{
  "abstract": "Understanding global ground-level ozone is hindered by significant monitoring gaps. To address this, we introduce a Bayesian Maximum Entropy data fusion framework that includes satellite ozone observations, generating a global 0.1° surface ozone dataset (2005–2022). Satellite observations (IASI+GOME2, OMI/MLS), adjusted to ground-level estimates, were analyzed using Flexible Regionalized Air Quality Model Performance to correct biases and estimate variance. Ground observations (hard data) and satellite-derived estimates (soft data) were then integrated, 2 using a composite of 10 atmospheric chemistry models and 3 reanalyses as the global spatialtemporal trend. Cross-validations demonstrated high overall accuracy (R2 = 0.83; RMSE = 3.73 ppb). Although integrating satellite-derived soft data yields only marginal performance gains in densely monitored areas, it substantially reduces model uncertainties and improves model accuracy across large spatial gaps. Notably, for the furthest unmonitored gap group at a 20° checkerboard, including satellite data increased R2 by 25.9% and decreased RMSE by 21.1% after the integration. Applying this optimized data fusion configuration from 2005 to 2022, global population-weighted ozone is estimated to have increased by 0.21 ppb/decade. Consequently, the global population exposed to ozone levels exceeding 50 ppb more than doubled over this 18-year period, rising from 17.2% to 41.0%.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Environmental Sciences & Engineering",
    "University of North Carolina",
    "Cooperative Institute for Research in Environmental Sciences",
    "University of Colorado",
    "NOAA Chemical Sciences Laboratory",
    "Jet Propulsion Laboratory",
    "California Institute of Technology",
    "NSF National Center for Atmospheric Research",
    "Univ. Paris Est Créteil and Université de Paris Cité, CNRS, LISA",
    "Meteorological Research Institute",
    "NOAA Geophysical Fluid Dynamics Laboratory",
    "Department of Marine, Earth and Atmospheric Sciences",
    "North Carolina State University",
    "NASA Goddard Space Flight Center"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Hantao Wang",
      "orcid": "0000-0003-3935-8272"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Marc L. Serre"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Kai-Lan Chang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Praful Dodda"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "George E. Heidkamp"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Kazuyuki Miyazaki"
    },
    {
      "affs": [
        7
      ],
      "name": "Rebecca R. Buchholz"
    },
    {
      "affs": [
        8
      ],
      "name": "Juan Cuesta"
    },
    {
      "affs": [
        9
      ],
      "name": "Makoto Deushi"
    },
    {
      "affs": [
        10
      ],
      "name": "Meiyun Lin"
    },
    {
      "affs": [
        11,
        12
      ],
      "name": "Zhen Qu"
    },
    {
      "affs": [
        13
      ],
      "name": "Luke D. Oman"
    },
    {
      "affs": [
        13
      ],
      "name": "Jerry R. Ziemke"
    },
    {
      "affs": [
        4
      ],
      "name": "Owen R. Cooper"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "J. Jason West"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1p4he1m3y9a6g.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15011001/v1",
  "fulltext_url": null,
  "id": "1p4he1m3y9a6g",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011001/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Air Quality Monitoring and Forecasting"
    ]
  ],
  "openalex": "W7223562613",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011001/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/1p4he1m3y9a6g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15011001",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011001/v2"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Air Quality Monitoring and Forecasting",
      "subfield": "environmental-science"
    }
  ],
  "title": "Assessing the Impact of Incorporating Bias-Adjusted",
  "title_text": "Assessing the Impact of Incorporating Bias-Adjusted",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/1p4he1m3y9a6g",
  "venue": "ChemRxiv",
  "versions": []
}