{
  "abstract": "Landfills are a major anthropogenic source of methane (CH4), contributing up to 20 % of global CH4 emissions. Although CH4 emissions from landfills are highly sensitive to meteorological conditions, their response to climate variations remains poorly understood, leading to substantial uncertainty in emission projections under climate change. This study evaluated the impact of meteorological factors on landfill CH4 generation, using a site-specific machine-learning-based model optimized for temperature and precipitation. The model optimized for meteorological conditions performed better than conventional models such as LandGEM and the IPCC model, with a root mean squared error (RMSE) of 6.57 million m3 CH4, a mean absolute error (MAE) of 4.91 million m3 CH4, and Pearson correlation coefficients of 0.89, when compared with field measurements. CH4 generation exhibited a linear correlation with increasing temperature, and a parabolic response to increasing precipitation. Quantification of the contributions of the meteorological variables, revealed that temperature accounted for 5.96±3.06 %, and precipitation for 7.38±0.58 % of the total modeled CH4 generation. These results highlight the high importance of incorporating meteorological variability into landfill CH4estimation to improve predictive accuracy, and emphasize the need of stronger and faster CH4 mitigation efforts under climate change.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Environmental Management, Graduate School of Environmental Studies, Seoul National University, Seoul, Republic of Korea",
    "Seoul National University (Seoul, Korea, Republic of)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Donghee Kim"
    },
    {
      "affs": [
        1
      ],
      "name": "Sujong Jeong",
      "orcid": "0000-0003-4586-4534"
    },
    {
      "affs": [
        0
      ],
      "name": "Dong Yeong Chang",
      "orcid": "0000-0003-2614-0397"
    },
    {
      "affs": [
        0
      ],
      "name": "Jaewon Joo",
      "orcid": "0000-0001-9754-4917"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cf7tvxhzd09p5.bib",
  "cited_by": 5,
  "doi": "10.5194/acp-26-7789-2026",
  "fulltext_url": null,
  "id": "cf7tvxhzd09p5",
  "issns": [
    "1680-7316",
    "1680-7324"
  ],
  "landing_url": "https://doi.org/10.5194/acp-26-7789-2026",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2311|Landfill Environmental Impact Studies"
    ]
  ],
  "openalex": "W7163170273",
  "pdf_url": "https://acp.copernicus.org/articles/26/7789/2026/acp-26-7789-2026.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-02",
  "publisher": "Copernicus Publications",
  "ris": "https://research.draconitic.com/paper/cf7tvxhzd09p5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:59Z",
      "native_id": "W7163170273",
      "source": "openalex",
      "url": "https://doi.org/10.5194/acp-26-7789-2026"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2311|Landfill Environmental Impact Studies",
      "subfield": "environmental-science"
    }
  ],
  "title": "Quantifying meteorological impacts on local landfill methane emissions by using field measurements and machine learning",
  "title_text": "Quantifying meteorological impacts on local landfill methane emissions by using field measurements and machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:55Z",
  "url": "https://research.draconitic.com/paper/cf7tvxhzd09p5",
  "venue": "Atmospheric chemistry and physics",
  "versions": []
}