Factors influencing concentrations of atmospheric speciated mercury measured at the farthest island West of South Korea

Atmospheric Environment 213 (2019) 239–249

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Factors influencing concentrations of atmospheric speciated mercury measured at the farthest island West of South Korea

T

Su-Hyeon Lee, Jae-In Lee, Pyung-Rae Kim1, Dae-Young Kim, Ji-Won Jeon, Young-Ji Han∗ Department of Environmental Science, College of Natural Science, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon, Gangwon-do, 24341, Republic of Korea

A R T I C LE I N FO

A B S T R A C T

Keywords: East Asia Total gaseous mercury Gaseous oxidized mercury Particulate bound mercury Marine boundary layer

East Asia is the largest emitter of mercury (Hg) in the world, but only a few studies of speciated Hg have been carried out in South Korea. In this study, total gaseous mercury (TGM), gaseous oxidized mercury (GOM), and particulate bound mercury (PBM) were measured at one of the most northwestern islands of South Korea. Concentrations of TGM and GOM were similar to those measured in other regions of Korea; however, PBM was found in much higher concentrations during cold months, most likely from coal and biomass burning in North Korea. Sources outside Korea significantly affected TGM and PBM, and GOM increased as air masses transported from domestic coal-burning power plants. During the warm months, GOM increased as gaseous elemental mercury (GEM) decreased when air masses were confined within the ocean area with minimal influence from anthropogenic emissions. This implies that there is an efficient process for the oxidation of GEM and GOM production in the marine boundary layer. The concentrations of ozone also decreased as an increase in GOM was observed, suggesting that halogens are primarily responsible for GEM oxidation.

1. Introduction Mercury (Hg) is the only metal that can exist naturally in the gas phase; hence, it can actively circulate between environmental media after being emitted. The most common form of atmospheric Hg is inorganic in nature and is currently measured as gaseous elemental (GEM), gaseous oxidized (GOM), and particulate bound mercury (PBM). GEM is the most abundant species (∼95%) and has a long residence time of 1/2 to 2 years. It can be oxidized to GOM by hydroxyl, nitrate, and halogen radicals, along with other oxidants (Pal et al., 2004; Sommar et al., 1997, 2001; Laurier et al., 2003; Raofie and Ariya, 2004; Holmes et al., 2010). Although the chemical forms of GOM have not yet been identified, they have been proposed to include HgCl2, HgBr2, HgO, HgSO4, Hg(NO2)2, and Hg(OH)2 (Gustin et al., 2013; Hynes et al., 2009). GOM is considered to have high dry and wet deposition velocities, which results in a short atmospheric residence time of only a few days (Valente et al., 2007). The atmospheric residence time of PBM is dependent on diameter, a property that varies seasonally and spatially (Kim et al., 2012). Atmospheric Hg can be released from natural sources and processes or because of human activity. Two of the most important anthropogenic sources globally are artisanal and small-

scale gold mining and combustion of coal (UNEP, 2013; EPA, 2014). In Korea, it was reported in 2010 that the national anthropogenic emission of Hg was 8.04 tons, with cement production being the largest source (AMAP/UNEP, 2013). The re-emission of Hg that had previously settled from air onto soils, surface waters, and vegetation from past emissions was the most important category, contributing about 60% of total emissions (UNEP, 2013). East and Southeast Asia contribute approximately 40% of global anthropogenic emissions, and about 75% of the Hg from this region originates in China (UNEP, 2013). Because Korea is situated in proximity to China, the long-range transport of Hg from China is likely significant. In Korea, independent programs and networks have been developed in the last decade to monitor atmospheric Hg and wet deposition. Total gaseous mercury (TGM) was first measured by Kim and Kim (2000) in the late 1980s in Seoul, Korea, and very high concentrations (14.4 ± 9.8 ng m−3) were reported. TGM concentrations have shown a clear decreasing trend since the 1980s, dropping to 2.1–3.9 ng m−3 in the 2000s and the 2010s (Kim and Kim, 2002; Kim et al., 2011; Shon et al., 2008; Gan et al., 2009; Han et al., 2014). Individual researchers have also been reporting concentrations of speciated Hg since 2009 (Gan et al., 2009; Han et al., 2014; Lee et al.,

∗

Corresponding author. E-mail address: [email protected] (Y.-J. Han). 1 Present address: Urban Forests Research Center, Forest Conservation Department, National Institute of Forest Science, 57 Heogi-ro, Dongdaemun-gu, Seoul, Republic of Korea, 02455. https://doi.org/10.1016/j.atmosenv.2019.05.063 Received 4 January 2019; Received in revised form 22 May 2019; Accepted 25 May 2019 Available online 28 May 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

industrial and metropolitan areas were located to the east (Fig. 1). Gyodong Island has a total area of about 411 km2, with forests and farmland covering about 43% and 40% of that area, respectively, with a population of 68,010. From August 2015 to September 2017, the three specified Hg species were measured during 10 intensive sampling periods (Table 1). The TGM concentration was measured every 5 min using Tekran 2537X, an automated measuring instrument for Hg. Outdoor air was continuously transported at a flow rate of 1.0 L min−1 through a heated 3-m-long Teflon line into the analyzer. Two gold cartridges collected and thermally desorbed Hg alternately, and desorbed Hg was quantified using a cold vapor atomic fluorescence spectrophotometer. Auto-calibration was performed by an internal permeation source every 24 h (2:00 a.m. to 2:22 a.m.). Manual calibration was also performed every 6 months by injecting five different volumes of saturated Hg vapor through the injection port located on the front of the Tekran 2537X instrument. From these calibrations, r2 was higher than 0.9995 between the mass injected and Tekran's reported area (p-value < 0.001). The average relative percent difference (RPD) between the auto-calibration and the manual calibration was less than 2%. A certain amount of Hg vapor was directly injected into the sampling line between the sample inlet and the Tekran 2537X in an ultra-high-purity (UHP) air stream before each intensive sampling. The recovery rate ranged from 93% to 134% (103.3 ± 14.6%, n = 8). GOM and PBM were manually collected using a KCl-coated annular denuder (URG Corporation) followed by a 47-mmdiameter quartz filter at a flow rate of 10 L min −1 every 2 h (except for the first and second sampling periods, during which GOM and PBM samples were taken every 6 h). A jet impactor was deployed upstream of the denuder to remove particles larger than 2.5 μm. A sampling system that consisted of a jet impactor, a KCl-coated denuder, and a filter pack was housed in a custom-made sampling box maintained at 45 °C to prevent the hydrolysis of KCl. To remove any remaining Hg, the denuder and quartz filter were heated in a tube furnace at 550 °C and 850 °C, respectively, before sampling. After sampling, the denuder and quartz filter were thermally desorbed at 525 °C and 850 °C, respectively, to convert Hg2+ to Hg0 in a UHP air stream, and Hg0 was then transported into a Tekran 2537X for quantification. The manual sampling and analysis method for GOM and PBM has been described in previous studies (Han et al., 2004; Lee et al., 2016). In this study, the sampling duration and analysis time for the GOM and PBM samples was 2 h for each; therefore, six samples were obtained in a 24 h period. For precision evaluation, collocated GOM and PBM samples were obtained from

2016), but the measurements are too scarce to identify spatial variation patterns. The reported GOM and PBM concentrations ranged from 2.7 to 27.2 and from 3.7 to 23.9 pg m−3, respectively (Han et al., 2014; Lee et al., 2016; Kim et al., 2009). Currently, there are 12 national Hg monitoring sites operating in Korea, but only the TGM concentrations are being monitored. In this study, atmospheric speciated Hg concentrations were measured in one of the most northwestern islands of South Korea, located between eastern China and the Korean mainland. Previously, our group measured Hg concentrations on Yongheung Island (located in the western part of Korea) during the spring, summer, and winter months, and sources and/or pathways of each type of speciated Hg were detected (Lee et al., 2016). However, GOM and PBM were measured during a 12 h period, which is a much longer period than is used for meteorological data, possibly leading to inaccurate results when it comes to evaluating the sources of speciated Hg. Other studies have also suggested that long sampling times can result in serious sampling artifacts (Malcolm and Keeler, 2007). In addition, there were six coalburning power plants on Yongheung Island. Each plant generated 800 MW, which could have significantly and consistently affected the Hg concentrations at the sampling site, making it difficult to evaluate exactly how much Hg was carried over through long-range transport from China and how much originated from the power plants on Yongheung. In this study, the sampling site was changed to Gyodong Island, located about 60 km north of the previous site, as it can approximately represent the background concentrations. The period used for sampling was also changed to 2 h for GOM and PBM, and sampling was conducted during all four seasons to better evaluate the effects of Korean domestic and foreign anthropogenic and natural sources and re-emission from the ocean. 2. Materials and methods 2.1. Field measurements TGM, GOM, and PBM were measured on the roof of a two-story building on Gyodong Island, the westernmost island in Korea (Fig. 1). The sampling site was located only 2 km away from North Korea and was also close to China (the distances to Shandong Peninsula and Liaoning Province are 322 and 300 km, respectively). Furthermore, there were no local anthropogenic Hg sources on the island. Several large coal-burning power plants were located south of the island, and

Fig. 1. Location of the sampling site (red star) on Gyodong Island. The right panel shows the anthropogenic Hg emission sources in Korea. The blue circle in the right panel indicates the area where the major Korean coal-burning power plants are located. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 240

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

Table 1 Summarized concentrations of speciated atmospheric Hg during 10 intensive sampling periods. Sampling period

TGM (ng m−3)

GOM (pg m−3)

PBM (pg m−3)

PM2.5 (μg m−3)

O3 (ppb)

NO2 (ppb)

CO (ppb)

SO2 (ppb)

1st: 2015.08.18–2015.08.21 2nd: 2016.01.07–2016.01.13 3rd: 2016.03.22–2016.03.26 4th: 2016.05.11–2016.05.17 5th: 2016.07.25–2016.08.01 6th: 2016.11.01–2016.11.07 7th: 2017.01.05–2017.01.14 8th: 2017.04.03–2017.04.11 9th: 2017.07.26–2017.08.01 10th: 2017.09.15–2017.09.21 Mar.–May Jun.–Aug. Sep.–Nov. Dec.–Feb. Total

2.5 1.9 3.5 2.8 1.5 1.9 3.4 3.3 1.4 4.2 3.2 1.7 3.1 2.8 2.7

5.2 ± 2.3 2.4 ± 1.4 4.5 ± .2 5.5 ± 5.1 11.1 ± 14.4 3.3 ± 4.2 4.0 ± 3.9 5.7 ± 5.2 2.4 ± .1.3 5.7 ± .3.5 5.3 ± 4.5 (N = 87) 6.5 ± 10.3 (N = 65) 4.6 ± 4.0 (N = 59) 3.6 ± 3.5 (N = 66) 5.0 ± 6.2 (N = 277)

8.5 ± 10.3 35.5 ± 16.8 47.8 ± 26.7 23.9 ± 3.2 6.6 ± 4.3 16.1 ± 15.9 41.9 ± 46.6 21.8 ± 20.7 9.7 ± 7.4 19.3 ± 26.0 29.2 ± 23.2 (N = 92) 8.2 ± 6.8 (N = 73) 17.7 ± 21.3 (N = 61) 40.4 ± 41.2 (N = 65) 24.0 ± 28.1 (N = 291)

31 19 – – 21 32 20 27 17 19 27 21 24 20 22

63 35 51 59 38 51 35 30 43 48 46 46 49 35 43

6 4 6 6 3 6 5 9 6 4 7 5 5 4 5

417 502 313 354 286 433 354 400 471 302 359 387 359 415 381

3± 3± 2± 3± 3± 3± 2± 2± 2± 2± 2± 3± 2± 2± 2±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.7 0.5 3.0 1.0 0.6 0.5 3.9 2.6 0.9 5.6 2.0 1.0 3.6 2.9 2.6

(N = 428) (N = 334) (N = 265) (N = 344) (N = 1371)

two samplers deployed side by side, and the average RPDs were 5.8 ± 5.9% (N = 8 set) for GOM and 6.5 ± 3.4% (N = 4 set) for PBM. At least more than one blank for each intensive sampling period was obtained, and these blanks ranged from 0.0 to 1.8 pg m−3 for GOM and from 1.0 to 1.8 pg m−3 (N = 10) for PBM. Meteorological data, including temperature, humidity, wind speed, and wind direction, were obtained from the automatic weather station at the sampling site. Hourly measurements of all the representative air pollutants other than Hg were obtained from the national air quality monitoring station located about 8 km southeast from the Hg sampling site.

Wij =

Three-day backward trajectories were calculated for each hour of sampling using HYSPLIT 4.0 (Hybrid Single-Particle Lagrangian Integrated Trajectory) with GDAS (Global Data Assimilation System) meteorological data having 0.5° latitude–longitude as spatial resolution. Hourly 3-day back-trajectories were calculated for each hour of sampling, and the arrival heights of 100, 300, 500, 700, and 1000 m were used to describe the local and regional transport meteorological patterns. On the basis of the trajectories, cluster analysis was performed based on the total spatial variance (TSV) method. On the basis of TSV, four clusters were chosen based on the methods of Draxler et al. (2009) and Kelly et al. (2012). A more detailed description of the clustering process can be found in Draxler et al. (2009).

mij

=

nij

3 2 4 4 3 3 4 7 5 1 6 4 2 3 5

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

140 190 129 172 286 149 163 85 102 188 139 216 183 189 184

2 1 1 2 1 1 1 1 1 1 1 1 1 1

1.0 for 3Nave < nij ⎧ ⎫ ⎪ 0.7 for nij < 1.5 ≤ 3Nave ⎪ ⎨ 0.4 for Nave < nij ≤ 1.5Nave ⎬ ⎪ ⎪ 0.2 for nij ≤ Nave ⎩ ⎭

(2)

The average TGM concentration was calculated as 2.7 ± 2.6 ng m−3 and ranged from 0.4 to 28.7 ng m−3 during the whole sampling period. This was considerably higher than the background concentrations of 1.5–1.7 ng m−3 found in the Northern Hemisphere. This indicated that domestic inland sources and/or longrange transport from foreign countries were likely to be significant, especially considering that there were no local anthropogenic sources on the island. The observed TGM concentrations were higher than those found in Japan, the USA, and Canada, but lower than those found at most sites in China (Marumoto et al., 2015; Ren et al., 2016; Cheng et al., 2014; Fu and Feng, 2015). There was distinct seasonal variations were observed, showing significantly lower TGM concentrations in warm months (Jun.–Aug.) (ANOVA test, p-value < 0.001, Tukey HSD) (Table 1, Table S1). In summer, the planetary boundary layer (PBL) height is generally higher than in other seasons owing to the large

N

(1)

N

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

3.1. General concentration pattern

The potential source contribution function (PSCF) value was calculated as

P (Bij )

8 12 12 13 8 20 13 10 18 10 14

26 10 11 13 17 18 6 8 18 20 17 22 19 8 18

3. Results and discussion

2.3. Potential source contribution function

P (Aij )

± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

where N is the total number of trajectory segment endpoints over the period of the study, nij is the number of trajectory segments falling into the ijth cell, and mij is the number of segment endpoints in the same ijth cell when the concentrations are higher than a predetermined criterion value. Therefore, if a certain grid cell has a high PSCF value, that cell likely contains the source of the atmospheric Hg. In this study, the criterion value was the top 25% concentrations, and a cell size of 1° by 1° was used. An arbitrary weight function Wij was used to reduce the uncertainty in a grid cell that contained a small number of endpoints. Wij was applied when the number of endpoints in a particular cell (nij) was less than three times the average number of endpoints (Nave) for all cells (Eq. (2)), as used in several other studies (Fu et al., 2011; Han et al., 2007; Polissar et al., 2001a,b).

2.2. Backward trajectory and cluster analysis

PSCF =

± 12 ±9

Table 2 Pearson correlation coefficients of speciated Hg with meteorological data and representative atmospheric pollutants for the entire sampling period.. TGM

**

.130

TGM GOM PBM

Note that

GOM

.130** .322** ∗

and

∗∗

−.015

PBM

TEMP **

.322 −.015

−.086 .239** −.430** **

RH

WS

.002 .027 −.251**

−.128 .070 −.029

PM10 **

**

.113 −.009 .197**

PM2.5 .064 .031 .074

*

O3 −.056 .113** −.138** *

NO2

CO

SO2

.029 .078* .032

−.015 −.021 .117**

−.172 .138** −.044

indicate that the correlation was statistically significant at a significance level of 0.05 and 0.01, respectively. 241

SOLAR **

−.039 .097* −.065

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

Fig. 2. Hourly variations of TGM concentration for each sampling day during 10 intensive sampling periods. The black bars indicate no data.

mechanism for GOM (Keeler et al., 2005; Lombard et al., 2011; Pehkonen and Lin, 1998). During the course of the study, it was noted that GOM significantly decreased after precipitation (t-test, p-value < 0.05). The average PBM concentration was found to be 24.0 ± 28.1 pg m−3, much higher than the concentrations measured on Yongheung Island in our previous study (Lee et al., 2016) (10.9 ± 11.2 pg m−3) and in other Korean cites (Han et al., 2014) (3.7 ± 5.7 pg m−3 in rural areas and 13.4 ± 12.0 pg m−3 in metropolitan areas). PBM showed a clear seasonal variation with high concentrations in winter months (Table 1, Table S1). There are two possible pathways leading to the seasonal variation of PBM: increased emissions from coal and residential wood burning (Lan et al., 2012; Zhang et al., 2012) and active secondary formation in cold months. The sampling site in this study is located only 2 km from North Korea. In North Korea, wood and biomass burning is the second largest source of energy, contributing to 20–30% of the country's total energy (Von Hippel and Hayes, 2012; Conti et al., 2016). Previous studies have observed that concentrations of PM2.5 and polycyclic aromatic hydrocarbons measured in South Korea were noticeably affected by biomass burning emissions from North Korea (Lee and Kim, 2007; Park et al., 2007; Kim and Yeo, 2013). Because substantial PBM can be emitted from biomass burning, contributing up to 50% of total Hg emissions (Obrist et al., 2008), it was likely that higher PBM concentrations were going to be observed at this sampling site than at other sites in Korea. In addition, coal produced in North Korea is known to have higher Hg content than the standard (Yonhap news agency, 2015). Gas–particle partitioning has been suggested as an important secondary formation pathway for PBM at low temperatures (Lee et al., 2016; Amos et al., 2012; Mao and Talbot, 2012). Assuming that GEM is not adsorbed on particles, the gas–particle partitioning coefficient Kp can be calculated by the following equation (Amos et al., 2012):

surface buoyancy effect from higher temperatures, causing an active vertical dispersion and a decrease in the concentrations of pollutants (Han et al., 2014; Stull, 2012). In addition, the abundant oxidants in summer can transform GEM to GOM. GOM is readily removed by rain scavenging, which can lower the TGM concentrations. There was a weak but statistically significant negative relationship between TGM and O3 at a significance level of 0.05 (Table 2), possibly suggesting oxidation of GEM. Meteorology and reactions with other pollutants in the atmosphere were more likely to affect the Hg concentrations at this background site because there were no local sources consistently influencing Hg concentrations. PBL height, which typically begins to rise in the morning (7–10 a.m.), reaches its maximum in the afternoon before sunset and subsides around 7 p.m. (Liu and Liang, 2010; Zhang et al., 2014). This too likely played a role in the diurnal variation of TGM. In this study, TGM concentrations were generally higher in the morning than in the afternoon, with the exception of a few days in the 7th sampling period (Fig. 2). The average GOM concentration was 5.0 ± 6.2 pg m−3, with higher values in summer months and lower values in winter months (Table 1), which is consistent with other studies (Han et al., 2014; Xu et al., 2015). One reason for the high GOM concentrations during summer months is likely the oxidation of GEM, explained by the positive correlation between GOM and O3 (Table 2). The positive correlation between O3 and GOM does not necessarily indicate that GEM was oxidized by O3, but that oxidants represented by O3 seem to play an important role in GOM production. Lan et al. (2012) suggested that the large GOM diurnal variation in the afternoon could be attributed to an increased temperature and the presence of halogen species creating favorable oxidizing conditions in the atmosphere. The photochemical oxidation of GEM is presumed to be an important factor affecting the GOM concentration in this study, especially considering the possibility of large halogen emissions (Swartzendruber et al., 2006; Sillman et al., 2007; Soerensen et al., 2010), such as reactive chlorine and bromine, from the Yellow Sea. In support of this hypothesis, the daily variation of GOM was more pronounced in warm months, peaking in the afternoon (Fig. S1). GOM also showed statistically significant correlations with SO2 and NO2 (Table 2), indicating that it was directly affected by anthropogenic sources such as the coal-burning power plants located to the south of the sampling site, as suggested in other studies (Gustin et al., 2012; Ren et al., 2014, 2016). GOM increased in the 5th sampling period (Fig. 3), in particular because the weather was very dry all summer. Wet deposition has been regarded as an important removal

Kp =

PBM GOM PM

(3)

where PM indicates the concentration of particulate matter. In this study, 2 h averaged PM2.5 concentrations were used, and a well-fitting statistically significant simple linear relationship of Kp with atmospheric temperature (T) was found (Eq. (4), Fig. S2) (r = 0.581, pvalue < 0.0001). The coefficient and y-intercept in Eq. (4) are within the range of the empirical gas–particle partitioning relationship that Amos et al. (2012) proposed using the Hg data obtained from five 242

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

Fig. 3. Time series of TGM, GOM, and PBM concentrations during the whole sampling period. The blue boxes indicate high-concentration events. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

be collected on a downstream particle filter, and analyzed as PBM, which results in an underestimation of GOM and an overestimation of PBM. Considering that the sampling site was in the marine boundary layer (MBL) in this study, the dominant GOM compounds were likely halogenated species, such as HgCl2 and HgBr2, which means the collection efficiency of GOM by the KCl denuder was approximately 50%. In addition, the relative humidity is typically higher in the MBL than at other sites, which could also affect the KCl-coated denuder's performance, as moisture could hydrolyze the KCl-coated surface. This uncertain collection efficiency might have resulted in relatively low GOM concentration and relatively high PBM concentrations at this sampling site. Also, the gas-particle partitioning coefficient, Kp was likely to be overestimated, and the range of Kp proposed for various sites in a previous study (Amos et al., 2012) was also presumed to be distorted, and future study will be needed. In this study, we re-calculated GOM concentration based on the

monitoring sites in the USA and Canada, as well as two laboratory experiments, suggesting that gas–particle partitioning is an important formation pathway for PBM, especially during the colder seasons.

1 2930 Log ⎛⎜ ⎟⎞ = 9.5 − T ⎝ Kp ⎠

(4)

It has been shown that GOM is significantly underestimated by the KCl-coated denuder (Gustin et al., 2013, 2016; Huang et al., 2013, 2017; Huang and Gustin, 2015; McClure et al., 2014). Ozone and water vapor are known to interfere with the KCl denuder method, and it also should be noted that the KCl-coated denuder has different collection efficiencies for different GOM compounds. According to Gustin et al. (2013, 2016), the collection efficiencies of KCl denuder are 62.5%, 43.5%, 41.7%, 27.0%, and 7.9% for HgBr2, HgSO4, HgCl2, HgO, and HgN2O6∙H2O, respectively. Gustin et al. (2013) and Lynam and Keeler (2005) suggested that GOM can pass through the KCl-coated denuder, 243

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

for the significant correlations between all three types of Hg observed during the 7th sampling period. In addition, both the wind speed and the daily maximum mixing depth (MMD) were very low (WS = 0.7 m s−1; daily MMD = 469.5 m) (note that the mixing depths were obtained from a HYSPLIT model) during Jan. 7–8, causing especially high concentrations of all three Hg species likely because of a lack of horizontal and vertical dilution (Fig. 3). Gas–particle partitioning was determined to be an unimportant factor for the high PBM concentrations during the 7th sampling period because PBM did not show any correlation with temperature. Among the 10 intensive sampling periods, the highest TGM average (4.2 ng m−3) was observed during the 10th period (Table 1). The first TGM peak appeared in the early morning on Sep. 17, and TGM was elevated again from 7 p.m. on Sep. 17 to 7 a.m. on Sep. 18 with a maximum hourly average of 28.7 ng m−3 (Fig. 3). The back-trajectories originated from several directions, including northeastern China, Russia, and Japan, during the 10th sampling period. However, none of the trajectories that caused the peaks of TGM that appeared in the early morning of Sep. 17 and Sep. 18 passed through China (Fig. S4). This indicates that the sources in mainland Korea and North Korea were the primary reason for high TGM concentrations during these days.

following Eq. (5) that McClure et al. (2014) suggested. Absolute humidity was obtained based on the vapor pressure calculated as a function of temperature using Eqs. (6) and (7) (Seinfeld and Pandis, 2016). When the estimated recovery was larger than 100%, the revised GOM was assumed to be equal to the measured GOM. Estimate recovery (%) = 124.6 – (absolute humidity [g/kg] × 5.681) – (O3 [ppb] × 0.2933) (5) o PH (T) = 1013.25exp[13.3185 a− 1.97a2 − 0.6445a3 − 0.1299a4] 2O

(6)

a = 1 − 373.15/ T

(7)

The average re-calculated GOM concentration was 15.8 ± 41.2 pg m−3, approximately 3 times higher than the measured GOM concentration, and there was a significant correlation between the measured GOM and the re-calculated GOM (Pearson r = 0.78, pvalue < 0.001) (Fig. S3). However, even if GOM was underestimated and PBM was overestimated, the main findings of this study, which include high GOM concentration in summer, significant correlation between GOM and O3, and high PBM concentration in winter, suggest that the negative sampling artifact for GOM and the positive artifact for PBM appeared at a similar rate regardless of the sampling date.

3.3. Source identification 3.2. High-concentration events 3.3.1. National vs. foreign sources Wind patterns have a significant influence on the concentrations of air pollutants and are often used as tools to identify the impact of local and regional sources and long-range transport of pollutants. During the entire sampling period, northeastern and southwestern winds prevailed, but the predominant wind direction that brought high concentrations was different for each Hg species. The winds blowing northeast (0–90°) were associated with high concentrations of TGM, whereas the western winds (180–300°) and eastern winds were associated with high PBM (Fig. 4). These results indicate that the concentration of TGM was elevated by Korean domestic sources located east of the sampling site, whereas PBM was more affected by Chinese sources. On the contrary, high GOM concentrations came from the southwestern direction, a direction different from that associated with both TGM and PBM (Fig. 4). Pollution rose for the top 10% of TGM, PBM, and GOM samples was also used, and the same results can be seen in Fig. 4 (Fig. S5). A previous study suggested that the ratio of GOM/ PBM could be a useful tool for identifying the relative significance of local sources vs. regional transport because the atmospheric residence time of GOM is widely regarded as shorter than that of PBM (Lee et al., 2016). Our data produced GOM/PBM ratios that were less than 1.0 in most cases because PBM concentrations were generally higher than GOM concentrations at this site; however, the winds blowing from the south and southwest were associated with higher GOM/PBM ratios (Fig. 4). Relatively high GOM/PBM ratios were also observed with NEE winds (around 60°), demonstrating the effect of mainland Korea. The reciprocal of the GOM/PBM ratio was used to calculate Kp, which indicates the secondary formation of PBM through gas–particle conversion. It is likely that the GOM/PBM ratio is affected by temperature, as shown in Eq. (2). To remove any temperature dependence, the GOM/ PBM ratios were traced with corresponding wind directions for each season. When only samples collected during the summer months were analyzed, a high GOM/PBM ratio was still associated with southerly winds (Fig. S6), indicating that the sources located south of the sampling site were responsible for the increased concentrations of GOM compared with PBM. In fall and winter months, high GOM/PBM ratios were observed with easterly winds, demonstrating that the domestic sources in Korea especially contributed to the increased GOM concentrations (Fig. S6). Ground meteorological data, including wind direction, can be used to depict the meteorological pattern on a local scale, but not a regional

Several high-concentration events were observed, and four evident events that occurred in the 3rd, 5th, 7th, and 10th sampling periods (Fig. 3) were chosen for closer study. The highest average of PBM and the highest median of TGM concentrations appeared during the 3rd sampling period (Table 1, Table S1). According to the National Institute of Environmental Research of Korea, the PM10 concentration was considerably elevated throughout Korea because of dust brought from the Mongolian desert during this sampling period (www.airkorea.or.kr). However, no correlation was observed between PBM and PM10 concentrations. This is probably because Hg is mainly associated with fine particles (< 2.5 μm) (Kim et al., 2012; Fang et al., 2012), whereas soil dust typically exists as coarse particles (> 2.5 μm) (Wilson and Suh, 1997). During this period, back-trajectories originating from the Mongolian desert passed through northern China and North Korea before arriving at the sampling site, resulting in high PBM and TGM concentrations. In addition, PBM showed a significant negative correlation with temperature during this period (r = −0.53, p-value < 0.01), along with the 2nd (r = −0.25, p-value < 0.01) and the 10th (r = −0.36, p-value < 0.01) sampling periods, possibly indicating that gas–particle partitioning was an important formation pathway for PBM. Very high GOM concentrations were observed during the 5th sampling period (July 25 to Aug. 1, 2016), along with the lowest PBM and the second lowest TGM averages (Table 1, Fig. 3). During this period, GOM had the highest correlation coefficient with NO2 (r = 0.51, pvalue < 0.01) and SO2 (r = 0.54, p-value < 0.01) among all the sampling periods, and back-trajectories were passing through the large coal-burning power plant area located south of the sampling site. TGM and PBM also showed significant correlations with NO2 and SO2 during this period, indicating that all three Hg concentrations were affected. However, only GOM concentrations were substantially increased by domestic fossil-fuel combustion. During the 7th sampling period (Jan. 2017), both the TGM and PBM concentration were elevated at the same time (Fig. 3), and all three Hg species were positively correlated (the Pearson correlation coefficients between TGM and GOM, between TGM and PBM, and between GOM and PBM were 0.46, 0.45, and 0.33, respectively, all p-values < 0.01). This was not observed during any other sampling period. During this time, back-trajectories were originating from either eastern China or inland Korea east of the sampling site. For the top 10% of TGM samples, the trajectories passed through inland Korea, which may be the reason 244

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

Fig. 4. Speciated Hg concentrations and GOM/PBM ratios with the associated wind direction.

Fig. 5. Mean back-trajectory and residence time of trajectories associated with each cluster.

northeastern China, and North Korea, whereas cluster 2 contains trajectories confined within the Korean peninsula and the adjacent ocean areas. In comparison, cluster 3 consists of trajectories passing through both northeastern China and a central part of the Korean peninsula. The average TGM and PBM concentrations were high with clusters 1 and 3; however, for GOM, the average, median, and even standard deviation

one. To identify the regional transport, the back-trajectories were grouped into four clusters using the trajectory cluster analysis feature of HYSPLIT, and the residence time of these trajectories was calculated in a 1° by 1° cell for each cluster (Fig. 5). Among the four clusters, clusters 1 and 4 represent northwesterly trajectories originating from Russia, the Mongolian desert area, 245

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

or pathways located in areas of Korea other than the central part were important to GOM. In cluster 1, all three Hg species showed relatively high concentrations. This is likely attributable to the trajectories passing through northeastern China, the Korean peninsula, and the ocean being included together. All these results derived by cluster analysis suggest that Korean sources and the ocean area were significant sources for GOM, whereas northeastern China, including Liaoning Province, was an important source area for TGM and PBM (note the location Liaoning Province in Fig. 7). Liaoning Province, where large nonferrous metal smelting industries are located, is one of the largest anthropogenic Hg emission areas in China (Fu et al., 2012). It should be noted that the sampling method possibly had an influence on the high GOM concentrations that appeared in cluster 2 because of the different collection efficiencies of the KCl-coated denuder for different GOM compounds. When air masses stayed over the ocean for long period of time, the dominant GOM compounds were likely to be halogenated species. Halogenated species are known to have a higher capture efficiency on the KCl denuder than other Hg forms, such as Hg-nitrogen, -sulfur, and -oxide compounds.

Table 3 Averaged concentrations of the three Hg species for each cluster. Figures in parenthesis refer to medians. Cluster

TGM (ng m−3)

GOM (pg m−3)

PBM (pg m−3)

1 2 3 4

3.2 2.1 3.1 2.5

5.1 5.9 3.5 3.7

28.3 11.2 34.0 26.0

(33%) (31%) (17%) (19%)

± ± ± ±

3.0 1.6 3.7 1.5

(2.3) (1.9) (2.1) (2.0)

± ± ± ±

4.9 8.6 3.1 3.1

(3.9) (4.1) (2.0) (2.7)

± ± ± ±

34.4 (16.8) 8.6 (6.8) 26.1 (29.5) 18.3 (24.3)

were the highest in cluster 2 (Table 3). The one-way ANOVA result indicates that the TGM, PBM, and GOM concentrations associated with cluster 2 were statistically different from those of the other clusters, indicating that GOM in cluster 2 was higher than that in clusters 3 and 4, and TGM and PBM in cluster 2 were lower than those in other clusters (Tukey HSD test, Table S2). Analysis of the clusters clearly establishes that the different sources and/or formation pathways had an impact on the different species of Hg. When air masses stayed stagnant within the Korean peninsula and over the ocean, as shown in cluster 2, GOM was enhanced whereas both TGM and PBM decreased (Table 3). However, a high GOM average was not found in cluster 3, which contained a significant portion of trajectories coming from a central part of Korea, indicating that sources and/

3.3.2. The ocean as a possible GOM source Cluster analysis shows that high GOM concentrations were associated with cluster 2 (Table 3). Cluster 2 contained trajectories that had

Fig. 6. Time series of GOM/GEM, GOM, NO2, and O3 measured during the 5th sampling period. Note that the unit of the y-axis for the GOM/GEM ratio is pg ng−1. 246

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

did not change when the measured GOM concentrations were replaced with the re-calculated GOM concentrations using Eq. (5). This once again indicates that the sampling artifact from the KCl-coated denuder did not significantly alter the overall results of this study. The idea that the ocean could be a possible source of GOM was also supported by the plotted back-trajectories for the top 10% of TGM, PBM, and GOM (Fig. 7). The area northeast of China was an important area for the top 10% of both TGM and PBM, whereas a large number of back-trajectories stayed in the Yellow Sea region between China and the Korean peninsula for the top 10% of GOM. The PSCF results corroborate the fact that the ocean was a potential source for GOM, because cells with high PSCF values appear in the ocean areas as well. For both TGM and PBM, possible sources are located north of the sampling site, situated in Liaoning Province in northeastern China and the western area of North Korea (Fig. 8). Nguyen et al. (2016) also observed highly elevated PBM concentrations near the Liaoning Province of China and the western region of North Korea from their shipboard measurements over the Yellow Sea and ground measurements made in areas around the Yellow Sea. However, although the Sea of Okhotsk and the East China Sea stand out, overland sources do not appear to be probable sources of GOM (Fig. 8). Considering that with a limited number of measurements PSCF may have a trailing effect, the PSCF results often identify areas upwind and downwind of real emission sources (Han et al., 2007). In this study, 277 GOM samples were used for PSCF in total; therefore, the trailing effect was presumed to be less pronounced. Another disadvantage of PSCF is that the cells near the sampling site are unlikely to have high PSCF values because there are fundamentally several trajectory endpoints. In this case, the denominator of the PSCF equation (Eq. (1)) increases as the cells get closer to the sampling site when the cell size is equivalent over the applicable area. If this is the case, sources located near the sampling site are likely omitted from the potential source area in the PSCF result. However, the neighboring area of the sampling site had already been identified as a possible source area for both TGM and PBM, which suggests that the ocean is a more important source than overland sources for GOM. In order to identify only the effect of long-range transport, the PSCF was also performed with samples that had a higher PBL (planetary boundary layer) than 200 m. The results were, in general, similar to the PSCF results shown in Fig. 8. However, the results at this altitude indicate that the Yellow Sea region is an even more important source of GOM than originally thought (Fig. S7).

a long residence time in the ocean area (Fig. 5), and these trajectories contributed 64% of the total residence time in the cluster 2. This was much higher than the residence time in the ocean area for clusters 3 and 4 (24% and 14%, respectively). The trajectories in cluster 2 were mostly associated with the samples obtained during summer months, including the 5th sampling period (Table S3). The highest GOM concentration event occurred during the 5th sampling period, described earlier (in Section 3.2), and the coalburning power plants located south of the sampling site were presumed to be an important factor because all three Hg species were statistically correlated with NO2 and SO2. However, the concentrations of NO2 and SO2 during the 5th sampling period were relatively low (Table 1). This suggests that the effect of fossil-fuel combustion is not enough to explain the considerably elevated GOM concentrations observed (Fig. 3). Another remarkable point regarding the 5th sampling period is that the residence time of the associated trajectories that stayed in the ocean area made up roughly 80% of the total residence time. In the marine boundary layer, it was suggested that the oxidation of GEM is an important pathway in the production of GOM, leading to the depletion of GEM by increasing the rate of deposition (Lan et al., 2012; Horvat et al., 2003; Subir et al., 2011). During the 5th sampling period, we observed GOM/GEM ratios that steadily increased with a decreasing GEM concentration, demonstrating the presence of an effective process of GEM oxidation and GOM production. In this study, GEM concentration was calculated as subtracting GOM from TGM measurements (Gustin et al., 2013; Temme et al., 2002). In Fig. 6, there are three sections of large GOM/GEM ratios. The second section occurring on July 29 showed that the GOM/GEM ratio increased at the same time that GEM and NO2 were increasing, possibly indicating the effect of coal-burning power plants. However, the large GOM/GEM ratios observed between July 25 and 26 and between July 30 and 31 appeared concurrently with the depletion of GEM and low O3 concentrations (Fig. 6). Timonen et al. (2013) also found elevated GOM concentrations, large GOM/GEM ratios, and anticorrelation between GOM and GEM during Asian long-range transport, supporting in situ oxidation in the marine boundary layer. A few other studies also observed in situ oxidation in the marine boundary layer (Swartzendruber et al., 2006; Sillman et al., 2007; Soerensen et al., 2010). CO concentrations were very low during the 5th sampling period (Table 1), suggesting that there was minimal influence from anthropogenic emissions or from biomass burning. This further supports the significant effect of the in situ oxidation of GEM, which leads to elevated concentrations of GOM. The concurrent decrease in O3 concentration coupled with the increase in GOM and the decrease in GEM suggests that halogens act as the primary GEM oxidant. Halogens are known to play an important role in atmospheric mercury depletion events and concurrent ozone depletion events in the Arctic and Antarctic regions, and even at mid-latitudes (Schroeder et al., 1998; Lindberg et al., 2002; Mastromonaco et al., 2016; Peleg et al., 2007). It should also be noted that the ratio of GOM/GEM observed during the 5th sampling period

4. Conclusion In this study, the speciated Hg concentrations were measured at one of the farthest Korean island northwest to determine both the effect of regional transport from China and North Korea and the effect of the ocean. The average TGM and PBM concentrations were considerably higher than the background concentrations found in the Northern

Fig. 7. Residence time of back-trajectories of the top 10% TGM (left), GOM (middle), and PBM (right) concentrations. 247

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

Fig. 8. PSCF results for TGM (left), GOM (middle), and PBM (right). The criterion value used was the top 25% highest concentrations.

Appendix A. Supplementary data

Hemisphere. The major factors influencing the three Hg species were different for each type. Since there were no nearby sources consistently influencing the concentrations of speciated Hg at this site, Hg concentrations were more likely to be affected by meteorology and reactions with other pollutants. In general, the concentration of TGM was higher in the morning than in the afternoon, as well as higher during colder months than in warmer months, as PBL height decreased. Because there was a positive correlation between GOM and ozone, the photochemical oxidation of GEM was presumed to be an important factor affecting GOM concentration. The daily variation of GOM was more pronounced in summer months, peaking in the afternoon when sunlight was most prevalent. Another factor in GOM concentration was found to be combustion and precipitation. It was also suggested that the high PBM concentrations during the winter months could be partially attributed to gas-particle partitioning and to biomass burning in North Korea. According to the back-trajectories based analysis, TGM and PBM concentrations were enhanced with the possible source areas of northeastern China. However, GOM was affected by domestic coalburning power plants as anticipated but was also found to be elevated during the summer months, when air masses were confined within the ocean area with minimal influence from anthropogenic emissions. The large GOM/GEM ratios appeared concurrently with the depletion of GEM and low ozone concentrations, supporting in situ oxidation of GEM possibly by halogens in the marine boundary layer. PSCF results also corroborate that the northeast of China was an important area for both TGM and PBM whereas the ocean was a potential source for GOM.

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.05.063. References AMAP/UNEP, 2013. Technical Background Report for the Global Mercury Assessment 2013: Final Technical Report; Output. Amos, H.M., Jacob, D.J., Holmes, C., Fisher, J.A., Wang, Q., Yantosca, R.M., Corbitt, E.S., Galarneau, E., Rutter, A., Gustin, M., 2012. Gas-particle partitioning of atmospheric Hg (II) and its effect on global mercury deposition. Atmos. Chem. Phys. 12 (1), 591–603. Cheng, I., Zhang, L., Mao, H., Blanchard, P., Tordon, R., Dalziel, J., 2014. Seasonal and diurnal patterns of speciated atmospheric mercury at a coastal-rural and a coastalurban site. Atmos. Environ. 82, 193–205. Conti, J., Holtberg, P., Diefenderfer, J., LaRose, A., Turnure, J.T., Westfall, L., 2016. International Energy Outlook 2016 with Projections to 2040; USDOE Energy Information Administration (EIA). Office of Energy Analysis, Washington, DC (United States). Draxler, R., Stunder, B., Rolph, G., Stein, A., Taylor, A., 2009. HYSPLIT4 User's Guide. September: 2014 Version 4.9. EPA,U.S., 2014. How People Are Exposed to Mercury. http://www.epa.gov/mercury/ how-people-are-exposed-mercury. Fang, G., Zhang, L., Huang, C., 2012. Measurements of size-fractionated concentration and bulk dry deposition of atmospheric particulate bound mercury. Atmos. Environ. 61, 371–377. Fu, X., Feng, X., 2015. Variations of atmospheric total gaseous mercury concentrations for the sampling campaigns of 2001/2002 and 2009/2010 and implications of changes in regional emissions of atmospheric mercury. Bull. Miner. Petrol. Geochem. 34, 242–249. Fu, X., Feng, X., Qiu, G., Shang, L., Zhang, H., 2011. Speciated atmospheric mercury and its potential source in Guiyang, China. Atmos. Environ. 45 (25), 4205–4212. Fu, X., Feng, X., Sommar, J., Wang, S., 2012. A review of studies on atmospheric mercury in China. Sci. Total Environ. 421, 73–81. Gan, S.-Y., Yi, S.-M., Han, Y.-J., 2009. Characteristics of atmospheric speciated gaseous mercury in Chuncheon, Korea. J. Korean Soc. Environ. Eng. 31 (5), 382–391. Gustin, M.S., Weiss-Penzias, P.S., Peterson, C., 2012. Investigating sources of gaseous oxidized mercury in dry deposition at three sites across Florida, USA. Atmos. Chem. Phys. 12 (19), 9201–9219. Gustin, M.S., Huang, J., Miller, M.B., Peterson, C., Jaffe, D.A., Ambrose, J., Finley, B.D., Lyman, S.N., Call, K., Talbot, R., 2013. Do we understand what the mercury speciation instruments are actually measuring? Results of RAMIX. Environ. Sci. Technol. 47 (13), 7295–7306. Gustin, M.S., Pierce, A.M., Huang, J., Miller, M.B., Holmes, H.A., Loria-Salazar, S.M., 2016. Evidence for different reactive Hg sources and chemical compounds at adjacent valley and high elevation locations. Environ. Sci. Technol. 50, 12225–12231. Han, Y.-J., Holsen, T.M., Hopke, P.K., 2007. Estimation of source locations of total gaseous mercury measured in New York State using trajectory-based models. Atmos. Environ. 41 (28), 6033–6047. Han, Y.J., Holsen, T.M., Lai, S.O., Hopke, P.K., Yi, S.M., Liu, W., Pagano, J., Falanga, L., Milligan, M., Andolina, C., 2004. Atmos. Environ. 6431–6446. Han, Y.-J., Kim, J.-E., Kim, P.-R., Kim, W.-J., Yi, S.-M., Seo, Y.-S., Kim, S.-H., 2014. General trends of atmospheric mercury concentrations in urban and rural areas in Korea and characteristics of high-concentration events. Atmos. Environ. 94, 754–764. Holmes, C.D., Jacob, D.J., Corbitt, E.S., Mao, J., Yang, X., Talbot, R., Slemr, F., 2010. Global atmospheric model for mercury including oxidation by bromine atoms. Atmos. Chem. Phys. 10 (24), 12037–12057. Horvat, M., Nolde, N., Fajon, V., Jereb, V., Logar, M., Lojen, S., Jacimovic, R., Falnoga, I., Liya, Q., Faganeli, J., 2003. Total mercury, methylmercury and selenium in mercury polluted areas in the province Guizhou, China. Sci. Total Environ. 304 (1–3), 231–256. http://english.yonhapnews.co.kr/news/2015/04/09/94/

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supporting information Further details on monitoring and modeling results including statistical test, cluster analysis, diurnal variation, and back-trajectory plots.

Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MISP) (Grant No. 2015R1A2A203008301) and by the Korea Ministry of Environment (MOE) as part of the "Environmental Health Action Program (2015001370001). 248

Atmospheric Environment 213 (2019) 239–249

S.-H. Lee, et al.

Peleg, M., Matveev, V., Tas, E., Luria, M., Valente, R.J., Obrist, D., 2007. Mercury depletion events in the troposphere in mid-latitudes at the Dead Sea, Israel. Environ. Sci. Technol. 41 (21), 7280–7285. Polissar, A.V., Hopke, P.K., Harris, J.M., 2001a. Source regions for atmospheric aerosol measured at Barrow, Alaska. Environ. Sci. Technol. 35 (21), 4214–4226. Polissar, A.V., Hopke, P.K., Poirot, R.L., 2001b. Atmospheric aerosol over Vermont: chemical composition and sources. Environ. Sci. Technol. 35 (23), 4604–4621. Raofie, F., Ariya, P.A., 2004. Product study of the gas-phase BrO-initiated oxidation of Hg0: evidence for stable Hg1+ compounds. Environ. Sci. Technol. 38 (16), 4319–4326. Ren, W., Zhang, Y., Chen, H.G., Gao, Z.F., Li, N.B., Luo, H.Q., 2016. Ultrasensitive labelfree resonance Rayleigh scattering aptasensor for Hg2+ using Hg2+-triggered exonuclease III-assisted target recycling and growth of G-wires for signal amplification. Anal. Chem. 88 (2), 1385–1390. Ren, X., Luke, W.T., Kelley, P., Cohen, M.D., Artz, R., Olson, M.L., Schmeltz, D., Puchalski, M., Goldberg, D.L., Ring, A., 2016. Atmospheric mercury measurements at a suburban site in the Mid-Atlantic United States: inter-annual, seasonal and diurnal variations and source-receptor relationships. Atmos. Environ. 146, 141–152. Ren, X., Luke, W.T., Kelley, P., Cohen, M., Ngan, F., Artz, R., Walker, J., Brooks, S., Moore, C., Swartzendruber, P., 2014. Mercury speciation at a coastal site in the northern Gulf of Mexico: results from the grand bay intensive studies in summer 2010 and spring 2011. Atmosphere 5 (2), 230–251. Schroeder, W., Anlauf, K., Barrie, L., Lu, J., Steffen, A., Schneeberger, D., Berg, T., 1998. Arctic springtime depletion of mercury. Nature 394 (6691), 331. Seinfeld, J.H., Pandis, S.N., 2016. Atmospheric Chemistry and Physics: from Air Pollution to Climate Change, third ed. John Wiley & Sons., Inc., Hoboken, New Jersey. Shon, Z.-H., Kim, K.-H., Song, S.-K., Kim, M.-Y., Lee, J.S., 2008. Environmental fate of gaseous elemental mercury at an urban monitoring site based on long-term measurements in Korea (1997–2005). Atmos. Environ. 42 (1), 142–155. Sillman, S., Marsik, F.J., Al‐Wali, K.I., Keeler, G.J., Landis, M.S., 2007. Reactive mercury in the troposphere: model formation and results for Florida, the northeastern United States, and the Atlantic Ocean. J. Geophys. Res.: Atmosphere 112 (D23). Soerensen, A.L., Sunderland, E.M., Holmes, C.D., Jacob, D.J., Yantosca, R.M., Skov, H., Christensen, J.H., Strode, S.A., Mason, R.P., 2010. An improved global model for airsea exchange of mercury: high concentrations over the North Atlantic. Environ. Sci. Technol. 44 (22), 8574–8580. Sommar, J., Gårdfeldt, K., Strömberg, D., Feng, X., 2001. A kinetic study of the gas-phase reaction between the hydroxyl radical and atomic mercury. Atmos. Environ. 35 (17), 3049–3054. Sommar, J., Hallquist, M., Ljungström, E., Lindqvist, O., 1997. On the gas phase reactions between volatile biogenic mercury species and the nitrate radical. J. Atmos. Chem. 27 (3), 233–247. Stull, R.B., 2012. An Introduction to Boundary Layer Meteorology, vol. 13 Springer Science & Business Media. Subir, M., Ariya, P.A., Dastoor, A.P., 2011. A review of uncertainties in atmospheric modeling of mercury chemistry I. Uncertainties in existing kinetic parameters–Fundamental limitations and the importance of heterogeneous chemistry. Atmos. Environ. 45 (32), 5664–5676. Swartzendruber, P.C., Jaffe, D.A., Prestbo, E., Weiss‐Penzias, P., Selin, N.E., Park, R., Jacob, D.J., Strode, S., Jaegle, L., 2006. Observations of reactive gaseous mercury in the free troposphere at the Mount Bachelor Observatory. J. Geophys. Res.: Atmosphere 111 (D24). Temme, C., Einax Jr., W., Ebinghaus, R., Schroeder, W.H., 2002. Measurements of atmospheric mercury species at a coastal site in the Antarctic and over the South Atlantic Ocean during polar summer. Environ. Sci. Technol. 37, 22–31. Timonen, H., Ambrose, J., Jaffe, D., 2013. Oxidation of elemental Hg in anthropogenic and marine airmasses. Atmos. Chem. Phys. 13 (5), 2827–2836. UNEP, 2013. The Global Atmospheric Mercury Assessment. UNEP Chemicals Branch. Valente, R.J., Shea, C., Humes, K.L., Tanner, R.L., 2007. Atmospheric mercury in the Great Smoky Mountains compared to regional and global levels. Atmos. Environ. 41 (9), 1861–1873. Von Hippel, D., Hayes, P., 2012. Foundations of Energy Security for the DPRK: 1990–2009 Energy Balances, Engagement Options, and Future Paths for Energy and Economic Redevelopment. Nautilus Institute Special Report: Sustainability, NIfSa. Wilson, W.E., Suh, H.H., 1997. Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. J. Air Waste Manag. Assoc. 47 (12), 1238–1249. Xu, L., Chen, J., Yang, L., Niu, Z., Tong, L., Yin, L., Chen, Y., 2015. Characteristics and sources of atmospheric mercury speciation in a coastal city, Xiamen, China. Chemosphere 119, 530–539. Zhang, L., Blanchard, P., Gay, D., Prestbo, E., Risch, M., Johnson, D., Narayan, J., Zsolway, R., Holsen, T., Miller, E., 2012. Estimation of speciated and total mercury dry deposition at monitoring locations in eastern and central North America. Atmos. Chem. Phys. 12 (9), 4327–4340. Zhang, Y., Gao, Z., Li, D., Li, Y., Zhang, N., Zhao, X., Chen, J., 2014. On the computation of planetary boundary-layer height using the bulk Richardson number method. Geosci. Model Dev. (GMD) 7, 2599–2611.

0200000000AEN20150409003800325F.html, 2015-04-09, 2019-03-22. Huang, J., Miller, M.B., Weiss-Penzias, P., Gustin, M.S., 2013. Comparison of gaseous oxidized Hg measured by KCl-coated denuders, and nylon and cation exchange membranes. Environ. Sci. Technol. 47, 7307–7316. Huang, J., Gustin, M.S., 2015. Uncertainties of gaseous oxidized mercury measurements using KCl-coated denuders, cation-exchange membranes, and nylon membranes: humidity influence. Environ. Sci. Technol. 49, 6102–6108. Huang, J., Miller, M.B., Edgerton, E., Gustin, M.S., 2017. Deciphering potential chemical compounds of gaseous oxidized mercury in Florida, USA. Atmos. Chem. Phys. 17, 1689–1698. Hynes, A.J., Donohoue, D.L., Goodsite, M.E., Hedgecock, I.M., 2009. Our current understanding of major chemical and physical processes affecting mercury dynamics in the atmosphere and at the air-water/terrestrial interfaces. In: Mercury Fate and Transport in the Global Atmosphere. Springer, pp. 427–457. Keeler, G.J., Gratz, L.E., Al-Wali, K., 2005. Long-term atmospheric mercury wet deposition at Underhill, Vermont. Ecotoxicology 14 (1–2), 71–83. Kelly, C., Toro, R., Di Martino, A., Cox, C.L., Bellec, P., Castellanos, F.X., Milham, M.P., 2012. A convergent functional architecture of the insula emerges across imaging modalities. Neuroimage 61 (4), 1129–1142. Kim, K.-H., Kim, M.-Y., 2002. A decadal shift in total gaseous mercury concentration levels in Seoul, Korea: changes between the late 1980s and the late 1990s. Atmos. Environ. 36 (4), 663–675. Kim, K.-H., Kim, M.-Y., 2000. The effects of anthropogenic sources on temporal distribution characteristics of total gaseous mercury in Korea. Atmos. Environ. 34 (20), 3337–3347. Kim, K.-H., Shon, Z.-H., Nguyen, H.T., Jung, K., Park, C.-G., Bae, G.N., 2011. The effect of man made source processes on the behavior of total gaseous mercury in air: a comparison between four urban monitoring sites in Seoul Korea. Sci. Total Environ. 409 (19), 3801–3811. Kim, P.-R., Han, Y.-J., Holsen, T.M., Yi, S.-M., 2012. Atmospheric particulate mercury: concentrations and size distributions. Atmos. Environ. 61, 94–102. Kim, S.-H., Han, Y.-J., Holsen, T.M., Yi, S.-M., 2009. Characteristics of atmospheric speciated mercury concentrations (TGM, Hg (II) and Hg (p)) in Seoul, Korea. Atmos. Environ. 43 (20), 3267–3274. Kim, Y.P., Yeo, M.J., 2013. The trend of the concentrations of the criteria pollutants over Seoul. J. Korean Soc. Atmos. Environ. 29 (4), 369–377. Lan, X., Talbot, R., Castro, M., Perry, K., Luke, W., 2012. Seasonal and diurnal variations of atmospheric mercury across the US determined from AMNet monitoring data. Atmos. Chem. Phys. 12 (21), 10569. Laurier, F.J., Mason, R.P., Whalin, L., Kato, S., 2003. Reactive gaseous mercury formation in the North Pacific Ocean's marine boundary layer: a potential role of halogen chemistry. J. Geophys. Res.: Atmosphere 108 (D17). Lee, G.-S., Kim, P.-R., Han, Y.-J., Holsen, T.M., Seo, Y.-S., Yi, S.-M., 2016. Atmospheric speciated mercury concentrations on an island between China and Korea: sources and transport pathways. Atmos. Chem. Phys. 16 (6), 4119–4133. Lee, J., Kim, Y., 2007. Source apportionment of the particulate PAHs at Seoul, Korea: impact of long range transport to a megacity. Atmos. Chem. Phys. 7 (13), 3587–3596. Lindberg, S.E., Brooks, S., Lin, C.-J., Scott, K.J., Landis, M.S., Stevens, R.K., Goodsite, M., Richter, A., 2002. Dynamic oxidation of gaseous mercury in the Arctic troposphere at polar sunrise. Environ. Sci. Technol. 36 (6), 1245–1256. Liu, S., Liang, X.-Z., 2010. Observed diurnal cycle climatology of planetary boundary layer height. J. Clim. 23 (21), 5790–5809. Lombard, M., Bryce, J., Mao, H., Talbot, R., 2011. Mercury deposition in southern New Hampshire, 2006–2009. Atmos. Chem. Phys. 11 (15), 7657–7668. Lynam, M.M., Keeler, G.J., 2005. Artifacts associated with the measurement of particulate mercury in an urban environment: the influence of elevated ozone concentrations. Atmos. Environ. 39, 3081–3088. Malcolm, E.G., Keeler, G.J., 2007. Evidence for a sampling artifact for particulate-phase mercury in the marine atmosphere. Atmos. Environ. 41 (16), 3352–3359. Mao, H., Talbot, R., 2012. Speciated mercury at marine, coastal, and inland sites in New England–Part 1: temporal variability. Atmos. Chem. Phys. 12 (11), 5099–5112. Marumoto, K., Hayashi, M., Takami, A., 2015. Atmospheric mercury concentrations at two sites in the Kyushu Islands, Japan, and evidence of long-range transport from East Asia. Atmos. Environ. 117, 147–155. Mastromonaco, M.N., Gårdfeldt, K., Jourdain, B., Abrahamsson, K., Granfors, A., Ahnoff, M., Dommergue, A., Méjean, G., Jacobi, H.-W., 2016. Antarctic winter mercury and ozone depletion events over sea ice. Atmos. Environ. 129, 125–132. McClure, C.D., Jaffe, D.A., Edgerton, E.S., 2014. Evaluation of the KCl denuder method for gaseous oxidized mercury using HgBr2 at an in-service AMNet site. Environ. Sci. Technol. 48, 11437–11444. Nguyen, D.L., Kim, J.Y., Shim, S.-G., Ghim, Y.S., Zhang, X.-S., 2016. Shipboard and ground measurements of atmospheric particulate mercury and total mercury in precipitation over the Yellow Sea region. Environ. Pollut. 219, 262–274. Obrist, D., Hallar, A.G., McCubbin, I., Stephens, B.B., Rahn, T., 2008. Atmospheric mercury concentrations at Storm Peak Laboratory in the Rocky Mountains: evidence for long-range transport from Asia, boundary layer contributions, and plant mercury uptake. Atmos. Environ. 42 (33), 7579–7589. Pal, B., Ariya, P.A., 2004. Studies of ozone initiated reactions of gaseous mercury: kinetics, product studies, and atmospheric implications. Phys. Chem. Chem. Phys. 6 (3), 572–579. Park, S.S., Kim, Y.J., Kang, C.H., 2007. Polycyclic aromatic hydrocarbons in bulk PM 2.5 and size-segregated aerosol particle samples measured in an urban environment. Environ. Monit. Assess. 128 (1–3), 231–240. Pehkonen, S.O., Lin, C.-J., 1998. Aqueous photochemistry of mercury with organic acids. J. Air Waste Manag. Assoc. 48 (2), 144–150.

Further reading Han, J., 2004. Study on the Vertical Atmospheric Structure and Development Mixed Layer over Jeonju Region. Master theses. Pusan National University.

249