Search Results (588)

Accurate observations of Ocean Surface Wind Speed (OSWS) are vital for predicting extreme weather and understanding ocean–atmosphere interactions. However, spaceborne sensors (e.g., ASCAT, SMAP) often experience data loss due to harsh weather and instrument malfunctions. Existing inpainting methods often rely on reanalysis data that is released with delays, which restricts their real-time capability. Additionally, deep-learning-based methods, such as Transformers, face challenges due to their high computational complexity. To address these challenges, we present the Multimodal Wind Speed Inpainting Dataset (MWSID), which integrates 12 auxiliary forecasting variables to support real-time OSWS inpainting. Based on MWSID, we propose the MMamba framework, combining the Multimodal Feature Extraction module, which uses mutual information (MI) theory to optimize feature selection, and the OSWS Reconstruction module, which employs Attention-Mamba-2 within a Residual-in-Residual-Dense architecture for efficient OSWS inpainting. Experiments show that MMamba outperforms MambaIR (state-of-the-art) with an RMSE of 0.5481 m/s and an SSIM of 0.9820, significantly reducing RMSE by 21.10% over Kriging and 8.22% over MambaIR in high-winds (>15 m/s). We further introduce MMamba-L, a lightweight 0.22M-parameter variant suitable for resource-limited devices. These contributions make MMamba and MWSID powerful tools for OSWS inpainting, benefiting extreme weather prediction and oceanographic research. Full article

The demand for robust safety monitoring in underground coal mines is increasing, yet traditional methods face limitations in long-term stability due to inadequate energy supply and high maintenance requirements. To address the critical challenges of high computational demand and energy constraints in this resource-limited environment, this paper proposes MineVisual, a battery-free visual sensing scheme specifically designed for underground coal mines. The core of MineVisual is an optimized lightweight deep neural network employing depthwise separable convolution modules to enhance computational efficiency and reduce energy consumption. Crucially, we introduce an energy-aware dynamic pruning network (EADP-Net) ensuring a sustained inference accuracy and energy efficiency across fluctuating power conditions. The system integrates supercapacitor buffering and voltage regulation for stable operation under wind intermittency. Experimental validation demonstrates that MineVisual achieves high accuracy (e.g., 91.5% Top-1 on mine-specific tasks under high power) while significantly enhancing the energy efficiency (reducing inference energy to 6.89 mJ under low power) and robustness under varying wind speeds. This work provides an effective technical pathway for intelligent safety monitoring in complex underground environments and conclusively proves the feasibility of battery-free deep learning inference in extreme settings like coal mines. Full article

Submicron particles (SPs), with diameters less than 1.0 μm, are a serious health risk, and urban meteorology variables (MVs), impacted by human activity, can support their sustainability. This study, in a city immersed in a basin geomorphology, is carried out during the summer period of high temperatures and variable relative humidity. An area of high urban density was selected, with the presence of high-rise buildings, urban canyons that favor heat islands, low forestation, intense vehicular traffic, and extreme conditions for MVs. Hourly measurements, in the form of time series, record the number of SPs (for diameters of 0.3, 0.5, and 1.0 μm) along with MVs (temperature (T), relative humidity (RH), and wind speed magnitude (WS)). The objective is to verify whether MVs (RH, T) promote the sustainability of SPs. For this purpose, Spearman’s analysis and a heavy-tailed probability function were used. The central tendency probability, a Gaussian distribution, was discarded since its probability does not discriminate extreme events. Spearman’s analysis yielded significant p-values and correlations between PM₁₀, PM_5.0, PM_2.5, and SPs. However, this was not the case between MVs and SPs. By applying a heavy-tailed probability analysis to extreme events, the results show that MVs such as T and RH act in ways that can favor the accumulation and persistence of SP concentrations. This tendency could have been exacerbated during the measurement period by heat waves and a geographical environment under the influence of a prolonged drought resulting from climate change and global warming. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

Accurate imputation of missing data in air quality monitoring is essential for reliable environmental assessment and modeling. This study compares two imputation methods, namely Random Forest (RF) and Bidirectional Recurrent Imputation for Time Series (BRITS), using data from the Mexico City air quality monitoring network (2014–2023). The analysis focuses on stations with less than 30% missingness and includes both pollutant (CO, NO, NO₂, NO_x, SO₂, O₃, PM₁₀, PM_2.5, and PMCO) and meteorological (relative humidity, temperature, wind direction and speed) variables. Each station’s data was split into 80% for training and 20% for validation, with 20% artificial missingness. Performance was assessed through two perspectives: local accuracy (MAE and RMSE) on masked subsets and distributional similarity on complete datasets (Two One-Sided Tests and Wasserstein distance). RF achieved lower errors on masked subsets, whereas BRITS better preserved the complete distribution. Both methods struggled with highly variable features. On complete time series, BRITS produced more realistic imputations, while RF often generated extreme outliers. These findings demonstrate the advantages of deep learning for handling complex temporal dependencies and highlight the need for robust strategies for stations with extensive gaps. Enhancing the accuracy of imputations is crucial for improving forecasting, trend analysis, and public health decision-making. Full article

Wind fields on tropical islands are among the most complex systems in atmospheric science, simultaneously influenced by large-scale monsoons, tropical cyclones, local sea-land circulation, and island topography. These interactions result in extremely complex responses to climate change, posing significant challenges for detailed assessment. This study examines how multi-scale processes have shaped the long-term evolution of the near-surface wind speed over Hainan, China’s largest tropical island. We developed a new high-resolution (5 km, hourly) regional climate reanalysis spanning 1961–2022, based on the WRF model and ERA5 data. Our analysis reveals three key findings: First, the long-term trend of wind speed over Hainan exhibits significant spatial heterogeneity, characterized by “coastal stilling and inland strengthening.” Wind speeds in coastal areas have decreased by −0.03 to −0.09 m/s per decade, while those in the mountainous interior have paradoxically increased by up to +0.06 m/s per decade. This pattern arises from the interaction between the weakening East Asian Winter Monsoon and the island’s complex terrain. Second, the frequency of extreme wind events has undergone seasonal reorganization: days with strong winds linked to the winter monsoon have significantly decreased (−0.214 days per decade), whereas days linked to warm-season tropical cyclones have increased (+0.097 days per decade), indicating asynchronous evolution of climate extremes. Third, the risk from 100-year extreme wind events is undergoing geographical redistribution, shifting from the coast to the mountainous interior (with an increase of 0.4–0.7 m/s in inland areas), posing a direct challenge to existing engineering design standards. Taken together, these findings demonstrate that local topography can significantly influence large-scale climate change signals, underscoring the critical role of high-resolution modeling in understanding the climate response of such complex systems. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article

Mass gatherings such as the annual Hajj pilgrimage in Makkah, Saudi Arabia, generate extreme, short-term anthropogenic emission loads with significant air quality and public health implications. This study assesses the spatiotemporal dynamics of key atmospheric pollutants—including nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), formaldehyde (HCHO), and aerosols—across Makkah and its holy sites before and during the Hajj seasons of 2023 and 2024. Using high-resolution Sentinel-5P TROPOMI satellite data, pollutant fields were reconstructed at 100 m spatial resolution via cloud-based geospatial analysis on the Google Earth Engine. During Hajj 2023, spatially resolved NO₂ concentrations ranged from 15.4 μg/m³ to 38.3 μg/m³ with an average of 24.7 μg/m³, while SO₂ during the 2024 event peaked at 51.2 μg/m³ in key hotspots, occasionally exceeding World Health Organization (WHO) guideline values. Aerosol index values showed episodic surges (up to 1.43), particularly over transportation corridors, parking areas, and logistics facilities. CO concentrations reached values as high as 1069.8 μg/m³ in crowded zones, and HCHO concentrations surged up to 9.99 μg/m³ during peak periods. Quantitative correlation analysis revealed that during Hajj, atmospheric chemistry diverged from urban baseline: the NO₂–SO₂ relationship shifted from strongly negative pre-Hajj (r = −0.74) to moderately positive during the event (r = 0.35), while aerosol–HCHO correlations intensified negatively from r = −0.23 pre-Hajj to r = −0.50 during Hajj. Meteorological analysis indicated significant positive correlations between wind speed and NO₂ (r = 0.35) and wind speed and CO (r = 0.35) during 2024, demonstrating that extreme emission rates overwhelmed typical dispersive processes. Relative humidity was positively correlated with aerosol loading (r = 0.37), pointing to hygroscopic growth patterns. These results quantitatively demonstrate that Hajj drives a distinct, event-specific pollution regime, characterized by sharp increases in key pollutant concentrations, altered inter-pollutant and pollutant–meteorology relationships, and spatially explicit hotspots driven by human activity and infrastructure. The integrated satellite–meteorology workflow enabled near-real-time monitoring in a data-sparse environment and establishes a scalable framework for evidence-based air quality management and health risk reduction in mass gatherings. Full article

The increasing frequency of extreme weather events presents serious challenges to both regional and global economies. This study focuses on quantifying the economic losses caused by three major types of extreme climate events (heatwaves, droughts, and heavy rain) in Hainan Island from 2001 to 2020. Moreover, a comprehensive dataset of related economic losses was developed. To support the analysis, we constructed an Extreme Climate Economic Loss Model (ECELM). Drought and heavy rain losses were estimated using a loss intensity index based on precipitation and typhoon landfall wind speeds. Heatwave-related losses were assessed through a threshold-based optimization approach. The results show that both heatwaves and heavy rain have exhibited increasing impacts from 2001 to 2020. Although heatwaves were the most frequent extreme event in more than half of the years, heavy rain caused the highest cumulative losses, reaching CNY 75 billion. Spatial analysis indicates that the southeastern coastal areas were the most severely affected. These findings provide valuable quantitative evidence for designing targeted regional climate adaptation strategies. Full article

Sea breezes alleviate coastal heat stress via cooling and humidifying. Sendai, Japan, in 2015 had a population of 1.08 million and an area of 786 km². Integrating the WRF model with RayMan, this study employs the PET index to assess spatiotemporal distributions of thermal comfort and heat stress, and their influencing factors, on typical summer sea-breeze days in Sendai, Japan. Results indicate that in the coastal zone, PET was primarily regulated by air temperature (Ta) and relative humidity (RH). In contrast, wind speed was the dominant influence on urban/inland zones, with Ta and RH contributing more during the evening. Sea breezes markedly improved the thermal environment in the coastal zone, suppressing PET increases. PET in urban and inland zones exhibited an initial rise followed by a decline, with the inland zone experiencing sustained extreme heat stress for 3 h. Among regions experiencing extreme heat stress, inland zones showed the highest proportion (17.75%), while coastal zones had the lowest (2.14%). Proportions across the three zones were similar under nighttime conditions with no thermal stress, with the urban zone exhibiting a slightly lower proportion. This study provides a theoretical basis for climate-adaptive urban planning leveraging sea breezes as a resource. Full article

Wind-induced erosion and extreme weather events pose growing risks to the structural integrity of masonry heritage buildings. However, current mitigation approaches often overlook ecological sustainability. This study investigates the wind-regulating effects of vegetation surrounding the Zhen Guo Tower, a 400-year-old masonry structure in Weihui, Henan Province, China. Using computational fluid dynamics (CFD) simulations, we first assess the protective performance of the existing vegetation layout and then develop and evaluate an optimized plant configuration. The results show that the proposed multilayered vegetation arrangement effectively reduces wind speeds by up to 13.57 m/s under extreme wind conditions, particularly within the 5–15 m height range. Wind protection efficiency improved by 28–68% compared to the baseline. This study demonstrates a replicable and ecologically integrated strategy for mitigating wind hazards in masonry heritage sites through vegetation-based interventions. Full article

The Ordos section of the Yellow River Basin represents a typical semi-arid zone in northern China. Due to dual pressures from natural drivers and human activities, this region is at the forefront of desertification. Therefore, rapidly and accurately identifying desertification and analyzing its evolutionary trends plays a vital role in desertification control. Using six-phase Landsat imagery (2000–2023) of Ordos City, this study extracted NDVI and Albedo to construct a fitting model, thereby analyzing desertification severity, spatial distribution patterns, and evolutionary dynamics. Through integrated analysis trends in meteorological and anthropogenic data, key driving factors of desertification processes were further investigated. Conclusions: (1) By 2023, the area of extremely severe and severe desertification reduction accounted for 12.67% of the total study area, the proportion of no desertification area increased by 11.27%, and the expansion of desertification was effectively curbed. (2) Desertification intensification cluster near residential zones and grazing lands, while improved areas concentrate in the western and southern of Mu Us Sandy Land vicinity. (3) Spatial autocorrelation analysis revealed statistically significant clustering patterns across the study area, predominantly characterized by distinct low–low and high–high aggregations. (4) Wind speed, temperature, and pastoral activities were major factors contributing to desertification. These research findings provided references for the ecological restoration and sustainable development of semi-arid areas in the Yellow River Basin. Full article

Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its driving mechanism remain unclear. This study takes Chengdu as an example, selects the air temperature (Ta), precipitation (P), wind speed (WS), and soil water content (SWC) within the period from 2001 to 2023 as influencing factors, and uses Theil-Sen median trend analysis and interpretable machine learning models (random forest (RF), BP neural network, support vector machine (SVM), and extreme gradient boosting (XG-Boost) models). The average absolute value of Shapley additive explanations (SHAPs) is adopted as an indicator to explore the key mechanism driving regional climate change in Chengdu in terms of NDVI changes. The analysis results reveal that the NDVI exhibited an extremely significant increasing trend during the study period (p = 8.6 × 10⁻⁶ < 0.001), and that precipitation showed a significant increasing trend (p = 1.2 × 10⁻⁴ < 0.001); however, the air temperature, wind speed, and soil-relative volumetric water content all showed insignificant increasing trends. A simulation of interpretable machine learning models revealed that the random forest (RF) model performed exceptionally well in terms of simulating the dynamics of the urban NDVI (R² = 0.746), indicating that the RF model has an excellent ability to capture the complex ecological interactions of a city without prior assumptions. The dependence relationship between the simulation results and the main driving factors indicates that the Ta and P are the main factors affecting the NDVI changes. In contrast, the SWC and WS had relatively small influences on the NDVI changes. The prediction analysis results reveal that a monthly average temperature of 25 °C and a monthly average precipitation of approximately 130 mm are conducive to the stability of the NDVI in the study area. This study provides a reference for exploring the responses of NDVI changes to regional climate change in the context of urban expansion and urban ecological construction. Full article

The Sixth Assessment Report of the IPCC highlights that global surface temperatures have risen by 1.1 °C above pre-industrial levels, with a marked increase in the frequency and intensity of extreme heat events in hot–humid regions. Buildings in these areas urgently require passive design strategies to enhance climate adaptability. Employing Zhupu Ancient Village in Chaoshan region in China as an example, this study analyzes and evaluates the wind-driven ventilation archetype and buoyancy-driven ventilation archetype of the village through integrated meteorological data analysis (ECMWF) and computational fluid dynamics (CFD) simulations. The results indicate that the traditional climate-adaptive archetype facilitates wind speeds exceeding 0.5 m/s in over 80% of outdoor areas, achieving unobstructed airflow and a discernible stack ventilation effect. Through archetype translation, the visitor center design incorporates open alleyway systems and water-evaporative cooling strategies, demonstrating that over 80% of outdoor areas attain wind speeds of 0.5 m/s during summer, thereby achieving enhanced ventilation performance. The research provides a climate-response-archetype translation-performance validation framework and practical case studies for climate-adaptive design of public buildings in hot–humid regions. Full article

The Bohai Bay is particularly vulnerable to storm surges triggered by extratropical storms or cold-air outbreaks. A coupled atmosphere–ocean–wave model with high resolution is presented and applied to simulate a cold-air outbreak that happened in late November 2004. The surge dynamics are examined in detail. Each model component is separately validated, demonstrating that the triply coupled system can reproduce intense winds, storm surge amplitudes, and significant surface waves with high fidelity. The potential coupling effects on the simulation results are investigated. Six experiments are performed covering various coupling models, and a two-way nesting technique is utilized during simulation. After comparison it shows that there is little difference in wind speed between the three numerical models and that the reanalysis data may significantly underestimate extreme winds. The evident improvements are obtained for peak values of water level when using the atmosphere–ocean coupled configuration versus uncoupled model simulation. It also can be found that the negative surge can be captured by each of the coupled and uncoupled models. The ocean–wave coupled configuration yields significant wave heights that closely match in situ measurements, underscoring the critical role of ocean–wave interaction in storm wave prediction. Our findings confirm that the fully coupled model is well-suited for forecasting extratropical storm surge in Bohai Bay. Northeast winds emerge as the primary driver, with the western coast of Bohai Bay bearing the greatest impact. Full article