Search Results (5,104)

Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. Full article

Urban CO₂ mitigation strategies typically aim at particular zones or sectors but do not account for spatial interdependencies among different components within the city. Understanding how land uses emit within and across districts can reveal systemic leverage points for climate-resilient urban planning. This study applies a bipartite spatial network approach using high-resolution Urban Atlas land-use data and a hierarchical spatial framework for emissions and sequestration estimation. The approach links urban land uses to their emissions profiles, offering a structural view of how different areas interconnect within urban carbon dynamics, moving beyond fragmented emission accounting. Using the Reggio Calabria Functional Urban Area in Italy as a case study, the analysis identifies influential areas and emission-intensive land uses. Subsequently, using centrality metrics highlights the spatial units with strong connections to emission-dense land uses, marking them as points of intervention. Results show that although 53% of districts act as net carbon sinks, their sequestration capacity is outweighed by the intensity of a smaller group of emitter districts. Among these, five central districts (IDs 94, 82, 107, 108, and 72) emit over 500 million kg CO₂ per year, making them leverage points for systemic mitigation. The integration of bipartite spatial network and multiscale territorial analysis provides a replicable, data-driven framework for urban CO₂ mitigation. Ultimately, the study demonstrates that mapping emissions through spatial interdependencies enables planners to target interventions where localized action yields the greatest network-wide climate impact. Full article

With the aging of China’s expressway network, the number of maintenance projects continues to increase, and issues such as construction safety, driving risk, and traffic efficiency have become increasingly prominent. This paper systematically reviews relevant research progress from four aspects: safety characteristics, traffic capacity, work-zone layout, and speed limit management. The review indicates that Western scholars have made extensive use of rich data resources—such as traffic parameters and accident records from expressway maintenance road sections—and have developed relatively systematic and well-established research frameworks in theoretical analysis, practical application, and evaluation methods. In contrast, Chinese studies have mainly relied on specific maintenance projects, commonly employing on-site investigations and traffic simulations to address particular problems, with limited systematization and generalization. Looking forward, it is essential to further strengthen the standardized collection and statistical analysis of traffic data (including accident data) for expressway maintenance road sections. Meanwhile, for complex scenarios such as multi-lane segments, special road sections, reconstruction and expansion sections, as well as extreme climatic conditions and nighttime operations, comprehensive research should be conducted by leveraging new-generation driving simulation, big data analytics, and artificial intelligence technologies, thereby providing scientific support and methodological foundations for building a systematic theoretical framework for traffic safety in expressway maintenance road sections. Full article

The Xishan–Yongding River cultural belt is a key component of the three major cultural belts of Beijing and its water heritage; as a representative of the intensive distribution of semi-arid climate, analyzing its spatial and temporal distribution characteristics is crucial for the development of systematic conservation strategies. This study is based on a dataset developed from field surveys and historical documentation and has been spatially analyzed using visual analytical methods and using a Geographic Information System (GIS). In this study, kernel density estimation was used to identify areas of high density, standard elliptic deviation was used to assess the distribution of water heritage sites over time, and the mean nearest neighbor index was used to determine the spatial clustering pattern of these sites. Regarding type and quantity, water heritage in the cultural belt is diverse, with non-water heritage sites, such as temples and inscriptions, being the most prevalent. In terms of temporal distribution, water heritage spans a long period, with the largest number dating to the Qing Dynasty. The centers of distribution across different periods exhibit a trend from south to north and from mountainous regions to plains, exhibiting a gradual concentration. Spatially, water heritage within the cultural belt follows a “multi-core, contiguous distribution” pattern, with three high-density zones, two medium-density zones, and six low-density zones. The distribution of water heritage is influenced by a combination of natural factors, such as river systems, settlements, elevation, and slope, alongside human factors, including historical culture and the political environment. The findings of this research offer a detailed analysis of the regional characteristics and underlying mechanisms of the temporal and spatial distribution of water heritage within the Xishan–Yongding River cultural belt. Full article

Montane shrubby grasslands, as one of the world’s important ecosystems, are highly sensitive to climate change and human activities, especially in the subtropical regions experiencing rapid economic development. However, little is known about how anthropogenic activities, such as firebreak construction, interact with elevation to influence plant diversity in these ecosystems. Shrub and herbaceous communities were surveyed in subtropical montane shrubby grassland within Baishanzu National Park, eastern China. Nine transects were established along firebreaks, each with two edge plots near firebreak and two interior plots away firebreak, and twelve additional control plots in adjacent undisturbed areas. Species diversity was assessed using the Hill index. Our results revealed distinct responses of shrubs and herbs to firebreak disturbance and elevation. Firebreaks reduced shrub diversity but enhanced herb diversity, and both groups exhibited contrasting elevational patterns. In control areas, shrub diversity decreased while herb diversity increased with elevation, whereas in firebreak zones, these relationships were altered, with edge plots showing a hump-shaped diversity pattern. Differences in shrub diversity but not herbs between interior and edge plots decreased with elevation. Species composition also differed significantly between firebreak and control areas, driven mainly by elevation in control areas and by soil properties near firebreaks. These findings demonstrate that firebreak construction reshapes the elevation–diversity relationships of both herbs and shrubs, highlighting the sensitivity of high-elevation montane shrubby grasslands to small-scale disturbances. Effective firebreak management should therefore account for both elevational context and disturbance intensity to maintain ecosystem biodiversity and stability. Full article

Air cycle systems, once largely replaced by vapour-compression technologies due to efficiency concerns, are now re-emerging as a viable and sustainable alternative for highly dynamic thermal applications and excel in ultra-low temperature. By using air as the working fluid, these systems eliminate the need for synthetic refrigerants and comply naturally with evolving environmental regulations. This study presents the conceptual design and simulation-based analysis of a novel air cycle machine developed for advanced automotive testing environments. The system is intended to replicate a wide range of climatic conditions—from deep winter to peak summer—through the use of fast-responding turbomachinery and a flexible control strategy. A central focus is placed on the radial turbine, which is designed and evaluated using a modular, open source framework that integrates geometry generation, off-design CFD simulation, and performance mapping. The study outlines a potential operating strategy based on these simulations and discusses a control architecture combining lookup tables with zone-specific PID tuning. While the results are theoretical, they demonstrate the feasibility and flexibility of the proposed approach, particularly the turbine’s role within the system. Full article

Most risk assessment and source apportionment studies of the heavy metals in the surface soils in China have focused primarily on East China, whereas studies focused on Northwest China, particularly regarding heavy metals in surface soils in the central and western areas, remain limited. In this study, surface soils in the central–western Ali region were investigated, and the concentrations of nine heavy metals were determined. Moreover, the distribution patterns and ecological risks of these heavy metals were elucidated via a combination of the geoaccumulation index, pollution load index ($PLI$), comprehensive potential ecological risk index ($RI$), and integrated X-ray diffraction (XRD)–multivariate statistical techniques. Additionally, the pollution characteristics and sources were analyzed. The results indicated the following: (1) The spatial distribution of heavy metal pollution is closely linked to the geological background, and high–pollution zones (e.g., Cr, Ni, Co, Cu, As, and Cd) conform well with the distributions of ultramafic rocks and iron/chromite ore beds. The geoaccumulation index revealed that Cd caused slight and moderate contamination at 29.1% and 5.5% of the sites, respectively, whereas As affected 14.6% of the sites. The pollution load index indicated moderate pollution in 20% of the sites, and the potential ecological risk index indicated that 41.8% of the sites posed moderate risks, which was largely driven by Cd (mean $E_r^i$ = 43.1). The comprehensive ecological risk index ($RI$ = 115) confirmed a moderate risk level overall. Principal component analysis revealed three primary sources: natural weathering (Cr–Ni–Co–Cu, 39.1%); a mixed source influenced by nonagricultural anthropogenic activities such as transport and regional deposition, combined with natural processes such as arid climate and alkaline soil conditions that influence Cd mobility (Cd–Mo–Pb, 20.8%); and industrial/mining activities (As–Sb, 14.2%). Mineralogical analyses further indicated that heavy metals are present via lattice substitution, adsorption, and precipitation. This study systematically clarifies the composite pollution pattern and sources of heavy metals in the alpine Ali region, supporting targeted contamination control. Full article

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

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

The northwest of Mexico has important zones for biodiversity conservation, denominated Priority Marine Regions (PMRs), and to study key oceanographic features related to ecological structure, it is necessary to understand environmental variability and observe climatic trends. Sea Surface Temperature (SST) is tightly associated with photosynthesis and serves as a control and driver for biological processes linked to the phytoplankton. Global climatic systems, like El Niño Southern Oscillation (ENSO), are responsible for the interannual and interdecadal variation in SST, since global circulation is modified by them. An important metric to assess phytoplanktonic biomass/photosynthesis is Chlorophyll a (Chl a), constituting the primary basis of the marine trophic web. The present study aims to examine the interannual oceanographic variability across 24 PMRs by employing monthly SST (°C) and Chl a (mg/m³) data derived from remote sensing instruments with spatial resolution of 4 km and 1 km from September 1997 to October 2018. We grouped the Priority Marine Regions into 18 main areas, based on a cluster analysis of Sea Surface Temperature. Significant differences were observed, showing higher SST levels during El Niño phase and higher Chl a concentration during La Niña phase, primarily in winter and spring, which will impact marine ecosystems. Full article

Phenology is a key factor influencing the accuracy of regional-scale winter wheat-yield estimation. This study proposes a yield-estimation modeling framework centered on phenological zoning. Based on the remote sensing monitoring results of the heading stage of winter wheat in the Huang-Huai-Hai region from 2016 to 2021, the KNN-Ward spatial constraint clustering method was adopted to divide the Huang-Huai-Hai region into four consecutive wheat phenological zones. The results indicate a consistent spatio-temporal gradient in the phenology of winter wheat across the Huang-Huai-Hai region, characterized by later development in the northern areas and earlier development in the southern areas. The median day of year (DOY) for the heading stage in each zone varies by approximately 4 to 5 days, demonstrating a high degree of interannual stability. Building upon the phenological zoning outcomes, a multi-source data-driven random forest model was developed for wheat-yield estimation by integrating remote sensing data and meteorological variables during the wheat grain filling stage. This model incorporates remote sensing vegetation indices, crop growth parameters, and climatic factors as key input variables. Results show that the phenological zoning strategy significantly improves model prediction performance. Compared with the non-zoning model (R² = 0.46, RRMSE = 13.02%), the phenological zone model shows strong performance under leave-one-year-out cross-validation, with R² ranging from 0.54 to 0.68 and RRMSE below 12.50%. The phenological zoning model also exhibits more uniform residuals and higher prediction stability than models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of phenology-based zoning for regional yield estimation and provide a reliable framework for fine-scale crop yield monitoring. The phenological zoning model also demonstrates superior residual uniformity and prediction stability compared with models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of the multi-factor-driven modeling framework based on crop phenological zoning for regional yield estimation, providing a robust methodological foundation for fine-scale yield monitoring at the regional level. Full article

This study assesses the long-term impact of mine dewatering on groundwater resources in the fractured–porous Upper Cretaceous chalk aquifer of the Chełm region, SE Poland. Using precipitation records (1994–2024) and groundwater levels from 50 sites (2009–2025), temporal trends were tested with Mann–Kendall, Sen’s slope, Kendall’s tau, and regression, while spatial patterns were evaluated with Local Moran’s I and Getis–Ord Gi*. Results show no significant changes in total annual precipitation but declining snow days (–1 to –1.5 days/year) and rising temperatures (0.02–0.05 °C/year), indicating reduced snowmelt recharge. In contrast, groundwater levels declined consistently, with a median Sen’s slope of –0.14 m/year and drawdowns > 22 m near the chalk mine. Spatial clustering confirmed coherent zones of decline in mining and watershed areas. These findings indicate that climate variability alone cannot explain the observed drawdown; mine dewatering is the dominant driver, reinforced by reduced winter recharge. The results highlight the urgent need for integrated monitoring and adaptive management to protect groundwater-dependent ecosystems. Sustainable water management in mining-affected aquifers must address both anthropogenic pressures and climate-induced reductions in recharge. Full article

The three northeastern provinces of China are the country’s most important grain-producing region, particularly for maize, soybean, and rice, and form its largest commercial grain base. Over the past two decades, cropping structures in this region have undergone notable shifts driven by both climate change and human activities. Generating long-term, high-resolution maps of multi-crop distribution and evaluating their suitability is essential for understanding cropping dynamics, optimizing land use, and promoting sustainable agriculture. In this study, we integrated multi-source satellite imagery from Landsat and Sentinel-2 to map the distribution of rice, maize, and soybean from 2000 to 2023 using a Random Forest classifier. A crop suitability assessment framework was developed by combining a multi-criteria evaluation model with the MaxEnt model. Reliable training samples were derived by overlaying suitability evaluation results with stable crop growth areas, and environmental variables—including climate, topography, soil, hydrology, and anthropogenic factors—were incorporated into MaxEnt to assess suitability. Furthermore, the spatial consistency between actual cultivation and suitability was evaluated to identify areas of misallocated land use. The results show that: (1) the six classification maps achieved an average overall accuracy of 91.05% and a Kappa coefficient of 0.857; (2) the cultivation area of all three crops expanded, with maize showing the largest increase, followed by soybean and rice, and the dominant conversion being from soybean to maize; (3) suitability areas ranked as soybean (376,692 km²) > maize (329,056 km²) > rice (311,869 km²), with substantial spatial overlap, particularly between maize and soybean, suggesting strong competition; and (4) in 2023, highly suitable zones accounted for 57.39% of rice, 39.69% of maize, and 28.89% of soybean cultivation, indicating a closer alignment between actual distribution and suitability for rice, weaker for maize, and weakest for soybean, whose suitable zones were often displaced by rice and maize. These findings provide insights to guide farmers in optimizing crop allocation and offer a scientific basis for policymakers in designing cultivated land protection strategies in Northeast China. Full article

Modern agricultural production faces challenges, caused by soil degradation, declining natural fertility, and a lack of organic matter and productive moisture in the arable layer, which is especially relevant in the context of global climate change and rising prices for fuel and lubricants, mineral fertilizers, and plant protection products. Five tillage systems (moldboard, flat-cut, adaptive, shallow and surface) and three fertilization options (no fertilization, by-product, by product + N₆₅P₆₀K₇₀) were tested. The combination of adaptive cultivation and organic-mineral fertilization resulted in the highest input of crop by-products (up to 1.26 g cm⁻³), elevated humus reserves (69.2 t ha⁻¹ in the 0–40 cm layer), reduced bulk density in the root zone (down to 1.26 g cm⁻³), improved soil moisture conditions, and, consequently, the highest grain yield—4.34 t ha⁻¹, which is 7.4–21.4% higher than in other treatments. The use of adaptive cultivation with differentiation of the depth and type of loosening allowed the humus reserve to be increased to 66.4 t ha⁻¹, the productive moisture in the 0–40 cm layer to reach 86 mm, and ensured an increase in the yield of the grain units to 4.34 t ha⁻¹. The obtained results prove the validity of the efficient integration of the plant biomass on light-textured soils with low physicochemical parameters and humus content as a renewable resource in sustainable agriculture technologies, especially in conditions of climate instability and the rising costs of the resources. Full article

The pine wood nematode (PWN), one of the globally significant forest diseases, has driven the demand for precise detection methods. Recent advances in satellite remote sensing technology, particularly ultra-high-resolution optical imagery, have opened new avenues for identifying PWN-infected trees. In order to systematically evaluate the ability of ultra-high-resolution optical remote sensing and the influence of spatial and spectral resolution in detecting PWN-infected trees, this study utilized a U-Net network model to identify PWN-infected trees using three remote sensing datasets of the ultra-high-resolution multispectral imagery from Beijing 3 International Cooperative Remote Sensing Satellite (BJ3N), with a panchromatic band spatial resolution of 0.3 m and six multispectral bands at 1.2 m; the high-resolution multispectral imagery from the Beijing 3A satellite (BJ3A), with a panchromatic band resolution of 0.5 m and four multispectral bands at 2 m; and unmanned aerial vehicle (UAV) imagery with five multispectral bands at 0.07 m. Comparison of the identification results demonstrated that (1) UAV multispectral imagery with 0.07 m spatial resolution achieved the highest accuracy, with an F1 score of 89.1%. Next is the fused ultra-high-resolution BJ3N satellite imagery at 0.3 m, with an F1 score of 88.9%. In contrast, BJ3A imagery with a raw spatial resolution of 2 m performed poorly, with an F1 score of only 28%. These results underscore that finer spatial resolution in remote sensing imagery directly enhances the ability to detect subtle canopy changes indicative of PWN infestation. (2) For UAV, BJ3N, and BJ3A imagery, the identification accuracy for PWN-infected trees showed no significant differences across various band combinations at equivalent spatial resolutions. This indicates that spectral resolution plays a secondary role to spatial resolution in detecting PWN-infected trees using ultra-high-resolution optical imagery. (3) The 0.3 m BJ3N satellite imagery exhibits low false-detection and omission rates, with F1 scores comparable to higher-resolution UAV imagery. This indicates that a spatial resolution of 0.3 m is sufficient for identifying PWN-infected trees and is approaching a point of saturation in a subtropical mountain monsoon climate zone. In conclusion, ultra-high-resolution satellite remote sensing, characterized by frequent data revisit cycles, broad spatial coverage, and balanced spatial-spectral performance, provides an optimal remote sensing data source for identifying PWN-infected trees. As such, it is poised to become a cornerstone of future research and practical applications in detecting and managing PWN infestations globally. Full article