Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis T. W. Chan1,* and M. Mozurkewich1 1Department of Chemistry and Centre for Atmospheric Chemistry, York University, Toronto, Ontario, Canada *now at: Environment Canada, Toronto, Ontario, Canada
Abstract. Principal component analysis provides a fast and robust method to reduce the
data dimensionality of an aerosol size distribution data set. Here we
describe a methodology for applying principal component analysis to aerosol
size distribution measurements. We illustrate the method by applying it to
data obtained during five field studies. Most variations in the
sub-micrometer aerosol size distribution over periods of weeks can be
described using 5 components. Using 6 to 8 components preserves virtually
all the information in the original data. A key aspect of our approach is
the introduction of a new method to weight the data; this preserves the
orthogonality of the components while taking the measurement uncertainties
into account. We also describe a new method for identifying the approximate
number of aerosol components needed to represent the measurement
quantitatively. Applying Varimax rotation to the resultant components
decomposes a distribution into independent monomodal distributions.
Normalizing the components provides physical meaning to the component
scores. The method is relatively simple, computationally fast, and
numerically robust. The resulting data simplification provides an efficient
method of representing complex data sets and should greatly assist in the
analysis of size distribution data.
Citation: Chan, T. W. and Mozurkewich, M.: Simplified representation of atmospheric aerosol size distributions using absolute principal component analysis, Atmos. Chem. Phys., 7, 875-886, doi:10.5194/acp-7-875-2007, 2007.