Kim, Sungwoo2024-12-052024-12-052024-12-04vt_gsexam:41544https://hdl.handle.net/10919/123737Atmospheric samples are complex mixtures that contain thousands of volatile organic compounds (VOCs) with diverse physicochemical properties and multiple isomers. These compounds can interact with nitrogen oxides, leading to the formation of ozone and particulate matter, which have detrimental effects on human health. Therefore, it is essential to apply effective analytical methods to obtain valuable information about the sources and transformation processes of these samples. Gas chromatography coupled with mass spectrometry (GC-MS) is a widely used method for the analysis of these complex mixtures due to its sensitivity and resolution. However, it presents significant challenges in data reduction and analyte identification due to the complexity and variability of atmospheric data. Traditional processing methods of large GC-MS datasets are highly time-consuming and may lead to the loss of potentially valuable information from relatively weak signals and incomplete characterization of compounds. This study addresses these challenges. An automated approach is developed that catalogs and identifies nearly all analytes in large chromatographic datasets by combining factor analysis and a decision tree approach to de-convolute peaks. This approach was applied to data from the GoAmazon 2014/5 campaign and cataloged more than 1000 unique analytes. A novel method is then introduced to automatically identify quantification ions for single-ion chromatogram (SIC) based peak fitting and integration to generate time series of analytes. Through these combined approaches, a complex GC-MS dataset of atmospheric composition is reduced and processed fully automatically. Additionally, a machine learning-based dimensionality reduction algorithm was applied to the generated time series data for systematic characterization and categorization of both identified and unidentified compounds, clustering them into 8 distinct groups based on their temporal variation. These data are then used to generate fundamental insight into the atmospheric processes impact composition. This analysis aimed to elucidate the effects of meteorological conditions on these compounds, particularly the impact of wet deposition through precipitation scavenging on gas- and particle-phase oxygenated compounds. Hourly removal rates for all analytes were estimated by examining the impacts of precipitation on their concentration.ETDenIn CopyrightGas chromatographymass spectrometrypositive matrix factorizationdimensionality reductionspherical k-meanswet depositionDissertation