Search Results (61)

This study provides a comprehensive spatiotemporal analysis of sand and dust storms (SDSs) in Uzbekistan using ground-based meteorological data from 2010 to 2023. The results reveal significant spatial heterogeneity in the SDS activity, with the highest frequency of SDS days observed in the southern and western regions, including Surkhandarya, Kashkadarya, Bukhara, Khorezm, and Republic of Karakalpakstan. In the most vulnerable areas, such as Karakalpakstan, Surkhandarya, and Kashkadarya, the annual number of SDS days can exceed 80 in certain years, reflecting a high recurrence of extreme dust events in certain climatic zones. About 53% of the SDS events were regional, affecting several stations, while 47% were localized, indicating a combination of large-scale dust transport and localized emissions. Seasonal patterns showed a peak SDS activity between March and August, coinciding with the dry season characterized by elevated temperatures, reduced soil moisture, and intense agricultural activity, all of which contribute to the surface exposure and increased vulnerability. This study found a significant variation in the event duration across regions, with Karakalpakstan and Surkhandarya experiencing the highest proportion of prolonged events due to its orography and persistent southerly wind patterns. Using ERA5 data and a decision tree regressor, the analysis identified the wind direction and mean wind speed as the most influential meteorological factors, followed by the maximum wind speed and soil temperature, with other variables such as solar radiation and soil moisture playing moderate roles. This study highlights the importance of regional wind patterns and geomorphology in SDS formation, with prevailing wind directions from the northwest, west, and south. The integration of the ERA5 reanalysis and machine learning techniques offers significant potential for improving SDS monitoring and studies. 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

Understanding aeolian sediment transport and wind erosion enhances our knowledge of desert dune formation and sand migration. The Makran region of southern Sistan and Baluchistan is prone to wind-driven erosion alongside frequent sand and dust storms (SDSs). Hourly wind data from two meteorological stations spanning 1994–2020 were analyzed to study erosive winds and sand transport. Wind energy analysis using drift potential (DP) indicated low energy (DP < 200 in vector unit) and minimal spatial variation across the Makran dune fields. The effective winds transporting sand particles were towards the east from November to May, and in the northwestern direction from June to October. The DP showed a gradual decline in the study area from 1990 to 2022, with no significant temporal trends. The sand dune morphology analysis indicates that bimodal wind regimes primarily form linear dunes and sand sheets, while crescentic, transverse, and topographic dunes are also present. Full article

This study provides a comprehensive evaluation of dust events over Iran, using synoptic data from 286 meteorological stations. The dust events are classified according to synoptic dust codes as suspended dust and others (i.e., blowing dust, dust storms) and based on their intensity with horizontal visibility ≤1, 3, 5, and 10 km. Severe events (visibility ≤ 1 km) of suspended dust (code 06) occurred primarily in the western parts of Iran, while blowing dust events of moderate or severe intensity dominated over the south and eastern Iran, thus revealing a contrasting spatial distribution regarding the type and frequency of dust events. Furthermore, a distinct seasonality is revealed in the number of dust events, since suspended dust maximized in SW Iran from March to July, highly associated with Shamal winds, while blowing dust storms over south and east Iran maximized from April to August. Zabol city, east Iran, and some stations along the coast of the Arabian Sea are highly impacted by this type of dust storm throughout the year. Trend analysis revealed a notable increase in frequency of dust events during the period 1994–2023, particularly in the western part of Iran, mostly attributed to transboundary dust from the Mesopotamian plains. The large increase in dust activity during 1994–2009 was followed by a decrease during the 2010s at many stations, while notable differences were observed in the spatial distribution of the trends in suspended and blowing dust. An inverse correlation between dust events and precipitation anomalies was observed, since years with abnormal precipitation (e.g., 2019; 138% increase) were related to a substantial decrease in dust occurrence. Over an 11-year period, surface dust concentrations exceeded the annual PM₁₀ threshold of 50 µg/m³ on more than 800 days, with maximum concentrations reaching up to 1411 µg/m³. This highlights the urgent need for effective management strategies to mitigate the impacts of dust storms on air quality and public health in Iran. Full article

Dust storms are frequent meteorological phenomena in the arid and semi-arid regions of Khuzestan province (KHP) in southwest Iran. These storms result in significant social and economic repercussions that extend beyond mere meteorological and climatic disturbances. Over the past decade, they have become the primary cause of substantial environmental and socio-economic damage in the region. In this study, we aim to assess the economic impacts of sand/dust storms (SDSs) on human health, agriculture (specifically Estamran dates), and migration in KHP. Our findings reveal the following economic consequences: Respiratory diseases incurred a financial loss of approximately USD 14 million, with more than 450 individuals requiring hospitalization at a cost exceeding USD 0.04 million between April and July 2022. In addition, cardiovascular diseases related to SDSs resulted in costs exceeding USD 1.9 million within the same time frame. Cities near the sources of dust storms experienced a cumulative damage cost of approximately USD 6.8 million. The local population in these cities also suffered more significant adverse effects compared to those in cities farther from the influence of dust storms in southwest Iran. We further evaluated the impact of SDSs on the quality of Estamran dates by analyzing 20 samples from key Estamran date production areas in KHP, including Ahvaz, Abadan, Khorramshahr, and Shadegan. The cost of damages (COD) in this sector was estimated at around USD 18.3 million, with Shadegan bearing the brunt of the loss at approximately USD 8.3 million. SDSs also have an important social economic impact due to deterioration of living conditions and migration in KHP. In total, the socio-economic costs of SDSs in these three sectors amounted to about USD 39 million. This is particularly concerning considering that Khuzestan province contributed 14.8% to Iran’s Gross Domestic Product (GDP) in 2020, representing 50% of the economic output of southwestern provinces. Therefore, the current findings represent an alarming situation regarding the socio-economic impacts of dust storms in SW Iran. Full article

In recent years, northeastern Iran, particularly Khorasan Razavi province, has experienced wind erosion and dust storms, although large-scale studies are limited. To assess wind patterns, sand drift, and dust events, hourly wind data were analyzed using Fryberger’s method, along with trend analysis through the Mann–Kendall and Sen’s slope tests. Additionally, MODIS satellite data and Google Earth Engine helped identify event frequency and spatial patterns. The results show that east (12%) and southeast winds (9.6%) are the most frequent, with an average annual wind speed of 4.39 knots. Sand drift potential (DP = 96, RDP = 21.6) indicates sand movement from southeast to northwest, with a multi-directional wind system (unidirectional index of 0.22). The results of the AOD index show that the amount of dust in the north and northwest part is more than other locations, and more than 500 events with dust has been registered over the last two decades. These findings suggest that policymakers should monitor these trends to mitigate the environmental and infrastructural damage caused by blowing sand. Full article

Sand and dust storms (SDS) pose a wide range of hazards to human society, affecting people in drylands and beyond. This paper, based on a wide-ranging review of the scientific and grey literature, presents, for the first time, a comprehensive synthesis of mitigation and adaptation interventions designed to manage the risks involved and thus build resilience to these SDS hazards in line with the Sendai Framework for Disaster Risk Reduction 2015–2030 (Sendai Framework) and the Sustainable Development Goals. It highlights case studies and good practice examples of measures available to reduce the risks and impacts associated with SDS beyond SDS source areas. These measures, which are interrelated and complementary, are summarized under education initiatives (for schools, specific sectors and vulnerable groups), risk/impact assessments (involving information on hazard, exposure and vulnerability), vulnerability assessment/mapping, integrated monitoring and early warning (using the World Meteorological Organization’s Sand and Dust Storm Warning Advisory and Assessment System, or SDS-WAS) and emergency response and risk reduction plans (including contingency planning). Many of these measures are developed for other hazards, but not for SDS. Data availability is an important issue in this regard, and the example of Kuwait illustrates that even with a relatively good understanding of SDS, many aspects of impact mitigation remain poorly understood. Developing appropriate responses to SDS hazards is a matter of some urgency given climate change projections that indicate more frequent and intense SDS emissions due to increased aridity and worsening drought conditions (frequency, severity and duration). Full article

The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study. Full article

Sand and dust storms (SDSs) are particularly concerning natural disasters in East Asia. At present, there is still a lack of comprehensive knowledge of the characteristics of the cross-border SDSs between Mongolia and Northern China and the associated weather systems. This study identifies and documents the spring cross-border SDSs between Mongolia and Northern China based on the MODIS AOD map and Himawari-8/9 dust RGB images and analyzes the corresponding weather system types. A total of 76 spring cross-border SDSs were identified during 2000–2023, accounting for 55.1% of the total SDSs in both countries. The vast majority of the cross-border SDSs (86.8%) were related to Mongolian cyclones (MCs). Among them, 53.9% of the cross-border SDSs were mainly driven by Mongolian cyclones alone, and 32.9% were driven by the combination of MCs and cold highs (MC-CH type). Significant differences in the horizontal distribution of the SDSs were observed for different weather types. MCs alone trigger SDSs in the southern halves of the MCs, so the horizontal extent of the SDSs is consistent with that of MCs but larger than that associated with cold fronts. For the MC-CH type, strong winds in the southern flanks of MCs and their rear cold highs jointly drive a large-scale zonally extensive SDS belt. In recent years, particularly in 2021 and 2023, the strong cross-border SDSs have been dominated by the MC–CH type. This study provides a reference for the forecasting and early identification of cross-border SDS disasters. Full article

The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur. Full article

Particulate-bound mercury (PBM) has a large dry-deposition rate and removal coefficient, both of which import mercury into terrestrial and marine ecosystems, causing global environmental problems. In order to illustrate the concentration characteristics, main sources, and health risk of PBM in the atmospheric environment during the spring dust storm period in Xi’an in 2022, PM_2.5 samples were collected in Xi’an in March 2022. The concentration of PBM and the PM_2.5 composition, including water-soluble ions and elements, were analyzed. The input of dust caused a significant increase in the concentration of PBM, Ca²⁺, Na⁺, Mg²⁺, SO₄²⁻, and metal elements in the aerosol. The research results revealed that the dust had a strong enrichment influence on the atmospheric PBM in Xi’an. Anthropogenic mercury emissions and long-distance migration in the sand source area promote the rise in PBM concentration and should be included in the mercury inventory. The values of the risk index for a certain metal (E_rⁱ) (572.78–1653.33) and the geo-accumulation index (Igeo) (2.47–4.78) are calculated during this study, showing that atmospheric PBM has a strong pollution level with respect to the ecological environment and that Hg mainly comes from anthropogenic mercury emissions. The non-carcinogenic health risk of atmospheric PBM in children (8.48 × 10⁻²) is greater than that in adults (1.01 × 10⁻²). The results show that we need to pay more attention to children’s health in the process of atmospheric mercury pollution control. This study discusses the distribution characteristics of PBM during spring sandstorms and the effects of atmospheric mercury on residents’ health, providing a basis for studying the sustainable development of environmental health and formulating effective strategies for mercury emission control. Full article

In the Inner Mongolia region, sand and dust storms are prevalent throughout the year, with sand erosion having a particularly significant impact on the performance of wind turbine blades. To enhance the performance stability of wind turbines and reduce operation and maintenance costs, this study delves into the specific impact of sand-laden wind erosion on the aerodynamic performance of scaled-down wooden wind turbine blades. The experiment conducts vehicle-mounted tests on scaled models of 1.5 MW wind turbine blades that have been eroded by wind-sand flows from different zones, analyzing the changes in aerodynamic performance of wind turbines caused by the erosion. The results indicate that with an increase in the angle of installation, both the overall power output and the wind energy utilization coefficient of the wind turbines show a declining trend. The power outputs of both the partially eroded group and the fully eroded group are unable to reach the rated power level of 100 W. Compared to the uneroded group, the leading-edge eroded group demonstrated higher power output and wind energy utilization coefficients across most wind speed ranges. This finding verifies the possibility that the drag-reducing effect caused by pits from leading-edge erosion has a positive impact on the aerodynamic performance of the blades. It also provides a new research perspective and strong evidence for the study of erosion effects on wind turbine blades and the optimization of their aerodynamic performance. Full article

The Taklimakan Desert in northwest China stands as a significant contributor to dust storms, with its fringe oases already designated as ecologically fragile due to the severe impacts of these storms. This study focuses on Moyu County, situated on the southwest edge of the Taklimakan Desert, examining the origin and transport pathways of dust storms from 2004 to 2021. The classification involves utilizing a 36 h backward trajectory model and the k-means clustering technique, resulting in three clusters displaying distinct transport pathways and entry directions. Air pollutant concentrations at the study site corresponding to each cluster are analyzed to elucidate the contribution of dust storms from different directions. The results categorize 1952 dusty days into three categories: NE-SE (cluster 1), N-N (cluster 2), and NW-W (cluster 3). The highest frequency of dust storms, accounting for 64% of the total suspended dust weather, originates from the northeast and southeast direction (NE-SE category), with relatively weak intensity, mainly as suspended dust (71.5%). Strong sand storms predominantly occur from the northwest direction (57.8%). Cluster 1 (the southeast direction) exhibits a higher concentration of SO₂, NO₂, and CO, mainly associated with its pathway over anthropogenically polluted areas. Conversely, Cluster 3 (northwest direction) shows higher PM10 and PM2.5 concentrations due to increased wind speed and stronger dust storm intensity. The study develops dust storm early warning schemes based on 15-day advance predictions, utilizing an 18-year trajectory model and local monitoring data. This proposed warning scheme serves as a predictive tool for potential dust storm events and air pollution levels, aiding in both scientific research and policy formulation for dust storm mitigation and adaptation. The data obtained also hols relevance for conducting further scientific research in this field. Full article

Global warming due to climate change has increased the frequency of sand and dust storms that affect air quality and ecosystems in general, contributing to air pollution. The Sahara Desert is the most potent emitter of atmospheric dust. The atmosphere is an extreme environment and microorganisms living in the troposphere are exposed to greater ultraviolet radiation, desiccation, low temperatures and nutrient deprivation than in other habitats. The Iberian Peninsula, and specifically the Canary Islands—due to its strategic location—is one of the regions that receive more Saharan dust particles annually, increasing year after year, although culturable microorganisms had previously never been described. In the present work, dust samples were collected from three calima events in the Canary Islands between 2021 and 2022. The sizes, mineralogical compositions and chemical compositions of dust particles were determined by laser diffraction, X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. Particle morphology and biological features were also studied by scanning electron microscopy (SEM-EDX) and confocal laser scanning microscopy (CLSM). The mineral–bacteria interactions were described from microscopic observations, which revealed the presence of iberulites and small neoformed kaolinite crystals in association with bacteria. This article defines the term “mineral atmospherogenesis” and its variant, “mineral bioatmospherogenesis”, through microbial interaction. This is the first described case of kaolinite produced through mineral bioatmospherogenesis. The bacterial growth in atmospheric dust was illustrated in SEM images, constituting a novel finding. Twenty-three culturable microorganisms were isolated and identified by 16S rRNA sequencing. Members of the phyla Pseudomonadota, Bacillota and Actinomycetota have been found. Some of these microorganisms, such as Peribacillus frigoritolerans, have Plant Growth-Promoting Rhizobacteria (PGPR) properties. Potential human pathogenic bacteria such as Acinetobacter lwoffii were also found. The presence of desert dust and iberulites in the Canary Islands, together with transported biological components such as bacteria, could have a significant impact on the ecosystem and human health. Full article

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks. Full article