Characterization of anthropogenic organic aerosols by TOF-ACSM with the new capture vaporizer

Abstract

Abstract. A new capture vaporizer (CV) has been developed and used recently in the Aerodyne aerosol mass spectrometer (AMS) and aerosol chemical speciation monitor (ACSM) instead of the standard vaporizer (SV) to reduce the particle bounce. It is important to characterize the CV performance in different environments. In this study, we characterized specific organic aerosols (OAs) from vehicle, cooking, biomass burning, and coal burning emissions by a time-of-flight ACSM (TOF-ACSM) with the CV. Their corresponding marker ions that have been defined in the previous SV-based analysis are still valid in the CV mass spectra. Spectra of OAs from cooking and vehicle exhaust show similarities in distinct alkyl fragments but different ratios of m∕z 55 and 57. Ions related to polycyclic aromatic hydrocarbons are present in the OA spectra obtained from burning lignite and bituminous coal but not in the spectra obtained from burning anthracite. Although the relative intensities of m∕z 60 and 73 are much lower in the CV spectra than in the SV spectra for biomass burning OA, they are still relatively greater compared with the spectra for other sources. Our data suggest an atmospheric background of f60 of below 0.03 % for CV. Moreover, we deployed the CV TOF-ACSM along with a SV AMS in urban Beijing during the winter of 2017 to characterize ambient OA with strong anthropogenic influences. The CV TOF-ACSM shows a collection efficiency (CE) of about unity. The CV and SV data show consistent mass concentrations of sulfate, nitrate, ammonium, and OA. Six OA factors are identified by the positive matrix factorization (PMF) analysis for both the CV and the SV data. The SV and CV PMF factors show good correlations in mass concentrations. The SV and CV factors related to coal combustion and cooking differ significantly in loadings, explained by the PMF uncertainty and the lack of understanding of the relative ionization efficiency and CE for primary OA. The CV factors related to secondary sources show greater loadings than the SV factors, which may be associated with the changes in signal-to-noise ratios of various ions in the PMF analysis. Our results support improved mass quantification and useful source identification by the CV for ambient particles in the polluted urban environment. The difference in factor loadings between SV and CV should be considered when interpreting or comparing the PMF results among studies.