Introduction
Mercury (Hg) is an environmental concern due to its health effects on humans
and wildlife (Mergler et al., 2007). This trace element
undergoes long-range transport in the atmosphere and is readily cycled at
the Earth's surfaces (Selin, 2009), and thus even the
remote Antarctic Plateau, a vast (about 5×106 km2) and
elevated (about 3 km above sea level) region of snow-covered ice, receives
significant mercury inputs (Dommergue et al., 2010).
Over the past decade, field studies have investigated mercury in air and/or
snow at a few inland Antarctic stations, i.e., Concordia Station (Dome C,
75∘ S, 123∘ E), Dome Argus (80∘ S, 77∘ E), Dome Fuji (77∘ S, 40∘ E), and the South Pole
(90∘ S), as well as along several transects on the plateau
(Brooks et al., 2008; Dommergue et al., 2012; Han et al., 2014; Li et
al., 2014; Angot et al., 2016b, c; Wang et al., 2016; Han
et al., 2017; Spolaor et al., 2018). Most of these studies only measured
atmospheric mercury in austral summer, whereas
Angot et al. (2016c) reported a year-round
observational record at Dome C. All these measurements suggest that in
summer (November–February), a photochemical mercury cycle occurs between the
atmospheric boundary layer and surface snowpack, including the oxidation of
gaseous elemental mercury (Hg0) in air, the deposition of oxidized
mercury (HgII) onto snow, the photoreduction of snow HgII, and the
re-emission of Hg0 from the snowpack surface. A clear diurnal cycle of
Hg0 (peaking at midday and decreasing to a minimum around midnight) was
observed in near-surface air and has been attributed to enhanced Hg0
re-emission in the daytime as a result of increasing solar radiation
(Dommergue et al., 2012; Angot et al., 2016c; Wang et al., 2016). The
summertime photochemical mechanism of Hg0 oxidation in air is unknown
but has been related to the high oxidizing capacity of the plateau, which is
characterized by high concentrations of NOx, OH, and other oxidants
within the Antarctic mixed layers (Eisele et al., 2008; Helmig et al.,
2008a, b; Neff et al., 2008; Kukui et al., 2014; Frey et
al., 2015). Interestingly, such summertime diurnal variations of Hg0
have not been seen at the polar inland Summit Station atop the Greenland ice
sheet (Brooks et al., 2011). As for other seasons,
observations at Dome C showed high atmospheric Hg0 in fall (March–April),
exceeding those measured at the Antarctic coast and southern hemispheric
midlatitude sites. Such seasonal cycles were repeatedly measured in
2012–2015 at Dome C (Angot et
al., 2016a). Moreover, in fall, the concentrations of Hg0 peaked during
the night. In winter (May–August), as expected, the diurnal cycle of Hg0
disappeared, and a gradual decline of Hg0 was seen in near-surface air.
Overall, these observed seasonal and diurnal features of atmospheric mercury
on the plateau are not well understood and not reproduced by global chemical
transport models, likely due to their imperfect representations of boundary
layer dynamics and chemical reaction pathways
(Angot et al., 2016a) and to the
singularity of their longitude–latitude grid at the poles. Here, we present
detailed box model calculations to interpret observational data collected at
Dome C in 2013, and to explore important chemical and physical processes
controlling diurnal and seasonal variations of atmospheric mercury. A better
knowledge of these characteristics is helpful for evaluating the potential
influence of the Antarctic Plateau on the coastal environment
(Bargagli, 2016) and for understanding processes occurring in
other polar regions.
Methods
We have built a multiple-layer box model to account for mercury chemistry
and transport in the lower troposphere and surface snow, and the exchange
between them. Details on the model setup are given in this section. The
modeling results are mainly compared with the measurement data of Hg0
in the year 2013. Briefly, Hg0 concentrations were measured at three inlets
(25, 210, and 1070 cm above the surface) of a meteorological tower located in
the “clean area” of Dome C (where snow is kept undisturbed). Hg0
concentrations were also measured in the near-surface air and snow
interstitial air with multi-inlet snow sampling manifolds (the so-called
“snow towers”). The mercury measurements were performed using a Tekran
2537A automated analyzer (Tekran Inc., Toronto, Canada). The experimental
details have been described in Angot et al. (2016c).
Major assumptions and simplifications made in the
mercury model.
Description
Note
Physical or chemical processes not considered
Horizontal transport
The model is not expected to capture day-to-day variability
Photoreduction of HgII in aqueous cloud/aerosol
The air is cold and dry
Wet deposition of HgII (snowfall and diamond dust)
Large uncertainty in its parameterization
Exchange with deep snowpack Hg
The diffusive transfer is expected to be slower
Simplifications for specific species or parameters
Free tropospheric Hg concentration
Specified based on CTMs
HOx concentration
Estimated based on OPALE measurements, NO, and J(NO2)
BrOx concentration
Specified based on CTMs
Air turbulent diffusion coefficient (Kz)
Modeled by MAR (with an optional adjustment for warming events)
Dry deposition velocities (Vd)
Typical values from the literature
Depth of surface snow layer
Specified based on e-folding light penetration depth
Air–snow molecular diffusion coefficient (Dm)
Typical value from the literature
Air–snow turbulent diffusion coefficient (Dt)
Parameterized based on surface level turbulent kinetic energy (TKE)
Model overview
The model accounts for vertical transport using outputs from a regional
climate model (Sect. 2.2). As shown in Fig. 1, Hg0 can be oxidized to
HgII by different gas-phase chemical schemes (Sect. 2.3). The
photoreduction of HgII in aqueous clouds and aerosols is not considered
in the model because its mechanism is poorly understood, and also because
the air above the plateau is cold and dry. The vertical resolution is
∼2 m near the surface and gradually decreases with height
above the surface, and there are 33 atmospheric layers in total below 500 m.
In the free troposphere, Hg0 and HgII concentrations are
prescribed (Sect. 2.4). Hg0 and HgII are transferred from air to
snow through dry deposition (Sect. 2.5). Wet deposition is not considered
due to low snow accumulation rates and large uncertainty in parameterizing
this process (France et al., 2011; Palerme et al., 2017). Note that
Spolaor et al. (2018) have recently suggested that frequent
snowfall and diamond dust (tiny ice crystals) events in summer may lead to
quick mercury deposition. However, a quantitative parameterization for this
process has not been available, and it is thus not included in this model.
The model tracks Hg0 and HgII in a surface snow reservoir, in
which HgII may be reduced to Hg0 photolytically or in the dark
(Sect. 2.5). The depth of the surface snow layer is set to 20 cm, equivalent
to one to two e-folding light penetration depths at Dome C
(France et al., 2011). The exchange of mercury
between the surface snowpack and the deeper snowpack is not considered in
the model because the photochemistry in the deeper snowpack is less active,
and also because the diffusive transfer of Hg0 between these two snow
layers should be slower. Our model calculations are not expected to capture
day-to-day variations, since horizontal transport is ignored, and are thus
compared with the average monthly and diurnal observations at Dome C as
reported in Angot et al. (2016c). Major
assumptions and simplifications made in the model are summarized in Table 1.
Chemical and physical processes represented in
the mercury box model. Hg0 can be oxidized to HgII by three
different gas-phase chemical schemes (OH, O3, or a two-step
Br-initiated scheme). Note that the concentrations of the intermediate
HgI in the two-step Br-initiated oxidation mechanism are not tracked
since its lifetime is short, and thus effective reaction rates are used to
describe the oxidation of Hg0 to HgII for this mechanism (Sect. 2.3).
The dark reduction of surface snow HgII may be only important for
the non-summer period (Sect. 2.5).
Meteorology
A surface-based temperature inversion layer exists at Dome C for most of the
year, mainly due to radiation imbalance, while a convective mixed layer up
to several hundred meters in depth develops during the daytime in summer in
response to surface heating (see the Supplement, Sect. S1)
(Pietroni et al., 2014). Here, the depth of the inversion/mixed
layers is specified as ∼500 m in our model, and the air above
is regarded as the free troposphere. The vertical atmospheric transport is
represented with turbulent diffusion coefficients (Kz) from the
polar-oriented regional climate model MAR (Modèle Atmosphérique
Régional) (Sect. S1). The MAR data have been used to
simulate several other atmospheric species (e.g., NOx and HONO) in the
2011–2012 summer Oxidant Production in Antarctic Lands and Export (OPALE)
campaign at Dome C (Legrand et al., 2014; Frey et al., 2015; Preunkert et
al., 2015). In general, MAR simulations agree well with meteorological
observations at Dome C (Gallée and Gorodetskaya, 2010; Gallée et
al., 2015), whereas the intermittent warming events occurring primarily
during the non-summer period, which decrease temperature inversion strength
and strongly enhance vertical turbulence (leading to large Kz values),
may not be well represented. The vertical temperature gradients measured at
a meteorological tower at Dome C indicate that the actual intensities of
warming events should be weaker than results from MAR
(Genthon et al., 2010). This is likely related to the cloud
microphysical scheme in MAR, which is responsible for estimating the cloud
cover and thus affects the estimation of surface temperature and buoyant
forcing of turbulence. For example, in the wintertime, when the cloudiness
is overestimated by the model, the downward infrared radiation is also
overestimated. This overestimation limits surface cooling and subsequently
the inhibition of turbulence, which is essentially generated by the wind
shear. An accurate estimate of the warming events is challenging, and here
we tentatively adjust MAR-modeled Kz values during warming events using
a rough empirical relationship between the temperature gradients and
Kz, resulting in weaker exchange between the surface layers and free
troposphere. It is important to note that such an adjustment is subject to
large uncertainties and tends to underestimate the strength of vertical
turbulence (Sect. S1). Thus, due to uncertainties in
estimating warming events and their effects on the vertical transport of
mercury in the non-summer period, both original and adjusted Kz values
are used to drive the mercury model in this study.
Gas-phase mercury reactions used in the mercury
model.
No.
Reaction
Rate constanta
Reference
R1
Hg0+O3→HgII
k1=1.7×10-18 (upper)
Schroeder et al. (1991)
k1=3×10-20 (lower)
Hall (1995)
R2
Hg0+OH→HgII
k2=3.2×10-13×(T/298)-3.06 (upper)
Goodsite et al. (2004)
k2=8.7×10-14 (lower)
Sommar et al. (2001)
R3
Hg0+Br→HgIBr
k3=3.2×10-12 (upper)
Ariya et al. (2002)
k3=1.46×10-32×(T/298)-1.86×[M] (lower)
Donohoue et al. (2006)
R4b
HgIBr→Hg0+Br
k4 [s-1] =k3/Keq
Dibble et al. (2012)
R5
HgIBr+Br→Hg0+Br2
k5=3.9×10-11
Balabanov et al. (2005)
R6
HgIBr+NO2→HgII
k6=8.6×10-11
Dibble et al. (2012); Wang et al. (2014)
R7
HgIBr+OH→HgII
k7=6.3×10-11
Dibble et al. (2012); Wang et al. (2014)
R8
HgIBr+HO2→HgII
k8=8.2×10-11
Dibble et al. (2012); Wang et al. (2014)
R9
HgIBr+Br→HgII
k9=6.3×10-11
Dibble et al. (2012); Wang et al. (2014)
R10
HgIBr+BrO→HgII
k10=1.1×10-10
Dibble et al. (2012); Wang et al. (2014)
a Rate constants are in cm3 molecule-1 s-1 unless
otherwise stated. T represents temperature in K. [M] is the number density
of air in molecule cm-3. The “upper” and “lower” indicate the highest and
lowest reaction rate constants determined by different kinetic studies (for
a review, see Ariya et al., 2015), respectively. The
uncertainty ranges of reaction rate constants of R4–R10 are unknown as only
computational kinetic data are available for these reactions
(Jiao and Dibble, 2017). b R3 and R4 are a pair of
reversible reactions. Keq=9.14×10-24e7801/Tcm3molecule-1 is the equilibrium constant estimated by
Dibble et al. (2012), which is very close to the value
of 9.25×10-23×(T/298)-2.76e7292/T cm3 molecule-1 calculated by Goodsite et al. (2012).
Atmospheric mercury chemistry
In the model, Hg0 is oxidized in the atmosphere to HgII, while the
oxidants, chemical kinetics, and oxidant concentrations are all uncertain.
As shown in Table 2, the rate constants of Hg0 reactions with O3
(R1), OH (R2), and Br (R3) from existing theoretical and experimental studies may
vary by factors of about 60, 8, and 4, respectively. While used in several
chemical transport models, O3- and OH-based chemical mechanisms are
unlikely as pure gas-phase reactions since the formation of HgO is
endothermic (Subir et al., 2011). The two-step
Br-initiated scheme (R3–R10) can explain polar atmospheric mercury depletion
events (Sprovieri et al., 2005; Steffen et al., 2008) and is likely the
dominant Hg0 oxidation pathway globally (Holmes et al., 2006;
Horowitz et al., 2017; Ye et al., 2018). The recombination of Hg0 and
Br forms unstable HgIBr, which either dissociates or is oxidized to
HgII by NO2, HO2, OH, Br, or BrO. The effective oxidation
rate constant of this two-step scheme is expressed in Eq. (1), assuming a
steady state of HgIBr, as it forms slowly by R3, and is oxidized readily
by R6–R10, where terms in brackets refer to concentrations, and
k3-k10 are reaction rates of R3–R10. The gas-phase oxidations of
Hg0 by other species and the aqueous and heterogeneous processes are
not considered here (Sect. S2) (Lin and Pehkonen, 1999; Subir
et al., 2011; Ariya et al., 2015).
keff=k3Br⋅(k6NO2+k7OH+k8HO2+k9Br+k10BrO)k4+k5Br+k6NO2+k7OH+k8HO2+k9Br+k10BrO‾
Concentrations of chemical species, including O3, HOx (OH,
HO2), BrOx (Br, BrO), and NOx (NO, NO2), are prescribed
based on the available measurements and global chemical transport model
(CTM) simulations (details in Sect. S3). Monthly and diurnal
averages are computed. The temporal variations of O3 and NOx are
specified based on in situ measurements in near-surface air (Angot et al.,
2016c; Legrand et al., 2016a; Helmig et al., 2018), and a uniform O3
vertical profile within the inversion/mixed layers is assumed, consistent
with aircraft observations on the plateau (Slusher et al., 2010; Legrand
et al., 2016a). The NOx vertical profile has not been measured and is
estimated assuming an exponential decay with height starting at the surface
(Slusher et al., 2010). The
previously reported potential bias in the measurement ratios of
[NO]/[NO2] (Frey et al., 2015) does not
significantly affect our model results, as suggested by a sensitivity test.
The HOx concentrations in summer are set based on measurements from the
OPALE campaign, and their values in other seasons are estimated using
relationships with J(NO2) and NO (Kukui et
al., 2014). The uncertainties in O3 and OH concentrations are assumed
to be 2 % and 50 %, respectively, as inferred from in situ measurements at Dome
C (Kukui et al., 2014).
For BrO concentrations, due to lack of measurements, we rely on two global
CTMs, GEOS-Chem and p-TOMCAT (Yang et al., 2005; Sherwen et al., 2016). We
assume no diurnal and vertical variations of BrO (Stutz et al., 2011;
Legrand et al., 2016b). The modeled BrO mixing ratios from these two CTMs
are similar: less than 0.1 pptv in winter and ∼0.4 pptv in
other seasons (Fig. S8 in the Supplement). The modeled BrO is likely at the lower
limits of its uncertainty range, as suggested by the comparison of the
modeled tropospheric BrO columns and their values retrieved from the Global Ozone Monitoring Experiment-2
(GOME-2) satellite (Sherwen et al., 2016).
Legrand et al. (2016b) measured total inorganic gaseous
bromine concentrations at Dome C and suggested that the upper limit of BrO
is ∼1 pptv. Based on the above information, the uncertainty
of BrO concentrations is set as a factor of 2.5. It is important to note
that the seasonal patterns of the modeled BrO by the CTMs may have biases,
as indicated by the total inorganic bromine measurements at Dome C
(Legrand et al., 2016b). The modeled BrO is likely
biased high in fall and spring, which affects Hg0 concentrations
simulated by the mercury model (Sect. 3.4). The concentrations of Br are
estimated assuming a photochemical steady state:
[Br]/[BrO]=(JBrO+kBrO+NO[NO])/(kBr+O3[O3])
(Holmes et al., 2010), where JBrO is the BrO
photolysis frequency, and kBrO+NO and kBr+O3 are rate constants for
BrO+NO→Br+NO2 and Br+O3→BrO+O2, respectively (Sander et al., 2011).
Mercury concentrations in the free troposphere
Due to lack of measurements, we rely on two global CTMs, GEOS-Chem (version
9-02) and the Global European Monitoring and Evaluation Programme (EMEP)
Multi-media Modelling System (GLEMOS), to specify the free tropospheric mercury concentrations
(Angot et al., 2016a; Travnikov et al., 2017). The former uses a Br
oxidation scheme, whereas the latter assumes OH and O3 to be the
oxidants of Hg0. Monthly Hg0 and HgII concentrations at 500 m
above ground level in the Dome C grid box are extracted from these two CTMs.
Studies have identified that the CTMs show significant seasonal biases in
modeled mercury concentrations when compared to mercury observations at two
southern hemispheric background stations, Amsterdam Island (38∘ S,
78∘ E) and Cape Point (34∘ S, 18∘ E) (Angot
et al., 2014; Song et al., 2015; Horowitz et al., 2017; Martin et al.,
2017), implying potential biases in modeled mercury budgets for the Southern
Hemisphere. Hence, we adjust the modeled free tropospheric mercury
concentrations using the scaling factors estimated by model–observation
comparisons for these two background stations:
Ri,j=Xobs,i,j‾/Xmod,i,j‾, where X‾ represents the average mercury
concentrations, and i and j indicate each month and model, respectively. The
two CTMs predict similar total gaseous mercury (HgT=Hg0+HgII) concentrations with annual means of ∼1.0 ng m-3, whereas the modeled HgII concentrations during the sunlit
period are much higher in GEOS-Chem than in GLEMOS due to their different
chemical mechanisms (Fig. S9). In our simulations, the free
tropospheric mercury data are chosen from either GEOS-Chem or GLEMOS
according to the chemical oxidation scheme (O3, OH, or Br) used in each
model scenario, for consistency. For example, the GEOS-Chem free
tropospheric mercury data are used when the Br scheme is assumed in the box
model simulation. Both CTMs use reaction rate constants at the lower limits.
When the upper-limit reaction rates are assumed in the model scenarios, we
expect more mercury should exist in its oxidized form, HgII, in the
free troposphere, and thus we adjust free tropospheric concentrations of
Hg0 and HgII according to this equation:
HgupperII/Hgupper0=R×HglowerII/Hglower0,
where R is the ratio between the upper- and lower-limit reaction
rate constants, whereas the total HgT concentrations remain unchanged.
Air–snow mercury exchange and snow mercury transformation
Dry deposition fluxes of Hg0 and HgII are determined by their
concentrations at the atmospheric ground level and prescribed deposition
velocities (Vd). The effects of wind speeds and snow properties on
Vd are not included here. As indicated by previous studies (Lindberg
et al., 2002; Brooks et al., 2006; Skov et al., 2006), the values of
Vd for Hg0 and HgII are set to 1×10-4 and
1 cm s-1, respectively (Zhang et al., 2009). These Vd
parameters are not well constrained, but we find that varying the values of
Vd by a factor of 2 does not change the main findings of this study. For
Hg0, the bidirectional fluxes between surface snow and air are
considered and estimated by Hg0 concentration differences and the
turbulent and molecular diffusion coefficients in the snow interstitial air.
Following Durnford et al. (2012), the molecular
diffusion coefficient (Dm) in our model is set to 6×10-6 m2 s-1. The turbulent diffusion coefficients
(Dt) can be estimated by an explicit representation of the vertical wind
pumping within the snowpack, which include several uncertain parameters,
such as the height and wavelength of sastrugi (snow-eroded grooves or
ridges) and the permeability of surface snowpack (Cunningham and
Waddington, 1993; Thomas et al., 2011; Zatko et al., 2013; Toyota et al.,
2014b). The estimated values of Dt using this approach and the air and
snow properties at Dome C may vary from the order of 10-6 to 10-4 m2 s-1 for the surface snowpack with a depth of 20 cm. Here, a
more simple approach is adopted following Durnford et al. (2012), in which Dt is set proportional to the atmospheric
ground-level turbulent kinetic energy (TKE) obtained from the MAR model: Dt=
TKE (m2 s-2) ×3×10-3 s. Dt varies
by season and by time of day and has an annual median value of 3×10-4 m2 s-1. The choice of the scaling factors (3×10-3 s by default in the model) is found to affect the modeled
Hg0 concentrations in the snow interstitial air (Sect. 3.2). A more
explicit consideration of the influence of air and snow properties on
air–snow exchange is recommended for future mercury modeling studies.
Previous studies have suggested that HgII can be reduced both
photolytically and in the dark, and the photolytic and dark oxidation of
Hg0 may also occur, but the reaction rates and reductants/oxidants of
individual pathways are largely unknown (for a review, see
Durnford and Dastoor, 2011). Sunlight, in particular UV-B
(280–320 nm) radiation, greatly enhances the formation of Hg0
(Poulain et al., 2004; Dommergue et al., 2007; Johnson et al., 2008).
Similar to previous models (Durnford et al., 2012; Toyota et al., 2014a),
we include a first-order photoreduction of HgII in the surface snowpack
and scale its rate by J(O(1D)), the photolysis frequency of O3. In
doing so, we assume that the supply of reductants is ample and that all
HgII is reducible (Durnford and Dastoor, 2011). The
photoreduction rate is poorly constrained, with a corresponding lifetime
(denoted as τPR) from a few days to several weeks
(Toyota et al., 2014a). We also include dark reduction
of snow HgII (the corresponding lifetime denoted as τDR) in
our model simulations for the non-summer period (Sect. 3.4).
Results and discussion
Atmospheric Hg0 oxidation rates
We have computed ranges of atmospheric Hg0 oxidation rates for
different schemes (O3, OH, and two-step Br), using the low (i.e.,
lower-limit) and high (i.e., upper-limit) rate constants listed in Table 2 and
uncertainties of oxidant concentrations (Sect. 2.3). As shown in Fig. 2, the
Hg0 oxidation rates for these schemes in the inversion/mixed layers
have large uncertainty ranges. Since the OH and Br concentrations are
largely determined by the amount of solar radiation, the oxidation rates of
Hg under these schemes exhibit strong seasonal and diurnal variations, while
the O3 scheme does not. In austral summer (November–February), the two-step Br
oxidation scheme (corresponding Hg0 oxidation lifetimes denoted as
τOX ∼1.7–22 days) is more efficient than the
O3 (τOX ∼19–1300 days) and OH (τOX ∼17–350 days) oxidation schemes. We find that the
fast two-step Br oxidation is favored by low ambient temperature, high
concentrations of NOx, and low concentrations of O3 at Dome C.
This is because the thermal dissociation rates of the intermediate
HgIBr decrease rapidly at a lower temperature, and because the
concentrations of Br are influenced by the concentrations of NOx and
O3 (Sect. 2.3). In austral winter (May–August), by contrast, the O3
oxidation scheme (τOX ∼13–900 days) is usually
more efficient than the others. A series of combinations of oxidation
schemes, oxidant concentrations, and chemical kinetics are tested in our
model simulations.
Uncertainty ranges of atmospheric
Hg0 oxidation rates within the
inversion/mixed layers: (a) O3, (b) OH, and (c) Br. Monthly and
diurnal variations in the year 2013 are shown in the shaded regions. Note that
the y axis is in log scale.
Comparison of seasonal and diurnal variations of
near-surface atmospheric Hg0 concentrations between
observations and model. Panels (a)–(c) show monthly and diurnal Hg0
observations in the year 2013 and modeling results from different scenarios.
Panels (d)–(f) show diurnal Hg0 ranges calculated from the maximum and minimum
hourly concentrations in each month. The shaded regions indicate 25 % and
75 % percentiles in observations. Observations were conducted at 25 cm
above the snow surface at Dome C. The name of each scenario reflects the
atmospheric oxidant, its concentration levels, chemical reaction rates (H indicates “high” or
“upper”; L indicates “low” or “lower”), and the photoreduction rates of snow mercury (in
days). For example, the scenario with name “O3_HH_3d” assumes O3 as the oxidant, and high oxidant
concentrations and high reaction rates are applied, and τPR is
set to 3 days.
Strong photochemistry in summer
During the summer months, the observed Hg0 concentrations in
near-surface Dome C air show a pronounced diurnal pattern, which usually
peaks in the daytime and is minimized at night, as shown in Figs. 3 and S10. The amplitudes of diurnal variations of observed Hg0
reach ∼0.4 ng m-3 in January and ∼0.3 ng m-3 in February
and November, respectively, which are higher than those during other seasons.
This characteristic has been attributed to enhanced re-emissions of Hg0
in the daytime (Angot et al., 2016c; Wang et al., 2016), highlighting a
dynamic Antarctic surface snowpack. The solar zenith angle has a diurnal
cycle during summer, and a convective layer develops in the daytime as a
response to surface heating, enhancing strengths of vertical mixing and snow
ventilation. Previous studies have suggested rapid recurring cycles of
oxidation and re-emission of Hg0 in summer, but chemical mechanisms have
not been well defined (Angot et al., 2016c; Wang et al., 2016). As
photochemical processes in the air and surface snow are of obvious
importance for summer, we have conducted a series of mercury model
sensitivity simulations by varying atmospheric oxidants (O3, OH, or
Br), their concentrations (high or low) and chemical reaction rate constants
(upper or lower), and surface snow HgII photoreduction rates (τPR from 3 days to 3 weeks). In total, we ran 24 model
sensitivity scenarios (Table S1 in the Supplement). Through comparing
modeling results to observations, key atmospheric Hg0 oxidants may be
identified, and surface snow HgII photoreduction rates may be
constrained. Some of these scenarios have large biases compared to
observations for the non-summer months, which is likely due to several
factors in these simulations that will be discussed in detail in Sect. 3.4:
(1) the adjusted Kz values during the warming events are used, which
tends to underestimate the mercury vertical transport from the free
troposphere, (2) the Br concentrations used in the model calculations are
likely overestimated in the non-summer period, and/or (3) the dark reduction
of snow HgII, which may be important in the non-summer period, is not
included.
The modeled Hg0 concentrations in near-surface air from various
scenarios are compared to observations in Fig. 3 and in Sect. S4 (only the data collected at 25 cm above the surface are shown, and the
model–observation comparison results for the data at 210 and 1070 cm are
similar). We find, during summer, that model scenarios using either OH or
O3 oxidation schemes do not reproduce the diurnal variations of
Hg0, and tend to overestimate atmospheric Hg0 concentrations,
even when high oxidant concentrations and upper-limit reaction rates are
assumed (resulting in τOX∼20 days). Among the
scenarios with the bromine oxidation scheme, BR_
HH_14d (using high Br concentrations and upper-limit reaction
rate constants; τOX∼2 days and τPR of
2 weeks in summer) best reproduces the concentrations of atmospheric
Hg0 and its diurnal patterns during the summer months (calculated
normalized root mean square errors of < 20 %; Sect. S4).
This scenario shows larger Hg0 diurnal variations in January–December than
February–November, consistent with observations (Angot et al., 2016c; Spolaor et
al., 2018). The differences in solar radiation in these summer months are
expected to influence the strength of photochemical activities (such as Br
concentration and photoreduction rates of snow HgII). Therefore, these
sensitivity simulations suggest that a fast oxidation for atmospheric
Hg0 occurs in the surface layers at Dome C in summer, and that the fast
oxidation of Hg0 may be provided by a two-step Br scheme with its
upper-limit reaction rates.
Summertime average Hg0
concentrations at different heights from observations and model. The
observations include the meteorological tower (25, 210, and 1070 cm above
the snow surface) and snow tower (50 cm above the snow surface and 10, 30, 50, and
70 cm below the snow surface). Model results from the BR_
HH_14d scenario are shown. Measurement data are from the snow tower
no. 1 as reported in Angot et al. (2016c). Error
bars indicate 25 % and 75 % percentiles.
The summertime average Hg0 concentrations modeled by the BR_HH_14d scenario
are also compared with those
measured at different sampling heights, as shown in Fig. 4. The snow tower
measurements indicate that Hg0 concentrations in the surface snow
interstitial air (10 cm below the surface) are about 0.2 ng m-3 higher than
those in the air (50 cm above the surface). The model predicts a similar
Hg0 difference of about 0.3 ng m-3. These results suggest the
snow-to-air transport of Hg0 and the production of Hg0 in the
surface snowpack. It is noted that the modeled difference in Hg0
concentrations depends on the assumed turbulent diffusion coefficients
(Dt). Larger Dt implies faster vertical mixing of Hg0 and thus
corresponds to smaller differences between the surface snowpack and
atmosphere (Fig. S12). The measured Hg0 concentrations in
the interstitial air of the deeper snowpack are lower than those in the
surface snowpack, suggesting that the production of Hg0 may mainly
occur in the snow near the surface. In the model, the production of Hg0 in
surface snow arises from the photoreduction of HgII during summer. The
photoreduction rates of surface snow (top 20 cm) HgII in
BR_HH_14d (τPR of 2 weeks) agree
well with observations at the South Pole in Brooks et al. (2008),
who estimated a lifetime of surface snow mercury (assumed to be the top 15 cm) of ∼16 days. The surface snow mercury concentrations
modeled by BR_HH_14d are ∼20 ng L-1 (Fig. S13). The available measurements suggest that
surface snow mercury concentrations were highly variable, ranging from
∼3 to 50 ng L-1 (Angot et al., 2016c; Spolaor et al.,
2018).
Summertime diurnal cycles of Hg0
concentrations and fluxes. Panel (a) shows the modeled vertical distributions of
Hg0 concentrations in near-surface air, (b) the modeled Hg0 fluxes
in the inversion/mixed layers, (c) the modeled Hg0 concentration
averaged for 0–50 m above the snow surface, and (d) the modeled Hg0 fluxes
for the air in 0–50 m above the snow surface. Model results from the BR_HH_14d scenario
are shown.
The summertime vertical and diurnal profiles of modeled Hg0
concentrations in near-surface air are shown in Fig. 5a. Model results are
from the BR_HH_14d scenario (using high Br
concentrations and upper-limit reaction rates; τOX
∼2 days and τPR of 2 weeks), which best reproduces
the observed Hg0 in summer. We find that the diurnal variation ranges
of Hg0 are greater than 0.2 ng m-3 only for near-surface levels
from snow to about 50 m above. As shown in Fig. 5b, the summertime Hg0
cycles in the inversion/mixed layers are primarily driven by diffusion from
snow and oxidation loss. The dry deposition and transport from the free
troposphere are insignificant. The amplitude of Hg0 oxidation loss
increases during the daytime due to enhanced photochemical activities.
Diffusion of Hg0 from surface snow is controlled by the rate of snow
HgII photoreduction, which also peaks in the daytime. The diurnal
profiles of the modeled Hg0 fluxes from simulations using the O3
and OH oxidation schemes are given in Fig. S14. As expected,
the amplitudes of their fluxes are much smaller than this bromine oxidation
model scenario. In order to elucidate the drivers of strong diurnal
variations of Hg0 in near-surface vertical levels in summer, we
calculated the diurnal cycles of Hg0 concentrations and all related
fluxes for 0–50 m above the snow (Fig. 5c and d). The net diffusion of
Hg0 refers to difference in its diffusion from snow and to upper
levels. The latter is controlled by the changing mixed layer heights, which
are low at night (< 50 m) and strongly increased during the daytime
(Angot et al., 2016c). Thus, at night, all
Hg0 diffused from snow remains inside the shallow mixed layer, while in
the daytime a large fraction is transferred to the air above 50 m. The net
Hg0 flux, the derivative of its diurnal variation, is determined by the
net diffusion and oxidation loss of Hg0. As shown in Fig. 5d, the net
flux is positive in the morning but becomes negative in the afternoon, thus
leading to the Hg0 maximum around noon. Overall, the diurnal variations
of Hg0 in near-surface levels in summer are determined by the changes
in the Hg0 oxidation loss, snow HgII photoreduction, and mixed
layer depth, all of which are in turn controlled by the strong photochemical
activity during this time period at Dome C.
Furthermore, our model results suggest that the air above Dome C is enriched
in HgII during summer, consistent with its strong photochemical
activity. As shown in Fig. 6, the predicted HgII by the BR_HH_14d scenario
increases with height, from
∼0.1 near the surface to ∼0.5 ng m-3 at 500 m. Such HgII concentrations are comparable to the
levels identified in the upper free troposphere for the midlatitudes
(Bieser et al., 2017). A
diurnal pattern of HgII with higher concentrations in the afternoon is
predicted in near-surface air by the model. These characteristics should be
verified by future measurement studies. Preliminary field sampling using
polyethersulfone cation-exchange membranes in a 2014–2015 summer campaign
obtained HgII of about 0.4 ng m-3 (average concentration from
three filter samples) (Angot, 2016).
Comparison with summertime data at Summit Station, Greenland
Dome C (75∘ S, 123∘ E; 3 km above sea level) and Summit
Station, Greenland (73∘ N, 38∘ W; 3.2 km above sea level), are both
located in high altitude and far from the ocean (hundreds of kilometers). As
a result, their meteorological and chemical conditions have similarities. In
summer, both stations have shallow boundary layers that are stable at night
but convective during the day (Helmig et al., 2002; Cohen et al., 2007;
Van Dam et al., 2013). Active bromine chemistry was found to occur at Summit Station
in summer (Thomas et al., 2011), and the average
BrO mixing ratios in near-surface air were 0.9–1.5 pptv (Liao et al.,
2011; Stutz et al., 2011), comparable to the 1 pptv upper limit at Dome C
(Legrand et al., 2016b). Thus, it is expected that these
two stations may have similar mercury variabilities in near-surface air.
Brooks et al. (2011) measured atmospheric mercury
concentrations in the summer of 2007–2008 at Summit Station but did not observe a
significant diurnal cycle of Hg0 peaking at noon as was seen at Dome C.
Based on our model analysis, we can identify several potential factors that
can contribute to differences in the diurnal cycles of Hg0 between
these two inland polar locations.
First, although BrO concentrations at Summit Station are comparable or higher than
at Dome C, the concentrations of Br at Summit Station, the primary oxidant of
Hg0, may be much lower. As described in Sect. 2.3, the
[Br]/[BrO]
ratios are positively related to the concentrations of NO and negatively
related to the concentrations of O3. Reported summertime NOx
concentrations at Summit Station (∼20 pptv) are lower than at Dome C
(∼300 pptv), whereas O3 at Summit Station (∼50 ppbv) is approximately 2 times that at Dome C (∼25 ppbv)
(Helmig et al., 2008a; Frey et al., 2015; Kramer et al., 2015; Van Dam et
al., 2015; Huang et al., 2017). The larger NOx concentrations at Dome C
have been suggested to arise in part from larger NOx emissions from
surface snow, which are in turn driven by the photolysis of nitrate in the
surface snowpack (Frey et al., 2015). A
back-of-the-envelope calculation shows, assuming the same BrO
concentrations, that Br concentrations at Dome C would be on average a
factor of 6 higher than at Summit Station. Second, the thermal dissociation rate of
the intermediate HgIBr at Summit Station should be 1 order of magnitude
greater than that at Dome C. This is because this rate strongly depends on
temperature (Table 2), and the ambient temperature at Summit Station is about 15 K
higher than at Dome C. Third, the oxidation of HgIBr by NO2 (the
dominant second step oxidant) is significantly slower at Summit Station than at Dome
C, due to their different concentrations of NO2. In fact, the rates of
oxidation by NO2 and dissociation of HgIBr are comparable at
Summit Station. This is in contrast with Dome C, where the oxidation by NO2 can
easily outcompete the thermal dissociation of HgIBr. All in all, we
expect that the Br-initiated oxidation of Hg0 should be slower at
Summit Station than at Dome C, leading to weaker oxidation/re-emission cycling of
Hg0 during summer. It is also noted that atmospheric circulation on
Greenland may be influenced by stronger synoptic-scale events than over the
Antarctic Plateau, because the air is thicker over the Greenland ice sheet
(leading to a weaker decrease of relative vorticity when a large-scale eddy
propagates from the ice sheet margin towards the center). However, the
impact of this circulation pattern on the diurnal cycle of Hg0 is
unclear.
Summertime diurnal and vertical profiles of atmospheric
HgII concentrations. Panel (a) shows both diurnal and
vertical distributions, and (b) only shows the average vertical profile.
Model results from the BR_HH_14d scenario are
shown.
Non-summer period
We showed above that the model simulations including the photoreduction of
snow HgII and a fast bromine oxidation of atmospheric Hg0 could
reasonably explain the observed atmospheric mercury variations during
summer. However, these simulations strongly underestimate Hg0
concentrations in the non-summer months (Fig. 3), when solar radiation is
weakened or completely absent. Based on our understanding of air and snow
mercury cycling (Fig. 1), such model–observation discrepancies may imply,
for the non-summer period, that in the model the vertical transport of
mercury from the free troposphere is underestimated, the reduction of snow
HgII is underestimated, and/or the oxidation of atmospheric Hg0 is
overestimated. All these processes are poorly constrained in the non-summer
period in part because previous studies have mainly focused on the summer
season. The model performance can be improved by modifying the
representation of these processes.
First of all, it is important to note in the above simulations that the
adjusted Kz values in the warming events are used to drive the mercury
model, which tends to underestimate the transport of mercury from the free
troposphere. We therefore conducted a sensitivity simulation
(BR_S1) to examine the possible effects of warming events on
modeling results. The difference between BR_S1 and
BR_HH_14d (using high Br concentrations and
upper-limit reaction rates; τOX∼2 days and
τPR of 2 weeks in summer) is that the original MAR-modeled
Kz values are used in BR_S1, which may overestimate the
transport of mercury from the free troposphere. As shown in Fig. 7a, in the
non-summer months, near-surface air Hg0 concentrations by
BR_S1 are close to the prescribed Hg0 concentrations in
the free troposphere and are significantly higher than those from
BR_HH_14d. However, the BR_S1 scenario
cannot reproduce the high atmospheric Hg0
concentrations of ∼1.2 ng m-3 in fall (exceeding its
levels at the Antarctic coastal regions and the southern hemispheric
midlatitude sites) and the diurnal cycles of Hg0 in fall peaking in
the night. This result indicates that Hg0 may be produced below the
atmospheric mixed layers at Dome C. In addition, surface snow Hg
concentrations by BR_S1 exhibit an increase during the
non-summer period (Fig. 7b), as a result of HgII transport in warming
events from the free troposphere (Fig. 7c). The deposited HgII is
accumulated in surface snow (photoreduction of HgII is weak in the
non-summer period). Such an enhancement of snow mercury was not measured at
Dome C (Angot et al., 2016c). Therefore, we
postulate that the existence of warming events during the non-summer period
can significantly enhance Hg0 concentrations in near-surface air but
is unlikely to be the only reason for the observed mercury variations.
Possible impacts of warming events on mercury
concentrations in the non-summer period. Panel (a) shows Hg0 observations at
25 cm above the snow at Dome C, and the shaded regions indicate 25 % and 75 %
percentiles. The modeled Hg0 concentrations from BR_
HH_14d and BR_S1 are also shown. Panel (b) shows
surface snow mercury concentrations from BR_HH_
14d and BR_S1. Panel (c) shows the exchange fluxes of HgII
from the free troposphere modeled by BR_HH_14d
and BR_S1.
Second, the reduction of snow HgII might occur in the dark, which would
produce Hg0 and sustain atmospheric concentrations of Hg0 through
snow-to-air diffusion and convective transport. The possibility of the
presence of dark reduction has been reported in previous laboratory and
field studies (Lalonde et al., 2003; Ferrari et al., 2004; Dommergue et
al., 2007; Faïn et al., 2007), although actual mechanisms remain
unclear. The reduction might be a continuation of photolytically initiated
reactions or through reactions requiring no insolation at all
(Durnford and Dastoor, 2011). The HO2 radical produced
in the dark surface snowpack may serve as a potential HgII reductant
(Dommergue et al., 2003; Ferrari et al., 2004). The dark reduction rates
estimated in these studies are much lower than the photoreduction rates of
HgII. Some observational evidence at Dome C supports the hypothesis of
snow HgII dark reduction. Near-surface air Hg0 concentrations
peaked in the night in fall, and Hg0 concentrations in snow
interstitial air were higher than air Hg0 in fall and winter
(Angot et al., 2016c). Thus, we have conducted a
sensitivity simulation, BR_S2, which added a first-order dark
reduction of snow HgII based on BR_HH_14d, in order to examine the possible effects of dark reduction on model
results. The reaction rate corresponds to an average τDR of
∼1 year for the non-summer period and is scaled by NOx
concentrations since this process is likely related to nitrogen chemistry.
As shown in Fig. 8, the hypothesized snow HgII dark reduction process
leads to a small increase in the snow-to-air diffusive fluxes of Hg0
(< 0.5 ng m-2 h-1), which can increase the concentrations
of atmospheric Hg0 in the non-summer period, especially in winter. This
scenario also better reproduces the diurnal variation of Hg0 in the
fall months.
Possible impacts of snow mercury dark reduction on
Hg0 concentrations and fluxes in the non-summer
period. Panel (a) shows Hg0 observations at 25 cm above the snow surface at Dome
C, and the shaded regions indicate 25 % and 75 % percentiles. The
modeled Hg0 concentrations from BR_HH_14d
and BR_S2 are also shown. Panel (b) shows the modeled Hg0
snow-to-air diffusion fluxes from BR_HH_14d
(left axis) and the difference of snow-to-air diffusive fluxes between
BR_S2 and BR_HH_14d (right
axis).
Third, oxidation of atmospheric Hg0 may be overestimated in our model
in the non-summer period. As described in Sect. 2.3, the modeled BrO
concentrations by the CTMs may have seasonal biases. Total inorganic bromine
measurements at Dome C (Legrand et al., 2016b) have
suggested that the modeled BrO is likely biased high by up to a factor of 3
in fall and spring. The reasons remain unknown but are probably related to
several factors, including depositions of Br-containing species, snow
re-emission or long-distance transport of Br2/BrCl, and photochemical Br
reactions (Xin Yang, British Antarctic Survey, personal communication, 2017).
In order to qualitatively evaluate this potential bias in BrO (and
Br) concentrations, we have conducted a sensitivity simulation that reduces
BrO (and thus Br) concentrations in fall by a factor of 3. We find that
reducing BrO in fall could increase the modeled air Hg0 concentrations
during the fall and winter months (Fig. S15).
Based on the above sensitivity analysis, we find that the all these three
processes (intermittent warming events, dark reduction of snow mercury, and
overestimation of bromine oxidation) can help explain the observed high
mercury concentrations in the non-summer period. Their relative
contributions, however, are difficult to constrain since the understanding
of these processes is limited.
Summary and future research needs
We have conducted box model calculations to explore important chemical and
physical processes controlling the diurnal and seasonal variations of
mercury at Dome C. The atmospheric Hg0 oxidation rates of the OH,
O3, and the two-step Br-initiated schemes all have large uncertainty
ranges due to uncertain chemical kinetics and oxidants concentrations. In
austral summer, the Br oxidation scheme, favored by low ambient temperature
and high concentrations of NOx, is more efficient than the OH and
O3 schemes. The model simulations support the hypothesis that rapid
recurring cycles of oxidation and re-emission of Hg0 occur in summer.
Among the model scenarios tested, the simulations using the Br oxidation
scheme (with upper-limit reaction rates) can best match mercury observations
in summer. The modeling results indicate that strong diurnal variations of
Hg0 in summer may be confined within several tens of meters above the
snow surface and are primarily determined by changes in Hg0 oxidation
loss, snow HgII photoreduction, and mixed layer depths. For the
non-summer period, the model–observation comparisons at Dome C suggest the
intermittent warming events and a hypothesized dark reduction of snow
HgII may be important processes controlling the mercury variations, but
their relative importance is uncertain. The Br-initiated oxidation of
Hg0 is expected to be slower at Summit Station, Greenland, because of high
temperatures, high O3, and low NOx conditions, which might
contribute to the observed differences in the summertime diurnal variations
of Hg0 between these two polar inland locations.
In order to obtain a better understanding of mercury cycling over the East
Antarctic Plateau, we suggest several areas for future research. (1) It is
essential to better constrain the concentration levels of bromine species,
especially BrOx, through more field experiments and modeling studies.
(2) It is important to reduce uncertainties in existing chemical kinetic
parameters of bromine oxidation mechanisms. The rate constant of Hg0
reaction with Br from existing theoretical and experimental studies varies
by a factor of 4. (3) Our modeling indicates relatively high atmospheric
HgII concentrations in summer, which remains to be verified by
additional field measurements. (4) A better characterization of atmospheric
vertical transport during the non-summer period is needed, in particular the
role of intermittent warming events. (5) The chemical mechanisms and
reaction rates for snow mercury processes, including photoreduction and
dark reduction, should be further investigated. Future modeling work should
also improve the representation of those processes (e.g., diamond dust)
shown in Table 2.
Given the rapid exchange of mercury between the surface snowpack and the
above atmosphere (especially during summer), regional modeling studies
should be conducted in the future in order to understand the total and
speciated mercury budgets over the entire Antarctic Plateau and the
influence of the plateau on the coastal environments.