{
  "abstract": "Carbon monoxide (CO) is an important atmospheric constituent affecting both air quality and climate. Using TROPOMI CO and HCHO column retrievals within the Model-Free Inversion Estimation Framework (MFIEF), we derived daily gridded CO emission estimates over Central Asia, including Xinjiang, Kazakhstan, Kyrgyzstan, and Uzbekistan, for 2019–2024. Regional mean CO emissions decreased by 37.9 % between 2019 and 2024, with a significant seasonally adjusted trend of -0.33µgm-2s-1yr-1. The identified emission hotspots also weakened over the study period. Higher emissions during winter and early spring are consistent with enhanced combustion associated with residential heating and industrial activity. A perturbation-based uncertainty analysis propagates assumed observational uncertainties through the inversion and indicates that approximately 69 % of the daily grid-cell emission estimates fall outside the adopted uncertainty bounds, highlighting the potential influence of atmospheric variability, dust loading, small sources, and observational uncertainty on the reliability of grid-scale emission estimates. The spatial distribution of satellite-derived grid-scale CO emission estimates reveals differences in urban emissions relative to bottom-up inventories, indicates a moderate positive association between coal consumption and inferred CO emissions at selected industrial locations, and highlights emissions associated with persistent underground coal fires, which are not explicitly represented as a separate source category in conventional anthropogenic emission inventories. This study demonstrates the potential of MFIEF as an observation-driven approach for characterizing regional CO emission patterns and temporal variability in data-sparse regions and provides complementary information for emission-inventory development and source characterization when production and location data are not available or are out of date.",
  "abstract_shown": true,
  "affiliations": [
    "China University of Mining and Technology (Xuzhou, China)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Ye Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Jason Blake Cohen",
      "orcid": "0000-0002-9889-8175"
    },
    {
      "name": "Xiaolu Li",
      "orcid": "0000-0001-7206-1230"
    },
    {
      "name": "Lingxiao Lu",
      "orcid": "0000-0002-3839-9083"
    },
    {
      "name": "Shuo Wang"
    },
    {
      "name": "Zhewen Liu",
      "orcid": "0009-0007-4692-8137"
    },
    {
      "name": "Lei Wang"
    },
    {
      "name": "Jian Liu",
      "orcid": "0000-0001-6022-2383"
    },
    {
      "name": "Kai Qin",
      "orcid": "0000-0002-1280-6330"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e8rs8y8s55qeh.bib",
  "cited_by": 0,
  "doi": "10.5194/acp-26-15139-2026",
  "fulltext_url": null,
  "id": "e8rs8y8s55qeh",
  "issns": [
    "1680-7324"
  ],
  "landing_url": "https://doi.org/10.5194/acp-26-15139-2026",
  "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/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "feed-journal",
      "earth/atmospheric"
    ],
    [
      "openalex",
      "2306|Atmospheric and Environmental Gas Dynamics"
    ]
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  "openalex": "W7118580054",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "Copernicus GmbH",
  "ris": "https://research.draconitic.com/paper/e8rs8y8s55qeh.ris",
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    {
      "first_seen": "2026-10-30T12:17:16Z",
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      "url": "https://doi.org/10.5194/acp-26-15139-2026"
    },
    {
      "first_seen": "2026-10-30T12:20:34Z",
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      "source": "crossref",
      "url": "https://acp.copernicus.org/articles/26/15139/2026/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Atmospheric and Environmental Gas Dynamics",
      "subfield": "climate"
    }
  ],
  "title": "Quantifying and attributing CO emissions over Central Asia using TROPOMI and explicit observational uncertainty",
  "title_text": "Quantifying and attributing CO emissions over Central Asia using TROPOMI and explicit observational uncertainty",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:34Z",
  "url": "https://research.draconitic.com/paper/e8rs8y8s55qeh",
  "venue": "Atmospheric Chemistry and Physics",
  "versions": [
    {
      "doi": "10.5194/egusphere-2025-5890",
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      "relation": "preprint",
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}