Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2)

Abstract

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.

Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: The Geostationary Environment Monitoring Spectrometer (GEMS) data fusion product and its proxies

“…To simulate the meteorological fields and ambient concentrations of gaseous air pollutants and aerosols for each of the study periods, we used the Weather Research and Forecasting (WRF) version 3.8 developed by the National Center for Atmospheric Research (NCAR) (Skamarock et al, 2008) and CMAQ version 5.2 developed by the U.S. Environmental Protection Agency (EPA) (Byun and Schere, 2006). Employing the same modeling setups and initial conditions used in our previous studies over East Asia (Jung et al, 2019Pouyaei et al, 2020Pouyaei et al, , 2021Park et al, 2022), we configured WRF and CMAQ to cover the modeling domain at a horizontal resolution of 27 km and 35 vertical variable thickness layers from the surface up to 100 hPa. Detailed model configurations are listed in Table S1.…”

Measurement report: Summertime and wintertime VOCs in Houston: Source apportionment and spatial distribution of source origins

“…To investigate the origins of VOCs in the region, we applied the CWT method, a widely used receptor-based model that spatially reflects the concentration levels of trajectories and explores the potential geographic origins of identified source locations (Pouyaei et al, 2020;Stein et al, 2015). Since the receptor location was inside the Ship Channel, an 440 area responsible for substantial air pollution in the region, we focused on studying local sources rather than on regional transport (Fig.…”

The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NOx Emission Adjustments Over East Asia

“…In the last several decades, transboundary and intercontinental air pollution has been a source of scientific interest and political concerns. Hence, to examine external influences on local air quality, a number of studies have used various observational data and numerical models together with source attribution (e.g., back trajectory tracking method and tagging source regions) and sensitivity tests with zero‐out or perturbed emissions with certain factors (Brown‐Steiner & Hess, 2011; Itahashi et al., 2013; Kunhikrishnan, 2004; Lee et al., 2014; Lee and Kim et al., 2019; Pouyaei et al., 2020; Reidmiller et al., 2009; Wang et al., 2011; Wu et al., 2009; Zheng et al., 2021). Especially, model‐based source apportionment approaches (e.g., the Comprehensive Air‐quality Model with Extensions Ozone (O 3 ) Source Apportionment Technology/Particulate Source Apportionment Technology, the Model for O 3 and Related chemical Tracers, the Weather Research and Forecasting (WRF) with Chemistry, and the Community Multiscale Air Quality (CMAQ) Integrated Source Apportionment Method (ISAM)) have developed to prove the contribution of emission sources from specific areas or at a specific location with the purpose of conserving bulk mass of base model in nonlinear systems (Cohan & Napelenok, 2011; Kwok et al., 2015).…”

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The Yellow Sea is a path of long-range transport of air pollutants because of its geographical location between sources of anthropogenic and natural air pollutants and downwind regions (Lee et al, 2014;Pouyaei et al, 2020;Zheng et al, 2021). Thus, the oceanic environment of the Yellow Sea, with an abundant supply of moisture, induces loss of air pollutants by wet deposition.

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