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A large fraction of secondary aerosols is produced from the condensation of precursor gases or nucleation via cloud processes (Ervens et al., 2011). Particulate matter (PM), a major air pollutant worldwide (Koulouri et al., 2008;Mukherjee & Agrawal, 2017), comes in two aerodynamic diameters of fine particles: less than 10 µm (PM 10 ) and less than 2.5 µm (PM 2.5 ) (United States Environmental Protection Agency [USEPA, 2020]). As PM is associated with respiratory and cardiovascular diseases and mortalities (Brunekreef & Holgate, 2002), acquiring accurate estimates of PM is important to assess its impact on human health. Estimates have shown that globally over 2 million deaths resulting from damage to the respiratory system per year are associated with PM pollution (K. H. Kim et al., 2015;Shah et al., 2013). A common product of satellites for estimating PM levels (Ghahremanloo et al., 2021) is the aerosol optical depth (AOD). Although researchers have devoted a great deal of effort to develop systems that improve PM forecasting, they have been limited by the quality and amount of data (Ghahremanloo et al., 2020) and the performance of chemical transport models (Park et al., 2011;Pouyaei et al., 2020). To address the limitations of models and improve the accuracy of models for atmospheric chemistry and weather forecasting, they have incorporated both in situ measurements and

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Abstract. This paper introduces a novel Lagrangian model (Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model, C-TRAIL version 1.0) output from a Eulerian air quality model for validating the source–receptor direct link by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we implement the trajectory-grid (TG) Lagrangian advection scheme in the CMAQ (Community Multiscale Air Quality) Eulerian model version 5.2. The TG algorithm follows the concentrations of representative air “packets” of species along trajectories determined by the wind field. The diagnostic output from C-TRAIL accurately identifies the origins of pollutants. For validation, we analyze the results of C-TRAIL during the KORUS-AQ campaign over South Korea. Initially, we implement C-TRAIL in a simulation of CO concentrations with an emphasis on the long- and short-range transport effects. The output from C-TRAIL reveals that local trajectories were responsible for CO concentrations over Seoul during the stagnant period (17–22 May 2016) and during the extreme pollution period (25–28 May 2016), highly polluted air masses from China were distinguished as sources of CO transported to the Seoul Metropolitan Area (SMA). We conclude that during the study period, long-range transport played a crucial role in high CO concentrations over the receptor area. Furthermore, for May 2016, we find that the potential sources of CO over the SMA were the result of either local transport or long-range transport from the Shandong Peninsula and, in some cases, from regions north of the SMA. By identifying the trajectories of CO concentrations, one can use the results from C-TRAIL to directly link strong potential sources of pollutants to a receptor in specific regions during various time frames.

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