Development of a volatility and polarity separator (VAPS) for volatility- and polarity-resolved organic aerosol measurement

E. Martinez, Raul; J. Williams, Brent; Zhang, Yaping; Hagan, David; Walker, Michael; Kreisberg, Nathan M.; Hering, Susanne V.; Hohaus, Thorsten; Jayne, John T.; Worsnop, Douglas R.

doi:10.6084/m9.figshare.2069755

UAST_1147645_Supplementary File.pdf (808.91 kB)

Development of a volatility and polarity separator (VAPS) for volatility- and polarity-resolved organic aerosol measurement

Version 2 2016-02-26, 22:42

Version 1 2016-02-03, 06:05

journal contribution

posted on 2016-02-26, 22:42 authored by Raul E. Martinez, Brent J. Williams, Yaping Zhang, David Hagan, Michael Walker, Nathan M. Kreisberg, Susanne V. Hering, Thorsten Hohaus, John T. Jayne, Douglas R. Worsnop

Discrepancies between modeled and measured atmospheric organic aerosol (OA) have highlighted the need for in situ instrumentation to better characterize the sources, formation mechanisms, and atmospheric evolution of ambient OA. We have developed the Volatility and Polarity Separator (VAPS) for hourly measurements of volatility- and polarity-resolved OA detected using high-resolution time-of-flight mass spectrometry (HR-ToF-MS). Here, atmospheric OA is inertially impacted onto a collection cell, material is transferred onto a short transfer line located inside a gas chromatography (GC) oven, the oven is heated to provide a first-dimension separation of volatility, then thermally pulsed through a short polar GC column for a second-dimension polarity separation, and finally detected by HR-ToF-MS. This novel instrument increases the mass throughput of ambient OA in comparison to traditional GC due to shorter transfer paths and passivated coatings. Molecular separation resolution is partially sacrificed for this increased mass recovery, but the high-resolution mass spectral data recovers information such as chemical classes and even some individual compounds along with elemental composition to determine aerosol oxidation states. Different techniques for interpreting and representing VAPS data are considered and its applicability to positive matrix factorization (PMF) analysis is demonstrated.