Source apportionment of molecular markers and organic aerosol. 2. Biomass smoke. Robinson, A., L., Subramanian, R., Donahue, N., M., Bernardo-Bricker, A., & Rogge, W., F. Environ. Sci. Technol., 40:7811-7819, 2006. Website abstract bibtex Chemical mass balance analysis was performed using a
large dataset of molecular marker concentrations to estimate the
contribution of biomass smoke to ambient organic carbon (OC) and
fine particle mass in Pittsburgh, Pennsylvania. Source profiles
were selected based on detailed comparisons between the ambient
data and a large number of published profiles. The fall and winter
data were analyzed with fireplace and woodstove source profiles,
and open burning profiles were used to analyze the spring and
summer data. At the upper limit, biomass smoke is estimated to
contribute on average 520 +/- 140 ng-C m(-3) or 14.5% of the
ambient OC in the fall, 210 +/- 85 ng-C m(-3) or 10% of the
ambient OC in the winter, and 60 +/- 21 ng-C/m(-3) or 2% of the
ambient OC in the spring and summer. In the fall and winter, there
is large day-to-day variability in the amount of OC apportioned to
biomass smoke. The levels of biomass smoke in Pittsburgh are much
lower than in some other areas of the United States, indicating
significant regional variability in the importance of biomass
combustion as a source of fine particulate matter. The calculations
face two major sources of uncertainty. First, the ambient ratios of
levoglucosan, resin acids, and syringhaldehyde concentrations are
highly variable implying that numerous sources with distinct source
profiles contribute to ambient marker concentrations. Therefore, in
contrast to previous CMB analyses, we find that at least three
distinct biomass smoke source profiles must be included in the CMB
model to explain this variability. Second, the marker-to-OC ratios
of available biomass smoke profiles are highly variable. This
variability introduces uncertainty of more than a factor of 2 in
the amount of ambient OC apportioned to biomass smoke by different
statistically acceptable CMB solutions. The marker-to-OC ratios of
source profiles are critical parameters to consider when evaluating
CMB solutions.
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. 2. Biomass smoke},
type = {article},
year = {2006},
identifiers = {[object Object]},
pages = {7811-7819},
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websites = {http://dx.doi.org/10.1021/es060782h},
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last_modified = {2017-03-14T17:32:24.802Z},
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abstract = {Chemical mass balance analysis was performed using a
large dataset of molecular marker concentrations to estimate the
contribution of biomass smoke to ambient organic carbon (OC) and
fine particle mass in Pittsburgh, Pennsylvania. Source profiles
were selected based on detailed comparisons between the ambient
data and a large number of published profiles. The fall and winter
data were analyzed with fireplace and woodstove source profiles,
and open burning profiles were used to analyze the spring and
summer data. At the upper limit, biomass smoke is estimated to
contribute on average 520 +/- 140 ng-C m(-3) or 14.5% of the
ambient OC in the fall, 210 +/- 85 ng-C m(-3) or 10% of the
ambient OC in the winter, and 60 +/- 21 ng-C/m(-3) or 2% of the
ambient OC in the spring and summer. In the fall and winter, there
is large day-to-day variability in the amount of OC apportioned to
biomass smoke. The levels of biomass smoke in Pittsburgh are much
lower than in some other areas of the United States, indicating
significant regional variability in the importance of biomass
combustion as a source of fine particulate matter. The calculations
face two major sources of uncertainty. First, the ambient ratios of
levoglucosan, resin acids, and syringhaldehyde concentrations are
highly variable implying that numerous sources with distinct source
profiles contribute to ambient marker concentrations. Therefore, in
contrast to previous CMB analyses, we find that at least three
distinct biomass smoke source profiles must be included in the CMB
model to explain this variability. Second, the marker-to-OC ratios
of available biomass smoke profiles are highly variable. This
variability introduces uncertainty of more than a factor of 2 in
the amount of ambient OC apportioned to biomass smoke by different
statistically acceptable CMB solutions. The marker-to-OC ratios of
source profiles are critical parameters to consider when evaluating
CMB solutions.
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.}
}
Downloads: 0
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The fall and winter\ndata were analyzed with fireplace and woodstove source profiles,\nand open burning profiles were used to analyze the spring and\nsummer data. At the upper limit, biomass smoke is estimated to\ncontribute on average 520 +/- 140 ng-C m(-3) or 14.5% of the\nambient OC in the fall, 210 +/- 85 ng-C m(-3) or 10% of the\nambient OC in the winter, and 60 +/- 21 ng-C/m(-3) or 2% of the\nambient OC in the spring and summer. In the fall and winter, there\nis large day-to-day variability in the amount of OC apportioned to\nbiomass smoke. The levels of biomass smoke in Pittsburgh are much\nlower than in some other areas of the United States, indicating\nsignificant regional variability in the importance of biomass\ncombustion as a source of fine particulate matter. The calculations\nface two major sources of uncertainty. First, the ambient ratios of\nlevoglucosan, resin acids, and syringhaldehyde concentrations are\nhighly variable implying that numerous sources with distinct source\nprofiles contribute to ambient marker concentrations. Therefore, in\ncontrast to previous CMB analyses, we find that at least three\ndistinct biomass smoke source profiles must be included in the CMB\nmodel to explain this variability. Second, the marker-to-OC ratios\nof available biomass smoke profiles are highly variable. This\nvariability introduces uncertainty of more than a factor of 2 in\nthe amount of ambient OC apportioned to biomass smoke by different\nstatistically acceptable CMB solutions. The marker-to-OC ratios of\nsource profiles are critical parameters to consider when evaluating\nCMB solutions.\nC1 Carnegie Mellon Univ, Dept Mech Engn, Pittsburgh, PA 15213 USA.\nCarnegie Mellon Univ, Dept Chem, Pittsburgh, PA 15213 USA. Carnegie\nMellon Univ, Dept Chem Engn, Pittsburgh, PA 15213 USA. Florida Int\nUniv, 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.","bibtex":"@article{\n title = {Source apportionment of molecular markers and organic aerosol. 2. 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Source profiles\nwere selected based on detailed comparisons between the ambient\ndata and a large number of published profiles. The fall and winter\ndata were analyzed with fireplace and woodstove source profiles,\nand open burning profiles were used to analyze the spring and\nsummer data. At the upper limit, biomass smoke is estimated to\ncontribute on average 520 +/- 140 ng-C m(-3) or 14.5% of the\nambient OC in the fall, 210 +/- 85 ng-C m(-3) or 10% of the\nambient OC in the winter, and 60 +/- 21 ng-C/m(-3) or 2% of the\nambient OC in the spring and summer. In the fall and winter, there\nis large day-to-day variability in the amount of OC apportioned to\nbiomass smoke. The levels of biomass smoke in Pittsburgh are much\nlower than in some other areas of the United States, indicating\nsignificant regional variability in the importance of biomass\ncombustion as a source of fine particulate matter. The calculations\nface two major sources of uncertainty. 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