Current Issue

Samples of PM_2.5 were collected on the roof of a four-story building of Kangwon National University (KNU) in Chuncheon, South Korea (37°87′N; 127°74′E) (Fig. 1). Chuncheon is a medium-sized residential city without large-scale anthropogenic sources, and is therefore expected to be affected more by regional- or long-range transport from the metropolitan and industrial areas located in the northwestern part of South Korea (Fig. 1). Samples of PM_2.5 were collected for 23 h from 10 am to 9 am the following day from May 15, 2021 to June 26, 2021. To quantify PM_2.5 mass concentration, a 47 mm PTFE membrane filter with supported polypropylene ring (pore size of 2.0 μm, GVS, Italy) and a cyclone were used in a PMS-204 sampler (APM Engineering, Bucheon-si, Korea) at a flow rate of 16.7 L min^-1. Carbonaceous components were collected on a pre-baked 47 mm quartz filter (pore size of 2.2 μm, Whatman, Maidstone, UK) with a carbon denuder (Sunset Laboratory Inc., USA) and a cyclone at a flow rate of 16.7 L min^-1. High volume air sampler (Hi-vol 3000, Ecotech, Australia) with quartz filter (203.2 mm×254 mm) was used to measure organic compounds. For ionic compounds, a 47 mm PTFE filter (pore size of 1.0 μm, GVS, Italy) was used with a cyclone and two 3-channel annular denuders (URG-2000-30x242-3CSS, URG Co., Chapel Hill, NC, USA) at a flow rate of 10 L min^-1. The annular denuder was coated with citric acid solution (100 mL of ethanol+1 g of citric acid+1 g of glycerol) to remove acid gases (SO₂, HNO₃, HNO₂) and Na₂CO₃ solution (50 mL of ethanol+50 mL of ultrapure water+1 g of Na₂CO₃+1 g of glycerol) to remove NH₃.

Fig. 1. 
The sampling location of this study in South Korea (left) and an enlarged map around the sampling site in Chuncheon (right). The nearest national air quality monitoring station was also indicated.

The Teflon filter was stored under controlled conditions of temperature (20°C) and RH (50%) for 24 h before and after sampling, and was then weighed three times using a microbalance (readability=10^-5 g, CP 225D, Satorius, Germany) equipped with a static eliminator (The Staticmaster 2U500, NRD, USA) to determine PM_2.5 mass concentration. To analyze ionic compounds, the filters were extracted with 10 mL of ultrapure water in an ultrasonic extractor for 2 h. The extract was filtered with 0.45 μm PTFE syringe filter, and analyzed with ion chromatography (Metrohm AG, Switzerland). For organic carbon (OC) and elemental carbon (EC) analysis, 1.5 m² of quartz filter was analyzed using the National Institute of Occupational Safety and Health (NIOSH) method 5040 for thermal-optical analysis. The detailed method can be found in other studies (Byun et al., 2020; Park et al., 2018).

For nonpolar organic compounds, the collected filters from high-volume air sampler were cut into pieces, and placed in amber glass vials containing 50 mL of dichloromethane (DCM, GC grade, Fisher chemical, USA)/methanol (HPLC grade, Fisher chemical, USA) mixture (3 : 1). After injecting internal standards into the vials, the samples were extracted twice in an ultrasonicator for 30 min. The extracts were concentrated to 10 mL at 40°C using N₂ (99.999% purity) gas evaporator (Turbo storm, SCINCO, Korea), filtered by 0.45 μm PTFE syringe filter (Pall Corp., USA), and reduced to the final volume of 0.5 mL at room temperature using a N₂ (99.999%) concentrator. The GC-MS (GC 7890A/MSD 59975C, Agilent Technologies, USA) was used for n-alkanes (C20-C34) and 11 PAHs (Table 1). Table S1 of the Supplementary Information (SI) describes the analysis condition of GC-MS.

Calibration curve was obtained using 7 different concentrations of standard solutions, and r² was higher than 0.997 (Table 1) when using the internal standard technique (NIER, 2011). Acenaphthene-d10 and Pyrene-d-10 were used as internal standards for PAHs, while Pentadecane-d32, Eicosane-d42, Tetracosane-d50, Dotriacontane-d66, and Hexatricontane-d74 were used as internal standards for n-alkanes. Method detection limit (MDL) was calculated as 3.14 times the standard deviation by analyzing the standard solution 7 times. The extraction recoveries for n-alkanes and PAHs were determined by spiking the standard solution in the blank filter, and ranged (62 to 114)% (Table 1).

Meteorological data (temperature, wind speed, and relative humidity) were measured every 5 min using meteorological equipment (Wireless Vantage Pro2 Weather Station, Davis Instrument, Hayward, CA, USA) at the sampling site. Meteorological data were measured every 5 min, but averaged to match the temporal resolution of PM_2.5 samples. It should be noted that there are possible uncertainties caused by using different timescales for the measurements of pollutants and for meteorological data. Concentrations of PM₁₀ and gaseous pollutants, including O₃, NO₂, SO₂, and CO, were observed from the nearest national ambient air quality monitoring station, which is located about 1.5 km distance from the sampling site (Fig. 1).

Average PM_2.5 concentration was (16.4±9.7) μg m^-3, and OC showed the highest concentration (3.2 μg m^-3), contributing about 20% of total PM_2.5 mass. The SO₄^2- was higher than NO₃^-, probably because of active photochemical sulfur oxidation in the warm season (Zhou et al., 2016; Gao et al., 2011). The PM_2.5 was better correlated with carbonaceous compounds (r²=0.65 with OC and 0.72 with EC) than with ionic compounds (r²=0.47-0.59) (Fig. 2). Average OC/EC ratio of 10.3 was much higher than the values reported in previous studies in South Korea (Choi et al., 2021; Park et al., 2021; Yu and Park, 2021), indicating that the portion of secondary OC was significant in this season. Since aged organic matter and secondarily formed organic matter contain more oxygen, which is not analyzed by thermal-optical analysis (Martinez et al., 2012; Turpin and Lim, 2001), organic matter estimated from OC concentration is likely to significantly contribute to PM_2.5 mass in this city.

Fig. 2. 
Daily variation of PM_2.5 and its major chemical constituents. The four high concentration events (HCEs) of PM_2.5 are indicated as thick yellow lines. The red dashed line in the top panel indicates the 24-h national ambient air quality standard (NAAQS) of Korea.

In this study, a high concentration episode (HCE) was defined when the measured PM_2.5 concentrations exceeded 30 μg m^-3. Four HCEs occurred during the study period, and a summary of measured data for the HCEs is presented in Table S2. The HCE1, HCE3, and HCE4 showed higher wind speed than the average WS of non-HCEs (Fig. 3), possibly indicating that atmospheric stagnation was not an important factor causing these HCEs. Asian dust event appeared on HCE1, and PM_2.5/PM₁₀ ratio was only 33%, indicating that crustal elements were dominant in the coarse mode. For HCE1, Ca²⁺ and Mg²⁺ concentrations were dramatically enhanced (9.2 times and 4 times higher than the average for Ca²⁺ and Mg²⁺, respectively), but SO₄^2- and NH₄⁺ showed even lower concentrations than for non-HCEs (Fig. 3). Increased NO₃^- and OC concentrations for HCE1 (Fig. 3) were probably derived from crustal sources (Lee et al., 2014). The ∑n-alkane also showed the highest concentration in HCE1 (Fig. 3), suggesting that soil resuspension affected these non-polar organic compounds, as found in previous research efforts (Alves et al., 2016). The ∑PAHs in HCE1 was higher than in non-HCE, and CHR and BkF, known as the major PAHs in temperate soil (Wilcke, 2000), increased the most (2.0 and 2.3 times higher for HCE1 than the average during the entire sampling period).

Fig. 3. 
PM_2.5 component characteristics with wind speed and relative humidity for each HCE event. O₃ concentrations were obtained from the nearest national ambient air monitoring station.

On the other hand, low wind speed (WS) and high relative humidity (RH) were observed for HCE2 (Fig. 3), providing suitable aqueous heterogeneous reactions. Both HCE2 and HCE4 showed significantly increased ionic constituents, but NO₃^- of HCE2 did not increase as much as that of HCE4, while SO₄^2- and NH₄⁺ were dramatically enhanced for both HCE2 and HCE4. The reaction rate of NO₂ with OH is an order of magnitude greater than that of SO₂ with OH (Ma et al., 2021; Seinfeld and Pandis, 2016), while heterogeneous chemistry was frequently found to be more important than gas-phase oxidation for the SO₄^2- formation mechanism (Zheng et al., 2015). High RH and low WS observed for the HCE2 possibly suggested that aqueous reactions played more important roles in SO₄^2- formation than in NO₃^- formation at this site. In the meantime, HCE4 showed largest increases on NO₃^-, SO₄^2-, and NH₄⁺, as well as on gaseous O₃ concentration; therefore, gas-phase oxidation mechanisms were significant, as O₃ is predominantly produced via a series of gas-phase reactions (Sun et al., 2011), which is also supported by the significant negative correlation between RH and O₃ observed in this study (Pearson r=0.47, p-value<0.001). The OC also significantly increased for both HCE2 and HCE4, but the relative increment rate in OC to EC concentrations was large for HCE2, compared with HCE4 (Fig. 3), suggesting that secondary OC formation was likely to be more important for HCE2 than for HCE4. The PM_2.5/PM₁₀ ratios for both HCE2 (88%) and HCE4 (94%) were also very high, supporting the importance of secondary aerosol formation. On the other hand, in HCE3, the concentrations of ionic constituents were rather reduced, while OC and EC increased about 1.5 and 1.9 times the average concentrations of non-HCEs (Fig. 3), suggesting that the primary sources emitting carbonaceous compounds played an important role in enhancing PM_2.5 concentration. Variation of ∑PAHs from HCE2 to HCE4 was similar to EC variation (Fig. 3).

To identify the possible source areas, backward trajectories for the top 10% of PM_2.5 were compared with those for the bottom 10% of PM_2.5. Back-trajectories associated with the top 10% of PM_2.5 were clearly distinct from those of the bottom 10% of PM_2.5, showing that high PM_2.5 samples originated from northeastern China, and passed through the western metropolitan area and industrial areas of Korea (Fig. S1 of the SI).

The highest average concentration among PM_2.5 ionic constituents was shown by SO₄^2- (1.8 μg m^-3), followed by NO₃^- (0.9 μg m^-3) and NH₄⁺ (0.8 μg m^-3). The neutralization ratio (NR) was calculated to describe the aerosol acidity:

where, concentrations are in equivalents. The [NH₄⁺] and [SO₄^2-]+[NO₃^-] were highly correlated with each other, indicating that most ionic compounds existed as NH₄NO₂ and (NH₄)₂SO₄ (Fig. 4a). Average NR was (0.72±0.26), indicating slightly acidic aerosols (Fig 4a). To investigate the degree of neutralization of secondary inorganic aerosol (SIA), NR was plotted according to the NH₄⁺ concentration (Fig. 4b). When NH₄⁺ exceeded about 50 nmole m^-3, the NR appeared constant around 1, and SIA was completely neutralized. In other cases, NR almost linearly increased as NH₄⁺ increased, possibly indicating that NH₃ was the limiting factor for SIA formation in this study. The HCE2 and HCE4, where SIA concentration was significantly enhanced (Fig. 3), showed the highest NR values (1.04 and 1.01, respectively) over the entire period, suggesting that NH₃ was likely to cause high PM_2.5 concentrations.

Fig. 4. 
Correlation between the molar concentrations of [NH₄⁺] and 2[SO₄^2-]+[NO₃^-] (left), and the change of NR ratio according to NH₄⁺ concentration.

The PM_2.5 component that increased the most during HCEs appeared to be NO₃^- on average. To identify the influence of NO₂ and SO₂ on NO₃^- and SO₄^2- formations, respectively, the sulfur oxidation ratio (SOR) and the nitrogen oxidation ratio (NOR) were calculated:

where, [SO₄^2-], [SO₂], [NO₃^-], and [NO₂] refer to the molar concentrations. In this study, SOR considers only the aerosol fraction since H₂SO₄ was not measured, but HNO₃ was considered for the total [NO₃^-] (T-NO₃^-) in NOR. The average SOR (of 0.20±0.10) was higher than the average NOR (of 0.08±0.04), but the NOR and the SOR during HCEs increased about 2.3 and 1.5 times, compared to those during non-HCEs, respectively. To determine whether the gaseous precursors of NO₂ and SO₂ were oxidized to form SIA, the correlation between NO₂ and T-NO₃^- and between SO₂ and SO₄^2- were investigated. Both regressions were significant (p-values< 0.001), but the r² was much higher between NO₂ and T-NO₃^- than between SO₂ and SO₄^2- (please note that one outlier, indicated as red point in Fig. 5, was excluded for the regression between NO₂ and T-NO₃^-). These results possibly indicate that in situ oxidation of NO₂ and/or gas-aerosol partitioning for HNO₃ and NO₃^- occurred, even in the relatively warm season.

Fig. 5. 
Influence of gaseous NO₂ on total NO₃^- (left) and gaseous SO₂ on SO₄^2- concentration (right).

N-alkanes are common organic compounds in PM_2.5, emitted from both anthropogenic and natural sources (Rogge et al., 1993; Simoneit, 1986). In this study, average ∑n-alkane concentration was (24.5±11.0) ng m^-3, and there was relatively strong correlation between ∑n-alkane and PM_2.5 concentrations (Pearson r=0.47). The average concentrations of ∑n-alkane were 14.0±10.3 ng m^-3 and 53.7±35.0 ng m^-3 at the background site (Anmyeon Island) (Kim et al., 2018) and in Seoul (Lee et al., 2015), respectively, indicating that the ∑n-alkane concentrations reported in this study were relatively higher than in Anmyeon Island and lower than in Seoul. The C29 (n-nonacosane) and C31 (n-hentriacontane) were the most abundant species with the largest coefficient of variation (standard deviation divided arithmetic mean) (Fig. 6). Previous studies suggested that high carbon numbers generally appear from biogenic sources, including plant wax and bacteria, while low carbon numbers indicate a large contribution from fossil fuel combustion (Sun et al., 2021; Chen et al., 2014; Feng et al., 2006). Other research shows that C_max ranges (C18 to C22) for petrol vehicles, and is C21 for diesel vehicles (Zhang et al., 2021). In this study, the relative concentration of C20-C23 was lower than the higher molecular weight alkanes (Fig. 6), indicating that fossil fuel combustion was not important. The carbon preference index (CPI, Eq. (8)) has often been used to identify the alkanes from natural emissions from the alkanes from anthropogenic emissions, because odd alkanes, rather than even alkanes, were preferred for biogenic sources.

Fig. 6. 
(a) Abundance of n-alkanes, (b) the relationship between wind speed and CPI value, and (c) the daily variation of C_max proportion.

When CPI is close to 1, n-alkanes are mainly from anthropogenic sources, such as fossil fuel combustion and biomass burning, because the emission distribution of even and odd alkanes from these sources is not selective (Gao et al., 2022; Choi et al., 2012). When 1<CPI<3, n-alkanes are influenced by both biogenic and anthropogenic sources, and a higher CPI indicates a higher effect of biogenic sources (Feng et al., 2021). In this study, odd alkanes generally showed higher abundance than even alkanes (Fig. 6), suggesting the importance of biogenic sources over fossil fuel combustion. Average CPI value was (1.73±0.92) with relatively large variation, ranging (0.57 to 3.99), and the two maximum CPIs were observed on May 24 and May 26 when the PM_2.5/PM₁₀ ratio was (0.33 and 0.39), respectively, indicating that natural sources were more significant than combustion source. Asian dust event occurred on May 24 when the highest ∑n-alkane concentration (64.8 ng m^-3) appeared during the sampling period (Fig. 2).

Relatively high ∑n-alkane concentrations were shown for HCEs (Fig. 3), but the relative abundance of each alkane varied. For HCE1 and HCE3, the odd alkanes of C27, C29, and C31 were dominant, contributing (65 and 48)%, respectively (Fig. 6). According to Li et al. (2010), alkanes are anticipated to be mainly emitted from biogenic sources if the odd alkanes among C27-C31 are higher than even alkanes. In HCE3, the concentrations of ionic constituents were low, while the highest OC concentration was shown among all HCEs (Fig. 3), and high CPI of 2.35 was observed; therefore, the high OC of HCE3 was partly influenced by biogenic sources, such as plant wax and microorganism. On the other hand, for both HCE2 and HCE4 categorized by SIA-derived event (Fig. 3), CPI values were relatively low at (0.68 and 1.47), respectively.

In this study, CPI was statistically correlated with WS (Pearson r=0.45) (Fig. 6), suggesting that the biogenic alkanes were significantly emitted from epicuticular wax with the effect of strong wind (Thao et al., 2014). Previous study also found that up to 50% of the leaf surface wax was lost after strong winds (Hall and Donaldson, 1963).

Average ∑PAHs concentration was (0.43±0.19) ng m^-3, and there was statistically significant (at a significance level of 0.05), but not high, correlation between ∑PAHs and ∑n-alkane (Pearson r=0.38, p-value=0.019). Among 11 PAHs measured, PYR and BbF showed the highest average concentrations of 0.08 ng m^-3. All PAHs concentrations were significantly lower than those measured at the same sampling site in 2014-2015 (Park et al., 2018). Previous studies showed that PAHs concentrations have dramatically decreased recently in other cities in Korea and in China (Zhang et al., 2021; Kang et al., 2020). In Seoul, the measured ∑PAHs concentration in 2018 were approximately one-fifth that of 2002, and the decreasing trend was more pronounced in summer (decreasing from 6.8 ng m^-3 in 2002 to 0.8 ng m^-3 in 2018 during summer) (Kang et al., 2020). The PAHs concentrations typically show clear seasonal variation with high values in winter and low values in summer (Shin et al., 2022; Nguyen et al., 2018; Ma et al., 2010), and low PAHs concentrations in this study were also likely to be reflected by seasonal influences. Concentrations of high molecular weight (HMW)-PAHs exceeded the medium-MW (MMW)-PAHs and low-MW (LMW)-PAHs in this study, indicating that vehicular emissions were more important than solid fuel combustion (Ali-Taleshi et al., 2021), or that LMW-PAHs showed much lower gas-particle partitioning coefficient, K_p than HMW-PAHs, especially in the warm season (Chen et al., 2016; Venkataraman et al., 1999).

Since PAH composition profiles are characteristics of each specific emission source, the diagnostic ratios (DRs) can be used to identify the source types. In this study, four DRs were used, of BaA/ (BaA+Chr), BaP/ BghiP, BaP (BaP+Chr), and IcdP/ (IcdP+BghiP). BaA/ (BaA+Chr) less than 0.2 indicates a petrogenic source, while it ranges (0.2 to 0.35) for petroleum combustion (Kang et al., 2020; Finardi et al., 2017; Simcik et al., 1999). In this study, the BaA/(BaA+Chr) was in the range (0.2 to 0.35) for most samples (Fig. 7), indicating the influence of vehicular emission. The BaP/BghiP ratio can further identify PAHs from gasoline (0.3-0.45) and diesel (0.45-0.8) (Zhang et al., 2021), indicating the major influence of diesel vehicle in this study (Fig. 7). The IcdP/(IdcP+BghiP) less than 0.5 and BaP/(BaP+Chr) ranging (0.3 to 0.7) indicate vehicular emission (Liu et al., 2017). A similar domestic study conducted using the PAHs diagnostic ratio showed that PAHs were greatly affected by vehicular emission in Seoul during summer (Kang et al., 2020). Previous study conducted at the same sampling site as this study showed that solid-fuel combustion, including biomass and coal burning, was predominant source of PAHs in winter (Park et al., 2018).

Fig. 7. 
Diagnostic ratios to identify the major sources of PAHs. Results in this study generally suggest that the vehicular emissions were important.

There are some discrepancies for DR values in characterizing the different sources in the scientific literature; however, it was generally speculated that PAHs were majorly influenced by vehicular emission, although some influence of solid fuel combustion, such as coal and biomass burning, was observed in this study (Fig. 7). The DRs are better to be used with consideration of different volatilities of compounds, to reduce the impact of ambient temperature (Shen et al., 2019); however, the ratio of BaP/(BaP+Chr), which does not meet this rule, was considered, because all samples were collected in the relatively warm season (May and June).