Date of Award
2-20-2025
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of health, with millions of attributable deaths annually. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing offers information to fill these gaps worldwide when augmented with information from a chemical transport model. More specifically, this satellite-derived PM2.5 is obtained from satellite retrieved aerosol optical depth (AOD) by applying the modeled PM2.5 to AOD relationship (η). Uncertainties, however, exist in the estimation of PM2.5, particularly from the modeled η. This dissertation focuses on understanding the spatial distribution of η and model developments for improved simulation of it. This dissertation includes three studies. The first aim of this study is to interpret aircraft observation and an aerosol microphysics scheme for insight into aerosol size representation in a chemical transport model (CTM). Size representation strongly affects mass scattering efficiency and, therefore, the PM2.5 to AOD relationship. An improved representation of mass scattering efficiency is developed by combining aerosol measurements from aircraft campaigns over the U.S. and South Korea and a CTM with an aerosol microphysics scheme, GEOS-Chem-TOMAS. The simple aerosol size parameterization proposed here significantly improves the agreement between modeled AOD and ground-based measured AOD globally. The second study aims to understand the spatial pattern and driving factors of the relationship by examining η from both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6,870 ground-based PM2.5 measurement sites and satellite retrieved AOD. The GEOS-Chem global chemical transport model, in its high performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. The spatial correlation of observed η with the driving factors reveals that the spatial variation of η is strongly influenced by aerosol composition and aerosol vertical profile. Sensitivity tests were done to quantify their effects on η spatial variability. Building on the second study, the third study aims to understand the global spatial pattern of sulfate, a major PM2.5 component, and to examine emission uncertainties in emission inventories. This study leverages sulfate measurements from the Surface PARTiculate mAtter Network (SPARTAN) and the GEOS-Chem chemical transport model. Three major global emission inventories, the Community Emissions Data System (CEDS), the Emissions Database for Global Atmospheric Research (EDGAR), and the Hemispheric Transport of Air Pollution (HTAP) - are examined. Simulation with CEDS generally reproduced the global sulfate distribution measured by SPARTAN. HTAP and EDGAR emission inventories exhibit weaker performance due to regional biases. SPARTAN data supports recent developments in CEDS and reveals potential regional biases in all three emission inventories. The three studies fill gaps in understanding and modeling η spatial distribution and highlight directions for future model development efforts.
Language
English (en)
Chair
Randall Martin
Committee Members
Jay Turner; Jeffrey Pierce; Jian Wang; Lu Xu