Abstract: In recent years, with the continuous growth of China's economy, anthropogenic emissions of nitrogen oxides remain high, causing regional air pollution. In order to investigate the uncertainty of the NO_x emissions inventory, the NO₂ tropospheric column data from OMI satellite, combined with the WRF-CMAQ modeling system were applied to verify the regional NO_x emissions inventory in the Yangtze River Delta (YRD) region, and the uncertainty of the NO_x emissions inventory was assessed. The results show that, based on the NO_x emissions inventory of the Yangtze River Delta in 2014, the average value of the regional NO₂ column density (4.66×10¹⁵-10.58×10¹⁵ mole/cm²) obtained from the WRF-CMAQ model and average OM INO₂ tropospheric column density (3.49×10¹⁵-11.47×10¹⁵ mole/cm²) were close to each other. The correlation was good (average R=0.65), the normalized mean bias (NMB) was from -7.71% to 33.52%, and the bias was between 0.06 and 0.28. Generally, the total amount of NO_x emissions in the YRD region in 2014 could basically reflect the regional NO₂ pollution situation. The study showed that the simulation results of NO_x emissions inventory were consistent with satellite data in total amount and spatial allocation. However, the satellite NO₂ column densities were lower than those simulated from CMAQ in south of Jiangsu, Shanghai, north of Zhejiang and other industrial areas, though in the surrounding economically underdeveloped areas the result was the opposite. This shows that the spatial distributions still need further optimization. By comparing the observed data with the model predicted results, we determined that the NO₂ concentrations of the near ground observation were higher than the model predicted data, which indicates that there would be some deviations in the results if only the ground observation data were used to verify the model results. The results show that the simulation results of NO_x emission inventory are consistent with the satellite data in terms of total amount and time, but there are some deviations in the spatial distribution.