Data-Driven Methods for Modeling Emissions and Atmospheric Composition

Abstract

This dissertation investigates how large remote sensing datasets of atmospheric composition can be combined with traditional chemical transport models to advance understanding of pollutant budgets. Chemical transport models can simulate past and future pollutant burdens by representing atmospheric transport, reactive processes, and pollutant sources and sinks, but are subject to error in any of these components. Recent advances in chemical data assimilation and machine learning offer novel methods to combine the strengths of chemical transport models with information encoded in large measurement libraries from satellites and other instruments. I further develop and apply these methods in two core areas: fine particulate matter concentrations (focusing in East Asia) and pollutant emissions quantification (focusing on methane). Specific topics addressed in my dissertation include the following: Quantifying surface fine particulate matter in East Asia using machine learning (Chapters 1 and 2). Inhalation of outdoor fine particulate matter (PM2.5) is a major public health burden. Surface instruments allow PM2.5 monitoring but cannot cover all areas, so satellite-based aerosol optical depth (AOD) measurements can be used in combination with machine learning to estimate gap-free surface PM2.5. Here I developed and applied a machine learning model to produce daily, high resolution maps of PM2.5 in East Asia. Pendergrass et al. (2022) Atmos. Meas. Tech., Pendergrass et al. (2025) Atmos. Env. Interpreting fine particulate matter trends in South Korea, 2011-2022 (Chapter 3). Despite steady reductions in precursor emissions, winter PM2.5 in South Korea has shown fluctuating trends. Here I apply results from Chapters 1 and 2 along with surface data and remote sensing products to analyze the drivers of PM2.5 concentrations. Results suggest a growing role for secondary PM2.5 production due in part to rising oxidant concentrations, sulfate reductions in favor of nitrate, and changing nighttime PM2.5 formation pathways. Pendergrass et al. submitted to Geophys. Res. Lett. Developing a chemical data assimilation platform and applying it to global methane emissions (Chapters 4 and 5). Satellite observations of pollutant concentrations do not offer direct information on pollutant sources. Bayesian optimization can fuse observational data with emissions inventories and constrain emissions based on both. Here I develop an open-source chemical data assimilation toolkit called CHEEREIO which uses the localized ensemble transform Kalman filter (LETKF) algorithm and the GEOS-Chem chemical transport model to optimize emissions and concentrations. I then apply CHEEREIO to methane, with a focus on explaining causes of the 2020-2022 methane surge. I attribute the surge to emissions from the tropics and use a satellite inundation product to suggest that wetlands play a key role. Pendergrass et al. (2023) Geosci. Mod. Dev., Pendergrass et al. submitted to Atmos. Chem. Phys.