Analysis of long-range transboundary transport (LRTT) effect on Korean aerosol pollution during the KORUS-AQ campaign

Lee, Seo-Young; Kim, Jhoon; Choi, Myungje; Hong, Jaemin; Lim, Ho Soo; Eck, T. F.; Holben, B. N.; Ahn, JaeKyoung; Kim, Jeongsoo; Koo, Ja Ho

doi:10.1016/j.atmosenv.2019.02.020

Cited by 71 publications

(32 citation statements)

References 48 publications

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“…In summary, moderate westerly flows from East-central China, including Shandong and/or Hebei province, most likely contributed to a higher regression slope between ∆CO and ∆CO 2 (>3%) from industrial sectors. This result was similar to the finding by Lee et al [33] that showed the air pollution from East-central China usually contributes towards high levels of air pollution concentrations in Korea. We investigated the regression slopes between ∆CO and ∆CO 2 originating in Korea and China to find that combustion sources in Korea are generally more efficient than those in China.…”

Section: Short-term Regression Slope Calculation Methodssupporting

confidence: 92%

In Situ Aircraft Measurements of CO2 and CH4: Mapping Spatio-Temporal Variations over Western Korea in High-Resolutions

Kim

et al. 2020

Remote Sensing

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A cavity ring-down spectroscopy (CRDS) G-2401m analyzer onboard a Beechcraft King Air 350, a new Korean Meteorological Administration (KMA) research aircraft measurement platform since 2018, has been used to measure in situ CO2, CH4, and CO. We analyzed the aircraft measurements obtained in two campaigns: a within-boundary layer survey over the western Republic of Korea (hereafter Korea) for analyzing the CO2 and CH4 emission characteristics for each season (the climate change monitoring (CM) CM mission), and a low altitude survey over the Yellow Sea for monitoring the pollutant plumes transported into Korea from China (the environment monitoring (EM) mission). This study analyzed CO2, CH4, and CO data from a total of 14 flights during 2019 season. To characterize the regional combustion sources signatures of CO2 and CH4, we calculated the short-term (1-min slope based on one second data) regression slope of CO to CO2 and CH4 to CO enhancements (subtracted with background level, present as ∆CO, ∆CO2, and ∆CH4); slope filtered with correlation coefficients (R2) (<0.4 were ignored). These short-term slope analyses seem to be sensitive to aircraft measurements in which the instrument samples short-time varying mixtures of different air masses. The EM missions all of which were affected by pollutants emitted in China, show the regression slope between ∆CO and ∆CO2 with of 1.8–6% and 0.3–0.7 between ∆CH4 and ∆CO. In particular, the regression slope between ∆CO and ∆CO2 increased to >4% when air flows from east-central China such as Hebei, Shandong, and Jiangsu provinces, etc., sustained for 1–3 days, suggesting pollutants from these regions were most likely characterized by incomplete fossil fuel combustions at the industries. Over 80% of the observations in the Western Korea missions were attributed to Korean emission sources with regression slope between ∆CO and ∆CO2 of 0.5–1.9%. The CO2 emissions hotspots were mainly located in the north-Western Korea of high population density and industrial activities. The higher CH4 were observed during summer season with the increasing concentration of approximately 6% over the background level, it seems to be attributed to biogenic sources such as rice paddies, landfill, livestock, and so on. It is also noted that occurrences of high pollution episodes in North-Western Korea are more closely related to the emissions in China than in Korea.

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Section: Short-term Regression Slope Calculation Methodssupporting

confidence: 92%

In Situ Aircraft Measurements of CO2 and CH4: Mapping Spatio-Temporal Variations over Western Korea in High-Resolutions

Kim

et al. 2020

Remote Sensing

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“…2 bottom panel, which shows the diurnal variations of gas concentrations simulated by CMAQ, together with AOD measured on 25 May 2016. This represents a long-range transport case resulting in high concentration during the joint Korea-United States KORUS-AQ field campaign (Lee et al 2019;Choi et al 2019). KORUS-AQ integrated models and measurements from ground-based, airborne, shipborne, and satellite platforms to diagnose AQ problems in Korea [National Institute of Environmental Research (NIER); NIER and NASA 2017].…”

Section: E4mentioning

confidence: 92%

“…It is expected that the aerosol and trace gas observations from GEMS will further improve the performance of coupled meteorology-chemistry models in forecasting both weather and AQ by providing tighter observational constraints on the DA through the high density of GEMS measurements. The GEMS observations are anticipated to be utilized in many applications including AQ forecasts, AQ reanalysis systems, radiative forcing estimation, and so on (e.g., Marécal et al 2015;Lee et al 2016;Benedetti et al 2009;Inness et al 2013;IPCC 2013;Lee et al 2014).…”

Section: E15mentioning

confidence: 99%

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

Kim

Jeong

Ahn

et al. 2020

Bulletin of the American Meteorological Society

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215

108

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The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

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“…3, the model domain, with a horizontal grid resolution of 27 km over East Asia, covers the eastern parts of China, the Korean Peninsula, and Japan. We use the 2010 MIX emission inventory (Li et al, 2017) at a 0.25 • spatial resolution. The emission inventory contains monthly averaged carbon bond version 5 (Sarwar et al, 2012) emission information, which includes 10 chemical species, including CO, in five different sectors.…”

Section: Setup and Validation Of The Modelmentioning

confidence: 99%

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)

et al. 2020

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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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Analysis of long-range transboundary transport (LRTT) effect on Korean aerosol pollution during the KORUS-AQ campaign

Cited by 71 publications

References 48 publications

In Situ Aircraft Measurements of CO2 and CH4: Mapping Spatio-Temporal Variations over Western Korea in High-Resolutions

In Situ Aircraft Measurements of CO2 and CH4: Mapping Spatio-Temporal Variations over Western Korea in High-Resolutions

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

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)

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