Next Article in Journal
Assessment and Correction of Solar Radiation Measurements with Simple Neural Networks
Previous Article in Journal
Modelling Canopy Actual Transpiration in the Boreal Forest with Reduced Error Propagation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 11
Issue 11
10.3390/atmos11111159
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

High PM2.5 Concentrations in a Small Residential City with Low Anthropogenic Emissions in South Korea

by
Jin-Yeo Byun
1,†,
Hekap Kim
1,
Young-Ji Han
1,*,
Sang-Deok Lee
2,* and
Sung-Won Park
3
1
Department of Environmental Science, Kangwon National University, 1-Kangwondaehak-gil, Chuncheon 24341, Gangwon-do, Korea
2
Division of Forest Science, Kangwon National University, 1-Kangwondaehak-gil, Chuncheon 24341, Gangwon-do, Korea
3
Department of Interdisciplinary Graduate Program in Environmental and Biomedical Convergence, Kangwon National Univeresity, 1-Kangwondaehak-gil, Chuncheon 24341, Gangwon-do, Korea
*
Authors to whom correspondence should be addressed.
†
Division of Chemical Research, National Institute of Environmental Research, Incheon 22689, Korea
Atmosphere 2020, 11(11), 1159; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111159
Submission received: 23 September 2020 / Revised: 16 October 2020 / Accepted: 23 October 2020 / Published: 27 October 2020
(This article belongs to the Section Aerosols)
Browse Figures
Versions Notes

Abstract

:
High particulate matter (PM2.5) concentrations have been considered a serious environmental issue in South Korea. Recent studies have focused mostly on metropolitan and industrial cities; however, high PM2.5 episodes have also been frequently observed even in small– and middle-sized cities. Thus, in this study, PM2.5 and its major chemical components were measured in a small residential city with low anthropogenic emissions for 2 years to identify the factors affecting the PM2.5 concentrations. Overall, the average PM2.5 concentration was 29.4 μg m−3: about two times higher than the annual ambient air quality standard value. In winter, when the PM2.5 concentrations were generally higher, relative humidity (RH) was significantly correlated with both PM2.5 mass and the PM2.5/PM10 ratio, suggesting that high RH promoted the formation of secondary PM2.5. In addition, SO42− and NO3− were found to be correlated with both NH4+ and K+ in winter, indicating that biomass burning was an important source in this city. Water-soluble organic carbon (WSOC) was also highly correlated with elemental carbon (EC) and K+ in fall and winter, when the burning of agricultural residues actively occurred. During high concentration episodes, NO3− exhibited the highest increase; nevertheless, other components (e.g., K+ and organic carbon) also significantly increased.

1. Introduction

Fine particulate matter (PM) is a major concern not only in South Korea, but also worldwide. It has been reported to be associated with increased hospitalization, emergency room visits, and mortality [1,2,3]. For this reason, the International Agency for Research on Cancer (IARC) has classified PM as carcinogenic to human beings. In particular, PM with aerodynamic diameter <2.5 μm (PM2.5) can stay in the atmosphere for several weeks, allowing its long-range transport and effectively reducing visibility [4]. In addition, PM2.5 is directly involved with respiratory and cardiovascular diseases as it can penetrate deep into small airways [5,6,7]. Notably, PM2.5 consists of ionic compounds, elemental carbon (EC), primary and secondary organic matter, sea salt, and other trace elements [8]. Secondary inorganic ions (e.g., NO3−, SO42−, and NH4+) are the main components of PM2.5, generally accounting for >30% of this PM fraction [9]. Organic aerosol (OA) contributes to about 20–50% of PM2.5 mass, and 40–80% of it is presumed to be water-soluble [10,11].
Carbonaceous compounds are often operationally defined as organic carbon (OC) or EC, depending on their thermal and optical properties. EC is emitted directly from the incomplete combustion of biomass and fossil fuel, while OC can be also newly formed via homogeneous and/or heterogeneous reactions in the atmosphere [12,13]. Water-soluble organic carbon (WSOC) can be either generated secondarily in atmospheric processes through gas-to-particle conversion after the oxidation of volatile organic compounds to semi-volatile forms [14] or directly emitted from biomass burning [15].
In South Korea, PM2.5 concentrations have remained at high levels for the last 10 years, which greatly exceeded the current annual National Ambient Air Quality Standard (NAAQS) of South Korea (15 μg m−3), while primary pollutants (e.g., SO2 and CO) have shown a dramatic decrease due to numerous efforts (e.g., the application of industry transfer and fuel conversion). The high PM2.5 concentrations observed in South Korea have partly been derived not only from national sources, but also from long-range transport from China to South Korea [16]. We measured PM2.5 concentrations in Chuncheon, a small residential city in South Korea from 2016 to 2018. Since Chuncheon is a water protection sanctuary, no large polluting industries can be established in this area; as a result, PM2.5 emissions from anthropogenic sources are very low here, according to the National Emissions Inventory of South Korea. However, the ambient PM2.5 concentrations in Chuncheon are among the highest observed in South Korea, including in metropolitan and major industrial areas [17,18]. Previous studies have shown that PM2.5 concentrations in Chuncheon often exceeded the NAAQS (35 μg m−3 and 15 μg m−3 for the daily and annual standards); moreover, the OC concentration and the ratio of OC to EC were particularly high in this city compared to those measured in other cities of South Korea [19]. These results suggest that secondary aerosol formation, the regional range transport of PM2.5 and its precursors from outside the city, and/or the sources not included in the National Emissions Inventory (e.g., open burning and meat cooking) might have significantly affected PM2.5 concentrations. Thus, this study was conducted to identify the factors contributing to the high PM2.5 concentrations observed in Chuncheon. PM2.5 samples were collected in the downtown area of the city, and their chemical compositions (including the ionic and carbonaceous compounds) were analyzed.

2. Experiments

2.1. Sampling

PM2.5 samples were collected in Chuncheon, South Korea (Figure 1). This relatively small city is surrounded by mountains, which limit the transport of air pollutants. The sampling site was located in the city center: on the roof of a four-story Natural Sciences building of Kangwon National University (KNU). The PM2.5 samples were collected during 24 h (00:00−00:00) every 6 days, from January 2016 to October 2017.
PM2.5 was collected on 47-mm Teflon filters (Whatman, pore size = 2 μm) using PMS-103 (APM Eng., South Korea), to determine the mass concentration, and 47-mm quartz filters (Whatman, pore size = 2.2 μm) using an FH95 (Thermo ESM Andersen, Erlangen, Germany) to determine the carbonaceous compounds at a flow rate of 16.7 L min−1. FH95 and PMS-103 are the devices designed to fit the US EPA PM2.5 measurement methods, and the flow rate was maintained within ±1% (FH95) and ±1.5% (PMS-103) errors by automatically calibrating temperature and pressure. Each quartz filter was baked for 24 h at 500 °C before sampling. For the determination of the ionic components, PM2.5 was collected using a cyclone (URG-2000-30EN), two 3-channel annular denuders (242 mm length, URG Co., USA), and a 3-stage Teflon filter pack (URG Co., Chapel Hill, NC, USA) in series in order to remove all particles with aerodynamic diameters ≥2.5 μm, as well as the acidic and basic gases, at a flow rate of 10 L min−1. To maintain an accurate flow rate, a mass flow controller and a dry gas meter were used. The first denuder, which was Na2CO3-coated, was used to trap out HNO3, SO2, HCl, HNO2, HF, and some organic acids. The second denuder, which was instead citric acid-coated, was used to trap NH3 [20]. In the 3-stage filter pack, 47-mm Zefluor filter (Pall Cor., Port Washington, NY, USA, pore size = 1 μm), nylon filter (Pall Co., Port Washington, NY, USA, pore size = 1 μm), and paper (Whatman, Maidstone, UK, pore size = 20 μm) filter soaked in 1% citric acid (1 g of citric acid in 100 mL of ethanol) were placed in order. The nylon and the paper membrane filters were used to capture nitric acid and ammonia, respectively, to compensate for volatilization. The cleaning and sampling procedures were in accordance with the US EPA Compendium Method IO-4.2 [20].
Meteorological data, including temperature, wind speed, wind direction, relative humidity (RH), and solar radiation, were also measured every 5 min at the sampling site using a meteorological tower (Vantage Pro2, Davis Instrument, Hayward, CA, USA).

2.2. Chemical Analysis

The Teflon filters were stored under controlled conditions of temperature (20 °C) and RH (50%) for at least 24 h before and after sampling; then, they were weighed at least twice using an analytical balance (Sartorius CP225D, readability = 10−5 g) after removing static electricity using a static eliminator (StaticMaster 2U500, NRD, USA). PM2.5 mass concentrations were calculated based on the mass increase observed for each filter divided by sampled air volume.
To determine the carbonaceous compounds, a small piece (1.5 cm2) was punched out from each quartz filter and analyzed using the National Institute of Occupational Safety and Health (NIOSH) method 5040 for thermal-optical analysis in which a sample is heated in pure helium (He) and subsequently in 2% oxygen (O2) and He atmosphere [21,22]. The temperature gradually increased up to 870 °C and decreased to 550 °C in the pure He condition, and after which it gradually increased again to 870 °C in the 2% O2 atmosphere (Table 1). Some organic carbonaceous aerosols that were not volatilized in the He stage remained in the filter as a pyrolyzed fraction, which was called pyrolyzed OC (PC). Thus, the total OC was calculated by adding the PC to the amount of carbon measured at the He stage, and the total EC was calculated by subtracting the PC from the carbon measured at the He-O2 stage. Calibration of OC and EC analysis was performed using a sucrose standard (50 μg-C/25 μL), and the recovery rate ranged from 100 to 102%. Average system blank values were 0.04 ± 0.04 μg cm−2 and 0.05 ± 0.04 μg cm−2 for OC and EC, respectively.
The remaining portion of quartz filter was then placed into a conical tube filled with 30 mL of ultrapure water and extracted in a sonicator for 1 h. The extract was then filtered using a 0.2-μm PTFE syringe filter (Pall Life Sciences), and the WSOC was quantified using a total organic carbon (TOC) analyzer (Sievers 5310C Laboratory, Boulder, CO, USA). TOC analyzer is based on the oxidation of organic compounds to form CO2 using UV radiation and a chemical oxidizing agent (NH4)2S2O8, and the CO2 is measured using a selective membrane-based conductivity detection technique. The concentration of the total inorganic carbon (TIC) is first determined, and the total carbon (TC) content is measured after the oxidation of the organic compounds. Then, the concentration of the total organic carbon (TOC) is calculated as the difference between TC and TIC [23].
In this study, we analyzed both anions (i.e., NO3− and SO42−) and cations (i.e., Na+, NH4+, K+, Ca2+, and Mg2+). The filters were first placed in a conical tube filled with 10 mL of ultrapure water and then extracted using a sonicator during 4 h at 60 °C. Afterward, the extract was filtered using a 0.45-μm PTFE syringe filter (Pall Life Sciences) and was analyzed by ion chromatography (IC) (Table S1 of the Supplementary Material). Sodium carbonate (Na2CO3) and methanesulfonic acid solutions were used as eluents for the anion and cation analysis, respectively. For IC analysis, calibration was performed by injecting five different concentrations of standard solution, and r2 was higher than 0.9993.
All instruments used for the sampling and analyses were previously washed with Alconox and ultrapure water. Field blanks (FB) were collected once every six samples, and the method detection limit (MDL) was calculated as three times the standard deviation of these field blanks. Concentration values lower than the MDL were substituted with 0.5 × MDL. The relative percent differences (RPD) between the triplicate (or duplicate) analyses were also obtained. The QA/QC results are shown in Table 2.

2.3. Meteorological Data and Other Pollutants

Meteorological data including temperature, wind speed, wind direction, and relative humidity were measured every 5 min at the sampling site using a meteorological tower (Vintage Pro2, DAVIS Inc., California, CA, USA). The concentrations of PM10 and NO2 were obtained from the national air quality monitoring station located about 1.5 km south of the PM2.5 sampling site. PM10 and NO2 were measured using beta attenuation and chemiluminescence methods, respectively. More detailed measurement methods can be found in Air Pollution Monitoring Network Installing and Operation Guidelines [24].

2.4. Backward Trajectories

Three-day backward trajectories were calculated using the NOAA HYSPLIT with GDAS (Global Data Assimilation System) meteorological data, which consist in 3-h, global 1° latitude by 1° longitude data sets of the pressure surface. The back-trajectories were then calculated every 2 h for the 24 h-averaged samples; moreover, arrival height of 500 m was considered to describe the regional transport meteorological pattern.

2.5. Statistical Analysis

Most of the statistical analysis including correlation and linear regression analysis was conducted using the SPSS (Statistical Package for the Social Sciences, Ver. 23, IBM, Armonk, NY, USA). However, to evaluate the trend of concentration for each PM2.5 component, the Mann-Kendall analysis was performed using Excel data analysis (XLSTAT). In the Mann-Kendall test, each data value was compared to all subsequent data values. The Mann-Kendall statistic, S, was initially assumed to be 0, and incremented by 1 if data from a later time period was higher than a data value from an earlier time period, or decremented by 1 if the data from a later time period was lower than a data value sampled earlier [25]. The final S was given by Equation (1).
S =   ∑ k = 1 n − 1 ∑ j = k + 1 n s i g n ( x j − x k )
where:
sign ( x j − x k ) = 1   i f   x j − x k > 0 = 0   if   x j − x k = 0 =   − 1   if   x j − x k < 0
A very high positive S and a very high negative S values indicate an increasing and a decreasing trends, respectively. The variance of S, VAR(S), and a normalized test statistic Z were then calculated by Equations (2) and (3), respectively.
V A R ( S ) = 1 18 [ n ( n − 1 ) ( 2 n + 5 ) − ∑ p = 1 g t p ( t p − 1 ) ( 2 t p + 5 ) ]
where, n is the number of data points, g is the number of tied groups, and tp is the number of data points in the pth group.
Z =   S − 1 [ V A R ( S ) ] 0.5   i f   S > 0 = 0   if   S = 0
Z =   S + 1 [ V A R ( S ) ] 0.5   i f   S < 0
Negative and positive Z values indicated the decreasing and increasing trends, respectively. If the p-value was less than the level of a significance, α, the null hypothesis, H0 indicated that there is no trend in the series is rejected.

3. Results and Discussion

3.1. General Trends

The average PM2.5 concentration during the study period was 29.4 (±16.8) μg m−3 (Table 3): about two times higher than the current annual NAAQS of South Korea (note that the annual PM2.5 NAAQS changed from 25 μg m−3 to 15 μg m−3 in 2018). In addition, approximately 24% of the samples exceeded the current daily standard of 35 μg m−3. The seasonal averaged PM2.5 values were the highest in winter, followed by spring, fall, and summer (Figure 2, Table 3; note that spring, summer, fall, and winter are indicated as March-May, June-August, September-November, and December-February, respectively). The same trend was observed by other researches in South Korea [16,26]. Of the two winters included in the study period, that between 2016 and 2017 showed higher average PM2.5 concentrations (36 ± 20 μg m−3 vs. 30 ± 16 μg m−3 of the winter between 2015 and 2016) and the maximum absolute PM2.5 concentrations (84 μg m−3 vs. 61 μg m−3 of the winter between 2015 and 2016).
The Mann-Kendall trend analysis method was used for the mass of PM2.5 and its chemical constituents. Many constituents including carbonaceous compounds (OC, EC, and WSOC) and cationic components (Na+, NH4+, and K+) showed a decreasing trend during the sampling period, whereas the remaining constituents and PM2.5 showed no trend at a significance level of 0.05 (Table 4). Carbonaceous compounds were measured from November 2016 to October 2017; therefore, the decreasing trend reflects the seasonal variation—higher concentration in late fall and winter and lower concentration in summer (Figure 2). The sampling period of about 22 months was not long enough to determine the temporal trend over years, thus, there is a need for long-term monitoring in the future.
During the whole sampling period, there were significant negative correlations between the PM2.5 concentrations and temperature (Pearson r = −0.289, p-value = 0.001) and wind speed (Pearson r = −0.332, p-value < 0.001), and a statistical significant multiple linear regression was identified based on the wind speed and temperature data (Equation (4)): PM2.5 concentrations generally increased in winter when the wind speed was low.
PM2.5 = −(0.406 ± 0.106)Ta − (0.819 ± 2.055)WS + 44.363, r2 = 0.203
In the above equation, Ta and WS indicate the atmospheric temperature (°C) and the wind speed (m s−1), respectively. Both of these variables and a constant in Equation (4) were statistically significant (p-value < 0.001).
In this study, seven ionic constituents were determined and measured: their total contribution to PM2.5 mass was found to be 28.5%. The average concentrations of NO3−, SO42−, and NH4+ at the KNU site were 2.0 (±2.7) μg m−3, 2.2 (±1.8) μg m−3, and 2.9 (±1.7) μg m−3, respectively (Table 3). The contribution of NH4+ to PM2.5 mass (12.7% ± 13.2%) was the highest among the seven ionic constituents, followed by SO42− (9.2% ± 7.4%) and NO3− (7.2% ± 7.2%). As anticipated, the proportion of SO42− and NO3− among the ionic compounds was enhanced in summer and in winter, respectively (Figure 3). Meanwhile, the proportion of NH4+ has generally decreased in winter and increased in spring. K+, which is typically used as a biomass burning marker, then increased in late fall and winter and was not abundant enough to be detected between May and September, along with Mg2+ and Ca2+. There was a positive correlation between Mg2+ and Ca2+ (Pearson r = 0.58, p-value< 0.001), indicating that they were affected by the same factors such as crustal dust. The highest average concentrations of Mg2+ and Ca2+ were obtained in spring (Table 3), probably because the soils that are frozen during winter are thawed in spring and subsequently suspended from the surface to the atmosphere [17].
The proportion of Na+ did not show any significant trend; however, there were four extraordinarily high peaks observed on March 16, 22, and 28, 2016, and January 10, 2017, showing Na+ concentrations above 3.0 μg m−3 (Figure 2). Ming et al. (2007) [27] identified the possible source of Na+ using the Na+/Ca2+ ratio because the two main sources include marine aerosol and crustal dust. In this study, a higher concentration of Na+ than Ca2+ (Table 3), resulted in an average Na+/Ca2+ ratio of 10.2 ± 11.8. The Na+/Ca2+ ratio was high, showing 24.8, 99.2, and 40.8 on March 16, 22, and 28, 2016, respectively, whereas it was 8.1 on January 10, 2017. Three high Na+ samples obtained in 2016 were likely to be affected by sea-salt aerosol because the back-trajectories of air-parcels showed a long residence time over the ocean (Figure 4a). On the other hand, the sample obtained on January 19, 2017 also showed the highest concentrations of both Mg2+ and Ca2+, indicating the effect of crustal dust. The corresponding 72-h back-trajectories originated from Russia and passed through Mongolia, China, and North Korea before arriving at the sampling site (Figure 4b).

3.2. Sourcing the Measured Species

Correlation coefficients of 7 ionic compounds were identified (Table 5). NO3− was correlated with all other ionic constituents, and the highest correlation coefficient was observed with K+. SO42− showed the strongest correlation with NH4+ which also correlated with NO3−, indicating the existence of (NH4)2SO4 and NH4NO3 in the PM2.5. Na+ was rather weakly but statistically correlated with NO3−, NH4+, K+, and Ca2+, suggesting that it was possibly affected by biomass burning and crustal dust. K+ was highly correlated with NO3−, but it showed a relatively weak correlation with SO42−, Na+, NH4+, and Mg2+, which indicates that KNO3 was one of the major constituents of the PM2.5.
The SO42− concentrations obtained in this study are significantly lower compared to those reported in a previous study performed at the same site between 2013 and 2014 (3.9 (±3.6) μg m−3 in Cho et al., 2016 [28]; check Table 3 for our results). This was probably because, according to the National Air Pollutants Emission Service of the Republic, SOX emissions have continuously decreased in South Korea since 2011. However, the NH4+ concentrations obtained in this study (Table 3) were considerably higher compared to those measured in 2013 and 2014 (2.0 (±1.9) μg m−3 in Cho et al., 2016 [28]). Here, we calculated an NH4+ availability index: J (Equation (5)) (Figure S1 of the Supplementary Material) [29]. The value of this index generally exceeded 100%, indicating an NH4+ surplus and the basicity of SO42− and NO3−. In this city, Chuncheon, a large portion of land cover is represented by agricultural areas. From there, significant amounts of NH3 are emitted due to fertilizer application and cattle farms.
J =   [ N H 4 + ] 2 × [ S O 4 2 − ] + [ N O 3 − ] × 100
In the above equation, [NH4+], [SO42−], and [NO3−] represent the molar concentration of NH4+, SO42−, and NO3−, respectively. J was the lowest in winter (182% ± 144%), despite still indicating, on average, an NH4+ surplus (Figure S1). The correlation between [NH4+] and 2[SO42−]+[NO3−] was very good during winter (when the J values were relatively low), indicating that NH3 was combined with H2SO4 and HNO3 in the form of (NH4)2SO4 and NH4NO3, respectively. However, the correlation between [NH4+] and 2[SO42−]+[NO3−] was clearly lower in other seasons (Figure 5). In cities with size and emission characteristics similar to Chuncheon, NH3 concentrations were reportedly higher in spring and lower in fall and winter [30]. In South Korea, NH3 has been observed to be lower in summer than in spring due to the occurrence of intensive monsoon-related raining episodes [30]. The poorer correlation observed between the two same variables in spring, summer, and fall (Figure 5) suggest that NH3 remained excessive even when combined with H2SO4 and HNO3 and, hence, it likely existed also in other forms (e.g., NH4Cl) [31]. Ammonia can react with hydrochloric acid to form NH4Cl [32,33], and according to Du et al. (2010) [31], NH4Cl is a common constituent of aerosols in non-sulfate NH4+, although NH4NO3 is more favorably formed than NH4Cl. Unfortunately, Cl was not measured in this study. Therefore, the possible existence of NH4Cl should be further investigated in future research. Notably, the correlation between [NH4+] and 2[SO42−]+[NO3−] in fall (Figure 5) became much stronger when excluding only three points. The three samples that had exceptionally low J in fall (Figure 5) due to the low NO3− and SO42− and high NH4+ concentrations showed no notable features. However, relatively high Na+, Mg2+, and Ca2+ and low K+ concentrations in those samples, compared to other samples obtained in fall indicate that the suspension of soil treated by nitrogen-based fertilizers was a possible source [34].
OC concentrations were relatively higher in Chuncheon than in other cities of South Korea, although their ECs were comparable [35,36], resulting in high OC/EC ratios (average = 9.6 ± 4.5). OC corresponded to approximately 40% of PM2.5 mass, ranging from 36.5% in winter to 42.4% in spring. However, OC concentrations were the highest in winter and the lowest in fall (Table 3). Moreover, the OC/EC ratios were about two times higher in summer (16.1 ± 5.0 in average of ratio) compared to the other seasons (8.0 ± 2.6), suggesting a major presence of secondary OC (SOC) in summer [37]. The correlation coefficient between OC and EC was also determined to be lower in summer (Pearson r = 0.68) than in the other seasons (Pearson r = 0.82) (Figure S2 of the Supplementary Material), likely indicating that a significant portion of the SOC was produced in summer. The high OC/EC ratios observed in all seasons implied the presence of other major sources besides mobile sources. The OC/EC ratios of vehicle exhaust emissions typically range from 1.0 to 4.2, while those of solid fuel combustions (including biomass and coal burning) are in a much higher range [37,38,39,40,41]. Using the EC tracer method (SOC = OCtot − [OC/EC]pri), first suggested by Turpin and Huntzicker (1995) [42], we calculated the average POC and SOC concentrations as 5.3 (±2.4) μg m−3 and 3.9 (±2.5) μg m−3, respectively. The [OC/EC]pri indicates the OC/EC ratio directly emitted from combustion sources, which has often been estimated from the minimum OC/EC ratio [42]. In this study, the minimum OC/EC ratio for the month was used as the [OC/EC]pri for each month. The POC concentrations were comparable to the SOC concentrations in summer and fall, while they have significantly exceeded SOC values in spring and winter (Figure S3 of the Supplementary Material). The average WSOC concentration was 4.4 (±2.4) μg m−3, and the contribution of WSOC to OC ranged from 36.2% in fall to 57.5% in spring (no WSOC data were available for the summer season).

3.3. Effect of Humidity

Previous studies have shown that RH affects the gas-particle portioning of semi-volatile species, including NH3, HNO3, HCl, and some organic acids [43,44,45]. A large number of lakes and reservoirs are located within Chuncheon, where the sampling site is located; therefore, fog episodes tend to be frequent and RH tends to be high. As indicated by Equation (1), PM2.5 was negatively correlated with temperature and wind speed, but it was not correlated with RH during the whole sampling period. However, when considering only the data obtained during winter, the PM2.5 concentrations and the PM2.5/PM10 ratio showed both a significant positive correlation with RH (Pearson r = 0.69 between PM2.5 concentrations and RH, Pearson r = 0.79 between PM2.5/PM10 ratio and RH, both p-values < 0.001) (Figure S4 of the Supplementary Material). Among the PM2.5 components, NO3− (Pearson r = 0.60), SO42− (Pearson r = 0.55), NH4+ (Pearson r = 0.60), K+ (Pearson r = 0.55), and OC (Pearson r = 0.68) were determined to be clearly correlated with RH at a significance level of 0.01, indicating that high RH promoted the formation of secondary inorganic and organic PM2.5 during winter. In addition, NO3− was highly correlated with K+ (Pearson r = 0.74, p-value < 0.001) and NH4+ (Pearson r = 0.88, p-value < 0.001), while it did not show any correlation with either Ca2+ or Mg2+, suggesting the possible formation of non-volatile KNO3 salt under high RH in winter. These results imply that high RH likely provides suitable conditions for HNO3, SO2, and NH3 to be condensed on humid particles in winter [45,46,47,48]. The prominent correlation between gaseous NO2 and NO3− (Pearson r = 0.70) and the high NO3−/NO2 ratio observed in winter (NO3−/NO2 = 0.051, 0.019, 0.025, and 0.074 ppm ppm−1 in spring, summer, fall, and winter, respectively) also suggest the effective conversion of gaseous NO2 to NO3− during winter.
Although the formation of secondary sulfate and secondary organic aerosols in the aqueous phase is significant in summer [49,50], in this study, there was no statistical correlation between RH and PM2.5 and its chemical constituents in summer. This could be because the number of samples in summer (Table 3) were not enough to show clearly the effect of RH, or it may be difficult to determine the influence of RH on PM2.5 and/or its constituents because the RH was relatively consistent in summer (the coefficient of variation (standard deviation/average) was 0.13 and 0.21 in summer and winter, respectively). Therefore, there is a need for further research on this.

3.4. Effect of Biomass Burning

The outskirts of Chuncheon (where the sampling site is located) consist mainly of forests and agricultural areas. There, the open burning of agricultural residue and household wastes frequently occurs. WSOC served as a proxy for secondary OC in many previous studies [14,51,52], but it is also known to be directly emitted, along with K+, during biomass burning [15,51,53]. In this study, WSOC was significantly correlated with EC (Pearson r = 0.58) and K+ (Pearson r = 0.52). This correlation became much stronger during fall and winter (Pearson r = 0.79 with EC and Pearson r = 0.83 with K+, both p-values < 0.001), when the burning of agricultural residue actively occurred. These results suggest that biomass burning was an important source of carbonaceous PM2.5 in Chuncheon, especially during cold months. Particles derived from the burning of fresh biomass mostly consisted of K+, Cl−, and OC, but can be readily converted to KNO3 and K2SO4 via heterogeneous reactions during their transport [54,55]. SO42− (Pearson r = 0.63) and NO3− (Pearson r = 0.74) were determined to be highly correlated with K+ in winter, suggesting the importance of aerosol produced from the burning of aged biomass. In addition, the deliquescence relative humidity (DRH) of KCl is 84.3%, which was lower than that of KNO3 (97.4%) and K2SO4 (92.5%) [56,57]. Therefore, at RH > ∼84%, chlorine gases can be released to the atmosphere, while KNO3 and K2SO4 still exist in their solid forms, resulting in a strong correlation of K+ with both NO3− and SO42−. These results suggest that biomass burning and high RH might have a synergistic effect, enhancing PM2.5 concentrations in the city.

3.5. High Concentration Episode

In this study, a high concentration episode (HCE) was defined for daily PM2.5 concentrations higher than the 24-h NAARS (35 μg m−3). In total, 33 samples were identified as corresponding to a HCE. Of these 13, 12, 6, and 2 samples were obtained in winter, spring, fall, and summer, respectively. The average PM2.5 concentration was 47.9 (±11.1) μg m−3 during the HCE, ranging from 36.3 to 83.8 μg m−3. Among them, the samples with the highest PM2.5 concentrations were those obtained on February 4, 2017 (83.8 μg m−3) and January 19, 2017 (81.5 μg m−3) (Figure 2). Despite the similar PM2.5 concentrations, the components of these samples showed different concentrations. On February 4, 2017, the OC, EC, K+, and NH4+ concentrations all showed an increase (Figure 2); moreover, the HCE sample collected on this day was characterized by the highest OC/EC ratio (OC/EC = 9.7), possibly indicating biomass burning as a source of PM2.5. On the other hand, NO3− and SO42− reached their highest concentrations on January 19, 2017, indicating that the gas-particle partitioning of HNO3 and heterogeneous reactions in the nitrate and sulfate formation were facilitated by low atmospheric temperatures (−2.4°C: the lowest temperature observed among HCE) and relatively high RH (77%: the average RH was 63% during winter) [58]. NH4+ was also high, but its increase was not noticeable compared to those of NO3− and SO42−, resulting in an NH4+ deficit (NH4+ availability index, J = 69.7%). On January 19, the SO42− concentration (9.3 μg m−3) was approximately two times higher than on February 4, 2017 (4.4 μg m−3), despite the similar PM2.5 concentrations of the corresponding samples. The increase of SO42− concentrations was possibly supported by the back-trajectories that originated from northeastern China and Mongolia on January 19, while the back-trajectories were much shorter and stayed for a longer time over the ocean on February 4 (Figure 6). Previous studies have also highlighted an increase of SO42− concentrations in parallel with long-range transport from China [16,59].
All of the PM2.5 components significantly increased during HCEs (by a factor of 1.3 to 2.4, compared to non-HCEs) (Figure 7). NO3− was determined to be the most important component for the HCE samples, and its concentration increased by approximately 2.4 times; moreover, this component showed the strongest correlation with PM2.5 mass (Pearson r = 0.77) among all components in the HCE samples, suggesting that the source increasing NO3− should have been crucial for reaching the high PM2.5 concentration observed in Chuncheon. With respect to mass, the highest enhancement was observed for OC: it increased by 4.6 μg m−3 (Figure 7). K+ was also significantly higher in the HCE, rather than in the non-HCE samples (by a factor of 1.7), although its contribution to PM2.5 mass was imperceptible.
In order to identify the characteristics of each HCE, we divided them into four groups: NO3−–driven, OC-driven, crustal element-driven, and summer HCE. Notably, all the 10 components (shown in Table 3) were analyzed only in 14 out of the 33 HCE samples. Six samples were classified as NO3−–driven HCE. These showed significantly enhanced K+ and NO3− concentrations (Figure 8), as well as the highest six K+ concentrations among all the HCE samples. All HCE samples showed a significant increase in K+ concentration (Figure 7; nevertheless, the values of this parameter were particularly high in the six NO3−–driven HCE samples (0.45 ± 0.08 μg m−3, Figure 8, suggesting the active formation of inorganic aerosol (e.g., NH4NO3 and KNO3) as a result of biomass burning. Of these six samples, the three collected in spring (on March 16, 22, and 28, 2016) have showed PM2.5 concentrations somewhat different from those of the four collected in winter: a distinct increase in Na+ and NH4+ occurred under low RH conditions (Figure 8; notably, no carbonaceous compounds were analyzed in these samples). High Na+ and NH4+ concentrations might have been possibly derived from the trajectories of air-parcels that had a long residence time over the ocean (Figure 4) and intense emissions of NH3 in spring [30], respectively.
Two HCE samples collected on 17 December 2016, and 4 January 2017, had very high OC fractions, contributing to 47.3% and 36.3% of the total PM2.5, respectively, and were classified as OC-driven (Figure 8). The nine PM2.5 constituents analyzed in this study contributed to 83.7% of the total PM2.5 mass of the HCE samples collected on 17 December 2016, indicating that metallic elements directly emitted from primary sources were insignificant, while secondary formation contributed considerably to the high PM2.5 concentrations.
On 29 March 2017, crustal and/or ocean elements (e.g., Na+, Mg2+, and Ca2+) clearly increased, showing much higher concentrations (∑3 elements = 2.9 μg m−3) and contributed to a higher fraction of the total PM2.5 mass (∑3 elements = 5.4%) compared to the other HCE samples (0.8 μg m−3 and 1.7%) (Figure 8). Two HCE samples collected in summer (on 15 and 30 June 2017) showed an increase in the SO42− and OC concentrations (Figure 8). The OC/EC ratios were also very high (13.1 and 9.6 on 15 and 30 June, respectively), and most of the endpoints of the 72-h backward trajectories were stagnant over the Yellow Sea (Figure 6). These results suggest that a combination effect of active photochemical oxidation reactions and stagnant air can lead to high PM2.5 concentration episodes in Chuncheon even during summer.
Back-trajectories were also calculated for the bottom 10% samples (when PM2.5 < 11 μg m−3), and they were relatively long and originated in the north, east, and south (Figure S5 of the Supplementary Material) whereas the back-trajectories for HCEs were short and originated in the west.

3.6. Comparison with Other Studies

The characteristics of the chemical constituents of PM2.5 varied in different regions. When the PM2.5 concentration was high, the air masses generally transported from eastern China and Seoul (the capital of South Korea) before arriving at Chuncheon [28,60,61]. Because Baengryeong island, located in the northernmost part of Korea, is the nearest region to China, it is the national background monitoring site in South Korea. Therefore, the PM2.5 characteristics in eastern China, Baengryeong island, Seoul, and Chuncheon were compared. The concentrations of PM2.5 and its ionic components were much higher than those in other sites (Table 6). One of the notable results is that both the OC concentration and the OC/EC ratio (9.6 as in average of ratio) were significantly high in this study when compared to that in Beijing, China (3.7), Baengryeong island (3.5), and Seoul (2.4) (Table 6). Considering the low OC/EC ratio generally in the metropolitans, such as Beijing and Seoul due to the large contribution of mobile sources [37,38,39,40,41], the significantly higher OC/EC ratio in Chuncheon than in Baengryeong island indicates the importance of biomass burning and/or large fractions of aged aerosol. Because PM2.5 generally increased with the long-range transport from China to Baengryeong island and Seoul, SO42− greatly increased in the haze episode [62]. However, in this study, NO3− was the most important constituent along with K+ and OC especially under the high RH condition, suggesting that the formation of secondary inorganic and organic components and biomass burning were the important processes for the high PM2.5 episode. In a recent study performed in Beijing, China [63], high nitrate to sulfate ratio (2.2) was observed, which increased up to 2.7 during haze events (Table 6). In this study, an average of nitrate to sulfate ratio was 0.9 was obtained, which increased to 1.3 for HCEs, indicating that the increase in NO3− was greater than that of SO42− and other components and that NO3− was concentrated as the PM2.5 increased.

4. Conclusions

Different policies should be applied to reduce regional PM2.5 concentrations by taking into account the unique characteristics of the correspondent PM2.5 generation mechanisms. Most PM2.5 studies in South Korea have been conducted in large and industrial cities, and consistent PM2.5 policies (e.g., reduction of diesel vehicles) are being implemented all over the country. This study was carried out in Chuncheon, a small residential city that has frequently shown high PM2.5 concentrations despite very low anthropogenic emissions. The average PM2.5 concentration (29.4 ± 16.8 μg m−3) was found to be approximately two times higher than the NAAQS of 15 μg m−3. By comparing our data with previous ones collected in the same city during 2013 and 2014, we noted that SO42− significantly decreased, while NH4+ considerably increased, resulting in a serious NH4+ surplus condition. However, when the Mann-Kendall trend analysis was used in this study, carbonaceous compounds and cationic components showed a decreasing trend, whereas the remaining constituents and PM2.5 showed no trend during the sampling period of 22 months. The high RH measured in Chuncheon was statistically related with increases of NO3−, SO42−, NH4+, K+, and OC, episodically resulting in high PM2.5 concentrations. Under conditions of high RH, we also observed a strong correlation between NO3− and K+ (Pearson r = 0.74). These results suggest an important contribution of aged biomass burning to the high PM2.5 concentrations detected in this city. This hypothesis is supported by the strong correlations between WSOC and EC (Pearson r = 0.58), and by those between WSOC and K+ (Pearson r = 0.52), especially during fall and winter.
Approximately 24% of the samples exceeded the daily standard of 35 μg m−3; in particular, NO3-showed the highest increment in the HCE (high concentration episode) samples, along with K+ and OC. Notably, two HCE samples showing significant increases in OC and SO42− were observed during summer, when the residence time of the back-trajectories over the Yellow Sea were relatively long, compared with the back-trajectories for the other HCE sample. Back-trajectories for the bottom 10% samples were relatively long and originated in the north, east, and south, whereas the back-trajectories for HCEs were short and originated in the west. When the PM2.5 characteristics of this study were compared with those in Beijing, China, Seoul, Korea (the capital of South Korea), and Baengryeong island (the national background monitoring site), both the OC concentration and the OC/EC ratio were significantly high. In general, the results obtained from the HCE samples and comparison with other studies suggest that the formation of secondary inorganic and organic aerosol and biomass burning were the two most important factors contributing to the high PM2.5 concentrations in Chuncheon.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/2073-4433/11/11/1159/s1, Nomenclature. Table S1. Information of instrument for the ionic constituents. Figure S1: Time series of J value, the NH4+ availability index. The y-axis is shown in logarithmic scale. Figure S2: Correlation between EC and OC in each season. Figure S3: Concentrations of POC (gray bars) and SOC (blue bars) and POC/OC ratio (green symbol) in each season. Figure S4: Significant correlations of RH with the PM2.5 and the PM2.5/PM10 ratio. Figure S5: Back-trajectories for the bottom 10% PM2.5 samples.

Author Contributions

The work presented in this case was carried out in collaboration between all authors. J.-Y.B. analyzed the data and wrote the paper. S.-D.L. interpreted the results and reviewed the paper. S.-W.P. and H.K. performed the experiments. Y.-J.H. acquired the funding, defined the research theme, interpreted the results, and wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a grant from the Ministry of Environment, Republic of Korea, and by a grant from the National Research Foundation of Korea (NRF-2017K1A3A1A12073373 and NRF-2020R1A2C2013445). This work was also supported by 2018 Research Grant (PoINT) from Kangwon National University. We would like to thank the Central Laboratory of Kangwon National University for helping with chemical analysis.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

References

  1. Medina-Ramón, M.; Zanobetti, A.; Schwartz, J. The effect of ozone and PM10 on hospital admissions for pneumonia and chronic obstructive pulmonary disease: A national multicity study. Am. J. Epidemiol. 2006, 163, 579–588. [Google Scholar] [CrossRef] [PubMed]
  2. Moolgavkar, S.H.; Luebeck, E.G.; Anderson, E.L. Air pollution and hospital admissions for respiratory causes in Minneapolis-St. Paul and Birmingham. Epidemiology 1997, 8, 364–370. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Pope, C.A.; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston, G.D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc. 2002, 287, 1132–1141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Lin, Y.; Zou, J.; Yang, W.; Li, C.Q. A review of recent advances in research on PM2.5 in China. Int. J. Environ. Res. Public Health 2018, 15, 438. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Anderson, H.R.; Spix, C.; Medina, S.; Schouten, J.P.; Castellsague, J.; Rossi, G.; Bacharova, L. Air pollution and daily admissions for chronic obstructive pulmonary disease in 6 European cities: Results from the APHEA project. Eur. Respir. J. 1997, 10, 1064–1071. [Google Scholar] [CrossRef] [Green Version]
  6. Tsai, S.S.; Yang, C.Y. Fine particulate air pollution and hospital admissions for pneumonia in a subtropical city: Taipei, Taiwan. J. Toxicol. Environ. Health 2014, 77, 192–201. [Google Scholar] [CrossRef]
  7. Slaughter, J.C.; Kim, E.; Sheppard, L.; Sullivan, J.H.; Larson, T.V.; Claiborn, C. Association between particulate matter and emergency room visits, hospital admissions and mortality in Spokane, Washington. J. Expo. Sci. Environ. Epidemiol. 2005, 15, 153–159. [Google Scholar] [CrossRef] [Green Version]
  8. Jorge, H.M.; Susana, R.R.; Jose, F.R.M.; Arturo, C.R.; Salvador, B.J.; Beatriz, C.G.; Darrel, G.B. Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America. Atmos. Pollut. Res. 2013, 4, 181–190. [Google Scholar]
  9. Cao, J.J.; Shen, Z.X.; Chow, J.C.; Watson, J.G.; Lee, S.C.; Tie, X.X.; Han, Y.M. Winter and summer PM2.5 chemical compositions in fourteen Chinese cities. J. Air Waste Manag. Assoc. 2012, 62, 1214–1226. [Google Scholar] [CrossRef]
  10. Timonen, H.; Aurela, M.; Carbone, S.; Saarnio, K.; Saarikoski, S.; Mäkelä, T.; Hillamo, R. High time-resolution chemical characterization of the water-soluble fraction of ambient aerosols with PILS-TOC-IC and AMS. Atmos. Meas. Tech. 2010, 3, 1063–1074. [Google Scholar] [CrossRef] [Green Version]
  11. Zhang, Q.; Jimenez, J.L.; Canagaratna, M.R.; Allan, J.D.; Coe, H.; Ulbrich, I.; Dzepina, K. Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced northern hemisphere midlatitudes. Geophys. Res. Lett. 2007, 34, L13801. [Google Scholar] [CrossRef] [Green Version]
  12. Gonçalves, C.; Alves, C.; Fernandes, A.P.; Monteiro, C.; Tarelho, L.; Evtyugina, M.; Pio, C. Organic compounds in PM2.5 emitted from fireplace and woodstove combustion of typical Portuguese wood species. Atmos. Environ. 2011, 45, 4533–4545. [Google Scholar] [CrossRef] [Green Version]
  13. Hamad, S.H.; Schauer, J.J.; Heo, J.; Kadhim, A.K. Source apportionment of PM2.5 carbonaceous aerosol in Baghdad, Iraq. Atmos. Res. 2015, 156, 80–90. [Google Scholar] [CrossRef]
  14. Kawamura, K.; Seméré, R.; Imai, Y.; Fujii, Y.; Hayashi, M. Masahiko Hayashi, Water soluble dicarboxylic acids and related compounds in Antarctic aerosols. J. Geophys. Res. Atmos. 1996, 101, 18721–18728. [Google Scholar] [CrossRef]
  15. Witkowska, A.; Lewandowska, A.U. Water soluble organic carbon in aerosols (PM1, PM2.5, PM10) and various precipitation forms (rain, snow, mixed) over the southern Baltic Sea station. Sci. Total Environ. 2016, 573, 337–346. [Google Scholar] [CrossRef] [PubMed]
  16. Heo, J.B.; Hopke, P.K.; Yi, S.M. Source apportionment of PM2.5 in Seoul, Korea. Atmos. Chem. Phys. 2009, 9, 4957–4971. [Google Scholar] [CrossRef] [Green Version]
  17. Han, Y.; Kim, H.; Cho, S.; Kim, P.; Kim, W. Metallic elements in PM2.5 in different functional areas of Korea: Concentrations and source identification. Atmos. Res. 2015, 153, 416–428. [Google Scholar] [CrossRef]
  18. Vellingiri, K.; Kim, K.; Ma, C.; Kang, C.; Lee, J.; Kim, I.; Brown, R.J. Ambient particulate matter in a central urban area of Seoul, Korea. Chemosphere 2015, 119, 812–819. [Google Scholar] [CrossRef]
  19. Park, J.M. Fine Particulate Organic Carbon in Chuncheon, Korea: Concentrations and Source Identification. Master’s Thesis, Kangwon National University, Gangwon-do, Korea, 2017. [Google Scholar]
  20. Center for Environmental Research Information Office of Research and Development US; Environmental Protection Agency. Compendium of Methods for the Determination of Inorganic Compounds in Ambient Air; EPA: Washington, DC, USA, 1999. [Google Scholar]
  21. Birch, M.E.; Cary, R.A. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Sci. Technol. 1996, 25, 221–241. [Google Scholar] [CrossRef]
  22. Birch, M.E. Occupational monitoring of particulate diesel exhaust by NIOSH method 5040. Appl. Occup. Environ. Hyg. 2002, 17, 400–405. [Google Scholar] [CrossRef]
  23. Park, S.S.; Schauer, J.J.; Cho, S.Y. Sources and their contribution to two water-soluble organic carbon fractions at a roadway site. Atmos. Environ. 2013, 77, 348–357. [Google Scholar] [CrossRef]
  24. Ministry of Environment. Available online: https://www.airkorea.or.kr/web/board/3/267/?pMENU_NO=145 (accessed on 10 October 2019).
  25. Hussain, F.; Nabi, G.; Boota, M.W. Rainfall trend analysis by using the Mann-Kendall test & Sen’s slope estimates: A case study of district Chakwal rain gauge, Barani area, Northern Punjab province, Pakistan. Sci. Int. (Lahore) 2015, 27, 3159–3165. [Google Scholar]
  26. Heo, J.H.; Oh, S.W. Characterization of annual PM2.5 and PM10 concentrations by real-time measurements in Cheonan, Chungnam. J. Korea Acad. Ind. Coop. Soc. 2012, 13, 445–450. [Google Scholar] [CrossRef]
  27. Ming, J.; Zhang, D.; Kang, S.; Tian, W. Aerosol and fresh snow chemistry in the East Rongbuk Glacier on the northern slope of Mt. Qomolangma (Everest). J. Geophys. Res. 2007, 112, 1–11. [Google Scholar] [CrossRef] [Green Version]
  28. Cho, S.H.; Kim, P.R.; Han, Y.J.; Kim, H.W. Characteristics of ionic and carbonaceous compounds in PM2.5 and high concentration episodes in Chuncheon, Korea. J. Korean Soc. Atmos. Environ. 2016, 32, 435–447. [Google Scholar] [CrossRef] [Green Version]
  29. Chu, S.H. PM2.5 episodes as observed in the speciation trends network. Atmos. Environ. 2004, 38, 5237–5246. [Google Scholar] [CrossRef]
  30. Park, J.; Ryoo, J.; Lee, J.; Song, M. Origins and distributions of atmospheric ammonia in Jeonju during 2019–2020. J. Korean Soc. Atmos. Environ. 2020, 36, 262–274. [Google Scholar] [CrossRef]
  31. Du, H.; Kong, L.; Cheng, T.; Chen, J.; Yang, X.; Zhang, R.; Han, Z.; Yan, Z.; Ma, Y. Insights into ammonium particle-to-gas conversion: Non-sulfate ammonium coupling with nitrate and chloride. Aerosol Air Qual. Res. 2010, 10, 589–595. [Google Scholar] [CrossRef]
  32. McMurry, P.H.; Takano, H.; Anderson, G.R. Study of the ammonia (gas)—Sulphuric acid (aerosol) reaction rate. Environ. Sci. Technol. 1983, 17, 347–352. [Google Scholar] [CrossRef]
  33. Wang, Y.; Zhuang, G.S.; Tang, A.; Yuan, H.; Sun, Y.L.; Chen, S.; Zhang, A. The ion chemistry of PM2.5 aerosol in Beijing. Atmos. Environ. 2005, 39, 3771–3784. [Google Scholar] [CrossRef]
  34. Bray, C.D.; Battye, W.; Aneja, V.P.; Tong, D.Q.; Lee, P.; Tang, Y. Ammonia emissions from biomass burning in the continental United States. Atmos. Environ. 2018, 187, 50–61. [Google Scholar] [CrossRef]
  35. Byun, J.Y. Source Estimation Based on Chemical Characteristics and Size Distribution of Fine Particle in Chuncheon, Korea. Master’s Thesis, Kangwon National University, Gangwon-do, Korea, 2018. [Google Scholar]
  36. Park, J.M.; Han, Y.J.; Cho, S.H.; Kim, H.W. Characteristics of carbonaceous PM2.5 in a small residential city in Korea. Atmosphere 2018, 9, 490. [Google Scholar] [CrossRef] [Green Version]
  37. Ming, J.; Xiao, C.; Sun, J. Carbonaceous particles in the atmosphere and precipitation of the Nam Co region, central Tibet. J. Environ. Sci. 2010, 22, 1748–1756. [Google Scholar] [CrossRef]
  38. Schauer, J.J.; Kleeman, M.J.; Cass, G.R.; Simoneit, B.R. Measurement of emissions from air pollution sources. 5. C1–C32 organic compounds from gasoline-powered motor vehicles. Environ. Sci. Technol. 2002, 36, 1169–1180. [Google Scholar] [CrossRef]
  39. Schauer, J.J.; Kleeman, M.J.; Cass, G.R.; Simoneit, B.R. Measurement of emissions from air pollution sources. 3. C1–C29 organic compounds from fireplace combustion of wood. Environ. Sci. Technol. 2001, 35, 1716–1728. [Google Scholar] [CrossRef]
  40. Chen, Y.; Zhi, G.; Feng, Y.; Fu, J.; Feng, J.; Sheng, G.; Simoneit, B.R. Measurements of emission factors for primary carbonaceous particles from residential raw-coal combustion in China. Geophys. Res. Lett. 2006, 33. [Google Scholar] [CrossRef]
  41. He, L.; Hu, M.; Huang, X.; Yu, B.; Zhang, Y.; Liu, D. Measurement of emissions of fine particulate organic matter from Chinese cooking. Atmos. Environ. 2004, 38, 6557–6564. [Google Scholar] [CrossRef]
  42. Turpin, B.J.; Huntzicker, J.J. Identification of secondary organic aerosol episodes and quantitation of primary and secondary organic aerosol concentrations during SCAQS. Atmos. Environ. 1995, 29, 3527–3544. [Google Scholar] [CrossRef]
  43. Zhao, M.; Wang, S.; Tan, J.; Hua, Y.; Wu, D.; Hao, J. Variation of urban atmospheric ammonia pollution and its relation with PM2.5 chemical property in winter of Beijing, China. Aerosol Air Qual. Res. 2016, 16, 1378–1389. [Google Scholar] [CrossRef] [Green Version]
  44. Jang, M.; Czoschke, N.M.; Lee, S.; Kamens, R.M. Heterogeneous atmospheric aerosol production by acid-catalyzed particle-phase reactions. Science 2002, 298, 814–817. [Google Scholar] [CrossRef] [PubMed]
  45. Galon-Negru, A.G.; Olariu, R.; Arsene, C. Chemical characteristics of size-resolved atmospheric aerosols I Iasi, north-eastern Romani: Nitrogen-containing inorganic compounds control aerosol chemistry in the area. Atmos. Chem. Phys. 2018, 18, 5879–5904. [Google Scholar] [CrossRef] [Green Version]
  46. Sharma, M.; Kishore, S.; Tripathi, S.N.; Behera, S.N. Role of atmospheric ammonia in the formation of inorganic secondary particulate matter: A study at Kanpur, India. J. Atmos. Chem. 2007, 58, 1–17. [Google Scholar] [CrossRef]
  47. Pathak, R.K.; Wu, W.S.; Wang, T. Summertime PM2.5 ionic species in four major cities of China: Nitrate formation in an ammonia-deficient atmosphere. Atmos. Chem. Phys. 2009, 9, 1711–1722. [Google Scholar] [CrossRef] [Green Version]
  48. Pathak, R.K.; Wang, T.; Wu, W.S. Nighttime enhancement of PM2.5 nitrate in ammonia-poor atmospheric conditions in Beijing and Shanghai: Plausible contributions of heterogeneous hydrolysis of N2O5 and HNO3 partitioning. Atmos. Environ. 2011, 45, 1183–1191. [Google Scholar] [CrossRef]
  49. Ying, Q.; Wu, L.; Zhang, H. Local and inter-regional contributions to PM2.5 nitrate and sulfate in China. Atmos. Environ. 2014, 94, 582–592. [Google Scholar] [CrossRef]
  50. Zhou, S.; Wang, T.; Wang, Z.; Li, W.; Xu, Z.; Wang, X.; Yuan, C.; Poon, C.N.; Louie, P.K.K.; Luk, C.W.Y.; et al. Photochemical evolution of organic aerosols observed in urban plumes from Hong Kong and the Pearl River Delta of China. Atmos. Environ. 2014, 88, 219–229. [Google Scholar] [CrossRef]
  51. Sullivan, A.P.; Weber, R.J. Chemical characterization of the ambient organic aerosol soluble in winter: 2. Isolation of acid, neutral, and basic fractions by modified size-exclusion chromatography. J. Geophys. Res. Atmos. 2006, 111. [Google Scholar] [CrossRef] [Green Version]
  52. Wber, R.J.; Sullivan, A.P.; Peltier, R.E.; Russell, A.; Yan, B.; Zheng, M.; De Gouw, J.; Warneke, C.; Brock, C.; Holloway, J.S. A study of secondary organic aerosol formation in the anthropogenic-influenced southeastern United States. J. Geophys. Res. Atmos. 2007, 112. [Google Scholar] [CrossRef]
  53. Yan, B.; Zheng, M.; Hu, Y.; Ding, X.; Sullivan, A.P.; Weber, R.J.; Baek, J.; Edgerton, E.S.; Russell, A.G. Roadside, urban, and rural comparison of primary and secondary organic molecular markers in ambient PM2.5. Environ. Sci. Technol. 2009, 43, 4287–4293. [Google Scholar] [CrossRef]
  54. Li, J.; Pósfai, M.; Hobbs, P.V.; Buseck, P.R. Individual aerosol particles from biomass burning in southern Africa: 2. Compositions and aging of inorganic particles. J. Geophy. Res. 2003, 108, 8484. [Google Scholar] [CrossRef] [Green Version]
  55. Liu, X.D.; Van Espen, P.; Adams, F.; Cafmeyer, J.; Maenhaut, W. Biomass burning in southern Africa: Individual particle characterization of atmospheric aerosols and savanna fir samples. J. Atmos. Chem. 2000, 36, 135–155. [Google Scholar] [CrossRef]
  56. Jing, B.; Peng, C.; Wang, Y.; Liu, Q.; Tong, S.; Zhang, Y.; Ge, M. Hygroscopic properties of potassium chloride and its internal mixtures with organic compounds relevant to biomass burning aerosol particles. Sci. Rep. 2017, 7, 43572. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Report to California Air Resources Board, Contract No. 04–336, Determination of the Spatial and Temporal Variability of Size-Resolved PM2.5 Composition and Mixing State in Multiple Regions in California; University of California: San Diego, CA, USA, 2009; p. 183.
  58. Kim, H.; Zhang, Q.; Heo, J. Influence of intense secondary aerosol formation and long-range transport on aerosol chemistry and properties in the Seoul Metropolitan Area during spring time: Results from KORUS-AQ. Atmos. Chem. Phys. 2018, 18, 7149–7168. [Google Scholar] [CrossRef] [Green Version]
  59. Choi, J.; Park, R.; Lee, H.; Lee, S.; Jo, D.S.; Jeong, J.I.; Henze, D.K.; Woo, J.; Ban, S.; Lee, M.; et al. Impacts of local vs. trans-boundary emissions from different sectors on PM2.5 exposure in South Korea during the KORUS-AQ campaign. Atmos. Environ 2019, 203, 196–205. [Google Scholar] [CrossRef]
  60. Jung, J.-H.; Han, Y.-J. Study on characteristics of PM2.5 and its ionic constituents in Chuncheon, Korea. J. Korean Soc. Atmos. Environ. 2008, 24, 682–692. [Google Scholar] [CrossRef] [Green Version]
  61. Han, Y.-J.; Kim, T.-S.; Kim, H. Ionic constituents and source analysis of PM2.5 in three Korean cities. Atmos. Environ. 2008, 42, 4735–4746. [Google Scholar] [CrossRef]
  62. Yu, G.-H.; Park, S.-S.; Park, J.S.; Park, S.M.; Song, I.H.; Oh, J.; Shin, H.J.; Lee, M.D.; Lim, H.B.; Kim, H.W.; et al. Pollution characteristics of PM2.5 observed during winter and summer in Baengryeongdo and Seoul. J. Korean Soc. Atmos. Environ. 2018, 34, 38–55. [Google Scholar] [CrossRef]
  63. Xu, Q.; Wang, S.; Jiang, J.; Bhattarai, N.; Li, X.; Chang, X.; Qiu, X.; Zheng, M.; Hua, Y.; Hao, J. Nitrate dominates the chemical composition of PM2.5 during haze event in Beijing, China. Sci. Total Environ. 2019, 689, 1293–1303. [Google Scholar] [CrossRef]
Atmosphere 11 01159 g001 550
Figure 1. Study area. (a) Location of South Korea. (b) Map of South Korea. The red area represents the city, Chuncheon, while the star represents the location of the sampling site; moreover, the green and blue areas represent the capital of South Korea (Seoul) and the major industrial regions (Incheon), respectively.
Figure 1. Study area. (a) Location of South Korea. (b) Map of South Korea. The red area represents the city, Chuncheon, while the star represents the location of the sampling site; moreover, the green and blue areas represent the capital of South Korea (Seoul) and the major industrial regions (Incheon), respectively.
Atmosphere 11 01159 g001
Atmosphere 11 01159 g002 550
Figure 2. Daily concentrations of PM2.5 and its components. The red and blue symbols indicate the concentrations of PM2.5 components of the samples having the two highest PM2.5 mass concentrations.
Figure 2. Daily concentrations of PM2.5 and its components. The red and blue symbols indicate the concentrations of PM2.5 components of the samples having the two highest PM2.5 mass concentrations.
Atmosphere 11 01159 g002
Atmosphere 11 01159 g003 550
Figure 3. Monthly cumulative fractions of each ionic component with respect to the sum of all PM2.5 ionic components.
Figure 3. Monthly cumulative fractions of each ionic component with respect to the sum of all PM2.5 ionic components.
Atmosphere 11 01159 g003
Atmosphere 11 01159 g004 550
Figure 4. Back-trajectories for the high Na+ samples obtained in (a) March 2016 and (b) January 2017.
Figure 4. Back-trajectories for the high Na+ samples obtained in (a) March 2016 and (b) January 2017.
Atmosphere 11 01159 g004
Atmosphere 11 01159 g005 550
Figure 5. Correlation between the molar concentrations [NH4+] and 2[SO42−]+[NO3−] in each season. The short dash indicates the 95% confidence interval for regression. The blue lines and r value represent the regression results when excluding three blue points for fall.
Figure 5. Correlation between the molar concentrations [NH4+] and 2[SO42−]+[NO3−] in each season. The short dash indicates the 95% confidence interval for regression. The blue lines and r value represent the regression results when excluding three blue points for fall.
Atmosphere 11 01159 g005
Atmosphere 11 01159 g006 550
Figure 6. Backward trajectories of the high concentration episode (HCEs).
Figure 6. Backward trajectories of the high concentration episode (HCEs).
Atmosphere 11 01159 g006
Atmosphere 11 01159 g007 550
Figure 7. Comparison between the PM2.5 components of the HCE and non-HCE samples. The red symbols indicate the increment ratio of each PM2.5 component during the HCE.
Figure 7. Comparison between the PM2.5 components of the HCE and non-HCE samples. The red symbols indicate the increment ratio of each PM2.5 component during the HCE.
Atmosphere 11 01159 g007
Atmosphere 11 01159 g008 550
Figure 8. Characteristics of each type of HCE: (a) NO3—–driven, (b) organic carbon (OC)–driven, (c) crustal element-driven, and (d) summer HCEs. The lower graphs show the K+ concentration in each HCE sample.
Figure 8. Characteristics of each type of HCE: (a) NO3—–driven, (b) organic carbon (OC)–driven, (c) crustal element-driven, and (d) summer HCEs. The lower graphs show the K+ concentration in each HCE sample.
Atmosphere 11 01159 g008
Table
Table 1. Detailed parameters for the thermal/optical analytical protocol for the carbonaceous compounds.
Table 1. Detailed parameters for the thermal/optical analytical protocol for the carbonaceous compounds.
GasHold Time (s)Temp.(°C)Component
He101
He80310OC1
He80475OC2
He80615OC3
He110870OC4
He45550PC
O2 in He45550EC1
O2 in He45625EC2
O2 in He45700EC3
O2 in He45775EC4
O2 in He45850EC5
O2 in He110870EC6
Table
Table 2. Summarized QA/QC results for particulate matter (PM2.5) components.
Table 2. Summarized QA/QC results for particulate matter (PM2.5) components.
NO3−SO42−Na+NH4+K+Mg2+Ca2+OCEC
F.B. (μg m−3)0.130.260.111.050.030.020.040.38 0.04
MDL (μg m−3)0.280.180.130.250.040.010.010.53 0.09
<MDL * (%)23.25.8022.41.2420.516.817.40.00 0.00
RPD (%)2.041.390.89 0.38 0.31 0.20 0.70 0.44 3.70
* indicates the percentage of samples below the method detection limit (MDL).
Table
Table 3. Seasonal and annual average PM2.5 mass concentrations and ionic components. Units of PM2.5 and all components are μg m−3.
Table 3. Seasonal and annual average PM2.5 mass concentrations and ionic components. Units of PM2.5 and all components are μg m−3.
TotalSpringSummerAutumnWinter
N(#)14137283838
PM2.526.9 ± 14.428.0 ± 14.021.3 ± 9.223.2 ± 10.833.5 ± 18.3
NO3−2.0 ± 2.72.0 ± 2.50.5 ± 0.61.4 ± 1.54.3 ± 3.8
SO42−2.2 ± 1.82.0 ± 1.82.6 ± 1.81.9 ± 1.72.2 ± 1.9
Na+0.45 ± 0.570.64 ± 0.940.27 ± 0.170.33 ± 0.170.50 ± 0.49
NH4+2.9 ± 1.73.2 ± 2.02.8 ± 1.62.4 ± 1.53.0 ± 1.6
K+0.15 ± 0.140.13 ± 0.120.06 ± 0.040.15 ± 0.150.21 ± 0.13
Mg2+0.05 ± 0.070.06 ± 0.08N.D.0.06 ± 0.070.053 ± 0.063
Ca2+0.10 ± 0.150.16 ± 0.210.01 ± 0.030.07 ± 0.070.13 ± 0.15
OC10.2 ± 4.110.0 ± 4.09.9 ± 3.19.0 ± 3.211.5 ± 5.2
EC1.2 ± 0.61.2 ± 0.50.7 ± 0.31.1 ± 0.51.6 ± 0.6
WSOC4.4 ± 2.45.1 ± 2.5–3.1 ± 2.04.8 ± 2.4
Table
Table 4. Results of Mann-Kendall trend test on PM2.5 and its constituents.
Table 4. Results of Mann-Kendall trend test on PM2.5 and its constituents.
PollutantsSVar(S)Zp-ValueTrend Estimation
NO3−−499166750.3−1.220.223–
SO42−−667166750.3−1.630.103–
Na+−1161224875.0−2.450.014decreasing
NH4+−1189224875.0−2.510.012decreasing
K+−1125224875.0−2.370.018decreasing
Mg2+461209250.31.010.315–
Ca2+−171204207.7−0.380.707–
OC−43642316.0−2.110.034decreasing
EC−91642316.0−4.45<0.0001decreasing
WSOC−36722220.7−2.460.014decreasing
PM2.5−704349302.0−1.190.234–
Table
Table 5. Pearson correlation coefficients between ionic constituents. Note that ** and * indicates the significant correlation at a significance level of 0.01 and 0.05, respectively.
Table 5. Pearson correlation coefficients between ionic constituents. Note that ** and * indicates the significant correlation at a significance level of 0.01 and 0.05, respectively.
NO3−SO42−Na+NH4+K+Mg2+Ca2+
NO3−–0.423 **0.293 **0.446 **0.632 **0.333 **0.216 *
SO42−0.423 **–0.0120.510 **0.230 *−0.011−0.039
Na+0.293 **0.012–0.323 **0.314 **0.1470.393 **
NH4+0.446 **0.510 **0.323 ** 0.334 **0.057−0.039
K+0.632 **0.230 *0.314 **0.334 ** 0.189 *0.103
Mg2+0.333 **−0.0110.1470.0570.189 * 0.626 **
Ca2+0.216 *−0.0390.393 **−0.0390.1030.626 **–
Table
Table 6. Comparisons of measured concentrations of PM2.5 and its chemical constituents with those reported in other studies. Unit of concentration is μg m−3.
Table 6. Comparisons of measured concentrations of PM2.5 and its chemical constituents with those reported in other studies. Unit of concentration is μg m−3.
LocationStudy PeriodPM2.5SO42−NO3−NH4+K+OCECRef.
Beijing, ChinaCleanFeb.~Nov. 201732.24.86.63.9 [63]
Slightly91.813.023.012.5
moderately167.517.944.520.9
Seoul, KoreaDec. 201349.15.59.05.20.35.82.4[62]
Baengryeong, KoreaDec. 201326.93.53.32.20.24.21.2[62]
This study (whole)Jan. 2016~Oct. 201726.92.22.02.90.210.21.2
This study (HCEs)47.93.34.84.30.314.81.9
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Byun, J.-Y.; Kim, H.; Han, Y.-J.; Lee, S.-D.; Park, S.-W. High PM2.5 Concentrations in a Small Residential City with Low Anthropogenic Emissions in South Korea. Atmosphere 2020, 11, 1159. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111159

AMA Style

Byun J-Y, Kim H, Han Y-J, Lee S-D, Park S-W. High PM2.5 Concentrations in a Small Residential City with Low Anthropogenic Emissions in South Korea. Atmosphere. 2020; 11(11):1159. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111159

Chicago/Turabian Style

Byun, Jin-Yeo, Hekap Kim, Young-Ji Han, Sang-Deok Lee, and Sung-Won Park. 2020. "High PM2.5 Concentrations in a Small Residential City with Low Anthropogenic Emissions in South Korea" Atmosphere 11, no. 11: 1159. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111159

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop