{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Mohammad Momen Haji Mirzaei"
    },
    {
      "name": "Yashar Mohammadi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0gnqkrd880z9q.bib",
  "cited_by": 0,
  "doi": "10.31223/x5wr36",
  "fulltext_url": null,
  "id": "0gnqkrd880z9q",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/14739/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Dust storms"
    ],
    [
      "bepress",
      "Earth Sciences"
    ],
    [
      "bepress",
      "Environmental Engineering"
    ],
    [
      "bepress",
      "Environmental Sciences"
    ],
    [
      "bepress",
      "Satellite remote sensing"
    ],
    [
      "bepress",
      "Trajectory clustering"
    ],
    [
      "bepress",
      "Transboundary air pollution"
    ],
    [
      "bepress",
      "Unsupervised machine learning"
    ],
    [
      "openalex",
      "2306|Atmospheric aerosols and clouds"
    ]
  ],
  "openalex": "W7204988392",
  "pdf_url": "https://eartharxiv.org/repository/object/14739/download/25633/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0gnqkrd880z9q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "14739",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/14739/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "bepress:Environmental Sciences",
      "subfield": "environmental-science"
    }
  ],
  "title": "Unsupervised Clustering for Identifying Dusty Days and Their Sources: A Long-Term Data Study in Sanandaj, Iran",
  "title_text": "Unsupervised Clustering for Identifying Dusty Days and Their Sources: A Long-Term Data Study in Sanandaj, Iran",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/0gnqkrd880z9q",
  "venue": "EarthArXiv",
  "versions": []
}