Analysis of some factors related to dust storms occurrence in the Sistan region

Namdari, Soodabeh; Kamran, Khalil Valizadeh; Sorooshian, Armin

doi:10.1007/s11356-021-13922-6

Cited by 8 publications

(3 citation statements)

References 55 publications

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“…Gholami et al (2020c) applied several data mining models for classifying land susceptibility to dust emissions in the Sistan watershed and they reported that most of the study area is classified as high and very high susceptibility levels for dust emissions. Namdari et al (2021) examined the relationship of dust storms with wind speed and vegetation cover in the Sistan watershed using RS data and found a positive feedback of wind and sparse vegetation to dust emissions. Recently, Ebrahimi-Khusfi et al (2021) predicted the number of dusty days in the Sistan watershed by means of feature selection and machine learning techniques, with satisfactory results about the model's performance.…”

Section: Introductionmentioning

confidence: 99%

Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

et al. 2021

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Dust storms in arid and desert areas affect radiation budget, air quality, visibility, enzymatic activities, agricultural products and human health. Due to increased drought and land use changes in recent years, the frequency of dust storms occurrence in Iran has been increased. This study aims to identify dust source areas in the Sistan watershed (Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia, using remote sensing (RS) and bivariate statistical models. Furthermore, this study determines the relative importance of factors controlling dust emissions using frequency ratio (FR) and weights of evidence (WOE) models and interpretability of predictive models using game theory. For this purpose, we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data. In addition, spatial maps of topographic factors affecting dust source areas including soil, lithology, slope, Normalized difference vegetation index (NDVI), geomorphology and land use were prepared. The performance of two models (WOE and FR) was evaluated using the area under curve (AUC) of the receiver operating characteristic curve. The results showed that soil, geomorphology and slope exhibited the greatest influence in the dust source areas. The 55.3% (according to FR) and 62.6% (according to WOE) of the total area were classified as high and very high potential dust sources, while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE, although they predict different fractions of dust potential classes. Based on Shapley additive explanations (SHAP), three factors, i.e., soil, slope and NDVI have the highest impact on the model's output. Overall, combination of statistic-based predictive models (or data mining models), RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions, which can be helpful for mitigation of negative effects of dust storms.

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Section: Introductionmentioning

confidence: 99%

Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

et al. 2021

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“…where B (band) represents surface reflectance measurements. We use daily AOD products at 550 nm obtained from Level 2 Collection 6 over land based on the Deep Blue algorithm of MODIS Terra at a resolution of 0.1 [42,43]. NDVI data are based on the MODIS Terra 16-day 500 m NDVI product (MOD13A1) [44,45].…”

Section: Data Selection and Methodsmentioning

confidence: 99%

Time Series of Remote Sensing Data for Interaction Analysis of the Vegetation Coverage and Dust Activity in the Middle East

Namdari

Alnasrawi²,

Ghorbanzadeh

et al. 2022

Remote Sensing

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Motivated by the lack of research on land cover and dust activity in the Middle East, this study seeks to increase the understanding of the sensitivity of dust centers to climatic and surface conditions in this specific region. In this regard, we explore vegetation cover and dust emission interactions using 16-day long-term Normalized Difference Vegetation Index (NDVI) data and daily Aerosol Optical Depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and conduct spatiotemporal and statistical analyses. Eight major dust hotspots were identified based on long-term AOD data (2000–2019). Despite the relatively uniform climate conditions prevailing throughout the region during the study period, there is considerable spatial variability in interannual relationships between AOD and NDVI. Three subsets of periods (2000–2006, 2007–2013, 2014–2019) were examined to assess periodic spatiotemporal changes. In the second period (2007–2013), AOD increased significantly (6% to 32%) across the studied hotspots, simultaneously with a decrease in NDVI (−0.9% to −14.3%) except in Yemen−Oman. Interannual changes over 20 years showed a strong relationship between reduced vegetation cover and increased dust intensity. The correlation between NDVI and AOD (−0.63) for the cumulative region confirms the significant effect of vegetation canopy on annual dust fluctuations. According to the results, changes in vegetation cover have an essential role in dust storm fluctuations. Therefore, this factor must be regarded along with wind speed and other climate factors in Middle East dust hotspots related to research and management efforts.

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“…Recent literature has drawn attention to the significant impact of dust storms on various economic and social aspects, particularly in Iran (Broomandi et al., 2022; Namdari et al., 2021). In addition to the well‐documented environmental consequences, such as air pollution and landscape changes, dust storms have been found to have severe implications for rangeland grasses, leading to food scarcity and the death of livestock (Hu et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring the impact of dust storms on pastoralists’ livelihoods in West Iran: Social, economic, and environmental consequences

Ahmadi,

Zamani,

Mirzaei

et al. 2023

Environmental Quality Mgmt

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This study investigates the economic, social, and environmental impacts of dust storms on pastoralists’ livelihoods in the Chaqakdo rangeland of West Iran. Data was collected through face‐to‐face interviews with 100 pastoralists in 2019. The research adopts a quantitative approach using Structural Equation Modeling (SEM). The results demonstrate a significant and positive relationship between pastoralists’ livelihoods and the three independent variables (environmental, economic, and social), with the strongest correlation observed in environmental factors. The path coefficients indicate positive effects on pastoralists’ livelihoods. Additionally, dust storms were found to have detrimental environmental effects, such as plant leaf pollution, reduced grazing, and decreased growth of grassland plants. Therefore, it is crucial to establish adaptive measures to mitigate the impacts of dust storms on pastoralists, considering their heavy reliance on the local pastures.

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Analysis of some factors related to dust storms occurrence in the Sistan region

Cited by 8 publications

References 55 publications

Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

Time Series of Remote Sensing Data for Interaction Analysis of the Vegetation Coverage and Dust Activity in the Middle East

Exploring the impact of dust storms on pastoralists’ livelihoods in West Iran: Social, economic, and environmental consequences

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