Search Results (143)

Dust aerosols can influence climate change, the ecological environment, human health, etc. and are one of the most important factors causing global change. The specific contributions of dust events, gusts, and dust devils to dust emission remain unclear in many regions. In this study, we quantified dust emissions generated by dust events, gusts, and dust devils in the center of the Taklimakan Desert of northwestern China and investigated their respective contributions to atmospheric dust aerosols. The results illustrated that monthly dust emissions and the dust emission time for dust events, gusts, and dust devils peaked in July, August, and June, respectively, and the average monthly contributions to dust emissions were 48.2, 10.6, and 41.2% and those to emission time were 60.5, 25.5, and 14.0%, respectively. Although the dust emissions for the dust event were comparable to the sum of gusts and dust devils, the average value of AOD corresponding to the dust event was roughly 2.5 times higher than that of a non-dust day. The results presented in this study not only highlight the undeniable contribution of gusts and dust devils to dust emissions but also indicate that the specific contributions to atmospheric dust aerosols from gusts and dust devils remain uncertain. Full article

Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article

Prolonged conflicts in Iraq over the past four decades have profoundly disrupted environmental systems, not only through immediate post-conflict emissions—such as residues from munitions and explosives—but also via long-term infrastructural collapse, population displacement, and unsustainable resource practices. Despite growing concern over air quality in conflict-affected regions, comprehensive assessments integrating long-term data and localized measurements remain scarce. This study addresses this gap by analyzing the environmental consequences of sustained instability in Mosul, focusing on air pollution trends using both remote sensing data (1983–2023) and in situ monitoring of key pollutants—including PM_2.5, PM₁₀, TVOCs, NO₂, SO₂, and formaldehyde—at six urban sites during 2022–2023. The results indicate marked seasonal variations, with winter peaks in combustion-related pollutants (NO₂, SO₂) and elevated particulate concentrations in summer driven by sandstorm activity. Annual average concentrations of all six pollutants increased by 14–51%, frequently exceeding WHO air quality guidelines. These patterns coincide with worsening meteorological conditions, including higher temperatures, reduced rainfall, and more frequent storms, suggesting synergistic effects between climate stress and pollution. The findings highlight severe public health risks and emphasize the urgent need for integrated urban recovery strategies that promote sustainable infrastructure, environmental restoration, and resilience to climate change. Full article

License plate recognition in sandstorm conditions faces challenges such as image blurriness, reduced contrast, and partial information loss, which result in significant limitations in the feature extraction and recognition accuracy of existing methods. To address these challenges, this study proposes a license plate recognition method based on an improved AlexNetBN network. By introducing Batch Normalization (BN) layers, the model achieves greater training stability and generalization in complex environments. A dedicated dataset tailored for license plate recognition in sandstorm conditions was constructed, and data augmentation techniques were used to simulate real-world scenarios for model training and testing. Experimental results demonstrate that, compared to the traditional AlexNet model, AlexNetBN achieves higher recognition accuracy and robustness in environments with frequent sandstorms and significant variations in lighting intensity. This study not only effectively enhances license plate recognition performance under sandstorm conditions but also offers new insights and references for applying CNN-based methods in low-visibility scenarios. Full article

This article is devoted to the study of a 20 MW large-scale photovoltaic power plant (LS-PVPP), connected to the grid and located in Naàma, Algeria. The power plant is included in the National Program for the Development of Renewable Energies 2015–2030. Among the parameters analyzed in detail in this work, the performance ratio recorded an average value of 67.55%, the capacity factor had an average of 17.10%, the total losses had an average of 2.10 kWh/kWp/day, the system efficiency had an average of 4.10 kWh/kWp/day and an annual average of 9.84% of the efficiency. A linear regression equation with a coefficient of determination R² of 0.91 confirms the importance of irradiation impact in the region; less significant linearity for the effect of temperature with a coefficient of determination R² = 0.28 is recorded for production. A comparative study conducted with the Adrar plant (Algeria) with an extremely hot desert climate and the Saida plant (Algeria) with a semi-arid climate demonstrated that the efficiency of the Naàma station is equal to 91.22% of the efficiency of Adrar and 73.47% of the efficiency of Saida. Naàma is known for its semi-arid climate; it is very cold in winter and hot in summer, with sandstorms becoming more frequent due to climate change. PVsyst software (Version 7.4.8) is used to validate the results. Full article

This study investigated the dust storm observation data from the Taklimakan Desert in 2018, focusing on analyzing horizontal dust flux (Q), vertical dust flux (F), their relationships with aerosol optical depth (AOD), and the relationship between HYSPLIT backward trajectories and dust storm dispersion direction. Key findings include: (1) at the Xiaotang (XT) station, Q values at low heights (1–10 m) exceeded those at higher altitudes, highlighting the role of flat terrain in dust accumulation, while Q values at the Tazhong (TZ) station remained relatively stable, suggesting dust redistribution influenced by undulating topography; (2) vertical dust flux (F) decreased with height, with significant seasonal variations in spring linked to frequent dust events; (3) at station XT, the contribution of F at 5 m height is relatively strong to AOD and its peak precedes AOD by 24–72 h, although the direct correlation is weak; and (4) dust dispersion directions aligned with HYSPLIT trajectories and high Q values corresponded with remotely derived dust dispersion patterns. Full article

Aerospace engines ingest small particles when operating in a particulate-rich environment, such as sandstorms, atmospheric pollution, and volcanic ash clouds. These micron particles enter their cooling channels, leading to film-cooling hole blockage and thus thermal damage to turbine blades made of nickel-based single-crystal superalloy materials. This work studied the collision and deposition mechanisms between the micron particles and structure surface. A combined theoretical and numerical study was conducted to investigate the effect of deposits on particle collision and deposition. Finite element models of deposits with flat and rough surfaces were generated and analyzed for comparison. The results show that the normal restitution coefficient is much lower when a micron particle impacts a deposit compared to that of particle collisions with DD3 nickel-based single-crystal wall surfaces. The critical deposition velocity of a micron particle is much higher for particle–deposit collisions than for particle–wall collision. The critical deposition velocity decreases with the increase in particle size. When micron particles deposit on the wall surface of the structure, early-stage particle–wall collision becomes particle–deposit collision when the height of the deposits is greater than twice the particle diameter. For contact between particles and rough surface deposits, surfaces with a shorter correlation length, representing a higher density of asperities and a steeper surface, have a much longer contact time but a lower contact area. The coefficient of restitution of the particle reduces as the surface roughness of the deposits increase. The characteristic length of the roughness has little effect on the rebounding rotation velocity of the particle. Full article

In earth science research, digital elevation models (DEMs) serve as essential tools for acquiring terrain information. However, existing research has primarily focused on geomorphic units like mountainous and forested regions, while research on extreme desert environments remains relatively scarce. This study systematically evaluates the vertical accuracy of six open-access DEMs in the hinterland of the Taklimakan Desert using ICESat-2 ATL08 data and unmanned aerial vehicle (UAV) data. Additionally, it examines the relationship between DEM errors and terrain characteristics, including slope, aspect, and terrain relief. The results reveal that the error distribution of different DEMs in the Taklimakan Desert hinterland follows a normal distribution pattern, but significant differences exist in both the magnitude and stability of the errors. Among the evaluated DEMs, Copernicus and AW3D30s exhibit superior performance, with moderate errors and high stability, making them suitable for high-precision terrain analysis. Further analysis indicates that terrain characteristics significantly influence DEM vertical accuracy in the TD hinterland. Specifically, increasing slope leads to a notable rise in errors across all assessed DEMs, with error fluctuations becoming more pronounced when the slope exceeds 15°. While slope aspect has a relatively minor impact on errors, certain DEMs exhibit error variations in the SE and NW directions. Similarly, increasing terrain relief results in greater errors. Moreover, research has demonstrated that ICESat-2 ATL08 data can effectively validate the vertical accuracy of DEMs in desert regions, offering valuable insights for DEM selection and correction in the hinterland of the Taklimakan Desert and similar arid environments. Full article

The global transition toward clean energy has driven the extensive deployment of overhead tower-lines in desserts, where such structures face unique challenges from wind–sand interactions. The current design standards often overlook these combined loads due to oversimplified collision models and inadequate computational frameworks. These gaps are bridged in the present study through the development of a refined impact force model grounded in Hertz contact theory, which captures transient collision mechanics and energy dissipation during sand–structure interactions. Validated against field data from northwest China, the model enables a comprehensive parametric analysis of wind speed (5–60 m/s), sand density (1000–3500 kg/m³), elastic modulus (5–100 GPa), and Poisson’s ratio (0.1–0.4). Our results show that peak impact forces increase by 66.7% (with sand density) and 148% (with elastic modulus), with higher wind speeds amplifying forces nonlinearly, reaching 8 N at 30 m/s. An increased elastic modulus shifts energy dissipation toward elastic rebound, reducing the penetration depth by 28%. The dynamic analysis of a 123.6 m transmission tower under wind–sand coupling loads demonstrated significant structural response amplifications; displacements and axial forces increased by 28% and 41%, respectively, compared to pure wind conditions. These findings reveal the importance of integrating coupling load effects into design codes, particularly for towers in sandstorm-prone regions. The proposed framework provides a robust basis for enhancing structural resilience, offering practical insights for revising safety standards and optimizing maintenance strategies in arid environments. Full article

Understanding the spatial differentiation of ecosystem service (ES) interactions and their underlying driving mechanisms is crucial for effective ecosystem management and enhancing regional landscape sustainability. However, comprehensive analyses of the effects of key influencing factors on ES interactions remains limited, especially regarding the nonlinear driving mechanisms of factors and their regional heterogeneity. We assessed and validated five key ES in the National Key Ecological Function Zones (NKEFZs) of China—net primary productivity (NPP), soil conservation (SC), sandstorm prevention (SP), water retention (WR), and biodiversity maintenance (BM). By integrating the optimal parameter geographical detector with constraint line methods, we further explored the complex responses of ES interactions to driving factors across different functional zones. The results showed that most ES exhibited significant spatial synergistic clustering. In contrast, widespread spatial trade-off clustering was detected in ES pairs related to WR, mainly distributed in the Tibetan Plateau, northeast China, and the Southern Hills region. Due to the improvement in ES, the overall synergies of ES enhanced from 2000 to 2020. The dominant factors in different functional zones influenced ES interactions in a non-stationary manner, with the same factors potentially showing diverse effect types in different sub-regions. Additionally, we detected the dominant role of landscape configuration factors in sub-regions for specific interaction types (e.g., WR-NPP interaction in the SP zones), suggesting the potential for achieving multi-ES synergies through landscape planning without altering landscape composition. This research provides valuable insights into understanding ES interactions and offers a scientific foundation for the implementation of ecological protection and restoration plans. Full article

This study’s field observations of Light Source Efficiency (LSE) in the Taklamakan Desert have unveiled significant daily average variations across different wavelengths, with LSE values ranging from 0.827 at 9.1 μm to a peak of 0.969 at 12.1 μm, and notably, a substantial daily variation (DV) of Δε = 0.080 in the 14.3 μm band. These findings underscore the necessity for wavelength-specific analysis in LSE research, which is crucial for enhancing the precision of remote sensing applications and climate models. This study’s high-temporal-resolution FTIR field observations systematically reveal the diurnal dynamics of infrared surface emissivity in the desert for the first time, challenging existing satellite-based inversion products and highlighting the limitations of traditional temperature–emissivity separation algorithms in arid regions. The diurnal fluctuations are governed by three primary mechanisms: the amplification of lattice vibrations in quartz minerals under high daytime temperatures, changes in the surface topography due to thermal expansion and contraction, and nocturnal radiative cooling effects. The lack of a significant correlation between environmental parameters and the emissivity change rate suggests that microclimate factors play a dominant indirect regulatory role. Model comparisons indicate that sinusoidal functions outperform polynomial fits across most wavelengths, especially at 12.1 μm, confirming the significant influence of diurnal forcing. The high sensitivity of the 14.3 μm band makes it an ideal indicator for monitoring desert surface–atmosphere interactions. This study provides three key insights for remote sensing applications: the development of dynamic emissivity correction schemes, the prioritization of the long-wave infrared band for surface temperature inversion in arid regions, and the integration of ground-based observations with geostationary high-spectral data to construct spatiotemporally continuous emissivity models. Future research should focus on multi-angle observation experiments and the exploration of machine learning’s potential in cross-scale emissivity modeling. Full article

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability. Full article

Dunes are key geomorphological features in aeolian environments, and their automated mapping is essential for ecological management and sandstorm disaster early warning in desert regions. However, the diversity and complexity of the dune morphology present significant challenges when using traditional classification methods, particularly in feature extraction, model parameter optimization, and large-scale mapping. This study focuses on the Gurbantünggüt Desert in China, utilizing the Google Earth Engine (GEE) cloud platform alongside multi-source remote sensing data from Landsat-8 (30 m) and Sentinel-2 (10 m). By integrating three deep learning models—DeepLab v3, U-Net, and U-Net++—this research evaluates the impact of the batch size, image resolution, and model structure on the dune segmentation performance, ultimately producing a high-precision dune type map. The results indicate that (1) the batch size significantly affects model optimization. Increasing the batch size from 4 to 12 improves the overall accuracy (OA) from 69.65% to 84.34% for Landsat-8 and from 89.19% to 92.03% for Sentinel-2. Increasing the batch size further to 16 results in a slower OA improvement, with Landsat-8 reaching OA of 86.63% and Sentinel-2 reaching OA of 92.32%, suggesting that gradient optimization approaches saturation. (2) The higher resolution of Sentinel-2 greatly enhances the ability to capture finer details, with the segmentation accuracy (OA: 92.45%) being 5.82% higher than that of Landsat-8 (OA: 86.63%). (3) The U-Net model performs best on Sentinel-2 images (OA: 92.45%, F1: 90.45%), improving the accuracy by 0.13% compared to DeepLab v3, and provides more accurate boundary delineation. However, DeepLab v3 demonstrates greater adaptability to low-resolution images. This study presents a dune segmentation approach that integrates multi-source data and model optimization, offering a framework for the dynamic monitoring and fine-scale mapping of the desert’s geomorphology. Full article

As an important means to monitor atmospheric vertical temperature and humidity, the ground-based microwave radiometer has been widely used in environmental monitoring, climate prediction, and other fields, but its application in desert areas is particularly limited. At Minfeng Station on the southern edge of the Taklimakan Desert, Global Telecommunications System (GTS) detection technology was used to evaluate the microwave radiometer observations under different weather conditions and at different altitudes. The planetary boundary layer height (PBLH) was calculated using the potential temperature gradient method, and the planetary boundary layer results were calculated by analyzing dust and rainfall events. The results show that the determination coefficients (R²) of the overall observed temperature (T), specific humidity (q), and water vapor density (${\rho }_v$) of the microwave radiometer are all above 0.8 under different weather conditions. When the relative humidity is 0–10%, the temperature is the best, and the R² is 0.9819. When the relative humidity is 70–80%, the R² of q and ${\rho }_v$ is the best, and the R² is 0.9630 and 0.9777, respectively. This is in good agreement with the temperature observed by the FY–4A satellite; the observation effect is the best in May, and its R² is 0.9142. Under the conditions of clear sky, precipitation day, and dusty weather, the R² of the atmospheric boundary layer height calculated by the microwave radiometer is greater than 0.7 compared to the GTS sounding calculation results. These results demonstrate the reliability of microwave radiometry in extremely arid environments, providing valuable insights for boundary layer studies in desert regions. Full article

Enhancing the visibility of outdoor images under sandstorm conditions remains a significant challenge in computer vision due to the complex atmospheric interference caused by dust particles. While existing image enhancement algorithms perform well in mild sandstorm scenarios, they often struggle to produce satisfactory results in more severe conditions, where residual color casts and chromatic artifacts become pronounced. These limitations highlight the need for a more robust and adaptable restoration method. In this study, we propose an advanced algorithm designed to restore sand-dust images under varying sandstorm intensities, effectively addressing the aforementioned challenges. The approach begins with a color correction step, achieved through channel compensation and color transfer techniques, which leverage the unique statistical properties of sand-dust images. To further refine the restoration, we improve the boundary constraints of the saturation line prior (SLP) by adjusting the local illumination in the atmospheric light map, enhancing the model’s robustness to environmental variations. Finally, the atmospheric scattering model is employed for comprehensive image restoration, ensuring that color correction and dust removal are optimized. Extensive experiments on real-world sandstorm images demonstrate that the proposed method performs on par with state-of-the-art (SOTA) techniques in weaker sandstorm scenarios, showing marked improvements in more severe conditions. These results highlight the potential of our approach for practical applications in outdoor image enhancement under challenging environmental conditions. Full article