Application of chemometric methods for classification of atmospheric particles based on thin-window electron probe microanalysis data

Abstract

Conventional single-particle electron probe microanalysis (EPMA) is widely used for evaluating the sources of atmospheric aerosol. The method is capable of simultaneously detecting the chemical composition and the morphology of each particle. Computer-controlled automatic EPMA allows the analysis of huge numbers of individual particles. Cluster as well as factor analysis are used for the classification of particles based on the obtained data set. However, the method is not able to detect low- Z elements (C, N, O), therefore, e.g. organic particles can only be identified by their typical inorganic content and high background. Using a thin-window X-ray detector, the capabilities of EPMA can be extended to determine low- Z elements. The recently developed quantification method based on Monte Carlo simulations is capable to evaluate elemental concentrations in single microscopic particles, including C, N and O. It was shown that also chemical species can be determined from the obtained concentrations. Hierarchical and non-hierarchical cluster analysis, as well as principal component analysis were applied for the classification of particles based on low- Z EPMA data. A mixture of standard particles as well as atmospheric aerosol samples were used to test the classification methods. Different input data (X-ray intensities or elemental concentrations) and scaling functions were used for the chemometric methods. Cluster and factor analysis appear to be efficient tools for classification of particles based on low- Z EPMA data. As an example, atmospheric ammonium sulphate and organic sulphur were classified in separate groups, which was not possible by conventional EPMA.