Search Results (1,998)

Rapid global urbanization has exacerbated the conflict between land expansion and ecosystem carrying capacity, making the enhancement of urban land use efficiency (ULUE), a critical pathway for sustainable development. While the digital economy offers a new engine for green transition, its spatiotemporal mechanisms remain underexplored. Taking China, a representative emerging economy, as a case study, this paper investigates the impact of digital transformation on ULUE from 2013 to 2020. By integrating the Super-EBM model with GTWR, we reveal a dynamic evolution where national efficiency improves while regional polarization intensifies. A key finding challenges traditional agglomeration theory, that population density increasingly exerts a negative impact on ULUE, suggesting that congestion costs and ecological pressures are outweighing agglomeration benefits in the digital era. Furthermore, digital infrastructure demonstrates a consistent positive effect by overcoming geographical barriers, whereas environmental regulation exhibits a J-curve effect that is initially constraining but eventually boosts efficiency. These insights provide a roadmap for developing nations to leverage digital tools for balancing economic growth with ecological sustainability, emphasizing the need for spatially differentiated strategies to manage the digital divide and urban congestion. Full article

Ground-level ozone (GLO₃) poses a critical threat to public health and the success of the Saudi Green Initiative, yet its long-term spatiotemporal evolution across the Arabian Peninsula remains poorly constrained. Utilizing CAMS-derived mixing ratios (1000–850 hPa) from 2003 to 2024, this study identifies a major systemic regime shift occurring in 2016–2017, marking a transition toward a more O₃-enriched atmospheric state across Saudi Arabia. While the early study period was characterized by pronounced spatial heterogeneity, post-2017 diagnostics reveal a synchronized intensification of GLO₃, particularly within the urban industrial belts of the Eastern and Western Provinces. Statistical trend metrics, including Mann–Kendall and regime-shift detection, show a persistent upward trend in GLO₃ concentrations, most significantly during winter and over the southwestern highlands. These trends are robustly coupled with increasing boundary-layer height, temperature, and UV-B radiation, alongside shifting precursor stoichiometry (CO, VOCs, NO_x) that separates titration-dominated from production-dominated regimes. Our results suggest that this mid-decade intensification reflects a convergence of anthropogenic forcing under Saudi Vision 2030 and shifting regional climatic drivers. By uncovering the transition from localized variability to kingdom-wide synchronization, this research provides a process-based foundation for targeted air quality management and the safeguarding of regional sustainability frameworks. Full article

Understanding the dynamics of soil organic carbon (SOC) in farmland is crucial for assessing soil health, quantifying ecosystem potential for SOC enrichment, and guiding sustainable agricultural management. Existing research on SOC sequestration and mineralization has focused mainly on the topsoil layer (0–20 cm), whereas systematic evidence on how deep SOC (>20 cm) responds to agricultural management, and on strategies to enhance deep carbon sequestration, remains limited. This study uses long-term fixed-site monitoring data from 120 farmland plots across 21 typical farmland ecosystem stations and farmland–complex ecosystem stations within the Chinese Ecosystem Research Network (CERN) over 17 years (2004–2020). Using spatial analysis, we characterize the spatiotemporal dynamics of SOC below 20 cm along soil profiles across seven major geographical zones in China. We then estimate the heterogeneous effects of fertilization and straw-management practices (S, straw returning; SCF, straw returning with chemical fertilizer; OF, organic fertilizer; OCF, organic fertilizer with chemical fertilizer), tillage modes, and farmland types on SOC in the 20–40 cm, 40–60 cm, and 60–100 cm layers using a panel fixed-effects model. The results indicate pronounced vertical heterogeneity in SOC below 20 cm and a clear spatial gradient. The 60–100 cm layer shows a significant increase in SOC content during the study period, with a cumulative increase of 4.07%. Relative to single organic inputs, the co-application of organic and inorganic materials improves deep soil SOC enhancement efficiency. Compared with reduced tillage and no-tillage, conventional tillage is less conducive to SOC enhancement in layers shallower than 60 cm, yet it has a significant positive impact on SOC in the 60–100 cm layer. Compared with dryland and irrigated land, paddy fields are less favorable for SOC enhancement below 20 cm. Consequently, regarding agricultural practice, a composite tillage regime combining “surface conservation tillage with periodic deep tillage” should be promoted to foster deep SOC enhancement. Full article

Against the backdrop of global climate warming and rapid urbanization, urban thermal environments exhibit strong spatiotemporal heterogeneity and diurnal contrasts. Based on the high-resolution, seamless land surface temperature dataset (GSHTD), this study systematically evaluates the evolution of extreme urban thermal environments across 107 cities in the Yangtze River Basin (YRB) from 2001 to 2020. Urban cores were delineated using high-density impervious surface area (ISA ≥ 50%), and rural background temperatures were elevation-corrected. To quantify the asynchrony between extreme heat intensification and seasonal background warming, we propose “Risk Amplification Index (Ri)”. The results reveal that: (1) The surface urban heat island intensity (SUHII) intensified across the entire basin, with daytime increases being significantly stronger and more spatially consistent than nighttime ones. (2) The intra-annual SUHII cycle exhibits a unimodal pattern peaking in August, with widening inter-city disparities during the warm season. (3) The intensification of extreme heat is often asynchronous with background warming. Combined with land-use change intensity (ΔISA), our analysis indicates that small and medium-sized cities undergoing rapid expansion (high ΔISA) exhibit a stronger heat-risk amplification effect (higher Ri), whereas mature megacities (high total ISA but low ΔISA) show relatively synchronous thermal evolution. The results suggest that an ISA density of around 70% may act as a threshold beyond which extreme-heat amplification is more likely to intensify. These findings suggest that future heat-risk governance should be time- and region-specific, shifting the focus of climate-adaptive planning from solely megacities to mitigating extreme-heat risk amplification during the rapid urbanization of small and medium-sized cities. Full article

Accurate large-scale crop mapping is critical for yield prediction, agricultural disaster monitoring, and global food security. Synthetic aperture radar (SAR), with its all-weather imaging capability, plays a vital role in remote sensing based on crop mapping studies. However, although feature-level fusion has been widely explored in remote sensing, existing VV and VH fusion approaches for rice mapping are still predominantly conducted at the data level and fail to adequately integrate their complementary information across the rice growth cycle, so the simplistic fusion methods yield features that are redundant or conflicting at field boundaries and in heterogeneous areas, thereby increasing classification errors. To address these challenges, this study proposes a novel spatiotemporal attention model (SimTA) for feature fusion to improve rice mapping. (1) A VV-VH feature-level fusion scheme is designed, integrated with a Content-Guided Attention (CGA) fusion method which effectively exploits the complementary information of the dual-polarized SAR data for achieving deep spatiotemporal dynamics fusion. (2) A Central Difference Convolution Spatial Extraction Conv (CDCSE Conv) Block is designed, enhancing sensitivity to edge variations in rice fields by combining standard and central difference convolutions. (3) To achieve efficient spatiotemporal feature integration across SAR time series, a Temporal–Spatial Attention (TSA) Block is developed, utilizing large-kernel convolutions for spatial feature extraction and a squeeze-and-excitation mechanism for capturing long-range temporal dependencies of rice time series. Extensive experiments were conducted by comparing SimTA with different models under five fusion schemes. Results demonstrate that feature-level fusion consistently outperforms other schemes, with SimTA achieving the best performance: OA = 91.1%, F1 score = 90.9%, and mIoU = 86.2%. Compared to the baseline Simple Video Prediction (SimVP), SimTA improves F1 score and mIoU by 0.8% and 2.1%, respectively. The CGA enhanced feature-level fusion further boosts SimTA’s performance to OA = 91.5% and F1 = 91.4%. SimTA bridges the gap between existing VV-VH deep fusion schemes and modern spatiotemporal modeling demands, offering a more accurate and generalizable approach for large-scale rice field mapping. Full article

To address the problems of rapid water cut increases and severe interlayer interference in offshore composite rhythmic edge-water reservoirs, this paper aims to reveal the three-dimensional spatiotemporal evolution laws of water-flooding fronts under complex heterogeneous conditions. A systematic study was carried out using a combination of three-dimensional large-scale physical simulation, mathematical derivation, and orthogonal numerical simulation. The results indicate that under composite rhythmic conditions, the dynamic interplay between the interlayer permeability differential and gravity segregation exacerbates bottom-water channeling, while a bottom low-permeability zone and a large formation dip angle effectively inhibit water underride. Crude oil viscosity and liquid production rate are the core factors affecting the recovery factor. Furthermore, the constructed water breakthrough time prediction model, which considers additional gravity potential energy, demonstrates a stable calculation error within 4.6%. The study confirms that promoting the longitudinally balanced advancement of multilayer oil–water fronts is the key to improving macroscopic sweep efficiency, and the optimized balanced sweep mode improves the ultimate recovery factor by up to 8.57% compared to the extreme channeling mode, providing scientific guidance for water control well selection and the optimization of liquid production schedules in offshore edge-water reservoirs. Full article

As a critical link between regional economic development and ecological security, understanding the dynamics of water retention is essential for sustainable water resource management in the Huaihe River Economic Belt. This study explores the spatio-temporal evolution and spatial explanatory factors of water retention across five temporal snapshots (2003, 2008, 2013, 2018, and 2023). Based on the InVEST model, we assessed water retention capacity at both grid and spatial development levels, thereby obtaining the retention characteristics of different land-use types and their responses to land-use transitions. Furthermore, a parameter-optimized geographical detector was employed to quantify the relative contributions of climatic-environmental and social-economic factors to the spatial variance of the modeled water retention index. Results indicate that the total water retention capacity exhibited significant interannual fluctuations, with the net capacity in 2023 being lower than the initial level in 2003. Retention values displayed obvious spatial heterogeneity, with high levels concentrated in the southwest and north and low levels distributed in the central area, closely mirroring precipitation distribution. While forest land exhibited the strongest unit water retention capacity, cropland contributed the most to the total volume (50.49%) due to its predominant areal proportion (73.92%). Notably, the conversion of forest to cropland was spatially associated with the most substantial loss in the modeled retention capacity. Soil saturated hydraulic conductivity and land-use type were identified as the dominant factors explaining the spatial variance of water retention. These findings underscore the methodological utility of coupling the InVEST model with a parameter-optimized geographical detector. For practical ecosystem management, the results suggest that spatial planning policies should strictly limit the conversion of ecological lands to agricultural use and prioritize targeted soil hydrological improvements in the central plains to secure long-term water resources. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

This study evaluates the performance of a semi-dynamic negative binomial model with cubic spline smoothing to characterize the spatiotemporal dynamics of COVID-19 mortality in Peru, a setting marked by significant data inconsistency and reporting delays. Using nationwide weekly mortality data, we compared a Poisson regression against a semi-dynamic NB model with a population offset and cubic splines (df = 6). The models were evaluated using Akaike Information Criterion and log-likelihood to handle overdispersion and temporal non-stationarity. The NB model demonstrated a superior fit, reducing the AIC from 136,596.4 to 75,668.25 and improving log-likelihood by over 30,000 points. Demographic analysis revealed an 81.6% higher risk of death in males (IRR = 1.816; 95% CI: 1.753–1.881) and an exponential gradient with age, peaking at an IRR of 4.717 (95% CI: 4.499–4.945) for individuals ≥80 years. Departmental fixed effects identified significant spatial heterogeneity, with higher diffusion in coastal regions. The semi-dynamic NB model with splines provides a robust, parsimonious, and scalable framework for epidemiological surveillance in resource-limited settings. By effectively correcting for overdispersion and stabilizing weekly reporting fluctuations, this approach offers a reliable tool for public health decision making in environments with fragmented data quality. Full article

Task allocation for heterogeneous unmanned aerial vehicle (UAV) swarms requires complex spatiotemporal coordination. While traditional algorithms struggle to interpret abstract semantic intents, general large language models (LLMs) often suffer from physical hallucinations and superficial tactical reasoning. To address these limitations, we propose a generative task allocation paradigm augmented by a heterogeneous toolchain, shifting the approach from rigid numerical optimization toward tool-grounded semantic planning. To implement this and overcome domain data scarcity, we design a decoupled dual-model architecture. This architecture is optimized through an execution-manifold-anchored orthogonal evolution training method. By utilizing simulated self-play within a stable execution environment, this approach prevents gradient conflicts and autonomously generates abundant training data. Furthermore, to resolve the credit assignment problem in long-horizon scenarios, we develop a Recursive Causal Probe (RCP) algorithm. By tracing failures backward through the simulation, RCP synthesizes counterfactual preference data, effectively translating tactical mistakes into precise corrections for the planning model. Extensive simulations demonstrate that our method achieves an 82.34% mission success rate in complex scenarios, requiring significantly fewer interactive corrections than general LLMs, fully verifying its physical feasibility and practical robustness. Full article

Net primary productivity (NPP) and its response to global climate change are one of the hot topics in global change research. Based on Net primary productivity remote sensing data and meteorological data, this study analyzed the spatiotemporal variation in vegetation NPP in Maowusu sandy land by using Sen trend analysis, Mann–Kendall significance test, coefficient of variation stability analysis, partial correlation and complex correlation analysis, and quantitatively analyzed the response of vegetation NPP to climate factors. The results showed that from 2001 to 2023, the overall vegetation NPP showed a significant upward trend, and the annual average increased from 124.28 g·(m⁻²·a)⁻¹ to 221.41 g·(m⁻²·a)⁻¹. The Theil–Sen median slope of NPP was +3.87 g·(m⁻²·a)⁻¹ with a coefficient of variation (CV) of 0.19, suggesting a robust but spatially variable greening trend. In total, 98.53% of the area showed an upward trend, with a very significant and significant increase area. The overall stability of vegetation NPP was strong, with an average coefficient of variation (CV) of 0.19 and a CV< of 0.30 in 97.96% of the regions, but the local area from southwest to east was highly volatile and there was a risk of secondary desertification. The influence of climate factors on vegetation NPP had significant spatial heterogeneity: precipitation was the key driving factor, and most areas were positively correlated. Potential evapotranspiration was positively correlated in the central and northern regions, and negatively correlated in some southern areas. The overall temperature has a negative effect, and only the local area has a weak promoting effect. Multi-correlation analysis shows that vegetation NPP is the result of the synergy of multiple climatic factors, and the hydrothermal coupling mechanism plays a decisive role in its spatial pattern. This study can provide a scientific basis for the restoration of vegetation ecosystems, environmental protection policy formulation, ecological protection and high-quality development of the Yellow River Basin in Maowusu Sandy Land. Full article

Biomass, composed of natural polymers such as cellulose, hemicellulose, and lignin, can be converted into circular chemical feedstocks through thermochemical conversion processes like pyrolysis. Char conversion is the rate-limiting step in the thermochemical conversion process, and thus, char reactivity is essential for determining the overall efficiency of pellet-based thermochemical processes. Pyrolysis experiments were conducted on rice straw pellets of different sizes (i.e., 8, 10, and 12 mm) in a vertical quartz tube reactor at 700 °C, and then the chemical structure of chars sampled at different stages and locations within a 10 mm pellet was analyzed using Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). The results indicate that increasing the pellet size facilitates the growth of polycyclic aromatic structures, as evidenced by the observed variations in the abundance of typical aromatic compounds in bio-oil. This also promotes volatile–char interactions, leading to greater deposition of large aromatic structures on the char surface, thereby enhancing char aromatization. Analogous to the spatial scale effect of pellet size on char structure, the evolution of the char structure within a single pellet exhibits distinct spatial heterogeneity during the initial devolatilization and subsequent char aromatization stages due to the location-dependent coupling of heat/mass transfer limitations and aromatization reactions during pyrolysis. Furthermore, the spatiotemporal evolution of the char structure leads to differences in the specific reactivity: during the devolatilization stage at 75 s, the center exhibits the highest reactivity, whereas the outer surface becomes the most reactive in the subsequent char aromatization stage at 300 s. Full article