Dust Storms Research Articles

Daily PM2.5 and Seasonal-Trend Decomposition to Identify Extreme Air Pollution Events from 2001 to 2020 for Continental Australia Using a Random Forest Model

Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m3. We predicted daily PM2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM2.5 attributable to extreme pollution events in different locations across the country.

Evaluation of Health Risk and Heavy Metal Pollution Caused by Dust Storms in Zabol City.

This study investigated the concentrations and compositions of specific elements in total suspended particulate (TSP) samples and the associated ecological, carcinogenic, and non-carcinogenic risks related to the inhalation, ingestion, and dermal absorption of heavy metals. The study focused on adult and pediatric populations in the highly dust-polluted city of Zabol, Iran, during 2022-2023. The samples were analyzed by using inductively coupled plasma optical emission spectrometry (ICP-OES). The concentration of metals in milligram per kilogram (mg/kg) was as follows: Al (75053) > Ca (8206) > Fe (5439) > Mg (5323) > Zn (15.27) > Pb (3.66) > Cr (3.38) > Cd (0.011); according to the results, the highest amount of metals related to aluminum and calcium in dust particles. The calculated individual indices, including the ecological risk index (ERI), pollution factor (Cfi), and geographic accumulation index (Igeo), indicated that calcium exhibited minimal pollution, while the metal samples (Al, Mg, Fe, Cd, Cr, Pb, and Zn) were categorized as uncontaminated. The results revealed that the ecological risk factor (Er) is below 40, indicating a low level of contamination. Additionally, the pollution level determined by the ecological risk potential (RI) was less than 150, suggesting a low probability of contamination. Ingestion posed the highest average hazard quotient (HQ) values for both children and adults in terms of non-carcinogenic risk assessment, while cutaneous and inhalation exposures showed lower values. Furthermore, the hazard index (HI) for heavy metals remained below the safe threshold of 1. The risk index (RI) values for lead, cadmium, and chromium were all below 1 × 10-6 in both adult and pediatric populations. The United States Environmental Protection Agency (USEPA) guidelines suggest an acceptable level of carcinogenicity for all heavy metals, indicating a potential risk of cancer associated with the presence of these metals in suspended particles in Zabol.

Geotechnical characteristics of levees subjected to sand boil due to high-water levels

Geotechnical characteristics of levees subjected to sand boil due to high-water levels

The role of refractive indices in measuring mineral dust with high-spectral-resolution infrared satellite sounders: application to the Gobi Desert

Abstract. Mineral dust significantly influences the Earth's climate system by affecting the radiative balance through the emission, absorption and scattering of solar and terrestrial radiation. Estimating the dust radiative effect remains challenging due to the lack of detailed information on the physical and chemical properties of dust. High-spectral-resolution instruments in the infrared (IR) spectrum, such as the Infrared Atmospheric Sounding Instrument (IASI), have demonstrated the ability to quantify these aerosol properties. A crucial parameter for characterizing mineral dust from space is the complex refractive index (CRI), as it links the dust's physical and chemical properties to its optical properties. This paper examines the impact of six prior laboratory CRI datasets to improve the characterization of dust microphysical properties using IASI. The CRIs include older measurements obtained through the classical pellet method, commonly employed in mineral dust applications, as well as newer datasets that incorporate the latest advancements in laboratory measurement techniques for aerosol generation. These datasets are tested on IASI measurements during a dust storm event over the Gobi Desert in May 2017. We evaluate the sensitivity of IASI to different CRI datasets using the Atmospheric Radiation Algorithm for High-Spectral Resolution Measurements from Infrared Spectrometer (ARAHMIS) radiative transfer algorithm and explore the datasets' impact on retrieving size distribution parameters by mapping their spatial distributions. The results indicate that the laboratory CRI datasets decrease the total error in the covariance matrix by 30 %. In addition, we assess the ability to accurately reconstruct IASI detections and the extent to which we can retrieve the microphysical properties of dust particles. The choice of CRI significantly impacts the accuracy of dust detection and characterization from satellite observations. Notably, datasets that incorporate recent aerosol generation techniques with higher spectral resolution and samples from the case study region show improved compatibility with IASI observations. The outcomes of this research emphasize two key points: the crucial link between chemical composition of dust and its optical properties and the importance of considering the specific composition of the CRI dataset for improved retrieval of the microphysical parameters. Furthermore, this study highlights the critical role of ongoing enhancements in CRI measurement approaches, as well as the potential of high-spectral-resolution infrared sounders for aerosol atmospheric investigation and for understanding the radiative impacts of sounders.

Sand and Dust Storm Risk Assessment in Arid Central Asia: Implications for the Environment, Society, and Agriculture

AbstractThis study aimed to assess sand and dust storm (SDS) risks in arid Central Asia during 2001–2021 from a multisectoral (environment, society, and agriculture) and comprehensive perspective on the Google Earth Engine (GEE) platform. The results show that the areas with moderate or greater SDS risk accounted for 18.75% of the total area of arid Central Asia. The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer. The SDS risk in the oasis area of southern Xinjiang increased significantly, while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years. Khwarazm of Uzbekistan, located in the Amu Darya River Delta, is the administrative district with the highest comprehensive risk of sandstorms, and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms. The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation.

Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.

Global characterization of the early-season dust storm of Mars year 36

Dust storms are one of the most important dust transport mechanisms on Mars with significant impacts on temperatures and atmospheric dynamics. Regional storms generally occur during the spring and summer of the southern hemisphere, but some of them have been observed earlier in the year. While these early-season storms have been little studied over the years, their occurrence produces important dust liftings. Their study, both vertically and horizontally, allows us to understand the mechanism of dust distribution that initiates them and the areas in which their onset takes place. In this study we compare the dust patterns of the early storms of MY 29 and 36. In addition, we analyzed globally, in each longitude, the early storm that occurred in MY 36 which was the most intense detected in such an early time. It presents maximum values of column dust optical depth (CDOD) of 0.5 and temperatures of 197 K. We use dust and temperature measurements provided by the Mars Climate Sounder (MCS) instrument and images by the Mars Color Imager (MARCI), both on board Mars Reconnaissance Orbiter (MRO).

Combined effects of ocean-land processes on spring precipitation variability in Mongolian Plateau

The Mongolian Plateau hosts one of the world's most fragile ecosystems, characterized by high volatility and frequent natural disasters due to rapid climate change and human activities in recent decades. Frequent dust storms notably mark spring in this region. Through observational analysis and numerical modeling, this study investigates the impacts of comprehensive ocean and land processes—including sea surface temperature (SST) in the North Atlantic and Pacific Oceans, as well as Eurasian land conditions—on the interannual fluctuations of spring precipitation in the Mongolian Plateau (SPMP) from 1979 to 2020. The ocean-land processes involve: a tripole pattern of North Atlantic SST anomalies triggers an eastward-propagating continental-scale wave train; Increased snow cover in Central Asia induces significant anomalous low pressure through the albedo effect; El Niño-Southern Oscillation initiate a teleconnection pattern over the upstream region of the Indian-western Pacific Ocean. These anomalous ocean and land conditions interact with large-scale atmospheric circulations, altering dynamical and hydrological conditions around the Mongolian Plateau, thereby contributing to SPMP variation. Further corroboration from numerical model experiments supports the observational analysis results. The combined effects of multiple ocean-land factors effectively explain precipitation variability across extensive areas of the Mongolian Plateau. A regression model constructed using these land-ocean factors captures the time evolution of the SPMP well.

Atmospheric Circulation on Other Planets: Venus, Mars, Jupiter

Abstract. This review provides a comparative analysis of atmospheric circulation across four planets in our solar system: Venus, Mars, Jupiter, and Earth. By examining the unique characteristics of each planet, including atmospheric composition, rotation rates, axial tilt, and solar radiation, the study explores how these factors influence the dynamics of their respective atmospheres. Venus, with its dense CO atmosphere and slow retrograde rotation, exhibits a super-rotating atmosphere leading to extreme and uniform climate conditions. Marss thin atmosphere and significant seasonal variations, combined with global dust storms, result in highly variable and dynamic atmospheric patterns. Jupiters rapid rotation and internal heat contribute to complex and powerful atmospheric dynamics, including strong jet streams and long-lasting storms like the Great Red Spot. In contrast, Earth's atmospheric circulation is driven by a combination of solar radiation, rotation, and the presence of oceans and continents, resulting in relatively stable and predictable weather patterns. The study highlights the diversity of atmospheric processes within our solar system, providing insights that are crucial for understanding planetary climates and assessing the potential habitability of exoplanets.

Dust over water: Analyzing the impact of lake desiccation on dust storms on the Iranian Plateau

The desiccation of lakes on the Iranian Plateau, driven by both natural processes and anthropogenic activities, has led to significant land cover changes, exposing lake beds and triggering adverse environmental consequences. These transformations pose serious threats to ecological conditions and the provision of ecosystem services. This study investigates the potential links between lake desiccation and the occurrence of dust storms across major lakes on the Iranian Plateau. By utilizing remote sensing data and time series statistical analysis, including the Mann-Kendall test, Ordinary Least Square (OLS) regression, and frequency analysis, we assessed changes in lake extent and their effects on Aerosol Optical Depth (AOD) and Particular Matter (PM2.5) from 1985 to 2020. An analysis of Landsat imagery reveals that lake desiccation commenced in the late 1990s, exhibiting a marked downward trend that has intensified in recent years. The results show a significant inverse correlation between aerosol parameters (AOD and PM2.5) and lake extent. Reduced lake area is associated with elevated AOD and PM2.5 concentrations, with the highest aerosol levels observed when lakes reach their smallest extent. Notably, despite an 81 % reduction in the lake area, dust storm occurrences have increased by 20%. These findings highlight the critical role of lake desiccation in exacerbating dust storms in the surrounding regions. Furthermore, the temporal alignment between fluctuations in lake extent and dust storm frequency is validated through regression analysis with a 95 % confidence level. The consistency between our results and existing literature underscores the reliability of the observed relationship, emphasizing the urgent need to address the environmental consequences of lake desiccation to mitigate its broader ecological impacts and potential human health impacts.Graphical Abstract

Unveiling the dynamics of shallow fronts in Australia during Southerly Buster episodes (1994–2020)

A Frontal Identification System (FIS), initially designed to track Galernas in the Bay of Biscay, has been adapted to monitor cold fronts across Australia using wind shifts derived from ERA5 hourly reanalysis data. This high-resolution system tracks shallow, cloud-free fronts during the warm season, which can trigger bushfires, dust storms, extreme heat, and coastal weather extremes like Southerly Busters (SB) on the east coast. SB episodes, marked by sudden, squally south winds, pose hazards for boating and aviation. Our analysis of 35 SB events from 1994 to 2020 indicates that SBs originate from frontogenesis in Bass Strait (40 %) or from prefrontal troughs crossing the strait (60 %). Preceding synoptic conditions involve a Southern Ocean cold front driving cool maritime winds into the Australian thermal low, creating shallow convergence fronts (∼1000 m deep) facing warm continental winds. Onshore acceleration into the thermal low sharpens these new fronts (Type 2 fronts of the southern coast) and weakens the trailing primary ocean front, which may disappear due to high-pressure wave propagation, cold advection, and subsidence over the sea. These fronts can penetrate deep inland (Central Australian fronts) and initiate SBs on the southeast coast after interacting with the Great Dividing Range. All 35 SB events show active shallow front frontogenesis/frontolysis affecting the southern coast and inland regions. Upper-level reversed pressure gradients between the thermal low over the continent and the ocean depression maintain a wind shear region over the shallow inland cold advection. Intense warm north-westerlies south of the surface front, with wind speeds of 35–50 m s−1 between 700 and 550 hPa, contribute to mesofront formation preceding SB episodes. This jet also sustains strong cross-mountain winds over the Great Dividing Range, causing the lee trough at the coastal strip that precedes all SB episodes on the eastern coast. Understanding these dynamics can help predict and manage these events more effectively.

Dust event identification and characterization with one-year online observations in Beijing.

Dust storms have a profound impact on air quality, atmospheric chemistry, and human well-being by carrying vast amounts of particles over distances of thousands of kilometers. However, the overall characteristics of these dust events and their influence on secondary pollution in the northern China region are not yet well understood, due to a lack of long-term, comprehensive observations and objective identification techniques. Based on principal component analysis combined with high-time-resolution observations of particulate matter components, here we developed a robust method to identify dust storm events and identified 14 dust events in Beijing in 2019. We further classified these 14 events into two distinct types using Lagrangian particle dispersion models and backward trajectory analysis. The first type (Type I, 9 cases) is characterized by synoptic patterns in Mongolia, originating from the north and directly impacting the Beijing area. The second type (Type II, 5 cases) involves air masses from the north or northwest that temporarily pass through polluted regions south of Beijing before being carried back into the city. Consistently, during Type I dust events, we observed a sharp decrease in secondary inorganic aerosols (SIA) from 65 % to 7 %, as well as in the sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) (from 0.52 to 0.19, and 0.27 to 0.018 respectively). In contrast, during Type II dust events, SIA concentrations increased by 91 %, along with an increase in SOR (1.7 %), NOR (69 %), and f44/f43 (3.0 %), suggesting an enhancement of secondary aerosol formation resulting from the interaction between dust aerosols and gaseous anthropogenic pollutants. Our results demonstrate that dust events and the sub-type of dust events can be identified in an objective manner using the protocol developed in this study and the dynamics should be considered when discussing impacts of dust events on atmospheric chemistry.

Enhancing wind erosion risk assessment through remote sensing techniques.

Preventing wind erosion and dust storms has always been a major concern in arid and semi-arid areas because of their negative effects on the environment. This study aims to utilize remote sensing and machine learning techniques to model, monitor, and predict the risk of wind erosion in Northeast Iran. Through an examination of relevant studies, a comprehensive review was conducted, leading to the identification of eight remote sensing indicators that exhibited the highest correlation with field data. These indicators were subsequently employed to model the risk of wind erosion in the study area. Various methods including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM) were employed to carry out the modeling process. The final method utilized a weighted average of the model, and the SDM statistical package was used to combine different approaches to decrease uncertainty when modeling and monitoring wind erosion in the area. The modeling results indicated that in 2008, the RF model performed the best (AUC = 0.92, TSS = 0.82, and Kappa = 0.96), while in 2023, the GBM model showed superior performance (AUC = 0.95, TSS = 0.79, and Kappa = 0.95). Therefore, the utilization of an ensemble model emerged as an effective approach to reduce uncertainty during the modeling process. By employing the ensemble model, the outcomes obtained accurately depicted an elevated intensity of wind erosion in the northeastern regions of the study area by 2023. Furthermore, considering the climatic scenarios and projected land use changes, it is anticipated that wind erosion intensity will experience a 23% increase in the central and southern parts of the study area by 2038. By taking into account the reliable results of the ensemble model, which offers reduced uncertainty, it becomes feasible to implement effective planning, optimal management, and appropriate measures to mitigate the progression of wind erosion.

Enhancing Sustainable Communities through the Protection of Natural Buffer Zones

Certain regions within Middle Eastern countries are vulnerable to climate-related hazards, including floods and dust storms, resulting in adverse consequences for these nations and hindering the sustainable development of urban communities. The preservation and development of natural buffer zones, such as green spaces and surface waters, offer nature-based solutions to shield urban areas from the impacts of these hazards. This article examines the significance and legal protection of these buffer zones and concludes that, despite their effectiveness in mitigating floods and dust storms, natural buffer zones have not received distinct recognition as protective zones for urban areas in international and national legal frameworks. The study recommends the integration of natural buffer zones preservation and development into national and regional urban planning and disaster risk management policies and strategies, highlighting their role as nature-based solutions for enhancing urban sustainability in the Middle East, especially Iran.

Saharan dust and respiratory health: Understanding the link between airborne particulate matter and chronic lung diseases (Review).

Saharan dust storms, which originate from the Sahara desert, have a significant impact on global health, especially on respiratory conditions of populations exposed to fine particulate matter that travels across continents. Dust events, characterized by the transport of mineral dust such as quartz and feldspar, lead to the suspension of particulate matter in the atmosphere, capable of traversing long distances and affecting air quality adversely. Emerging research links these dust episodes with increased incidence and exacerbation of lung diseases, including asthma and chronic obstructive pulmonary disease, especially during peak dust emission seasons from November to March. The present review aims to synthesize existing scientific evidence concerning the respiratory health impacts of Saharan dust, examining the environmental dynamics of dust transmission, the physical and chemical properties of dust particles, and their biological effects on human health. Further, it assesses epidemiological studies and discusses public health strategies for mitigating adverse health outcomes. Given the complexity of interactions between atmospheric dust particles and respiratory health, this review also highlights critical research gaps that need attention to better understand and manage the health risks associated with Saharan dust.

Characterization of water-soluble inorganic ions and carbonaceous aerosols in the urban atmosphere in Amman, Jordan

The urban particulate matter (PM) carbonaceous and water-soluble ions were investigated in Amman, Jordan during May 2018–March 2019. The PM2.5 total carbon (TC) annual mean was 7.6 ± 3.6 μg/m3 (organic carbon (OC) 5.9 ± 2.8 μg/m3 and elemental carbon (EC) 1.7 ± 1.1 μg/m3), which was about 16.3% of the PM2.5. The PM10 TC annual mean was 8.4 ± 3.9 μg/m3 (OC 6.5 ± 3.1 μg/m3 and elemental carbon (EC) 1.9 ± 1.1 μg/m3), about 13.3% of the PM10. The PM2.5 total water-soluble ions annual mean was 7.9 ± 1.9 μg/m3 (about 16.9%), and that of the PM10 was 10.1 ± 2.8 μg/m3 (about 16.0%). The minor ions (F−, NO2−, Br−, and PO43−) constituted less than 1% in the PM fractions. The significant fraction was for SO42− (PM2.5 4.7 ± 1.6 μg/m3 (10.0%) and PM10 5.3 ± 1.9 μg/m3 (8.3%)). The NH4+ had higher amounts of PM2.5 (1.3 ± 0.6 μg/m3; 2.7%) than that PM10 (0.9 ± 0.4 μg/m3; 1.4%). During sand and dust storm (SDS) events, TC, Cl−, and NO3− were doubled in PM, SO42− did not increase significantly, and NH4+ slightly decreased. Regression analysis revealed: (1) carbonaceous aerosols come equally from primary and secondary sources, (2) about 50% of the OC came from non-combustion sources, (3) traffic emissions dominate the PM, (4) agricultural sources have a negligible effect, (5) SO42− is completely neutralized by NH4+ in the PM2.5 but there could be additional reactions involved in the PM10, and (6) (NH4)2SO4, was the major species formed by SO42−and NH4+ instead of NH4HSO4. It is recommended to perform long-term sampling and chemical speciation for the urban atmosphere in Jordan.

An Ensemble Machine Learning Model to Enhance Extrapolation Ability of Predicting Coarse Particulate Matter with High Resolutions in China.

Accurate exposure assessment is important for conducting PM10-2.5-related epidemiological studies, which have been limited thus far. In this study, we aimed to develop an ensemble machine learning method to estimate PM10-2.5 concentrations in mainland China during 2013-2020. The study was conducted in two stages. In the first stage, we developed two methods: the indirect method refers to developing models for PM2.5 and PM10 separately and subsequently calculating PM10-2.5 as the difference between them; and the direct method refers to establishing a model between PM10-2.5 measurements and relevant predictors directly. In the second stage, we employed an ensemble method by integrating predictions from both indirect and direct methods. Internal and external cross-validation (CV) were performed to validate the extrapolation capacity of models. The ensemble method demonstrated enhanced extrapolation accuracy in both internal and external CV compared to indirect and direct methods. The predictions produced by the ensemble method captured the spatiotemporal pattern of PM10-2.5, even in the sand and dust storm seasons. Our study introduces an ensemble strategy leveraging the strengths of both indirect and direct methods to estimate PM10-2.5 concentrations, which holds significant potential to support future epidemiological studies to address knowledge gaps in understanding the health effects of PM10-2.5.

Air quality and wheeze symptoms in a rural children's cohort near a drying saline lake

BackgroundIn California, climate change and competing water demands are intensifying the desiccation of the Salton Sea, a large land-locked “sea” situated near the southeastern rural US-Mexico border region known as the Imperial Valley. MethodsTo examine the possible effects of living near a saline lake on children's respiratory health, we analyzed the relationship between respiratory health symptoms and ambient PM concentrations among a predominantly Latino/Hispanic cohort of 722 school age children. Guardians completed a survey of their child's wheeze and respiratory health symptoms over the past 12 months, adapted from the International Study of Asthma and Allergies in Childhood (ISAAC). Exposure to dust storm hours (hourly concentrations >150 μg/m3 for PM10) was estimated using a network of regulatory monitors. ResultsBetween 2017 and 2019, children were exposed to 98 to 395 dust event hours annually. We observed disparate effects for dust events and wheeze among children living near the Salton Sea. Every additional 100 dust storm hours per year among children living near the Sea (<11 km) was associated with a 9.5 percentage point increase in wheeze (95% CI: 3.5, 15.4), a 4.6 percentage point increase in bronchitic symptoms (95% CI: 0.18, 10.2) and a 6.7 percentage point increase in sleep disturbance due to wheeze (95% CI: 0.96, 12.4). Similarly, increases in PM10 were also associated with greater reported wheeze and bronchitic symptoms among those living near the Sea, compared to children living ≥11 km from the Sea. There was no association of dust storms or PM10 with wheeze or bronchitic symptoms among the children residing farther from the Sea. ConclusionWe observed stronger adverse impacts of PM10 and dust events on respiratory health among those living closer to the drying Salton Sea, compared to children living farther away. In this community of predominantly low-income residents of color, these impacts raise environmental justice concerns about the effects of the drying Salton Sea on public health.

The impact of technologies for the development and use of virgin, fallow lands on agroecological stability and soil fertility

Relevance. The research was carried out in order to analyze the impact of technologies for the development of virgin and fallow lands on erosion processes in the steppe zone in the 60s and modern conditions.Methods. The analysis of approaches to the prevention of soil degradation and desertification of the territory is carried out based on materials obtained in the steppe zone of the Republic of Khakassia in the second half of the XX century and in modern conditions. We used the results of the application of technologies for the development of virgin and fallow lands of Tselinnoye LLC in different periods. Information about dust storms was taken from data from the Bey and Shire weather stations. In 1954–1960 when plowing the chernozem of the southern virgin lands, technology was used using plows, huskers and other tillage tools. The lack of adapted ways of developing and using new lands has led to an outbreak of wind erosion of soils over large areas, including in the south-east of the country.Results. The dump system of soil treatment during the mass development of virgin and fallow lands in the 60s, without taking into account soil, climatic and other conditions, led to a strong development of wind erosion. The number of days with dust storms in Khakassia reached up to 4.8–11.6 per year. In modern conditions, the removal of highly ventilated soils from arable lands and the use of strip placement of crops and minimal tillage on the rest of the arable land contribute to increasing the erosion resistance of agricultural landscapes. The use of the Tornado 500 herbicide in August during the development of the deposit reduces the erodibility by 2.5 times, compared with the technology with its introduction in early summer.

Evaluating the Prediction Performance of the WRF-CUACE Model in Xinjiang, China

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m−3 during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m−3 and 29.10 µg·m−3, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data.