@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},
 file_attached = {false},
 profile_id = {363623ef-1990-38f1-b354-f5cdaa6548b2},
 group_id = {02267cec-5558-3876-9cfc-78d056bad5b9},
 last_modified = {2017-03-14T17:32:24.802Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Robinson:EST:2006c},
 source_type = {article},
 private_publication = {false},
 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.}
}

{"_id":{"_str":"5411bfbb480d3bb058002a09"},"__v":0,"authorIDs":[],"author_short":["Robinson, A., L.","Subramanian, R.","Donahue, N., M.","Bernardo-Bricker, A.","Rogge, W., F."],"bibbaseid":"robinson-subramanian-donahue-bernardobricker-rogge-sourceapportionmentofmolecularmarkersandorganicaerosol3foodcookingemissions-2006","bibdata":{"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","file_attached":false,"profile_id":"363623ef-1990-38f1-b354-f5cdaa6548b2","group_id":"02267cec-5558-3876-9cfc-78d056bad5b9","last_modified":"2017-03-14T17:32:24.802Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Robinson:EST:2006c","source_type":"article","private_publication":false,"abstract":"The chemical mass balance model is applied to a large\ndataset of organic molecular marker concentrations to apportion\nambient organic aerosol to food cooking emissions in Pittsburgh,\nPennsylvania. Ambient concentrations of key cooking markers such as\npalmitoleic acid, oleic acid, and cholesterol are well correlated,\nwhich implies the existence of well-defined source profiles.\nHowever, significant inconsistencies exist between the ambient data\nand published source profiles. Most notably, the ambient ratio of\npalmitoleic-acid-to-oleic-acid is more than a factor of 10 greater\nthan essentially all published source profiles. This problem is not\nunique to Pittsburgh. The reason for this discrepancy is not known\nbut it means that both acids cannot be fit simultaneously by CMB.\nCMB analysis is performed using three different combinations of\nfood cooking source profiles and molecular markers. Although all\nthree solutions have high statistical quality, the amount of OC\napportioned to food cooking emissions varies by a factor of 9.\nDifferences in fitting species and source profile\nmarker-to-organic-carbon ratios cause most of the large systematic\nbiases between the different solutions. The best CMB model includes\ntwo alkanoic acids as fitting species in addition to other cooking\nmarkers, which helps constrain the source contribution estimates.\nIt also includes two meat cooking source profiles to account for\nthe variability in the ambient data. This model apportions 320 +/-\n140 ng-C m(-3) or 10% of the study average ambient organic carbon\nto food cooking emissions. Although these results illustrate the\nsignificant challenges created by source profile variability, the\nstrong correlations in the ambient dataset underscore the\nsignificant promise that molecular markers hold for source\napportionment analysis. C1 Carnegie Mellon Univ, Dept Mech Engn,\nPittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem,\nPittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem Engn,\nPittsburgh, PA 15213 USA. Florida Int Univ, Dept Civil & Environm\nEngn, 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.","bibtex":"@article{\n title = {Source apportionment of molecular markers and organic aerosol. 3. Food cooking emissions},\n type = {article},\n year = {2006},\n identifiers = {[object Object]},\n pages = {7820-7827},\n volume = {40},\n websites = {http://dx.doi.org/10.1021/es060781p},\n id = {1d8e9276-d871-30f3-b58e-adb36b2096c3},\n created = {2014-10-08T16:28:18.000Z},\n file_attached = {false},\n profile_id = {363623ef-1990-38f1-b354-f5cdaa6548b2},\n group_id = {02267cec-5558-3876-9cfc-78d056bad5b9},\n last_modified = {2017-03-14T17:32:24.802Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Robinson:EST:2006c},\n source_type = {article},\n private_publication = {false},\n abstract = {The chemical mass balance model is applied to a large\ndataset of organic molecular marker concentrations to apportion\nambient organic aerosol to food cooking emissions in Pittsburgh,\nPennsylvania. Ambient concentrations of key cooking markers such as\npalmitoleic acid, oleic acid, and cholesterol are well correlated,\nwhich implies the existence of well-defined source profiles.\nHowever, significant inconsistencies exist between the ambient data\nand published source profiles. Most notably, the ambient ratio of\npalmitoleic-acid-to-oleic-acid is more than a factor of 10 greater\nthan essentially all published source profiles. This problem is not\nunique to Pittsburgh. The reason for this discrepancy is not known\nbut it means that both acids cannot be fit simultaneously by CMB.\nCMB analysis is performed using three different combinations of\nfood cooking source profiles and molecular markers. Although all\nthree solutions have high statistical quality, the amount of OC\napportioned to food cooking emissions varies by a factor of 9.\nDifferences in fitting species and source profile\nmarker-to-organic-carbon ratios cause most of the large systematic\nbiases between the different solutions. The best CMB model includes\ntwo alkanoic acids as fitting species in addition to other cooking\nmarkers, which helps constrain the source contribution estimates.\nIt also includes two meat cooking source profiles to account for\nthe variability in the ambient data. This model apportions 320 +/-\n140 ng-C m(-3) or 10% of the study average ambient organic carbon\nto food cooking emissions. Although these results illustrate the\nsignificant challenges created by source profile variability, the\nstrong correlations in the ambient dataset underscore the\nsignificant promise that molecular markers hold for source\napportionment analysis. C1 Carnegie Mellon Univ, Dept Mech Engn,\nPittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem,\nPittsburgh, PA 15213 USA. Carnegie Mellon Univ, Dept Chem Engn,\nPittsburgh, PA 15213 USA. Florida Int Univ, Dept Civil & Environm\nEngn, Miami, FL 33199 USA.},\n bibtype = {article},\n author = {Robinson, A L and Subramanian, R and Donahue, N M and Bernardo-Bricker, A and Rogge, W F},\n journal = {Environ. Sci. 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