Number concentrations of fine and ultrafine particles containing metals

Michael P. Tolocka, Derek A. Lake, Murray V. Johnston, Anthony S. Wexler

Research output: Contribution to journalArticle

39 Scopus citations

Abstract

Typical classification schemes for large data sets of single-particle mass spectra involve statistical or neural network analysis. In this work, a new approach is evaluated in which particle spectra are pre-selected on the basis of an above threshold signal intensity at a specified m/z (mass to charge ratio). This provides a simple way to identify candidate particles that may contain the specific chemical component associated with that m/z. Once selected, the candidate particle spectra are then classified by the fast adaptive resonance algorithm, ART 2-a, to confirm the presence of the targeted component in the particle and to study the intra-particle associations with other chemical components. This approach is used to characterize metals in a 75,000 particle data set obtained in Baltimore, Maryland. Particles containing a specific metal are identified and then used to determine the size distribution, number concentration, time/wind dependencies and intra-particle correlations with other metals. Four representative elements are considered in this study: vanadium, iron, arsenic and lead. Number concentrations of ambient particles containing these elements can exceed 10,000particlescm-3 at the measurement site. Vanadium, a primary marker for fuel oil combustion, is observed from all wind directions during this time period. Iron and lead are observed from the east-northeast. Most particles from this direction that contain iron also contain lead and most particles that contain lead also contain iron, suggesting a common emission source for the two. Arsenic and lead are observed from the south-southeast. Particles from this direction contain either arsenic or lead but rarely both, suggesting different sources for each element.

Original languageEnglish (US)
Pages (from-to)3263-3273
Number of pages11
JournalAtmospheric Environment
Volume38
Issue number20
DOIs
StatePublished - Jun 2004

Keywords

  • Ambient aerosol
  • Chemical composition
  • Metals
  • Particle number concentration
  • Real-time single-particle mass spectrometry

ASJC Scopus subject areas

  • Atmospheric Science
  • Environmental Science(all)
  • Pollution

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