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Abstract
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Dust storms are among the most significant environmental hazards affecting arid and
semi-arid regions of Iran, yet their long-term behavior remains insufficiently characterized
at the national scale. This study provides a comprehensive 20-year assessment (2003–2022)
of dust-day variability across 50 synoptic stations using an integrated framework that
combines descriptive statistics, trend analysis, extreme-event analysis based on the gen-
eralized extreme event GEV distribution, spatial clustering, and machine-learning-based
forecasting. Results reveal strong spatial heterogeneity, with eastern and southeastern
regions—particularly Zabol, Zahedan, Tabas, Naein, and Yazd—emerging as persistent
dust hotspots due to arid climate, extensive desert surfaces, and dominant wind systems
such as the Sistan 120-day wind. Trend analysis shows mixed behavior across the country,
with significant increases in several central and western stations and notable decreases in
southeastern stations, indicating that dust dynamics are driven by localized environmental
and hydrological changes rather than uniform national forcing. Extreme value analysis
demonstrates that high-impact dust years occur almost annually in eastern Iran, while
extreme events remain rare in western and northern regions. K-means clustering identifies
three coherent dust regimes—high-dust east/southeast, moderate-dust central region, and
low-dust west/north—providing a practical basis for regional dust management. Long
Short-Term Memory (LSTM) forecasts suggest stable to moderately variable dust activity
over the next decade, although model performance declines in stations with high temporal
variability, such as Naein. Overall, the findings highlight the spatial concentration and
temporal complexity of dust activity in Iran and underscore the need for region-specific
mitigation strategies, improved land and water management, and enhanced monitoring
systems to reduce the environmental and health impacts of dust storms.
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