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
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.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "026:00" ] |
| contactPoint |
{ "fn": "undefined", "@type": "vcard:Contact", "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", "title": "Download this dataset", "format": "HTML", "mediaType": "text/html", "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" }, { "@type": "dcat:Distribution", "title": "View documentation related to this dataset", "format": "HTML", "mediaType": "text/html", "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" }, { "@type": "dcat:Distribution", "title": "Get a related visualization", "format": "JPEG", "mediaType": "image/jpeg", "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" }, { "@type": "dcat:Distribution", "title": "Google Scholar search results", "format": "HTML", "mediaType": "text/html", "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" } ] |
| 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 |
{ "name": "SEDAC", "@type": "org:Organization" } |
| 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 |
