Search Results (1,079)

Geopolymer mortars were produced from construction and demolition waste using a binary binder of recycled brick powder/recycled concrete powder (RBP/RCP = 70/30 wt%), activated with a hybrid alkaline solution (NaOH/Na₂SiO₃/KOH) and reinforced with sisal fibres at 0–2 wt%. Mechanical performance (compression and three-point bending) and microstructure–phase evolution (XRD, FTIR, SEM-EDS) were assessed after low-temperature curing. Sisal addition delivered a strength–toughness trade-off with a reproducible optimum at ~1.0–1.5 wt%; at 2.0 wt%, fibre clustering and connected porosity reduced the effective load-bearing section, penalising flexure more than compression. Microstructural evidence indicates coexistence and co-crosslinking of N-A-S-H and C-(A)-S-H gels—enabled by Ca from RCP—leading to matrix densification and improved fibre–matrix anchorage. Fractographic features (tortuous crack paths, bridging, and extensive pull-out at ~1.5 wt%) are consistent with an extended post-peak response and higher fracture work without compromising early-age strength. This study achieves the following: (i) it identifies a practical reinforcement window for sisal in RBP/RCP geopolymers, (ii) it links gel chemistry and interfacial phenomena to macroscopic behaviour, and (iii) it distils processing guidelines (gradual addition, workability control, gentle deaeration, and constant A/S) that support reproducibility. These outcomes provide a replicable, low-embodied-CO₂ route to fibre-reinforced geopolymer mortars derived from CDW for non-structural and semi-structural applications where flexural performance and post-peak behaviour are critical. Full article

Phloeosinus cupressi Hopkins is an invasive bark beetle that poses a serious threat to Cupressus trees, with potential ecological and economic impacts globally. Native to North America, it has spread to Australia and New Zealand, and climate change may further alter its range. Global trade increases the risk of spread, highlighting the need for predictive modeling in management. In this study, we employed CLIMEX and random forest (RF) models to project the potential global distribution of P. cupressi, incorporating host distribution data for Cupressus. Climatic suitability is concentrated in temperate, subtropical, and Mediterranean zones, including Europe, the U.S., South America, China, Australia, and New Zealand, totaling 10,165.22 × 10⁴ km². Coldest-quarter precipitation (bio19) and annual temperature range (bio7) were identified as the most influential variables. Under RCP6.0 scenarios, suitable areas are projected to expand northward, increasing by ~18%. Regional shifts include contraction in southern Europe and South China, expansion in southern Argentina, southeastern Australia, and coastal New Zealand. Temperature sensitivity is expected to exceed precipitation, enhancing colonization. Due to global Cupressus trade, quarantine and monitoring should focus on high-risk regions. Our findings support early detection, long-term monitoring, and control measures for managing P. cupressi under climate change. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

Soil organic carbon fractions and pool management indices are critical for the ecosystem function of bamboo forests; however, their response to varying degrees of expansion of Chimonobambusa utilis (EDCU) and altitudinal gradients remains poorly understood in high-altitude karst regions. In this study, 225 samples (three replicate soil samples, each with five duplicate samples) were collected from 45 typical soil sites in the Jinfo high-altitude karst mountains, China. This study investigated the effects of three EDCUs (low, moderate, and high expansion) and five altitudinal gradients (1300–1500 m, 1500–1700 m, 1700–900 m, 1900–2100 m, and 2100–2300 m) on root elemental composition, soil properties, soil organic fractions, and pool management indices. The results revealed that root total C, N, R_C:P, and R_N:P decreased with increasing altitude, whereas root total C, N, P, and R_C:N also increased significantly with increasing EDCU. Compared with those at low and moderate EDCU, the POC:SOC (34.12%), HFOC (32.73 g kg⁻¹), and HFOC:SOC (37.07%) ratios were highest at high EDCU along the altitudinal gradient of 1700–1900 m. Meanwhile, the L (2.38), LI (2.01), and CMI (174.55) ratios reached their highest values at moderate expansion degrees of Chimonobambusa utilis within the altitudinal gradient of 1900–2100 m. Moreover, redundancy discriminant analysis (RDA) and structural equation modeling (SEM) revealed that the soil carbon pool management index was significantly positively associated with soil properties through direct pathways and negatively correlated with root elemental composition through indirect pathways. In general, the quality of the carbon pool in Chimonobambusa utilis is optimal within the moderate expansion degrees of Chimonobambusa utilis within the altitudinal gradient of 1900–2100 m. The findings of this study establish a theoretical basis for the expansion of Chimonobambusa utilis in high-altitude karst regions and provide scientific evidence to support the increase in the carbon sequestration capacity of bamboo forest ecosystems in these mountainous areas. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

A thorough understanding of the dispersion characteristics of graphene oxide (GO), its micro-pore enhancement mechanisms, and correlations with mechanical properties are crucial for advancing high-strength, durable green concrete. Introducing recycled concrete powder (RCP) can weaken the interfacial transition zone (ITZ) and inhibit hydration reactions, degrading the pore structure and affecting mechanical strength and durability. However, traditional methods struggle to accurately characterize and quantitatively analyze GO-modified pore structures due to their nanoscale size, microstructural diversity, and characterization technique limitations. To address these challenges, this study integrates deep learning-based backscattered electron image analysis with deep Taylor decomposition feature extraction. This innovative method systematically analyzes pore characteristic evolution and the correlation between porosity and mechanical strength. The results indicate that GO promotes Calcium Silicate Hydrate gel growth, refines pores, and reduces pore connectivity, decreasing the maximum pore size by 33.4–45.2%. Using a Convolutional Neural Network architecture, BSE images are efficiently processed and analyzed, achieving an average recognition accuracy of 94.3–96.9%. The optimized degree of GO coating on enhanced regions reaches 30.2%. Fitting porosity with mechanical strength and chloride ion permeability coefficients reveals that enhanced regions exhibit the highest correlation with mechanical strength and durability in regenerated cementitious materials, with R² values ranging from 0.79 to 0.99. The deep learning-assisted pore structure characterization method demonstrates high accuracy and efficiency, providing a critical theoretical basis and data support for performance optimization and engineering applications of recycled cementitious materials. This research expands the application of deep learning in building materials and offers new insights into the relationship between the microstructural and macroscopic properties of recycled cementitious materials. Full article

The cover-management factor (C-factor) plays a pivotal role in soil erosion control and is the most easily influenced by policymakers. Despite the availability of numerous C-factor estimation methods, systematic comparisons of their applicability and associated uncertainties remain limited, particularly for future projections under climate change scenarios. This study systematically evaluates multiple widely used C-factor estimation models and projects potential C-factor changes under future scenarios up to 2070, using 2015 as a baseline. Results reveal substantial spatial variability among models, with the land use/land cover-based model (CLu) showing the strongest correlation with the reference model (r = 0.960) and the lowest error (RMSE = 0.048). Using the CLu model, global average C-factor values are projected to increase across all Shared Socioeconomic Pathways–Representative Concentration Pathways (SSP-RCP) scenarios, rising from 0.077 to 0.079–0.082 by 2070. Statistically significant trends were observed in 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas, identified as hotspot regions (HRs). In these HRs, mean C-factor values are expected to increase by 16.1% and 33.4%, respectively, relative to the 2015 baseline. Economic development analysis revealed distinct trajectories across income categories. Low-income countries (LICs, World Bank classification) exhibited a pronounced dependency on development pathways, with C-factor values decreasing by −50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5 compared to 2015. In contrast, lower-middle-income, upper-middle-income, and high-income countries exhibited consistent C-factor increases across all scenarios. These variations were closely linked to cropland dynamics, with cropland areas in LICs decreasing by 64.6% under SSP1-RCP2.6 but expanding under other scenarios and income categories between 2015 and 2070. These findings highlight the critical importance of sustainable land-use policies, particularly in LICs, which demonstrate the highest magnitude of both improvement and degradation under varying scenarios. This research provides a scientific foundation basis for optimizing soil conservation strategies and land-use planning under future climate and socioeconomic scenarios. Full article

Hydrological droughts represent a growing challenge for northern watersheds in Thailand, where climate change is projected to intensify seasonal water stress and destabilize agricultural productivity and water resource management. This study employed the Composite Hydrological Drought Index (CHDI) to evaluate the spatiotemporal characteristics of future droughts under representative concentration pathway (RCP) scenarios. The findings revealed a pronounced seasonal contrast: under RCP8.5, the CHDI values indicated more severe drought conditions during the dry season and greater flood potential during the wet season. Consequently, the region faces dual hydrological threats: prolonged water deficits and increased flood exposure within the same annual cycle. Drought persistence is expected to intensify, with maximum consecutive drought runs extending up to 10–11 months in future projections. The underlying mechanisms include increased actual evapotranspiration, which accelerates soil moisture depletion, enhanced rainfall variability, which drives the sequencing of floods and droughts, and catchment storage properties, which govern hydrological resilience. These interconnected processes alter the timing and clustering of drought events, concentrating hydrological stress during periods that are sensitive to agriculture. Overall, drought behavior in northern Thailand is projected to intensify in a spatially heterogeneous pattern, emphasizing the need for localized, integrated adaptation measures and flexible water management strategies to mitigate future risks of drought. Full article

The main objective of this study is to enhance the understanding of the physical and chemical processes operating in a still understudied basin and to establish methodologies for assessing the impacts of climate change in an arid region of southern Spain. The work aims to identify areas that are vulnerable, or potentially vulnerable, to climate change and to evaluate the system’s response in terms of both water quantity and quality. To this end, we analyze the evolution of streamflow, suspended sediments, and nitrates, using the SWAT (Soil and Water Assessment Tool) model. A clear lack of observed data was the main limitation improving river flow calibration; however, the validation process showed a very satisfactory coefficient of determination (R²) for the two stations considered (R² = 0.78 and R² = 0.70). Due to the limited water quality dataset, the calibration and validation of nitrates and suspended sediments were performed using the LOAD ESTimator (LOADEST) program. Satisfactory results were obtained at both stations during the validation period for nitrates (R² = 0.52 and R² = 0.92) and suspended sediment (R² = 0.83 and R² = 0.95) load. Finally, the model was applied under two climate change scenarios (Representative Concentration Pathways, RCP 4.5 and RCP 8.5). Reduced precipitation, combined with temperature increases exceeding 1 °C in some areas, leads to decreased flows along the main channel, affecting suspended sediment concentrations. Nitrate levels generally decrease across the basin, although they increase from October to April at the river mouth. This area emerges as highly vulnerable to climate change, particularly regarding alterations in water flow and nitrate concentration. Full article

Climate change strongly influences the phenology of temperate fruit species, yet its long-term effects on Romanian plum orchards (Prunus domestica L.) remain insufficiently quantified. This study analyzes projected changes in the start (SGS), end (EGS), and duration (GSL) of the growing season under two emission scenarios (RCP 4.5 and RCP 8.5) throughout the 21st century. Using temperature-based phenological thresholds, SGS and EGS were modeled for six orchard clusters representing distinct regional and altitudinal conditions across Romania. Results reveal a consistent advancement of SGS and a marked extension of GSL, particularly under RCP 8.5, where the growing season may lengthen by up to 60 days compared with early-century conditions. Under RCP 4.5, changes are more moderate but directionally similar, indicating a robust climatic signal across all clusters. These findings highlight that earlier and longer vegetation periods may enhance fruit development potential but also increase risks associated with late spring frosts, heat stress, and pollination mismatches. Despite inherent model uncertainties, the convergence of trends suggests reliable projections that can support adaptive orchard management and long-term strategies for sustainable fruit production under a changing climate. Full article

Traditional methods for picking small-target crops like pepper are time-consuming, labor-intensive, and costly, whereas deep learning-based object detection algorithms can rapidly identify mature peppers and guide mechanical arms for automated picking. Aiming at the low detection accuracy of peppers in natural field environments (due to small target size and complex backgrounds), this study proposes an improved Yolov8n-based algorithm (named Yolov8n-RCP, where RCP stands for RVB-CA-Pepper) for accurate mature pepper detection. The acronym directly reflects the algorithm’s core design: integrating the Reverse Bottleneck (RVB) module for lightweight feature extraction and the Coordinate Attention (CA) mechanism for background noise suppression, dedicated to mature pepper detection in complex crop environments. Three key optimizations are implemented: (1) The proposed C2F_RVB module enhances the model’s comprehension of input positional structure while maintaining the same parameter count (3.46 M) as the baseline. By fusing RepViTBlocks (for structural reparameterization) and EMA multi-scale attention (for color feature optimization), it improves feature extraction efficiency—specifically, reducing small target-related redundant FLOPs by 18% and achieving a small-pepper edge IoU of 92% (evaluated via standard edge matching with ground-truth annotations)—thus avoiding the precision-complexity trade-off. (2) The feature extraction network is optimized to retain a lightweight architecture (suitable for real-time deployment) while boosting precision. (3) The Coordinate Attention (CA) mechanism is integrated into the feature extraction network to suppress low-level feature noise. Experimental results show that Yolov8n-RCP achieves 96.4% precision (P), 91.1% recall (R), 96.2% mAP0.5, 84.7% mAP0.5:0.95, and 90.74 FPS—representing increases of 3.5%, 6.1%, 4.4%, 8.1%, and 11.58FPS, respectively, compared to the Yolov8n baseline. With high detection precision and fast recognition speed, this method enables accurate mature pepper detection in natural environments, thereby providing technical support for electrically driven automated pepper-picking systems—a critical application scenario in agricultural electrification. Full article

In studies of potential climate change (CC) impacts on freshwater ecosystems, water temperature is a primary abiotic factor. Still, it is insufficient to describe the specific habitat conditions that have changed for the biological elements of water quality affecting fish. In this study, special attention is focused on the fish bioindicator species, Barbus balcanicus. For two future scenarios of CC impact (RCP4.5 (2020–2050) and RCP8.5 (2070–2100)), in a Sutla River water body case study, fish life stage models are developed based on the fundamental abiotic factors (water flow, depth, velocity, temperature, and dissolved oxygen) to describe the ecological requirements of the selected fish bioindicator species. Two future CC impact scenarios and their results—water flow, dissolved oxygen, and nutrients, prepared by SWAT—have been analysed. To determine the most important abiotic factors, for water temperature, depth, and velocity, models have been developed by the machine learning tool Weka. The modelled biological elements of water quality were combined with previously calculated dissolved oxygen, flow, and E-flow values during dry periods and the spawning period. For both selected CC scenarios, the results indicate that in approximately 60–70% of the life stages of the bioindicator species Barbus balcanicus, the conditions are acceptable. Full article

Groundwater depletion causes a critical risk for the sustainability of urban and agricultural resilience in semi-arid regions such as Tangshan city. This study deployed an integrated framework that combines understandable machine learning (Shapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), spatial autocorrelation (Local Indicators of Spatial Association or LISA), and scenario-based recharge forecasting to evaluate the spatial drivers and patterns of groundwater stress and project potential future risks. Using spatial groundwater table data from 2022 and key environmental and anthropogenic variables such as evapotranspiration (ET), population, temperature, precipitation, and land use and land cover changes, an XGBoost (Extreme Gradient Boosting) regression model was trained to capture nonlinear spatial patterns. SHAP analysis found that ET and population density are prominent contributors to groundwater depletion in agricultural and urban zones. To capture spatial heterogeneity, GWR was utilized to estimate localized coefficients and construct a Vulnerability and Resilience Index (VRI) from normalized coefficients and residuals. LISA validated vulnerability zones and revealed transitional stress regions. Future risks are also projected using Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data and land-use data to run recharge modeling from 2023 to 2049 for both representative concentration pathway (RCP) 4.5 and RCP 8.5. Results show that RCP 8.5 demonstrates highly unstable recharge with frequent negative episodes (ET > P), while RCP 4.5 shows relatively stable patterns of groundwater table. Furthermore, coupled with urban and agricultural expansion, RCP 8.5 intensifies depletion risks. This combined framework provides analytical understandings of spatial driver patterns and scenario-based risk assessments under climate and land use change. The findings of the study recommend priority zones for intervention and underline the importance of adaptive, scenario-sensitive groundwater governance in semi-arid, urbanizing regions. Full article

Heatwaves are intensifying across China under global warming. Although previous SSP-RCP studies project more frequent and intense events, systematic evaluations of exposure mechanisms among the elderly in China remain limited. The purpose of the paper is to reveal the spatiotemporal dynamics and inequality of heatwave exposure among China’s urban elderly and to disentangle the driving influences of climate change, ageing, and urbanization. Historical and future heatwaves across China are identified and analyzed, exposure inequality is evaluated using the Gini coefficient, and the relative contributions of key drivers are quantified through factor separation. Results showed that heatwave frequency and duration increased from 2000 to 2019, with high-risk provinces clustering in the Yangtze River Basin, North China Plain, and Sichuan Basin. Future projections indicate substantial growth in elderly exposure to heatwaves, while under the SSP3-70 scenario, inter-provincial inequality in exposure tends to alleviate rather than intensify. Climate change was identified as the dominant driver, while ageing amplified risks and urbanization partly mitigated growth. These findings highlighted the urgent need for place-based adaptation and health protection strategies, aligned with climate mitigation, demographic transition, and sustainable urban planning. Full article