Interpreting spatial and temporal variations in global air quality using a chemical transport modeling framework

Air quality is a major health concern. Exposure to fine particulate matter (PM2.5) and nitrogen oxides (NOx = NO + NO2) is a leading mortality risk factor across the world. Numerous cohort studies conducted over the past two decades have identified strong associations between ambient air pollution and human mortality, highlighting the adverse health effects of PM2.5 and NO2 exposure. My thesis includes three studies that contribute to a better understanding of air quality. In many regions, including South Asia, elevated emissions from various sectors and sparse ground monitoring are significant challenges. The concurrent development and use of the high-performance configuration of GEOS-Chem (GCHP) provide unprecedented opportunities to assess the primary sources of PM2.5 as a function of emissions, meteorology, chemistry, and deposition. The application of satellite observations with high-resolution source-based information can enhance scientific understanding of air pollution patterns and their impact on human health. Our findings indicate that residential combustion (28%) and biofuels (31%) are major sources of PM2.5 linked to mortality in South Asia. NOx affects air quality and human health directly by contributing to premature mortality and asthma for children and adults and indirectly by acting as precursors for tropospheric ozone (O3) formation and nitrate aerosols. Pandora sun photometers provide hourly ground-based NO2 observations, which can be used to validate satellite observations. Pandora’s observed columns are affected by vertical variations in temperature and local solar time. We used the GCHP model to correct Pandora observations, improving accuracy by 8% through adjustments for temperature and local solar time variations. Furthermore, we used fine-scale GCHP simulations for better estimation of NO2 columns for an accurate representation of NO2 column-to-surface relationships. NOx emissions from bottom-up inventories are limited by uncertainty and latency. Satellite instruments offer NO2 column measurements that can be used to infer surface NOx emissions through inverse modeling. We simulated synthetic NO2 column densities as observed by the Tropospheric Ozone Monitoring Instrument (TROPOMI) over eastern North America to test the ability of the iterative finite difference mass balance (IFDMB) method to recover NOx emissions. Additionally, we applied a resolution-optimized mass balance approach (ROMBA) to retrieve surface NOx emissions from TROPOMI NO2 columns for June–August 2019. Our tests include the use of multiple grid resolutions of 200 km, 100 km, and 55 km, revealed that simulations with 100 km resolution most accurately recovered true emissions. Our global top-down estimates for annual land surface NOx emissions (42.1 Tg NO2) closely match the CEDS a priori estimate (38.4 Tg), with the highest agreement over the eastern United States (10–15%). We observed significant regional variations, with top-down NOx emissions 30–60% higher in regions such as northern South America, Africa, southern Europe, western Canada, the United States, and the Middle East. In contrast, we found top-down NOx emissions 50–100% lower in southern South America, Africa, India, and parts of Europe and China. Overall, my doctoral dissertation makes three key contributions to the field of atmospheric sciences: 1) identifying residential combustion and biofuel emissions as major drivers of ambient and household air pollution and mortality in South Asia; 2) correcting NO2 observation systems and interpreting hourly variations in NO2 concentrations for more accurate satellite-based analysis; and 3) enhancing satellite-based emissions estimation using inverse modeling framework.

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