New Insights into the Composition of Organics in the Atmosphere Enabled by Advanced Processing Techniques for Existing Chromatographic Datasets

SUNGWOO KIM, Lindsay Yee, Allen Goldstein, Nathan Kreisberg, Susanne Hering, Gabriel Isaacman-VanWertz, Virginia Tech

     Abstract Number: 469
     Working Group: Instrumentation and Methods

Abstract
Ambient aerosols may contain hundreds or thousands of unique organic compounds, and even minor components which may provide valuable insight into particle sources and formation chemistry. However, due to the complexity of this data and the highly dynamic nature of aerosol composition, conventional analysis methods tend to be highly time consuming and also result in a substantial fraction of data never being 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) in Manacapuru, Brazil during the wet season of 2014 to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results find 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.