Understanding the impact of long-range transport (LTI) on concentrations of particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) is crucial for accurately assessing air quality in affected areas. We developed an integrated approach combining emissions adjustment and model bias correction to improve the replication of observed PM2.5 concentrations and estimate LTI contributions in South Korea, a representative downwind area in Northeast Asia. Using ground observations, we first adjusted emissions of sulfur dioxide, nitrogen oxides, and primary PM2.5 in China, which is upwind of South Korea. Refining factors were applied to further reduce systematic biases in estimating upwind PM2.5 concentrations and enhance LTI calculations. The results demonstrated that our approach reduced both random and systematic biases in simulated PM2.5 concentrations in China, achieving a correlation coefficient of 0.99 between the observed and simulated concentrations. These results were used to refine LTI estimates in South Korea, leading to reduced mean bias between observed and simulated concentrations. The improvements aligned well with observed PM2.5 concentration trends in both countries, highlighting the critical role of accurate LTI estimates in understanding air pollution dynamics in South Korea. Moreover, this approach was effective for assessing both short- and long-term population exposure, enhancing the accuracy of identifying “unhealthy” PM2.5 days and calculating population-weighted concentrations in South Korea. By analyzing PM2.5 concentrations, long-term trends, changes in local emission impacts, and population exposure in areas influenced by long-range transport, this method has substantial potential for broader applicability.
