WIT Press


Data Handling Of Complex GC-MS Signals To Characterize Homologous Series As Organic Source Tracers In Atmospheric Aerosols

Price

Free (open access)

Paper DOI

10.2495/AIR080341

Volume

116

Pages

9

Page Range

335 - 343

Published

2008

Size

522 kb

Author(s)

M. C. Pietrogrande, M. Mercuriali & D. Bacco

Abstract

A description is given of a chemometric approach used to extract information on the characteristics of n-alkane and n-alkanoic acid homologous series as useful markers for PM source identification and differentiation. The key parameters of the homologous series – number of terms and Carbon Preference Index – are directly estimated by the Autocovariance Function (EACVF) computed on the acquired chromatogram. The homologous series properties – relevant as the chemical signature of specific input sources – can be efficiently extracted from the complex GC-MS signal thus reducing the labour, time consumption and the subjectivity introduced by human intervention. Keywords: aerosol chemical composition/homologous series/GC-MS analysis/ signal processing/ multicomponent mixtures. 1 Introduction Atmospheric aerosols consist of a complex mixture of hundreds of compounds belonging to many different compound classes: despite this complexity, in environmental monitoring and assessment studies, the sample chemical analysis is usually limited to selected compounds to adequately represent a chemical signature of the possible input sources [1–3]. Homologous series of n-alkanes and n-alcanoic acids are especially suited for use as molecular tracers: they are common to multiple sources and they give information relevant to differentiating aerosols of anthropogenic origin (i.e. associated with industrial and urban activities) from those of natural, biogenic origin [4–6]. The key parameters to characterize specific sources are the number of terms and the carbon preference index (CPI, i.e., the sum of the concentrations of the odd/even carbon number

Keywords

aerosol chemical composition/homologous series/GC-MS analysis/ signal processing/ multicomponent mixtures.