Unsupervised Clustering for Identifying Dusty Days and Their Sources: A Long-Term Data Study in Sanandaj, Iran

Mohammad Momen Haji Mirzaei , Yashar Mohammadi

Dust storms are a persistent environmental hazard in the Middle East, yet long-term monitoring remains limited by sparse ground observations and the lack of objective, reproducible event definitions. This study develops a transferable, data-driven framework for objectively identifying dusty days in data-scarce regions and explicitly addresses two key issues: defining dust events using defensible multi-indicator criteria instead of arbitrary thresholds, and linking objectively detected events to dominant dust source regions. Long-term PM₁₀ records were combined with satellite and reanalysis aerosol indices, including Aerosol Optical Depth at 550 nm (AOD₅₅₀), Dust Optical Depth (DOD), and Dust Aerosol Optical Depth (DUAOD₅₅₀) from CAMS and MERRA-2. Quantitative thresholds derived using statistical analyses and K-Means clustering produced physically consistent criteria (PM₁₀ ≥ 75 μg m⁻³; AOD₅₅₀ ≥ 0.45; DOD ≥ 0.20; DUAOD₅₅₀ ≥ 0.20), capturing the episodic, right-skewed nature of dust variability. Among Boolean rules, OR logic provided the most comprehensive detection (616 dusty days) and remained robust under ±2% perturbations, while Meteorological Aerodrome Reports (METAR) dust and visibility codes confirmed higher recall than restrictive schemes. Using these objectively identified events, 72-h Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectories with resampling, Principal Component Analysis (PCA), and K-Means clustering revealed two distinct regimes: regional transport from the Iraq-Iran borderlands and southern Mesopotamia, and longer-range intrusions from the Syrian Desert and northern-western Mesopotamia. Unlike previous single-indicator studies, this work provides a unified framework integrating objective thresholding, independent validation, and trajectory source attribution, supporting scalable monitoring, early warning, and risk management in arid and semi-arid regions.