Search Results (2,893)

Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. Full article

Understanding the historical patterns of drought changes is important to effectively manage and mitigate drought. This paper aims to provide a quantitative assessment of the spatiotemporal drought patterns in Saudi Arabia from 1985 to 2022. The study used the Standardized Precipitation Index (SPI) to examine drought patterns on both monthly and yearly timescales. The findings indicate a significant trend of increasing drought conditions in certain regions of the Kingdom from 1985 to 2022. The average rates of change for SPI-03, SPI-06, and SPI-12 were found to be −0.003 yr⁻¹, −0.0034 yr⁻¹, and −0.0099 yr⁻¹, respectively. Droughts were more frequent and persistent in the northern regions of the country, while the western region experienced severe and intense droughts. There were fewer drought occurrences before 2000, but droughts became more frequent after 2000, with large-scale impacts occurring during 2007–2008 and 2013–2014. These findings have important implications for water management strategies and can help mitigate the effects of drought, as they identify hotspot regions across Saudi Arabia at different timescales. Overall, it is important to implement province-specific efforts to reduce environmental vulnerabilities to droughts. Full article

Analyzing the spatial and temporal changes in the carbon budget and its influencing factors is the basis for formulating effective measures to reduce emissions and increase sinks. This study establishes a carbon budget assessment model for forest enterprises, calculating forest carbon stocks and enterprise emissions using volume-derived biomass and emission factor methods. The spatiotemporal evolution characteristics of carbon budgets for forest enterprises in the key state-owned forest area (2017–2021) were analyzed using various methods, including the Mann-Kendall (MK) test and hotspot analysis. Influencing factors are identified through correlation analysis and the optimal parameter geographical detector (OPGD), while their spatial-temporal variations and causal relationships are analyzed using the geographical and temporal weighted regression model (GTWR) and structural equation modeling (SEM). The carbon budget in the Greater Khingan Range state-owned forest area averaged 10.16 × 10⁶ t CO₂-eq from 2017 to 2021, showing a gradual upward trend. The average annual carbon budget of forest enterprises was 1.02 × 10⁶ t CO₂-eq, which was highest in the central regions and lowest in the periphery. Soil pH, forest area, and elevation are the primary factors. The interaction between paired factors enhances the explanatory power of their impact, and the effects of different influencing factors exhibit both positive and negative variations across forest enterprises. In addition, the middle-aged forest tending area and average annual precipitation positively influenced forest area and soil pH, indirectly enhancing the carbon budget through multifactor interactions. This research can enhance the understanding of the carbon budget in forest enterprises, providing scientific support for the ecological protection of state-owned forests and contributing to the development of sustainable forestry practices that indirectly benefit societal well-being and economic resilience. Full article

Erosive rainfall is essential for initiating surface runoff and soil erosion to occur. The analysis on its temporal and spatial distribution characteristics is crucial for calculating rainfall erosivity, predicting soil erosion, and implementing soil and water conservation. This study utilized daily rainfall observation data from 90 meteorological stations in Henan from 1981 to 2020, and conducted geostatistical analysis, M-K mutation test analysis, and wavelet analysis on erosive rainfall to reveal the spatiotemporal distribution characteristics over the past 40 years. Building on this foundation, the correlation between erosive rainfall, rainfall, and rainfall erosivity were further explored. The findings indicated that the average annual rainfall in Henan Province varied between 217.66 mm and 812.78 mm, with an average yearly erosive rainfall of 549.24 mm and a standard deviation of 108.32 mm. Erosive rainfall constitutes for 77% of the average annual rainfall on average, and the analysis found that erosive rainfall is highly correlated with rainfall volume. The erosive rainfall increased from northwest to southeast, and had the same spatial distribution characteristics as the total rainfall. The number of days with erosive rainfall was 20.5 days and the annual average sub-erosive rainfall was 26.86 mm. The average annual rainfall erosivity in Henan Province ranged from 1341.81 to 6706.64 MJ·mm·ha⁻¹·h⁻¹, averaging at 3264.63 MJ·mm·ha⁻¹·h⁻¹. Both the erosive rainfall and the rainfall erosivity are influenced by the monsoon, showing a unimodal trend, with majority of the annual total attributed to rainfall erosivity from June to September, amounting to 80%. The results can provide a basis for forecasting of heavy rainfall events, soil conservation and planning, ecological treatment, and restoration. Full article

This study aims to investigate the impact of ionospheric models on Global Navigation Satellite System (GNSS) positioning and proposes an ionospheric prediction method based on a Transformer deep learning model. We construct a Transformer-based deep learning model that utilizes global ionospheric maps as input to achieve spatiotemporal prediction of Total Electron Content (TEC). To gain a deeper understanding of the model’s prediction mechanism, we employ integrated gradients for explainability analysis. The results reveal the key ionospheric features that the model focuses on during prediction, providing guidance for further model optimization. This study demonstrates the efficacy of a Transformer-based model in predicting Vertical Total Electron Content (VTEC), achieving comparable accuracy to traditional methods while offering enhanced adaptability to spatial and temporal variations in ionospheric behavior. Furthermore, the application of advanced explainability techniques, particularly the Integrated Decision Gradient (IDG) method, provides unprecedented insights into the model’s decision-making process, revealing complex feature interactions and spatial dependencies in VTEC prediction, thus bridging the gap between deep learning capabilities and explainable scientific modeling in geophysical applications. The model achieved positioning accuracies of −1.775 m, −2.5720 m, and 2.6240 m in the East, North, and Up directions respectively, with standard deviations of 0.3399 m, 0.2971 m, and 1.3876 m. For VTEC prediction, the model successfully captured the diurnal variations of the Equatorial Ionization Anomaly (EIA), with differences between predicted and CORG VTEC values typically ranging from −6 to 6 TECU across the study region. The gradient score analysis revealed that solar activity indicators (F10.7 and sunspot number) showed the strongest correlations (0.7–0.8) with VTEC variations, while geomagnetic indices exhibited more localized impacts. The IDG method effectively identified feature importance variations across different spatial locations, demonstrating the model’s ability to adapt to regional ionospheric characteristics. Full article

The ecological urban agglomeration around Poyang Lake represents a critical development area in the Yangtze River basin. The spatiotemporal characteristics of the net primary productivity (NPP) of vegetation are explored from the perspective of the city’s functional position, providing important insights for the city to achieve the dual-carbon target and green development. The study evaluates the spatiotemporal variations in NPP from 2003 to 2022 in the eco-urban agglomeration around Poyang Lake, using the CASA model. Its variation characteristics were explored in detail from a completely new perspective and scope using indicators such as cycle amplitudes, CV coefficients, Hurst indices, and others. Results indicate seasonal fluctuations and significant variations between urban areas and vegetation, with implications for sustainable development. The annual NPP ranged from 200 to 800 gC/(m²·a), with a change rate of 0.58 gC/(m²·a) and evident seasonal fluctuations in the study area. Notably, urban core cities like Jiujiang and Nanchang exhibit lower NPP and decreasing trends. Scenic areas showed high forest cover and vigorous NPP changes, highlighting the need for targeted urban ecological management to enhance green development. Additionally, the seasonal fluctuations in NPP were notably influenced by specific land use types and local economic conditions. In areas with high vegetation cover, the seasonal characteristics of NPP are pronounced, while they are less evident in regions with strong urban economic conditions. Full article

Currently, land drought events have become a frequent and serious global disaster. How to address these droughts has become a major issue for researchers. Traditional response strategies for land drought events have been determined by experts based on the severity levels of the events. However, these methods do not account for temporal variations or the specific risks of different areas. As a result, they overlooked the importance of spatio-temporal multi-scale strategies. This research proposes a multi-scale response strategy recommendation model for land drought events. The model integrates characteristics of drought-causing factors, disaster-prone environments, and hazard-bearing bodies using case-based reasoning (CBR). Additionally, the analytic hierarchy process (AHP) and entropy weighting methods (EWMs) are introduced to assign weights to the feature attributes. A case retrieval algorithm is developed based on the similarity of these attributes and the structural similarities of drought cases. The research further classifies emergency strategies into long-term and short-term approaches. Each approach has a corresponding correction algorithm. For short-term strategies, a correction algorithm based on differential evolutions is applied. For long-term strategies, a correction algorithm based on drought risk assessment is developed. The algorithm considers factors such as drought risk, vulnerability, and exposure. It facilitates multi-scale decision-making for drought events. The candidate case obtained using the correction algorithm shows an overall attribute similarity of 94.7% with the real case. The emergency response levels match between the two cases. However, the funding required in the candidate case is CNY 327 million less than the actual expenditure. Full article

Net primary productivity (NPP) is a key metric for evaluating ecosystem carbon sink capacity and defining vegetation. Despite extensive research on vegetation NPP, much relies on coarse spatial resolution data, which often overlooks regional spatial heterogeneity, causing inaccuracies in NPP estimates. Therefore, this study employed the improved CASA model, based on GF-SG and kNDVI methods, to estimate vegetation NPP at a 30 m spatial resolution on the Zoigê Plateau from 2001 to 2020. The effects of anthropogenic and climatic factors on NPP were quantified through residual and partial correlation analyses. These results indicated the following: (1) NDVI derived from the GF-SG fusion method aligns closely with Landsat NDVI (R² ≈ 0.9). When contrasted with using NDVI alone, incorporating kNDVI into the CASA model enhances NPP assessment accuracy. (2) Vegetation NPP on the Zoigê Plateau has fluctuated upward by 2.09 gC·m⁻²·a⁻¹ over the last two decades, with higher values centrally and lower at the edges. (3) Monthly partial correlation analysis indicates almost no temporal effects in NPP response to temperature (97.42%) but significant cumulative effects in response to precipitation (80.3%), with longer accumulation periods in the south. Annual analysis reveals that NPP correlates more strongly with temperature than precipitation. (4) NPP changes are jointly influenced by climate change (48.46%) and human activities (51.54%), with the latter being the dominant factor. This study deepens the understanding of NPP dynamics in the Zoigê Plateau and offers insights for estimating NPP at high spatial-temporal resolutions. Full article

Droughts are long-term natural disasters and encompass many unknown factors. Herein, yearly and seasonal standardized precipitation evapotranspiration index (SPEI) values were calculated by analyzing monthly temperature and precipitation data from 1971 to 2020. A cloud model was employed to obtain the spatiotemporal variations in the yearly distribution of drought weather. The cross-wavelet transform results revealed the relationship between the SPEI and atmospheric circulations. The results indicated that the average reduction rates of the SPEI-3 and SPEI-12 in Yinshanbeilu were 0.091 and 0.065 yr⁻¹, respectively, and the annual drought occurrence frequency reached 30.37%. The annual station ratio and drought intensity showed increasing trends, whereas the degree of drought slightly decreased. The overall drought conditions indicated an increasing trend, the entropy (E_n) and hyper entropy (H_e) values demonstrated increasing trends, and the expectation (E_x) showed a downward trend. The fuzziness and randomness of the drought distribution were relatively low, and the certainty of drought was relatively easy to measure. The variation in the drought distribution was relatively low. There were resonance cycles between the SPEI and various teleconnection factors. The Pacific Decadal Oscillation (PDO) and the El Niño–Southern Oscillation (ENSO) exhibited greater resonance interactions with the SPEI than did other teleconnection factors. The cloud model exhibits satisfactory application prospects in Yinshanbeilu and provides a systematic basis for early warning, prevention, and reduction in drought disasters in this region. Full article

In this study, a diagnostic model for lower atmospheric ducts was developed using the polar weather research and forecasting model. A five-year simulation was then conducted across the entire Arctic region to investigate the temporal and spatial characteristics of lower atmospheric ducts. The model demonstrated excellent performance in simulating modified atmospheric refractivity, with root mean square errors ranging from 0 M to 5 M. The five-year simulation results revealed that duct occurrence rates across the Arctic region were all below 1% and exhibited a negative relationship with latitude. Regarding the difference between surface ducts and elevated ducts, a higher frequency of surface ducts was detected in the Arctic region. The height and thickness of surface ducts were generally lower than those of elevated ducts, but the strength of surface ducts was slightly greater. Regionally, surface ducts mainly occurred in the land areas surrounding the Arctic Ocean, while more elevated ducts were found in the North Atlantic Sea area. Additionally, a negative correlation was observed between the polar vortex indices and the characteristics of ducts, particularly for surface ducts. The ducts in Greenland were notably influenced by polar vortex activity, whereas the ducts in other regions, such as the Norwegian Sea and Kara Sea, were less affected. Full article

Exploring the characteristics and driving factors of carbon storage change in different terrain gradient variations can provide important insights for formulating the agricultural ecological protection policy for regional development. Previous studies have used the fixed value of carbon density to evaluate the change characteristics of carbon storage but ignored the spatio-temporal heterogeneity of carbon storage at the block scale and the impact of policy factors. Thus, this paper takes Sanjiang Plain, Heilongjiang Province, China, as a study area, and the spatio-temporal variation of carbon storage at different topographic gradients was revealed using hot and cold spot analysis and zonal statistics. Through the geographic detector and estimation of the soil carbon density model, the driving factors and intensity of carbon storage spatial distribution are revealed from 1990 to 2020. We conducted analyses on aboveground biomass, underground biomass, and soil carbon storage across three elevation levels (0–200 m, 200–500 m, 500–999 m) to reveal the quantitative distribution features of carbon storage. The study analysis finds that carbon storage indicates a sawtooth evolution during the study period. Carbon storage was dominant at elevation I (range is 0–200 m), slope I (range is 0–2°), and relief amplitude I (range is 0–30 m). Additionally, the carbon storage losses were severe at elevation II (range is 200–500 m), slope II (2–6°), and relief amplitude II (30–70 m). In contrast, the carbon storage losses at elevation III (500–999 m), slope III (6–15°), and relief amplitude III (70–186 m) were insignificant. The spatial pattern of carbon storage varies significantly under different topographic gradients from 1990 to 2020. The most critical driving factors influencing the spatial distribution pattern of carbon storage were land use and annual average temperature. Distance to urban centers and soil texture also moderately influence the distribution of carbon storage. As the topographic gradient increases, the dominant factors of carbon storage gradually change from annual mean temperature and the extent of land use to policy factors and other socio-economic factors. Therefore, this study emphasizes the importance of implementing policies that convert farmland to forests and wetlands and promote the green transformation of agriculture. Full article

This study examines the spatio-temporal variations of ambient air pollution from fine particulates below 2.5 µm (PM_2.5) and particulate matter below 10 µm (PM₁₀) in three of the most polluted provinces in Iran, namely Tehran, Isfahan, and Khuzestan, over a 6-year period (2016–2021). The results reveal distinct patterns of PM₁₀ and PM_2.5 concentrations since in Tehran, the highest PM₁₀ and PM_2.5 levels occur in winter, while PM_2.5 is lowest from March to May. Khuzestan experiences the highest pollution levels in summer due to dust storms, while Isfahan exhibits pollution levels and annual patterns similar to Tehran. Strong correlations are observed between PM₁₀ and PM_2.5 concentrations at stations in Tehran and Khuzestan Provinces, suggesting common sources and variation in both coarse and fine PM, with average PM_2.5/PM₁₀ ratios of 0.39–0.42, suggesting the dominance of dust. Furthermore, the analysis identifies the role of atmospheric stability, wind speed, and dust storms in controlling the PM levels in the three provinces. Lifetime cancer risks have been identified as unacceptably high, exceeding the threshold limit of 10⁻⁴, while Hazard Quotient (HQ) values above 1 indicate a high non-carcinogenic potential risk, particularly at stations in Khuzestan Province. The Excess Lifetime Cancer Risk (ELCR) values for PM_2.5 exposure in the most populated Tehran Province range from 139.4 × 10⁻⁶ to 263.2 × 10⁻⁶, underscoring significant cancer risks across various monitoring sites. This study emphasizes the urgent need for targeted pollution control measures in each province to effectively mitigate the adverse health effects associated with high PM concentrations. Full article

Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens of millions of people worldwide. Currently, the most effective diagnostic method employs the monitoring of brain activity through electroencephalogram (EEG). However, it is critical to predict epileptic seizures in patients prior to their onset, allowing for the administration of preventive medications before the seizure occurs. As a pivotal application of artificial intelligence in medical treatment, learning the features of EEGs for epilepsy prediction and detection remains a challenging problem, primarily due to the presence of intra-class and inter-class variations in EEG signals. In this study, we propose the spatio-temporal EEGNet, which integrates contractive slab and spike convolutional deep belief network (CssCDBN) with a self-attention architecture, augmented by dual-task learning to address this issue. Initially, our model was designed to extract high-order and deep representations from EEG spectrum images, enabling the simultaneous capture of spatial and temporal information. Furthermore, EEG-based verification aids in reducing intra-class variation by considering the time correlation of the EEG during the fine-tuning stage, resulting in easier inference and training. The results demonstrate the notable efficacy of our proposed method. Our method achieved a sensitivity of 98.5%, a false-positive rate (FPR) of 0.041, a prediction time of 50.92 min during the epilepsy prediction task, and an accuracy of 94.1% during the epilepsy detection task, demonstrating significant improvements over current state-of-the-art methods. Full article

Exploring and predicting the spatiotemporal evolution characteristics and driving forces of carbon storage in typical mountain forest ecosystems under land-use changes is crucial for curbing the effects of climate change and fostering sustainable, eco-friendly growth. The existing literature provides important references for our related studies but further expansion and improvements are needed in some aspects. This study first proposed an integrated framework comprising gray multi-objective optimization (GMOP), Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), the Patch-level Land Use Simulation Model (PLUS), and optimal parameter-based geographical detector (OPGD) models to further expand and improve on existing research. Then, the integrated model was used to analyze the spatial–temporal variation in land-use pattern and carbon storage at the county scale in China’s Daiyun Mountain’s Rim under four scenarios in 2032, and analyze the driving force of spatial differentiation of carbon storage. The results indicated that (1) land-use change primarily involves the mutual transfer among forest, cultivated, and construction land, with approximately 7.2% of the land-use type area undergoing a transition; (2) in 2032, the natural development scenario projects a significant reduction in forest land and an expansion of cultivated, shrub, and construction lands. Conversely, the economic priority, ecological priority, and economic–ecological coordinated scenarios all anticipate a decline in cultivated land area; (3) in 2032, the natural development scenario will see a 2.8 Tg drop in carbon stock compared to 2022. In contrast, the economic priority, ecological priority, and economic–ecological coordinated scenarios are expected to increase carbon storage by 0.29 Tg, 2.62 Tg, and 1.65 Tg, respectively; (4) the spatial differentiation of carbon storage is jointly influenced by various factors, with the annual mean temperature, night light index, elevation, slope, and population density being the key influencing factors. In addition, the influence of natural factors on carbon storage is diminishing, whereas the impact of socioeconomic factors is on the rise. This study deepened, to a certain extent, the research on spatiotemporal dynamics simulation of carbon storage and its driving mechanisms under land-use changes in mountainous forest ecosystems. The results can serve to provide scientific support for carbon balance management and climate adaptation strategies at the county scale while also offering case studies that can inform similar regions around the world. However, several limitations remain, as follows: the singularity of carbon density data, and the research scope being confined to small-scale mountainous forest ecosystems. Future studies could consider collecting continuous annual soil carbon density data and employing land-use simulation models (such as PLUS or CLUMondo) appropriate to the study area’s dimensions. Full article

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning. Full article