Constraining Regional and Global Atmospheric Emissions and Chemistry Using Models and Observations
Author(s)
Xi, Shengjun
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Abstract
This thesis presents three interconnected studies advancing our understanding of atmospheric chemistry through investigations of biogenic emissions, ozone pollution control, and interhemispheric transport. In the first work, we developed the Speciated Isoprene Emission Model with the MEGAN Algorithm for China (SieMAC), a comprehensive biogenic isoprene emissions model specifically designed for China's diverse ecosystems. By integrating extensive local emission factor measurements, high-resolution vegetation distributions, and plant functional type-specific leaf area indices, SieMAC significantly improves upon existing MEGAN versions. The model incorporates optional water stress effects through vapor pressure deficit calculations and modified temperature response algorithms for boreal grasslands. Evaluation against ground-based observations and satellite formaldehyde data demonstrates superior performance compared to MEGAN v2.1 and v3.1, particularly in northern China where previous models showed systematic underestimation. The higher estimate of isoprene emissions in SieMAC highlights the significant role of biogenic emissions in China's ozone pollution. In the second work, through analysis of ~1,400 monitoring sites across China and comparison with historical data from the United States and Europe, we identified two key mechanisms explaining China's limited ozone response to substantial NOx emission reductions since 2013. First, China's high-ozone regions exhibit weak or negative correlations between odd oxygen (Ox = O3 + NO2) and NO2, indicating NOx-saturated chemical regimes resistant to initial emission cuts. A critical NO2 threshold is identified, below which ozone begins responding positively to further NOx reductions. Second, using modeling, we found that the emission redistribution process creates a "redistribution penalty" that undermines reduction benefits. In the third work, we evaluated and optimized the transport processes in the 12-box AGAGE atmospheric model, which is widely used for inversion of greenhouse gas emissions. Using SF6 observations, we optimized the model transport parameters to improve representation of large-scale atmospheric circulation. The tuned transport settings provide a more realistic foundation for future simulations of long-lived species distribution, including CH4 source apportionment studies.
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2025-08-20
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Dissertation (PhD)