Search Results (102,426)

Climate change and its impacts on precipitation patterns have intensified the occurrence of prolonged dry periods in agricultural regions of Brazil, particularly in the MATOPIBA region (comprising the states of Maranhão, Tocantins, Piauí, and Bahia). This study analyzes the seasonal variability and trends of the Consecutive Dry Days (CDDs) index in the MATOPIBA region from 1981 to 2023. Daily precipitation data from the Brazilian Daily Weather Gridded Data (BR-DWGD) dataset were used for the analysis. The novelty of this work lies in its focus on the seasonal characterization of CDD across the entire MATOPIBA field of agriculture, addressing the following main research question: how have the frequency and persistence of dry spells evolved during the rainy and dry seasons over the past four decades? The methodology involved trend detection using the Mann–Kendall test and Sen’s Slope estimator. The results indicated that during the rainy season, the average CDD ranged from 20 to 60 days, with higher values concentrated in the states of Piauí and Bahia. In contrast, during the dry period, averages exceeded 100 days across most of the region. Trend analysis revealed a significant increase in CDD over extensive areas, particularly in Tocantins and Southern Bahia. The increasing trends were estimated at 1 to 4 days per decade during the rainy season and 4 to 14 days per decade in the dry period. Although a decreasing CDD trend was observed in small areas of Northern Maranhão, possibly associated with the influence of the Intertropical Convergence Zone, the overall scenario indicates a greater persistence of long dry spells. This pattern suggests an increase in vulnerability to water scarcity and agricultural losses. These findings highlight the need for implementing adaptation strategies, such as the use of drought-tolerant cultivars, conservation management practices, irrigation expansion, and public policies aimed at promoting climate resilience in the MATOPIBA region. Full article

Heatwaves are intensifying across Mediterranean cities, where the Urban Heat Island (UHI) effect amplifies thermal stress. This study updates the spatial characterization of the Surface Urban Heat Island (SUHI) in Tarragona using multi-sensor remote sensing data within a Local Climate Zone (LCZ) framework. Land surface temperature, albedo, and the Normalized Difference Vegetation Index (NDVI) were analyzed during heatwaves from 2015–2025 to assess spatial patterns and drivers of urban heating. Results reveal a daytime urban cool island associated with low albedo and scarce vegetation, and a nocturnal SUHI caused by heat retention in dense built-up areas. High-resolution mapping identifies industrial and commercial zones as hotspots, while vegetated and water-covered areas act as cooling sites. These findings clarify the spatial dynamics and key biophysical controls of SUHI and provide an actionable basis for prioritizing locally tailored adaptation strategies in Mediterranean coastal cities. Full article

Earthen heritage in desert oases reflects local identity, craftsmanship and traditional knowledge but is facing increasing threat of disappearance from material vulnerabilities, social abandonment and unsuitable interventions. This study develops and validates a novel decision-making tool to guide stakeholders in selecting the most suitable building technology for the adaptive reuse of earthen heritage complexes to ensure their long-term sustainability while maintaining their cultural and social values. The proposed methodology combines the Integrated Value Model for Sustainability Assessment (MIVES) and Delphi technique to evaluate the cultural, economic, environmental and social aspects. Quantitative and qualitative indicators were defined through literature review and weighted by experts in two rounds of Delphi to obtain comparable sustainability index for each building technology. The evaluation of economic and environmental aspects was based on literature data, while cultural and social aspects were assessed through a third round of Delphi with local participants. The tool was applied to the Adrere Amellal Ecolodge in Siwa Oasis, Egypt, comparing three building technologies: Karshif traditional earthen technique, commonly used red bricks and innovative 3D-printed saltblocks. Karshif achieved the highest sustainability index (0.77) due to its cultural values, social acceptance and environmental performance. The findings demonstrate the potential of traditional earthen techniques to ensure sustainable adaptive reuse, providing a replicable method for sustainable adaptive reuse of earthen heritage in desert oases in Egypt and worldwide. Full article

Durum wheat (Triticum durum Desf.) underpins semolina value chains in water-limited regions, yet Peru remains import-dependent due to constrained local adaptation. We evaluated eleven elite lines plus the commercial variety ‘INIA 412 Atahualpa’ across three contrasting semi-arid sites in Arequipa (Santa Elena, San Francisco de Paula, Santa Rita) during 2023–2024 to identify genotypes maximizing performance and stability. Grain yield, thousand-kernel weight (TKW), hectoliter weight, and plant height were analyzed with combined analysis of variance (ANOVA), the additive main effects and multiplicative interaction (AMMI) and genotype and genotype-by-environment (GGE) biplots, complemented by AMMI stability value (ASV) and weighted average of absolute scores and best yield index (WAASBY). Grain yield and hectoliter weight showed significant genotype × environment (G × E) interaction, while plant height was driven mainly by genotype and environment with limited interaction. For grain yield, AMMI (PC1: 55.2%) and GGE (PC1 + PC2: 90.2%) revealed crossover responses and three practical mega-environments: TD-053 “won” at San Francisco de Paula, TD-037 at Santa Elena, and TD-033 at Santa Rita. Additionally, WAASBY-integrated rankings favored TD-033 (93.7%) and TD-014 (84.72%), followed by TD-026/TD-020 (>57%), whereas TD-062 (9.1%) and TD-043/TD-061 underperformed. Quality traits highlighted TD-044 and TD-014 for high hectoliter weight and TD-014/TD-062 for high TKW with contrasting stability. Overall, TD-033 and TD-014 were adaptable across environments, providing selection guidance to strengthen Peru’s durum breeding pipeline under climate variability. Full article

Urban trees are exposed to multiple co-occurring stressors, including heat, drought, and pollution driven by intensified urbanization and climate change. These environmental pressures can compromise tree vitality by disrupting photosynthetic performance and oxidative balance. In this study, we assessed the structural, physiological, and biochemical responses of three common urban tree species (Tilia platyphyllos, Celtis occidentalis, and Platanus × hispanica) growing under urban environmental conditions in Novi Sad, Serbia. Leaf traits were measured during June and August to capture seasonal stress variation. Structural indicators (SPAD, leaf thickness, leaf temperature differential), chlorophyll fluorescence traits (Φ_II, Φ_NPQ, F_v′/F_m′), oxidative stress biomarkers (TBARS, proline, GSH), and antioxidant enzyme activities (APX, CAT) were quantified. The Tree Health Risk Index (THRI) was calculated to integrate multilevel responses. Results revealed species-specific differences, with Tilia exhibiting the highest sensitivity, characterized by notable photochemical declines and oxidative stress under urban conditions. Celtis showed moderate resilience, while Platanus demonstrated the most robust performance and emerged as a promising candidate for climate-resilient urban sites. Heatmap clustering and trait contribution analyses confirm oxidative stress biomarkers and chlorophyll fluorescence traits as key indicators of urban stress. This study emphasizes the importance of integrating functional trait-based approaches for assessing tree health in urban greening. Full article

The field experiment aimed to evaluate the effect of different nitrogen rates on accumulation of aboveground dry mass (AGDM), leaves area index (LAI), and intercepted photosynthetically active radiation (iPAR) of pea (Pisum sativum L.) varieties. The experiment was arranged in a factorial randomized block design consisting of three levels of the first factor (variety) and seven levels of the second factor (NPK fertilization treatments were used: (1) NPK 0:0:0 (control), (2) NPK 0:40:80, (3) NPK 15:40:80, (4) NPK 30:40:80, (5) NPK 45:40:80, (6) NPK 15 + 15:40:80, (7) NPK 60:40:80). The growth indicators (LAI and AGDM) and iPAR were assessed three times during the growing season. Nitrogen fertilization positively influenced LAI, but significant differences in LAI were found only under splitted N30 (N15 + N15), N45, and N60 applications, compared to the treatment N0 P40K80. In the dry 2015 and the optimal moisture 2016, N30, N45, and N60 rates significantly increased AGDM. The influence of fertilization on iPAR varied between experimental years, and it was strongest in the dry 2015, when applying N15+15 and N60 fertilization significantly increased iPAR, compared to the control. According to LAI and iPAR data, pea varieties were ranked in descending order: Simona, Ieva DS, and Respect. LAI significantly (p ≤ 0.01) correlated with AGDM and iPAR, but the relationship weakened as peas reached later growth stages. These results provide valuable knowledge, and it will be useful for researchers in developing new cultivation methodologies to achieve higher semi-leafless pea productivity by applying different combinations of nutrition and new varieties. Full article

Drought is a major climatic hazard affecting water resources, agriculture, and livelihoods in semi-arid regions, with increasing severity under climate change. This study assessed long-term drought in Dura City, Palestine, from 2000 to 2023 using the Standardized Precipitation Index (SPI) at 3-, 6-, and 12-month timescales. Monthly precipitation and temperature data were obtained from local meteorological stations, with mean annual precipitation of 408 mm and average summer and winter temperatures of 28 °C and 12 °C, respectively. Trends were analyzed using the Mann–Kendall test and Sen’s slope estimator. SPI-3 values ranged from −3.13 to 3.87, including 67 moderates to severe drought months and 12 extreme wet months. SPI-6 ranged from −2.97 to 2.53, showing 34 drought months and 40 wet months, while SPI-12 ranged from −1.94 to 2.32, reflecting generally stable long-term precipitation. Annual rainfall exhibited no significant trend (Sen’s slope = −1.34 mm/year, p = 0.785), whereas yearly average temperature increased significantly by 0.054 °C/year (p = 0.02), raising evapotranspiration and drought risk. Results indicate high short- and medium-term drought variability despite stable annual precipitation, underscoring the need for integrated water management strategies, including rainwater harvesting, groundwater protection, and efficient irrigation, to improve resilience under evolving climate conditions. Full article

Economic development and its sustainability are influenced not only by material, human, financial, and intellectual factors, but also by psychological factors. In particular, the levels of business expectations, trust, and confidence significantly affect the resilience of the economy, especially in crucial sectors such as industry and, more specifically, industrial production. Based on political, economic, social, and legal stability, businesses are likely to assess their opportunities more optimistically and realistically. This, in turn, enables them to look confidently toward the future and provides a foundation for investing in further development. Conversely, a decline in business expectations and confidence can slow socio-economic development, potentially leading to recession or depression. The purpose of the article is to identify the association between business confidence (Impact of the Business Confidence Indicator, IBCI) and the level of industrial production (Industrial Production Index, IPI), as a crucial aspect of ensuring sustainable economic development. A correlation–regression analysis conducted using Ukraine as a case study—a country candidate for EU accession—and statistical data from the State Statistics Service of Ukraine (SSSU) for the period from 1 February 2022 to 1 September 2024 demonstrated that there is a stable, positive, and strong relationship between IBCI and IPI levels (r = 0.7; D = 0.49). The constructed linear correlation model indicates that, with other factors held constant, a one-percentage-point increase in positive business expectations may lead to a 2.23-point rise in the industrial production activity of enterprises in Ukraine’s manufacturing sector. Furthermore, approximately 49.0% of the variation in industrial production levels is likely explained by changes in business expectations. Verification of the constructed regression equation and assessment of its parameters indicate that it is statistically reliable and consistent with real economic processes. Specifically, the Fisher coefficient (F = 5.30) exceeds the critical (tabular) value (Ft = 2.04), with Se = 0.45 and C_95% = 1.96; the causality test based on the Granger methodology revealed the presence of a causal relationship, indicating that the IBCI influences the IPI. The obtained statistical results for the applied models and tests are as follows: MDF (p < 0.05), KPSS (p > 0.10), Durbin–Watson ≈ 2.0, Breusch–Godfrey (p = 0.32), White (p = 0.41), ARCH (p = 0.27), and SER (p = 0.36). The constructed correlation–regression equation also allowed forecasting based on trend line modeling—how IPI levels will change depending on business confidence. According to the forecast, the IPI in Ukraine at the beginning of 2030 is expected to increase by 63.48 percentage points compared to the beginning of 2024, reaching 153.6%. Full article

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects. Full article

As a geographical development mode, can urban agglomeration solve the problem of intensive land use that cannot be solved on the urban scale? What is the degree of balanced development between urbanization and intensive land use? This study constructs the index system of the coupling system between urbanization development and intensive land use, and evaluates the urbanization development subsystem and the intensive land use subsystem using the coupling Comprehensive Gravity–Gram–Schmidt Orthogonalization model (CG-GSO) and the entropy weight method, based on the coupling coordination degree model to explore coordinated development, and, finally, it analyzes the driving factors. The results showed the following: (1) the urbanization development and the intensive land use subsystems were rising in the two urban agglomerations; (2) in the coupling system, the driving factors were the economic development and the land input level dimensions in the Jing-Jin-Ji urban agglomeration, and the economic development and the land output level dimensions in the Yangtze River Delta urban agglomeration; and (3) the Jing-Jin-Ji urban agglomeration was always in the land input stage, while the Yangtze River Delta urban agglomeration had experienced the land utilization stage, the land input stage and the land output stage. In general, urban agglomeration, as a development mode, had indeed solved the imbalance in the coupling system. Although the coordination degree was unbalanced from 2003 to 2020, it increased and had a strong development momentum, approaching the balanced development (the Jing-Jin-JI urban agglomeration was 0.3493 and the Yangtze River Delta was 0.3611) in 2020, and achieving slightly balanced development in 2023, with barely balanced development in 2034 and superiorly balanced development in 2043 (Jing-Jin-Jin urban agglomeration) and in 2044 (Yangtze River Delta urban agglomeration). The research provides ideas for other countries to solve the uncoordinated development between urbanization and intensive land use. Full article

Establishing robust geochemical baselines in the hyper-arid Atacama Desert remains challenging because of extreme climatic gradients, polymetallic mineralisation, and decades of intensive mining. To disentangle natural lithogeochemical signals from anthropogenic inputs, a region-wide, multi-institutional soil dataset (1404 samples; 32 elements) was compiled. The analytical workflow integrated compositional data analysis (CoDA) with isometric log-ratio transformation (ILR), principal component analysis (PCA), robust principal component analysis (RPCA), and consensus anomaly detection via hierarchical (HC) and spectral clustering (SC), applied both with and without spatial coordinates to capture compositional structure and geographic autocorrelation. Optimal cluster solutions differed among laboratory subsets (k = 2–17), reflecting instrument-specific biases. The dual workflows flagged 76 (geochemical-only) and 83 (geo-spatial) anomalies, of which 33 were jointly identified, yielding high-confidence exclusions. Regional baselines for 13 priority elements were subsequently computed, producing thresholds such as As = 66.9 mg · kg⁻¹, Pb = 53.6 mg · kg⁻¹, and Zn = 166.8 mg · kg⁻¹. Incorporating spatial variables generated more coherent, lithology-aligned clusters without sacrificing sensitivity to geochemical extremes (Jaccard index = 0.26). These findings demonstrate that a reproducible, compositional-aware machine learning workflow can separate overlapping geogenic and anthropogenic signatures in heterogeneous terrains. The resulting baselines provide an operational reference for environmental monitoring in northern Chile and a transferable template for other arid mining locations. Full article

Microplastics (MPs) are pervasive in freshwater and may threaten aquatic ecosystem health. We surveyed surface waters of Shijiu Lake and its inflowing tributaries during the dry (January–March) and rainy (May–July) seasons of 2024. MP abundance ranged within 17.54–30.93 items/L, with higher values in the rainy than in the dry season (28.18 ± 6.03 vs. 24.53 ± 5.68 items/L; one-way ANOVA, p < 0.05). Abundance correlated positively with turbidity (r = 0.44; R² = 0.20; p < 0.05), whereas associations with total nitrogen, total phosphorus, and suspended solids were not significant (p > 0.05). Small particles (38–75 μm) dominated and were slightly more prevalent in the dry season, while the fraction of larger particles (>150 μm) was relatively higher in the rainy season. Granules predominated across sites, but their share decreased in the rainy season, accompanied by a notable increase in fibers. The Pollution Load Index (PLI) indicated slight but spatially uneven pollution (PLI = 1.00–1.43), generally higher during the rainy season and consistently elevated at the lake center; the Nongkan River exhibited the lowest levels. Ecologically, the patterns indicate rainfall-driven inputs and hydrodynamic controls (runoff, resuspension, residence time), identifying the lake center and tributary interfaces as priority zones for monitoring and mitigation. These results provide lake-scale evidence to refine seasonal monitoring and inform source-reduction strategies in similar inland waters. Full article

Variability in blood RNA-Seq data can obscure transcriptional changes that reflect tumor responses, and conventional normalization methods such as RLE/DESeq2 or TMM/edgeR often fail to capture these changes consistently. To address this challenge, we applied DiRT (DEG-by-index Ratio Transformation), a normalization and analysis strategy previously used in insect models, to 111 blood RNA-Seq datasets from mouse tumorigenesis models. DiRT achieved clearer separation between tumor and control samples as early as three days after tumor induction and maintained consistent marker signals across all stages of disease progression. In contrast, standard methods typically revealed differences only at later or scattered time points. KEGG pathway analysis further showed that DiRT-derived differentially expressed genes (DEGs) were enriched in the platelet activation signaling pathway, a pathway not identified using RLE/DESeq2 or TMM/edgeR. These findings demonstrate that DiRT enhances both sensitivity and reproducibility, enabling earlier and more consistent detection of transcriptional changes in blood during tumor development. Full article

In Haiti, uncontrolled urbanization is increasing pressure on agricultural landscapes, compromising both their ecological integrity and productivity. This study examines spatio-temporal land-use changes across three agricultural plains, Cul-de-Sac, Maribahoux, and Léogâne, between 1997 and 2024, using Landsat imagery and landscape metrics of composition (percentage of landscape, PLAND) and configuration (largest patch index, LPI). The findings reveal a rapid expansion of built-up areas, primarily at the expense of farmland. In the Cul-de-Sac plain, built-up areas and bare soil grew by 152%, from 41.26 km² to 104.11 km², while agricultural land became highly fragmented (LPI dropping from 94.51% to 57.63%). In Maribahoux, urbanization was more moderate, partly offset by a temporary rise in woody vegetation that peaked at 20.04% in 2022 before declining. The Léogâne plain experienced a 17.38 km² increase in built-up areas and bare soil, alongside a slight decrease in woody vegetation. Population density showed limited differences in Maribahoux and Léogâne, but marked disparities in Cul-de-Sac, where landscape transformation was more pronounced. These findings highlight increasing fragmentation of agricultural landscapes, threatening ecological connectivity and functionality, and stress the urgent need for land-use planning that curbs urban growth, protects farmland, and safeguards biodiversity. Full article

Deep learning reduced macadamia planting year error at a national scale, achieving a pixel-level Mean Absolute Error (MAE) of 1.2 years and outperforming a vegetation index threshold baseline (MAE 1.6 years) and tree-based models—Random Forest (RF; MAE 3.02 years) and Gradient Boosted Trees (GBT; MAE 2.9 years). Using Digital Earth Australia Landsat annual geomedians (1988–2023) and block-level, industry-supplied planting year data, models were trained and evaluated at the pixel level under a strict Leave-One-Region-Out cross-validation (LOROCV) protocol; a secondary block-level random split (80/10/10) is reported only to illustrate the more optimistic setting, where shared regional conditions yield lower errors (0.6–0.7 years). Predictions reconstruct planting year retrospectively from the full historical record rather than providing real-time forecasts. The final model was then applied to all Australian Tree Crop Map (ATCM) macadamia orchard polygons to produce wall-to-wall planting year estimates. The approach enables fine-grained mapping of planting patterns to support yield forecasting, resource allocation, and industry planning. Results indicate that sequence-based deep models capture informative temporal dynamics beyond thresholding and conventional machine learning baselines, while remaining constrained by regional and temporal data sparsity. The framework is scalable and transferable, offering a pathway to planting year mapping for other perennial crops and to more resilient, data-driven agricultural decision-making. Full article