The paper describes the characterization of n-alkane homologous series present in PM samples performed by Gas Chromatography-Mass Spectrometry analysis. The PM samples were collected in three locations in northern Italy: Milan, a large urban area, Oasi Bine, a rural site far from big city centers, and Alpe San Colombano a remote, high altitude site in the Alps. They represent different particle sizes (PM10.PM2.5, PM1) and seasons (summer, fall and winter). The analyzed samples were characterized in terms of PM total mass, total concentration of n-alkanes and carbon preference index, CPI , to quantify the relative abundance of odd versus even n-alkanes. As alternative to the conventional method based on peak integration, a chemometric approach based on computation of the Autocovariance Function was applied to extract homologous series property information, as useful chemical markers for tracking the biogenic and anthropogenic origins of organic input sources.

Data handling based on AutoCoVariance Function for decoding complex signals from environmental monitoring: identification of organic tracers in atmospheric aerosol

MERCURIALI, Mattia;PIETROGRANDE, Maria Chiara
2009

Abstract

The paper describes the characterization of n-alkane homologous series present in PM samples performed by Gas Chromatography-Mass Spectrometry analysis. The PM samples were collected in three locations in northern Italy: Milan, a large urban area, Oasi Bine, a rural site far from big city centers, and Alpe San Colombano a remote, high altitude site in the Alps. They represent different particle sizes (PM10.PM2.5, PM1) and seasons (summer, fall and winter). The analyzed samples were characterized in terms of PM total mass, total concentration of n-alkanes and carbon preference index, CPI , to quantify the relative abundance of odd versus even n-alkanes. As alternative to the conventional method based on peak integration, a chemometric approach based on computation of the Autocovariance Function was applied to extract homologous series property information, as useful chemical markers for tracking the biogenic and anthropogenic origins of organic input sources.
environmental monitoring; Data handling; AutoCoVariance Function; organic tracers; atmospheric aerosol
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1400565
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