Structural characterization of organic aerosol using Fourier transform ion cyclotron resonance mass spectrometry: Aromaticity equivalent approach

Abstract

A challenge of atmospheric particulate matter (PM) analysis is the understanding of the sources and chemistry of complex organic aerosols, especially the water-soluble organic compounds (WSOC) fraction, a key component of atmospheric fine PM (PM(2.5)). The sources of WSOC are not well understood and, thus, the molecular characterization of WSOC is important because it provides insight into aerosol sources and the underlying mechanisms of secondary organic aerosols formation and transformation. In this study, molecular characterization of WSOC was achieved using Fourier transform ion cyclotron resonance mass spectrometry. The aromaticity equivalent (X(c)), a new parameter calculated from the assigned molecular formula, is introduced to improve the identification and characterization of aromatic and condensed aromatic compounds in WSOC. Diesel PM (DPM) and atmospheric PM samples were used to study the applicability of the proposed method. Threshold values of X(c) ≥2.5000 and X(c) ≥2.7143 are proposed as unambiguous minimum criteria for the presence of aromatics and condensed aromatics, respectively. By using these criteria, 36% of precursors were defined as aromatics and condensed aromatics in the DPM. For comparison, 21% of aromatic and condensed aromatic compounds were defined using the Aromaticity Index (AI) classification. The lower estimates by the AI approach are probably due to the failure to recognize aromatics and condensed aromatics with longer alkyl chains. The estimated aromatic and condensed aromatic fractions in the atmospheric aerosol samples collected in an industrial area affected by biomass burning events were 51.2 and 50.0%, respectively. The advantage of employing this parameter is that X(c) would have a constant value for each proposed core structure regardless of the degree of alkylation, and thus visual representation and structural interpretations of the spectra become advantageous for characterizing and comparing complex samples. In addition, the proposed parameter complements the AI classification and identification of aromatic and condensed aromatic structures in complex matrices.