Characterization of Short-Term Particulate Matter Events by Real-Time Single Particle Mass Spectrometry

Abstract

Single particle measurements were made in Baltimore, Maryland from March to December 2002 using a real-time single particle mass spectrometer, RSMS-3. Particle composition classes were identified that indicated how the aerosol composition changed with time. The results were compared with collocated instruments giving particle number concentrations and size distributions, sulfate, nitrate, organic, and elemental carbon mass concentrations and total mass. Examination of these measurements revealed several particulate matter (PM) events in which the 24 h averaged PM 2.5 mass exceeded 30 μ g/m 3 . Three of these events were studied in further detail by comparing number and mass concentrations obtained by RSMS-3 with standard methods. For all three events, the number concentrations obtained with RSMS-3 and a scanning mobility particle sizer were highly correlated (R 2 ∼ 0.7). For the event characterized by a high sulfate mass concentration, the RSMS-3 provided an accurate measure of time-dependent nitrate and carbon mass concentrations, but not for sulfate and total mass. For the two events characterized by high carbon mass concentrations (one from a transcontinental wildfire and the other from stagnation during a period of high traffic), RSMS-3 provided an accurate measure of time-dependent nitrate mass, carbon mass and total mass when the aerosol was not dominated by particles outside the size limit of RSMS-3. While the time dependencies were strongly correlated, the absolute mass or number concentrations determined by RSMS-3 were sometimes off by a constant value, which permitted the relative detection efficiencies of some particle classes to be estimated. Other factors that inhibit reconciliation of mass- and number- based concentration measurements are discussed including the difficulty of detecting ammonium sulfate by laser ablation/ionization and the varying size ranges of different particle measurement methods.