As a global pollutant, mercury (Hg) is of particular concern in East Asia,
where anthropogenic emissions are the largest. In this study, speciated Hg
concentrations were measured on Yongheung Island, the westernmost island in
Korea, located between China and the Korean mainland to identify the
importance of local and regional Hg sources. Various tools including
correlations with other pollutants, conditional probability function, and
back-trajectory-based analysis consistently indicated that Korean sources
were important for gaseous oxidized mercury (GOM) whereas, for total gaseous
mercury (TGM) and particulate bound mercury (PBM), regional transport was
also important. A trajectory cluster based approach, considering both Hg
concentration and the fraction of time each cluster was impacting the site,
was developed to quantify the effect of Korean sources and out-of-Korean
sources. This analysis suggests that contributions from out-of-Korean sources
were similar to Korean sources for TGM whereas Korean sources contributed
slightly more to the concentration variations of GOM and PBM compared to
out-of-Korean sources. The ratio of GOM
Mercury (Hg) is the only metal that exists as a liquid at standard conditions (US EPA, 1997) which results in it having a significant vapor pressure and presence in the atmosphere. In the atmosphere, Hg generally does not constitute a direct public health risk at the level of exposure usually found (Driscoll et al., 2007). However, once Hg is deposited into aquatic systems, it can be transformed into methyl-mercury (MeHg) which is very toxic and readily bioaccumulates through the food web (Mason et al., 1995). Many studies show that one of the major sources of MeHg in aquatic and terrestrial system is atmospheric deposition of inorganic Hg (Landis and Keeler, 2002; Mason and Sullivan, 1997). Fish consumption has been considered to be the major exposure pathway of Hg for humans (Mergler et al., 2007; UNEP, 2013). In Korea, You et al. (2012) showed that MeHg concentrations in blood were affected by fish consumption as well as by gender difference. However, rice consumption was also found to be the predominant pathway of MeHg exposure for the inhabitants residing in a highly contaminated area of China (Zhang et al., 2010).
Atmospheric mercury exists in three major inorganic forms, including gaseous
elemental mercury (GEM, Hg
In the atmosphere, Hg species can be interconverted through various redox
reactions. It is known that GOM can be produced by homogeneous and
heterogeneous reactions of GEM with O
The region of largest anthropogenic Hg emissions is East and Southeast Asia,
contributing 39.7 % of the total global anthropogenic emissions (UNEP,
2013). In Korea, atmospheric Hg emissions have generally decreased since 1990
(Kim et al., 2010). However, Hg levels in Korea are likely to be highly
susceptible to Chinese emissions because China alone accounts for about one
third of the global total (UNEP, 2013) and Korea is situated just east (and
generally downwind) of China. According to the recent studies, Hg
concentrations in the blood of Koreans are more than 4–8 times higher than those
found in US and Germany, and approximately 26 % of Koreans have higher
blood mercury concentrations than a USA guideline level
(
This study was designed to identify the contribution of various Hg sources including direct emissions from anthropogenic and natural sources and indirect secondary formation processes to atmospheric Hg concentrations in Korea. In order to achieve these objectives, Hg concentrations were measured in the westernmost island in Korea, located in between eastern China and the Korean mainland, so that, depending on wind patterns, the effects of Chinese and Korean Hg emissions could be evaluated. Previously, our group qualitatively evaluated the impact of local Korean sources and regional Chinese sources on TGM concentrations at the same sampling site (Lee et al., 2014). However, that work was unable to identify the effect of sources on Hg levels in Korea because only TGM was measured whereas all three Hg species are needed since they have very different physical and chemical properties. In this study, the importance of sources and pathways was both qualitatively and quantitatively evaluated using all three Hg species' concentrations measured throughout the extended sampling period.
Summarized concentrations of speciated Hg and other typical pollutants for each sampling period.
TGM, GOM, and PBM were measured on the roof of a three-story building on
Yongheung Island (YI), the westernmost island in Korea (Fig. 1). YI is a
small island located about 15–20 km west from mainland Korea with a
population of 5815. The Yongheung coal-fired power plant (YCPP), located
approximately 4.5 km southwest of the sampling site (Fig. 1c), emits about
0.11 ton yr
From January 2013 to August 2014, three atmospheric mercury species: TGM
(GEM
GOM and PBM were collected manually using an annular denuder coated with KCl
followed by a quartz filter, respectively, at a flow rate of
10 L min
The sampling methods used in this study are currently the most accepted methods for the measurement of atmospheric GOM and PBM, however there are many studies reporting that these methods are subject to interferences from ozone, water vapor, and possibly other compounds (Lyman et al., 2010; Talbot et al., 2011; Jaffe et al., 2014; Finley et al., 2013; Gustin et al., 2013; Huang et al., 2013; McClure et al., 2014) although recent side-by-side measurements with two Tekran systems showed good agreement and no impact from added ozone and increasing relative humidity (Edgerton, 2015). Also, it should be noted that the concentrations of PBM measured during 12 h of sampling time (all sampling periods except in the 7th) may have been biased due to Hg loss from filters over the long sampling period (Malcolm and Keeler, 2007; Wang et al., 2013); however for model development any loss of PBM is assumed to be the same for each sampling period. Therefore, it should be noted that the GOM and PBM measurements reported in this study may be somewhat biased even though, at present, it is not possible to quantify the magnitude of these uncertainties.
Meteorological data including temperature, wind speed, wind direction, relative humidity, and solar radiation were also measured every 5 min at the sampling site using a meteorological tower (DAVIS Inc weather station, Vintage Pro2™).
Hourly concentrations of SO
Three-day backward trajectories were calculated using the NOAA HYSPLIT 4.7
with GDAS (Global Data Assimilation System) meteorological data which
supplies 3-h, global 1
The backward trajectories were clustered into groups with similar transport patterns using NOAA HYSPLIT 4.7. This method minimizes the intra-cluster differences among trajectories while maximizing the inter-cluster differences. The clustering of trajectories is based on the total spatial variance (TSV) method. TSV is the sum of all the cluster spatial variances (SPVAR) which is the sum of the squared distances between the endpoints of the cluster's component trajectories and the mean of the trajectories in that cluster. In this study, five clusters were chosen based on a large increase in TSV for larger clusters (Fig. S4), as described in Draxler et al. (2014) and Kelly et al. (2012). A more detailed description of the clustering process can be obtained in Draxler et al. (2014).
The conditional probability that a given concentration from a given wind
direction will exceed a predetermined threshold criterion, was calculated
using the following equation:
The PSCF model counts each trajectory segment endpoint that terminates
within given grid cell. A high PSCF value signifies a potential source
location. The PSCF value was calculated as
Comparisons of measured Hg concentrations with those reported in other studies.
To maintain the consistency of the sampling duration, the 12 h averaged GOM
and PBM concentrations were used for the 7th sampling period to identify the
general trends of Hg species. The average TGM, GOM, and PBM concentrations
were 2.8
TGM, GOM, and PBM concentrations measured during the eight sampling periods. TGM was measured every 5 min while GOM and PBM were measured during 12 h except for the 2 h measurements during May 2014.
When the data were grouped into three categories including the first (April,
May 2013, March, May 2014), the second (August 2013, August 2014), and the
third (January, February 2013) periods, both TGM (ANOVA/Tukey test,
Box-and-whisker plot for the concentrations of TGM, GOM, and PBM during three different periods. The red dash lines indicate the arithmetic mean.
The TGM concentration varied diurnally, generally showing morning maximums
(07 a.m.–12:00 a.m.) and minimums during the
nighttime. In urban areas, TGM concentrations are typically higher during the
nighttime due to a combination of decreased GEM loss by daytime oxidation,
increased use of household heating systems and decreased mixing heights at
night (Kim et al., 2012; Han et al., 2014). In contrast, daytime peaks have
been observed in rural and remote areas, likely due to increased
volatilization of Hg
GOM concentrations were highest in spring
(10.7
In Korea emissions of PBM from anthropogenic sources are much smaller then
gaseous emissions (the proportion of GEM, GOM, and PBM released are 64.4,
28.8, and 6.8 %) (Kim et al., 2010). The fact that PBM concentrations are
similar to GOM even though significantly less PBM is released suggests that a
significant portion of atmospheric PBM may be due to secondary formation
through gas-particle partitioning. This process is characterized by a
partition coefficient,
The relationship between
Some previous studies suggested that all gaseous mercury species including
Hg
Han et al. (2014) also found a significant multiple linear relationship
between the ratio of PBM
Correlation coefficients and
The gas-particle partitioning coefficient,
In previous researches, GOM concentration measured using KCl denuder is
subject to interferences under the conditions of high ozone and relative
humidity (Gustin et al., 2013, 2015; Huang et al., 2013)
although it is currently the most accepted method. To evaluate the possible
error on interpreting result,
Correlations between Hg and other pollutant concentrations are often used to
identify sources. For example good correlations with SO
CPF plot shows that the top 25 % TGM concentrations were associated with winds from the NNW and eastern direction, pointing towards northeastern China and inland Korean sources; however, when the criterion was set to the top 10 % the winds from NNW became less important and the sources located in southern and eastern areas from the sampling site were identified as an important source direction (Fig. 5). The CPF plot for GOM is significantly different from the one for PBM. High PBM concentrations were associated with northern winds while GOM concentrations were enhanced during southeastern winds.
CPF plots for TGM using the top 25 % (left upper panel) and the top 10 % (right upper panel) as a criterion, and for GOM (left bottom panel) and for PBM (right bottom panel). For both GOM and PBM, the criterion of the top 25 % concentration was used.
These results suggest that for PBM regional transport from Chinese and North
Korea sources were more important than Korean sources; in contrast coal-fired
power plants located in the southern direction rather than regional transport
impacted GOM concentration. It should be noted that this result is in
apparent conflict with the finding that there was no relationship between GOM
and SO
Among the eight sampling periods, the second period (April 2013) had the
highest TGM, PBM and the second highest GOM average concentration, and
SO
In contrast the fifth sampling period had the lowest GOM, PBM and the second
lowest TGM concentrations (Table 1). Note however that the TGM concentrations
for the first couple of days reached approximately 5 ng m
According to the CPF results, the winds from NW and NE of the sampling site
were responsible for the elevated PBM concentrations while easterly winds
pointing towards inland Korea were associated with increased GOM
concentrations (Fig. 5). The finding that regional transport of TGM and PBM
to the site is important is supported by their significant correlation with
CO (Table 3). In order to identify the relative importance of local sources
relative to regional transport, the ratio of GOM
Frequency of wind direction with different GOM
PSCF results for TGM (left), PBM (middle), and GOM (right) using the top 25 % of concentrations as criteria.
The reciprocal of this ratio (i.e. GOM
In order to locate potential source areas in more detail, PSCF was used. For
TGM, potential sources were located in Liaoning, Shandong, and Henan
provinces of China along with the southern area of Korea (Fig. 7). Liaoning
province, where large non-ferrous smelters are situated, is the province with
the largest Hg emissions in China; Shandong and Henan provinces are also
large Hg emission areas, emitting about 30–40 ton yr
The probable source areas of PBM identified by PSCF were similar to those for TGM, indicating that both Chinese and inland Korean sources enhanced PBM concentrations, with the exception of metropolitan (Seoul) and industrial (Incheon) areas located in northwestern South Korea which emerged as more prominent source areas for PBM than for TGM (Fig. 7). Only Korean sources including metropolitan (Seoul) and the industrial areas in southern Korea were identified as probable source areas for GOM (Fig. 7); regional transport of GOM from China was not important. The Yellow Sea between China and Korea was also associated with high PSCF values, possibly indicating the shipping ports located on the western coast of Korea as important source areas. However, it should be noted that it might be a trailing effect derived by relatively short sampling duration. A trailing effect is often observed, especially with a limited number of measurements or short sampling period, since PSCF evenly distributes weight along the path of trajectories so that PSCF results often identify areas upwind and downwind of real sources as a source area (Han et al., 2007). However, it should be also noted that the marine boundary layer provides good conditions for active Hg oxidation reactions due to an abundance of oxidants (Auzmendi-Murua et al., 2014); therefore, the possibility of areas over the ocean being a GOM source should not be excluded.
Estimated contribution of Korean and out-of-Korean sources on variations of speciated Hg concentration.
Back trajectories for clusters 1 through 5. The left top panel indicates the mean back trajectory and contribution for each cluster.
It should be noted that different temporal resolutions for trajectories (hourly) and concentrations (every 12 h) were used for GOM and PBM. Since trajectory directions can significantly change over the course of 12 h there is a possibility that some source areas could be misidentified, especially for more distant regional sources. However, upon investigation it was determined that over the 12 h sampling periods the trajectories did not diverge significantly at this sampling site for most sampling periods.
In an effort to quantify the contribution of national and foreign sources to the measured Hg concentrations the back trajectories were grouped into five clusters using the trajectory cluster analysis feature of HYSPLIT. Among the five clusters, clusters 1 and 5 represent trajectories originating from outside (South) Korea whereas the trajectories grouped in the cluster 4 originated and passed through the (South) Korean peninsula (Fig. 8). Clusters 2 and 3 contain trajectories from China and the Korean peninsula, but cluster 2 was more associated with Liaoning province and North Korea while cluster 3 originated more from Shandong and Henan provinces. Clusters 1 through 5 contributed 12, 31, 26, 20, and 11 % of the total time, respectively, and the associated concentrations with each cluster are shown in Table 4. Concentration ranges of three Hg species for each cluster were shown as the box-and-whisker plots in the Supplement (Fig. S5). When considering that cluster 4 is associated with the local transport from inland Korea and cluster 1 and 5 are associated with the regional transport from outside of (South) Korea, the maximum and 75th percentile values as well as the arithmetic average are higher in cluster 4 for GOM and PBM than those in clusters 1 and 5 (Fig. S5).
The TGM concentration was the highest for cluster 5; however, GOM and PBM
concentrations had the lowest averages for this cluster. Cluster 5 contains
the back trajectories originating from Mongolia and Russia and passing
through northeastern China before arriving at the sampling site, which
suggests regional transport was important for this cluster (Fig. 8). Average
CO concentrations were pretty similar for all clusters, but it was the second
highest for cluster 5 (cluster 2 was highest). The highest total average GOM
and PBM concentrations were associated with cluster 4 which includes
trajectories distributed over the Korean peninsula, suggesting that Korean
sources were responsible for the enhanced GOM and PBM concentrations. For
cluster 4, the highest Pearson correlation coefficient between GOM and PBM
concentrations (
In order to consider both Hg concentration and the fraction of time for each
cluster, the following equation was used to quantify the effect of Korean and
out-of-Korean sources to the Hg concentration at the receptor
site.
In order to quantify the contribution of Korean vs. out-of-Korean sources (note that “Korean” means “South Korean” throughout the manuscript), the source contributions of the clusters were used. Clusters 1 and 5 were used to represent the effect of sources outside of Korea and cluster 4 was used to indicate the effect of sources in Korea. Since clusters 2 and 3 contain mixed trajectories from Korea and out-of-Korea their contribution was divided evenly between in and out of Korea. The results indicate that the sources in Korea and outside Korea contributed about 50 % each to the concentration variation of TGM measured at the sample site during the sampling period while the Korean sources affected GOM and PBM more significantly, accounting for approximately 52.3 and 53.4 %, respectively (Table 4). These results augment the CPF and PSCF results which only use concentrations that are in the top 25th percentile. While CPF and PSCF found that for high concentration events Korean sources were most important for GOM while for TGM and PBM regional transport from China and North Korea were also important, the cluster-based approach suggests that for all three species Korean and out-of-Korean sources contributed approximately 50 % each to the concentration variations seen by the site. When the geometric mean concentrations were used for each cluster a similar result was obtained for relative contributions as the results using arithmetic mean concentrations.
It should be noted that errors always exist in calculating trajectories, causing uncertainties in all trajectory-based approaches. Trajectory errors vary considerably from case to case; Stohl (1998) suggested uncertainties might be 20 % of the distance traveled by trajectories while Draxler (1996) found that the final error was about 10 % of the travel distance.
This study was initiated to identify the sources affecting speciated mercury concentrations measured on an island located between mainland Korea and Eastern China. Various tools were used to locate and quantify the sources, including correlations with other pollutants, CPF, and the back-trajectory-based analysis (PSCF and cluster analysis). The results consistently show that Korean sources are most important for GOM while for other Hg species (TGM and PBM) regional transport from China and North Korea were also important. Existing methods including PSCF and CPF are able to locate the source direction and areas, but do not consider the frequency of the wind directions which can affect the long-term concentrations at the receptor site. For example, if the Hg concentration is high with easterly winds both CPF and PSCF identify the eastern areas as important source areas even if, in fact, winds are rarely blowing from the east. In this work, it is true that sources located in the eastern direction from the sampling site are likely to be important for enhancing Hg concentrations, but based only on CPF and PSCF results it cannot be said that their contribution to the concentration variations at the site is also high.
To address this problem a new approach that considers both the cluster frequency and the Hg concentration associated with each cluster was used to quantify the source contribution at the sampling site. On average, contributions from out-of-Korean sources were similar to Korean sources for TGM whereas Korean sources contributed slightly more to the concentration variations of GOM and PBM compared to the out-of-Korean sources. However, in general, conclusions using this approach are more uncertain when the concentration ranges are similar between clusters. Additional work is needed with this approach to determine if a different statistic (other than mean) would provide better results when there are no distinct concentration differences between clusters. In addition, uncertainties exist in the source attribution approach based on cluster analysis because the trajectories inevitably overlap between different clusters since the cluster analysis accounts for both variations in transport speed and direction simultaneously. Nevertheless, this new approach can augment existing methods including CPF and PSCF to help identify source contributions to the concentration variations at the sampling site.
The ratio of GOM
The work presented here was carried out in collaboration between all authors. Gang S. Lee analyzed data and wrote the paper. Pyung R. Kim performed the experiments and interpreted the results. Young J. Han defined the research theme, interpreted the results, and wrote the paper. Yong S. Seo, Seung. M. Yi, and Thomas M. Holsen also interpreted the results and approved the final paper.
This work was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1A2A2A03008301) and the Korea Ministry of Environment (MOE) as “the Environmental Health Action Program”. This research was also supported by 2014 Research Grant from Kangwon National University (No. C1011758-01-01). Edited by: L. Zhang