Dust Storms Research Articles

Dust Events over the Urmia Lake Basin, NW Iran, in 2009–2022 and Their Potential Sources

Nowadays, dried lake beds constitute the largest source of saline dust storms, with serious environmental and health issues in the surrounding areas. In this study, we examined the spatial–temporal distribution of monthly and annual dust events of varying intensity (dust in suspension, blowing dust, dust storms) in the vicinity of the desiccated Urmia Lake in northwestern (NW) Iran, based on horizontal visibility data during 2009–2022. Dust in suspension, blowing dust and dust storm events exhibited different monthly patterns, with higher frequencies between March and October, especially in the southern and eastern parts of the Urmia Basin. Furthermore, the intra-annual variations in aerosol optical depth at 500 nm (AOD550) and Ångström exponent at 412/470 nm (AE) were investigated using Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Urmia Lake Basin (36–39°N, 44–47°E). Monthly distributions of potential coarse aerosol (AE < 1) sources affecting the lower troposphere over the Urmia Basin were reconstructed, synergizing Terra/Aqua MODIS AOD550 for AE < 1 values and HYSPLIT_4 backward trajectories. The reconstructed monthly patterns of the potential sources were compared with the monthly spatial distribution of Terra MODIS AOD550 in the Middle East and Central Asia (20–70°E, 20–50°N). The results showed that deserts in the Middle East and the Aral–Caspian arid region (ACAR) mostly contribute to dust aerosol load over the Urmia Lake region, exhibiting higher frequency in spring and early summer. Local dust sources from dried lake beds further contribute to the dust AOD, especially in the western part of the Urmia Basin during March and April. The modeling (DREAM8-NMME-MACC) results revealed high concentrations of near-surface dust concentrations, which may have health effects on the local population, while distant sources from the Middle East are the main controlling factors to aerosol loading over the Urmia Basin.

Anthropogenic forest degradation and its impact on soil erosion in the mountainous region of northern China

Soil erosion has significant negative effects on the eco-environment, and it also threatens the sustainability of agricultural production. However, studies on soil erosion in northern China during the historical period, and its relationship with climate change and human activities, are rare. Here we present a 2900-year pollen and geochemical record from a sediment core from Lingganhai Lake, an alpine freshwater lake on the Chinese Loess Plateau in northern China. We observed an abrupt increase in the influx of detrital elements at ∼ 750 CE, accompanied by decreased tree pollen and increased cereal pollen percentages, which suggest severe soil erosion induced by intensified human activities. Coeval pollen and fungal spore records from other sites in the vicinity of Lingganhai Lake also indicate reduced forest cover, intensified agricultural and grazing activity, and increased dust storm frequency. During 1250–1380 CE, decreased soil erosion at Lingganhai Lake was accompanied by forest recovery and a reduction in the intensity of human activities. However, after ∼ 1380 CE, intensive soil erosion was renewed, likely caused by enhanced human activity, which partially overlapped with intensified dust activity in northern China during the Little Ice Age. Overall, our results demonstrate the close link between vegetation cover and soil erosion, and they highlight the importance of the maintenance of montane forest in northern China for soil preservation.

The Effects of Dust Storms on People Living in Beijing: A Qualitative Study.

Dust storms, which are common aversive occurrences in northern China, result from high winds, dry soil or dust, and soil surface disturbance. Exposure to dust storms, regardless of duration, can induce varying mental and physical distress levels. Recognizing the urgency of comprehending the impact of dust storms on residents and the scarcity of information on their effects on the indigenous civilians there, this study aims to address this gap by qualitatively sampling 29 participants from Beijing, a typical city in northern China. The current study seeks to gain insights into residents' dust storm experiences and explore their perspectives on effective coping mechanisms. The findings align with existing knowledge regarding the mental and physical repercussions of dust storms while identifying some emerging patterns of coping mechanisms already employed by residents in Beijing. Concerns regarding mental well-being, either directly influenced by the environmental conditions or indirectly stemming from disruptions to life routines on a broader scale, persistently dominate people's perceptions of dust storms. New themes emerged following the step-by-step exploration of feelings and coping mechanisms. This study aims to enlighten the public about the ramifications of the dust storms in Beijing and advocate for essential policy support.

Primary Factors and Synoptic Pattern Classification of Mega Asian Dust Storms in Korea

Primary Factors and Synoptic Pattern Classification of Mega Asian Dust Storms in Korea

Dust model sensitivity to dust source mask, sandblasting efficiency, air density, and land use: Implications for model improvement

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared with a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 12 times more dust than does a physics-based expression. Simulations using model-predicted versus constant air density differ by only 8%. However, this difference (often overlooked) could range between 12% and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The difference between simulations within each of the four pairs is generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Effect of vegetation introduction versus natural recovery on topsoil properties in the dried Aral Sea bed

AbstractDesiccation of the Aral Sea has resulted in the emergence of vast saline and flat terrains, jeopardizing human health and agricultural activities because of sand and dust storms. Vegetation, mainly indigenous Haloxylon species, has been introduced to ameliorate the soil. As a critical indicator of rehabilitation, the physicochemical properties of soil after the introduction of vegetation remain poorly understood. This study examined (1) the changes in topsoil properties after vegetation establishment based on a 30‐year chronosequence and employed (2) site comparisons of topsoil properties between two cases of natural versus introduced vegetation on a dried Aral Sea bed. Twelve paired areas that were naturally or artificially vegetated during 1990, 2000, 2005, 2008, 2013, and 2017 were selected for examination. Irrespective of vegetation type, increases in organic matter and nutrients (TOC >90%, TN >143%, and P2O5 >23%) were detected in the surface soil (0–10 cm) along the 30‐year chronosequence. In addition, decreases in ECe (92% and 69%), CEC (41% and 11%), Ca2+ (38% and 12%), and TIC (81% and 11%) were observed in both natural and introduced vegetation, respectively. The introduced vegetation was associated with a slightly greater accumulation of soil K+ and TN than the natural vegetation. Our results indicate vegetation‐derived nutrient and organic matter accumulation as well as the possible removal of salts by root exudates in the surface soil. Overall, vegetation contributed to soil amelioration, with similar effects observed at naturally and artificially vegetated sites.

Application of global dust detection index (GDDI) for sand and dust storm monitoring over Kingdom of Saudi Arabia

Application of global dust detection index (GDDI) for sand and dust storm monitoring over Kingdom of Saudi Arabia

Performance Evaluation of mm-Wave Based System in GFDM- 5G and Beyond Channel Model with Dust Storm Scenario

Telecommunication has made tremendous improvements in terms of bandwidth, requiring good frequency location, high data rates, and wideband spectrum availability. One solution to these requirements is the millimeter wave frequency band of 30 GHz. However, communication in this band is facing new challenges due to climate effects such as humidity, dust storms, and temperature. For fifth-generation (5G) mobile networks and beyond, Generalized Frequency Division Multiplexing (GFDM) has been proposed as a compelling candidate to substitute Orthogonal Frequency Division Multiplexing (OFDM). The GFDM's ability to adapt the block size and type of pulse shaping filters enables it to meet various crucial requirements, including low latency, low Out-Of-Band(OOB) radiation, and high data rates. This paper evaluated the overall GFDM performance and investigated the Bit Error Rate (BER) across a Rayleigh channel under various weather conditions. The simulation results show that GFDM outperforms the current OFDM candidate system. Also, GFDM offers better resistance to the Rayleigh channel with moderate and heavy dust storms in terms of BER.

Cross-Border Sand and Dust Storms between Mongolia and Northern China in Spring and Their Driving Weather Systems

Cross-Border Sand and Dust Storms between Mongolia and Northern China in Spring and Their Driving Weather Systems

Compound events of heatwave and dust storm in the Taklamakan Desert

Compound events of heatwave and dust storm in the Taklamakan Desert

Climate Change and Cardiovascular Health

Climate change may increase the risk of adverse cardiovascular outcomes by causing direct physiologic changes, psychological distress, and disruption of health-related infrastructure. Yet, the association between numerous climate change-related environmental stressors and the incidence of adverse cardiovascular events has not been systematically reviewed. To review the current evidence on the association between climate change-related environmental stressors and adverse cardiovascular outcomes. PubMed, Embase, Web of Science, and Cochrane Library were searched to identify peer-reviewed publications from January 1, 1970, through November 15, 2023, that evaluated associations between environmental exposures and cardiovascular mortality, acute cardiovascular events, and related health care utilization. Studies that examined only nonwildfire-sourced particulate air pollution were excluded. Two investigators independently screened 20 798 articles and selected 2564 for full-text review. Study quality was assessed using the Navigation Guide framework. Findings were qualitatively synthesized as substantial differences in study design precluded quantitative meta-analysis. Of 492 observational studies that met inclusion criteria, 182 examined extreme temperature, 210 ground-level ozone, 45 wildfire smoke, and 63 extreme weather events, such as hurricanes, dust storms, and droughts. These studies presented findings from 30 high-income countries, 17 middle-income countries, and 1 low-income country. The strength of evidence was rated as sufficient for extreme temperature; ground-level ozone; tropical storms, hurricanes, and cyclones; and dust storms. Evidence was limited for wildfire smoke and inadequate for drought and mudslides. Exposure to extreme temperature was associated with increased cardiovascular mortality and morbidity, but the magnitude varied with temperature and duration of exposure. Ground-level ozone amplified the risk associated with higher temperatures and vice versa. Extreme weather events, such as hurricanes, were associated with increased cardiovascular risk that persisted for many months after the initial event. Some studies noted a small increase in cardiovascular mortality, out-of-hospital cardiac arrests, and hospitalizations for ischemic heart disease after exposure to wildfire smoke, while others found no association. Older adults, racial and ethnic minoritized populations, and lower-wealth communities were disproportionately affected. Several environmental stressors that are predicted to increase in frequency and intensity with climate change are associated with increased cardiovascular risk, but data on outcomes in low-income countries are lacking. Urgent action is needed to mitigate climate change-associated cardiovascular risk, particularly in vulnerable populations.

Changes in the severity of compound hot-dry-windy events over global land areas

Hot-dry-windy events (HDWEs), characterized by concurrent high temperature, low humidity, and strong wind speeds, are associated with reduced crop yields, accelerated spread of wildfire, increased human health risks, and dust storms across various regions. A comprehensive understanding of global HDWE severity is crucial for mitigating these detrimental consequences. However, current assessments of HDWE show notable regional constraints due to the lack of a universal definition for its severity. In this study, we introduce a standardized hot-dry-windy index (SHDWI) using multi-source datasets to assess spatial and temporal changes in the severity of HDWEs across the globe. Influenced by significantly rising temperatures, intensified wind speeds, and declining humidity, a notable decreasing trend (-0.09/decade, p < 0.01) in SHDWI indicates a rise in warm-season HDWE severity on average across the global land areas, particularly in South America, North Africa, western Europe, and southeastern Asia. Geospatially, Europe, Africa, South America, and Asia have experienced substantial increases in HDWE severity and expanded areas affected by strong HDWEs between 1980–1998 and 1999–2017. Notably, HDWE severity is predominated by temperature in the south of 30°N but by humidity in the north of 30°N. The standardized index proposed in this study would be beneficial for a worldwide assessment of future HDWE risks under climate change.

Airborne particle radioactivity during desert dust days in Cyprus

Mediterranean countries are often affected by desert dust storms, which have significant effects on the environment and public health. We compared airborne particle radioactivity levels during desert dust and non-dust days in Cyprus.Gross α- and β-radioactivity from Total Suspended Particle (TSP) samples, collected at two urban routine monitoring stations in Limassol and Nicosia, were available for the period 2017–2020 and 2008–2020, respectively. Radionuclides 137Cs and 40K, from TSP samples, were also available from a semi-industrial monitoring station in Nicosia during 2008–2020. Information on desert dust presence, dust origin, particulate matter (PM) levels, and solar activity (KP index and solar sunspot numbers - SSN) were also obtained. We used linear regression models adjusting for seasonality and long-term trends, and solar activity to assess the effect of dust storms on TSP gross α- and β-, and 137Cs and 40K radioactivity levels.Gross α- and β-radioactivity, and 137Cs and 40K radioactivity levels were significantly higher on days with desert dust compared to days characterized with no influence of desert dust. Levels of gross α- and β-radioactivity during dust days were higher when dust originated from the Middle East deserts than from the Sahara Desert. The same trend was observed for the ratios 137Cs to 40K and 137Cs to PM10. Conversely, ratios of TSP gross α- and β-radioactivity to PM10 were significantly lower during desert dust days in comparison to days without dust influence.This study suggests that desert dust increase both TSP gross α- and β-radioactivity, as well as 137Cs and 40K radioactivity levels. Further studies should clarify the contribution of anthropogenic and other natural sources to the emission or transportation of particles radioactivity, to better mitigate future exposures.

DustSCAN: A Five Year (2018-2022) Hourly Dataset of Dust Plumes From SEVIRI

Airborne mineral dust significantly impacts air quality, human health, and the global climate. Due to sparse ground sensors, particularly in source regions, dust monitoring relies mainly on remote sensing through Aerosol Optical Depth (AOD) retrievals from polar-orbiting satellite optical instruments. These are valuable but lack the temporal resolution for precise plume tracking and source characterization. We introduce DustSCAN, a five-year, hourly dust plume dataset derived from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) images on geostationary-orbit Meteosat satellites. Using multi-channel infrared images, we detect atmospheric dust and track hourly dust-affected pixels. These are clustered into discrete plumes using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DustSCAN includes 9950 discrete plumes over 2018-2022 across the Sahara, the Arabian Desert, and Western and Central Asia. It complements existing resources and provides a framework for detailed analysis of dust sources, trajectories, and impacts. Its distinctive event-based and spatio-temporal detail offers an advancement in unraveling the complexities of dust storm dynamics.

Comparing annual extreme winds in Iran predicted by numerical weather forecasting and Gram-Charlier statistical model with meteorological observation data

Iran has been experiencing severe dust storms due to strong wind speeds that cause sand erosion. This erosion is particularly pronounced in the semi-arid and arid zones. Wind speed predictions using numerical weather forecasting (NWF) are indispensable; however, the applicability of NWF for predicting extreme annual wind speeds is not well known. To validate the NWF model, this study compared NWF-model-predicted 3-hour averaged wind speeds over one year and those observed at 390 weather stations. In addition, a statistical model was employed to estimate the probability densities of wind speeds for both the observational data and the NWF output. As an NWF model, the mesoscale Weather Research and Forecasting (WRF) model was utilized to simulate wind speeds. The results indicate that the observation data are consistent with the modeled datasets regarding the relationships among the mean, standard deviation, and skewness, whereas the WRF model tends to overestimate the mean wind speeds. In addition, the predictability of annual extreme wind speeds was determined using the peak factor. Moreover, skewness has emerged as an influential parameter for predicting extreme winds. Finally, the Gram-Charlier series model was utilized to estimate probability density functions of the wind speeds, demonstrating its effectiveness in capturing positively skewed distributions. The present analyses broaden the use of both NWF outputs and statistical methods to predict extreme wind speeds in Iran.

Association of Asian dust storms and PM2.5 with clinical visits for respiratory diseases in children

East Asia dust storms originated in China, bringing abundant suspended particulate matters (PMs), are investigated to examine the association between their arrivals and clinical visits for respiratory diseases (RCV – Respiratory disease Clinical Visit). Taiwan, located in the downwind with a well-documented health database from the National Health Insurance, is selected for the study. Children under the age of 17 years, because of their regular daily activities and outdoor exposures, who are diagnosed with respiratory disease, are our targeted population. We conducted a case-crossover design and used the conditional regression analysis to explore the relationship between the dust storms and RCV, for cases between 2000 and 2012, when severe dust storms were mostly recorded. In total, 2,253,092 patients were recruited. The analysis shows that the coming of dust storms significantly affects the condition of RCV in children. After adjusting for temperature, relative humidity, and air pollutant concentrations, dust storm in springtime is associated with an increase in RCV. In contrast, dust storm impact in wintertime is associated with a decrease in RCV. In addition, PM2.5 concentrations associated positively with RCV among children, especially in the southern region of Taiwan in springtime and in the central and southern regions of Taiwan in wintertime. In short, the results showed that the occurrence of dust storms affects the RCV in children. PM2.5 exposure is also responsible for the increase in children's RCV.

Investigating the Occurrence of Dust Storms and its Zoning Using GIS During 2018-2021: A Case Study in Ahvaz, Iran

Background: A dust storm is a critical environmental issue and natural hazard in arid and semiarid regions worldwide. Particulate matters including PM10 and PM2.5 are of the primary air pollutants, which can adversely affect human health. This research aimed to determine the fine dust concentration spatiotemporally and estimate the suspended particulate matter (PM10 and PM2.5) during 2018-2021 in Ahvaz. Methods: An analytical- descriptive method and the data from synoptic weather stations in the studied area were used in this study. The zoning of dust was done using the kriging method by ArcGIS software. Results: The results indicated that the year 2021 experienced the highest frequency of dusty days, with 54 occurrences, while the year 2019 exhibited the lowest frequency, with only 28 dusty days. Notably, Ahvaz city recorded the highest dust concentration in 2020, reaching an average of 4667 mg/m3 . Based on the findings of PM concentration over the four-year period, it is evident that 2021 recorded the highest PM10 concentration (326.14 mg/m3 ), while 2020 exhibited the highest PM2.5 concentration (148.26 mg/m3 ). Conclusion: The results revealed that there was an increase in dust concentration in the east and north. This is probably attributed to the proximity to dust centers.

Martian Dust Storm Spatial‐Temporal Analysis of Tentative Landing Areas for China's Tianwen‐3 Mars Mission

AbstractChina's first Mars sampling return mission (Tianwen‐3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA‐A, TLA‐C and TLA‐U) are selected based on elevation &lt;−2,000 m and latitude between 17° and 30°N. As a dominant feature of Martian meteorology, dust storms manifest in all seasons and affect the accuracy and safety of Mars exploration missions. Tianwen‐3's landing, sampling and ascent phases are in the dust storm season. Therefore, analyzing dust storm activity around landing areas is significant for the Tianwen‐3 mission. According to Mars Daily Global Maps taken by Mars Orbiter Camera spanning Martian years 24–28, 2,476 dust storm events around the three TLAs were identified in this research. Dust storm temporal probabilities within TLA‐A, TLA‐C and TLA‐U were calculated as 0%–44.69%, 0%–66.29% and 0%–33.64%, separately. Dust storm spatial probabilities around the TLA‐A, TLA‐C and TLA‐U were computed, with ranges of 0%–10.71%, 0%–14.55% and 0%–19.96% during the T1 period (Ls = 161–309°), and 0%–6.75%, 0%–7.65% and 0%–8.26% during the T2 period (Ls = 342‐55°), respectively. Finally, considering the temporal and spatial distribution of dust storms, we recommend the T2 period as the launch scenario. Three safe periods (Ls = 2–18°, 4–12°, and 356–4°) were assigned for the entry‐descent‐landing (EDL) phase, along with one period (Ls = 45–55°) for the take‐off and ascent phase. Five circular landing zones with dust storm spatial probability &lt;3% were selected for the Tianwen‐3 mission.

Intelligent Dust Monitoring System Based on IoT

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

Spatio-temporal characterization of PM10 concentration across Abu Dhabi Emirate (UAE)

The abundance and recurrence of particulate matter in Abu Dhabi Emirate (ADE), are often derived from different emission sources such as the combustion of hydrocarbon, producing much of the PM2.5 found in outdoor air, as well as a significant proportion of PM10. Wind-blown dust from open desert areas and construction sites, landfills and agriculture, brush/waste burning, and industrial sources, has contributed markedly to the problem of the spread of haze and the long-range movement of pollutants in the country. In this study, the spatio-temporal characterization of PM10 concentration across the Emirate was analyzed utilizing geospatial interpolation, spanning the period between 2013 and 2017. The results suggest that the fluctuations of the PM10 concentration can be decomposed into three dominant types, each characterizing different spatial and temporal variations. First, the western region with PM10 showing a peak concentration during the summer season i.e., when the winds are predominantly northerlies or northwesterly, and a minimal concentration during the winter season. Second, the central region with the PM10 exhibiting a concentration surge in July–August, as a result of a mix of strong winds and high temperatures. Third, the eastern region with a low concentration of PM10. Seasonally, this component exhibits two concentration maxima during quarters 2 and 3 (summer), and two minima during quarters 1 and 4 (winter). Indeed, the seasonal variability of PM10 concentration in desertic countries like the UAE is closely linked to the seasonal variation of heat waves and dust storms, which are characteristic of the dryland climate. During the summer months, the UAE experiences high temperatures and arid conditions, creating favorable conditions for the formation of heat waves. Furthermore, it was noticed that the PM10 concentration also fluctuated markedly throughout the study period with anomalies detected in open desert areas and regions characterized by extensive industrial operations.