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Sand and dust storms (SDS) are global phenomena that significantly impact the socio-economy, human health, and the environment. The characterization of SDS intensity is a fundamental aspect of SDS issues and studies. In this study, a sand and dust storms index (SDSI) is developed to characterize SDS intensity by addressing the potential impacts of sand and dust storms on sensitive elements. Compared with other indices, SDSI includes four SDS-related components: SDS frequency, SDS visibility, SDS duration, and SDS wind speed. Using SDSI, this study characterizes the SDS intensity in the Three-North Forest Shelterbelt Program (TNFSP) region of China. The SDSI results show that high values of SDSI are mostly concentrated in southern Xinjiang, western and central Inner Mongolia, western and central Gansu, and northern Ningxia. By analyzing the SDSI components, over half of the stations experienced sand and dust storms no more than once per year on average. Most of the SDS events reduced horizontal visibility to less than 500 m, one-third of SDS events last more than two hours, and the wind speed of over half of the SDS events varied between 10–17 m/s. In comparison with SDS frequency, SDSI performs better in reflecting the spatial and temporal variation of SDS events. Therefore, instead of SDS frequency, SDSI can be applied to studies relevant to SDS intensity. Finally, five major SDS transportation routes were identified based on the surface prevailing wind direction, SDSI, and the existing literature. The SDS routes, combined with SDSI, could help governments and policy-makers cooperate on a regional level to combat SDS events more effectively.

Airborne dust and dust storms are natural disasters that transport dust over long distances from the source basin, sometimes reaching hundreds of kilometers. Today, Iraq is a basin that produces dust storms that strike all neighboring countries such as Iran, Kuwait and Saudi Arabia. These storms affect the productivity and capacity of the photovoltaic modules and reduce the amount of electricity that is generated clearly. Airborne dust reduces the intensity of solar radiation by scattering and absorbing it. In addition, the dust accumulated on the photovoltaic modules causes a deterioration in their productivity. In this study, an extensive review of wind movement and its sources, especially those that hit the city of Baghdad, the capital of Iraq, was conducted. Practical experiments were also carried out during a storm to measure important variables that had not been measured practically before at this site. The experimental tests were carried out starting from 1 April 2022 and continued until 12 April. Within this period, a dust storm occurred that lasted for three consecutive days that was considered one of the most severe storms that the city of Baghdad had experienced in the last few years. Practical measurements showed a deterioration in the solar radiation intensity by up to 54.5% compared to previous days. The air temperature during the storm decreased by 21.09% compared to the days before the storm. From the measurements of ultrafine aerosol particles PM1 and PM2.5, there was a significant increase of 569.9% and 441% compared to the days before the storm, respectively. Additionally, the measurements showed an increase of 217.22% and 319.21% in PM10 and total suspended particles, respectively. Indoor performance experiments showed a deterioration of current, voltage, power and electrical efficiency by 32.28%, 14.45%, 38.52% and 65.58%, respectively, due to dust accumulated during the storm days compared to the previous days. In the outdoor experiments, the rates of deterioration of current, voltage, power and electrical efficiency were greater, reaching 60.24%, 30.7%, 62.3% and 82.93%, respectively, during the storm days compared to the days before it. During a storm, cleaning the panels is futile due to the high concentration of dust in the air, especially by water. However, the photovoltaic modules can be dry cleaned with bristle brushes after the storm has subsided.

This study aims to determine the reasons for the increase in the frequency of sand and dust storms in the Middle East and to identify their sources and mitigate them. A set of climatic data from 60 years (1960–2022) was analyzed. Sand storms in Iraq are a silty sand mature arkose composed of 72.7% sand, 25.1% silt, and 2.19% clay; the clay fraction in dust storms constitutes 70%, with a small amount of silt (20.6%) and sand (9.4%). Dust and sand storms (%) are composed of quartz (49.2, 67.1), feldspar (4.9, 20.9), calcite (38, 5), gypsum (4.8, 0.4), dolomite (0.8, 1.0), and heavy minerals (3.2, 6.6). Increasing temperatures in Iraq, by an average of 2 °C for sixty years, have contributed to an increase in the number of dust storms from 75 to 200 times annually. North African storms affect the Middle East, with a monthly average exceeding 300 g/m3 in peak dust seasons. To reduce the negative impacts on public health, property, and infrastructure, the study suggests solutions to mitigate them, including reducing carbon dioxide gas emissions to prevent the expansion of drought and the afforestation of the desert with plants adapted to drought using advanced techniques and avoiding land overuse.

This study commences by extracting hourly data on PM 10 and PM 2.5 concentrations to discern occurrences of sand and dust events in the North China region spanning from 2015 to 2023. Subsequently, the HYSPLIT model is employed to precisely locate the sources and track the migration routes of specific representative sand and dust weather occurrences. Additionally, the study investigates the interplay between meteorological factors and dust events to elucidate the triggering mechanisms of these phenomena. Building on these findings, used to forecast PM 10 concentrations for North China through random forest model. The findings indicate In March 19–23, 2023, North China witnessed dust events, with the episode from being the most intense and widespread in recent years. An analysis of the paths and sources of this sand and dust event revealed that the severe sandstorm in North China was the result of a synergistic effect of dust sources from Mongolia and Northwest China. The study identified a combination of meteorological factors—maximum wind speed > 6.1 m/s, relative humidity < 56%, and solar radiation > 128 w/m 2 —as having the most significant impact on sandstorm in North China. Leveraging these research outcomes, the study established an hourly PM 10 prediction model for North China using random forest approach, with the highest accuracy for 1–3 h forecasts. This study has advanced our understanding of dust and sand weather events to a certain extent, furnishing a theoretical and scientific basis for the effective management of sand and dust in ecologically vulnerable regions.

Using 30 years of satellite observations, two sand and dust storms (SDS) source locations (hotspots) were detected on the southern side of the Mesopotamian Flood Plain. Around 40 million people in the region are affected by the two hotspots, including populations in Iraq, Iran, Kuwait, Saudi Arabia, Qatar, Bahrain, and Emirates. Both hotspots encompass roughly 8212 km2 and contribute 11% to 85% in 2005 and 2021, respectively, of the total SDS in the region. Dust physical (particle surface area and size percentages) and chemical (mineralogy, major and trace elements, and radionuclides) properties show close similarities between source and downwind samples during SDS originated solely from the two hotspots. Deposited dust size particles show a finning trend towards the north in the Middle East compared to the south. A comprehensive assessment of the chemical and physical properties of soil and dust samples was conducted as an essential step in developing and implementing a mitigation plan in order to establish a success story in reducing SDS, improving air quality, and benefiting the gulf countries and neighboring regions.

This study investigates sand and dust storms under RCP scenarios over the West Asia. The RegCM 4.7 was employed to simulate the dust event with non-hydrostatic core, a horizontal grid spacing of 20 km and 18 vertical sigma levels with the model top at 10 hPa. Data for running RegCM were extracted from the ICTP website and HadGEM2 database. The simulations were conducted for historical (1996–2005) and future (2021–2032) periods. The evaluation of HadGEM2 model using AERONET and MISR data revealed that the model could reproduce the aerosol features with a very high confidence. During the historical period, the AOD and DCB were strong in spring and summer with higher values mostly situated in Rab’ al Khali desert of Arabian Peninsula, Red Sea, and the east of Dasht-e Lut. The atmospheric pattern at SLP and 850 hPa levels revealed that these regions are affected by strong dust outflows from the dipole in pressure between Indian-Pakistan low-pressure and high-pressure systems in the north of Africa and Caspian Sea plus cyclogenesis during dust storms. Also, the positive values of aerosol asymmetry parameter proved that aerosols in inland area are associated with dust outflows from the Rab’ al Khali and the Dasht-e Lut deserts along with air pollution from industrial activities and the Sahara Desert dust in the coastal region. The future dust emission under RCP scenarios indicated that AOD and DCB values would diminish in all months especially in spring and the highest AOD would happen in the center and south of Arabian Peninsula, Red Sea, and southeast of Iran. Also, the dust emission flux would decrease in spring and increase in summer and fall as compared to present. The South Sinai and Arabian Peninsula would have the most frequent dust emission flux too, but the dust emission flux would significantly drop in the southeast of Iran. The results from RCPs also demonstrated that the wind speed mean would decrease and the mean of soil moisture increase by about 6–6.5 kg/m2 up to a depth of 10 cm in all RCP scenarios.

Dust storms can suspend large quantities of sand and cause haze in the boundary layer over local and regional scales. Iraq is one of the countries that is often impacted to a large degree by the occurrences of dust storms. The time between June 29 to July 8, 2009 is considered one of the worst dust storm periods of all times and many Iraq is suffered medical problems as a result. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS Surface Reflectance Daily L2G Global 1 km and 500 m data were utilized to calculate the Normalized Difference Dust Index (NDDI). The MYD09GA V006 product was used to monitor, map, and assess the development and spread of dust storms over the arid and semi-arid territories of Iraq. We set thresholds for NDDI to distinguish between water and/or ice cloud and ground features and dust storms. In addition; brightness temperature data (TB) from the Aqua /MODIS thermal band 31 were analyzed to distinguish sand on the land surface from atmospheric dust. We used the MODIS level 2 MYD04 deep blue 550 nm Aerosol Option Depth (AOD) data that maintains accuracy even over bright desert surfaces. We found NDDI values lower than 0.05 represent clouds and water bodies, while NDDI greater than 0.18 correspond to dust storm regions. The threshold of TB of 310.5 K was used to distinguish aerosols from the sand on the ground. Approximately 75% of the territory was covered by a dust storm in 5 July 2009 due to strong and dry northwesterly winds.

Climate-related hazards such as sand and dust storms (SDS) have various impacts on human health, socio-economy, environment, and agroecosystems. Iran has been severely affected by domestic and external SDS during the last two decades. Considering the fragile economy of Iran’s rural areas and the strong dependence of livelihood on agroecosystems, SDS cause serious damage to human communities. Therefore, there is an urgent need to conduct a vulnerability assessment for developing SDS risk mitigation plans. In this study, various components of SDS vulnerability were formulated through a geographic information system (GIS)-based integrated assessment approach using composite indicators. By implementing a GIS multiple-criteria decision analysis (GIS-MCDA) model using socioeconomic and remote sensing data, a map of rural vulnerability to SDS was produced. Our results show that about 37% of Iran’s rural areas have experienced high and very high levels of vulnerability to SDS. Rural areas in the southeast and south of Iran, especially Sistan and Baluchestan and Hormozgan provinces are more vulnerable to SDS. The findings of this study provide a basis for developing SDS disaster risk-reduction plans and enabling the authorities to prioritize SDS mitigation policies at the provincial administrative scale in Iran.

Photovoltaic (PV) clusters in deserts such as the Gobi and other regions are frequently affected by sand and dust, which causes great deviation in power prediction and seriously threatens the safe operation of new power systems. For this reason, this paper proposes a short-term cluster PV power prediction method based on statistical upscaling, considering the effect of sand and dust. Firstly, the sand and dust events are identified, and then time series generative adversarial networks (TimeGANs) are used to solve the problem of small sample scarcity in sand and dust and construct a power correction model for sand and dust scenes. Secondly, for different weather scenes, a combination of conventional prediction and correction prediction is used to solve the problem of large differences in the predictability of a single model. Finally, a statistical upscaling method is utilized to calculate the cluster prediction power to solve the prediction difficulties of large-scale newly installed PV field stations. Through a case study and comparison with other models and methods, the cluster prediction method established in this paper effectively improves the prediction accuracy of the power of large-scale PV clusters affected by sand and dust, with the RMSE reduced by 8.28%.