{
  "accessLevel": "public",
  "bureauCode": [
    "020:00"
  ],
  "contactPoint": {
    "fn": "Keith Appel",
    "hasEmail": "mailto:appel.wyat@epa.gov"
  },
  "describedBy": "https://pasteur.epa.gov/uploads/10.23719/1506002/documents/HogrefeChristian_Data_Dictionary_LuoEtAl_ACP.docx",
  "describedByType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
  "description": "Files containing daily average total and speciated PM2.5 observations and WRF/CMAQ simulations that were contributed by EPA/ORD/CEMM/AESMD researchers to the manuscript “Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition”. \n\nThis dataset is associated with the following publication:\nLuo, H., M. Astitha, C. Hogrefe, R. Mathur, and S.T. Rao. Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition.   Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir,  TURKEY, 20(22): 13801-13815, (2020).",
  "distribution": [
    {
      "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1506002/sitecompare_data_3sites_LuoEtAl_ACP_2020.zip",
      "mediaType": "application/zip",
      "title": "sitecompare_data_3sites_LuoEtAl_ACP_2020.zip"
    }
  ],
  "identifier": "https://doi.org/10.23719/1506002",
  "keyword": [
    "CMAQ",
    "air quality modeling",
    "model evaluation"
  ],
  "license": "https://pasteur.epa.gov/license/sciencehub-license.html",
  "modified": "2019-12-31",
  "programCode": [
    "020:094"
  ],
  "publisher": {
    "name": "U.S. EPA Office of Research and Development (ORD)",
    "subOrganizationOf": {
      "name": "U.S. Environmental Protection Agency",
      "subOrganizationOf": {
        "name": "U.S. Government"
      }
    }
  },
  "references": [
    "https://doi.org/10.5194/acp-20-13801-2020"
  ],
  "rights": null,
  "title": "Data contributed by EPA/ORD/CEMM/AESMD researchers to the manuscript “Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition”"
}