Evaluation of polycyclic aromatic hydrocarbons in fine particulate matter censoring-related bias.csv
The case study data are selected from an EPA study using cookstove combustion experiments to measure polycyclic aromatic hydrocarbon (PAH) concentrations in particulate matter (PM) and were determined using gas chromatography-mass spectrometry (GC-MS) (Shen et al. 2017). Chromatograms of particle emissions reported in Shen et al. (2017) were reanalyzed for the case study to quantify previously censored measurements where the signal-to-noise ratio was >1.
This dataset is associated with the following publication:
George, B., L. Gains-Germain, K. Broms, K. Black, M. Furman, M. Hays, K. Thomas, and J.E. Simmons. Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(6): 3786-3795, (2021).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Barbara George",
"hasEmail": "mailto:george.bj@epa.gov"
}
|
| describedBy | https://pasteur.epa.gov/uploads/10.23719/1517601/documents/data%20dictionary%20-%20Evaluation%20of%20polycyclic%20aromatic%20hydrocarbons%20in%20fine%20particulate%20matter%20censoring-related%20bias.docx |
| describedByType | application/vnd.openxmlformats-officedocument.wordprocessingml.document |
| description | The case study data are selected from an EPA study using cookstove combustion experiments to measure polycyclic aromatic hydrocarbon (PAH) concentrations in particulate matter (PM) and were determined using gas chromatography-mass spectrometry (GC-MS) (Shen et al. 2017). Chromatograms of particle emissions reported in Shen et al. (2017) were reanalyzed for the case study to quantify previously censored measurements where the signal-to-noise ratio was >1. This dataset is associated with the following publication: George, B., L. Gains-Germain, K. Broms, K. Black, M. Furman, M. Hays, K. Thomas, and J.E. Simmons. Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(6): 3786-3795, (2021). |
| distribution |
[
{
"title": "Evaluation of polycyclic aromatic hydrocarbons in fine particulate matter censoring-related bias.csv",
"mediaType": "application/vnd.ms-excel",
"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1517601/Evaluation%20of%20polycyclic%20aromatic%20hydrocarbons%20in%20fine%20particulate%20matter%20censoring-related%20bias.csv"
}
]
|
| identifier | https://doi.org/10.23719/1517601 |
| keyword |
[
"bias",
"censoring",
"nondetects"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2019-11-14 |
| 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/10.1021/acs.est.0c02256"
]
|
| rights |
null
|
| title | Evaluation of polycyclic aromatic hydrocarbons in fine particulate matter censoring-related bias.csv |
