Assessing mass balance-based inverse modeling methods via a pseudo-observation test to constrain NOx emissions over South Korea

Using the mass balance-based inverse modeling method, this study constrains NOx emissions and then examines the optimization of inverse modeling conditions via a pseudo-observation test for South Korea, a country with complex topography. We apply the following mass balance-based inverse modeling methods: basic mass balance (BMB), finite difference mass balance (FDMB), and iterative finite difference mass balance (IFDMB). We perform a numerical simulation of air quality using the Community Multi-scale Air Quality (CMAQ) model to calculate the NO2 column density required for the inverse modeling. Then we conduct a pseudo-observation test according to the season, the modeling resolution, and the regridding methodology of satellite observational data to identify various conditions while applying inverse modeling for South Korea. Comparing the inverse modeling results from the BMB, FDMB, and IFDMB methods, we find IFDMB the most effective method for constraining NOx emissions in the South Korean region since it minimizes the smearing effect (i.e., transport-induced errors) through iterative calculations. Our findings show that the accuracy of the constrained NOx emissions using mass balance-based inversions in South Korea is the highest in the summer season, in which the lowest smearing effect occurs, and the most efficient resolution for the inverse modeling of the region is 9 km. In addition, the results of inverse modeling differ depending on the regridding method, suggesting that the use of a regridding method suitable for satellite observational data and the modeling resolution is important. This study uses pseudo observations, not actual observations; we expect that in the future, inversions will be applied to inverse modeling studies based on actual satellite data.