Characterization of Polycyclic Aromatic Hydrocarbon Profiles by Multivariate Statistical Analysis

Abstract

In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. The PCA was useful in identifying the main emission sources in each study area. It showed that in the city of La Plata the most important pollution sources are traffic emissions and the industrial activity associated with oil and petrochemical plants. In Leipzig, the main sources are those associated with traffic and also a power plant. The combined PCA and DDA methods applied to PAH distributions is a valuable tool in characterizing types of emissions burdens and also in obtaining a differentiation of sample identity according to study areas and sampling times.