Current Issue

PM₁₀, PM_2.5, and PM_1.0 samples were collected at the Korea University campus in Seoul (N 37.59°, E 127.03°) from March 2007 to June 2008 (Fig. 1). Sampling was basically conducted for 24 h with six days interval but 7 sets of 6 or 12 h intensive sampling in (9% of total samples) carried out for detail understanding of chemical compositions of PMs in three haze events. Samples were collected during times of no precipitation; therefore, only a few samples were collected during the summer monsoon season (July-August) (Table 1), whereas sampling was more frequent during Asian dust and haze episode, which mostly occurred in spring and winter.

Fig. 1. 
(a) Map showing Seoul (which includes Korea University) (triangle) where aerosols were sampled and Dongdaemun-Gu station (cross) where gaseous species in South Korea were measured. (b) Map of South Korea in relation to China and Japan.

Samples were collected on 37 mm filters using Teflon coated Aluminum sharp cut cyclone (URG, USA) at 16.7 LPM. The cumulative air flow was measured with a dry gas-meter (URG, USA). Following collection by cyclone, the air passed through two annular denuders before reaching the filters: one coated with 1% Na₂CO₃ to remove acidic gases (SO₂, HNO₃) and the other coated with 1% citric acid to remove basic gas (NH₃). Teflon, quartz, and glass fiber filters were used to measure mass concentrations and water-soluble ions, carbonaceous compounds (PM_2.5 and PM_1.0), and trace metals (PM₁₀ and PM_2.5), respectively (Pall corp., USA). The Teflon filters were pre-dried during 24 h under constant temperature and humidity condition prior to sampling, and the quartz and glass fiber filters were pre-baked at 500°C for 6 h.

The Teflon filter equilibrated for 24 h under the clean condition after sampling and then the gravimetric masses of PM₁₀, PM_2.5, and PM_1.0 were measured using a micro-balance. The weight of all Teflon filters was averaged over three consecutive measurements. All processes performed under strict quality control to eliminate the possibility of sample contamination. Water-soluble ions,

including Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺, Cl^-, NO₂^-, NO₃^-, and SO₄^2-, were analyzed by ion chromatography (Dionex 4500, Dionex, USA), and organic carbon (OC) and elemental carbon (EC) were determined using the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance protocol (TOR) method at the Desert Research Institute (DRI) (Chow et al., 1993). OC consists of OC1, OC2, OC3, OC4, and pyrolyzed OC (OP), and EC is the sum of EC1, EC2, and EC3 minus OP. OP was determined as reflectance returning to its initial value after analysis of OC components with injection of O₂ (98% with 2% He) (Chow et al., 2005).

Trace elements, such as Al and Fe, were analyzed by Inductively Coupled Plasma Mass Spectrometer (ICP-MS) at the Korea Basic Science Institute. Reactive gases, including O₃, NO₂, CO, and SO₂, were measured by the National Institute of Environmental Research (NIER) at a monitoring station of Dongdaemun-Gu (37.58°N, 127.03°E) near Korea University (about 1,000 m distance) (Fig. 1). Meteorological factors, including wind speed and direction, temperature, relative humidity, and solar radiation, were obtained every 1 min by an Automatic Weather Station (AWS) at the campus. The backward trajectories of air masses arriving at 1,000 m above sea level (a.s.l.) were calculated for 72 h every 6 h (at 03:00, 09:00, 18:00, and 21:00 local time) using the NOAA Air Resources Laboratory (ARL) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (version 4) (Draxler and Rolph, 2012; Rolph, 2012, http://www.arl.noaa.gov/ready/hysplit4.html).

Aerosol sources were examined using non-negative matrix factorization (NMF), which is a receptor model that combines principal components analysis (PCA) and vector quantization (VQ) (Lee and Seung, 1999) and has been widely used for environmental studies in conjunction with positive matrix factorization (PMF) (e.g., Shang et al., 2018b; Laing et al., 2015; Oh et al., 2011; Liang and Fairley, 2006). It is similar to the two-way PMF, in which two solution matrices (source contribution and source profile) with specified dimensions are calculated by the input data matrix that includes the number of samples and species (Liang and Fairley, 2006). It can be expressed by the following eq. (1),

where X is the input data matrix (n samples×m species), G is the source contribution matrix with p sources, F is the source profile matrix, and E is the residual matrix between observed and calculated values (Xie et al., 2008). The NMF minimizes the conventional least-squares error (Q) and enables the weighting of variables and samples (Liang and Fairley, 2006). To reduce the effect of outliers on the fitting of contributions and profiles, Q is obtained from a robust mode (Williams et al., 2010; Reff et al., 2007). In this study, the optimum number of factors were determined by Q values using a comparison between Q_theory and Q_robust at a range of p values (e.g., Williams et al., 2010; Reff et al., 2007); as a result, the five factors were selected. Furthermore, data were prepared using a method employed in previous studies (e.g., Alleman et al., 2010; Reff et al., 2007). Missing data and values that were below the detection limit (DL) were replaced with mean concentrations and DL/3, respectively (Heo et al., 2009). Further details about the NMF and PMF methods can be found in Lee and Seung (1999) and Reff et al. (2007).

Seventy-eight sets of samples were obtained throughout the entire period, for which the average mass concentrations of PM_1.0, PM_2.5, and PM₁₀ were 19.7 μg/m³, 26.0 μg/m³, and 48.2 μg/m³, respectively. While PM_2.5 was in good correlation with both PM_1.0 (γ=0.79) and PM₁₀ (γ=0.52), the correlation between PM_1.0 and PM₁₀ was relatively poor (γ=0.37). The PM_2.5 mass concentrations are presented in relation to those of PM_1.0 and PM₁₀ in Figs. 2 and 3, where it shows that, in general, PM_2.5 is proportionally increased with PM₁₀ and PM_1.0. Note that there are two types of outliers that are either due to more enhanced PM₁₀ or less enhanced PM₁₀ relative to PM_1.0. PM_2.5 is generally defined as a fine aerosol in mode distribution. However, our results show that PM_2.5 increased with a sharp increase in PM₁₀ or with a dominant increase in PM_1.0. It likely indicates that measured PM_2.5 mass can be affected by both fine and coarse particles.

Fig. 2. 
Correlation between PM₁₀ and PM_1.0 mass concentrations (color-coded circles represent various PM_2.5 concentrations and dashed and solid lines represent linear regression between PM₁₀ and PM_1.0 and 95% confidence interval).

Fig. 3. 
For the three groups: (a) mean concentrations of PM_1.0, PM_2.5, and PM₁₀ mass and ratio of PM_2.5/PM₁₀ and PM_2.5/PM_1.0 (b) mean concentrations of SO₄^2-, NO₃^-, of PM₁₀, PM_2.5, and PM_1.0 with standard deviation (error bar); (c) mean concentrations of OC, and EC of PM₁₀, PM_2.5, and PM_1.0 with standard deviation (error bar); and (d) mean concentrations of SO₂, NO₂, CO, and O₃.

Fig. 2 illustrates the relationship of PM_1.0 and PM₁₀ mass concentrations in Seoul during the whole experiment period, where PM_2.5 mass concentrations were color-coded. While PM_1.0 and PM₁₀ mass concentrations are well correlated, sample data points can be grouped mainly by 3 domains; (i) samples within a 95% confidence interval (“Group 1”, 31% data over total data), (ii) samples with clearly deviated PM₁₀ mass concentrations (“Group 2”, 17%), and (iii) samples with relatively higher PM_1.0 and lower PM₁₀ mass concentrations (“Group 3”, 52%). Table 1 shows the number of samples in each group in accordance with the seasons.

The mass concentrations and other chemical characteristics are summarized in Table 2 and Fig. 3. Of the three groups, Group 2 is distinguished by the highest PM₁₀ (103.0 μg/m³) and PM_2.5 (34.6 μg/m³) with the highest ratio of PM_2.5 to PM_1.0 (1.6) and the lowest ratio of PM_2.5 to PM₁₀ (0.3) (Fig. 3a). Group 1 has similar mean concentrations of PM_2.5 (31.6 μg/m³) to Group 2, with the best correlation between PM_1.0 and PM₁₀ (γ=0.89), and Group 3 has the lowest PM₁₀ mass level, but ratios of PM_2.5/PM₁₀ (0.7) is the highest and PM_2.5/PM_1.0 (1.2) is the lowest, which is considered to be associated with the submicron nature of particles. From the seasonal frequency and mass characteristics of the three groups, it is quite likely that Group 2 represents events when the mass of coarse mode particles was enhanced and that Group 1 includes events with high concentrations of fine mode particles (Table 1 and Fig. 3). These results suggest that the PM_2.5 variation in Seoul can be understood in accordance with the collective figures from these three groups, which have distinct mass and chemical composition characteristics. Therefore, detailed characteristics of the three groups were further examined with respect to the major composition of air masses, air mass origins, and source profiles.

Compared to the other groups, the major aerosol composition of Group 1 consisted of the highest concentrations of OC, EC, and NO₃^- (Fig. 3), and the average concentrations of reactive gases, including SO₂, CO, NO₂, and O₃, were also the highest (Fig. 3). For Group 1, high PM_2.5 mass concentrations were observed when air masses had moved through urbanized regions in China (Fig. 4), which implies that high concentrations of secondary ions, such as NO₃^- and trace gases, are associated with Chinese outflows (Zhang et al., 2015; Huang et al., 2014; Zhang et al., 2013; Wang et al., 2006). In particular, EC is strongly correlated with SO₄^2-, which suggests the influence of coal combustion (Hou et al., 2011; Seinfeld and Pandis, 2006).

Fig. 4. 
(a) Three-day air mass trajectories for Group 1, Group 2, and Group 3, and (b) their vertical variations. One representative backward trajectory was selected for each sample, and concentrations of PM_2.5 mass are color-coded in trajectories.

In Group 2, NO₃^- and SO₄^2- concentrations were highly elevated in PM_2.5 and PM₁₀ (Fig. 5); however, their fractions against mass were the lowest among the three groups because the highest PM₁₀ and PM_2.5 mass concentrations exceeded the annual standard (Table 2). Ca²⁺ was highly enriched in coarse mode particles in PM₁₀−PM_2.5, which indicates the impact of Asian dust (Kang et al., 2013; Lim et al., 2012; Lee et al., 2007). Backward trajectories analysis shows that air masses were originated from desert regions of Inner Mongolia, passed through urbanized northeastern China, and finally arrived in Seoul with high PM_2.5 loadings (Fig. 4). It is a common pathway of Asian dust plumes coming to Korean Peninsula (Lim et al., 2012). It is thus highly likely that the observed coarse-mode NO₃^- and SO₄^2- concentrations for Group 2 are associated with heterogeneous reactions of anthropogenic oxidized vapors on abundant soil particles during transport (Geng et al., 2009; Lin et al., 2007; Wang et al., 2007; Jacobson, 2002, 2001). Although there were only a few samples, the concentrations of EC and SO₄^2- were noticeably increased when air masses had passed over industrial regions in northern and northeastern China. These results imply that Groups 1 and 2 relate to episodes of high PM concentrations that are mainly influenced by Chinese outflows. In particular, high PM_2.5 masses that exceed the national standard are associated with haze occurrence in Group 1 and Asian dust events in Group 2, and these are clearly distinguished by the relation between PM₁₀ and PM_1.0 mass concentrations.

Fig. 5. 
Mean concentrations of chemical constituents for: (a) PM_10-2.5 (coarse mode), (b) PM_2.5-1.0 (fine mode), and (c) fraction of chemical constituents against PM_2.5 mass in each group.

In Group 3, the carbonaceous fraction (sum of OC and EC) against PM_2.5 mass was noticeably high (up to 0.63) along with a high OC/SO₄^2- ratio of 2.85, and most of the OC and EC was enriched in PM_1.0 (Table 2, and Fig. 5). In addition, OC and EC exhibited a significant correlation with NO₃^- (γ ≥ 0.72), which suggests a fossil fuel combustion source (Zhang et al., 2013; Hou et al., 2011; Seinfeld and Pandis, 2006). Fig. 4 shows that backward trajectories of Group 3 are scattered around the Korean peninsula. Of these, trajectories for high PM_2.5 concentrations, which are associated with high EC, NO₃^-, and SO₄^2- concentrations, linger around the area southwest of Seoul under stagnant conditions (Fig. 4). It is quite likely that domestic and local influences mostly prevail in Group 3. It is also worth mentioning that high OC, EC, and SO₄^2- concentrations of PM_2.5 were intermittently observed in air that had been rapidly transported from the Liaoning and Jilin regions of China during winter, which indicates that the Chinese influence is prevalent during the cold season.

A comparison between the chemical compositions of the three groups confirms that the characteristics of PM_2.5 can be distinguished by the relation between PM₁₀ and PM_1.0. It also highlights that the PM mass concentrations of Seoul are largely dependent on the episodic occurrence of high concentration events associated with Chinese outflows. On the other hand, when local influences dominate under stagnant condition, aerosol mass concentrations are relatively lower but contributions from carbonaceous compounds are greater.

Table 4 shows that from the NMF analysis of 78 samples of PM_2.5, five factors were identified in relation to source type as follows: primary urban emissions mostly from traffic related sources, and secondary urban sources that are aged, biomass combustion, coal-fired industry, and soil dust. The five sources are seasonally and geographically distinct and are more evident in high-concentration episodes. It is known that in northeast Asia, the PM_2.5 source and degree of atmospheric processing is largely dependent on synoptic meteorological conditions (e.g., Shang et al., 2018a; Lim et al., 2012), and these are summarized as plume characteristics in Table 4. In addition, the sources of PM_2.5 are estimated and compared for each mass group.

Urban primary aerosols (Factor 1) are represented by high concentrations of OC, EC1, SO₄^2-, NO₃^-, and trace metals such as Pb, V, Cr, and Mn. As the contribution of V relates to both natural and anthropogenic sources, the ratio of V/Mn has been suggested as an indicator of vehicle emissions (Cao et al., 2005; Chow et al., 2004; Rodríguez et al., 2004). The contribution of Factor 1 was the largest in Group 3: 28% and rising to 33% when air had been rapidly transported from urban areas of Beijing, Dalian, and Harbin area during cold months or when the air was stagnant in warm months.

Factor 2 is characterized by high NO₃^-, NH₄⁺, and SO₄^2- loading, which relates to carbonaceous components, and accounts for 19% of the PM_2.5 mass. These species were found to be highly elevated during haze events and responsible for a reduction in visibility in China (Zhang et al., 2015). The contribution of Factor 2 was evidently higher in Group 1 (23%) and Group 2 (24%) than in Group 3 (13%). In particular, the contribution of Factor 2 in Groups 1 and 2 was greatest when air masses had been slowly transported from eastern China (including Shanghai) during the warm season. It is thus considered that Factor 2 represents aged and secondary urban aerosols (Shang et al., 2018b; Künzi et al., 2015; Huang et al., 2014).

K⁺, Cl^-, NO₃^-, and Mg²⁺ were identified as major components in Factor 3, and these are related to biomass combustion emissions (Chen et al., 2017; Han et al., 2007; Chow et al., 2004; Duan et al., 2004). The contribution of Factor 3 was the highest in Group 1 (13%) and particularly related to eastern Chinese outflows from the Shandong region. In eastern China, biomass combustion frequently occurs in association with agricultural clearing fires and the use of biofuel for heating, which have been identified as the major sources of haze events (Chen et al., 2017; Han et al., 2017; Yin et al., 2017; Zhang et al., 2012; Cao et al., 2006; Yang et al., 2005; Duan et al., 2004).

Factor 4 is distinguished by high concentrations of NO₃^-, SO₄^2-, refractory components of OC (OC3 and OC4), and EC with relatively high concentrations of trace metals, and thus represents emissions from coal-fired industries (Lim et al., 2014; Duan and Tan, 2013; Heo et al., 2009; Han et al., 2006; Cao et al., 2005; Chow et al., 2004; Rodríguez et al., 2004). Trace elements, Pb, Zn, and Cd are known to be enriched in coal dust (Bozlaker et al., 2013; Duan and Tan, 2013). It is of note that Group 3 had the highest contribution of Factor 4, and the fractions of OC and EC were high with relatively low concentrations of PM_2.5 and reactive gases. For these samples, air masses had been rapidly transported during the cold months from northeastern China, including the Liaoning and Jilin regions, and through North Korea. These regions are recognized for their heavy use of anthracite, bituminite, and brown coal in industries, power plants, and for household heating (Li et al., 2017; Kim, 2015; Han et al., 2009; Zhang et al., 2008; Cao et al., 2006). This result indicates that the influence of Chinese outflows is persistent during the cold season but is not associated with high PM_2.5 concentrations.

With high loadings of Ca²⁺, Na⁺, Cl^-, and some metals Cu, Ni, Cr, Co, Mn, Factor 5 represents the influence of soil dust (Lim et al., 2012; Zhu et al., 2012; Lee et al., 2007; Wang et al., 2006). The contribution of Factor 5 was particularly high (18%) in Groups 1 and 2 (Fig. S1). In particular, the concentrations of Cl^- and Na⁺ were enhanced when air masses had been transported from Inner Mongolia and northeastern China, where saline dust is commonly observed (Shang et al., 2018b; Zhu et al., 2012; Aldabe et al., 2011). Trace elements such as Cu were likely added to the air stream when passing over the Beijing region on the way to the sampling site (Duan and Tan, 2013).

Overall, NMF analysis shows that traffic-related urban primary (Factor 1) and coal-fired industry (Factor 4) emissions are the main sources of (and equal contributors to) PM_2.5 in Seoul. The contributions of these two factors were greatest in Group 3 (accounting for 67%). In Groups 1 and 2, the contribution of an aged urban source (Factor 2) was as large as Factors 1 and 4, and secondary ions such as SO₄^2- and NO₃^- were highly elevated. In addition, sources with the least contributions, including biomass combustion (Factor 3) and soil dust (Factor 5) were significant in these two groups but not in Group 3. These results imply that the air masses in Groups 1 and 2 were more influenced by Chinese outflows than those in Group 3.

Fig. 6. 
Contributions of the five sources of PM_2.5 for: (a) all samples, (b) Group 1, (c) Group 2, and (d) Group 3.

Interestingly, the traffic-related urban primary source was distinct not only in the cold season but also in the warm season when air masses were stagnant around the Seoul Metropolitan Area (SMA). It is thus probably representative of emissions from the SMA, and it caused high-mass episodes during the warm season. The development of stagnant conditions that promote pollution episodes during the warm season (from May to June) have previously been observed in Seoul (Lee et al., 2015). Loadings from the coal-fired industry (Factor 4) were elevated with respect to strong northerly winds during cold seasons. Under these conditions, industrial emissions are transported quickly from Far East China and North Korea. Although the mass concentrations of Group 3 were not as high as those of Groups 1 or 2, the fractions of OC and EC against the PM_2.5 mass were significantly higher in Group 3. In this respect, and with respect to the harmful effects of carbonaceous aerosols on health, these low PM_2.5 masses should not be disregarded (Atkinson et al., 2015; Künzi et al., 2015; Kennedy, 2007; Sørensen et al., 2003).