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ISSN : 1225-6692(Print)
ISSN : 2287-4518(Online)
Journal of the Korean earth science society Vol.40 No.5 pp.476-486

Dominant Synoptic Patterns Controlling PM10 Spatial Variabilities over the Korean Peninsula

Hyo-Jin Park1,2, Jieun Wie1, Byung-Kwon Moon1*
1Division of Science Education/Institute of Fusion Science, Jeonbuk National University, Jeonju 54896, Korea
2Gimje Girls’ High School, Gimje 54393, Korea
Corresponding author: Tel: +82-63-270-2824
October 22, 2019 October 26, 2019 October 28, 2019


This study examines the controlling role of synoptic disturbances on PM10 spring variability in the Korean Peninsula by using empirical orthogonal function (EOF) and back trajectory analyses. Three leading EOF modes are identified, and a lead-lag analysis suggests that PM10 variabilities be closely related to the synoptic weather systems. The first EOF shows the spatially homogeneous distribution of PM10, which is influenced by travelling anticyclonic disturbance with negative precipitation and descending motion. The second and third modes exhibit the dipole structures of PM10, being associated with propagating cyclones. Furthermore, the back-trajectory analysis suggests that the transport of pollutants by anomalous winds associated with synoptic disturbances also contribute to the altered PM10 concentration. Hence, a substantial synoptic control should be considered in order to fully understand the PM10 spatiotemporal variability.


    1. Introduction

    Particulate matter (PM), an environmental cause of premature deaths (Zhang et al., 2017), is primarily produced from fossil fuel combustion, and therefore anthropogenic emissions have a strong effect on such air pollutants (Li et al., 2013). The concentration of PM with diameters less than 10 μm (PM10) is highly dependent on the synoptic system, which determines day-to-day changes in weather (Zhang et al., 2009; Beaver et al., 2010; Gao et al., 2011; Fortelli et al., 2016). For example, high PM10 events are often caused by stagnant high pressure (anticyclone) systems with subsidence and dry conditions, thus providing favorable conditions for an accumulation of locally emitted pollutants (Kallos et al., 1993; Triantafyllou et al., 2002). Anomalous winds associated with synoptic systems also play an important role in changing PM10 levels by modulating the long-range transport of pollutants from source regions (Lee et al., 2013; Oh et al., 2015). In contrast, mid-latitude cyclones lead to decreased air-pollution levels because of wet deposition (Pranesha and Kamra, 1997; Choi et al., 2008).

    South Korea, located downstream of the East Asia continent, has frequently suffered from heavy PM10 events associated with local emission sources (Park et al., 2004), as well as long-range transport from external sources (Kim et al., 2007;Lee and Kim, 2018). Corresponding to these two effects, Lee et al. (2011) identified two synoptic weather conditions that favor high PM10 episodes in Seoul: anomalous anticyclones located in the northern regions of the Korean Peninsula with easterly anomalies, and anomalous anticyclones that have moved to southern Korea with upper-level westerlies. Both anticyclones contribute to increases in PM10 of Seoul via accumulation of local emissions, and transboundary influx of pollutants, respectively. Identification of synoptic patterns associated with high-PM10 conditions can provide a promising way for prediction of air quality (Hur et al., 2016), because mid-latitude synoptic system can be easily forecast in advance.

    While previous studies have focused on synoptic patterns which may affect PM levels at a particular location (e.g., Seoul, the capital city of South Korea; Lee et al., 2013), little attention has been given to the spatially inhomogeneous distribution of PM in South Korea. There is the possibility that migratory anticyclones and cyclones passing through the Korean Peninsula could affect spatial variation, depending on their pathways.

    The present study aimed to analyze the spatial modes of variability in PM10 levels in South Korea and their association with the passage of synoptic weather systems. Three spatial modes of PM10 associated with synoptic atmospheric patterns were identified using an empirical orthogonal function (EOF) analysis.

    2. Data and Methods

    The PM10 concentrations used in this study were hourly data from 318 monitoring sites over South Korea, collected during five springs (March-April- May) from 2011-2015, provided by the Korea Environment Corporation (downloaded from https:// Using these hourly data, we calculated daily average concentrations of PM10, and excluded the 44 days when Asian dust was observed, since Asian dust has its own distinct sources and transport routes (Chun et al., 2001; Kim, 2008). Figure 1a shows the time series of the averaged daily PM10 concentrations together with Asian dust events during the analysis period. The spatial distribution of daily average PM10 levels was highest around the Seoul Metropolitan Area (Fig. 1b), which is most likely attributable to anthropogenic sources (Sharma et al., 2014). In metropolitan cities there were many observation stations located within close proximity to each other, making it difficult to discriminate between and express each value individually. To reduce the resolution, the results were latticed within latitudes 33° N-39° N and longitudes 124° E-131° E, at a resolution of 0.25° ×0.25° . The average PM10 concentration of the stations located in each grid was defined as the representative value of the grid (Fig. 1c). We used the re-gridded PM10 concentration data to examine the relationship between PM10 spatial modes and synoptic disturbances. The day-to-day variability in PM10 was made evident through the power spectrum analysis (Fig. 1d), reflecting the influence of synoptic-scale meteorological conditions.

    Moderate Resolution Imaging Spectroradiometer Terra satellite aerosol optical depth (AOD) data (Tanre et al., 1997; Remer et al., 2002) were used to compare the results of the EOF analysis. The horizontal resolution for these data is 1° ×1° . For the atmospheric data, the National Centers for Environmental Prediction Reanalysis dataset (NCEP2), with a horizontal resolution of 2.5° ×2.5° , was used for the 1000 hPa temperature, 850 hPa and 500 hPa geopotential heights, 850 hPa U- and V-winds, and 500 hPa vertical velocity (ω) (Kanamitsu et al., 2002). The precipitation dataset from the Global Precipitation Climatology Project, with a 1° ×1° horizontal resolution, was used (Huffman et al., 2001). We also used the National Oceanic and Atmospheric Administration HYSPLIT model (Draxier and Hess, 1998) to identify the transboundary transport of PM10. Three-day (72 h) back trajectory paths were simulated at altitudes of 1,000 m at 6-h intervals.

    3. Results

    The Empirical Orthogonal Function (EOF) was applied to determine the spatiotemporal variability of PM10 (Fig. 2). The first mode shows a uniformly positive signal throughout the Korean Peninsula, accounting for 69.0% of total variation. The correlation coefficient between PC1 and the variation in average Korean PM10 concentration (Fig. 1a) was very high (r=0.99), indicating that the first EOF mode (EOF1) may be related to the anomalous high pressure system (e.g., Lee et al., 2011) over the Korean Peninsula. The second EOF mode (EOF2) exhibits a dipole pattern with a positive northwestern region and a negative southeastern region, which accounted for 7.5% of the total variance. In the third mode (EOF3), the dipole structure is also evident in the northeastern and southwestern parts of Korea, which explains 5.7% of the total variation.

    Given that these modes, particularly EOF2 and EOF3, may yield non-physical modes due to the orthogonal constraints, it is critical to understand whether the dipoles-type PM10 anomalies in Korea are related to East Asian large-scale patterns. The regressed satellite retrieval AOD distributions with EOF modes confirmed that Korean PM10 variability extended to the East Asian region. For example, the PC1-regressed AOD shows an increased AOD in the zonally elongated region extending from the Yellow Sea to the East/Japan Sea (Fig. 3a). Similarly, the EOF2 and EOF3 modes of PM10 over Korea (Fig. 2b, c) can be related to AOD variations over the East Asia (Fig. 3b, c, respectively). These AOD results suggest that spatiotemporal variations (Fig. 2) are significant and reflect the synoptic patterns associated with variation in PM10 concentrations over Korea.

    The lead-lag regressed atmospheric patterns with PC1 show that overall positive PM10 anomalies (Fig. 2a) are associated with the high pressure system that passes through the Korean Peninsula (Fig. 4). While PM10 levels were low over the northwestern part of Korea with a lag of -3 day, PM10 concentrations increased as the high pressure anomaly begins to dominate over Korea during the lag period -2 to 0 days, which may lead to PM10 accumulation (Kim et al., 2014). Note that the westward tilting geopotential heights and associated temperature, winds, precipitation and vertical motions are clearly observed at lag 0. During the lag +1 to +2 days, the center of the high pressure system moved toward Japan, and southerly winds prevailed throughout Korea. Simultaneously, Korea was influenced by positive precipitation and upward motion, causing decreasing concentrations in PM10. Moreover, southerly winds could also contribute to lowering PM10 by transporting particle-poor ocean air masses to Korea. Lag +2 days displayed weak high pressure and wind speed over the peninsula.

    Figure 5 shows the PC2-regressed evolution of atmospheric synoptic disturbances. The positive PM10 anomaly appears over most of Korea with a lag of -3 days, except in the southeastern region. Subsequently, the dipole PM10 pattern develops up to zero lag with anomalous easterly winds and convection activities around Korea, which may have caused negative anomalies from Japan to eastern China (Fig. 3b). It should be noted that these anomalous synoptic patterns can be related to the mid-latitude cyclone that formed in southern China with a lag of -2 days and travelled across the East China Sea. The warm sector between the cold and warm fronts was also consistently propagated eastward (second column in Fig. 4). The dipole pattern shows local positive PM10 anomalies around the Seoul metropolitan area, even under increased precipitation, which may indicate the significant role of local emission of pollutants (e.g., Kim and Kim, 2000; Park et al., 2004). At present, however, it remains unclear which factors lead to positive PM10 anomalies around the Seoul metropolitan area.

    In the case of PC3, the PM10 levels decreased in the northeastern regions of Korea, while increasing in the southwestern areas during the 3 to 1 day lag period and eventually led to a dipole PM10 pattern at lag 0 day. Simultaneously, the Korean Peninsula experienced a predominantly descending motion anomaly, which was associated with the high pressure system. This suppressed convection generally favors accumulation of air pollutants, thus causing the positive PM10 concentration in Korea at lag 1 and 2 days. Negative anomalies in northeastern areas may also be attributed to anomalous northerly winds of the western flank of cyclones, which tends to dilute pollution due to the influence of relatively clean air masses from the Siberian region (Fig. 7i).

    We used the HYSPLIT model to identify air parcel trajectories associated with each EOF mode. In the first mode, the trajectories were analyzed for Seoul, Busan and Gwangju. Fig. 7a-c show the frequencies of passage of air parcels that arrived at those cities on the 29 days when PC1 exceeded +1.5σ. These days exhibited positive PM10 anomalies in most areas of Korea (Fig. 3a) because these air parcels mainly originated in China (Fig. 7a-c), where pollution levels are high (Fig. 7i). Therefore, the transboundary transport of PM10 particles from China may play a significant role in elevating PM10 concentrations across the Korean Peninsula (Oh et al., 2015). A similar transport from the Gobi Desert in Mongolia appeared for trajectories arriving at Seoul in EOF2 (Fig. 7d), where PM10 levels exhibited positive anomalies. However, in this case, the trajectories were more diverse and confined around the Seoul metropolitan area, which indicates the potential impact of local emission, as previously discussed. In contrast, the trajectories arriving at Busan (Fig. 7e) broadly extend to the East/Japan Sea, where the air is clean (Fig. 7i), which in turn decreased the PM10 levels there (Fig. 5). Similarly, high frequencies of back trajectories at Gwangju were related to polluted continental air masses in the third mode (Fig. 7g, i), while the source region to Gangreung was largely related to clean air flows from northern Korea and the East/Japan Sea. These results clearly indicate that PM10 concentrations in Korea were strongly influenced by background source regions, as well as local emissions (Lee et al., 2011). Furthermore, EOF mode analysis suggests that synoptic weather system can influence the air parcels trajectory to the Korean Peninsula, thus leading to altered PM10 concentrations.

    4. Summary

    In this study, we used the daily average PM10 concentrations in the spring of 2011-2015 in Korea to identify the three leading modes, and then we analyzed their associated time evolution of meteorological fields (surface temperature, 850 and 500 hPa geopotential heights, 850 hPa winds, 500 hPa vertical velocity and precipitation). The EOF1 exhibited spatially homogeneous mode with 69.0% of explained variance. The EOF2 and EOF3, which explained 7.5 and 5.7% of variance respectively, were characterized by dipole patterns of PM10. These distinct spatial structures reflect the influences of different synoptic meteorological patterns, which were described by the lead-lag regression between PC time series and meteorological fields.

    The EOF1 showed that the positive PM10 anomalies throughout Korea were formed as a high pressure system propagated eastward (Fig. 4). The associated downward motion and dryness favor the accumulation of pollutants, thus increasing PM10 concentrations. It should be noted that the regression analysis is linear; therefore, Fig. 4 also demonstrates that mid-latitude cyclones passing through the Korean Peninsula can create negative PM10 levels across the Korean Peninsula due to enhanced wet deposition.

    Similar synoptic controls of daily PM10 air pollution were also observed in the second and third modes. The EOF2 exhibited a dipole PM10 anomaly pattern between northwestern and southeastern regions of Korea (Fig. 2b). The lead-lag regression revealed that this dipole structure coexisted with the transient cyclone that passed over the south sea of Korea (Fig. 5). Subsequently, precipitation increased in Korea, with anomalous southeasterly winds, causing a reduction in PM10 levels in this region. However, the Seoul metropolitan area (i.e., northwestern region of Korea) experienced enhanced levels of PM10 pollution. Since population and transportation densities are concentrated in the Seoul Metropolitan Area, the accumulation of local pollution may play a significant role in enhancing PM10 levels (Fig. 7d) and producing the dipole anomaly distribution. This spatially inhomogeneous distribution of PM10 requires further investigation. The EOF3 illustrated that positive PM10 values are observed in the southwestern part of Korea, which experiences anticyclonic conditions with downward motions (Fig. 6). In contrast, the northeastern region of Korea is located along the western flank of an anomalous cyclone, where northerly winds are dominant, bringing particle-depleted cold air masses from the Siberian area (Fig. 7h), thereby leading to a decrease in PM10 concentration.

    We also investigated the trajectories of air parcels to relate PM10 changes associated with EOF modes to transport pathways to four cities in Korea. The results demonstrated that positive PM10 anomalies coincide with the polluted air parcels originating from industrial areas in China (Fig. 7a-c, g), and partly from the Gobi Desert (Fig. 7d), indicating the long-range transport of pollutants. As previously discussed, the analyses of trajectories also confirmed that air parcels originating in particle-depleted oceans tend to disperse pollutants (Fig. 7e, h).

    Our study focused on the effect of synoptic disturbances on PM10 variability, using unprecedented spatial and temporal scales, in the Korean Peninsula. This differs from previous studies that have analyzed shorter periods and only limited regions (e.g., Lee et al., 2011; Lee et al., 2013; Oh et al., 2015). The EOF analysis allowed us to identify the region-wide variability in PM10 and, furthermore, back trajectory analysis revealed the significant role of transport pathways in controlling pollution levels, although the accumulation of local pollutants can still not be ruled out. Finally, our main findings of dominant synoptic patterns controlling PM10 spatial variabilities in Korea and associated pathways of transport of parcels of air tend to be seasonally dependent. Moreover, one would expect large interannual variability in PM10 due to teleconnections (e.g., Wie and Moon, 2017; Kim et al., 2019), as well as in transport pathways. These issues need further investigation.


    This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A2C1008549). H.J.P. and J.W. were also supported by the Korea Ministry of Environment (MOE) as “Climate Change Correspondence Program.”



    (a) Time series of the spatial-mean daily PM10 concentration (μg m−3 ) from 318 monitoring sites with 44 Asian dust days highlighted in brown; 15 Asian dust days in 2011, 3 in 2012, 5 in 2013, 6 in 2014, and 15 in 2015. Note that vertical dashed lines divide each year. (b) Spatial distribution of daily mean PM10 concentrations (μg m−3 ). (c) As in (b) but averaged values over the 0.25° ×0.25° grid. (d) Power spectrum for the daily PM10 during springtime. The green line is the mean red noise spectra and dashed upper and lower lines are 10% and 5% significance levels, respectively.


    (a-c). Three leading EOF modes of the daily PM10 concentrations in Korea, and (d) their corresponding PC time series with 44 Asian dust days highlighted in brown.


    Linear regression maps of AOD against PC1, (b) PC2, and (c) PC3 from Fig. 2. The black dots represent statistical significance at the 90% confidence level.


    Linear lead-lag regression maps of atmospheric anomaly variables against PC1. The leftmost column shows PM10. The second row shows the surface temperature (TS) with contour interval of 0.3 K. The third panel shows the 500 hPa (contour; H500) and 850 hPa (shading; H850) geopotential heights and the 850 hPa wind field (vector; UV850). The contour intervals of the 500 hPa and 850 hPa geopotential heights are 4 hPa and 2 hPa, respectively. The right most column shows precipitation (shading; PREC) and vertical velocity (contour; ù500) with contour intervals of 0.2 mm d–1 and 0.01 Pa s–1 , respectively.


    Same as Fig. 4, except for lead-lag regressed patterns against PC2.


    Same as Fig. 4, except for lead-lag regressed patterns against PC3.


    Air parcel passage frequencies from back trajectories arriving at (a-c) Seoul (37.57 º N, 126.97 º E), Busan (35.18 º N, 129.07 º E), and Gwangju (35.16 º N, 126.85 º E) on 29 days when PC1 time series exceeded +1.5σ respectively. (d-e) As (a-c), but trajectories from Seoul and Busan for PC2 (30 days). (g-h) Trajectories from Gwangju and Gangreung for PC3 (27 days). (f) Locations of four cities. (i) Annual mean AOD in East Asia.



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