = 0.7–0.8). Structural change and trade openness show no robust independent association once energy intensity is controlled; educational scale is insignificant throughout; and no structural break appears around 2014, 2016, or 2020. The findings support a regime-sensitive, diagnostic reading of Vision 2030 prioritizing energy-mix decarbonization and cautious attention to urban demographic scale over uniform composition instruments. The study informs Sustainable Development Goals 7, 8, 9, 11, and 13. Full article

Pollen exposure drives allergic disease in millions of Americans, yet no standardized, publicly available national pollen dataset has existed until now. We describe the first nationally standardized and publicly available dataset of pollen season timing and airborne pollen concentrations. The data were derived from the National Allergy Bureau™ (NAB™), the only pollen and mold measuring network in the United States certified by the American Academy of Allergy, Asthma & Immunology (AAAAI), and curated, processed, and disseminated by the U.S. Centers for Disease Control and Prevention (CDC). The 2003–2024 dataset provides standardized measures of (1) taxa-specific historical average main pollen season (MPS) concentrations and timing (e.g., start dates, peak dates, end dates, season length); (2) taxa-specific yearly MPS concentrations and timing; (3) grouped weekly MPS concentrations, levels, and timing; and (4) grouped daily pollen levels and MPS timing. Pollen concentrations are reported as pollen grains per cubic meter (PPCM). MPS timing is computed using a 3-day consecutive method: season start occurs after the first occurrence of three consecutive days with concentrations > 1.0 PPCM; season peak is the day of maximum concentration; and season end occurs after the first occurrence of three consecutive days with concentrations < 1.0 PPCM after the peak. Historical average timing is calculated in a 365-day-of-year format and converted to calendar dates using 2024 as a reference year for display and consistency. By combining long-term data from monitoring sites across the country, this dataset shows how pollen levels vary over time and across geographic locations. This resource supports tracking pollen trends, linking pollen with weather and climate factors, and informing public health action, clinical care, and communication about population exposure and the impact to allergic diseases such as asthma and hay fever. Full article

Road construction contributes to embodied carbon in infrastructure, with asphalt-bound layers often dominating construction-stage greenhouse gas emissions in flexible pavements. Reclaimed asphalt pavement (RAP) and high-modulus asphalt concrete can reduce virgin material demand and improve structural efficiency, but their sustainability benefit depends on maintaining equivalent pavement performance. This study develops a climate-informed, mechanistic, environmental, and economic Pareto optimization framework for RAP-modified high-performance asphalt concrete (RAP-HPAC) pavement sections in Alberta. The framework couples fitted dynamic modulus master curves, monthly pavement temperature inputs, ALVA layered elastic analysis, Asphalt Institute fatigue and rutting criteria, A1–A5 global warming potential (GWP), and Alberta 2026 installed unit-price cost data. The RAP-HPAC mixture contains 50% RAP and was designed through a balanced mix design to target approximately 80% effective RAP binder activation. Three traffic classes were evaluated: 731, 1300, and 5426 ESAL/day/direction, each with 2% annual compound growth over a 20-year design period. Relative to independently optimized conventional HMA controls, Pareto-selected RAP-HPAC sections reduced P50 construction-stage GWP by approximately 19–30% and first cost by approximately 6–11% at a conservative 0.90× RAP-HPAC cost multiplier. The results show that RAP-HPAC is most beneficial when used as a structural-bound base that replaces conventional asphalt-bound capacity while preserving sufficient granular support. The framework provides a reproducible design-stage approach for comparing recycled high-modulus asphalt mixtures using performance, carbon, and cost criteria simultaneously. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

Post-fire rehabilitation structures are widely used in Mediterranean burned landscapes to reduce runoff and sediment transfer, yet their ecological associations with early vegetation recovery remain insufficiently documented. This observational study assessed vascular plant composition, species richness, vegetation cover, plant density, aboveground biomass, and soil properties across log barriers, wattles, and log dams in the burned landscape of Ancient Olympia, western Greece. The study area belongs to the humid climatic class of the United Nations Environment Programme (UNEP) aridity framework based on the Thornthwaite aridity index, providing a comparatively wetter Mediterranean post-fire context. Paired depositional and eroded microsites in operationally restored post-fire areas were monitored in 2022 and 2023. The sampling design comprised nine plots and 18 microsites (n = 9 plots, 18 microsites). Generalized estimating equations (GEE), change-score models, principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) were performed to examine associations of monitoring year, microsite condition and rehabilitation structure type with soil and vegetation patterns. A total of 27 vascular plant species belonging to 16 families were recorded. The average vegetation cover increased from 39.17 ± 21.44% in 2022 to 75.11 ± 12.90% in 2023. Model-based marginal estimates with 95% confidence intervals indicated a large positive increase in vegetation cover over this period. Further, rapid early recovery was indicated by large increases in species richness, plant density and biomass. Depositional microsites were associated with stronger recovery signals than eroded ones, characterized by a larger increase in vegetation cover, density, biomass and species richness. Among rehabilitation structures, log dams showed the highest cumulative floristic richness and a broader observed floristic spectrum, although the species-level contingency analysis provided only marginal evidence for structure-associated differences in floristic composition. Changes in selected soil properties including total nitrogen (total N), ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), pH, electrical conductivity (EC), and exchangeable calcium (Ca), magnesium (Mg), and potassium (K), were detected between 2022 and 2023; the multivariate soil pattern was driven primarily by mineral nitrogen, pH, and EC. These findings suggest that, under operational post-fire restoration conditions, rehabilitation structures are associated not only with erosion-control functions but also with microsite differentiation that may shape early plant establishment and biodiversity recovery in Mediterranean burned landscapes. Full article

The article presents the development of a multi-zone temperature control model in an Internet of Things (IoT) environment, designed for the cold chain of pharmaceutical products transported by sea. The model is based on the Elephant Herding Optimization (EHO) algorithm, which is used to regulate cooling modes in three independent temperature zones. The study is designed as a simulation-based proof-of-concept rather than as a full-scale experimental validation on an industrial refrigerated container. The proposed framework evaluates whether an EHO-based controller can generate spatially differentiated cooling decisions under synthetic but controlled disturbance scenarios. The variability of sensor readings reflects conditions typical of long-distance maritime transport. These include transitions across different climate zones, changes in solar exposure, and local differences in thermal load. The simulation results indicate that EHO maintains the temperature within the target range required for pharmaceutical cargo, i.e., 0–8 °C. The algorithm responds effectively to local disturbances and to asymmetry between zones. The proposed model provides a basis for further research on autonomous monitoring and control methods in IoT-based cold chain systems; however, validation using measurements from real refrigerated containers, physical heat-transfer modelling, refrigeration-unit response delays, and IoT communication disturbances remains necessary before operational deployment. Full article

Invasive alien species (IAS) threaten coastal wetland ecosystems, and smooth cordgrass (Spartina alterniflora) is among the most damaging invaders along the coast of China. We compiled occurrence records from the invaded range (China) and native range (United States) and retained 358 and 291 spatially thinned occurrences after quality control and definition of coastal-accessible areas. We assembled climatic, topographic, land use, soil and anthropogenic predictors and fitted species distribution models using the biomod2 ensemble-modeling framework, complemented by an ecospat-based comparison of native and invaded niche spaces. The ensemble model (EM) showed high predictive accuracy (China: AUC = 0.98, TSS = 0.99; USA: AUC = 0.99, TSS = 0.94). Elevation (73.6%) and human influence (6.0%) were the strongest predictors, highlighting the role of intertidal geomorphology and human-mediated propagule pressure. Niche overlap between ranges was low (Schoener’s D = 0.13), and the invaded niche showed substantial unfilling (0.36), indicating additional environmental space at risk of colonization in China. The current suitable habitat forms a continuous coastal belt from the Bohai Rim through the Yellow Sea–East China Sea to the South China Sea. Projections under future climate change suggest predominantly stable suitable areas with localized expansions but potential contractions in some periods. Our results may support the early warning, surveillance prioritization, and adaptive management of S. alterniflora under climate change. Full article

Improving the productivity and stability of winter wheat under increasingly variable climatic conditions remains a major challenge for sustainable agriculture. This study evaluated the effects of pre-sowing seed treatment with a microbial preparation (Nando BioExpert) and a biostimulant (Vitazyme), applied individually and in combination, on crop establishment, yield components, and grain yield of winter wheat under unstable moisture conditions in the Right-Bank Forest-Steppe of Ukraine. A three-year field experiment demonstrated that both treatments positively influenced plant growth, while their combined application produced a pronounced synergistic effect. Seed treatment enhanced plant establishment, increasing plant density at emergence from 242 plants m⁻² in the control to 372 plants m⁻² under the combined treatment. This improvement contributed to increased stand-level productive tiller density per unit area. Consequently, grain yield was consistently improved across years, with the combined treatment producing the highest average yield (6.04 t ha⁻¹), corresponding to a 37% increase relative to the control. The results indicate enhanced winter wheat resilience to environmental stress under biological seed treatment. Overall, integrating microbial inoculants with biostimulants represents an effective strategy for improving winter wheat productivity under moisture-limited conditions and supports the transition toward sustainable and resource-efficient crop production systems. Full article

Residential buildings must now be designed and retrofitted as adaptive climate–health–work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. This review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is a hypothesis-generating, decision-support and benchmark-development framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance. Full article