Search Results (602)

The terrain and climate of the Qinghai–Tibetan Plateau make it hard to assess satellite precipitation. GSMaP (Global Satellite Mapping of Precipitation) is a widely used rainfall dataset, but direct comparisons of its versions and products over the Plateau are still limited. In this study, we evaluate four GSMaP products—Gauge, GNRT, MVK and NRT—across four versions (v05–v08) using daily station precipitation data from 2001 to 2022 as the reference. We assess both precipitation amount and precipitation event detection. The analysis is carried out at the station scale and then examined by month, season, year, rainfall intensity and space. We also compare regional patterns across the Plateau. The results show that GSMaP performance generally improves in later versions. Among them, v08 is usually more stable and more consistent, especially for gauge-corrected products. This improvement appears not only in better agreement with station data but also in smaller differences among stations for some products. Still, the size of the improvement is not the same for all products, seasons, rainfall classes and regions. The improvement is more clear in wetter areas and in warm seasons. By contrast, uncertainty is still relatively large in cold seasons, under strong rainfall and in the high-elevation interior of the Plateau. Non-gauge products also show wider variation than the Gauge product, which suggests that gauge correction still plays an important role in improving consistency. In general, version updates help improve GSMaP performance under some conditions, but the gains are not the same across different climate settings, rainfall intensities, or elevation zones. This study provides a systematic evaluation of GSMaP over the Qinghai–Tibetan Plateau for 2001–2022 and offers practical support for choosing and using GSMaP products in complex terrain. Full article

This study investigates a typical valley city, Lanzhou, China, to reveal the nonlinear relationships and interaction mechanisms between gray–green space morphology and seasonal diurnal land surface temperature (LST) using multi-source remote sensing and land use data. A comprehensive morphological indicator system encompassing scale, complexity, connectivity, and structural integrity was constructed through landscape metric screening and the CRITIC objective weighting method, combined with the XGBoost-SHAP explainable machine learning framework. The findings highlight that: (1) Gray–green space impacts on LST exhibit significant seasonal and diurnal variations—daytime LST is predominantly governed by gray space morphology (e.g., fragmentation degree), while nighttime LST is driven by green space morphology (e.g., coverage intensity). (2) Key indicators demonstrate pronounced nonlinear and threshold characteristics: the cooling effect of green space coverage intensity (GCI) saturates beyond 0.25; gray space morphological structure factor (GRMSF) demonstrates cooling potential when exceeding 0.25, mitigating its warming effect. (3) Significant synergistic interaction effects exist between gray and green spaces. Interaction analysis reveals that “high green coverage with low structural connectivity of gray space” produces optimal synergistic cooling effects, representing the most effective spatial configuration for nighttime LST mitigation. This study deepens theoretical and methodological understanding of the complex relationships between spatial morphology and thermal environments, providing quantified, temporally differentiated spatial optimization guidance for climate-adaptive planning in valley cities. Full article

Amidst global urbanization and rising psychological stress, urban green spaces are increasingly recognized as critical infrastructure for sustainable urban development and public health. However, the mechanisms by which summer vegetation structure mediates both physiological and psychological restoration, and the interplay between these two dimensions, remain poorly understood. Understanding these mechanisms is essential for designing sustainable, health-promoting urban environments that can support growing urban populations in a warming climate. This study employed a controlled field experiment in Xi’an during summer to examine the effects of five vegetation structure types (Single-Layer Grassland, single-layer woodland, tree–shrub–grass composite woodland, tree–grass composite woodland, and a non-vegetated square) on university students’ physiological (heart rate variability) and psychological (perceived restorativeness and affective states) restoration. Following stress induction, 300 participants engaged with the green spaces through both quiet sitting and walking. The results revealed three key findings: (1) the tree–shrub–grass composite woodland consistently showed the most favorable trends other vegetation types across all psychological restoration dimensions, while also showing favorable trends in physiological recovery, underscoring the importance of structural complexity for restorative quality; (2) walking significantly enhanced physiological recovery compared to seated observation across all settings, confirming the role of physical activity as a critical activator of green space benefits; (3) correlation analysis identified a specific cross-system association: the R-R interval recovery value showed a weak but significant correlation with positive affect (PA) scores, suggesting that physiological calmness and positive emotional experience are linked, yet their weak coupling under short-term exposure indicates they may operate as parallel processes with distinct temporal dynamics. These findings indicate that the restorative potential of summer green spaces emerges from an integrated framework combining vegetation complexity and activity support. We propose that future sustainable landscape design should prioritize multi-layered vegetation structures as nature-based solutions that simultaneously enhance human well-being and urban resilience. These findings provide empirical evidence for integrating health-promoting green infrastructure into sustainable urban planning frameworks, supporting multiple Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

This study of the Ethiopian Rift Valley meso-climate (5° N–9° N, 37° E–40° E) employed space–time statistical methods over the period 1981–2025. Links between weather conditions at Hawassa (7.1° N, 38.5° E, 1700 m) and the Indian Ocean Dipole (IOD) were uncovered, among 3–4 yr oscillations and a weak upward trend. Seasonal anomalies of local dewpoint temperature (Td) and IOD cross-correlated at R = 0.61 over the four-decade study. Mean annual cycling revealed a narrow range for Td from April to October, in contrast with bi-modal rainfall and asymmetric runoff. Diurnal cycle analysis indicated that evening rainfall was driven by midday heat (0.6 mm/h) and moisture fluxes (0.1 mm/h). A case study revealed how shallow cloud bands extend westward from cool, forested highlands to the warm Rift Valley. Composite differences between warm and cool IOD events exhibited contrasting effects for zonal and meridional airflows, which explains why the equatorial trough and its associated rainfall are confined to the southeastern escarpment of Ethiopia. While earlier studies had anticipated drying trends, wetter conditions during the warm IOD events of 2019 and 2023 resulted in rising lake levels (1.8 m) and crop yields (4 T/ha). These findings enhance our understanding of regional climate dynamics to support adaptive management. Full article

The ongoing rise in temperatures due to climate change is one of the most critical considerations in the design of outdoor recreational spaces. Thermal conditions can affect people’s visitation patterns, satisfaction, health and well-being. In many developing countries, including Jordan, rapid urbanisation often occurs without sufficient planning for public outdoor spaces, thereby diminishing their quality. This study is the first to investigate the effects of temperature on visitor patterns and user satisfaction in Jordanian urban parks. A mixed-methods approach was employed, combining continuous measurements of outdoor temperature (Ta) and relative humidity (Rh) with a survey assessing users’ thermal sensation, satisfaction, and preferences across six urban parks in Amman, Jordan. Data were collected from 718 respondents in summer 2025. Visitation records for the surveyed parks were also obtained from local authorities for the monitored period. The results show that the mean Ta exceeded 30 °C in all surveyed parks during the monitoring period, with peak readings exceeding 41 °C. This resulted in a warm-to-hot thermal sensation among participants, with many preferring cooler conditions. A significant inverse relationship between temperature and park visitation rates (R² = 0.67, p = 0.001) was observed, with a 1 °C increase in outdoor temperature associated with approximately a 2.03 visitor decrease. Participants’ satisfaction was higher in parks with adequate amenities, such as shading, disability access, and green zones, than in parks with fewer amenities (p = 0.01, d = 0.63). The most reported areas for improvement included facilities, shaded seating areas, and perceived safety. The findings highlight the importance of considering outdoor thermal conditions when designing urban parks, as they shape public outdoor activity patterns, particularly in hot climates. Full article

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km² area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

This study addresses the critical challenge of optimizing outdoor thermal comfort for the aging population in old residential communities within China’s Hot-Summer and Cold-Winter (HSCW) climate zones. Against the backdrop of urban regeneration and rapid demographic aging, it investigates how key landscape elements—Square Reflectance, Greening Type, and Pergola Condition—influence the microclimate of community public spaces. The research employed an integrated methodology centered on numerical simulation. Using the ENVI-met 5.9.0 software and an L₉(3⁴) orthogonal experimental design, it simulated the microclimatic effects of nine combined scenarios on typical summer and winter days for a case study in Nanjing. The comprehensive thermal comfort index, Physiological Equivalent Temperature (PET), was used as the primary evaluation indicator to assess the thermal comfort performance for elderly occupants, with the assistance of air temperature, wind speed, and relative humidity, and the results were analyzed via range analysis and ANOVA. The key findings indicate that: (1) Greening Type and Pergola Condition are the dominant factors affecting microclimate and annual thermal comfort across seasons, while Square Reflectance has a comparatively minor influence. (2) The combination of deciduous trees with lawn achieves the optimal cross-seasonal PET gain. It provides effective shading and cooling in summer while allowing beneficial solar penetration for warming in winter, substantially outperforming evergreen-dominated configurations. (3) The presence of a pergola consistently enhances comfort by providing essential shade in summer and acting as a windbreak in winter. The combination dominated by deciduous trees + lawn and pergola yields an overall PET gain 1.097 °C higher than that of evergreen trees + shrub without pergola. This study provides evidence-based, elderly specific landscape design strategies to inform the thermal environment optimization of public spaces in old residential areas undergoing renewal. Full article

Natural ventilation remains a key strategy for improving indoor environmental quality (IEQ), lowering energy demand, and increasing resilience in low-rise residential buildings, especially in warm climates where mechanical ventilation is costly or unreliable. Classical ventilation studies are very often performed on computational fluid dynamics (CFD) or simplified thermal models, but they are computationally resource-heavy, data-dependent, or at odds with early design scenarios. Thus, this study proposes a Multi-Scale BIM-Driven Framework for Predictive Ventilation Opportunity Mapping (PVOM), presenting a geometry-based, data-light approach for investigating ventilation potential over micro-, meso-, and macro-scale spatial dimensions. Based on BIM models of two single-story residential buildings (Building A—author-developed and Building B—public reference model), the framework combines LOD 300 spatial modeling, multi-scale ventilation morphometrics, pathway prediction, and design optimization via opening repositioning, resizing, and envelope porosity adjustments. The outcomes indicate that PVOM correctly detects airflow constraints, stagnation pockets, and underperforming spaces, while simultaneously identifying geometrical areas for improvement on cross-ventilation. Performance for optimization scenarios indicated enhanced air change potential (ACH-P), cross-ventilation score (CVS), and spatial airflow continuity (SAC), thereby indicating the framework is adequate in facilitating early-stage sustainable design. This study presents a reproducible BIM-based method on natural ventilation assessment without CFD or advanced sensing systems, indicating PVOM as a scalable approach toward architects, engineers, and sustainability practitioners. BIM; natural ventilation; PVOM; ventilation morphometrics; low-rise buildings; sustainable design; performance optimization. Full article

Against the backdrop of global warming and the urgent demand for sustainable development, blue–green spaces (BGSs) play a vital role in carbon reduction and sequestration, yet the multi-scale spatial mechanisms by which blue–green space patterns (BGSPs) regulate carbon storage (CS) remain unclear. Taking the Zhengzhou Metropolitan Area as the study area, this research clarifies the BGSP-CS correlations at both class and landscape levels and quantifies their spatial interaction mechanisms, providing scientific support for integrated BGS planning that aligns with sustainable development objectives. Using the InVEST model coupled with regional carbon density correction, the total CS of the area is estimated at 1112.27 × 10⁶ t. Spearman’s correlation analysis shows that at the class level, area–edge and shape complexity indicators (e.g., Landscape Shape Index, LSI: r = −0.427) are negatively correlated with CS, while connectivity indicators exert no significant effect. At the landscape level, Shannon’s Diversity Index (SHDI: r = −0.635) and area–edge indicators inhibit CS, whereas Shannon’s Evenness Index (SHEI: r = 0.602), Largest Patch Index (LPI: r = 0.618) and shape complexity indicators exert positive effects. A comparative analysis of three regression models reveals that the multi-scale geographically weighted regression (MGWR) model outperforms the ordinary least squares (OLS) and geographically weighted regression (GWR) models, with R² values of 0.505 (class level) and 0.484 (landscape level). It effectively captures the “west–strong and east–weak” spatial heterogeneity of BGSP impacts on CS. This study identifies key BGSP indicators regulating CS and their spatial mechanisms, providing scientific support for integrated BGS planning, regional carbon sink enhancement, the achievement of “dual carbon” goals, and the promotion of sustainable development in metropolitan areas. Future research may optimize model parameters through field surveys and explore the coupling mechanism between BGSPs, land surface temperature and CS to better align BGS management with sustainable development agendas. Full article

The natural resources and local communities of Madre de Dios, Peru, face severe environmental degradation due to illegal mining, deforestation, and the expansion of agricultural activities, threatening one of the most ecologically sensitive regions of the Amazon. This research proposes a low-carbon and bioclimatic architectural design for a Sustainable Interpretation and Research Center dedicated to the conservation of the ecosystems of Manu National Park. The study is based on an analysis of the surrounding environment in terms of flora, fauna, and climate, applying bioclimatic strategies focused on sustainability and supported by specialized digital tools (Revit 2024, Canva, Global Mapper 2024, SketchUp 2024, Photoshop 2022, and Illustrator 2022). The project presents a bioclimatic architectural design that integrates constructive techniques ensuring thermal comfort in a warm-humid climate, while promoting the use of clean technologies such as photovoltaic solar systems generating 15,571.8 kWh per year and a rainwater harvesting system collecting 70,675 L annually. The infrastructure is built with bamboo and locally sourced wood, renewable materials that ensure durability and low environmental impact. In addition, the design includes the reforestation of 17.92% of the total area and 3.46% of public spaces, incorporating native species such as Brazil nut, rosewood, and capirona to reinforce local biodiversity. Overall, this research demonstrates how low-carbon construction, renewable materials, and bioclimatic design can contribute to sustainable development, environmental awareness, and the preservation of natural ecosystems in tropical regions. Full article

This study investigates the properties of concrete incorporating recycled aggregates (RAs) for rigid pavement applications. A 15-point three-level experimental design was used to vary three composition factors: Portland cement substitution with fly ash (FA), and dosages of a superplasticizer (SP) and polypropylene fibers (PFs). A set of experimental–statistical models (ES models) was developed to predict the concrete strength, abrasion and frost resistance (FR), water absorption (WA), and global warming potential (GWP). This study aimed to develop a material that achieves both adequate mechanical performance for pavement applications and enhanced environmental sustainability by incorporating RAs and FA. The results demonstrate that replacing up to 13% of cement with FA does not compromise the splitting tensile strength or FR. For non-fibrous concrete, this substitution increases FR by approximately 50 freeze–thaw cycles. Application of PFs (2.4–3 kg/m³) enhances splitting tensile strength by 14–16% and improves FR by about 50 cycles. Using response surface methodology (RSM), optimal concrete compositions were identified that meet all target criteria: compressive strength ≥ 40 MPa, flexural strength ≥ 5 MPa, FR ≥ F200 (cycles), and abrasion resistance (AR) ≤ 0.5 g/cm², while simultaneously minimizing GWP. An additional optimum composition was determined by imposing a constraint on splitting tensile strength of ≥4.5 MPa. This graphical optimization approach, utilizing two-factor interaction diagrams, provides an effective and visual methodology for practical concrete mixture design. The novelty of the method lies in the discretization of the factor space, which enables efficient identification of optimal concrete mixture compositions. Full article

Currently, hydrogen is considered a primary option for replacing fossil fuels across various processes, which can reduce greenhouse gas emissions and mitigate global warming. To achieve these goals, hydrogen should be produced using non-polluting processes, such as water electrolysis powered by renewable energy sources. This method requires feeding the converter with an unregulated voltage source. A quadratic step-down converter can be connected between a DC source and a Proton Exchange Membrane (PEM) electrolyzer to produce hydrogen. To mitigate variations in the generated output voltage and intermittent power supply to a PEM electrolyzer, a DC-DC converter is used as an interface. A converter model can be combined with a static or dynamic model of the PEM electrolyzer to yield switched models and, after averaging, linear state-space models. These models can be used to design robust controllers for green hydrogen production, thus significantly reducing greenhouse gas emissions. This work presents experimental and simulation results. Full article

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration. Full article

Rapid urbanisation and increasing heat extremes pose significant challenges for megacities in the Global South. This study develops a configuration-sensitive assessment of blue-green space (BGS) cooling in Delhi, a Global South megacity facing intensified heat. Using satellite imagery and statistical modelling, we quantify how land cover and patch structure regulate land surface temperature (LST). Satellite imagery was used to derive LST, and six land-cover classes were mapped using supervised classification. Spectral indices and proximity metrics were calculated, land-cover patches were delineated, and their thermal behaviour was analysed using patch-level LST statistics. Delhi exhibits a heterogeneous urban heat island (UHI) surface, with LST spanning 19.8–38.6 °C and built-up land dominating (743.50 km²), while BGS remains limited and fragmented. Warming scaled almost linearly with built-up patch size (R² = 0.98), with mean LST rising from 22.6 °C (<20,000 m²) to 27.4 °C (>500,000 m²). Cooling strengthened with BGS spatial dominance as dense vegetation declined from 23.8 to 22.1 °C (R² = 0.98), sparse vegetation from 24.3 to 22.2 °C, and water bodies from 21.4 to 18.8 °C (R² = 0.89) across increasing size classes. Correlations identified impervious surfaces as primary warming controls, while moisture and vegetation were cooling indicators. Random Forest-SHAP confirmed modified bare soil index (MBSI) and normalised difference built-up index (NDBI) as dominant predictors, with cooling from modified normalised difference water index (MNDWI) and comparatively conditional effects of normalised difference vegetation index (NDVI). Impervious and exposed surfaces govern Delhi’s thermal baseline, while BGS acts as a modifier whose benefits emerge when patches are large, connected, and integrated. These findings support shifting from area-based greening targets to morphology-based planning that protects connected blue-green corridors. Full article