Search Results (751)

Spatiotemporal prediction of groundwater quality is of great significance for regional water environmental safety assessment, pollution risk identification, and urban groundwater resource management. To address the difficulty of existing methods in simultaneously characterizing multi-indicator coupling relationships, temporal evolution processes, and spatial heterogeneity, this study proposes a spatiotemporal groundwater quality prediction model under multi-indicator coupling constraints. First, indicators including dissolved oxygen, total nitrogen, electrical conductivity, dissolved organic carbon, pH, permanganate index, and total phosphorus are uniformly mapped into a risk space to construct an integrated groundwater quality risk index. Then, based on monthly groundwater monitoring data from Yiyang City during 2000–2023, continuous regional grid sequences are generated. In terms of model design, the Temporal Difference Interaction Module (TDIM) is introduced to enhance multi-scale temporal variation modeling, Region-Guided Feature Modulation (RGFM) is used to strengthen regional heterogeneity representation, and Spatiotemporal Boundary-Aware Loss (STB Loss) is adopted to maintain spatiotemporal boundary consistency. The experimental results show that the proposed method achieves a Structural Similarity Index Measure (SSIM) of $0.9814\pm 0.0085$, a Peak Signal-to-Noise Ratio (PSNR) of $40.47\pm 2.19$, a Mean Absolute Error (MAE) of $2.80\times {10}^{-3}\pm 1.50\times {10}^{-3}$, and a Root Mean Square Error (RMSE) of $9.70\times {10}^{-3}\pm 2.69\times {10}^{-3}$, outperforming comparison models overall and providing effective support for dynamic groundwater quality prediction and water environmental safety assessment. Full article

Urea discharge can become unstable as interparticle cohesion increases under moisture-affected conditions. This study combined bulk-solid mechanics, discrete element method (DEM) simulations, contact parameter calibration, and bench testing to investigate urea arching and vibration-assisted discharge. Dry-contact parameters for urea particles and a polypropylene (PP) hopper were calibrated using angle-of-repose and sliding tests. The calibrated simulations differed from the physical target values by 1.71% for the angle of repose and 3.98% for the sliding friction angle. In a separate DEM sensitivity analysis, JKR surface energy was prescribed at 0, 0.05, 0.15, and 0.30 J·m⁻² as an effective adhesion parameter rather than as a calibrated moisture state. The maximum EDEM-exported Total Force signal increased from 2.283 N at 0 J·m⁻² to 2.704 N at 0.30 J·m⁻² (18.5%), whereas the mean particle velocity during the common 5–18 s pre-discharge interval decreased from 0.0613 to 0.0397 m·s⁻¹ (35.3%). Two combined excitation settings were evaluated: 29.17 Hz/0.2 mm and 58.33 Hz/1.2 mm. Because frequency and amplitude changed simultaneously, their individual effects could not be isolated. The bench tests yielded mean discharged masses of 566.808, 492.435, and 464.305 g for the 58.33 Hz/1.2 mm, 29.17 Hz/0.2 mm, and non-vibrating conditions, respectively. The 58.33 Hz/1.2 mm setting increased the mean mass discharged during the 30 s collection interval by approximately 22.1% relative to the non-vibrating control. The corresponding between-run coefficients of variation were 3.628%, 3.580%, and 4.303%; these values describe repeatability between replicate runs rather than temporal or spatial discharge uniformity. Overall, increasing prescribed adhesion reduced particle mobility in the DEM simulations, whereas the 58.33 Hz/1.2 mm combined excitation increased discharged mass under the tested conditions. The experiments do not directly demonstrate crystal bridge rupture or isolate an independent frequency effect. Full article

Lake ice is a reliable indicator of climate variability and change. This research investigates annual and spatial variations in lake ice phenology of small lakes (<2 km²) in the Cascade Mountain Range (U.S.A.) using high-temporal frequency remote-sensing data and a probabilistic model. Data from the Harmonized Landsat-Sentinel-2 (HLS-2) project were analyzed to detect the presence of lake ice for 2872 small lakes and characterize ice phenology during each of the 10 ice seasons (2014–2023). A multiple logistic regression model (Accuracy ~0.80 and AUC 0.88 for cross-validation) was used to predict the probability of ice for every non-imaged day during each ice season. From the time-continuous combination of HLS-2 observations and modelled ice presence, we extracted three ice phenology metrics: ice-on date, ice-off date, and total number of ice days (TID), and we analyzed their variabilities across ice seasons and across the Cascade Range. Both ice-on date and ice-off date distributions exhibited high inter- and intra-season temporal variability. All median TID fell between 100 and 150 days. Our research quantifies the variability of a large sampling of small alpine lakes in a climate-sensitive region and demonstrates the efficacy of extracting ice phenology metrics from daily ice presence probabilities based on remote-sensing data. Full article

This paper presents an analytical and numerical benchmark verification framework for explicit time integration procedures applied to the one-dimensional wave equation. The study combines an exact analytical solution, a closed-form discrete central-difference solution, and explicit finite element models implemented in LS-DYNA and in the in-house academic FEM code PAK-Multiphysics. Two benchmark problems with the same first sinusoidal spatial mode and homogeneous Dirichlet boundary conditions are considered. The first problem, defined by sinusoidal initial displacement and zero initial velocity, is used to analyse the central-difference discretization and its convergence behaviour. The closed-form discrete response enables direct comparison with the analytical solution and confirms the expected second-order accuracy under coupled mesh and time-step refinement at a fixed Courant number. The second problem, defined by zero initial displacement and sinusoidal initial velocity, introduces a phase-shifted temporal response suitable for explicit finite element verification. Four meshes are analysed over five periods of the first longitudinal mode. The numerical responses are assessed using displacement histories, maximum absolute errors, RMS errors, relative RMS errors, amplitude errors, and an energy check. The PAK-Multiphysics results show very close agreement with the analytical solution and a systematic reduction of the error measures, consistent with the lumped-mass central-difference formulation. The LS-DYNA results provide an independent commercial-code comparison, showing decreasing displacement errors under refinement and bounded total-energy variation. The proposed framework provides a transparent and reproducible benchmark for verifying one-dimensional explicit wave propagation models. Full article

Severe soil salinization and low fertility significantly constrain sustainable agricultural development in the Yellow River Delta, one of the three major estuarine deltas in China. Despite their ecological importance, the regional-scale spatial interactions between soil salinity and nutrients, particularly regarding their vertical variability, remain poorly understood. This study analyzed 228 soil samples from 76 sites distributed across a distinct salinity gradient, which was determined by constructing a spatial salinity distribution map after sampling. Samples were collected at three depths (0–15, 15–30, and 30–45 cm) to investigate the spatial distribution of soil organic carbon (SOC), total nitrogen (TN), and the C/N ratio, along with their underlying driving factors. SOC and TN exhibited similar spatial patterns, with higher values distributed along both banks of the Yellow River. Horizontally, SOC and TN in the 0–15 cm layer decreased gradually from west to east, whereas the 15–30 cm and 30–45 cm layers showed an opposite trend, increasing eastward. Vertically, SOC and TN contents declined significantly with soil depth (p < 0.05), although the magnitude of this decline varied regionally: the 0–15 cm layer in the western area contained markedly higher nutrient levels than deeper layers, while vertical variation was less pronounced in the eastern and estuarine regions. Both variables were positively associated with total phosphorus (TP), available potassium (AK), soil moisture content (MC), clay content, and pH, but negatively correlated with electrical conductivity (EC), particularly in the 0–15 cm layer. Our results highlight that soil texture, moisture, and salinity affect the spatial heterogeneity and vertical decline of SOC and TN in the Yellow River Delta. Future research should focus on the long-term temporal distribution of the coupling of multiple elements under changing hydrological and salinity regimes. Full article

High-altitude wetlands in the Caucasus Anatolia transition zone remain among the least-documented avian habitats in the Western Palearctic. Standardised avian biodiversity assessment in such systems provides essential evidence for sustainable wetland management, supporting the conservation planning and long-term ecological monitoring needed to safeguard these ecosystems under increasing anthropogenic pressure. We report on the avifauna of Çalı Lake (2237 m a.s.l.; 391 ha; Kars Province, Türkiye), a nationally designated wetland located on the Central Asian Flyway, based on five systematic survey periods conducted from March 2024 to Spring 2026 using line transects and point counts, combined with a 25 × 25 m grid-based GIS analysis encompassing 498 cells. Approximately 31 ha of the core open-water and marsh perimeter within the 391 ha designated boundary is covered; upland steppe and pasture zones beyond the active survey perimeter were excluded. A total of 154 species belonging to 18 orders and 41 families were recorded, representing approximately 30.5% of Turkey’s national checklist. IUCN status assessment identified two Endangered species, Neophron percnopterus and Oxyura leucocephala, two Vulnerable, five Near Threatened, and 145 Least Concern species. Grid-level species richness averaged 1.47 ± 1.20 per cell per period; cumulative richness per grid reached 7.62 ± 2.73 across all five survey periods. Spearman rank correlation between per-grid richness (S) and abundance (N) was consistently strong across all five periods (ρ = 0.52–0.60; all p < 0.001). A Friedman test indicated significant overall variation across periods (χ²(4) = 127.73, p < 0.001, Kendall’s W = 0.064, a negligible effect size by conventional benchmarks, indicating that the statistically significant variation reflects trivially small per-cell richness differences at this block size). Bonferroni-corrected post hoc Wilcoxon tests revealed that all significant contrasts involved the 2024 Spring–Summer period or the 2026 partial Spring window, while the four fully comparable 2024 Autumn–2025 periods showed no significant differences. A Lorenz concentration curve yielded a Gini coefficient of 0.351, with the top 10% of grid cells concentrating 24.0% of all individual detections in the central and south-western lake zones. Collectively, these findings document Çalı Lake as a species-rich high-altitude wetland with significant conservation value, and establish a reproducible spatial and temporal baseline for long-term ornithological monitoring. These results demonstrate the value of standardised biodiversity assessment as a practical tool for sustainable wetland governance and align with international sustainability frameworks, including the UN Sustainable Development Goals on life on land and clean water and sanitation. Full article

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

This study investigates pre-seismic ionospheric Total Electron Content (TEC) disturbances preceding the Mw 6.9 Northern Aegean Sea earthquake of 24 May 2014 using observations from 13 Global Navigation Satellite System (GNSS) stations. A pronounced negative TEC disturbance was identified on 22 May 2014, approximately two days before the earthquake, under comparatively quiet solar and geomagnetic conditions. Station-wise Z-score analysis, which expresses the TEC departure from the reference mean in units of standard deviation, revealed significant negative deviations across the network, while inter-station correlations indicated a temporally coherent but spatially heterogeneous ionospheric response. To characterize the disturbance beyond peak-based measures, the TEC Anomaly Significance Index (TASI) was developed by integrating the mean absolute Z-score, coefficient of variation, and Shannon entropy. TASI showed strong agreement with the maximum absolute Z-score ranking (Spearman’s $\rho =0.89$, $p<0.001$) while providing greater sensitivity to cumulative and persistent anomaly behaviour. It exhibited stronger associations than maximum $\mid Z\mid$ for six of the seven evaluated temporal descriptors, particularly those representing anomaly duration and consecutive persistence. Among the analyzed GNSS stations, KASI recorded the highest TASI despite not being the nearest station to the epicenter, indicating that anomaly significance was not governed solely by epicentral distance. The proposed framework provides a multidimensional, network-based approach for characterizing potential pre-seismic ionospheric disturbances and establishes a basis for future multi-event and control-period validation. Full article

Cyanobacterial blooms threaten water resource security and ecological health, and understanding their spatiotemporal dynamics is crucial for effective management. To investigate the spatiotemporal dynamics of cyanobacterial blooms in Lake Taihu under wind forcing, a framework integrating empirical orthogonal function (EOF) analysis and machine learning was developed to reconstruct bloom distributions from wind vector sequences. First, EOF analysis was applied to decompose daily spatial distributions of blooms into a set of spatial modes and corresponding temporal coefficients, thereby characterizing the spatiotemporal variability of bloom aggregation areas. The relationships between the temporal coefficients and wind vectors were then examined. Subsequently, a machine learning model based on a bidirectional gated recurrent unit with an attention mechanism (Bi-GRU-Attention) was employed to estimate the temporal coefficients from wind vector sequences. The estimated coefficients were combined with the corresponding spatial modes to reconstruct bloom distributions. Using MODIS remote sensing imagery and ERA5-Land wind vector reanalysis data for Lake Taihu from 2009 to 2023, the first four spatial modes collectively explained 57.7% of the total variance. Their temporal coefficients (PCs) captured the spatiotemporal variability of cyanobacterial blooms. Among them, PC1 was significantly positively correlated with annual mean bloom coverage (r = 0.928). PC1 was also significantly negatively correlated with wind speed (r = −0.271), whereas PC2 and PC4 were significantly associated with southeasterly and northeasterly winds, respectively, indicating distinct bloom aggregation areas under different wind conditions. Considering the relationship between wind conditions and bloom spatiotemporal dynamics, the proposed framework achieved an average structural similarity index (SSIM) of 0.629, an intersection-over-union (IoU) of 0.821, and a percent bias (PBIAS) of −1.11% compared with the observed distributions. These results demonstrate that EOF analysis provides an effective means of characterizing the spatiotemporal variations in bloom aggregation areas. The relationships between EOF temporal coefficients and wind vectors further confirm the important role of wind in influencing bloom dynamics. This EOF–machine learning framework provides a new perspective for representing the large-scale spatial distribution of cyanobacterial blooms and contributes to enhancing lake early warning capabilities for sustainable water resource management. Full article

Sandy beaches in the Central Region of Veracruz (CRV) face constant anthropogenic pressure from port and urban activities. This study aimed to evaluate total hydrocarbon (TH) concentrations in the intertidal interstitial water of five beaches in the CRV, analyzing their variability by depth (15 and 30 cm) and seasonality (northerly winds, dry, and rainy seasons). TH determination was performed using gas chromatography (GC-FID), following the NMX-AA-117-SCFI-2001 and NOM-138-SEMARNAT/SSA1-2012 standards. Results showed concentrations ranging from 0.86 to 6.53 µg L⁻¹. Significant spatial differences were identified (p < 0.05); Antepuerto beach presented the highest levels due to its proximity to the port, while Farallón showed the lowest concentrations, confirming its role as a reference site. No significant variations were detected by depth or season (p > 0.05), indicating temporal stability associated with continuous anthropogenic inputs. Although levels comply with Mexican regulations, the continuous presence of TH represents a potential risk to benthic biota and the integrity of the Veracruz Reef System (SAV). This study provides a critical baseline for strengthening coastal ecosystem management strategies in the Gulf of Mexico. Full article

High-rise building fires pose substantial challenges to conventional firefighting operations due to restricted rescue space and the difficulty of delivering suppression resources rapidly. To improve response efficiency, this study proposes a two-stage mission planning framework for multi-station UAV-based firefighting. The proposed methodology simultaneously accounts for environmental wind, building obstacles, fire evolution, and UAV payload constraints. In the first stage, an improved particle swarm optimization (PSO) algorithm is employed to generate time-optimal flight paths satisfying both spatial obstacle-avoidance and wind-field constraints. In the second stage, based on the actual flight times derived from the first stage, the multi-UAV resource scheduling problem is formulated as a mixed-integer linear programming (MILP) model to minimize the total fire suppression mission duration. Additionally, an isochrone-based firefighting coverage circle is introduced to optimize the layout of additional fire stations. Simulation results indicate that while optimized paths remain geometrically similar under varying wind conditions, wind-induced flight time variations significantly affect UAV arrival sequences and flight times. In the scheduling stage, differences in station layouts and fire scales alter projectile release timing; under unfavorable conditions, such temporal differences can increase the total mission duration by more than 28%. Notably, the optimized addition of fire stations effectively enhances response redundancy in high-rise clusters, reducing fire suppression time in adjacent scenarios by approximately 50%. The proposed method provides theoretical support and methodological guidance for cooperative UAV firefighting and emergency resource optimization in urban environments. Full article

Understanding seasonal patterns of forest biogenic volatile organic compounds (BVOCs) is essential for clarifying their ecological functions and potential health benefits. In this study, BVOC samples were collected from ten representative forest stands in Caoyutang Forest Park across four seasons using adsorption tube sampling coupled with thermal desorption–gas chromatography/mass spectrometry (TD-GC/MS). Pronounced spatial and temporal variations in BVOC composition and relative abundance were observed among different forest stands. The number of detected BVOCs varied considerably among seasons, following the order autumn > winter > spring > summer, while relative BVOC abundance was highest in autumn and lowest in spring. Thujopsis dolabrata stands showed relatively high BVOC relative abundance in spring, whereas several mixed forest stands exhibited greater compound richness and relative abundance during autumn. Seasonal shifts in dominant BVOC groups differed among forest stands, with esters becoming prominent in Cunninghamia lanceolata–Hydrangea mixed stands during summer, while aromatic hydrocarbons and alkanes dominated many stands during winter. Across all samples, alkanes and esters represented major compound classes, whereas aromatic hydrocarbons, aldehydes, and esters contributed substantially to relative abundance. A total of 10 terpenoid compounds were identified, with camphene representing one of the major components, and a higher relative abundance of terpenoids was observed in Abies firma stands. Principal component analysis indicated distinct compositional patterns among Cunninghamia lanceolata-dominated mixed stands, highlighting the influence of associated tree species and stand structure on BVOC profiles. This study elucidates spatial and seasonal BVOC patterns of BVOC composition in relation to forest structure, providing a scientific basis for planning forest therapy bases and selecting therapeutic plant species in landscape design. Full article

Protected estuaries provide reference conditions for assessing coastal biodiversity, but information on swimming crab assemblages in such systems remains limited. This study evaluated the composition, dominance structure, diversity, body size, and spatio-seasonal variation in Callinectes swimming crab assemblages in the Una do Prelado River estuary, within the Barra do Una Sustainable Development Reserve, south-eastern Brazil. Seasonal sampling was conducted at four sites along the estuarine gradient using baited traps. Species composition, abundance, diversity indices and multivariate patterns were analyzed in relation to site and climatic season. A total of 146 individuals from four species were recorded: Callinectes exasperatus, C. danae, C. sapidus and C. bocourti. The assemblage was strongly dominated by C. exasperatus, which represented 81.5% of the total catch. Species richness was low, consistent with the restricted taxonomic scope and environmental filtering expected in estuaries. Abundance varied more clearly than species composition, with higher catches in autumn and at the intermediate estuarine site. Diversity indices indicated low evenness where C. exasperatus was dominant, whereas multivariate analyses detected no significant compositional differences among sites or seasons. As the first study conducted in this protected estuary, our findings provide a baseline for future monitoring of this system. Given the spatial and temporal scope of the study, these results should not be generalized to other protected estuarine systems. Full article

The Yellow River Basin (YRB) is a key ecological barrier and socio-economic region in China, but the spatiotemporal dynamics of its ecosystem carbon sink and the non-linear effects of environmental drivers remain insufficiently understood. This study estimated Net Ecosystem Productivity (NEP) in the YRB from 2001 to 2024 using MODIS Net Primary Productivity (NPP) data and an empirical soil heterotrophic respiration model, analyzed NEP trends with the Theil–Sen estimator and Mann–Kendall test, and quantified non-linear responses to climatic, temporal, and spatial factors using a Generalized Additive Model (GAM). The YRB acted as a persistent and strengthening net carbon sink, with annual total NEP increasing significantly from 39.85 Tg C yr⁻¹ in 2001 to 176.44 Tg C yr⁻¹ in 2024, at a rate of 5.65 Tg C yr⁻¹. NEP showed a clear southeast-to-northwest decreasing gradient, and 85.6% of the basin exhibited increasing trends, particularly on the Loess Plateau. The GAM captured non-linear associations of NEP with temperature, precipitation, solar radiation, relative humidity, year, and spatial location, and achieved a moderate pooled spatial block cross-validated R² of 0.723. NEP displayed a unimodal association with temperature—with a fitted peak near 0 °C reflecting the spatial transition from cold high-altitude to warmer water-limited regions—and a saturation-type response to precipitation, highlighting the joint control of hydrothermal conditions and pervasive water limitation. The fitted spatial smooth further revealed residual spatially structured variation that may be partly associated with irrigation and land management. These findings improve the understanding of carbon-sink dynamics in the YRB and provide scientific support for climate-adaptive ecosystem management and the regional implementation of China’s “dual carbon” goals. Full article

Following the 2021 Montiferru wildfire, one of the largest wildfire events in modern Italian history, assessing the suitability of olive-growing environments for high-quality extra virgin olive oil (EVOO) production is crucial for supporting sustainable agricultural restoration. This study models the spatial distribution and temporal stability of total polyphenol concentration in EVOO (cv. Bosana) across a complex Mediterranean landscape. Olive samples from georeferenced sites were collected during the 2022 (22 samples) and 2023 (37 samples) harvest seasons and processed using a standardized protocol. Spatial modeling was performed via Empirical Bayesian Kriging Regression Prediction (EBKRP), integrating seven bioclimatic and topographic covariates. Cross-validation demonstrated high predictive accuracy with negligible bias (RMSE = 53.3 and 58.8 mg kg⁻¹ for 2022 and 2023, respectively). While single-predictor correlations were weak, multi-variable analysis highlighted a strong interaction between topography and water balance driving phenolic accumulation. The mean prediction map identified regional hotspots approaching600 mg kg⁻¹ of polyphenols content in the obtained olive oils. Spatial overlay analysis successfully delineated “Stable High-Phenolic Core Areas” (>500 mg kg⁻¹ with interannual variation < 100 mg kg⁻¹), filtering out high-altitude marginal zones. This geostatistical approach provides a valuable territorial decision-making tool to support post-fire agricultural reconversion and the valorization of high-quality monovarietal EVOO terroirs. Full article