Abstract View
New Insights into Complex Atmospheric Chromatograms Enabled by Advanced Data Processing Techniques
SUNGWOO KIM, Lindsay Yee, Allen Goldstein, Nathan Kreisberg, Susanne Hering, Gabriel Isaacman-VanWertz, Virginia Tech
Abstract Number: 580
Working Group: Instrumentation and Methods
Abstract
Ambient aerosols may contain hundreds or thousands of unique organic compounds, and even minor components may provide valuable insight into particle sources and formation chemistry. To gain insight into sources and transformations, individual components are frequently identified and quantified using gas chromatography/mass spectrometry. However, due to the complexity of this data and the highly dynamic nature of aerosol composition, a substantial fraction of data is never analyzed or interpreted. We have recently developed new methods to more comprehensively identify the full suite of analytes in a complex dataset by coupling positive matrix factorization (PMF) with a peak-filtering decision tree. In this presentation, we apply these methods to existing datasets collected by the Semi-Volatile Thermal Desorption Aerosol Gas chromatograph (SV-TAG) to identify new analytes and interpret their variability and transformations in the atmosphere. Comprehensive analysis of chromatograms collected in Manacapuru, Brazil in the wet season of 2014, finds roughly 1000 potential analytes of interest. We apply two matrix size reduction techniques, PMF and hierarchical cluster analysis (HCA) to yield new insights into the covariance and sources of previously overlooked analytes. In particular, we seek to understand the removal of these compounds through gas scavenging by precipitation. Comparison of analyte groups before, during, and after a precipitation event are used to qualitatively and quantitatively understand the impacts of this process on atmospheric composition.