{
  "abstract": "Reduced-form models of the Community Multiscale Air Quality Modeling System (CMAQ) enable efficient prediction of air quality responses to emission changes. In this study, we developed a reduced-form CMAQ model based on the least absolute shrinkage and selection operator (LASSO) to approximate CMAQ outputs in high-dimensional settings where the number of emission variables exceeds the number of training samples. CMAQ simulations were generated using 118 emission scenarios covering seven emission sectors across 17 provincial-level administrative regions in South Korea. To account for the bounded nature of pollutant concentrations and to better represent nonlinear responses, an adaptive logit transformation was applied within the LASSO framework. The model was trained on 100 simulations and evaluated on 18 test scenarios, achieving mean root mean square errors of 0.1 ppb for ozone and 0.1 μg/m3 for PM2.5. The model also identifies a small subset of influential sector–region emission variables, enabling interpretable analysis of emission impacts and supporting the design of targeted emission scenarios. A web-based interface was developed to demonstrate the applicability of the approach, allowing interactive exploration of pollutant responses to emission changes. The reduced-form model enables rapid prediction of air quality responses with substantially lower computational cost than CMAQ simulations.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Engineering, Graduate School, Soongsil University, Seoul, RepublicofKorea",
    "Environmental Planning Institute, Seoul National University, Seoul, RepublicofKorea",
    "Department of Advanced Technology Fusion, Konkuk University, Seoul, RepublicofKorea",
    "Department of Technology Fusion Engineering, Konkuk University, Seoul, RepublicofKorea",
    "Graduate School of Environmental Studies, Seoul National University, Seoul, Republic of Korea"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Da-Bin Lee"
    },
    {
      "affs": [
        1
      ],
      "name": "Hyun-Uk Kang"
    },
    {
      "affs": [
        2
      ],
      "name": "Gaeun Seo"
    },
    {
      "affs": [
        3
      ],
      "name": "Jinseok Kim"
    },
    {
      "affs": [
        4
      ],
      "name": "Bomi Kim"
    },
    {
      "name": "Jung-Hun Woo"
    },
    {
      "name": "Kyu-Baek Hwang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eda6gs9zn959r.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0347073",
  "fulltext_url": null,
  "id": "eda6gs9zn959r",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0347073",
  "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",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Atmospheric chemistry/Air quality"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Environmental chemistry/Atmospheric chemistry/Air quality"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Pollution/Air pollution"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Asia/Korea"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Asia/South Korea"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Chemical compounds/Ozone"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Environmental chemistry/Atmospheric chemistry/Air quality"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling"
    ]
  ],
  "openalex": "W7213291618",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0347073&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/eda6gs9zn959r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0347073",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0347073"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "plos:environmental chemistry",
      "subfield": "environmental"
    },
    {
      "field": "earth",
      "origin": "plos:atmospheric science",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "plos:pollution",
      "subfield": "environmental-science"
    }
  ],
  "title": "A LASSO-based reduced-form CMAQ model for predicting ozone and PM2.5 responses to emission changes in South Korea",
  "title_text": "A LASSO-based reduced-form CMAQ model for predicting ozone and PM2.5 responses to emission changes in South Korea",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/eda6gs9zn959r",
  "venue": "PLOS One",
  "versions": []
}