Search Results (16,664)

This paper is based on data from 121 cities in China’s ecologically fragile regions from 2008 to 2022; it constructs an indicator system for the efficiency of pollution control and carbon reduction in agricultural practices. This system includes expenditures on agriculture, forestry, and water affairs, arable land area, agricultural laborers, total agricultural output value, agricultural carbon emissions, and agricultural non-point source pollution. It uses a super-efficiency SBM model that incorporates non-desirable outputs to measure the synergistic efficiency and analyzes its dynamic evolution using the Malmquist–Luenberger index to reveal the spatiotemporal characteristics of the synergistic efficiency. A Tobit model identifies the influence of factors, such as the level of rural economic development, crop planting structure, the strength of fiscal support for agriculture, rural education level, urbanization rate, and mechanization level on the synergistic efficiency. The results show that, from a temporal perspective, the average synergistic efficiency was only 0.58, significantly below the effective value of 1, indicating substantial room for overall improvement. Only 10 cities met the benchmark, with distinctly different reasons for compliance, while the remaining 111 cities remained inefficient. Regarding influencing factors, crop planting structure, the strength of fiscal support for agriculture, and urbanization rate significantly and positively drive efficiency; the level of rural economic development and mechanization level significantly inhibit efficiency, and rural education level shows no significant impact. These findings provide targeted policy recommendations for the synergy effect in ecologically fragile areas, as well as for low-carbon agricultural development. Full article

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

Slope aspect is the primary topographic factor controlling the surface thermal state in mountainous cold regions. By modulating the magnitude and timing of solar radiation on slopes, it systematically affects soil temperature, maximum frost depth, and freeze–thaw timing, and it drives differentiation of the coupled hydrothermal process between sunny and shady slopes. However, the quantitative patterns of slope aspect freeze–thaw dynamics in high-latitude seasonally frozen soils and their response mechanisms to climate warming have not been systematically revealed. Therefore, based on field monitoring, this study used the SHAW model to simulate the soil freeze–thaw process and designed multiple warming scenarios to evaluate the evolving trend of the aspect effect. The results showed that: (1) the SHAW model effectively simulated soil temperature dynamics (R² = 0.939, NSE = 0.913, RMSE = 1.71 °C); (2) the profile-mean soil temperature on sunny slopes was 3.10 °C higher than on shady slopes, with a maximum frost depth approximately 61.2 cm shallower, freezing onset about 18 days later, complete thawing 59–77 days earlier, and freezing and thawing rates approximately 28% and 50% higher, respectively; and (3) under the SSP2-4.5 scenario, various freeze–thaw differentiation metrics did not exhibit a systematic convergence trend, and the aspect effect remained robust against climate warming. These findings offer a quantitative basis for ecological and hydrological assessment, water-resource scheduling, and foundation-stability design in cold regions, thereby supporting ecosystem conservation, sustainable water-resource use, and climate-resilient infrastructure development, and informing sustainable development planning and policy-making in high-latitude regions under a warming climate. Full article

Groundwater is a key strategic resource underpinning water security, and its effective management requires reliable, high-frequency monitoring data. In mountainous regions such as the flysch Carpathians in southern Poland, natural springs are particularly sensitive indicators of aquifer system dynamics. This study analyzes the role of springs in the national groundwater observation and research network and identifies barriers to the implementation of automated monitoring of spring discharge. The research covered 28 springs operating within the regional monitoring network of the Polish Geological Institute—National Research Institute in the Carpathian region. Classical hydrogeological spring classifications were applied and complemented with proprietary criteria addressing formal-legal, technical, and environmental conditions affecting the feasibility of automation. The results show that all of the analysed springs exhibited a Meinzer’s variability index (V) exceeding 100%, and numerous objects showed a coefficient of variation (CV) above 150%, providing quantitative evidence that standard weekly manual measurements statistically fail to capture rapid flow dynamics and peak discharge events. To bridge the gap between hydrodynamic observations and monitoring logistics, this study introduces a novel methodological contribution: the F-T-S-N screening framework. This proprietary, multi-criteria classification quantifies Formal-legal, Technical, Structural, and Nature-environmental barriers to telemetry implementation. The application of this framework demonstrates that the main obstacles to modernization are non-technological. The proposed classification serves as a practical, transferable tool that supports the rational planning of monitoring network automation in other mountainous regions with similar hydrogeological conditions. Full article

Offshore wind energy is an important source of clean energy. Single-post platforms, due to their simple structure and strong stability, can adapt to deep water environments through buoyancy and ballast systems, have small motion responses, and have low construction and maintenance costs. They are suitable for offshore wind energy development in deep-sea areas and help expand the application of offshore wind power. This paper conducts a coupled response analysis of offshore wind turbine foundations and mooring systems, as well as an optimization study on the form and number of mooring lines. Under the premise of considering the safety and economy of floating wind turbines, the mooring lines have been optimally arranged. The study calculates frequency-domain responses, time-domain responses, and mooring line forces under the constraints of the original three-line mooring system. Based on this benchmark, the study further optimizes the mooring forms and numbers for the same platform, analyzing four, six, and eight single mooring lines, as well as three groups of single-line, double-line, and triple-line mooring configurations. Finally, using AQWA software (2024 R1), the responses and mooring line forces of different mooring configurations were calculated, and the preferred mooring arrangement for this stepped single-post platform was determined to be a three-group, three-line system (a total of nine mooring lines). The mooring line tension decreased substantially from the original 3.2 × 10⁶ N to 1.8 × 10⁶ N, while the dynamic response was reduced to one-sixth of its original level. Meanwhile, this study provides strong support for the utilization of offshore wind energy and the construction of offshore wind turbine platforms and mooring systems. Full article

The Lower Permian M Formation marine carbonate gas reservoir in Block X of the Sichuan Chongqing exploration area has extreme working conditions with moderate H₂S content (0.57–0.97%), moderate CO₂ content (2.59–5.59%), and high formation pressure (70–80 MPa). Gas wells face challenges such as multi medium synergistic corrosion, large productivity differences, and limited economic viability. This article addresses the above issues for the first time by analyzing the dual corrosion mechanism, selecting corrosion-resistant pipes (nickel-based alloys/nickel–tungsten alloy coatings), evaluating the adaptability of corrosion inhibitor processes, and real-time monitoring and warning of corrosion risks. A collaborative anti-corrosion technology system of “mechanism material process monitoring” is constructed, and the first successful field implementation was carried out in this block. The experiment shows that the uniform corrosion rate of nickel–tungsten alloy coating under extreme working conditions (122 °C/85 MPa) is only 0.004 mm/a, which is more economical than traditional nickel-based alloys (cost reduction of 69%); CT2 series corrosion inhibitors can selectively inhibit the corrosion rate of gas wells with different water contents (efficiency > 82%). The combination of electromagnetic flaw detection and multi arm wellbore logging technology has achieved dynamic monitoring of downhole pipe corrosion. This system has been successfully applied in seven gas wells in Block X, achieving controllable corrosion risks, cost reduction and efficiency improvement, and providing a replicable technical paradigm for the safe and economic development of marine high-sulfur gas reservoirs. Full article

The Aedes aegypti mosquito exhibits a critical behavioral adaptation through its oviposition strategy, laying eggs in dry and wet environments just above the water level, allowing eggs to resist desiccation and hatch only when submerged by rain. To investigate this mechanism, we developed a nonlinear dynamic model incorporating climate-driven parameters affecting egg hatching and adult emergence. Theoretical analysis revealed an imperfect pitchfork bifurcation giving rise to a phenomenon we term basin-mediated hysteresis. Unlike classical hysteresis, which relies on coexisting stable states, this mechanism results from the progressive collapse of the extinction basin boundary. As the control parameter approaches its critical value, the basin of attraction of the trivial equilibrium shrinks. Once the population establishes itself above the threshold, returning the parameter below unity does not restore extinction, leading to an irreversible transition governing population persistence. The model was validated using field data from mosquito traps in a Brazilian city, showing strong agreement with observed seasonal patterns of female captures. Parameters were optimized using the Differential Evolution algorithm, yielding high correlation between model and field data. The results demonstrate that the dual oviposition strategy underlies population persistence and seasonal peaks, providing information for planning interventions amid global arbovirus expansion. Full article

Coastal ecosystems, particularly semi-enclosed gulfs, are increasing anthropogenic pressures from urbanization and industrialization with profound impacts on biodiversity maintenance, energy transfer, and biogeochemical cycling. However, how metazoan communities—key components of marine food webs—respond to spatiotemporal variability and human disturbance remains insufficiently understood. This study applied eDNA metabarcoding targeting the mitochondrial COI gene to investigate metazoan communities across 68 stations in the Beibu Gulf, spanning bay, coastal, and island regions, during wet and dry seasons. In total, 878 metazoan ASVs from 13 phyla were detected. Arthropoda dominated both seasons (wet: 85%; dry: 55%), whereas Chordata increased during the dry season (wet: 0.16%; dry: 37%). At the α-diversity level, diversity peaked in the bay region during the dry season and shifted toward the coastal region during the wet season. At the β-diversity level, community composition differed significantly between seasons and spatial regions, with seasonal variation exerting a stronger influence than spatial differentiation. Co-occurrence networks revealed higher complexity during the dry season. β-diversity was overwhelmingly driven by species turnover (94.4%). The island region exhibited the highest community uniqueness, while the human-impacted bay region showed reduced distinctiveness. Redundancy analysis further identified anthropogenically influenced inorganic nitrogen, together with water temperature, transparency, and salinity, as key environmental drivers shaping community structure. βNTI analysis indicated that community assembly was governed by the combined effects of deterministic and stochastic processes. Overall, this study highlights how environmental gradients and human pressures jointly regulate metazoan dynamics, providing insights for biodiversity conservation in human-impacted coastal seas. Full article

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas. Full article

Silicon is increasingly applied in agriculture to improve plant productivity under both abiotic and biotic stress constraints. Nevertheless, its mechanisms of action are often studied separately at the soil, plant, or microbiome levels, limiting a comprehensive understanding of its overall impact on agroecosystem functioning. This review proposes an integrated perspective of the soil–plant–microbiome continuum, linking silicon chemistry in soil solutions with the effects of silicon amendments on soil properties and the processes of uptake, transport, and deposition in the plants. We show that silicon bioavailability depends on maintaining a pool of dissolved silicon dominated by orthosilicic acid, regulated by mineral weathering, adsorption–desorption dynamics, polymerization, pH, iron and aluminum oxides, and organic matter. In soils, silicon inputs can improve structure, modulate acidity and cation exchange balances, influence nutrient availability, and reduce the mobility of certain metals. They may also affect enzymatic activities and microbial community composition. In plants, silicon uptake and transport, mediated by specific transporters, contribute to tissue silicification, the maintenance of leaf architecture, and the regulation of water, ionic, and redox homeostasis. These processes provide a basis for enhanced tolerance to drought, salinity, and metal toxicity, as well as biotic stress caused by pathogens and pests. Finally, we discuss key limitations to the agronomic application of silicon, including the diagnosis of the silicic status of soils, the choice of source and mode of application, and the genotypic variability of acquisition, as well as the need for multi-site tests and more robust mechanistic validations. This synthesis provides a coherent mechanistic framework to better define the conditions under which silicon can serve as a reliable tool for sustainable crop management under climate change. Full article

Coastal port ecosystems serve as critical interfaces between marine environments and anthropogenic activities, yet zooplankton community dynamics in these transitional zones remain poorly understood. This study investigated seasonal variations in zooplankton assemblages and their environmental drivers in the coastal waters surrounding Lanshan Port, northern Yellow Sea, through quarterly field surveys spanning spring to winter. A total of 33 zooplankton species and 16 planktonic larval categories were identified, with Hydromedusa, Copepoda, and planktonic larvae comprising the three dominant groups. Marked seasonal disparities were observed in species richness (spring: 21 species and 11 larvae categories; winter: 8 species and 3 larvae categories), biomass (autumn: 333.7 mg/m³; winter: 34.0 mg/m³), and abundance (spring: 185.3 ind/m³; winter: 25.7 ind/m³). Notably, Aidanosagitta crassa maintained perennial dominance across all seasons. Principal component analysis of dominant zooplankton taxa across seasons indicated that the first two principal components explained 70.05% and 15.97% of the total variance in zooplankton community structure, respectively, with distinct seasonal clustering of sampling sites along PC1 reflecting pronounced seasonal succession in community composition. Redundancy analysis revealed seasonal-specific correlations between dominant taxa and nutrients: nitrate concentration was negatively correlated with the relative abundance of Sergestidae in both spring and summer, whereas ammonium concentration was negatively correlated with Hydromedusa; by contrast, the abundances of Chaetognatha and Tunicata exhibited a significant positive correlation with nitrate. We also found water temperature only drove communities in autumn, while salinity had little effect. These findings elucidate the mechanisms structuring zooplankton communities in temperate coastal port ecosystems and underscore the necessity of seasonally resolved monitoring frameworks for effective marine environmental management. Full article

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article

Understanding how land management influences soil carbon (C) and nitrogen (N) dynamics is critical for improving ecosystem resilience and carbon sequestration potential in semiarid rangelands. This study used classical field- and laboratory-based methods to assess soil organic carbon (SOC), organic matter (OM), and N content at 13 sites across four ecological provinces in eastern Oregon, USA. Treated sites—where traditional rangeland restoration and management practices had been applied to them (i.e., juniper removal, sagebrush removal, post-fire grass seeding, and land conversion to pasture)—were paired with adjacent untreated control sites. Soil samples were collected at two depths, 0 to 10 cm and 15 to 25 cm and analyzed for C, N, OM, bulk density (BD), soil volumetric water content (SVWC), porosity, and texture. Soil C and N stocks were calculated on an area basis (t ha⁻¹), and statistical analyses were conducted using one-way ANOVA and correlation tests. Treated sites generally exhibited higher soil C, N, and OM content compared to untreated sites, particularly in the upper 10 cm of soil. Data obtained from the two soil depths (0 to 10 cm and 15 to 25 cm) were averaged and assumed to represent the top 30 cm of the soil profile, corresponding to the effective rooting zone at each field. The site where sagebrush removal was followed by grass seeding exhibited the highest soil C and N stocks (115.8 t C ha⁻¹ and 9.2 t N ha⁻¹, respectively). This site also had the highest OM content (9.53%), which was observed in the topsoil layer (0 to 10 cm) across all sites and depths. Strong positive correlations between C and N were detected across all sites (mean r = 0.92), while negative correlations were observed between soil C and bulk density at several locations. Results suggest that vegetation management practices such as woody plant removal and grass establishment can enhance soil C storage and nutrient retention in semiarid rangeland ecosystems. These findings provide baseline data to inform land management strategies aimed at improving soil health and carbon sequestration potential in the Pacific Northwest region in the USA. Full article

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

Jasmonates (JAs) are a diverse group of jasmonic acid (JA)-linked metabolites, including the biosynthetic intermediate 12-oxophytodienoic acid (OPDA). Although changes in JAs have been associated with plant responses to abiotic stress, the involvement and kinetics of specific forms such as JA, JA-Ile and OPDA require further clarification. This study analyzed jasmonate profiles in roots and leaves of two oat genotypes differing in drought tolerance. Jasmonates were quantified using UPLC-MS/MS, expression of key biosynthetic genes was assessed by qRT-PCR, and JA/OPDA treatments were applied to evaluate their effects on physiological and morphological responses to drought. Drought induced contrasting jasmonate dynamics in roots and leaves, with overall JA levels increasing in leaves and decreasing in roots, with genotype- and compound-specific differences. JA and JA-Ile ((+)-7-iso-jasmonoyl-L-isoleucine) showed similar trends, whereas OPDA displayed a distinct pattern. The tolerant genotype exhibited an early and marked reduction in root OPDA, while the susceptible one showed minimal change. Exogenous OPDA increased drought symptoms, reduced leaf relative water content and strongly decreased root length by limiting the formation of new thin roots. In contrast, JA application alleviated drought symptoms, reflected in a lower area under the drought progress curve, without affecting root length. Results suggest that under water deficit, reduced OPDA, likely due to its conversion into JA and JA-Ile, is associated with the development of small-diameter roots essential for maintaining water status in oat. Together, these results highlight tissue-specific differences in jasmonate dynamics during drought and show that OPDA and JA treatments lead to distinct drought-related responses in both leaves and roots. Full article