Search Results (3,661)

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

Accurate landslide susceptibility assessment remains challenging in mountainous regions with complex terrain, heterogeneous geology, and clustered landslide inventories. This study develops a slope–unit–based landslide susceptibility assessment framework for Tongren County, Qinghai Province, China, using a landslide inventory of 217 events, multi–source environmental data, Certainty Factor (CF)–based conditioning–factor analysis, and machine learning models. Eighteen conditioning factors derived from remote sensing, geological survey, and meteorological datasets were extracted at the slope–unit scale, and their collinearity was evaluated using Pearson’s correlation and the Variance Inflation Factor (VIF). Eight models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Decision Tree (DT), XGBoost, K–Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—were evaluated under a 70:30 train/test split. The results show clear performance differences among the tested models: SVM achieved the best overall balance between discrimination and landslide detection (AUC = 0.9489; recall = 0.879). The tested CNN baseline showed relatively weak performance under the current slope–unit–based tabular–data setting. Susceptibility zoning results showed that high– and very–high–susceptibility zones were mainly concentrated along the Longwu River and its tributaries, where middle–elevation dissected terrain, weak lithological materials, river–valley erosion, and human engineering activities spatially coincide. These results provide a practical basis for slope monitoring and land–use planning in Tongren County. Full article

As a strategic node of the Silk Road Economic Belt and a prototypical valley-type city, Lanzhou is subject to the dual constraints of rapid urbanization and an inherently fragile ecological foundation, making the coordination between urban expansion and ecosystem services a critical issue for regional sustainability. Drawing upon multi-temporal land use remote sensing datasets provided by the Chinese Academy of Sciences Resource and Environment Science Data Center, in conjunction with soil, meteorological, and socio-economic data, this study integrates a land use transition matrix, the InVEST model, a modified coupling coordination degree model, and the geographic detector to comprehensively examine land use dynamics, the spatiotemporal evolution of urban expansion, and the spatial heterogeneity of ecosystem services (i.e., carbon storage, water yield, habitat quality, and soil conservation) in Lanzhou. In addition, the coupling coordination relationship and its underlying driving mechanisms are systematically explored. The results demonstrate the following: (1) Between 1980 and 2020, urban land area in Lanzhou increased from 103.87 km² to 286.83 km², accounting for 2.17% of the total area, with cropland constituting the dominant source of expansion and exhibiting a fluctuating “high–low–high” conversion trajectory. (2) Ecosystem services exhibit pronounced spatial heterogeneity, with carbon storage and habitat quality displaying a pattern of “low in the southeast and high in the northwest”, water yield showing an increasing gradient from southeast to northwest, and soil conservation characterized by “lower values in central areas and higher values in peripheral regions”; (3) Urban expansion has accelerated significantly, with Yongdeng County and Gaolan County emerging as principal expansion hotspots during 2010–2020. (4) The dominant driving mechanism gradually shifted from natural factors to the synergistic interaction between natural and socioeconomic factors, and the interaction among driving factors markedly enhanced the explanatory power for ecosystem service evolution. (5) The coupling coordination degree has transitioned from widespread imbalance to a spatially differentiated pattern, characterized by relatively coordinated conditions in peripheral areas and persistent imbalance within the central urban core. These findings provide a robust scientific basis for territorial spatial optimization and the synergistic development of ecological and economic systems in valley-type cities, and offer important implications for sustainable development in arid and semi-arid regions. Full article

To address the challenges of strong volatility, pronounced non-stationarity, and the inability of single models to simultaneously capture local dynamics and global dependencies in photovoltaic (PV) power series under complex weather conditions, this study proposes a short-term PV power forecasting framework that integrates weather-based clustering, signal decomposition, parameter optimization, and hybrid neural networks. First, a density-based clustering algorithm, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is employed to partition historical samples into distinct weather regimes, thereby mitigating the impact of heterogeneous meteorological conditions on model stability. Second, to handle the strong non-stationarity of PV power series, Variational Mode Decomposition (VMD) is introduced to decompose the original signal into multiple intrinsic components. The Crested Porcupine Optimizer (CPO) is further utilized to adaptively optimize key VMD parameters, including the number of modes and the penalty factor, thereby improving decomposition quality. Finally, a hybrid LSTM–Transformer forecasting model is constructed to jointly capture local temporal dynamics and long-range dependencies. The Newton–Raphson-Based Optimizer (NRBO) is employed to optimize critical hyperparameters, including the learning rate, regularization coefficient, and the number of hidden units, thereby enhancing model performance. The proposed method is validated using real-world data from a PV power station in Alice Springs, Australia. Experimental results demonstrate that, compared with the LSTM–Transformer baseline, the proposed model achieves reductions in RMSE of 0.086, 0.082, and 0.097 kW, and reductions in MAE of 0.062, 0.082, and 0.081 kW under clear-sky, cloudy, and rainy/snowy conditions, respectively. The corresponding R² values reach 0.993, 0.968, and 0.958. These results indicate that the proposed framework exhibits strong predictive performance across different weather scenarios and provides a reliable reference for short-term PV power forecasting and grid dispatching decisions. Full article

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

High-latitude, cold-climate cities have long faced inherent climatic disadvantages due to prolonged frigid winters, historically limiting their attractiveness for population inflow. Global warming, particularly the polar amplification effect, offers potential for improving climate livability in such cities, creating new opportunities for population redistribution and urban development. Taking Harbin, a representative cold-climate city in Northeast China, as a case study, this research integrates meteorological data (2010–2023) with questionnaire responses from 1053 recent in-migrants. Using Analytic Hierarchy Process and Structural Equation Modeling, we systematically examine how climate livability is associated with migration decisions. The key findings are as follows. (1) Harbin’s climate livability improved significantly from 2010 to 2023. (2) In-migrants display a spatial pattern of “proximate dominance and distant diversity”, with pronounced heterogeneity in climate perception and satisfaction across origin regions. (3) Climate livability is associated with settlement intention through a partial mediation pathway: the indirect effect via deep adaptation (β = 0.330) accounts for 37.2% of the total effect, while a significant direct effect (β = 0.559) also exists, correcting an earlier inflated full-mediation estimate. (4) This perception–adaptation–decision pathway remains stable across subgroups and is not significantly moderated by origin climate contrast or occupational exposure, although boundary conditions apply. (5) Migration decisions involve trade-offs between climatic experience and socioeconomic rationality: climate livability reinforces long-term settlement intentions through psychological adaptation, while short-term migration is chiefly driven by socioeconomic factors. These findings provide empirical evidence and policy insights for cold-climate cities, such as Harbin, aiming to establish inclusive adaptation support systems and implement phased, context-sensitive population strategies. Full article

Drought exhibits a complex coupling response to regional meteorological factors, hydrological characteristics, land cover, and large-scale teleconnection climate indices, while their direct and indirect influences on multi-scale meteorological and hydrological droughts remain insufficiently understood, particularly in karst basins. This study investigated drought dynamics in China’s Yujiang River Basin using an integrated framework combining run theory, drought propagation analysis, and the partial least squares–structural equation model (PLS-SEM). We analyzed the 1-, 3-, 6-, and 12-month standardized precipitation index (SPI) and standardized streamflow index (SSI) at four hydrological stations during 1984–2014, together with meteorological factors, land cover indices, large-scale climate indices, areal precipitation, and naturalized streamflow. The results show that precipitation and streamflow exhibited slight declining tendencies with marked seasonal variability, and that drought durations of all severity levels generally decreased with increasing time scales. At the same time scale, SSI was more stable than SPI, and both indices tended to become more stable as the time scale increased. SPI-3 and SSI-1 were identified as the optimal time scales for monitoring meteorological and hydrological drought, respectively, providing a practical basis for drought identification and early warning in karst basins. Hydrological drought lagged meteorological drought by 1–3 months, indicating a measurable propagation time that is valuable for improving drought preparedness and water resources regulation. PLS-SEM further revealed that precipitation and streamflow were the dominant direct drivers of drought development, while land cover exerted a persistent negative effect, and climate-related factors mainly influenced drought indirectly. These findings enhance the understanding of drought propagation and multi-factor coupling mechanisms in karst basins and provide scientific support for regional drought monitoring and water resources management. Full article

During wet deposition, particulate matter and gaseous species in the atmosphere are ultimately transported to the Earth’s surface via precipitation and subsequently incorporated into terrestrial ecosystems. Therefore, investigating the fluxes, chemical compositions, and source apportionment of regional wet deposition is of great scientific importance. An analysis of the concentrations, deposition fluxes, spatiotemporal variations, and source apportionment of water-soluble ions in wet deposition can further enhance our understanding of the water-soluble ion characteristics, atmospheric pollution profiles, and potential ecosystem impacts of wet deposition in the Yangtze River Delta and Pearl River Delta regions. Coastal cities in China are most developed regions, and also areas suffering from severe air pollution. This study investigates the chemical characteristics, sources and wet deposition fluxes of water-soluble inorganic ions in precipitation in two typical coastal urban agglomerations of China: Ningbo in the Yangtze River Delta and Guangzhou in the Pearl River Delta. Precipitation samples were collected and analyzed to determine the concentrations of major ions. The results revealed distinct ionic compositions between the two regions. In Ningbo, NO₃⁻ and SO₄²⁻ were the predominant ions accounting for 16.98% to 23.22% of the total, reflecting the influence of anthropogenic emissions from fossil fuel combustion and mobile sources with the NO₃⁻/SO₄²⁻ ratio of 0.90 and 0.70. In Guangzhou, precipitation was characterized by high contributions of SO₄²⁻, NO₃⁻, NH₄⁺, and Ca²⁺, accounting for 17.22% to 23.29% of the total, indicating a mixed influence of industrial emissions, agricultural activities, and construction dust with the NO₃⁻/SO₄²⁻ ratio of 0.92 and 0.87. A clear inverse relationship between rainfall amount and ion concentration was observed at all sites (p < 0.05), demonstrating a significant dilution effect. Seasonality played a crucial role in deposition fluxes. In Ningbo, fluxes peaked during summer from 4667 to 5156 mg·m⁻², while in Guangzhou, distinct dry and rainy season patterns influenced the scavenging efficiency of different ion species. Urban sites exhibited enhanced scavenging of crustal and anthropogenic ions (e.g., Ca²⁺, NH₄⁺) during the rainy season, whereas the coastal site showed elevated fluxes of marine-derived ions (Na⁺, Cl⁻, Mg²⁺, SO₄²⁻) during the same period. The observed trends in ion fluxes suggest a gradual improvement in regional air quality over the study period. These findings elucidate the complex interactions between anthropogenic activities, natural sources, and meteorological factors in shaping the wet deposition chemistry in coastal urban environments, providing essential data for developing regional deposition models and assessing the ecological impacts of atmospheric pollution. Full article

This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for short-, medium-, and long-term forecasting of solar energy production. Approximately four years of hourly data from four photovoltaic power plants in Karabük were used. In addition to production data, meteorological and environmental variables were included through a multivariate forecasting approach. The model was tested under three scenarios at different time scales. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R²) metrics. Results showed high prediction accuracy, particularly with seasonal data, where R² values exceeded 0.90 in most cases. In forecasts based on monthly data, performance was more variable, and the shorter data window limited the model’s learning capacity. Long-term analyses indicated that the model successfully captured overall production trends and achieved high accuracy across all Photovoltaic (PV) systems. The findings also revealed that incorporating meteorological and environmental variables significantly improved prediction performance. In particular, air pollution parameters were effective in long-term production forecasting. Overall, the study demonstrates that Long Short-Term Memory (LSTM)-based models are reliable and effective tools for solar energy forecasting, with strong potential for energy planning and smart grid applications. Full article

Overhead distribution systems may experience concurrent wind and rainfall loading during typhoon events, but most existing studies still emphasize individual components, single-hazard descriptions, or network-level consequences. To address this gap, this paper develops a probabilistic assessment framework for distribution pole–line segments exposed to compound typhoon wind–rain hazards. A three-dimensional finite-element model of a representative segment with three poles, two spans, and three-phase conductors is constructed, and uncertainties in structural properties and loading-related coefficients are incorporated explicitly. Correlated turbulent wind histories are synthesized using the Davenport spectrum and harmonic superposition method, whereas rainfall actions are represented through an impact-based raindrop spectrum formulation. Nonlinear dynamic analyses are performed for multiple combinations of basic wind speed and rainfall intensity, and the resulting peak conductor tension and pole-base bending moment are used as engineering demand parameters. Logarithmic probabilistic demand models are then fitted to derive failure-probability surfaces for the conductor, the pole, and the pole–line segment. Segment failure is defined through the maximum normalized demand among the central pole and the six connected conductors, thereby extending the assessment from component-level failure to local segment-level risk. The results show that basic wind speed governs the overall evolution of failure probability, whereas rainfall acts as a secondary but non-negligible amplifying factor that shifts the probability transition zone toward lower wind-speed levels. For the adopted configuration, the segment-level failure probability is governed mainly by pole response. Additional model checks and event-based comparisons support the consistency of the proposed segment-level probability formulation. The proposed methodology can support risk screening, warning-threshold setting, and maintenance decision making for overhead distribution systems subjected to compound meteorological hazards. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Synergistic governance of air pollution and carbon is crucial for green transition against the backdrop of global climate change. This study explores the spatial synergistic characteristics and driving mechanisms between air pollutants and CO₂ across China in 2021 from both emission and concentration perspectives, filling the gap of single-perspective analysis. We used the Weather Research and Forecasting coupled with the Vegetation Photosynthesis and Respiration Model (WRF-VPRM) to simulate CO₂ concentrations, integrating the China High Air Pollutants (CHAPs) air pollution data, anthropogenic emission inventories, the coupling and coordination degree (CCD) model, and Geodetector analysis. Results show significant regional and seasonal differences in carbon–pollutant coordination. High-emission and high-coordination zones are concentrated in North China, southern Northeast China, and eastern coastal areas, with CO, NO₂, and O₃ exhibiting stronger coordination with CO₂ than PM₁₀, PM_2.5 and SO₂. Emission synergy is mainly driven by population and GDP with strong GDP-related two-factor enhancement, while concentration synergy is mainly driven by air temperature and temperature–NDVI coupling. These findings highlight the joint effects of socioeconomic, meteorological, and ecological factors, supporting targeted pollution reduction and carbon mitigation strategies and providing a scientific basis for China’s dual carbon strategy and sustainable development. Full article

Low-altitude UAV scheduling and planning has become a critical technological pillar in disaster response systems; however, systemic challenges in complex environments and under uncertain risk conditions remain insufficiently understood. Although substantial progress has been achieved in model formulation and algorithm design in recent years, scheduling and planning frameworks still lack a systematic representation of key risk factors, such as meteorological disturbances, terrain damage, and communication constraints, thereby undermining operational safety and decision reliability. This study conducts a systematic review of low-altitude UAV scheduling and planning research over the past decade, covering representative disaster scenarios including forest fires, large building fires, earthquakes, floods, major public health emergencies, and traffic accidents. By comparatively analyzing scheduling objectives and technical pathways across the pre-disaster, during-disaster, and post-disaster stages, this paper summarizes the dominant research paradigms and limitations of multi-UAV coordination, air–ground coordination, and risk reduction-oriented scheduling and planning. This review reveals that existing approaches generally lack explicit modeling of dynamic risks and uncertainties, highlighting an urgent need to incorporate risk-aware considerations and reliability analysis frameworks into scheduling and planning to enhance the overall robustness and decision credibility of UAV systems in disaster environments. Full article

Life expectancy in the United States varies significantly by region, a gap often explained by socioeconomic factors like income and education. However, the relative contribution of atmospheric exposures is less understood. We identify formaldehyde exposure and wet-bulb temperature as leading predictors of county-level life expectancy. Our analysis of 22,540 county-year observations (2012–2019) shows that formaldehyde ranked as the second-strongest predictor, surpassed only by educational attainment. Wet-bulb temperature, a physiological measure of heat stress, ranked sixth and was the leading meteorological predictor. We identified these patterns using XGBoost with SHAP analysis, integrating atmospheric exposures, livestock density, socioeconomic conditions, and smoking prevalence within an external exposome framework. These results suggest that air pollutants and heat stress provide predictive information beyond traditional socioeconomic indicators. Full article