Abstract

Lead (Pb) in individual aerosol particles was measured using single particle aerosol mass spectrometer (ATOFMS) in the summer of 2007 in Shanghai, China. Pb was found in 3% of particles with diameters in the range 0.1–2.0 μm. Single particle data were analyzed focusing on the particles with high Pb content which were mostly submicron. Using the ART-2a neural network algorithm, these fine Pb-rich particles were classified into eight main classes by their mass spectral patterns. Based on the size distribution, temporal variation of number density, chemical composition and the correlation between different chemical species for each class, three major emission sources were identified. About 45% of the Pb-rich particles contained organic or elemental carbon and were attributed to the emission from coal combustion; particles with good correlation between Cl and Pb content were mostly attributed to waste incineration. One unique class of particles was identified by strong phosphate and Pb signals, which were assigned to emissions from phosphate industry. Other Pb-rich particles included aged sea salt and particles from metallurgical processes.

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