Data collected in Ostrava, Czech Republic and applied in a source apportionment model
These data support a published journal paper described as follows:
A 14-week investigation during a warm and cold seasons was conducted to improve understanding of air
pollution sources that might be impacting air quality in Ostrava, the Czech Republic. Fine particulate
matter (PM2.5) samples were collected in consecutive 12-h day and night increments during spring and
fall 2012 sampling campaigns. Sampling sites were strategically located to evaluate conditions in close
proximity of a large steel works industrial complex, as well as away from direct influence of the
industrial complex. These samples were analyzed for metals and other elements, organic and elemental
(black) carbon, and polycyclic aromatic hydrocarbons (PAHs). The PM2.5 samples were supplemented
with pollutant gases and meteorological parameters. We applied the EPA PMF v5.1 model with uncertainty estimate features to the Ostrava data set. Using the model's bootstrapping procedure and other considerations, six factors were determined to provide the optimum solution. Each model run consisted of 100 iterations to ensure that the solution represents a global minimum. The resulting factors were identified as representing coal (power plants), mixed Cl, crustal, industrial 1 (alkali metals and PAHs), industrial 2 (transition metals), and home heat/transportation. The home heating source is thought to be largely domestic boilers burning low quality fuels such as lignite, wood, and domestic waste.
Transportation-related combustion emissions could not be resolved as a separate factor. Uncertainty
estimates support the general conclusion that the factors identified as representing coal power and home
heat/transportation dominate the percent contribution to fine mass. Apportionment of regulated individual
species is also presented.
Two data files are provided to support the dataset. One provides the input data (concentrations and uncertainties) as used in the PMF source apportionment model. The other provides the processed data that directly support all quantitative information presented in the journal paper main text and supporting material.
This dataset is associated with the following publication:
Conner , T., L. Černikovský, J. Novák, and R. Williams. Source apportionment with uncertainty estimates of fine particulate matter in Ostrava, Czech Republic using Positive Matrix Factorization. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 7(3): 503-512, (2016).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Teri Conner",
"hasEmail": "mailto:conner.teri@epa.gov"
}
|
| description | These data support a published journal paper described as follows: A 14-week investigation during a warm and cold seasons was conducted to improve understanding of air pollution sources that might be impacting air quality in Ostrava, the Czech Republic. Fine particulate matter (PM2.5) samples were collected in consecutive 12-h day and night increments during spring and fall 2012 sampling campaigns. Sampling sites were strategically located to evaluate conditions in close proximity of a large steel works industrial complex, as well as away from direct influence of the industrial complex. These samples were analyzed for metals and other elements, organic and elemental (black) carbon, and polycyclic aromatic hydrocarbons (PAHs). The PM2.5 samples were supplemented with pollutant gases and meteorological parameters. We applied the EPA PMF v5.1 model with uncertainty estimate features to the Ostrava data set. Using the model's bootstrapping procedure and other considerations, six factors were determined to provide the optimum solution. Each model run consisted of 100 iterations to ensure that the solution represents a global minimum. The resulting factors were identified as representing coal (power plants), mixed Cl, crustal, industrial 1 (alkali metals and PAHs), industrial 2 (transition metals), and home heat/transportation. The home heating source is thought to be largely domestic boilers burning low quality fuels such as lignite, wood, and domestic waste. Transportation-related combustion emissions could not be resolved as a separate factor. Uncertainty estimates support the general conclusion that the factors identified as representing coal power and home heat/transportation dominate the percent contribution to fine mass. Apportionment of regulated individual species is also presented. Two data files are provided to support the dataset. One provides the input data (concentrations and uncertainties) as used in the PMF source apportionment model. The other provides the processed data that directly support all quantitative information presented in the journal paper main text and supporting material. This dataset is associated with the following publication: Conner , T., L. Černikovský, J. Novák, and R. Williams. Source apportionment with uncertainty estimates of fine particulate matter in Ostrava, Czech Republic using Positive Matrix Factorization. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 7(3): 503-512, (2016). |
| distribution |
[
{
"title": "Ostrava PMF dataset.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"downloadURL": "https://pasteur.epa.gov/uploads/159/Ostrava%20PMF%20dataset.xlsx"
},
{
"title": "Ostrava PMF model input data.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"downloadURL": "https://pasteur.epa.gov/uploads/159/Ostrava%20PMF%20model%20input%20data.xlsx"
}
]
|
| identifier | A-n8q2-159 |
| keyword |
[
"Czech Republic",
"Fine Particulate Matter",
"home heating",
"source apportionment",
"steel manufacturing"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2015-11-17 |
| 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.apr.2015.12.004"
]
|
| rights |
null
|
| title | Data collected in Ostrava, Czech Republic and applied in a source apportionment model |