Alireza Yousefi Kebriya; Ali Khalili; Hasan Rezaei
Abstract
Introduction
Dust storms have emerged as one of the most significant environmental challenges in arid and semi-arid regions, and their frequency and intensity have notably increased in Ilam Province in recent years. These storms have had wide-ranging impacts on public health, urban infrastructure, agriculture, ...
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Introduction
Dust storms have emerged as one of the most significant environmental challenges in arid and semi-arid regions, and their frequency and intensity have notably increased in Ilam Province in recent years. These storms have had wide-ranging impacts on public health, urban infrastructure, agriculture, and the sustainability of natural resources. The province’s geographical location along the borders of Iraq and Syria makes it particularly vulnerable to transboundary dust storms originating from desertified areas in neighboring countries. Accordingly, precise monitoring of the spatiotemporal dynamics of dust storms and identifying their sources are essential for developing effective mitigation strategies and reducing their adverse impacts.
Materials and methods
In this study, PM concentration data from 2020 to 2025 were collected from air quality monitoring stations in Mehran and Dehloran as ground-based observations. In parallel, satellite-based indices were utilized, including the Aerosol Optical Depth (AOD) from MODIS, the Absorbing Aerosol Index (AAI) from Sentinel-5P TROPOMI, the Normalized Difference Dust Index (NDDI), and the Dust Event Count Map (DECM). All datasets were processed and analyzed using Google Earth Engine. To track the transport pathways of dust plumes, the HYSPLIT model was applied with a 24-hour backward trajectory simulation. Additionally, MODIS True Color images were employed to visually validate the HYSPLIT model outputs.
Results and discussion
Analysis of the DECM index from 2020 to 2024 revealed an upward trend in the frequency of dust events in Ilam Province. In 2020, the lowest number of events was recorded, although even in that year, Dehloran and Abdanan experienced over 30 events. In 2021, the number rose to over 120 events in border regions, reaching a critical peak in 2022 with more than 200 dust events recorded in Mehran, Dehloran, Eyvan, and southern Ilam. Although the numbers slightly decreased to 182 and 172 in 2023 and 2024, respectively, the spatial concentration of dust activity remained in the border areas. The Absorbing Aerosol Index (AAI) extracted from Sentinel-5P data further confirmed the severity of the situation. In 2020, the mean AAI values in Mehran, Dehloran, and Abdanan were around 0.28, increasing to 0.32 in 2021, and exceeding 1.3 in 2022 -indicative of very unhealthy conditions for the general population. Despite slight declines in 2023 (0.87) and 2024 (0.86), values remained in the unhealthy range. MODIS-derived AOD data also played a key role in assessing dust intensity. In 2020, AOD levels surpassed 1 in border areas and exceeded 1.6 in some regions in 2021. The critical peak occurred in 2022, when AOD values reached over 1.85 in southern Ilam and western Dehloran. Even central parts of the province saw AOD values greater than 0.5 in the same year. In 2023 and 2024, the values were 1.3 and 1.18, respectively, remaining within hazardous levels. The NDDI index, which reflects dust deposition on surfaces, peaked in 2021 with values exceeding 0.9 in some border areas. In 2022, the index dropped to approximately 0.5, possibly indicating airborne dust with limited ground deposition. It reached its lowest point in 2023 (below 0.5), followed by a slight increase to 0.54 in 2024. The HYSPLIT model was used to simulate dust transport pathways for two critical events in 2025. On April 15, 2025, the model identified western Iraq as the main dust source. Simulations showed that the dust plume reached the Ilam border at 11:00 AM and Dehloran station by 12:00 PM. Vertical profiles indicated that dust particles initially traveled at 500 meters altitude and later descended into the boundary layer, corroborating the recorded AQI level of 500 in Dehloran. In the second event on May 25, 2025, the dust originated from the deserts of eastern Syria. The particles formed at an altitude of 2000 meters and traveled across Iraq, reaching Mehran station at 12:00 PM. The trajectory showed a gradual descent to 500 meters, leading to severe surface-level pollution. Trajectory frequency maps indicated that more than 90% of paths passed through Syria, confirming the combined influence of Iraqi and Syrian sources. This event also saw an AQI level of 500 in Mehran. Overall, the results underscore the spatial stabilization of dust hotspots in Ilam’s border regions and highlight the critical role of transboundary dust sources in Iraq and Syria, as well as the synoptic wind patterns that facilitate their transport.
Conclusions
The findings demonstrate a notable increase in the frequency and intensity of dust storms in Ilam Province in recent years, with a clear spatial concentration in border areas. Transboundary sources, particularly desert regions in Iraq and Syria, have significantly contributed to the worsening dust pollution. The integration of satellite indices with the HYSPLIT model enabled the precise identification of dust origins, transport paths, and intensity. Consequently, implementing control strategies such as the restoration of drought-resistant vegetation, soil stabilization, land moistening, establishment of greenbelts along the borders, enhancement of regional cooperation with neighboring countries, and deployment of satellite-based early warning systems is essential. Without such interventions, the current trajectory may lead to a chronic crisis and exacerbate environmental, social, and economic vulnerabilities in the region.
Sina Nabizadeh; Ali asghar Naghipour; Ataollah Ebrahimi; Hamidreza Keshtkar; Elham Ghehsareh
Abstract
Introduction
Land use/land cover (LULC) maps are among the key tools for natural resource management, regional planning, and achieving sustainable development; therefore, the need for their accurate and up-to-date monitoring is increasingly emphasized. Continuous changes in land use driven by natural ...
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Introduction
Land use/land cover (LULC) maps are among the key tools for natural resource management, regional planning, and achieving sustainable development; therefore, the need for their accurate and up-to-date monitoring is increasingly emphasized. Continuous changes in land use driven by natural and human-induced factors have significantly affected local and global ecosystems. In recent decades, advances in remote sensing technologies and machine learning algorithms have led to major improvements in the extraction and classification of spatial data. The Google Earth Engine (GEE) platform, as a powerful cloud-based infrastructure for processing large-scale spatiotemporal data, provides an efficient framework for producing accurate and updated maps. Within this context, the present study focuses on the integration of multi-temporal satellite images, the use of auxiliary data, and the comparison of three machine learning algorithms over a large and heterogeneous watershed (Karun 1), aiming to improve classification accuracy and enhance the capability for long-term monitoring of LULC changes.
Materials and methods
To assess LULC changes in the Karun 1 watershed, Landsat 7 ETM+ (2002) and Landsat 8 OLI (2024) images with cloud cover less than 10% and considering long-term mean precipitation were retrieved and processed as surface reflectance products in the GEE platform. Composite images were generated from nine Landsat scenes during the peak growing season (May to July) using a median filter and were then clipped to the watershed boundary. A total of 1,920 training samples representing seven LULC classes based on the Anderson classification scheme were collected using field survey data, aerial photographs, and Google Earth imagery. The reference dataset was randomly split into training (60%) and evaluation (40%) subsets Auxiliary variables (such as NDVI, NDBI, NDWI, and a DSM) were derived and included alongside original spectral bands. Classification experiments were implemented in GEE using three supervised algorithms: CART, RF, and SVM. Model hyper parameters and training procedures were configured to ensure reproducibility and consistency across methods.
Results and discussion
The results showed that the CART, RF, and SVM algorithms produced classified maps with excellent accuracy. The incorporation of vegetation indices and auxiliary data improved both the overall accuracy and the Kappa coefficient for both study years. The highest overall accuracy and Kappa coefficient were achieved by the SVM algorithm, with values of 93% and 91.5% in 2002, and 93% and 92% in 2024, respectively. According to the results of all three algorithms, rangelands constitute the largest proportion of the watershed area (on average about 40%), followed by forests (approximately 27%). The temporal analysis indicated a decreasing trend in the area of rangelands and forests, as well as a notable reduction in water bodies, particularly based on the SVM results.
Conclusions
The results of this study demonstrated that applying machine learning algorithms within the Google Earth Engine platform enables the production of accurate land use/land cover maps and the effective monitoring of environmental changes over a large and heterogeneous watershed. The obtained results can be used as an efficient tool for land use planning, natural resource management, monitoring vegetation degradation, and controlling land use changes in the study area. However, limitations related to the spatial resolution of Landsat imagery and pixel-mixing errors, particularly along the boundaries between LULC classes, are considered among the main challenges of this study. Accordingly, it is recommended that future research utilize higher spatial resolution data, such as Sentinel imagery, to improve classification accuracy, especially for vegetation cover mapping.
