Search Results (1,247)

The Russian Far East is one of the regions of the country with the least investigated processes affecting the air quality and related climate changes of the region. In the current study 3D numerical modeling (WRF-Chem) together with the ground- and satellite-based observation data of the particular atmospheric pollutants (NO₂, CO, SO₂, O₃, aerosols) were applied to demonstrate how wildfires and transboundary pollution from China could influence air quality in the Far East of Russia (with focus on the Amur region) in July 2015 and January 2023. The WRF-Chem modeling system represents a near-surface air temperature with bias (compared to observations) of 0.5–2 °C and standard deviation, or STD, of 2–5 °C. In general the model overestimates near-surface wind speed—the bias varies in the range 0.8–1.9 m/s with STD of ~2 m/s. This fact should affect the model performance of near-surface gaseous and aerosol composition. Robust Pearson correlation coefficient (from ~0.5) in both periods was found only between modeled and observed near-surface NO₂ and CO. Significant correlation for O₃ (0.73) was found only in January. By using WRF-Chem regional modeling it was demonstrated that seasonal wildfires in the northern Amur region, Zabaykalsky Krai, and the Republic of Yakutia (July 2015) and transboundary pollution from northeastern China (January 2023) could cause the degradation of air quality in the Amur region. Additionally, the possible effect on air quality from the domestic anthropogenic emissions of the Amur region was found in January 2023. According to the modeling, in July 2015 monthly mean NO₂ concentration higher than state standards was found in the territory of the Amur region. The highest monthly mean near-surface NO₂ concentrations exceeding state standards were modeled in northeastern China (~0.05 ppm). The modeled concentrations of other pollutants in the Russian Far East fit the state norms in both periods. The effect of wildfires and transboundary pollution episodes on air quality in the Russian Far East can be considered for the evaluation in the future state air quality reports. Full article

Atmospheric fine particles (PM_2.5, aerodynamic diameter ≤ 2.5 µm) have a serious effect on human health. This study combined concentration weighted trajectory (CWT) analysis with the HYSPLIT trajectory ensemble (Ens-HYSPLIT-CWT), to separate the sources of PM_2.5 transported to Beijing, and further investigate the effect of PM_2.5 originated from different sources on human health. We found that north region air masses usually come with clean events under the blessing of meteorological conditions, combined with the clean air mass transported from the north, as high wind speed near the surface promotes the horizontal diffusion of pollutants. Additionally, north region air masses contribute to the decrease in aerosol optical depth (AOD) at Beijing and surrounding areas, with AF (daily attributable fraction associated with short-term PM_2.5 exposure) values of Beijing only at 0.14. During the study period (from January to March 2024), south region air masses usually come with high PM_2.5 values, which is correlated to the meteorological conditions and pollutant spatial distribution. The air masses coming from the south region contain high temperature and relative humidity (RH), promoting the occurrence of high pollution events. AOD spatial distribution observed from satellites showed that except for the dominance of north region air mass sources, the south region presents high AOD values, further resulting in the highest AF value of 0.75 obtained at Beijing, which is 5 times higher than the north region’s dominant AF mean value. It is worth noting that the air mass originated from the east region, which originally contributed relatively clean air masses before emission reduction, increased its contribution to air mass pollution after emission reduction due to the decrease in pollution concentration in other regions. As a result, the mean PM_2.5 in this source area was second only to south region air masses and local emission sources, and the AF value even exceeded local emissions, second only to south region air mass sources, reaching 0.5. This result emphasizes that in future pollution control policy adjustments and research on human health, attention needs to be paid to the contribution of eastward air masses. Full article

Accurate simulation and assimilation of CO₂ concentrations are of great significance for global carbon cycle research, carbon emission monitoring, and climate policy formulation. In this study, we conducted simulation and assimilation of CO₂ concentrations over central, eastern, and southern China from March to August 2020 using the WRF-Chem model (Weather Research and Forecasting model coupled with Chemistry) coupled with the Ensemble Adjustment Kalman Filter (EAKF) assimilation method. We designed four progressive experiments (CTRL, MET_DA, CO₂_DA, and FULL_DA) to evaluate the impacts of assimilating meteorological observations and multi-satellite fused XCO₂ concentrations on CO₂ simulation performance. Compared to the CTRL simulation, the MET_DA experiment shows that the correlation coefficients (R) for meteorological elements, including wind speed, temperature, and relative humidity, improved by approximately 9.68%, 2.03%, and 16.05%, respectively. The CO₂_DA experiment showed improved accuracy in CO₂ concentration simulation. The validation against WDCGG (World Data Centre for Greenhouse Gases) and TCCON (Total Carbon Column Observing Network) observations demonstrated that R increased to 0.970 and 0.830, respectively, with corresponding RMSEs reduced to 2.598 ppm and 2.042 ppm. Building upon the improvements of CO₂_DA, the FULL_DA experiment achieved greater accuracy, with R reaching 0.972 and 0.875, and RMSE reduced to 2.309 ppm and 1.693 ppm, respectively. In addition, the bias was lowered by 46.74% and 77.58%. The results show that assimilation of both meteorological and multi-source fused XCO₂ datasets achieves optimal performance in enhancing the accuracy of CO₂ concentration simulations. This study employs an hourly, multi-source fused CO₂ dataset that features an increased number of observations and greater spatial coverage. By assimilating this dataset, we achieve more accurate simulations of CO₂ concentrations, thereby providing reliable support for carbon monitoring and emission estimation. Full article

To meet the growing requirements of agricultural mechanization, a newly designed 9.5 m span frame has been introduced to replace the traditional 8.0 m span frame, which is constrained by limited internal space. However, as the structural dimensions increase, the failure mechanisms of arch frames under wind loads remain insufficiently understood. In particular, the influences of crop loads, initial geometric imperfections, pipe cross-sectional properties, and cable reinforcement on these failure mechanisms have not yet been systematically investigated. This study aims to reveal the mechanical mechanisms governing the wind-bearing capacity of standard 8.0 m span and newly designed 9.5 m span frames through comparative analysis, and to further investigate how crop loads, initial geometric imperfections, pipe cross-sectional properties, and cable reinforcement modify these mechanisms. The load combinations considered included the following: (1) permanent load + wind load and (2) permanent load + crop load + wind load. The crop load was applied to the frames via a 5-point hanging system. Simulation results indicate that the 9.5 m span frame exhibits a lower allowable wind speed (v_a) than the 8.0 m span frame due to strength failure. Further analysis reveals that the failure is governed by decreased stiffness resulting from the dimensional expansion. Notably, crop loads and initial geometric imperfections were found to amplify second-order bending moments, thereby further decreasing v_a. Moreover, a positive linear correlation is observed between the section modulus of pipes and v_a. However, replacing the circular pipe with rectangular, oval, or elliptical pipes of a similar cross-sectional area does not increase the v_a of the 9.5 m span frame. Conversely, reinforcing the 9.5 m span frame with cables provides strong lateral constraints and effectively suppresses the amplification of bending moments arising from crop loads and initial geometric imperfections. Thus, limiting lateral displacement through reinforcement measures can markedly increase the wind-bearing capacity of frames. The reinforced 9.5 m span frame proves to be a viable replacement for the 8.0 m span frame, meeting the modern demands of facility agriculture in Southern China. Full article

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks to enhance wind speed forecasting accuracy in Australia. Thirteen years of hourly wind speed data were collected from two wind-rich potential sites in Tasmania, Australia. The CNN component effectively captures local temporal patterns, while the DBiLSTM layers model long-range dependencies in both forward and backward directions. The proposed CNNDBiLSTM model was compared against three traditional benchmark models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Categorical Boosting (CatBoost). The proposed framework can effectively support wind farm planning, operational reliability, and grid integration strategies within the renewable energy sector. A comprehensive evaluation framework across both Australian study sites (Flinders Island Airport, Scottsdale) showed that the CNNDBiLSTM consistently outperformed the baseline models. It achieved the highest correlation coefficients (r = 0.987–0.988), the lowest error rates (RMSE = 0.392–0.402, MAE = 0.294–0.310), and superior scores across multiple efficiency metrics (ENS, WI, LM). The CNNDBiLSTM demonstrated strong adaptability across coastal and inland environments, showing potential for real-world use in renewable-energy resource forecasting. The wind speed analysis and forecasting show Flinders with higher and consistent wind speed as a more viable option for large-scale wind energy generation than Scottsdale in Tasmania. Full article

A clear understanding of water droplet formation and distribution dynamics is fundamental to improving the hydraulic performance and operational efficiency of sprinkler irrigation systems. This study presents an experimental investigation of droplet characteristics using high-speed photography under controlled laboratory conditions. The objective was to analyze droplet diameter, ellipticity, frequency, and velocity at working pressures of 0.2, 0.25, and 0.3 MPa. Median droplet diameters measured at 6–8 m from the nozzle were 2.79 mm, 3.41 mm, and 3.68 mm at 0.2 MPa, with a reduction of up to 17.7% as pressure increased to 0.3 MPa. Smaller droplets were predominantly concentrated near the nozzle and decreased with radial distance, influencing water application uniformity. Morphological parameters such as uniformity (1.3), ellipticity (2.13), and circularity (0.81) were quantified. Cumulative frequency curves revealed 12% droplet fragmentation at 7–8 m under higher pressures, illustrating the dynamic nature of droplet breakup. A strong linear correlation between droplet diameter and calibrated reference diameter confirmed the reliability of the measurement technique. These findings demonstrate that high-speed photography is a robust method for droplet characterization and provides accurate, repeatable data essential for optimizing sprinkler designs to reduce water loss due to evaporation and wind drift. The study contributes to precision irrigation research by offering a detailed understanding of droplet behavior under varying operating pressures. Full article

The sycamore lace bug, Corythucha ciliata, a globally invasive pest that damages Platanus spp., harbors a bacterial microbiome that may help it adapt to different geographical environments. However, the geographical differentiation patterns of its bacterial community and the underlying driving mechanisms remain unclear. In this study, we standardized rearing of three C. ciliata populations (collected from Beijing, Lianyungang, and Nanjing) for three generations to reduce immediate environmental interference, then analyzed their bacterial communities via 16S rRNA gene amplicon sequencing. The principal coordinate analysis revealed a significant separation of the bacterial community in the Nanjing population, while the Beijing and Lianyungang populations were more similar. Bacterial alpha diversity followed the gradient of “Nanjing > Lianyungang > Beijing”, with the Nanjing population exhibiting significantly higher species richness and evenness than the Beijing population. All three populations shared core bacterial taxa (e.g., phyla Proteobacteria, Bacteroidota; genera Cardinium, Serratia), but their relative abundances differed significantly: Cardinium dominated the Beijing population (50.1%), Serratia dominated the Lianyungang population (45.86%), and the Nanjing population harbored unique dominant genera such as Sphingomonas. For the three target populations, monthly average temperature and wind speed were positively correlated with bacterial diversity, while latitude was negatively correlated (Pearson correlation coefficient: 0.6564 < |r| < 0.7010, p < 0.05). Core bacterial functions (e.g., substance transport) were conserved across populations, whereas differential functions (e.g., detoxification, lipid metabolism) were linked to geographical adaptation. This study confirms the climate-driven geographical differentiation of the C. ciliata bacterial community provides insights into the “insect–microbiome” interactive invasion mechanism that is present here. Full article

Objective: This study aims to examine the relationship between atopic dermatitis (AD), one of the most common dermatological conditions in children, and environmental factors, including meteorological variables and air pollution. Methods: This retrospective cross-sectional study analyzed the medical records of 21,407 pediatric patients aged 0 to 18 years who presented to the city hospital in Agri, Turkey, between 2020 and 2024. Admission dates were matched with meteorological data (wind speed, atmospheric pressure, humidity, temperature) and air pollution indicators (PM₁₀, SO₂, NO₂, NOx, NO, O₃). Statistical analyses included t-tests, correlation analyses, binary logistic regression, and a CHAID decision tree model. Results: AD accounted for 10.1% of all dermatology-related visits. AD admissions increased particularly during the first half of the year and were significantly associated with higher O₃ levels, whereas increased PM₁₀ levels were associated with a lower likelihood of AD admissions. Logistic regression showed that age, sex, semiannual period, atmospheric pressure, PM₁₀, and O₃ were significant predictors of AD. The decision tree model identified age, period, and O₃ as the strongest discriminating variables for AD. Conclusion: AD was found to be more sensitive to environmental and seasonal variations compared with other dermatitis types. In particular, elevated ozone levels and temporal factors played a notable role in increasing AD presentations. These findings may inform environmental risk management and preventive strategies for children with AD. Full article

Atmospheric nitrogen deposition in coastal areas has a significant impact on water nutrients, with increasing emission of atmospheric nitrogen-containing pollutants. Clarifying the characteristics, source, and nutrient impact of atmospheric inorganic N deposition is therefore critical for targeted eutrophication control in coastal areas. Dry and wet atmospheric nitrogen deposition samples were collected and integrated into the atmospheric deposition model to analyze the influence of the deposition flux and source on coastal water nutrients. The results showed that inorganic nitrogen in the atmosphere over Xiamen’s coast was mainly composed of NH₄⁺-N and NO₃⁻-N. A high concentration of nitrogen was found in the cold season. Source apportionment analysis revealed that NH₄⁺-N mainly originated from agricultural sources, while NO₃⁻-N was primarily derived from traffic sources (24%) and secondary sources (25%). The wet deposition flux of NH₄⁺-N and NO₃⁻-N was significantly larger than the dry deposition flux. The NO₃⁻-N wet deposition flux was elevated during winter and summer, whereas the dry deposition flux peaked in spring and winter. A high NH₄⁺-N wet deposition flux was also found in spring and summer. Spatially, the inorganic nitrogen deposition flux was higher in offshore areas than in the inner bay, which was attributed to the higher wind speed in the offshore region. The atmospheric inorganic nitrogen input accounted for only 0.9% to 1.8% of the inorganic nitrogen input from the Jiulong River to Xiamen Bay; however, the NO₃⁻ concentration in Xiamen Bay showed a significant positive correlation with the dry deposition flux of atmospheric nitrogen (p < 0.05). Atmospheric nitrogen deposition directly affects coastal water nutrients without estuarine filtration. This study clarifies the different sources of atmospheric inorganic nitrogen deposition and their contribution to coastal water nutrients, providing an important basis for eutrophication in coastal areas, as well as pollutant control and emission reduction efforts. Full article

With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R² by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration. Full article

The evaporation duct, formed above the ocean surface by sharp vertical gradients of humidity, would significantly influence electromagnetic wave propagation. It is a quasi-permanent feature over the sea, and its strength is quantified by the evaporation duct height (EDH). While previous studies have focused on how local factors influence evaporation ducts, the impact of El Niño–Southern Oscillation (ENSO) on EDH in the South China Sea (SCS) remains undocumented. Using correlation analysis, empirical orthogonal function (EOF) decomposition, and wavelet transform, this study shows that evaporation is the dominant environmental factor controlling EDH variability across seasonal and inter-annual timescales in the SCS, while wind speed and relative humidity play secondary roles with contrasting effects between the northern and southern regions. ENSO drives the inter-annual variability of EDH by modulating evaporation. During El Niño events, anomalous anticyclonic circulations near the Philippine Sea, which weaken (strengthen) the evaporation in the northern (southern) SCS, alter EDH and contribute to the formation of the meridional dipole structure, particularly within the 2-to-6-year ENSO band. These results provide new insights into the mechanisms controlling EDH in the SCS and highlight the critical role of ENSO in shaping its spatial distribution. Full article

This paper presents the design and experimental investigation of a multifunctional friction test bench, aiming to characterize the frictional and transmission efficiency of rope–drum systems in high-altitude wind power generation. The study addresses a critical gap in the experimental validation of key components for this demanding application. The test bench, comprising loading, power, test, and data acquisition modules, was designed to measure the equivalent friction coefficient (a comprehensive macro-parameter, not the traditional material friction coefficient) between an ultra-high-molecular-weight polyethylene (UHMWPE) fiber rope and a drum, as well as the transmission efficiency of pulleys. Key parameters, including contact angle, gasket material (steel vs. polyamide (PA)), groove type (U vs. V), and rotational speed, were systematically tested using tension and speed and torque sensors for data acquisition. Experimental results show that the equivalent friction coefficient initially increased and then decreased with the contact angle, reaching a maximum of approximately 0.15 at 100°. The coefficient was positively correlated with rotational speed, increasing by about 40% for steel and 10% for PA linings as speed rose from 25 to 100 rpm. Steel linings exhibited a significantly higher equivalent friction coefficient (0.14–0.17) than PA linings (0.10–0.13). Similarly, in transmission tests, steel pulleys demonstrated superior efficiency compared to PA pulleys, while V-grooves slightly reduced efficiency compared to U-grooves. Furthermore, pulley misalignment was found to decrease transmission efficiency. This work provides essential experimental data and a robust testing platform, laying a foundation for optimizing the efficiency and reliability of high-altitude wind energy systems. Full article

Radar altimeters receive radio-wave reflections from nadir and determine surface parameters from the strength and shape of the return signal. Over the oceans, the strength of the return, termed sigma0 (${\sigma }^0$), is strongly related to the small-scale roughness of the ocean surface and is used to estimate near-surface wind speed. However, a number of other factors affect ${\sigma }^0$, and these need to be estimated and compensated for when developing long-term consistent ${\sigma }^0$ records spanning multiple missions. Aside from unresolved issues of absolute calibration, there are various geophysical factors (sea surface temperature, wave height and rain) that have an effect. The choice of the waveform retracking algorithm also affects the ${\sigma }^0$ values, with the four-parameter Maximum Likelihood Estimator introducing a strong dependence on waveform-derived mispointing and the use of delay-Doppler processing leading to apparent variation with spacecraft radial velocity. As all of these terms have strong geographical correlations, care is required to disentangle these various effects in order to establish a long-term consistent record. This goal will enable a better investigation of the long-term changes in wind speed at sea. Full article

Through research on the effects of soil and meteorological factors on poplar wood properties, poplar clones with enhanced cold tolerance, drought resistance, and salt–alkali tolerance were selected for large-scale cultivation in the Western Songnen Plain, Northern China. We evaluated wood physical properties (basic density) and anatomical characteristics (annual ring width—RW, vessel number—CNO, vessel lumen area—LA) of 15-year-old Populus simonii × P. nigra, Populus alba × P. berolinensis, P. euramericana N3016 × P. ussuriensis, and Populus pseudo-cathayana × P. deltodides clones in the typical black soil area and saline–alkali land. The results showed that black soil region was more suitable for poplar growth, which was influenced by both soil and meteorological factors. Among soil factors, pH was the primary factor influencing the radial growth of poplar clones, exhibiting a negative correlation for all clones except P. alba × P. berolinensis. Furthermore, P. euramericana N3016 × P. ussuriensis was affected by organic carbon, while P. simonii × P. nigra and P. alba × P. berolinensis were more influenced by potassium. Among climatic factors, basic wood density, annual ring characteristics, and vessel structural parameters in all clones were primarily influenced by wind speed and sunshine, with air temperature having the least effect. Among the four clones, P. alba × P. berolinensis displayed better growth performance (higher RW) and basic wood density (0.29–0.41 g/cm³) at both sites, while P. simonii × P. nigra proved suitable for cold regions. Both clones showing dual adaptability to saline–alkali and black soil environments in Northeast China. Full article

Soil moisture plays a crucial role in hydrological processes and serves as a key driver of rainfall-induced landslides, especially in regions with steep terrain and intense precipitation. Traditional landslide risk models often oversimplify soil moisture and infiltration dynamics, which limits their predictive accuracy. This study presents a deep learning-based framework for generating high-resolution, spatiotemporal Surface Soil Moisture (SSM) maps for Prince George’s County, Maryland—a region highly susceptible to rainfall-triggered landslides—aimed at improving infiltration modeling and landslide prediction. A Convolutional Long Short-Term Memory (ConvLSTM) network integrates static spatial features (elevation, slope, soil type) with multi-temporal meteorological variables (precipitation, temperature, humidity, wind speed, evapotranspiration) and vegetation indices. The model is trained using dense SSM maps derived from Sentinel-1 SAR data processed through a change detection algorithm, providing a physically meaningful alternative to sparse in-situ observations. To address data imbalance, a two-pass patch extraction strategy was implemented to enhance representation of high-SSM conditions. The framework leverages high-performance computing resources to process large-scale, multi-temporal raster datasets efficiently. Evaluation results show strong predictive performance, with the two-day model achieving ${\mathrm{R}}^2$ = 0.72, correlation = 0.85, RMSE = 0.154, and MAE = 0.103. The results demonstrate the model’s capability to produce fine-resolution, wall-to-wall SSM maps that capture the spatial and temporal dynamics of surface soil moisture, supporting the development of early warning systems and landslide hazard mitigation strategies. Full article