Search Results (296)

The Andean–Amazonian transition zone in northeastern Peru is one of the most climatically diverse and complex regions in South America, yet knowledge of its ecoregions under climate change remains limited. This study assessed projected changes in mean annual temperature, accumulated annual precipitation, and incident solar radiation in the Amazonas department using an ensemble of five global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP5-8.5 scenarios for the periods 2031–2060 and 2071–2100. Historical data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) were used to analyze the spatial distribution of climate, climate anomalies, variability among ecoregions, and inter-model variability. The results show a generalized temperature increase across the department, with average anomalies between 1.20 and 3.96 °C, while precipitation increased between 3.93 and 16.59%, but with greater spatial heterogeneity and inter-model variability. Solar radiation showed moderate changes, with increases of up to 2.07 W m⁻² toward the end of the century. Ecoregions maintained their climatic differences, but the largest relative increase in precipitation was projected for montane grasslands and shrublands. Temperature showed the greatest agreement in the direction of projected change among models, while precipitation and solar radiation showed greater inter-model variability. These results provide spatially explicit climate information that can contribute to land-use planning, ecosystem conservation, and the design of regional climate change adaptation strategies in the Andean–Amazonian transition zone. These findings may also provide useful climate information for landscape restoration planning and for assessing future ecosystem responses to changing climatic conditions. Full article

Drawing on ERA5 reanalysis data for 2005–2025, this study examines long-term environmental change across China’s eight major deserts and on this basis develops a data-informed framework for integrating geoscientific evidence into geographical fieldwork education. Temperature change, surface radiation and vertical soil moisture profiles are used to construct inquiry tasks on regional warming, land–atmosphere interactions and dryland hydrology. A four-dimensional resource assessment integrating solar resource abundance, thermal stability, soil moisture support and wind exposure stability is then applied using multi-criteria decision analysis (MCDA) to compare relative resource characteristics and ecological constraints across desert regions. The contrasting resource profiles of the Taklimakan, Tengger, and Kubuqi Deserts provide the basis for a scenario-based decision-making activity incorporating equal-weight, energy-priority, and ecology-priority perspectives on renewable energy development and ecological protection. Rather than treating deserts as static landscapes, the proposed framework positions them as dynamic coupled human–environment systems and is designed to engage students in interpreting long-term datasets, comparing regional processes and evaluating sustainability trade-offs. The study illustrates how environmental datasets can be organised into fieldwork questions, analytical tasks and decision scenarios, offering an adaptable framework in which quantitative reasoning, systems thinking and evidence-based sustainability learning are specified as intended educational outcomes in higher geographical education. Full article

Urban vegetation contributes to climate regulation and environmental quality, but its seasonal activity is difficult to compare across heterogeneous city landscapes. This study assessed the city-region monthly correspondence among TROPOMI solar-induced chlorophyll fluorescence (SIF), Sentinel-2 NDVI and NIRv, the radiation-scaled index NIRvP, and model-derived MODIS gross primary production (GPP) in Tel Aviv, Rome, Washington DC, Johannesburg, Beijing, and Buenos Aires from January 2019 to October 2020. ESA WorldCover 2020 was used to compare full-region and vegetation-masked Sentinel-2 indices. Raw SIF–GPP relationships were strong across the six cities (R² = 0.772–0.950), and NIRvP also showed strong raw correspondence in several cities. However, harmonic residualization and PAR-controlled analyses showed that much of the raw correspondence reflected shared annual seasonality and radiation forcing. Adjusted SIF–GPP relationships remained statistically significant only in Rome and Johannesburg. NIRvP had higher adjusted correlations than NIRv in Tel Aviv, Rome, and Johannesburg, but lower correlations in Washington DC, Beijing, and Buenos Aires, indicating no consistent cross-city advantage. Direct comparison of full-region and vegetation-masked results produced a median absolute ΔR² of 0.011 across 18 city-by-index comparisons, although larger changes occurred for NDVI in Buenos Aires and NIRv in Tel Aviv and Buenos Aires. TROPOMI footprint-selection effects were generally small where many footprints were available but were larger in Tel Aviv and Buenos Aires under strict vegetation thresholds. The findings therefore describe period-specific seasonal consistency and processing sensitivity among satellite-derived indicators at a coarse city-region scale, rather than validation of true urban GPP or fine-scale urban vegetation productivity. Full article

This study describes the principal outcomes from twelve years of a multistage research program on a solar updraft tower with an inherent focus on the system geometry and integration. It reviews the main conclusions from the different stages of this program in a synergistic manner. The core objectives and the contribution are in presenting the key findings in one document that intrinsically links the methodologies, component analysis and the development of the research question that connected the simple concept of the solar updraft tower as a system to its implementation in the local environment including the limitations. In this work, all validations were done under the closest accessible matched conditions. The first stage of the program encompassed prototype development and the verification of the system simulation model. Based on this work, a geometrical relation was established for optimizing future system designs. In the following stage, the reduction in the collector area was studied, and a new concept was introduced. Thermal concentration was included as a means for compensating the reduction in the thermal volume using a tracking mirror that reflected the solar thermal radiation onto the tower base. A simulation model was later developed to investigate the ability of the solar updraft tower to adapt to alpine terrain, which mimics the local landscape. In the final stage, the turbine blade profile was optimized and validated, but due to time and convergence limitations, it could not be incorporated into the overall system simulation. Instead, the reverse fan model was adopted with the sloped collector solar tower, imposing a margin of deviation but allowing for the prediction of an operation domain for the turbine. Full article

Urban and peri-urban regions increasingly face climate-related pressures, competing land-use demands, and the need to expand renewable-energy infrastructure while maintaining agricultural land and landscape functions. Agrivoltaics, which combines photovoltaic energy generation with agricultural production, represents a potentially multifunctional approach to land use; however, regional GIS assessments often stop at environmental suitability surfaces without translating those results into planning-relevant cadastral inventories. This study develops and applies a planning-oriented Geographic Information System (GIS) framework for preliminary statewide agrivoltaic screening in Connecticut. Annual global solar radiation and terrain slope were integrated through a weighted suitability model, while incompatible land-cover classes were treated as hard exclusions through a binary land-cover mask. The workflow subsequently excluded protected and open-space lands, associated suitable areas with cadastral parcels, normalized and dissolved parcel identifiers using ParcelKey, and a recalculated suitable area from the resulting unique parcel geometries and then applied a minimum requirement of 1 ha of cumulative suitable area per retained parcel. The final baseline inventory contained 3497 normalized unique cadastral parcels encompassing 16,366.49 ha of GIS-identified suitable area, with suitable land representing an average of 42.46% of total parcel area. Peri-urban contexts accounted for the largest share of the final suitable area, containing 2497 parcels and 73.16% of the total, compared with 476 urban and 524 rural parcels. Sensitivity analysis indicated strong stability under alternative weighting schemes, with spatial overlap exceeding 99% relative to the baseline. Reducing the suitability-score threshold from 3.0 to 2.5 produced only minor changes, whereas increasing it to 3.5 reduced the inventory to 3095 parcels and 13,712.89 ha. From a sustainability perspective, the framework provides a spatial decision-support approach for coordinating renewable-energy planning with agricultural land stewardship, conservation constraints, and more efficient use of already fragmented land resources. By making the effects of exclusions, parcel thresholds, and analytical assumptions explicit, the approach supports more transparent and reproducible evaluation of land-use trade-offs relevant to sustainable development. The resulting inventory is intended as a first-stage planning resource rather than a determination of project feasibility or site-level sustainability performance. Full article

Assessing the impacts of large-scale photovoltaic plants on soil properties is critical for sustainable land management in arid regions. This study examined a 50 MWp utility-scale PV facility in Siziwangqi, Inner Mongolia, northern China, a cold semi-arid desert steppe where renewable-energy development overlaps with fragile wind-eroded ecosystems. A spatially resolved sampling design contrasted soils at key micro-positions relative to the panels, namely Front, Under, and Behind, with adjacent natural Controls. By integrating laser diffraction analysis, multifractal modeling, and the EPIC erodibility equation, we evaluated soil particle-size probability redistribution and EPIC-estimated intrinsic erodibility. Compared with the silt-dominated surface soil of the natural steppe, soils within the photovoltaic plant exhibited fine-particle retention and lower information and correlation dimensions (D1 and D2), indicating stronger local clustering and a less even particle-size probability distribution. The EPIC-estimated erodibility factor decreased from 0.452 in the Control to approximately 0.361 in the PV micro-locations. These associations should be interpreted as texture- and SOC-based model estimates rather than direct measurements of wind erosion. Overall, multifractal parameters provide complementary proxy descriptors for detecting PV-induced particle sorting and potential changes in intrinsic soil erodibility, underscoring the need for field validation and adaptive management in dryland PV landscapes. These findings provide physical soil evidence for evaluating the environmental sustainability of dryland PV development and for supporting adaptive land management in solar farms. Full article

Cutaneous melanoma and basal cell carcinoma (BCC) represent the two primary malignancies driven by solar ultraviolet (UV) radiation. To map the spatial topology and distance dependence of their mutational signatures, we deployed new analytical bioinformatics workflow utilizing two convergent strategies: a data-driven read-level phasing framework to discriminate between collateral mutational events versus independent ones and a hypothesis-driven spatial permutational simulation model to capture density-dependent local deviations. A whole-genome analysis employing this framework unexpectedly reveals two fundamentally distinct lesion-processing landscapes, present in both BCC and melanoma. While independent mutations consistently reproduce canonical UV signatures (SBS7a–c) in both tumor types, collateral mutations tell a distinct and more varied narrative between BCC and melanoma. These collateral mutations are unusually abundant for non-UV characteristics, such as age-related SBS1 and SBS5, and display a notable 3′ to 5′ asymmetry near UV-induced lesions in pyrimidine dimers. We also demonstrated that BCC displays a pronounced enrichment of dinucleotide base substitutions flanking UV-signature sites on the 3′ side, particularly CA>TG and CG>TA changes. Ultimately, these topological patterns in both tumors indicate that a primary photoproduct seeds secondary mutagenesis within its local chromatin environment, dramatically altering our understanding of UV-induced lesion processing. Full article

As solar photovoltaic (PV) infrastructure expands across open landscapes, understanding its belowground effects in grassland ecosystems is important for sustainable solar development. PV panels can create spatially heterogeneous microsites by altering light availability, precipitation redistribution, and soil environmental conditions, but their effects on soil bacterial community structure remain insufficiently understood. Here, we investigated soil physicochemical properties and bacterial communities across four PV-associated microsites: undisturbed grassland outside the PV array (Control), the front edge of the panel (FE), beneath the center of the panel (BP), and the uncovered interspace between adjacent panel rows (IS). Soil bacterial communities were characterized using 16S rRNA gene sequencing. Most measured soil physicochemical properties did not differ significantly among microsites. Available potassium was the only variable showing an overall microsite effect and was significantly lower in FE and BP than in the Control. Shannon index and Chao1 richness did not differ significantly among microsites. In contrast, Bray–Curtis-based principal coordinate analysis (PCoA) showed that FE samples occupied a relatively distinct ordination position, whereas Control, BP, and IS samples partially overlapped; permutational multivariate analysis of variance (PERMANOVA) indicated a significant overall difference in bacterial community composition among microsites. At the phylum level, no dominant bacterial phylum differed significantly among microsites after false discovery rate (FDR) correction. Redundancy analysis (RDA) indicated that alkali-hydrolyzable nitrogen and available potassium were the measured soil variables most closely associated with bacterial community composition at the operational taxonomic unit level in the final model. Together, these results indicate that differences among PV-associated microsites were detected in overall bacterial community composition but not in Shannon index or Chao1 richness. Alpha-diversity and community-composition analyses therefore provide complementary information for evaluating belowground bacterial responses to PV development in grasslands. Full article

The transition towards decarbonised energy systems, often characterised by high photovoltaic penetration, imposes crucial challenges for operational security, flexibility and grid resilience. In this landscape, meteorological-data reliability has emerged as a strategic pillar to mitigate systemic risks arising from forecasting uncertainty, including grid imbalances, electricity-market volatility, and structural asset safety during extreme weather. This study provides a comparative analysis of four forecasting providers, evaluating their accuracy across atmospheric and solar irradiance parameters through heterogeneous datasets spanning diverse climatic zones and seasons. The analysis is performed by employing a dual-source validation framework that benchmarks every forecast against independent, real-world references rather than the providers’ own model-derived observations: first, the atmospheric variables are compared with real surface-station measurements; second, plane-of-array irradiance is benchmarked directly against on-site sensors at operational photovoltaic plants. This dual-source approach isolates systematic model biases relative to real-world environmental conditions, yielding a provider-independent estimate of accuracy against the true atmospheric and irradiance state. This work therefore proposes not merely a comparative analysis but a validation methodology that addresses a specific limitation of existing forecast-comparison approaches, offering actionable insights to minimise financial and operational risks while fostering a secure, resilient, and sustainable energy infrastructure. Full article

Outdoor public spaces are important built-environment settings that support health, social interaction, psychological restoration, and everyday urban life. In cold-region cities, however, winter conditions such as low temperature, snow and ice, short daylight, reduced vegetation, low solar altitude, and declining outdoor activity substantially weaken their usability, attractiveness, and vitality. Existing studies have mainly addressed these problems through thermal comfort, microclimate adaptation, snow safety, and physical environmental optimization, but they provide limited explanation of how users visually perceive winter spaces, allocate attention, and form subsequent spatial interpretations, perceptual evaluations, and behavioral intentions. To address this gap, this conceptual article develops a built-environment framework for explaining winter visual perception mechanisms and proposes an agenda for future eye-tracking-based validation. Through conceptual synthesis across cold-region public-space research, outdoor thermal comfort, environmental psychology, landscape visual perception, eye-tracking studies, public-space behavior, and architectural and built-environment design, the study conceptualizes winter public spaces as seasonal perceptual environments. It identifies five categories of winter visual stimuli: surface-related, vegetation-related, building interface-related, lighting-related, and activity-related stimuli. The framework clarifies how visual attention may serve as an observable mediating process between winter visual stimuli and inferred spatial interpretation, perceptual evaluation, and behavioral intention. Rather than empirically confirming these relationships, the article formulates a testable conceptual model and future validation agenda that should be examined through eye-tracking, behavioral observation, subjective evaluation, and environmental measurements. For architectural and built-environment research, the framework provides a theoretical basis for evaluating and optimizing façades, ground-floor interfaces, entrances, canopies, semi-outdoor spaces, path boundaries, lighting systems, vegetation configuration, and winter activity nodes. Full article

Precipitation use efficiency (PUE) links ecosystem carbon uptake to precipitation input, but endpoint ratios alone cannot show where carbon–water coupling differs along ecohydrological pathways. This limitation is especially relevant in karst landscapes, where thin soils and heterogeneous hydrological pathways can decouple rainfall, soil moisture, evapotranspiration, and plant carbon gain. Here, we developed a PUE chain decomposition framework based on gross primary productivity (GPP), transpiration (T), evapotranspiration (ET), soil moisture (SM), and precipitation (PRE): PUE = GPP/T × T/ET × ET/SM × SM/PRE. In this framework, GPP/T represents carbon fixation per unit transpiration, T/ET the transpiration fraction of evapotranspiration, ET/SM evapotranspiration output relative to soil moisture, and SM/PRE soil moisture status relative to precipitation input. We used multi-source remote-sensing and reanalysis data from 2003 to 2022 to compare karst and non-karst landscapes in Southwest China, applied variance decomposition to quantify the contributions of chain terms and their interactions, and used Stacking ensemble learning with Shapley additive explanations (SHAP) to interpret model-inferred environmental associations. Mean PUE was 1.16 g C m⁻² mm⁻¹ in non-karst areas and 1.08 g C m⁻² mm⁻¹ in karst areas, and all four chain components differed significantly between landform types. Variance decomposition identified SM/PRE and its interaction terms as the largest contributors to PUE variability, mainly reflecting a precipitation-sensitive diagnostic signal and soil moisture status relative to precipitation input. Machine learning interpretation showed that solar radiation, leaf area index, aridity, and groundwater storage were associated with different chain components; karst areas showed stronger groundwater-storage signals and lower model-inferred response thresholds. These findings indicate that PUE differences in Southwest China arise from multiple linked diagnostic stages rather than from endpoint carbon uptake or precipitation alone. The framework can help locate water-use constraints and support landform-specific ecological restoration and water management. Full article

This scientific article presents a comprehensive case study detailing the design of a fully off-grid household in Poland, utilizing an energy solution that combines high-pressure hydrogen energy storage and photovoltaic (PV) technology. In response to the growing demand for sustainable and self-sufficient energy sources, the current study investigates the efficiency and yearly energy balance of this innovative system. The off-grid household is powered by a hybrid system that seamlessly integrates PV panels to harness solar energy and a high-pressure hydrogen energy storage system for long-term energy management. The presented case study examines the design and performance of a system integrating solar energy production with hydrogen storage. Through an analysis of real-world data and operational parameters, this research contributes valuable insights into the viability of such an off-grid solution in Polish environmental conditions. These findings provided an interesting approach to off-grid residential systems, offering a glimpse into the possible future of residential energetic autonomy in the pursuit of a greener and more resilient energy landscape. Full article

The intensive care unit (ICU) plays a crucial role in modern hospitals. ICU patients endure severe physical and mental conditions, making it essential to create a healing environment that reduces stress and promotes recovery. Among common environmental parameters, lighting conditions are particularly critical, as patients often face challenges with mobility and body positioning. Kinetic facades with adjustable external shading elements have gained attention for their ability to regulate sunlight effectively. However, their complexity poses challenges for design and implementation. This study proposes a reinforcement learning-based method, using Q-learning to handle discrete facade configurations and adaptive control under varying solar conditions for optimizing facade configurations in ICU rooms. The method aims to: (1) reduce direct sunlight glare and heat; and (2) maximize landscape views. A case study at Providence Alaska Medical Center demonstrates the method’s effectiveness, showing reduced glare and heat gain and improved landscape view availability through simulation. The results highlight the potential of reinforcement learning to address ICU-specific environmental challenges. Full article

Satellite-based vegetation monitoring has evolved from mapping vegetation canopy properties at single points in time toward analysing time-resolved dynamics of vegetation traits and process-related variables. Retrieved traits and solar-induced chlorophyll fluorescence (SIF) are inherently defined by sensor-specific spatial resolution, temporal integration, and spectral response. Modifying these characteristics alters the retrieval problem itself: under nonlinear retrievals and heterogeneous landscapes, aggregation and retrieval are generally non-commutative, and error components scale differently with resolution. Consequently, increasing spatial, spectral, or temporal detail does not guarantee improved ecological accuracy; a phenomenon we term the resolution–accuracy paradox. These interacting processes define the effective scale of vegetation products, which may differ from nominal sensor resolution and governs the interpretation of retrieved vegetation traits. When products with differing resolutions or compositing strategies are combined, scale effects can induce systematic artefacts in spatial patterns and derived dynamic indicators that cannot be resolved through improved per-pixel accuracy alone. This review establishes a scale-aware conceptual framework that treats scale as an explicit diagnostic dimension linking observation characteristics, retrieval formulations, trait definitions, and aggregation operators. We analyse how scale interactions influence spatial patterns, temporal dynamics, disturbance signals, and multiresolution data fusion, and derive diagnostic principles, best-practice guidelines, and research priorities for the scale-consistent interpretation of vegetation trait dynamics and SIF-constrained productivity and stress indicators across spatial and temporal scales. Framed in the context of upcoming hyperspectral missions such as CHIME and FLEX, which increase spectral information content, robust interpretation of vegetation products requires scale-consistent analysis and uncertainty-aware processing. For practitioners, this implies that vegetation products should be interpreted, validated, and compared at their effective scale rather than assuming that a finer spatial, spectral, or temporal resolution necessarily yields more reliable ecological information. Full article

Agrotextiles are critical for enhancing climate resilience and food security in modern agriculture. This scoping review maps the global research landscape to identify primary functions, applications, and emerging sustainability challenges. Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, 206 studies published between 2000 and 2025 and indexed in Scopus and WoSCC were systematically analysed using a hybrid qualitative–quantitative approach. Results demonstrate that pest exclusion (37.4%) and solar radiation management (34.5%) are the dominant functional roles, with research heavily concentrated in high-value crops such as tomato (22.2%) and pepper (13.8%). Although synthetic polymers prevail, a substantial reporting gap remains, as 51.9% of studies do not explicitly specify base materials. Nevertheless, a clear shift toward sustainability is emerging, with environmental themes accounting for 77.8% of publications in 2025, particularly focusing on biodegradable materials and pesticide reduction. Overall, while applied performance research in agrotextiles is relatively mature, the field remains fragmented in terms of material transparency and structural standardisation. Future advances should integrate circular economy principles, establish technical reporting standards, and expand applications into extensive and tropical cropping systems to support global agricultural resilience. Full article