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Aging effects in analysis of polycyclic aromatic hydrocarbon aerosols
Author(s) -
Bulatov Valery,
Gridin Vladimir V.,
Fisher Michal,
Sluszny Chanan,
Schechter Israel
Publication year - 2008
Publication title -
israel journal of chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.908
H-Index - 54
eISSN - 1869-5868
pISSN - 0021-2148
DOI - 10.1560/ijc.47.2.195
Subject(s) - aerosol , chemistry , pyrene , polycyclic aromatic hydrocarbon , particulates , anthracene , perylene , hydrocarbon , characterization (materials science) , environmental chemistry , mass spectrum , mass spectrometry , analytical chemistry (journal) , photochemistry , organic chemistry , molecule , chromatography , nanotechnology , materials science
Polycyclic aromatic hydrocarbon (PAH) aerosols undergo time‐dependent processes such as various aging effects and material loss due to sublimation. Therefore, when their analysis is delayed, the results may not properly represent the true environmental composition as sampled. Moreover, even when fast analysis is performed, one may still be interested in the initial concentration of these aerosols, at the moment of their formation. In both cases, a characterization of the time dependence of aerosol mass losses and other possible modifications is of interest. In this study we monitored these time‐dependent processes for three different PAH aerosol particulates (anthracene, perylene, and pyrene). We applied two experimental techniques: Fourier transform spectral imaging microscopy (FT‐SIM) and traditional HPLC analysis. The former provided a full fluorescence spectrum at each pixel of the particulate, as well as PAH identification and quantification. The chemical imaging information was also used for morphological characterization of the PAH aerosols, which was applied for developing of a simple mathematical model. The effect of the time‐delay upon the actual aerosol mass can thus be estimated. The original (as‐formed or as‐sampled) aerosol mass can be calculated using the proposed empirical model.