Distribution of n-Alkanes in the Northern Italy Aerosols: Data Handling of GC-MS Signals for Homologous Series Characterization

• Published in: Environmental science & technology 2010-06, Vol.44 (11), p.4232-4240
• Main Authors: Pietrogrande, M. C, Mercuriali, M, Perrone, M. G, Ferrero, L, Sangiorgi, G, Bolzacchini, E
• Format: Article
• Language: English
• Subjects: Aerosols; Aerosols - chemistry; Alkanes - analysis; Applied sciences; Chemical compounds; Chromatography; Emissions; Environmental Measurements Methods; Environmental science; Exact sciences and technology; Gas Chromatography-Mass Spectrometry - methods; Italy; Mass spectrometry; Particle Size; Pollution
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/es1001242

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 (PM1, PM2.5, PM10) and seasons (summer, fall, and winter). The analyzed samples were characterized in terms of PM total mass, total concentration of C20−C32 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 autocovariance function (EACVF) computation was found reliable to characterize the homologous series. In particular two parameters have proven useful chemical markers for tracking the biogenic and anthropogenic origins of n-alkanes: CPIEACVF and series %, estimating the % n-alkanes abundance relative to total alkane concentration. The investigated samples display a large variation in the n-alkanes relative abundance: the lowest values (series % = 1−14%) were found in summer and the highest (series % = 24−48%) in winter. In addition, a considerable seasonal variation of CPIEACVF values can be identified for all the sampling sites: the CPIEACVF values are close to 1 (CPIEACVF = 0.8−1.2) in the cold seasons, revealing a strong contribution from anthropogenic emissions, while spreader values (CPIEACVF = 0.9−3) were found in the warm season, that is, reflecting a variable contribution from biogenic sources in combination with anthropogenic emissions.