Speciated atmospheric mercury including gaseous elemental mercury (GEM),
gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM) were
measured continuously for a 1-year period at a suburban site, representing a
regional transport intersection zone, in east China. Annual mean
concentrations of GEM, PBM, and GOM reached 2.77 ng m
Mercury (Hg) is a global pollutant of great concerns for environment and
human health (Wright et al., 2018). Atmospheric mercury is
operationally divided into three forms, i.e., gaseous elemental mercury (GEM),
particulate-bound mercury (PBM), and gaseous oxidized mercury (GOM).
GEM is the predominant form in the atmosphere (
Both natural processes and anthropogenic activities release mercury into the
atmosphere (Zhang et al., 2017; Pirrone et al., 2010). Natural sources of
mercury include the ocean volatilization, volcanic eruption, evasion from
soils and vegetation, geothermal activities, and weathering minerals.
Re-emissions of mercury that previously deposited onto the environmental
surfaces are also considered natural sources. As for the anthropogenic
emission sources of mercury, coal combustion, nonferrous smelters, cement
production, waste incineration, and mining are considered to be the main
sources. After being emitted into the atmosphere, mercury undergoes
speciation, which plays an important role in its biogeochemical cycle.
Previous studies suggest that the oxidation of GEM in the terrestrial
environments was generally initiated by
Anthropogenic mercury emissions in Asia accounted for more than 50 % of the global total mercury emissions (Pacyna et al., 2016); among these approximately 27 % were from mainland China (Hui et al., 2017). The Yangtze River Delta (YRD) is one of the most industrialized and urbanized regions in China. Early field measurements in urban Shanghai suggested that total gaseous mercury (TGM) was most likely derived from coal-fired power plants, smelters, and industrial activities (Friedli et al., 2011). Field measurements in urban Nanjing indicated that natural sources were important while most sharp peaks of TGM were caused by anthropogenic sources (Zhu et al., 2012). Modeling studies of atmospheric mercury in eastern China showed that natural emissions, accounting for 36.6 % of the total emissions, were the most important source for GEM in eastern China (Zhu et al., 2015). Measurements made at Chongming (an island belonging to Shanghai) observed a downward trend of GEM concentrations from 2014 to 2016, likely caused by the reduction of domestic emissions (Tang et al., 2018). Source apportionment studies for atmospheric mercury are very limited in this region (Zhu et al., 2015), although some studies were made in the other regions of China (Wan et al., 2009; Fu et al., 2019)
In this study, 1-year continuous measurements of GEM, GOM, and PBM were conducted at Dianshan Lake Station (DSL), a suburban site in Shanghai. DSL is located in the junction area of Shanghai, Zhejiang, and Jiangsu provinces and is close to the East China Sea (ECS). There are few local mercury sources in this area, making it a unique station for studying the main pollution sources and transport pathways of Hg. The mercury data were analyzed for (1) investigating the impact of meteorology on mercury distribution, (2) exploring the GEM oxidation process, (3) revealing the GOM adsorption process to ambient particles, and (4) identifying potential mercury sources. Knowledge gained from this study provides scientific basis for establishing future mercury emission control policies in this region.
Field sampling was conducted on the top of a four-story building
(
The location of the Dianshan Lake (DSL) site in Shanghai, China. The red dots in the map represent the anthropogenic atmospheric Hg emissions by each province in 2014 (Wu et al., 2016).
GEM, GOM, and PBM were measured from June 2015 to May 2016 using the Tekran
2537B/1130/1135 system (Tekran Inc., Canada), an instrument that has been
widely used worldwide (Landis and Keeler, 2002). In general,
GEM, GOM, and PBM in the atmosphere were collected by dual gold cartridges, a
KCl-coated annular denuder, and a regenerable quartz fiber filter,
respectively. In this study, GEM was collected at an interval of 5 min
with a flow rate of 1 L min
Quality control procedures are strictly followed. The KCl-coated denuder, Teflon-coated glass inlet, and impactor plate were replaced weekly and quartz filters were replaced monthly. Before sampling, denuders and quartz filters were prepared and cleaned according to the methods in Tekran technical notes. The Tekran 2537B analyzer was routinely calibrated using its internal permeation source every 47 h, and was also cross-calibrated every 3 months against an external temperature-controlled Hg vapor standard. Two-point calibrations (including zero calibration and span calibration) were performed separately for each pure gold cartridge. Manual injections were performed to evaluate these automated calibrations using the standard saturated mercury vapor. Extremely high concentrations (especially for GOM and PBM) were occasionally observed. If the values were several times higher than the previous hour, those data were regarded as outliers and were excluded in the data analysis.
Water-soluble inorganic anions (
Heavy metals (Pb, Fe, K, Ba, Cr, Se, Cd, Ag, Ca, Mn, Cu, As, Hg, Ni, Zn, V)
in PM
The hourly meteorological data including air temperature, relative humidity (RH),
wind speed, and wind direction were simultaneously monitored at the
observation site by the automatic weather station (AWS). The data of
planetary boundary layer (PBL) height were retrieved from the US National
Oceanic and Atmospheric Administration (
PSCF is a useful tool to diagnose the possible source areas with regard to
the levels of air pollutants when setting a contamination concentration
threshold at the receptor site. Back trajectory models are used to simulate
the airflows. The principle of PSCF is to calculate the ratio of the total
number of back trajectory segment endpoints in a grid cell (
In this study, we set the threshold concentration as the mean value of the
whole sampling period. The mean GEM, PBM, and GOM concentrations were
2.77 ng m
The PMF model is widely used to quantitatively determine the source
contributions of specific air pollutants (Gibson et al.,
2015). The essential principle of PMF is that the concentration of each
sample is determined by source profiles and different contributions. The
equation of the PMF model is shown as Eq. (3):
Before the model determines the optimal non-negative factor contributions
and factor profiles, an objective function, which is the sum of the square
difference between the measured and modeled concentrations weighted by the
concentration uncertainties, has to be minimized (Cheng et al., 2015).
The equation that determines the objective function is given by Eq. (4):
Time series of atmospheric Hg (GEM, PBM, and GOM) concentrations during the whole study period at DSL. The red dashed lines represent the mean concentrations of Hg species in each season.
Figure 2 displays the time series of atmospheric GEM, PBM, and GOM
concentrations from 1 June 2015 to 31 May 2016 at DSL. The annual
average concentrations of GEM, PBM, and GOM at DSL were
The concentrations of speciated atmospheric mercury in this study and at other sites around the world.
The monthly patterns of GEM, PBM, and GOM during the whole sampling period
are shown in Fig. 3. The seasonal mean GEM concentrations were slightly
higher in winter (2.88 ng m
Monthly variation in GEM, PBM, and GOM concentrations. The 10th, 25th, median, 75th, and 90th percentile values are indicated in the box plots. The red dots represent the mean values.
Figure 4 shows the diurnal variations in GEM, PBM, and GOM during the whole sampling period. To ensure the time resolutions were consistent among all the three mercury species, the temporal resolution of measured GEM was converted from 5 min to the 2 h average. As shown in Fig. 4, GEM concentrations were higher during daytime with the maximum in the morning at around 10:00 LT and minimum at midnight around 02:00 LT. The diurnal trends of GOM were similar to those of GEM, except that the minimum GOM occurred at around 20:00 LT in the evening. The diurnal trends of PBM were different from those of GEM and GOM, exhibiting relatively higher concentrations during nighttime. The PBM maximum occurred in the early morning at around 06:00 LT and the minimum in the afternoon at 18:00 LT. The diurnal trends of GEM, PBM, and GOM were similar to those observed in Nanjing (Zhu et al., 2012), but different from those in Guiyang, Xiamen, and Guangzhou (Wang et al., 2016; Chen et al., 2013). Since DSL and Nanjing both belong to the Yangtze River Delta region, the similar meteorology and emission characteristics within the Yangtze River Delta region may explain the similar diurnal patterns of Hg species between these two sites. The elevated GEM concentrations at DSL during daytime were likely related to the stronger emissions from both human activities and natural releases. GOM and GEM showed similar diurnal variations and both peaked at 10:00 LT, probably suggesting that GOM and GEM were affected by common sources (e.g., coal-fired power plants and industrial boilers). The high PBM concentrations at night were likely derived from the adsorption of Hg species onto the preexisting particles and the subsequent accumulation in the shallow nocturnal boundary layer. Wind speed was relatively low while relative humidity was high at night, which were conducive to the adsorption of GOM onto the particles (Fig. 4).
Annual mean diurnal variation in GEM, PBM, and GOM concentrations.
Figure 5 shows the relationship between wind direction–speed and atmospheric
mercury species. One-third of the prevailing winds came from the east and
16 % from the north at DSL during the study period (Fig. 5a). Wind speed
from all directions during the study period was mainly in the range of
0–6 m s
In order to confirm this hypothesis, the relationship between temperature and Hg concentrations at DSL was investigated. Seasonal temperature in ascending order was divided into different groups and the corresponding mean Hg concentrations are plotted in Fig. 6. In spring, GEM concentration increased with increasing temperature. In the other seasons, similar trends were observed when temperature increased to a certain value. Such a phenomenon was likely caused by temperature-dependent surface emissions, noting that the increasing PBL height with increasing temperature should have offset some of the increased mercury concentration. GOM concentration showed a clearly positive correlation with temperature in summer, largely due to the in situ oxidation of GEM under high-temperature heavy ozone pollution (Qin et al., 2018). Such a good correlation did not exist in the other seasons. As for PBM, it appeared to have weakly negative correlation with PBL height, suggesting the atmospheric diffusion conditions were influential on the concentrations of PBM.
The variation in atmospheric Hg (GEM, PBM, and GOM) and PBL as a function of temperature in all four seasons.
PSCF analysis results suggested that the potential sources for GEM were
mainly located in Anhui, Jiangxi, and Zhejiang provinces, and also possibly
in Shandong Province (Fig. 7a). Seasonal dominant sources affecting GEM at
the DSL site were those in Jiangsu and Zhejiang provinces in spring, in Anhui
and Jiangxi provinces in summer, in Jiangsu Province in autumn, and in Anhui
and Zhejiang provinces in winter (Fig. S1 in the Supplement). In addition, sources in Henan
and Shandong provinces seemed to also play a role in GEM in winter,
suggesting the importance of long-range transport in this season. There
were substantial high PSCF signals in the southern areas, stronger than
those in the northern areas, despite lower anthropogenic emissions in the
south. For example, southern provinces such as Zhejiang and Jiangxi were
estimated to release 25 t yr
Potential source regions of atmospheric Hg (GEM, PBM, and GOM) at the observational site according to PSCF analysis.
The PSCF pattern of PBM was quite different from that of GEM (Fig. 7b). The annual potential source regions for PBM were mainly from the northern areas of Jiangsu and Anhui provinces and from northeastern China including Shandong and Hebei provinces. These provinces were regarded as the main Hg sources areas in China and accounted for approximately 25.2 % of the Chinese anthropogenic atmospheric Hg emissions (Wu et al., 2016). Seasonal sources for PBM were similar in spring, autumn, and winter, but not in summer, for which high PSCF values were mainly located in the southern areas of Shanghai, likely due to the prevailing winds in summer from the south, southeast, and southwest where Zhejiang and Jiangxi provinces were important mercury source regions.
The annual potential sources for GOM were mainly located in Anhui and Zhejiang provinces and the coastal areas along Jiangsu Province (Fig. 7c). The PSCF pattern of GOM was similar to that of GEM but not PBM. The potential source regions of GOM were more from southern than northern China, likely due to the higher atmospheric oxidant levels in the southern regions. Seasonal PSCF patterns for GOM showed dominant sources in Zhejiang and Jiangxi provinces in summer, major sources in inland areas and moderate sources over the East China Sea and Yellow Sea in spring, major sources in Zhejiang Province and moderate sources over the Yellow Sea in autumn, and major sources from the coastal areas of Jiangsu to the vast Yellow Sea in winter. One previous study suggested that the marine boundary layer could provide considerable amounts of oxidants such as chlorine and bromine, which were beneficial for the production of GOM by oxidizing GEM (van Donkelaar et al., 2010) and this mechanism may explain the substantial PSCF signals over the ocean.
The PSCF analysis results discussed above demonstrated the relative contributions from the short-range transport from the adjacent areas of Shanghai and regional and long-range transport from northern and southern China as well as from the ocean on contributing to speciated atmospheric mercury at DSL (more discussion below).
According to the relationships between wind direction and Hg concentration
as well as the PSCF analysis results discussed above, the elevated GEM, GOM,
and PBM concentrations at DSL were generally associated with the wind
sectors from the southwest and north directions. To reveal the relative
importance of local sources and regional transport, the ratio of GOM
Frequency of wind directions under different ranges of GOM
The relationships among GEM, CO, secondary inorganic aerosols (SNAs), and
GOM
GEM concentration fluctuated with a mean value of less than 2.6 ng m
The GEM concentrations as a function of the GOM
PMF modeling has been widely used to apportion the sources of atmospheric pollutants. In this study, GEM together with heavy metals and soluble ions, measured online synchronously, were introduced into the EPA PMF5.0 model to apportion the major anthropogenic sources of GEM. A six-factor solution was selected based on the results of multiple model runs, which can explain the measured concentrations of the introduced species well. The profiles of the six identified PMF factors and contributions of major anthropogenic sources to GEM are shown in Fig. 10. It has to be noted that since no tracers for the natural emissions (e.g., soil, vegetation, and ocean) were available in this study, the identification of natural mercury sources was not possible based on the PMF modeling.
A six-factor source apportionment for anthropogenic GEM based on PMF analysis.
Factor 1 had high loadings of Se, As, Pb,
Factor 2 displayed particularly high loadings of Ni and V. The major sources of Ni in the atmosphere can be derived from coal and oil combustions (Tian et al., 2012), and oil combustion accounted for 85 % of anthropogenic V emissions in the atmosphere (Duan and Tan, 2013). In general, Ni and V have been considered good tracers of heavy oil combustion, which has been commonly used in marine vessels (Viana et al., 2009). Thus, this factor was identified as shipping emissions. The sampling site is adjacent to the East China Sea and is located in Shanghai, which has the largest port in the world. Figure S4 showed similar patterns of Ni and V with high concentrations mainly from the northeast, east, and southeast. To further validate this factor, the time series of GEM concentrations from the shipping factor based on the PMF modeling were extracted and digested into the PSCF modeling (Fig. S5), which showed that the potential source regions were mainly located over the East China Sea and coastal regions. This indicated a factor of 2 from PMF should be representative of the shipping sector as well as oil combustion in motor vehicles and inland shipping activities. Overall, this factor accounted for 19.6 % of anthropogenic GEM and ranked as the second largest emission sector, highlighting the urgent need of controlling the marine vessel emissions.
Factor 3 showed high loadings of Ca and moderate loadings of Ba and Fe. Ca and Fe are rich in the crust and can be used for cement production. As mercury could be released during industrial processes of cement production, Factor 3 was assigned as cement production and accounted for a minor fraction of 6.3 % of the anthropogenic GEM.
Factor 4 was characterized by high loadings of Cr and moderate loadings of Mn, Fe, Ni, and Cu. These species together served as markers of metal smelting. Metal smelting is known to emit large sources of Hg to the atmosphere (Pirrone et al., 2010), especially in the YRD, one of the most developed and industrialized areas in China. This factor accounted for 7.6 % of the anthropogenic GEM.
Factor 5 had high loadings of
Factor 6 was characterized by high loadings of Cd, Ag,
As discussed above, surface emissions were likely to be important sources of
the observed atmospheric mercury. As PMF modeling did not resolve the natural
sources of mercury, PCA (principal component analysis) was applied for
further source apportionment by introducing the temperature parameter. Four
factors are resolved, which totally explained 75.32 % of the variance as
shown in Table 2. Factor 1 accounted for 34.15 % of the total variance
with high loadings for
PCA (principal component analysis) for GEM at DSL. Note that bold
values represent high loading (factor loading
A typical case from 24 to 27 July 2015 was chosen to investigate the
possible formation process of GOM. As shown in Fig. 11, the shaded episodes
represented nighttime from 18:00 to 06:00 LT the next day. Both GEM and GOM
exhibited rising trends during nighttime (Fig. 11a), which was ascribed to
the nighttime accumulation effect due to the very shallow boundary layer
(Fig. 11c). Starting from 06:00 LT in the morning, GEM concentration began to
gradually decline as the boundary layer developed. In contrast, the
concentration of GOM continued to rise from 06:00 LT until it reached the peak
value at around 10:00 LT. During this period, ozone and temperature also kept
rising until they surpassed 200
A case study of GEM oxidation from 24 to 27 July 2015. The time series
of GEM, GOM,
Statistical relationships among GOM,
Temperature variations in each bin of the GOM
The variations in PBM and GOM as a function of PM
A case study of gas–particle portioning between GOM and PBM from
30 December 2015 to 1 January 2016, which was divided into different stages.
The time series of PBM, GOM, PM
Previous studies have shown that PBM can be emitted directly from various
anthropogenic sources such as coal-fired power plants and industries
(Cheng et al., 2014a; Wu et al., 2016). In addition, gas–particle
partitioning was considered to be an important pathway for the formation of
PBM (Zhang et al., 2015; Amos et al., 2012). Since most of the areas in
the YRD belong to non-attainment areas in regard to particulate pollution
and the concentrations of GOM were particularly high at DSL as discussed
above, the role of gas–particle partitioning in the formation of PBM should
be investigated. Previous studies reported that the concentrations of
PM
A short episode from 30 December 2015 to 1 January 2016 was chosen to
further investigate this phenomenon. As shown in Fig. 14, in Stage 1, the
concentrations of PM
In this study, a year-long observation of speciated atmospheric Hg concentrations was conducted at the Dianshan Lake (DSL) Observatory, located on the typical transport routes from mainland China to the East China Sea. Different from many sites in China, GEM at DSL exhibited high concentrations in winter, summer, and spring, which was due to the strong re-emission fluxes from natural surfaces in summer and enhanced coal combustion for residential heating over northern China in winter. The relatively high GOM concentrations in summer indicated that the formation of GOM from GEM oxidation was likely crucial. PBM exhibited high concentrations in winter, indicating the impact of long-range transport. The diurnal patterns of GEM and GOM were similar with relatively high levels during daytime. For GEM, this was likely attributed to both human activities and re-emission from natural surfaces during daytime. For GOM, in addition to direct emissions, high concentrations during daytime were partially ascribed to photochemical oxidation of GEM. The PBM concentrations were higher during nighttime, which was ascribed to the accumulation effect within the shallow nocturnal boundary layer.
The relationship between meteorological factors and atmospheric Hg species
showed that the high Hg concentrations were generally related to the winds
from the south, southwest, and north and positively correlated with
temperature. Both anthropogenic sources and natural sources contributed to
the atmospheric mercury pollution at DSL. Higher GOM
The formation processes of GOM and PBM based on episodic studies were also
investigated. The high GOM concentrations were partially attributed to
strong local photochemical reactions under the conditions of high
All data used in this paper are available by contacting Kan Huang (huangkan@fudan.edu.cn).
The supplement related to this article is available online at:
XQ, KH, and CD conceived the study and wrote the paper. XW, YL, and DW performed the measurements and collected data. All authors have contributed to the data analysis and review of the paper.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
This work was supported by the Natural Science Foundation of China (NSFC, grant nos. 91644105, 21777029), the National Key R & D Program of China (grant nos. 2018YFC0213105), and the Environmental Charity Project of the Ministry of Environmental Protection of China (201409022).
This paper was edited by Leiming Zhang and reviewed by three anonymous referees.