Source apportionment of molecular markers and organic aerosol. 3. Food cooking emissions. Robinson, A., L.; Subramanian, R.; Donahue, N., M.; Bernardo-Bricker, A.; and Rogge, W., F. Environ. Sci. Technol., 40:7820-7827, 2006.
Source apportionment of molecular markers and organic aerosol. 3. Food cooking emissions [link]Website  abstract   bibtex   
The chemical mass balance model is applied to a large dataset of organic molecular marker concentrations to apportion ambient organic aerosol to food cooking emissions in Pittsburgh, Pennsylvania. Ambient concentrations of key cooking markers such as palmitoleic acid, oleic acid, and cholesterol are well correlated, which implies the existence of well-defined source profiles. However, significant inconsistencies exist between the ambient data and published source profiles. Most notably, the ambient ratio of palmitoleic-acid-to-oleic-acid is more than a factor of 10 greater than essentially all published source profiles. This problem is not unique to Pittsburgh. The reason for this discrepancy is not known but it means that both acids cannot be fit simultaneously by CMB. CMB analysis is performed using three different combinations of food cooking source profiles and molecular markers. Although all three solutions have high statistical quality, the amount of OC apportioned to food cooking emissions varies by a factor of 9. Differences in fitting species and source profile marker-to-organic-carbon ratios cause most of the large systematic biases between the different solutions. The best CMB model includes two alkanoic acids as fitting species in addition to other cooking markers, which helps constrain the source contribution estimates. It also includes two meat cooking source profiles to account for the variability in the ambient data. This model apportions 320 +/- 140 ng-C m(-3) or 10% of the study average ambient organic carbon to food cooking emissions. Although these results illustrate the significant challenges created by source profile variability, the strong correlations in the ambient dataset underscore the significant promise that molecular markers hold for source apportionment analysis. C1 Carnegie Mellon Univ, Dept Mech Engn, Pittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem, Pittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem Engn, Pittsburgh, PA 15213 USA. Florida Int Univ, Dept Civil & Environm Engn, Miami, FL 33199 USA.
@article{
 title = {Source apportionment of molecular markers and organic aerosol. 3. Food cooking emissions},
 type = {article},
 year = {2006},
 identifiers = {[object Object]},
 pages = {7820-7827},
 volume = {40},
 websites = {http://dx.doi.org/10.1021/es060781p},
 id = {1d8e9276-d871-30f3-b58e-adb36b2096c3},
 created = {2014-10-08T16:28:18.000Z},
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 profile_id = {363623ef-1990-38f1-b354-f5cdaa6548b2},
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 last_modified = {2017-03-14T17:32:24.802Z},
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 citation_key = {Robinson:EST:2006c},
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 abstract = {The chemical mass balance model is applied to a large
dataset of organic molecular marker concentrations to apportion
ambient organic aerosol to food cooking emissions in Pittsburgh,
Pennsylvania. Ambient concentrations of key cooking markers such as
palmitoleic acid, oleic acid, and cholesterol are well correlated,
which implies the existence of well-defined source profiles.
However, significant inconsistencies exist between the ambient data
and published source profiles. Most notably, the ambient ratio of
palmitoleic-acid-to-oleic-acid is more than a factor of 10 greater
than essentially all published source profiles. This problem is not
unique to Pittsburgh. The reason for this discrepancy is not known
but it means that both acids cannot be fit simultaneously by CMB.
CMB analysis is performed using three different combinations of
food cooking source profiles and molecular markers. Although all
three solutions have high statistical quality, the amount of OC
apportioned to food cooking emissions varies by a factor of 9.
Differences in fitting species and source profile
marker-to-organic-carbon ratios cause most of the large systematic
biases between the different solutions. The best CMB model includes
two alkanoic acids as fitting species in addition to other cooking
markers, which helps constrain the source contribution estimates.
It also includes two meat cooking source profiles to account for
the variability in the ambient data. This model apportions 320 +/-
140 ng-C m(-3) or 10% of the study average ambient organic carbon
to food cooking emissions. Although these results illustrate the
significant challenges created by source profile variability, the
strong correlations in the ambient dataset underscore the
significant promise that molecular markers hold for source
apportionment analysis. C1 Carnegie Mellon Univ, Dept Mech Engn,
Pittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem,
Pittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem Engn,
Pittsburgh, PA 15213 USA. Florida Int Univ, Dept Civil & Environm
Engn, Miami, FL 33199 USA.},
 bibtype = {article},
 author = {Robinson, A L and Subramanian, R and Donahue, N M and Bernardo-Bricker, A and Rogge, W F},
 journal = {Environ. Sci. Technol.}
}
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