Quantifying wintertime O3 and NOx formation with relevance vector machines
Underlying data associated with figures in publication. Portions of this dataset are inaccessible because: Data is now available for public access. They can be accessed through the following means: Data available through Data.gov and EDG. Format: Excel spreadsheet.
This dataset is associated with the following publication:
Olson, D., T. Riedel, J. Offenberg, M. Lewandowski, R. Long, and T. Kleindienst. Quantifying wintertime O3 and NOx formation with relevance vector machines. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 259: 118538, (2021).
Resources
1 resource available
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ScienceHub entry for RVM Utah (Olson et al., 2021).xlsx
APPLICATION/VND.OPENXMLFORMATS-OFFICEDOCUMENT.SPREADSHEETML.SHEET
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "020:00" ] |
| contactPoint |
{ "fn": "David Olson", "hasEmail": "mailto:olson.david@epa.gov" } |
| description | Underlying data associated with figures in publication. Portions of this dataset are inaccessible because: Data is now available for public access. They can be accessed through the following means: Data available through Data.gov and EDG. Format: Excel spreadsheet. This dataset is associated with the following publication: Olson, D., T. Riedel, J. Offenberg, M. Lewandowski, R. Long, and T. Kleindienst. Quantifying wintertime O3 and NOx formation with relevance vector machines. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 259: 118538, (2021). |
| distribution |
[ { "title": "ScienceHub entry for RVM Utah (Olson et al., 2021).xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1520921/ScienceHub%20entry%20for%20RVM%20Utah%20%28Olson%20et%20al.%2C%202021%29.xlsx" } ] |
| identifier | https://doi.org/10.23719/1520921 |
| keyword |
[ "Ozone", "Secondary Organic Aerosol", "air quality", "fine particulate matter (PM2.5)", "machine learning" ] |
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2021-06-15 |
| 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/10.1016/j.atmosenv.2021.118538" ] |
| rights |
null
|
| title | Quantifying wintertime O3 and NOx formation with relevance vector machines |
