Atmospheric Chemistry and Physics Discussions
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2008
10.5194/acpd-8-6729-2008
http://www.atmos-chem-phys-discuss.net/8/6729/2008/
http://www.atmos-chem-phys-discuss.net/8/6729/2008/acpd-8-6729-2008.html
http://www.atmos-chem-phys-discuss.net/8/6729/2008/acpd-8-6729-2008.pdf
6729
6791
2008-04-09
Interpretation of organic components from positive matrix factorization of aerosol mass spectrometric data
I. M. Ulbrich
M. R. Canagaratna
Q. Zhang
D. R. Worsnop
J. L. Jimenez
jose.jimenez@colorado.edu
Cooperative Inst. for Research in the Environmental Sciences (CIRES), Boulder, CO, USA
Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
Aerodyne Research, Inc., Billerica, MA, USA
Atmos. Sci. Res. Center, University at Albany, State University of New York, Albany, NY, USA
The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer
(Q-AMS) collected at the Pittsburgh Air Quality Study in September 2002 was
analyzed for components with Positive Matrix Factorization (PMF). Three
components – hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated
OA (OOA-I) that correlates well with sulfate, and a less-oxygenated,
semi-volatile OA (OOA-II) that correlates well with nitrate and chloride –
are identified and interpreted as primary combustion emissions, aged SOA,
and semivolatile, less aged SOA, respectively. The complexity of
interpreting the PMF solutions of unit mass resolution (UMR) AMS data is
illustrated by a detailed analysis of the solutions as a function of number
of components and rotational state. A public database of AMS spectra has
been created to aid this type of analysis. A sensitivity analysis with
realistic synthetic data is also used to characterize the behavior of PMF
for choosing the best number of factors, rotations of non-unique solutions,
and the retrievability of more (or less) correlated factors. The ambient and
synthetic data indicate that the variation of the PMF quality of fit
parameter (Q, a normalized chi-squared metric) vs. number of factors in the
solution is useful to identify the minimum number of factors, but more
detailed analysis and interpretation is needed to choose the best number of
factors. The maximum value of the rotational matrix is not useful for determining the
best number of factors. In synthetic datasets, factors are "split" into
two or more components when solving for more factors than were used in the
input. Elements of the "splitting" behavior are observed in solutions of
real datasets with several factors. Significant structure remains in the
residual of the real dataset after physically-meaningful factors have been
assigned and an unrealistic number of factors would be required to explain
the remaining variance. This residual structure appears to be due to
variability in the spectra of the components (especially OOA-II in this
case), which is likely to be a key limit of the retrievability of components
from AMS datasets using PMF and similar methods that need to assume constant
component mass spectra. Methods for characterizing and dealing with this
variability are needed. Values of the rotational parameter (FPEAK) near zero
appear to be most appropriate for these datasets. Interpretation of PMF factors must be done
carefully. Synthetic data indicate that PMF internal diagnostics and
similarity to available source component spectra together are not sufficient
for identifying factors. It is critical to use correlations between factor
time series and external measurement time series to support factor
interpretations. Components with <5% of the mass or with high
correlation (<I>R</I>>0.9) with other components are suspect and should be
interpreted with care. Results from this study may be useful for interpreting
the PMF analysis of data from other aerosol mass spectrometers.
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