In this work, we investigate the NOx emissions inventory in Seoul, South Korea using a regional NASA Ozone Monitoring Instrument (OMI) NO 2 product. We first develop a regional OMI NO 2 product by re-calculating the air mass factors using a high-resolution (4 × 4 km 2 ) WRF-Chem model simulation, which better captures the NO 2 shape profiles in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the sub-pixel variability. These two modifications yield OMI NO 2 values in the regional product that are 1.37 larger in the Seoul metropolitan region and > 2 times larger near large industrial sources. These two modifications also yield an OMI NO 2 product that is in better agreement with the Pandora NO 2 spectrometer measurements acquired during the Korea U.S.-Air Quality (KORUS-AQ) field campaign. NO x emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO 2 products. We first apply the top-down approach to a model simulation to ensure that the method is appropriate: the WRF-Chem simulation utilizing the bottom-up emission inventory yields a NO x emission rate of 227 ± 94 kton/yr, while the bottom-up inventory itself yields a NO x emission rate of 198 kton/yr. Using the top-down approach on the regional OM NO 2 product, we derive the NO x emissions rate from Seoul to be 484 ± 201 kton/yr, and a 353 ± 146 kton/yr NO x emissions rate using the standard NASA OMI NO 2 product. This suggests an underestimate of 53 % and 36 % using the regional and standard NASA OMI NO 2 products respectively. To supplement this finding, we compare the NO 2 simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NO x emissions in the WRF-Chem model are doubled, there is better agreement with KORUS-AQ aircraft observations. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality region-specific top-down NO x emission estimates.