Characterization of PM_2.5 Mass in Relation to PM_1.0 and PM₁₀ in Megacity Seoul

1. INTRODUCTION

Particulate matter (PM) affects visibility and air quality on local and regional scales and exacerbates global climate change (Zhang et al., 2015; IPCC, 2013; Liu et al., 2013; Fowler et al., 2009; Isaksen et al., 2009). PM concentrations are continually increasing, particularly in urban areas, and more than 85% of the world’s population now lives in areas where the World Health Organization’s Air Quality Guideline is exceeded (WMO/IGAC, 2012). PM has been recognized as one of the major leading causes of death (Lelieveld et al., 2015), but its impact is highly variable and depends on the size, chemical composition, and optical properties of aerosols, which are determined by emission sources and atmospheric processing (Theodosi et al., 2011). While supermicron particles (PM₁₀) generally consist of trace metals (e.g. Al, Fe) and soluble ions (e.g. Ca²⁺, SO₄^2-) emitted from natural sources such as soil dust and sea salts (Jacobson, 2012), the major constituents of submicron particles (PM_1.0), which include SO₄^2-, NO₃^-, organic carbon, and elemental carbon, are derived from anthropogenic sources, and mostly from combustion processes (Monks et al., 2009; Querol et al., 2009; Brasseur et al., 1999).

Currently, PM_2.5 serves as an air quality standard that represents fine aerosols. However, it often contains mechanically generated particles such as dust particles under the influence of dust plumes, particularly in East Asia (Pérez et al., 2008; Finlayson-Pitts and Pitts Jr, 2000), where rapid economic growth is related to the occurrence of severe haze pollution. Air pollution abatement has thus become a top priority for policy makers (Fang et al., 2017; Huang et al., 2014; WMO/IGAC, 2012), and intensive studies are consequently being conducted throughout the world, such as in the US (Bozlaker et al., 2013; Stanier et al., 2012; Bahadur et al., 2011; Pasch et al., 2011), Europe (de Hoogh et al., 2018; Li et al., 2018; Bressi et al., 2014; Pateraki et al., 2012a, b), Mexico (Minguillón et al., 2014; Querol et al., 2008), India (Guo et al., 2017; Ram and Sarin, 2010), China (Shang et al., 2018b; Yang et al., 2018; Tao et al., 2017; Zhang et al., 2012), and Japan (Chowdhury et al., 2018; Sasaki and Sakamoto, 2005).

Seoul, the capital of the Republic of Korea, is surrounded by heavily populated satellite cities. The Seoul Metropolitan Area (SMA) ranks as the second most-populated megacity in the world (WMO/IGAC, 2012) with its combined population of over 20 million (Jeong et al., 2011). In past decades, studies on PM have focused on PM₁₀ with an emphasis on Asian dust events (e.g., Choi et al., 2001). In Korea, PM₁₀ has been an environmental standard since 1995, and the annual and daily standard is 50 μg/m³ and 100 μg/m³, respectively in the present (NIER, 2018). The powerful regulation and management base on Clean Air Act contributed to that the annual average PM₁₀ concentration in Seoul had gradually decreased from 78 μg/m³ in 1995 to 35 μg/m³ in 2020. However, it is still higher than in other cities such as Tokyo (16 μg/m³ in 2019), Los Angeles (29 μg/m³ in 2020), Paris (19 μg/m³ in 2020), and London (16 μg/m³ in 2020) (NIER, 2021). Recently the focus of concern has shifted from PM₁₀ to PM_2.5 because of its effect on air quality and human health (Lee et al., 2017; Seo et al., 2017; Ghim et al., 2015; Heo et al., 2009).

The research on PM_2.5 in South Korea has also been carried steadily in keeping pace with policy changes. Heo et al. (2009) investigated the emission sources of chemical compositions of PM_2.5 in Seoul. Ghim et al. (2017) compared the concentration changes of major chemical components in PM_2.5 connected with Asian dust and smog events in Seoul and Deokjeok Island to understand the influence of long-range transport. Park et al. (2013) showed the influence of biomass burning plumes originated from south China and local SO₂ emission on PM_2.5 from the measurement of carbonaceous and inorganic species of PM_2.5 at Gwangju, southwest urban site in Korea. However, these previous studies were of the features of PM_2.5 itself rather than relative character among PM_1.0 and PM₁₀.

The westerlies are dominant in northeast Asia, and Chinese outflows significantly affect both the composition and concentration of coarse and fine mode particles (e.g., Shang et al., 2018a). In addition, PM_1.0 has been investigated as a better criterion representing anthropogenic aerosols, because of the impact of soil particles on PM_2.5 (Shang et al., 2018b; Lim et al., 2012). In this context, the mass and chemical characteristics of PM_2.5 need to be thoroughly examined in relation to those of PM₁₀ and PM_1.0, to establish an effective abatement policy for PM_2.5.

In this current study, the mass and chemical compositions of PM_1.0, PM_2.5, and PM₁₀ were measured in Seoul, and their chemical characteristics and source profiles were compared with the ultimate aim of contributing to mitigating air quality for PM_2.5 in Seoul.

2. METHODS

2. 1 Sampling and Chemical Analysis

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.

Table 1.

Number of samples by group and season (number of haze or dust* impacted samples is provided in parentheses).

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).

3. RESULTS AND DISCUSSION

3. 1 Characteristics of PM_2.5 and Relationship with PM_1.0 and PM₁₀

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 PM1.0 mass concentrations (color-coded circles represent various PM2.5 concentrations and dashed and solid lines represent linear regression between PM10 and PM1.0 and 95% confidence interval).

Fig. 3.

For the three groups: (a) mean concentrations of PM1.0, PM2.5, and PM10 mass and ratio of PM2.5/PM10 and PM2.5/PM1.0 (b) mean concentrations of SO42-, NO3-, of PM10, PM2.5, and PM1.0 with standard deviation (error bar); (c) mean concentrations of OC, and EC of PM10, PM2.5, and PM1.0 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.

Table 2.

Average concentrations of major chemical constituents in PM10, PM2.5, and PM1.0 for three groups.

3. 2 Characteristics of Major Composition and Origin of Air Masses

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 PM2.5 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) PM10-2.5 (coarse mode), (b) PM2.5-1.0 (fine mode), and (c) fraction of chemical constituents against PM2.5 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.

Table 3.

Correlation between soluble ions and carbonaceous species in PM2.5.

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.

3. 3 Source Apportionment

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.

Table 4.

Source profiles of PM2.5 estimated from Non-negative Matrix Factorization (NMF) analysis.

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 PM2.5 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).

4. CONCLUSIONS

PM_1.0, PM_2.5 and PM₁₀ were collected at the Korea University Campus in Seoul, South Korea, during March 2007-June 2008. A total of 78 sets of PM_1.0, PM_2.5 and PM₁₀ samples were analyzed for mass and chemical compositions including soluble ions, carbonaceous compounds, and trace metals.

In general, PM_2.5 proportionally increased with PM₁₀ and PM_1.0 (Group 1), although there were two types of outliers due to either more (Group 2) or less (Group 3) enhanced PM₁₀ relative to PM_1.0. In Group 1 (31%), the best correlation was found between PM_1.0 and PM₁₀ mass concentrations (γ=0.89). In this group, OC and NO₃^- were discernibly higher (with reactive gases such as CO, SO₂, and NO₂) when air masses had been transported through urbanized regions in Eastern China. The average PM₁₀ and PM_2.5 concentrations were highest in Group 2 (17%) with highly enriched SO₄^2- and Ca²⁺ in coarse modes of PM₁₀-PM_2.5, which indicates the impact of soil dust. The PM_2.5 concentration in Seoul is often highly elevated during haze and dust outbreaks, and samples were mostly found in Groups 1 and 2, respectively. Although the masses of PM₁₀ and PM_2.5 were lowest in Group 3 (52%), the carbonaceous fraction (OC and EC) against PM_2.5 mass was the highest (63%), and OC and EC concentrations were well correlated with NO₃^-, which suggests fossil fuel sources.

The source of PM_2.5 was apportioned using the Nonnegative Matrix Factorization (NMF) receptor model into the following sources: traffic-related urban primary (28%), coal-fired industry (27%), aged urban secondary (19%), soil dust (16%), and biomass combustion (10%). Seasonality is implicit in these five sources and their contributions differed between cold and warm seasons, although traffic-related urban primary emissions were significant in both seasons. Along with the mass and chemical characteristics of PM, the results of source apportionment reveal that the aerosol mass concentration of Seoul is largely dependent on the occurrence of high mass events; these were captured in Groups 1 and 2 and are mainly associated with a Chinese outflow. However, local impacts were distinguished by traffic-related urban emissions under stagnant conditions during the warm months, when aerosol mass concentrations were relatively low, but when the carbonaceous contribution was greater. The high contribution from the coal-fired industry, which was not coupled with high PM_2.5 concentrations, highlights the influence from Far East China and North Korea during the cold months.

Acknowledgments

This paper is published with the support of the Korea Institute of Science and Technology (KIST) through a grant 2E31290-21-P009. J.H. is grateful for the support of Seoul Institute of Technology (SIT) (2021-AE-007, A basic study for the management of high ozone episodes by autonomous districts (Gu): Focused on the current status of air pollutants and health effects). A part of this study was done for the Ph.D. thesis of Jihyun Han. Specially, the authors would like to thank the National Institute of Environmental Research (NIER) for supporting the measurement.

References

• Aldabe, J., Elustondo, D., Santamaría, C., Lasheras, E., Pandolfi, M., Alastuey, A., Querol, X., Santamaría, J.M. (2011) Chemical characterisation and source apportionment of PM_2.5 and PM₁₀ at rural, urban and traffic sites in Navarra (North of Spain). Atmospheric Research, 102, 191-205. [https://doi.org/10.1016/j.atmosres.2011.07.003]
• Alleman, L.Y., Lamaison, L., Perdrix, E., Robache, A., Galloo, J.C. (2010) PM₁₀ metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone. Atmospheric Research, 96, 612-625. [https://doi.org/10.1016/j.atmosres.2010.02.008]
• Atkinson, R.W., Mills, I.C., Walton, H.A., Anderson, H.R. (2015) Fine particle components and health — a systematic review and meta-analysis of epidemiological time series studies of daily mortality and hospital admissions. Journal of Exposure Science and Environmental Epidemiology, 25, 208-214. [https://doi.org/10.1038/jes.2014.63]
• Bahadur, R., Feng, Y., Russell, L.M., Ramanathan, V. (2011) Impact of California’s air pollution laws on black carbon and their implications for direct radiative forcing. Atmospheric Environment, 45, 1162-1167. [https://doi.org/10.1016/j.atmosenv.2010.10.054]
• Bozlaker, A., Buzcu-Güven, B., Fraser, M.P., Chellam, S. (2013) Insights into PM₁₀ sources in Houston, Texas: Role of petroleum refineries in enriching lanthanoid metals during episodic emission events. Atmospheric Environment, 69, 109-117. [https://doi.org/10.1016/j.atmosenv.2012.11.068]
• Brasseur, G.P., Orlando, J.J., Tyndall, G.S. (1999) Atmospheric chemistry and global change. Oxford University Press, New York.
• Bressi, M., Sciare, J., Ghersi, V., Mihalopoulos, N., Petit, J.E., Nicolas, J.B., Moukhtar, S., Rosso, A., Féron, A., Bonnaire, N., Poulakis, E., Theodosi, C. (2014) Sources and geographical origins of fine aerosols in Paris (France). Atmospheric Chemistry and Physics, 14, 8813-8839. [https://doi.org/10.5194/acp-14-8813-2014]
• Cao, G., Zhang, X., Zheng, F. (2006) Inventory of black carbon and organic carbon emissions from China. Atmospheric Environment, 40, 6516-6527. [https://doi.org/10.1016/j.atmosenv.2006.05.070]
• Cao, J.J., Wu, F., Chow, J.C., Lee, S.C., Li, Y., Chen, S.W., An, Z.S., Fung, K.K., Watson, J.G., Zhu, C.S., Liu, S.X. (2005) Characterization and source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi’an, China. Atmospheric Chemistry and Physics, 5, 3127-3137. [https://doi.org/10.5194/acp-5-3127-2005]
• Chen, J., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M.S., Wang, S., Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B., Morawska, L., Thai, P., Lam, Y.F., Pereira, G., Ding, A., Huang, X., Dumka, U.C. (2017) A review of biomass burning: Emissions and impacts on air quality, health and climate in China. Science of the Total Environment, 579, 1000-1034. [https://doi.org/10.1016/j.scitotenv.2016.11.025]
• Choi, J.C., Lee, M., Chun, Y., Kim, J., Oh, S. (2001) Chemical composition and source signature of spring aerosol in Seoul, Korea. Journal of Geophysical Research: Atmospheres, 106, 18067-18074. [https://doi.org/10.1029/2001JD900090]
• Chow, J.C., Watson, J.G., Kuhns, H., Etyemezian, V., Lowenthal, D.H., Crow, D., Kohl, S.D., Engelbrecht, J.P., Green, M.C. (2004) Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study. Chemosphere, 54, 185-208. [https://doi.org/10.1016/j.chemosphere.2003.07.004]
• Chow, J.C., Watson, J.G., Louie, P.K.K., Chen, L.W.A., Sin, D. (2005) Comparison of PM_2.5 carbon measurement methods in Hong Kong, China. Environmental Pollution, 137, 334-344. [https://doi.org/10.1016/j.envpol.2005.01.006]
• Chow, J.C., Watson, J.G., Pritchett, L.C., Pierson, W.R., Frazier, C.A., Purcell, R.G. (1993) The Dri Thermal Optical Reflectance Carbon Analysis System - Description, Evaluation and Applications in United-States Air-Quality Studies. Atmospheric Environment - PT A General Topics, 27, 1185-1201. [https://doi.org/10.1016/0960-1686(93)90245-T]
• Chowdhury, P.H., Okano, H., Honda, A., Kudou, H., Kitamura, G., Ito, S., Ueda, K., Takano, H. (2018) Aqueous and organic extract of PM_2.5 collected in different seasons and cities of Japan differently affect respiratory and immune systems. Environmental Pollution, 235, 223-234. [https://doi.org/10.1016/j.envpol.2017.12.040]
• de Hoogh, K., Héritier, H., Stafoggia, M., Künzli, N., Kloog, I. (2018) Modelling daily PM_2.5 concentrations at high spatio-temporal resolution across Switzerland. Environmental Pollution, 233, 1147-1154. [https://doi.org/10.1016/j.envpol.2017.10.025]
• Draxler, R.R., Rolph, G.D. (2012) HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website (http://ready.arl.noaa.gov/HYSPLIT.php, ), NOAA Air Resources Laboratory, Silver Spring, MD.
• Duan, F., Liu, X., Yu, T., Cachier, H. (2004) Identification and estimate of biomass burning contribution to the urban aerosol organic carbon concentrations in Beijing. Atmospheric Environment, 38, 1275-1282. [https://doi.org/10.1016/j.atmosenv.2003.11.037]
• Duan, J., Tan, J. (2013) Atmospheric heavy metals and Arsenic in China: Situation, sources and control policies. Atmospheric Environment, 74, 93-101. [https://doi.org/10.1016/j.atmosenv.2013.03.031]
• Fang, W., Andersson, A., Zheng, M., Lee, M., Holmstrand, H., Kim, S.-W., Du, K., Gustafsson, Ö. (2017) Divergent Evolution of Carbonaceous Aerosols during Dispersal of East Asian Haze. Scientific Reports, 7, 10422. [https://doi.org/10.1038/s41598-017-10766-4]
• Finlayson-Pitty, B.J., Pitts Jr. J.J. (2000) Chemistry of the upper and lower atmosphere: theory, experiments, and applications, Academic press, San Dieago, California, USA.
• Fowler, D., Pilegaard, K., Sutton, M.A., Ambus, P., Raivonen, M., Duyzer, J., Simpson, D., Fagerli, H., Fuzzi, S., Schjoerring, J.K., Granier, C., Neftel, A., Isaksen, I.S.A., Laj, P., Maione, M., Monks, P.S., Burkhardt, J., Daemmgen, U., Neirynck, J., Personne, E., Wichink-Kruit, R., Butterbach-Bahl, K., Flechard, C., Tuovinen, J.P., Coyle, M., Gerosa, G., Loubet, B., Altimir, N., Gruenhage, L., Ammann, C., Cieslik, S., Paoletti, E., Mikkelsen, T.N., Ro-Poulsen, H., Cellier, P., Cape, J.N., Horváth, L., Loreto, F., Niinemets, Ü., Palmer, P.I., Rinne, J., Misztal, P., Nemitz, E., Nilsson, D., Pryor, S., Gallagher, M.W., Vesala, T., Skiba, U., Brüggemann, N., Zechmeister-Boltenstern, S., Williams, J., O’Dowd, C., Facchini, M.C., de Leeuw, G., Flossman, A., Chaumerliac, N., Erisman, J.W. (2009) Atmospheric composition change: Ecosystems-Atmosphere interactions. Atmospheric Environment, 43, 5193-5267. [https://doi.org/10.1016/j.atmosenv.2009.07.068]
• Geng, H., Park, Y., Hwang, H., Kang, S., Ro, C.U. (2009) Elevated nitrogen-containing particles observed in Asian dust aerosol samples collected at the marine boundary layer of the Bohai Sea and the Yellow Sea. Atmospheric Chemistry and Physics, 9, 6933-6947. [https://doi.org/10.5194/acp-9-6933-2009]
• Ghim, Y.S., Chang, Y.-S., Jung, K. (2015) Temporal and Spatial Variations in Fine and Coarse Particles in Seoul, Korea. Aerosol and Air Quality Research, 15, 842-852. [https://doi.org/10.4209/aaqr.2013.12.0362]
• Ghim, Y.S., Kim, J.Y., Chang, Y.-S. (2017) Concentration Variations in Particulate Matter in Seoul Associated with Asian Dust and Smog Episodes. Aerosol and Air Quality Research, 17, 3128-3140. [https://doi.org/10.4209/aaqr.2016.09.0414]
• Guo, H., Kota, S.H., Sahu, S.K., Hu, J., Ying, Q., Gao, A., Zhang, H. (2017) Source apportionment of PM_2.5 in North India using source-oriented air quality models. Environmental Pollution, 231, 426-436. [https://doi.org/10.1016/j.envpol.2017.08.016]
• Han, J., Lee, M., Lee, G., Emmons, L.K. (2017) Decoupling peroxyacetyl nitrate from ozone in Chinese outflows observed at Gosan Climate Observatory. Atmospheric Chemistry and Physics Discussion, 2017, 1-29. [https://doi.org/10.5194/acp-17-10619-2017]
• Han, J.S., Moon, K.J., Lee, S.J., Kim, Y.J., Ryu, S.Y., Cliff, S.S., Yi, S.M. (2006) Size-resolved source apportionment of ambient particles by positive matrix factorization at Gosan background site in East Asia. Atmospheric Chemistry and Physics, 6, 211-223. [https://doi.org/10.5194/acp-6-211-2006]
• Han, Y., Cao, J., Chow, J.C., Watson, J.G., An, Z., Jin, Z., Fung, K., Liu, S. (2007) Evaluation of the thermal/optical reflectance method for discrimination between char- and soot-EC. Chemosphere, 69, 569-574. [https://doi.org/10.1016/j.chemosphere.2007.03.024]
• Han, Y.M., Lee, S.C., Cao, J.J., Ho, K.F., An, Z.S. (2009) Spatial distribution and seasonal variation of char-EC and soot-EC in the atmosphere over China. Atmospheric Environment, 43, 6066-6073. [https://doi.org/10.1016/j.atmosenv.2009.08.018]
• Heo, J.B., Hopke, P.K., Yi, S.M. (2009) Source apportionment of PM_2.5 in Seoul, Korea. Atmospheric Chemistry and Physics, 9, 4957-4971. [https://doi.org/10.5194/acp-9-4957-2009]
• Hou, B., Zhuang, G., Zhang, R., Liu, T., Guo, Z., Chen, Y. (2011) The implication of carbonaceous aerosol to the formation of haze: Revealed from the characteristics and sources of OC/EC over a mega-city in China. Journal of Hazardous Materials, 190, 529-536. [https://doi.org/10.1016/j.jhazmat.2011.03.072]
• Huang, R.-J., Zhang, Y., Bozzetti, C., Ho, K.-F., Cao, J.-J., Han, Y., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., Haddad, I.E., Prevot, A.S.H. (2014) High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514, 218-222. [https://doi.org/10.1038/nature13774]
• IPCC (Intergovernmental Panel on Climate Change) (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M. Eds), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p. 1535.
• Isaksen, I.S.A., Granier, C., Myhre, G., Berntsen, T.K., Dalsøren, S.B., Gauss, M., Klimont, Z., Benestad, R., Bousquet, P., Collins, W., Cox, T., Eyring, V., Fowler, D., Fuzzi, S., Jöckel, P., Laj, P., Lohmann, U., Maione, M., Monks, P., Prevot, A.S.H., Raes, F., Richter, A., Rognerud, B., Schulz, M., Shindell, D., Stevenson, D.S., Storelvmo, T., Wang, W.C., van Weele, M., Wild, M., Wuebbles, D. (2009) Atmospheric composition change: Climate-Chemistry interactions. Atmospheric Environment, 43, 5138-5192. [https://doi.org/10.1016/j.atmosenv.2009.08.003]
• Jacobson, M.Z. (2001) Global direct radiative forcing due to multicomponent anthropogenic and natural aerosols. Journal of Geophysical Research-Atmospheres, 106, 1551-1568. [https://doi.org/10.1029/2000JD900514]
• Jacobson, M.Z. (2002) Atmospheric pollution: history, science, and regulation. Cambridge. [https://doi.org/10.1017/CBO9780511802287]
• Jacobson, M.Z. (2012) Air Pollution and Global Warming: History, Science, and Solutions, 2nd ed. Cambridge Univ. Press, Cambridge. [https://doi.org/10.1017/CBO9781139109444]
• Jeong, U., Kim, J., Lee, H., Jung, J., Kim, Y.J., Song, C.H., Koo, J.H. (2011) Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: A PSCF model approach. Journal of Environmental Monitoring, 13, 1905-1918. [https://doi.org/10.1039/c0em00659a]
• Kang, E., Han, J., Lee, M., Lee, G., Kim, J.C. (2013) Chemical characteristics of size-resolved aerosols from Asian dust and haze episode in Seoul Metropolitan City. Atmospheric Research, 127, 34-46. [https://doi.org/10.1016/j.atmosres.2013.02.002]
• Kennedy, I.M. (2007) The health effects of combustion-generated aerosols. Proceedings of the Combustion Institute, 31, 2757-2770. [https://doi.org/10.1016/j.proci.2006.08.116]
• Kim, K. (2015) Statistics of North Korea Energy (in Korean).
• Künzi, L., Krapf, M., Daher, N., Dommen, J., Jeannet, N., Schneider, S., Platt, S., Slowik, J.G., Baumlin, N., Salathe, M., Prévôt, A.S.H., Kalberer, M., Strähl, C., Dumbgen, L., Sioutas, C., Baltensperger, U., Geiser, M. (2015) Toxicity of aged gasoline exhaust particles to normal and diseased airway epithelia. Scientific Reports, 5, 11801. [https://doi.org/10.1038/srep11801]
• Laing, J.R., Hopke, P.K., Hopke, E.F., Husain, L., Dutkiewicz, V.A., Paatero, J., Viisanen, Y. (2015) Positive Matrix Factorization of 47 Years of Particle Measurements in Finnish Arctic. Aerosol and Air Quality Research, 15, 188-207. [https://doi.org/10.4209/aaqr.2014.04.0084]
• Lee, D.D., Seung, H.S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788-791. [https://doi.org/10.1038/44565]
• Lee, G., Park, R., Kim, J., Megacity Air Pollution Studies-Seoul (MAPS-Seoul) Whitepaper (2015) National Institute of Environmental Research (NIER).
• Lee, H.-M., Park, R.J., Henze, D.K., Lee, S., Shim, C., Shin, H.-J., Moon, K.-J., Woo, J.-H. (2017) PM_2.5 source attribution for Seoul in May from 2009 to 2013 using GEOS-Chem and its adjoint model. Environmental Pollution, 221, 377-384. [https://doi.org/10.1016/j.envpol.2016.11.088]
• Lee, M., Song, M., Moon, K.J., Han, J.S., Lee, G., Kim, K.-R. (2007) Origins and chemical characteristics of fine aerosols during the northeastern Asia regional experiment (Atmospheric Brown Cloud-East Asia Regional Experiment 2005). Journal of Geophysical Research-Atmospheres, 112, D22S29. [https://doi.org/10.1029/2006JD008210]
• Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A. (2015) The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525, 367. [https://doi.org/10.1038/nature15371]
• Li, J., Chen, B., de la Campa, A.M.S., Alastuey, A., Querol, X., de la Rosa, J.D. (2018) 2005-2014 trends of PM₁₀ source contributions in an industrialized area of southern Spain. Environmental Pollution, 236, 570-579. [https://doi.org/10.1016/j.envpol.2018.01.101]
• Li, Q., Jiang, J., Wang, S., Rumchev, K., Mead-Hunter, R., Morawska, L., Hao, J. (2017) Impacts of household coal and biomass combustion on indoor and ambient air quality in China: Current status and implication. Science of the Total Environment, 576, 347-361. [https://doi.org/10.1016/j.scitotenv.2016.10.080]
• Liang, J., Fairley, D. (2006) Validation of an efficient non-negative matrix factorization method and its preliminary application in Central California. Atmospheric Environment, 40, 1991-2001. [https://doi.org/10.1016/j.atmosenv.2005.11.035]
• Lim, S., Lee, M., Kim, S.W., Yoon, S.C., Lee, G., Lee, Y.J. (2014) Absorption and scattering properties of organic carbon versus sulfate dominant aerosols at Gosan climate observatory in Northeast Asia. Atmospheric Chemistry and Physics, 14, 7781-7793. [https://doi.org/10.5194/acp-14-7781-2014]
• Lim, S., Lee, M., Lee, G., Kim, S., Yoon, S., Kang, K. (2012) Ionic and carbonaceous compositions of PM₁₀, PM_2.5 and PM_1.0 at Gosan ABC Superstation and their ratios as source signature. Atmospheric Chemistry and Physics, 12, 2007-2024. [https://doi.org/10.5194/acp-12-2007-2012]
• Lin, C.Y., Wang, Z., Chen, W.N., Chang, S.Y., Chou, C.C.K., Sugimoto, N., Zhao, X. (2007) Long-range transport of Asian dust and air pollutants to Taiwan: observed evidence and model simulation. Atmospheric Chemistry and Physics, 7, 423-434. [https://doi.org/10.5194/acp-7-423-2007]
• Liu, X.G., Li, J., Qu, Y., Han, T., Hou, L., Gu, J., Chen, C., Yang, Y., Liu, X., Yang, T., Zhang, Y., Tian, H., Hu, M. (2013) Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China. Atmospheric Chemistry and Physics, 13, 4501-4514. [https://doi.org/10.5194/acp-13-4501-2013]
• Minguillón, M.C., Campos, A.A., Cárdenas, B., Blanco, S., Molina, L.T., Querol, X. (2014) Mass concentration, composition and sources of fine and coarse particulate matter in Tijuana, Mexico, during Cal-Mex campaign. Atmospheric Environment, 88, 320-329. [https://doi.org/10.1016/j.atmosenv.2013.09.032]
• Monks, P.S., Granier, C., Fuzzi, S., Stohl, A., Williams, M.L., Akimoto, H., Amann, M., Baklanov, A., Baltensperger, U., Bey, I., Blake, N., Blake, R.S., Carslaw, K., Cooper, O.R., Dentener, F., Fowler, D., Fragkou, E., Frost, G.J., Generoso, S., Ginoux, P., Grewe, V., Guenther, A., Hansson, H.C., Henne, S., Hjorth, J., Hofzumahaus, A., Huntrieser, H., Isaksen, I.S.A., Jenkin, M.E., Kaiser, J., Kanakidou, M., Klimont, Z., Kulmala, M., Laj, P., Lawrence, M.G., Lee, J.D., Liousse, C., Maione, M., McFiggans, G., Metzger, A., Mieville, A., Moussiopoulos, N., Orlando, J.J., O’Dowd, C.D., Palmer, P.I., Parrish, D.D., Petzold, A., Platt, U., Pöschl, U., Prévôt, A.S.H., Reeves, C.E., Reimann, S., Rudich, Y., Sellegri, K., Steinbrecher, R., Simpson, D., ten Brink, H., Theloke, J., van der Werf, G.R., Vautard, R., Vestreng, V., Vlachokostas, C., von Glasow, R. (2009) Atmospheric composition change - global and regional air quality. Atmospheric Environment, 43, 5268-5350. [https://doi.org/10.1016/j.atmosenv.2009.08.021]
• NIER (National Institute of Environmental Research) (2021) Annual Report of Ambient Air Quality in Korea, 2020 (in Korean). Incheon, Korea.
• Oh, M.S., Lee, T.J., Kim, D.S. (2011) Quantitative source apportionment of size-segregated particulate matter at urbanized local site in Korea. Aerosol and Air Quality Research, 11, 247-264. [https://doi.org/10.4209/aaqr.2010.11.0099]
• Park, S.-S., Jung, S.-A., Gong, B.-J., Cho, S.-Y., Lee, S.-J. (2013) Characteristics of PM_2.5 Haze Episodes Revealed by Highly Time-Resolved Measurements at an Air Pollution Monitoring Supersite in Korea. Aerosol and Air Quality Research, 13, 957-976. [https://doi.org/10.4209/aaqr.2012.07.0184]
• Pasch, A.N., MacDonald, C.P., Gilliam, R.C., Knoderer, C.A., Roberts, P.T. (2011) Meteorological characteristics associated with PM_2.5 air pollution in Cleveland, Ohio, during the 2009-2010 Cleveland Multiple Air Pollutants Study. Atmospheric Environment, 45, 7026-7035. [https://doi.org/10.1016/j.atmosenv.2011.09.065]
• Pateraki, S., Asimakopoulos, D.N., Flocas, H.A., Maggos, T., Vasilakos, C. (2012a) The role of meteorology on different sized aerosol fractions (PM₁₀, PM_2.5, PM_2.5-10). Science of the Total Environment, 419, 124-135. [https://doi.org/10.1016/j.scitotenv.2011.12.064]
• Pateraki, S., Assimakopoulos, V.D., Bougiatioti, A., Kouvarakis, G., Mihalopoulos, N., Vasilakos, C. (2012b) Carbonaceous and ionic compositional patterns of fine particles over an urban Mediterranean area. Science of the Total Environment, 424, 251-263. [https://doi.org/10.1016/j.scitotenv.2012.02.046]
• Pérez, N., Pey, J., Querol, X., Alastuey, A., López, J.M., Viana, M. (2008) Partitioning of major and trace components in PM₁₀-PM_2.5-PM₁ at an urban site in Southern Europe. Atmospheric Environment, 42, 1677-1691. [https://doi.org/10.1016/j.atmosenv.2007.11.034]
• Querol, X., Alastuey, A., Pey, J., Cusack, M., Pérez, N., Mihalopoulos, N., Theodosi, C., Gerasopoulos, E., Kubilay, N., Koçak, M. (2009) Variability in regional background aerosols within the Mediterranean. Atmospheric Chemistry and Physics, 9, 4575-4591. [https://doi.org/10.5194/acp-9-4575-2009]
• Querol, X., Pey, J., Minguillón, M.C., Pérez, N., Alastuey, A., Viana, M., Moreno, T., Bernabé, R.M., Blanco, S., Cárdenas, B., Vega, E., Sosa, G., Escalona, S., Ruiz, H., Artíñano, B. (2008) PM speciation and sources in Mexico during the MILAGRO-2006 Campaign. Atmospheric Chemistry and Physics, 8, 111-128. [https://doi.org/10.5194/acp-8-111-2008]
• Ram, K., Sarin, M.M. (2010) Spatio-temporal variability in atmospheric abundances of EC, OC and WSOC over Northern India. Journal of Aerosol Science, 41, 88-98. [https://doi.org/10.1016/j.jaerosci.2009.11.004]
• Reff, A., Eberly, S.I., Bhave, P.V. (2007) Receptor modeling of ambient particulate matter data using positive matrix factorization: Review of existing methods. Journal of the Air and Waste Management Association, 57, 146-154. [https://doi.org/10.1080/10473289.2007.10465319]
• Rodríguez, S., Querol, X., Alastuey, A., Viana, M.-M., Alarcón, M., Mantilla, E., Ruiz, C.R. (2004) Comparative PM₁₀-PM_2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain. Science of the Total Environment, 328, 95-113. [https://doi.org/10.1016/S0048-9697(03)00411-X]
• Rolph, G.D. (2012) Real-time Environmental Applications and Display sYstem (READY) Website (http://ready.arl.noaa.gov, ). NOAA Air Resources Laboratory, Silver Spring, MD.
• Sasaki, K., Sakamoto, K. (2005) Vertical differences in the composition of PM₁₀ and PM_2.5 in the urban atmosphere of Osaka, Japan. Atmospheric Environment, 39, 7240-7250. [https://doi.org/10.1016/j.atmosenv.2005.09.004]
• Seinfeld, J.H., Pandis, S.N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (2nd Edition). John Wiley & Sons.
• Seo, J., Kim, J.Y., Youn, D., Lee, J.Y., Kim, H., Lim, Y.B., Kim, Y., Jin, H.C. (2017) On the multiday haze in the Asian continental outflow: the important role of synoptic conditions combined with regional and local sources. Atmospheric Chemistiry and Physics, 17, 9311-9332. [https://doi.org/10.5194/acp-17-9311-2017]
• Shang, X., Lee, M., Han, J., Kang, E., Kim, S.W., Gustafsson, Ö., Chang, L. (2018a) Identification and Chemical Characteristics of Distinctive Chinese Outflow Plumes Associated with Enhanced Submicron Aerosols at the Gosan Climate Observatory. Aerosol and Air Quality Research, 18, 330-342. [https://doi.org/10.4209/aaqr.2017.03.0115]
• Shang, X., Zhang, K., Meng, F., Wang, S., Lee, M., Suh, I., Kim, D., Jeon, K., Park, H., Wang, X., Zhao, Y. (2018b) Characteristics and source apportionment of fine haze aerosol in Beijing during the winter of 2013. Atmospheric Chemistry and Physics, 18, 2573-2584. [https://doi.org/10.5194/acp-18-2573-2018]
• Stanier, C., Singh, A., Adamski, W., Baek, J., Caughey, M., Carmichael, G., Edgerton, E., Kenski, D., Koerber, M., Oleson, J., Rohlf, T., Lee, S.R., Riemer, N., Shaw, S., Sousan, S., Spak, S.N. (2012) Overview of the LADCO winter nitrate study: hourly ammonia, nitric acid and PM_2.5 composition at an urban and rural site pair during PM_2.5 episodes in the US Great Lakes region. Atmospheric Chemistry and Physics, 12, 11037-11056. [https://doi.org/10.5194/acp-12-11037-2012]
• Sørensen, M., Daneshvar, B., Hansen, M., Dragsted, L.O., Hertel, O., Knudsen, L., Loft, S. (2003) Personal PM_2.5 exposure and markers of oxidative stress in blood. Environmental Health Perspectives, 111, 161-166. [https://doi.org/10.1289/ehp.111-1241344]
• Tao, J., Zhang, L., Cao, J., Zhang, R. (2017) A review of current knowledge concerning PM_2.5 chemical composition, aerosol optical properties and their relationships across China. Atmospheric Chemistry and Physics, 17, 9485-9518. [https://doi.org/10.5194/acp-17-9485-2017]
• Theodosi, C., Grivas, G., Zarmpas, P., Chaloulakou, A., Mihalopoulos, N. (2011) Mass and chemical composition of size-segregated aerosols (PM₁, PM_2.5, PM₁₀) over Athens, Greece: Local versus regional sources. Atmospheric Chemistry and Physics, 11, 11895-11911. [https://doi.org/10.5194/acp-11-11895-2011]
• Wang, Y., Zhuang, G., Tang, A., Zhang, W., Sun, Y., Wang, Z., An, Z. (2007) The evolution of chemical components of aerosols at five monitoring sites of China during dust storms. Atmospheric Environment, 41, 1091-1106. [https://doi.org/10.1016/j.atmosenv.2006.09.015]
• Wang, Y., Zhuang, G.S., Sun, Y.L., An, Z.S. (2006) The variation of characteristics and formation mechanisms of aerosols in dust, haze, and clear days in Beijing. Atmospheric Environment, 40, 6579-6591. [https://doi.org/10.1016/j.atmosenv.2006.05.066]
• Williams, B.J., Goldstein, A.H., Kreisberg, N.M., Hering, S.V., Worsnop, D.R., Ulbrich, I.M., Docherty, K.S., Jimenez, J.L. (2010) Major components of atmospheric organic aerosol in southern California as determined by hourly measurements of source marker compounds. Atmospheric Chemistry and Physics, 10, 11577-11603. [https://doi.org/10.5194/acp-10-11577-2010]
• WMO/IGAC (2012) GAW report no. 205, WMO/IGAC impacts of megacities on air pollution and climate. Geneva, Switzerland., p. 314.
• Xie, S.D., Liu, Z., Chen, T., Hua, L. (2008) Spatiotemporal variations of ambient PM₁₀ source contributions in Beijing in 2004 using positive matrix factorization. Atmospheric Chemistry and Physics, 8, 2701-2716. [https://doi.org/10.5194/acp-8-2701-2008]
• Yang, F., He, K., Ye, B., Chen, X., Cha, L., Cadle, S.H., Chan, T., Mulawa, P.A. (2005) One-year record of organic and elemental carbon in fine particles in downtown Beijing and Shanghai. Atmospheric Chemistry and Physics, 5, 1449-1457. [https://doi.org/10.5194/acp-5-1449-2005]
• Yang, Y., Christakos, G., Yang, X., He, J. (2018) Spatiotemporal characterization and mapping of PM_2.5 concentrations in southern Jiangsu Province, China. Environmental Pollution, 234, 794-803. [https://doi.org/10.1016/j.envpol.2017.11.077]
• Yin, S., Wang, X., Xiao, Y., Tani, H., Zhong, G., Sun, Z. (2017) Study on spatial distribution of crop residue burning and PM_2.5 change in China. Environmental Pollution, 220, 204-221. [https://doi.org/10.1016/j.envpol.2016.09.040]
• Zhang, F., Xu, L., Chen, J., Chen, X., Niu, Z., Lei, T., Li, C., Zhao, J. (2013) Chemical characteristics of PM_2.5 during haze episodes in the urban of Fuzhou, China. Particuology, 11, 264-272. [https://doi.org/10.1016/j.partic.2012.07.001]
• Zhang, Q., Quan, J., Tie, X., Li, X., Liu, Q., Gao, Y., Zhao, D. (2015) Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Science of the Total Environment, 502, 578-584. [https://doi.org/10.1016/j.scitotenv.2014.09.079]
• Zhang, X.Y., Wang, Y.Q., Niu, T., Zhang, X.C., Gong, S.L., Zhang, Y.M., Sun, J.Y. (2012) Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmospheric Chemistry and Physics, 12, 779-799. [https://doi.org/10.5194/acp-12-779-2012]
• Zhang, Y., Schauer, J.J., Zhang, Y., Zeng, L., Wei, Y., Liu, Y., Shao, M. (2008) Characteristics of Particulate Carbon Emissions from Real-World Chinese Coal Combustion. Environmental Science and Technology, 42, 5068-5073. [https://doi.org/10.1021/es7022576]
• Zhu, B.Q., Yang, X.P., Liu, Z.T., Rioual, P., Li, C.Z., Xiong, H.G. (2012) Geochemical compositions of soluble salts in aeolian sands from the Taklamakan and Badanjilin deserts in northern China, and their influencing factors and environmental implications. Environmental Earth Sciences, 66, 337-353. [https://doi.org/10.1007/s12665-011-1243-1]