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
-
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 |
