{
  "abstract": "Fine particulate matter (PM2.5) is a major environmental concern, yet pollution assessment models are rarely tested across fundamentally different climate regimes. We develop a climate-stratified machine-learning framework using 49,997 monthly MERRA-2 satellite-assimilated samples (2019–2023) across five latitude-band climate zones, evaluated through Leave-One-Climate-Out (LOCO) cross-validation with nested hyperparameter tuning to prevent data leakage. Tuned LightGBM achieves a global LOCO R2 = 0.367 (MAE =6.43 μg m-3), but predictability varies substantially by zone: subtropical regions reach R2 = 0.604, while tropical regions achieve only R2 = 0.142, reflecting fundamental climate-dependent limits on meteorology-only models. SHAP analysis shows geographic coordinates—acting as proxies for emission patterns and climatological transport pathways—account for 64% of predictive importance on average across zones, compared to 23% for meteorological variables, indicating that PM2.5 spatial structure is driven more by where emissions occur than by month-to-month weather variability. To verify that coordinates add genuine predictive value beyond spatial autocorrelation, a retrain ablation confirms that removing latitude and longitude collapses model performance across all zones (mean R2 drops from 0.367 to −0.099), while an IDW spatial baseline confirms that the ML model captures meteorological signal beyond simple proximity interpolation (+98% R2 improvement over IDW). Temporal validation (2019–2021 training, 2022–2023 testing) yields R2 = 0.544, confirming LOCO is the more demanding test. Ensemble stacking degrades performance relative to the zone-tuned model, providing evidence of feature-set saturation rather than model weakness. This reproducible, open-source framework offers a useful benchmark for reanalysis-based PM2.5 assessment, particularly in data-sparse regions where ground monitoring is limited.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia",
    "Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Someyo Kamal Utsho"
    },
    {
      "affs": [
        1
      ],
      "name": "Md Rawjatul Fahim"
    },
    {
      "name": "Ruhaidah Samsudin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/046f2132329yc.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0347591",
  "fulltext_url": null,
  "id": "046f2132329yc",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0347591",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Air Quality Monitoring and Forecasting"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Climatology"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology"
    ],
    [
      "plos",
      "/Earth sciences/Geography"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Cartography/Latitude"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Cartography/Longitude"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Pollution/Air pollution"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Mixtures/Aerosols"
    ]
  ],
  "openalex": "W7212221163",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0347591&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/046f2132329yc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0347591",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0347591"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:meteorology",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "plos:climatology",
      "subfield": "climate"
    },
    {
      "field": "earth",
      "origin": "plos:pollution",
      "subfield": "environmental-science"
    }
  ],
  "title": "Climate-stratified assessment of PM2.5 using machine learning: Geographic controls dominate meteorological factors",
  "title_text": "Climate-stratified assessment of PM2.5 using machine learning: Geographic controls dominate meteorological factors",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/046f2132329yc",
  "venue": "PLOS One",
  "versions": []
}