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Journal article

Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data

Ulbrich I, Canagaratna M, Zhang Q, Worsnop D, Jimenez J ...see all

Atmos. Chem. Physics, vol. 9, issue 9 (2009) pp. 2891-2918

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Abstract

The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer
(Q-AMS) collected at the Pittsburgh Air Quality Study (PAQS) in September
2002 was analyzed with Positive Matrix Factorization (PMF). Three
components - hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated
OA (OOA-1) that correlates well with sulfate, and a less-oxygenated,
semi-volatile OA (OOA-2) 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 forcing. A public
web-based database of AMS spectra has been created to aid this type
of analysis. Realistic synthetic data is also used to characterize
the behavior of PMF for choosing the best number of factors, and
evaluating the rotations of non-unique solutions. 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 are 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-2 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. 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 and external
measurement time series and other criteria to support factor interpretations.
True components with accurately. Results from this study may be useful for interpreting
the PMF analysis of data from other aerosol mass spectrometers. Researchers
are urged to analyze future datasets carefully, including synthetic
analyses, and to evaluate whether the conclusions made here apply
to their datasets.

Author-supplied keywords

  • QQPMF
  • QQSA

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Authors

  • I M Ulbrich

  • M R Canagaratna

  • Q Zhang

  • D R Worsnop

  • J L Jimenez

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