Search Results (5,298)

Accelerated urbanization and intensified urban development globally lead to increased sewage discharge, challenging environmental protection. Therefore, exploring the correlation mechanism between the economic development level (EDL) and sewage discharge intensity (SDI) is crucial for sustainable development. This study uses panel data from 288 Chinese cities between 2003 and 2021, employs spatial analysis techniques to uncover the spatiotemporal evolution characteristics of SDI, and investigates the influence of economic development on this intensity using spatial panel models. The results reveal that (1) while the spatial distribution of SDI in China generally exhibits a downward trend, changes in the Northeast region are relatively modest, with SDI remaining higher than in other regions. Global autocorrelation analysis further indicates significant spatial agglomeration and positive correlation effects in urban SDI. (2) Economic development exerts a notable inhibitory effect on SDI, with a 0.570% decrease for every 1% rise in GDP per capita, thus demonstrating a significant spatial spillover effect. (3) For megacities, large cities, and small and medium-sized cities, EDLs have significant negative spatial spillover effects on SDI, with a more pronounced impact on large cities. This study provides a theoretical foundation for sewage management and empirical support for environmental policies, crucial for sustainable urban development. Full article

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

Semantically enriched trajectories have attracted growing interest in recent research, driven by the need for more expressive and context-aware movement data analysis. Two primary approaches have emerged for the storage and management of such data: moving object databases, which operate at the transactional or operational level, and trajectory data warehouses (TDWs), which support analytical processing within decision support systems. Conventional TDW methodologies typically model semantic aspects of trajectories by introducing new dimensions into the data warehouse schema. However, this approach often requires structural modifications to the schema in order to accommodate additional semantic attributes, potentially resulting in significant disruptions to the architecture and maintenance of the underlying decision support systems. To overcome this limitation, we propose a novel TDW model that supports dynamic and extensible integration of semantic aspects, without necessitating changes to the schema. This design enhances flexibility and promotes seamless adaptability to domain-specific requirements. To enable such extensibility, we propose an innovative approach to representing semantic trajectories by leveraging natural language processing (NLP) techniques. without relying on traditional spatiotemporal features. This enables the analysis of semantic movement patterns purely through textual context. Finally, we present a comprehensive framework that implements the proposed model in real-world application scenarios, demonstrating its practical extensibility. Full article

An EF1 tornado struck Nansha District, Foshan, Guangdong, on 28 July 2024. To explore the dynamic and thermodynamic changes during the tornado’s life cycle, high-resolution spatiotemporal data from Foshan’s X-band phased-array radar and the direct wind field synthesis algorithm were used to reconstruct the 3D wind field. The dynamics and 3D structure of the tornado were analysed, with a new parameter, vorticity volume (VV), introduced to study its variation. The observation results indicate that the tornado moved from southwest to northeast. During the tornado’s early stage (00:10–00:20 UTC), arc-shaped and annular echoes emerged and positive vorticity increased (peaking at 0.042 s⁻¹). Based on the tornado’s movement direction, the right side of the vortex centre was divergent, while the left side was convergent, whereas the vorticity area and volume continued to grow centrally. During the mature stage (00:23–00:25 UTC), the echo intensity weakened and, at 00:24, the vorticity reached its peak and touched the ground, with the vorticity area and volume also reaching their peaks at the same time. During the dissipation stage (00:25–00:30 UTC), the vorticity and echo features faded and the vorticity area and volume also declined rapidly. The analysis showed that the vorticity volume effectively reflects the tornado’s life cycle, enhancing the understanding of the dynamic and thermodynamic processes during the tornado’s development. Full article

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article

Rapid urbanization has intensified global settlement density, significantly increasing the importance of urban street environmental quality, which profoundly affects residents’ physical and psychological well-being. Traditional methods for evaluating urban environmental quality have largely overlooked dynamic perceptual changes occurring throughout the day, resulting in incomplete assessments. To bridge this methodological gap, this study presents an innovative approach combining advanced deep learning techniques with time-series street view imagery (SVI) analysis to systematically quantify spatio-temporal variations in the perceived environmental quality of pedestrian-oriented streets. It further addresses two central questions: how perceived environmental quality varies spatially across sections of a pedestrian-oriented street and how these perceptions fluctuate temporally throughout the day. Utilizing Golden Street, a representative living street in Shanghai’s Changning District, as the empirical setting, street view images were manually collected at 96 sampling points across multiple time intervals within a single day. The collected images underwent semantic segmentation using the DeepLabv3+ model, and emotional scores were quantified through the validated MIT Place Pulse 2.0 dataset across six subjective indicators: “Safe,” “Lively,” “Wealthy,” “Beautiful,” “Depressing,” and “Boring.” Spatial and temporal patterns of these indicators were subsequently analyzed to elucidate their relationships with environmental attributes. This study demonstrates the effectiveness of integrating deep learning models with time-series SVI for assessing urban environmental perceptions, providing robust empirical insights for urban planners and policymakers. The results emphasize the necessity of context-sensitive, temporally adaptive urban design strategies to enhance urban livability and psychological well-being, ultimately contributing to more vibrant, secure, and sustainable pedestrian-oriented urban environments. Full article

Heatwaves intensified by climate change increasingly threaten urban populations, especially the elderly. However, most existing studies have concentrated on short-term or single-scale analyses, lacking a comprehensive understanding of how land cover changes and urbanization affect the vulnerability of the elderly to extreme heat. This study aims to investigate the spatiotemporal distribution patterns of heat-related health risks among the elderly in Nanchang City and to identify their key driving factors within the context of rapid urbanization. This study employs Crichton’s risk triangle framework to the heat-related health risks for the elderly in Nanchang, China, from 2002 to 2020 by integrating meteorological records, land surface temperature, land cover data, and socioeconomic indicators. The model captures the spatiotemporal dynamics of heat hazards, exposure, and vulnerability and identifies the key drivers shaping these patterns. The results show that the heat health risk index has increased significantly over time, with notably higher levels in the urban core compared to those in suburban areas. A 1% rise in impervious surface area corresponds to a 0.31–1.19 increase in the risk index, while a 1% increase in green space leads to a 0.21–1.39 reduction. Vulnerability is particularly high in economically disadvantaged, medically under-served peripheral zones. These findings highlight the need to optimize the spatial distribution of urban green space and control the expansion of impervious surfaces to mitigate urban heat risks. In high-vulnerability areas, improving infrastructure, expanding medical resources, and establishing targeted heat health monitoring and early warning systems are essential to protecting elderly populations. Overall, this study provides a comprehensive framework for assessing urban heat health risks and offers actionable insights into enhancing climate resilience and health risk management in rapidly urbanizing regions. Full article

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article

This study investigates the spatio-temporal dynamics of Climate Potential Productivity (CPP) in Central Africa during 1901–2019 using the Thornthwaite Memorial model coupled with Mann–Kendall tests based on high spatial and temporal resolution data. The results demonstrate the climate–vegetation interactions under global warming: (1) Central Africa exhibited a statistically significant warming trend (r² = 0.33, p < 0.01) coupled with non-significant rainfall reduction, suggesting an emerging warm–dry climate regime that parallels meteorological trends observed in North Africa. (2) Central Africa exhibited an overall increasing trend in CPP, with temporal fluctuations closely aligned with precipitation variability. Specifically, the CPP in Central Africa has undergone three distinct phases: an increasing phase (1901–1960), a decreasing phase (1960–1980), and a slow recovery phase (1980–2019). The multiple intersection points between the UF and UB curves indicate that Central Africa’s CPP has been significantly affected by climate change under global warming. (3) The correlation of CPP–Temperature was mainly positive, mainly distributed in the Lower Guinea Plateau and the northern part of the Congo Basin (r² = 0.26, p < 0.1). The relationship of CPP–Precipitation showed predominantly a very strong positive correlation (r² = 0.91, p < 0.01). Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

Exploring the evolution of ecosystem services value (ESV) and its drivers is pivotal for optimizing the land-use structure and improving the value of ecosystem services. Using the 1980–2020 land-use/land-cover (LULC) dataset of the Mu Us Sandy Land, this study quantitatively evaluated ESV through LULC change, analyzing the spatiotemporal evolution characteristics of ESV and its driving forces. The results showed that (1) the LULC changes were stable from 1980 to 2020, and the ESV showed a slight downward trend in general. Grassland and water ecosystem services predominantly influenced ecosystem service function value fluctuations across the study area. (2) ESV demonstrated strong positive spatial autocorrelation, with high-value areas concentrated primarily in Red Alkali Nur, Dawa Nur, Batu Bay, and Ulanmulun Lake and low-value areas mainly distributed in unused land and certain agricultural zones. (3) The land-use degree and human activity intensity index were the main factors leading to the differentiation of ESV. The synergistic effects of human activities, landscape pattern changes, and natural factors led to the spatial differentiation of ESV in the study area. Beyond artificial ecological restoration projects, policies for ecosystem service management should pay more attention to the role of geodiversity in service provision. Full article

The koala (Phascolarctos cinereus) is recognised as threatened across two thirds of its distribution and identified as particularly susceptible to climate change. The aim of this study was to assess the spatio-temporal variation in microclimate across koala home ranges and determine any tendency for koalas to exploit this variability. Temperature data loggers were set out in a grid pattern across the study site on St Bees Island, Queensland. Resident koalas were collared with GPS units recording location at night or during the day. Our results revealed that temperature variation across the landscape was greatest on the hottest days (~10 °C). During the day, koalas were found in areas of the landscape that recorded lower daytime temperatures, and during the night, they were found in areas that recorded the highest daytime temperatures. We postulate that koalas avoided the hottest areas of their range during summer days and were more likely to use cooler non-fodder trees but utilised them at night because these areas corresponded with the location of fodder trees. From our results, we suggest that the microclimate of non-fodder trees both (a) explains their selection by koalas during the day and (b) highlights their importance to koala persistence, in addition to the known fodder species. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

Understanding future changes in crop phenology and climate suitability is essential for sustaining winter wheat production in the Huang-Huai-Hai (3H) Plain under climate change. This study integrates bias-corrected CMIP6 climate projections, the DSSAT CERES-Wheat crop model, and Random Forest analysis to assess spatiotemporal shifts in winter wheat phenology and climate suitability. The assessment focuses on the mid- (2041–2060) and late 21st century (2081–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicate that the vegetative and whole growing periods (VGP and WGP) will be extended in the mid-century but shorten by the late century. In contrast, the reproductive growing period (RGP) will be slightly reduced in the mid-century and extended under high emissions in the late century. Temperature suitability is projected to increase during the VGP and WGP but decline during the RGP. Precipitation suitability generally improves, except for a decrease during the reproductive period south of 32° N. Solar radiation suitability is expected to decline across all stages. Temperature is identified as the primary driver of phenological changes, with solar radiation and precipitation playing increasingly important roles in the mid- and late 21st century, respectively. Adaptive strategies, including the adoption of heat-tolerant varieties, longer reproductive periods, and earlier sowing, are recommended to enhance yield stability under future climate conditions. Full article

Accurate rainfall quantile estimation is critical for urban flood management, particularly given the escalating climate change impacts. This study comprehensively compared at-site frequency analysis and regional frequency analysis for sub-hourly rainfall quantile estimation, using data from 27 sites across Seoul. The analysis focused on Seoul’s disaster prevention framework (30-year and 100-year return periods). Employing L-moment statistics and Monte Carlo simulations, the rainfall quantiles were estimated, the methodological performance was evaluated, and Seoul’s current disaster prevention standards were assessed. The analysis revealed significant spatio-temporal variability in Seoul’s precipitation, causing considerable uncertainty in individual site estimates. A performance evaluation, including the relative root mean square error and confidence interval, consistently showed regional frequency analysis superiority over at-site frequency analysis. While at-site frequency analysis demonstrated better performance only for short return periods (e.g., 2 years), regional frequency analysis exhibited a substantially lower relative root mean square error and significantly narrower confidence intervals for larger return periods (e.g., 10, 30, 100 years). This methodology reduced the average 95% confidence interval width by a factor of approximately 2.7 (26.98 mm versus 73.99 mm). This enhanced reliability stems from the information-pooling capabilities of regional frequency analysis, mitigating uncertainties due to limited record lengths and localized variabilities. Critically, regionally derived 100-year rainfall estimates consistently exceeded Seoul’s 100 mm disaster prevention threshold across most areas, suggesting that the current infrastructure may be substantially under-designed. The use of minute-scale data underscored its necessity for urban hydrological modeling, highlighting the inadequacy of conventional daily rainfall analyses. Full article