Search Results (2,072)

Urban green infrastructure plays a crucial role in enhancing environmental resilience in cities, particularly in arid regions characterized by water scarcity, soil salinity, and high climatic stress. However, arid coastal cities remain insufficiently studied with regard to spatially explicit assessments of the structure and dynamics of green infrastructure. This study evaluates the state and spatial organization of urban green infrastructure in Aktau, Kazakhstan, over the period 2015–2025, with the most recent satellite observations obtained in June 2025. Sentinel-2 satellite imagery was used to calculate seasonal Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values, and zonal statistics were applied to assess intra-urban differentiation across functional zones. In addition, inventory-based indicators—Green Planting Density (GPD), Structural Composition of Greenery (SCG), and Protective Green Infrastructure (PGI)—were integrated to complement the remote sensing analysis. The results indicate a moderate overall increase in mean NDVI values (from 0.21 to 0.28), with the most significant growth observed in central and coastal areas (ΔNDVI = +0.12; ΔSAVI = +0.21), while industrial and newly developed zones exhibit only limited changes. Despite these localized improvements, the spatial configuration of green infrastructure remains fragmented, reflecting a persistent center–periphery asymmetry in urban greening. These results underline the importance of irrigation practices and spatially targeted greening strategies for improving vegetation conditions in arid urban environments. The proposed integrated approach combining satellite-derived vegetation indices and inventory-based indicators can serve as a useful tool for monitoring urban green infrastructure and supporting evidence-based planning in arid coastal cities. Full article

Secondary salinization in mulched drip-irrigated cotton fields of arid oasis–desert transition zones in Xinjiang imposes coupled root-zone constraints, including salt-induced aggregate structural degradation and ionic stress. However, field evidence remains limited on whether integrating a structure-oriented soil conditioner with humic acid can generate stable improvements across growing seasons. A two-year field experiment with a randomized block design (three replicates) was conducted to evaluate four treatments: control (CK), polyacrylamide (PAM, 30 kg ha⁻¹), humic acid (HA, 450 kg ha⁻¹), and PAM + HA. Soil physical and chemical properties and aggregate-size distribution were determined after harvest, while enzyme activities and root traits were assessed at the flowering–boll stage. Structural equation modeling (SEM) and random forest (RF) analysis were used to explore soil–root–yield linkages and identify key soil predictors associated with yield variation. Treatment effects were most evident in the 0–20 cm layer, with PAM + HA showing the greatest overall improvement. In the topsoil, PAM + HA lowered soil pH from 8.35 to 7.88 in 2024 (p < 0.05), increased soil organic carbon (SOC) to 4.29 g kg⁻¹ in 2025 (p < 0.01), and increased NO₃⁻–N to 25.51 and 30.27 mg kg⁻¹ in 2024 and 2025, respectively (both p < 0.05). PAM + HA also enhanced cellulase activity from 6.17 to 16.85 mg glucose g⁻¹ 72 h⁻¹ in 2024 and increased seed cotton yield to 6683.69 and 5996.89 kg ha⁻¹ in 2024 and 2025, with a 51.0% yield increase over CK in 2024. SEM showed that root development had the strongest direct positive effect on yield (β = 0.79, R² = 0.63; goodness of fit (GOF) = 0.74), while random forest identified alkaline phosphatase, cellulase, and NO₃⁻–N as the main yield predictors (out-of-bag R² (OOB R²) = 0.672, p = 0.01). This study elucidated the effects of the combined application of a structure-oriented soil conditioner and humic acid on the root-zone environment of mulched drip-irrigated cotton fields in arid regions, providing a theoretical basis for the coordinated regulation of soil structural improvement and nutrient activation in saline–sodic cotton fields. Full article

Yucca decipiens is a native species from arid and semi-arid regions with emerging nutritional and biotechnological potential. This study evaluated its proximate composition, elemental profile determined by inductively coupled plasma mass spectrometry (ICP-MS), and growth performance under nursery conditions. Proximate analysis revealed a high dietary fiber content in leaves (58.93%) and higher carbohydrate levels in stems (28.83%). Free amino acid content was significantly higher in stems (2.75 g histidine equivalents kg⁻¹) than in leaves (1.76 g kg⁻¹). Multi-elemental profiling (63 elements) showed organ-specific accumulation patterns, with essential macro- and micronutrients predominantly concentrated in leaves, including potassium (28,334 ppm) and calcium (15,345 ppm), while iron was the most abundant trace element in stems (1253 ppm). Principal component analysis (PCA) revealed clear organ-specific mineral partitioning between leaves and stems, indicating differentiated physiological roles and potential selective biomass utilization. Growth assessment conducted over a two-year period demonstrated steady biomass accumulation and good adaptive performance under nursery conditions. Overall, the results highlight the emerging nutritional and agroindustrial relevance of Yucca decipiens for applications in semi-arid environments. Full article

The Dammam Metropolitan Area (DMA) has been experiencing tremendous growth driven by increasing population and the oil industry. This has culminated in the development of the DMA, where the transportation system is reliant on automobiles, wide arterials, and a disjointed pedestrian environment. With the increasing progression of the Vision 2030 initiative, the Kingdom of Saudi Arabia (KSA) is focusing on livability and sustainable mobility. However, despite the massive efforts, the concepts of humanizing active mobility corridors remain insufficiently developed across Saudi cities. The paper will discuss the conceptual framework for developing the active mobility corridors of the DMA, an initiative of walkability, livability, and sustainable mobility with specific regard to the study region’s climatic and cultural environment. The methodology relies on qualitative desktop research supported by a structured and iterative literature synthesis using snowballing techniques. The resulting framework positions active mobility not merely as a transport function, but as a multidimensional system that promotes inclusion, comfort, and environmental resilience. Offering design and policy principles tailored to hot-arid Gulf contexts that contributes to national efforts to advance Quality of Life objectives under Vision 2030. Ultimately, this framework aims to contribute in human-centered mobility across the KSA and similar urban areas. Full article

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article

This study provides sedimentological and stratigraphic insights into the Ediacaran fluviolacustrine successions of the Amane-n’Tourhart and Tifernine basins. The Amane-n’Tourhart Basin developed in a post-caldera volcanic setting along the margin of the Oued Dar’a Caldera, whereas the Tifernine Basin formed in a pre-caldera tectono-volcanic context associated with caldera development. The successions provide valuable information about the sedimentary processes operating in late Ediacaran continental environments. Field observations, facies analysis, and petrography reveal a variety of siliciclastic, carbonate, mixed siliciclastic–carbonate, and volcaniclastic facies. These facies form associations indicative of alluvial fan, floodplain, and shallow-water lacustrine settings. Alluvial fan deposits are dominated by conglomerates and sandstones forming braided systems. Fluviolacustrine successions show a transition from clay-rich siltstones with calcareous nodules to nodular and massive limestones, marking a gradual shift from fluvial to lacustrine conditions. Laminated limestones and stromatolites indicate intermittent microbial activity that contributed to carbonate precipitation. Sedimentation was strongly influenced by volcanic inputs and climatic fluctuations, alternating between humid and arid conditions. These factors drove cycles of channel incision, sediment infill, and lake expansion–contraction, illustrating the dynamic interplay of volcanism and climate that modulated deposition in these Ediacaran continental basins, with broad relevance to our understanding of this critical window in the Earth’s history. Full article

Accurate mapping of near-surface air temperature ($T_{\mathrm{air}}$) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of $T_{\mathrm{air}}$ fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise $R^2\approx 0.88$) across diverse environments from humid coasts ($R^2\approx 0.89$) to arid interiors ($R^2\approx 0.84$). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide. Full article

Managing tillage intensity and diversifying crop rotation are important sustainability levers for conservation agriculture (CA) with the potential to enhance crop resilience, resource efficiency, and yield stability. Accordingly, this study aimed to determine the effect of reduced tillage intensities and cereal–legume rotation systems on the agronomic and physiological performance of rainfed durum wheat grown under Mediterranean semi-arid conditions. To this end, a two cropping seasons field experiment was conducted in northeast Tunisia where the combined effects of two reduced tillage intensities (minimum and no-tillage; MT and NT) and two legume-based crop rotation systems (biennial and triennial; B and T) were compared to the more traditional conventionally tilled monocropping system (CT and M). Crop rotation, particularly when integrated with no-tillage (NT), significantly improved wheat development and grain yield, along with key yield attributes such as thousand-kernel weight and spike density. The interaction between tillage and crop sequence was highly influential; for instance, the NT × T (no-tillage × triennial rotation) combination achieved the highest grain yields (240 and 236 g m⁻² in 2020–2021 and 2021–2022, respectively), while the CT × M (conventional tillage × monoculture) interaction resulted in the lowest productivity (143 and 135 g m⁻²). Physiologically, the integration of reduced tillage and legume–cereal rotations optimized the photosynthetic apparatus, as evidenced by significantly improved chlorophyll fluorescence parameters. However, a prominent trade-off was identified: while NT × T maximized productivity, conventional tillage (CT) maintained superior grain protein (18.6%) and gluten concentrations, indicating a nitrogen dilution effect in high-yielding conservation systems. These results demonstrate that while no-tillage and triennial rotations (faba bean–wheat–barley) are robust strategies for climate-resilient yields in semi-arid environments, they must be coupled with optimized nitrogen management to offset quality declines. Consequently, this study establishes the NT × T interaction as a superior model for sustainable rainfed farming, provided that nutrient synchronization is addressed to ensure nutritional security under increasingly unpredictable Mediterranean climates. Full article

Arid and semi-arid regions are critical to terrestrial ecosystems and regional carbon cycle regulation, directly contributing to peak carbon and carbon neutrality goals. However, the fragmented landscapes in these regions pose significant challenges to conventional pixel-based classification, which often struggles with mixed pixel issues and lacks biophysical interpretability. To address these limitations, this study develops an Ecologically Constrained Deep Learning Autoencoder (ECO-DEAU) framework for sub-pixel land cover mapping by integrating biophysical constraints. Specifically, ECO-DEAU employs spectral indices to extract standard spectral signatures for five primary land cover types, which serve as initial weights to guide the autoencoder in estimating fractional abundances. The model was trained across ten representative landscape zones in the Inner Mongolia section of the Yellow River Basin and validated against high-resolution Gaofen-2 data. Results demonstrated that ECO-DEAU yielded an average $R^2$ of 0.687, reaching a maximum $R^2$ of 0.749 in spatially heterogeneous transition zones, representing a substantial improvement over the baseline unconstrained Deep Autoencoder (DEAU). By effectively resolving the blind source separation problem and improving decomposition accuracy, ECO-DEAU serves as a robust tool for addressing mixed pixel challenges in heterogeneous environments, thereby facilitating large-scale, high-resolution carbon sink monitoring. Full article

Potential evapotranspiration (PET) and water balance (WB) are key indicators of hydroclimatic conditions and water availability, particularly in arid and semi-arid regions. This study investigates the interannual variability, long-term trends, and abrupt regime shifts in annual PET and WB across Saudi Arabia using multi-station observational data spanning 1985–2022. PET was estimated using a temperature-based approach suitable for data-scarce arid environments, and WB was calculated as the difference between precipitation and PET. Non-parametric statistical methods were applied to assess trend magnitude and significance, while Pettitt’s change-point test was used to identify abrupt shifts at both regional and station scales. The main findings show a widespread and spatially coherent increase in atmospheric evaporative demand, with predominantly positive PET trends at both regional and station scales, accompanied by persistently negative and increasingly declining WB values, indicating a long-term intensification of water deficit across much of the country. Spatial patterns of PET and WB closely follow gradients in energy availability and temperature, confirming the dominant influence of warming-driven processes on hydroclimatic conditions in this arid environment. Change-point analysis identifies a statistically significant regional hydroclimatic regime shift during the late 1990s, characterized by an abrupt increase in PET and a concurrent deterioration of WB, marking the onset of a more water-limited climatic regime. At the station scale, the timing and significance of detected change points display pronounced spatial heterogeneity, reflecting the modulation of regional climatic forcing by local climatic and geographic factors. Overall, the results demonstrate that increasing evaporative demand, rather than precipitation variability alone, has become a primary control on water availability across Saudi Arabia, highlighting the importance of explicitly accounting for hydroclimatic non-stationarity in water resource assessment and long-term planning under continued warming conditions. Full article

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km², increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km² (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km² in 2023 and are expected to reach 3018.44 km² (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development. Full article

Rapid urbanisation in arid regions has increased reliance on energy-intensive desalinated water, intensifying environmental and financial pressures on the built environment. Although non-potable water (NPW) reuse is promoted within regional water strategies, empirical validation of decentralised systems at asset scale remains limited. This study applies a greenhouse gas (GHG) intensity metric (kgCO₂e/m³) to multi-year operational data from a large healthcare facility in Abu Dhabi. The analysis integrates calibrated water balance records, onsite pumping energy (Scope 2), embedded desalination emissions (Scope 3), and a 20-year discounted cash flow framework. Three configurations are evaluated: a fully desalinated baseline, the observed mixed-supply system, and an optimised NPW configuration. The baseline exhibits an emission intensity of 19.53 kgCO₂e/m³. The observed configuration reduces desalinated supply but achieves only marginal decarbonisation (0.40 kgCO₂e/m³) due to continued dependence on desalinated make-up water. The optimised configuration reduces outdoor water demand by 36.7% and achieves 10.94 kgCO₂e/m³ net decarbonisation while improving life-cycle cost (LCC) performance. The results show that GHG intensity is primarily driven by water source substitution and system configuration rather than volumetric reuse alone, providing asset-level evidence for evaluating decentralised NPW systems in arid-climate buildings. Full article

Roadside green spaces function as critical ecological barriers in urban environments, and their plant communities play a key role in improving regional air quality. This study investigates typical roadside plant communities in southern Xinjiang, a region characterized by extreme aridity and frequent dust storms. By quantifying indicators such as dust retention capacity at both individual and community levels, together with leaf surface microstructural characteristics, we evaluate the comprehensive dust retention performance of different community configuration patterns. The results show that: (1) Among the studied species, Juniperus chinensis ‘Kaizuca’ exhibited the highest dust retention capacity per unit leaf area, followed by Juniperus chinensis L. and Rosa rugosa Thunb. Among trees, Platanus acerifolia (Aiton) Willd showed the greatest dust retention capacity per individual plant; among shrubs, Rosa rugosa Thunb. performed strongly, and among herbaceous species, Lolium perenne L. exhibited relatively high dust retention capacity. (2) Leaf dust retention is governed by the synergistic effects of multiple traits, including leaf aspect ratio, stomatal aspect ratio, stomatal protrusion, stomatal density, wax layer characteristics, and surface roughness. Leaf aspect ratio exerts a significant positive direct effect on dust retention, whereas stomatal aspect ratio shows a significant negative direct effect. (3) At the community level, the multi-layered tree–shrub–herbaceous configuration dominated by Platanus acerifolia (Aiton) Willd exhibited the strongest dust retention capacity, making it the most effective configuration for roadside green spaces. Overall, this study provides a robust theoretical framework and empirical evidence for the scientific selection and optimized configuration of roadside vegetation in arid regions, thereby supporting the sustainable improvement of urban roadside air quality in southern Xinjiang. Full article

Precise water quality forecasting is vital for sustainable resource management and public health, especially in semi-arid environments. This study investigates the predictive capabilities of ten Machine Learning (ML) algorithms using a dataset of 308 drinking water samples collected from various districts in Şanlıurfa Province, Türkiye. We evaluated ten predictive models, including Support Vector Regressor (SVR) and Extreme Gradient Boosting (XGBoost), both integrated with dimensionality reduction and hyperparameter optimization. Nineteen physicochemical and microbiological parameters—Temperature, chlorine (Cl⁻), pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), sulfate (SO₄²⁻), Free Chlorine (Cl₂), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), fluoride (F⁻), trihalomethanes (THMs), Escherichia coli, Enterococci, Total Coliform—were used as input features. The dataset was split into training (75%) and testing (25%) subsets, and model performance was assessed through 10-fold cross-validation and hold-out testing procedures. To improve model generalization and mitigate the effects of class imbalance, we implemented the Adaptive Synthetic Sampling (ADASYN) technique. ML algorithms were evaluated using standard regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The LSTM model optimized using Randomized Search outperformed the SVR and XGBoost models, demonstrating the highest accuracy and generalization capability, as evidenced by the superior R² value of 0.999 following ADASYN balancing and the lowest RMSE (1.206). These findings underscore the effectiveness of the LSTM framework in modeling the complex variance of the Weighted Arithmetic Water Quality Index (WAWQI). The findings of this study are expected to support future water quality monitoring strategies, inform policy development, and contribute to sustainable water resource management in arid and semi-arid regions. Full article

Neltuma juliflora (Sw.) Raf. (syn. = Prosopis juliflora (Sw.) DC.) is among the world’s most aggressive woody invaders, yet its ecological impacts remain poorly quantified in hyper-arid environments, where soils are calcareous and ecosystems recover slowly from disturbance. In this study, we tested two hypotheses: (1) the presence of N. juliflora changes native plant diversity, as well as soil and key physicochemical properties in hyper-arid Qatar, and (2) agricultural farms act as primary sources of N. juliflora invasion. Using a comparative observational design across 62 sites (45 invaded and 17 non-invaded), we applied a generalised additive model (GAM) and a generalised linear mixed model (GLMM) to quantify invasion drivers and the impact of invasion on perennial species diversity, respectively. Additionally, we used the Wilcoxon rank-sum test to compare the soil properties in the invaded and non-invaded sites. Our results indicate that N. juliflora is positively associated with farms, with the probability of occurrence declining by ca. 20% for each kilometre farther away from agricultural farms. This pattern suggests substantial propagule pressure from agricultural farms. Perennial species richness declined from 7.5 species at 0% N. juliflora cover to 4.8 species at full cover (36% reduction). Invaded sites were characterised by higher amounts of coarse sand (16%); reduced silt–clay fractions (5%); and elevated salinity indicators, including electrical conductivity (0.744 dS m⁻¹) and total dissolved solids (476 mg L⁻¹), while major N–P–K pools remained unchanged. These findings demonstrate measurable invasion-related changes in soil conditions and native perennial diversity in hyper-arid ecosystems and highlight the role of agricultural land use as a key driver of biological invasion. From a sustainability perspective, early detection, targeted control near agricultural and grazing zones, and integration of invasive species monitoring into land-use planning frameworks are essential to prevent further ecosystem degradation, protect biodiversity, and enhance the resilience of desert landscapes under increasing climate and land-use pressures. Full article