{
  "@type": "dcat:Dataset",
  "accessLevel": "public",
  "bureauCode": [
    "026:00"
  ],
  "contactPoint": {
    "@type": "vcard:Contact",
    "fn": "undefined",
    "hasEmail": "mailto:metadata@ciesin.columbia.edu"
  },
  "description": "The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 �m (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components are �g/m^3. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.",
  "distribution": [
    {
      "@type": "dcat:Distribution",
      "description": "Data Download Page",
      "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/aqdh-pm2-5-component-ec-nh4-no3-oc-so4-50-m-1-km-contiguous-us-2000-2019/data-download",
      "format": "HTML",
      "mediaType": "text/html",
      "title": "Download this dataset"
    },
    {
      "@type": "dcat:Distribution",
      "description": "Documentation Page",
      "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/aqdh-pm-2-5-component-ec-nh4-no3-oc-so4-50-m-1-km-contiguous-us-2000-2019/docs",
      "format": "HTML",
      "mediaType": "text/html",
      "title": "View documentation related to this dataset"
    },
    {
      "@type": "dcat:Distribution",
      "description": "Sample browse graphic of the data set.",
      "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/maps/aqdh/aqdh-pm2-5-component-ec-nh4-no3-oc-so4-50m-1km-contiguous-us-2000-2019/sedac-logo.jpg",
      "format": "JPEG",
      "mediaType": "image/jpeg",
      "title": "Get a related visualization"
    },
    {
      "@type": "dcat:Distribution",
      "description": "Search results for publications that cite this dataset by its DOI.",
      "downloadURL": "https://scholar.google.com/scholar?q=10.7927%2F10.7927%2Fwj3-en73",
      "format": "HTML",
      "mediaType": "text/html",
      "title": "Google Scholar search results"
    }
  ],
  "identifier": "C2673736502-SEDAC",
  "issued": "2023-04-28",
  "keyword": [
    "aerosols",
    "air-quality",
    "atmosphere",
    "earth-science"
  ],
  "language": [
    "en-US"
  ],
  "modified": "2025-07-17",
  "programCode": [
    "026:001"
  ],
  "publisher": {
    "@type": "org:Organization",
    "name": "SEDAC"
  },
  "spatial": "-180.0 17.0 -65.0 72.0",
  "temporal": "2000-01-01T00:00:00Z/2019-12-31T00:00:00Z",
  "theme": [
    "AQDH",
    "geospatial"
  ],
  "title": "Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1"
}