Search Results (1,830)

Microplastics (MPs) pose a significant threat to soil ecosystems based on their small size and resistance to biodegradation. Soil organic carbon (SOC) in saline–alkaline ecosystems has significantly affected maintain the ecological balance. This paper aims to review the mechanisms underlying the influence of MPs on SOC in saline–alkaline soils combining bibliometric mapping (VOSviewer). The results revealed that: (1) MPs mainly enter the saline–alkaline soil through water irrigation, sewage sludge, and agricultural films. (2) The interaction between the salt ions in saline–alkaline soils and the negatively charged surface of MPs will intensify the dispersion of soil aggregates, resulting in a significant decline in soil structure stability and nutrient imbalance. (3) MPs and the high-salt environment of saline–alkaline soils form a synergistic stress, significantly reducing the activities of key enzymes such as catalase and dehydrogenase in the soil, and it selectively promotes the enrichment of salt-tolerant bacterial communities (such as Halomonas and Bacillus species). (4) Using biodegradable plastic materials, setting up ecological buffer zones and planting halophytic plants (in coastal saline–alkaline areas), adding windbreak and sand-fixing buffer zones (in inland desert-type saline–alkaline areas), promoting precise irrigation and fertilization technologies (in areas with uneven irrigation conditions), and emergency soil amendment treatment (for severely polluted and ecologically fragile saline–alkaline soils) were all effective measures to dealing with the MPs pollution in saline–alkaline soils. This review provides a theoretical basis for the prevention and control of MPs pollution and the sustainable use of saline–alkaline soils. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

In the context of climate change, the hydrological processes and water resource system vulnerabilities in inland river basins of arid regions are intensifying. Understanding their evolutionary patterns and driving mechanisms is crucial for sustainable water resource management, agricultural development, and the protection of ecological security. This study focuses on the Kaidu River Basin, systematically analyzing the temporal and spatial variations in hydrological cycle elements in the basin from 1998 to 2023 based on multi-source precipitation data, the SWAT hydrological model, and the glacier degree-day model. The study also identifies the main driving factors using a geographic detector. The results show that the SWAT model performs well (calibration period R² and NSE ≥ 0.75, validation period R² and NSE of 0.75 and 0.70, respectively), indicating reliable simulation results. The surface water resources and the contribution of glacier meltwater to runoff in the basin both show a fluctuating downward trend, while potential evapotranspiration increases. The contribution of glacier meltwater during the ablation season decreased from 69.86% in 2014–2016 to 45.01% in 2017–2021. The hydrological processes exhibit a spatial pattern of “mountain areas generating runoff, non-mountain areas consuming water”. The geographic detector results indicate that precipitation is the decisive factor for the spatial differentiation of hydrological processes (influence degree q = 56.9%), with temperature, potential evapotranspiration, and altitude playing important synergistic roles. Moreover, the explanatory power of multi-factor interactions is much greater than that of individual factors. The findings of this study provide a scientific basis for the optimized allocation of watershed water resources, efficient agricultural irrigation, and the sustainable development of oasis ecosystems under changing environmental conditions, thereby supporting the goals of water security and sustainable development in inland river basins of arid regions. Full article

Inland waters are critical nodes in the global nitrogen cycle, where microbial processes govern transformations that impact water quality and ecosystem functioning. Inland waters are critical nodes in the global nitrogen cycle, where microbial processes govern transformations that impact water quality and ecosystem functioning. To systematically map the knowledge structure and to identify evolving trends in this field, a bibliometric analysis was conducted using CiteSpace on 2459 publications from the Web of Science Core Collection (1990–2024). The results reveal a significant increase in publications after 2010, peaking at 228 in 2024, with China (1541 articles) and the Chinese Academy of Sciences (776 articles) being the leading country and institution, respectively. Keyword co-occurrence and cluster analyses identify a core conceptual framework centered on microbial communities, nitrogen transformation processes (e.g., denitrification, anammox), and aquatic habitats (e.g., lakes, rivers). Based on keyword emergence and temporal trends, the analysis suggests an evolution in research focus across four dimensions: research subjects (from microbial biomass to keystone taxa), core questions (from process rates to predictive manipulation), methodological tools (from culturing to multi-omics), and mechanistic understanding (from linear pathways to complex networks). These observed patterns indicate a progressive refinement of the field. The findings provide a structured overview of the literature and may inform future research directions, but should be interpreted as bibliometric trends rather than definitive conclusions about the state of the science. Full article

Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer’s accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. Full article

In northwestern China, there is an abundance of saline-alkali water resources, but their high alkalinity severely restricts the development of inland saline-alkali water aquaculture. As an important aquaculture species, the whiteleg shrimp, Litopenaeus vannamei, shows an unclear physiological adaptation mechanism under high-alkaline stress. In this study, multi-omics and physiological methods were used to systematically reveal the effects of high-alkaline stress on the molt, antioxidation response, and immune defense in L. vannamei. The results showed that high-alkaline stress caused damage to the intestinal tissues of the shrimp and weakened the mucous barrier function, which was accompanied by a significant decrease in the activities of antioxidant enzymes (SOD, CAT, and GPx) and non-specific immune indicators (PO and LZM) (p < 0.05). The transcriptome results showed that the expression of genes related to chitin metabolism and calcium ion binding was upregulated, whereas that of genes related to muscle contraction and cell skeleton construction was downregulated. The structure of the intestinal microbiota changed significantly, with a decrease in microbiota diversity, whereas the abundance of potential pathogenic species (e.g., Photobacterium) increased. These results provide a theoretical basis for clarifying the molting response and antioxidant defense mechanism of L. vannamei in high-alkaline environments, with significance for saline-alkali water aquaculture practices. Full article

Remote-sensing inversion of water-quality parameters is a critical interdisciplinary field, integrating remote-sensing technology, environmental science, and water resources management, providing key technical support for precise water resources monitoring and ecological governance. To address the lack of comprehensive systematic reviews in this field, this study conducted a bibliometric-based narrative review, selecting 2812 valid English studies published during 1980–2026 from the Web of Science Core Collection (WOSCC) and performing in-depth knowledge mapping analysis via CiteSpace software. The results showed that global research in this field has gone through three stages: initial exploration (1980–2000), slow growth (2001–2015), and rapid explosion (2016–2026). China ranks first in publication volume worldwide, with a collaborative research pattern dominated by core institutions, including the Chinese Academy of Sciences, Wuhan University, and the National Aeronautics and Space Administration (NASA). The core research hotspots focus on multi-source data fusion, AI-driven inversion-model optimization, and the research shift from coastal to inland water bodies. Current research faces three key challenges: poor adaptability of multi-source data-fusion technologies to water-quality monitoring, inadequate integration of geospatial and thematic factors in inversion models, and an insufficient systematic approach of inland-water-body research. Accordingly, future research should focus on advancing remote-sensing data-fusion methods, further optimizing water-quality inversion models, and strengthening inland-water-body studies. This study clarifies the field’s development context and research characteristics, providing valuable references for subsequent academic exploration and practical applications in water resources management. Full article

Monitoring thermal effluent is critical for assessing aquatic ecosystem health, yet traditional satellite remote sensing and in situ point measurements often fail to capture fine-scale thermal dynamics in narrow streams and complex coastal areas due to spatiotemporal resolution limitations. This study establishes a high-precision surface water temperature mapping protocol using a low-cost Unmanned Aerial Vehicle (UAV) equipped with an uncooled thermal infrared sensor (FLIR Vue Pro R) to overcome these observational gaps. We investigated two distinct hydrological environments—an inland stream and a coastal sea—to provide initial evidence for the applicability of an in situ-based linear regression calibration model across contrasting aquatic settings. The initial uncalibrated radiometric temperatures exhibited significant bias errors reaching up to 9.2 °C in the stream and 9.4 °C in the coastal area, primarily driven by atmospheric attenuation and environmental factors. However, the proposed calibration method dramatically reduced these discrepancies, achieving Root Mean Square Errors (RMSE) of 0.43 °C and 0.42 °C, respectively, with high determination coefficients (R² > 0.87). The derived high-resolution thermal maps successfully visualized the detailed diffusion patterns of thermal plumes, revealing a steep temperature gradient of approximately 13 °C in the stream discharge zone and a distinct 5 °C elevation in the coastal effluent area relative to the ambient water. These findings demonstrate that UAV-based thermal remote sensing, when coupled with a rigorous radiometric calibration strategy, can serve as a cost-effective and reliable tool for environmental monitoring, bridging the critical scale gap between local point measurements and regional satellite observations. Full article

The study is based on Land Surface Temperature (LST) and Air Temperature data and Nonparametric Data Envelopment Analysis (DEA) technique to evaluate heat efficiency and detect anomalies in the thermal regime in the Eastern Black Sea Region, particularly in Hopa and Artvin, during the period 2000–2024. The regulating role of the Black Sea has resulted in Hopa having the warmest and most stable temperature patterns, with daytime temperatures 1.8 to 3.7 °C higher than Artvin. Previous DEA analysis of daytime temperatures has shown that the 2018–2020 period had the highest daily temperatures, while the 2001–2010 decade was characterized by the highest nighttime temperatures. A future heat map based on Monte Carlo simulation using six climate change scenarios indicates that in the most optimistic case, assuming a temperature increase of +0.8 °C, efficiency scores could increase as high as 0.995. On the other hand, if global warming leads to a sudden temperature increase above +7.2 °C, there is a 21.7% climate efficiency loss. Sensitivity analysis showed that technological innovation and good governance are the main positive factors affecting climate efficiency. Random Forest (RF) and SHapley Additive Explanations (SHAP) analyses were applied to determine the impact of climate factors on DEA scores and also indicated areas requiring risk assessment. The findings highlight the importance of considering location-specific climate adaptation strategies. Based on the observed thermal contrasts between coastal and inland environments, potential adaptation considerations may include urban heat management and agricultural water stress in coastal areas such as Hopa, and cold-climate resilience and energy-efficient infrastructure in inland locations such as Artvin. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

The decarbonization of the residential sector is a critical component of the European Green Deal, particularly in transition economies like Poland. This study proposes a comprehensive techno-economic optimization of a deeply retrofitted single-family house aiming for net-zero energy building (NZEB) status. The research specifically focuses on the Polish coastal climate zone, characterized by distinct humidity, wind, and temperature profiles compared to inland regions, which significantly influence the efficiency of air-to-water heat pumps (ASHP). Based on a real-world energy audit, the study simulates the synergy between a deep thermal envelope upgrade and a hybrid system comprising an ASHP, photovoltaics (PV), and battery energy storage (BES). This paper presents a detailed economic analysis of such hybrid systems under the new Polish ‘net-billing’ prosumer mechanism. The study evaluates the impact of electricity tariff structures (flat-rate G11 vs. time-of-use G12w) on the investment’s profitability. By calculating key performance indicators—including the levelized cost of energy (LCOE), net present value (NPV), and self-sufficiency ratio (SSR)—the research assesses various system configurations. The initial evaluation indicates that while deep retrofitting significantly reduces heating demand, integrating battery storage plays a critical role in enhancing economic returns under the net-billing framework. The analysis demonstrates that the optimized hybrid system (9.0 kWp PV + 10 kWh BESS) achieves an average annual self-sufficiency ratio (SSR) of 49.8% and reduces the non-renewable primary energy (EP) indicator to 0.0 kWh/(m²·year). Economically, the investment yields a positive NPV of €3194, an IRR of 5.25%, and a LCOE of €0.184/kWh, which is 34% lower than projected grid prices. Furthermore, switching to a time-of-use tariff (G12w) generates an additional 11% (€139) in annual savings. These quantitative findings provide actionable guidelines for policymakers and investors, confirming the financial viability and environmental benefit (annual reduction of 6.12 MgCO₂) of NZEB standards in coastal areas. Full article

The biological effects of weak low-frequency magnetic fields (LFMFs) remain controversial, particularly regarding frequency-specific resonance-like responses. Many previous studies tested different frequencies sequentially, potentially introducing uncontrolled environmental variability. This study aimed to evaluate frequency-dependent effects of LFMFs on the growth of juvenile Daphnia magna under strictly synchronized and temperature-controlled conditions. Genetically identical neonates from a single parthenogenetic brood were simultaneously exposed to sinusoidal 50 μT magnetic fields at 20, 25, 30, 35, or 40 Hz using spatially separated Helmholtz coils integrated into a closed-loop thermal stabilization system. Body length was measured after 48, 96, and 144 h of exposure. No significant growth differences were detected after 48 h. After 96 h, a significant biological effect was observed only at 30 Hz. The most pronounced responses occurred after 144 h, with significant growth stimulation at 25, 30, and 35 Hz and a maximal effect at 30 Hz. The frequency–response relationship exhibited a dome-shaped pattern that became less sharply peaked with prolonged exposure. These findings demonstrate duration-dependent and frequency-specific stimulation of juvenile daphnid growth with weak LFMFs. It suggests that exposure time critically influences the manifestation and breadth of resonance-like magnetobiological effects. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over inland waters, where coherent scattering dominates and traditional ocean models produce large systematic biases. Unlike open oceans, inland waters are dominated by coherent scattering due to limited fetch, resulting in Delay-Doppler Maps (DDM) with highly concentrated energy and minimal spreading. These characteristics render conventional ocean-based retrieval models—built on incoherent scattering assumptions—often inadequate. To overcome this, we develop a lightweight convolutional neural network (CNN) tailored to the coherent regime, using raw CYGNSS DDM as input for end-to-end wind speed regression. Cross-seasonal validation over Lake Victoria and Lake Hongze shows that the model robustly captures wind-driven spatiotemporal patterns aligned with ERA5. Notably, ERA5 reanalysis winds exhibit uncertainties over inland waters, with a root mean square error (RMSE) of 1.5–2.5 m/s against in situ buoys. The model yields a low RMSE (<0.7 m/s) in reconstructing ERA5-resolved wind patterns. This work extends GNSS-R to inland waters, offering a lightweight, deployable remote sensing solution for wind energy and lake–atmosphere research. Full article

Against the backdrop of intensified climate change and increasingly prominent imbalances in resource supply and demand, achieving multi-objective collaborative optimization of the Water-Energy-Food-Carbon (WEFC) nexus under uncertain conditions has become a pivotal task for regional sustainable development. Taking the Heihe River Basin, a typical arid inland river basin in northwest China with a complex WEFC nexus, as the research area, this study develops a multi-objective collaborative optimization model for the WEFC nexus, targeting three core goals: maximizing crop irrigation water productivity, minimizing carbon emissions, and enhancing low-carbon agricultural competitiveness. The model embeds constraints of regional water security, food security, land policy, and total water resource availability, introduces the uncertainty parameter τ to quantify fluctuations in available surface water, and adopts the ideal point method to convert the multi-objective problem into a single-objective optimization task by minimizing the Euclidean distance between feasible solutions and the ideal solution, with a case application in the oasis area of the basin’s middle reaches. Results show the model exhibits excellent stability across varying uncertainty levels: crop irrigation water productivity stabilizes around 1.5 kg/m³, low-carbon agricultural competitiveness at approximately 0.1003 kg/yuan, and spatial differences in resource allocation are evident. Linze gains the most water resources (16.47 × 10⁸ m³) due to geographical advantages, while Gaotai obtains the least (6.51 × 10⁸ m³). In terms of planting structure, vegetables dominate the sown area owing to low carbon emissions and high water use efficiency, while wheat planting is relatively limited by climate adaptability and market demand. Carbon sink analysis confirms vegetables as the primary carbon sequestration contributor in Ganzhou and Linze, offering a practical pathway for agricultural carbon reduction. These findings provide tailored theoretical and practical support for balancing food security, efficient resource utilization, low-carbon development, and ecological protection in arid and semi-arid regions, facilitating regional carbon neutrality and sustainable agricultural development. Full article

Inland water bodies are vital to the Earth’s ecosystem, global water cycles, and climate regulation. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for water detection, particularly with the deployment of the Fengyun-3 (FY-3) E, F, and G satellites. This study proposes an inland water body detection method by integrating the Z-score algorithm with specular point land surface reflectivity (SR_sp) derived from FY-3 Level-1 GNSS-R data. Using 2024 observations, the method was validated in the Amazon and Congo basins against optical water body products. The results demonstrate high detection performance, achieving overall accuracies of 95.39% and 97.38% in the two regions, respectively. Analysis of reflectivity expressed in decibels (dB) reveals that while dB-units enhance the detection of small tributaries, they are more susceptible to noise-induced misclassification compared to linear units. Furthermore, a comparative assessment of GNSS constellations shows that multi-system combination significantly reduces noise compared to single-system approaches. Notably, the Galileo system exhibited limited sensitivity to small tributaries due to lower observational density. Sensitivity analyses further reveal that interpolation methods and Z-score threshold selection are important factors influencing detection accuracy. As the first systematic evaluation of FY-3 GNSS-R data for inland water detection, this research provides a critical benchmark for future multi-platform and multi-constellation land surface retrieval studies. Full article