Introduction
Mercury (Hg) is a ubiquitous trace metal that cycles between the atmosphere,
ocean, land, and biosphere (Selin, 2009). Atmospheric mercury transports
globally (Driscoll et al., 2013) and, in aquatic systems, can be converted
to methylmercury, a bioaccumulative toxic compound (Mergler et al., 2007).
Human activities have strongly affected the mercury global cycle by both
unintentional and intentional releases (Streets et al., 2011). Since mercury
deposited to terrestrial and ocean surfaces can remobilize, the atmosphere
continues to be affected by its historical releases (Lindberg et al., 2007;
Amos et al., 2013). Atmosphere–surface fluxes of mercury are still poorly
constrained, limiting our ability to fully understand timescales of its
global biogeochemical cycle (Pirrone et al., 2010; Mason et al., 2012). A
better knowledge of these fluxes is important for assessing its impacts on
humans and evaluating the effectiveness of policy actions (Selin, 2014).
Current estimates of mercury fluxes to the atmosphere are mainly built on a
bottom-up approach. Anthropogenic inventories are based on emission factors,
activity levels, and abatement efficiency (Pacyna et al., 2010; S. Wang et
al., 2014; Muntean et al., 2014). Flux estimates from ocean and terrestrial
surfaces extrapolate limited direct measurements to larger scales and use
simplified process models (Mason, 2009; Kuss et al., 2011). The top-down or
inverse approach, combining observations and atmospheric modeling, has been
widely used to derive sources and sinks of greenhouse gases and
ozone-depleting substances (Gurney et al., 2002; Xiao et al., 2010). Inverse
studies have addressed mercury at a regional scale (Roustan and Bocquet, 2006;
Krüger et al., 1999). For example, a hybrid inversion combining back
trajectories and a regional chemical transport model (CTM) identified
Hg0 emission using year-long urban observations (de Foy et al., 2012).
This scheme was expanded to estimate sources of oxidized Hg (de Foy et al.,
2014).
In this paper, we apply a top-down approach at global scale to
quantitatively estimate present-day mercury emission sources (emission
inversion) as well as key parameters in a CTM (parameter inversion), in
order to better constrain the global biogeochemical cycle of mercury.
Section 2 describes the overall methodology. We combine ground-based
observations of atmospheric Hg0 (Sect. 2.1) and simulations with the
GEOS-Chem global CTM (Sect. 2.2). Reference (also known as a priori) emissions are
from GEOS-Chem parameterizations and agree well with bottom-up estimates
(Sect. 2.3). We adopt a Bayesian inversion method (Sect. 2.4) to obtain the
optimized (a posteriori) emissions, with a monthly time step, taking into account
uncertainties associated with both reference emissions and ground-based
observations (Sect. 2.6). Section 3 presents results and discussion.
Comparisons of observations and model outputs are given in Sect. 3.1. The
optimized emissions from ocean and terrestrial surfaces and from
anthropogenic sources are shown in Sect. 3.2. We use results of the emission
inversion to identify key uncertain model parameters, and optimize them in
the parameter inversion (Sects. 2.5 and 3.3). Finally, we discuss
implications of our inversion results for the global biogeochemical mercury
cycle (Sect. 3.4) and summarize our conclusions (Sect. 4).
Methods
Atmospheric mercury observations
Tropospheric mercury exists mainly as gaseous elemental mercury (GEM) but
also as two operationally defined species, gaseous oxidized mercury (GOM)
and particle-bound mercury (PBM) (Valente et al., 2007). Manual methods of
measuring GEM or total gaseous mercury (TGM = GEM + GOM) were applied in
the 1970s (Slemr et al., 1981). High-frequency measurements (time resolution
< 1 h, e.g., using Tekran automated ambient air analyzers) became
available in the 1990s and have substantially replaced manual sampling (time
resolution of about several hours). We only use GEM and TGM observations in
this study because we are not able to quantify the uncertainty in GOM and
PBM measurements (Jaffe et al., 2014; McClure et al., 2014).
We identify high-frequency observations of GEM and TGM concentration for our
inversions using two criteria. First, we choose sites in rural/remote areas
not strongly affected by local emissions. Second, we require that
observations at different sites are minimally correlated (Brunner et al.,
2012). Data sets are drawn from the Atmospheric Mercury Network (AMNet) (Gay
et al., 2013), the Canadian Measurement Networks (including the Canadian Air
and Precipitation Monitoring Network (CAPMoN) and other sites sponsored by
Environment Canada) (Cole et al., 2014), and the European Monitoring and
Evaluation Programme (EMEP) (Tørseth et al., 2012). We use data from
2009 to 2011, when all these networks were active. To expand spatial coverage
of observations, we also collected data from individual sites for recent
years (2007–2013). Some sites are included in the Global Mercury Observation
System (GMOS) (Pirrone et al., 2013). All sites use Tekran analyzers,
operated in sampling intervals of 5–30 min. We calculate Pearson's
correlation coefficients between each pair of sites using hourly data.
Several sites are excluded due to strong correlations within each other, as
shown in Table S1 in the Supplement. Table 1 shows the names, locations, and
affiliated networks of the 27 ground-based sites used in our inversion. Site
locations are also plotted in Fig. 1. For most of these sites GEM data are
used, and for a few sites where GEM data are not available we use TGM data
(see Table 1). The concentration difference between measured GEM and TGM
concentrations in remote near-surface air is usually < 1 % (Lan et
al., 2012; Fu et al., 2012a; Weigelt et al., 2013; Steffen et al., 2014)
and thus we do not distinguish between measured GEM and TGM concentrations
and use Hg0 to represent them in the paper. These sites are all
uncorrelated or only weakly correlated (-0.3 < r < 0.4, n=103–104) (see Table S2 in the Supplement).
Original observational data are converted into hourly averages and then into
monthly averages (Fig. S1 in the Supplement). We require > 30 min
data to derive an hourly average and > 10-day data to derive a
monthly average. Where full data are available, median values are used to
suppress the influence of high Hg0 due to local or regional pollution
events (Weigelt et al., 2013; Jaffe et al., 2005) or occasional low Hg0
due to non-polar depletion events (Brunke et al., 2010). For a few
individual sites (see Table 1), the original data are not available and
monthly arithmetic means are used. Finally, multiple-year averages are
calculated. Hg0 concentrations are given in nanograms per cubic meter at standard
temperature and pressure.
Locations of ground-based observational sites.
Four polar sites are included (ALT, ZEP, and ADY in the Arctic and TRS in
Antarctica, see Table 1). Episodically low Hg0 is observed at these
sites in polar spring (Cole et al., 2013; Pfaffhuber et al., 2012). These
atmospheric mercury depletion events (AMDEs) result from rapid Hg0
oxidation and deposition driven by halogens (Steffen et al., 2008).
Volatilization of the deposited Hg and the large quantities of imported
mercury from circumpolar rivers to the Arctic Ocean are hypothesized to
contribute to the observed summer Hg0 peak in the Arctic region
(Dastoor and Durnford, 2013; Fisher et al., 2012). The lack of understanding
of the above physical and chemical processes limits GEOS-Chem's ability to
reproduce Hg0 in the polar spring and summer. For these reasons we
remove Hg0 data at polar sites for this period (i.e., March–September in
the Arctic and October–March in Antarctica).
We also include three mountaintop sites (LUL, MBO, and MLO, see Table 1).
These sites are affected by upslope surface air during the day and downslope
air from the free troposphere at night (Sheu et al., 2010; Fu et al., 2010).
The downslope air usually contains higher levels of GOM than the upslope air
due to oxidation of Hg0 to GOM in the free troposphere (Timonen et
al., 2013). Therefore, Hg0 at mountaintop sites peaks in the afternoon
whereas GOM peaks between midnight and early morning (Fig. S2 in the
Supplement), showing an opposite diurnal pattern to most low-elevation sites
(Lan et al., 2012). The minimum hourly Hg0 at night is calculated to be
∼ 90 % of the all-day average. Thus, to represent Hg0
modeled at a vertical layer in the free troposphere (this layer is obtained
by matching observed air pressure), the observed mountaintop Hg0 data
are multiplied by 0.9.
Information for ground-based observational sites of atmospheric
mercury.
IDa,b
Location
Time
Lat
Long
Altc
Networkd
Observational errorse
Mismatch
NRMSEf
period
σIP
σIC
σSF
error (σMM)e
reference
emission
parameter
simulation
inversion
inversion
ALT
Alert, NU, Canada
2009
83
-62
210
1
28
138
3
36
0.06
0.03
0.02
ZEP
Zeppelin, Ny-Ålesund, Norway
2009–2011
79
12
474
2
34
169
6
14
0.13
0.19
0.18
ADY
Andøya, Norway
2010–2011
69
16
380
2
36
181
4
13
0.16
0.22
0.23
BKN
Birkenes, Norway
2010–2011
58
8
219
2
36
178
6
32
0.19
0.22
0.24
MHD
Mace Head, Ireland
2009–2011
53
-10
15
2
29
145
5
8
0.08
0.08
0.09
WLD
Waldhof, Germany
2009–2011
53
11
74
2
33
163
10
114
0.14
0.10
0.12
BRL
Bratt's Lake, SK, Canada
2009–2010
50
-105
587
1
25
127
5
23
0.18
0.11
0.13
SAT
Saturna, BC, Canada
2009–2010
49
-123
178
1
28
140
8
28
0.16
0.12
0.13
KEJ
Kejimkujik, NS, Canada
2009–2011
44
-65
158
3
28
138
6
14
0.07
0.05
0.09
EGB
Egbert, ON, Canada
2009–2010
44
-80
251
1
25
126
5
49
0.21
0.11
0.11
MBO
Mt. Bachelor, OR, USA
2009–2010
44
-122
2763
4
26
128
6
10
0.04
0.04
0.06
HTW
Huntington Wildlife Forest, NY, USA
2009–2011
44
-74
502
3
26
131
8
29
0.13
0.06
0.08
CBS
Mt. Changbai, JL, China
2008–2010
42
128
741
4
32
160
14
134
0.17
0.16
0.23
ATS
Athens Super Site, OH, USA
2009–2011
39
-82
274
3
28
137
6
39
0.17
0.04
0.07
SCZ
Santa Cruz, CA, USA
2010–2011
37
-122
150
3
30
148
5
23
0.07
0.05
0.04
WLG
Waliguan, QH, China
2007–2008
36
101
3816
4
38
188
20
223
0.21
0.26
0.24
YKV
Yorkville, GA, USA
2009–2011
34
-85
394
3
24
122
6
48
0.30
0.15
0.13
NMC
Nam Co Lake, XZ, China
2011–2013
31
91
4730
4
25
124
6
23
0.07
0.06
0.07
GRB
Grand Bay NERR, MS, USA
2009–2011
30
-88
1
3
28
141
5
41
0.08
0.07
0.08
SGR
Shangri-La, YN, China
2009–2010
28
100
3580
4
50
250
30
544
0.37
0.40
0.37
OKN
Okinawa, Japan
2009–2011
27
128
60
4
39
195
13
37
0.24
0.24
0.22
LUL
Mt. Front Lulin, Taiwan
2009–2011
24
121
2862
4
29
145
12
52
0.12
0.13
0.13
MLO
Mauna Loa, HI, USA
2011
20
-156
3384
3
25
123
16
8
0.11
0.13
0.11
NWN
Nieuw Nickerie, Suriname
2007–2008
6
-57
5
4
25
126
22
105
0.22
0.13
0.18
CPT
Cape Point, South Africa
2009–2011
-34
18
230
4
18
91
4
13
0.26
0.08
0.16
AMS
Amsterdam Island, Indian Ocean
2012–2013
-38
78
55
4
21
103
3
7
0.16
0.08
0.07
TRS
Troll Research Station, Antarctica
2009–2011
-72
3
1275
4
22
107
3
33
0.15
0.13
0.09
Avg.
29
146
8
63
0.16
0.13
0.14
a Observational sites without original data are MBO, CBS, WLG,
NMC, SGR, LUL, and NWN.
b Observational sites where we use TGM data are ALT, BRL, SAT,
EGB, CBS, WLG, NMC, SGR, and NWN. For all other sites, we use GEM data.
c Unit for altitude is meters.
d Network affiliations: (1) Canadian networks, (2) EMEP, (3)
AMNet, and (4) individual observational sites. More information about these
individual sites can be found in Weiss-Penzias et al. (2006) for MBO, Fu et
al. (2012b) for CBS, Fu et al. (2012a) for WLG, Zhang et al. (2015) for SGR,
MOEJ (2013) for OKN, Sheu et al. (2010) for LUL, Müller et al. (2012) for
NWN, Slemr et al. (2011) for CPT, Angot et al. (2014) for AMS, and Slemr et
al. (2015) for the Southern Hemispheric sites.
e Unit for errors is picograms per cubic meter.
f Equation of NRMSE (quantity without unit) is given in
Sect. 3.1.
We do not use over-water Hg0 observations (i.e., from ship cruises) in
the inversion because they are very limited and usually cover large areas,
making their observational errors difficult to estimate. Instead, we use
over-water observations as an independent check of our inversion results.
The North Atlantic Ocean is the most densely sampled ocean basin. Soerensen
et al. (2012) assembled Hg0 measurements from 18 ship cruises in this
region during 1990–2009 and found a statistically significant decrease of
-0.046 ± 0.010 ng m-3 yr-1. However, previous GEOS-Chem
simulations of Hg0 concentration did not take this multidecadal trend
into account in evaluating its seasonal variability (Soerensen et al.,
2010a). Here we add a new ship cruise and adjust observed Hg0
concentrations (Hgobs0) from all 19 ship cruises to Hg0
levels consistent with year 2009 based on a fitted decline trend (Table S3
and Fig. S3 in the Supplement). Seasonal variation is estimated by dividing
the normalized Hg0 (Hgnor0) by month of measurement. As shown
in Fig. 2, Hgnor0 are smaller and show less seasonal variability
compared to Hgobs0.
GEOS-Chem model
GEOS-Chem (v9-02) is a CTM driven by assimilated meteorological fields from
the NASA Goddard Earth Observing System (Bey et al., 2001). The
original GEOS-5 has a resolution of 1/2∘ × 2/3∘
and is degraded to 2∘ × 2.5∘ for input into our
simulations. The GEOS-Chem global mercury simulation was described and
evaluated in Selin et al. (2007) and Strode et al. (2007), with updates by
Selin et al. (2008), Holmes et al. (2010), Soerensen et al. (2010b), and
Amos et al. (2012). It couples a three-dimensional atmosphere, a
two-dimensional mixed layer slab ocean, and a two-dimensional terrestrial
reservoir. For consistency with most ground-based observations, we use
meteorological years 2009–2011 for analysis after a spin-up period of
4 years.
Three mercury tracers (representing GEM, GOM, and PBM) are simulated in the
atmosphere in GEOS-Chem. Models have assumed that Hg0 is oxidized by
OH, ozone, and/or halogens (Lei et al., 2013; De Simone et al., 2014;
Travnikov and Ilyin, 2009; Durnford et al., 2010; Grant et al., 2014). Some
studies suggested the gas-phase reaction with Br was the most important
Hg0 oxidation process globally (Seigneur and Lohman, 2008; Hynes et
al., 2009), and here we use Br as the only oxidant of Hg0 (Holmes et
al., 2010; Goodsite et al., 2012). Tropospheric Br fields are archived from
a full chemistry GEOS-Chem simulation (Parrella et al., 2012). Models also
hypothesize gas- and/or aqueous-phase reductions of oxidized Hg and scale
their kinetics to match atmospheric observations (Holmes et al., 2010;
Pongprueksa et al., 2011; Selin et al., 2007). However, an accurate
determination of potential pathways is lacking (Subir et al., 2011, 2012),
and their atmospheric relevance is unknown (Gårdfeldt and Jonsson,
2003). Thus, we do not include atmospheric reduction of oxidized Hg in our
simulations.
Observed and modeled monthly Hg0 concentrations over
the North Atlantic Ocean. The observational data and related references are
given in the Supplement. Hgobs0 are the concentrations observed
from 19 ship cruises during 1990–2009, whereas Hgnor0 are the
concentrations normalized to levels consistent with year 2009. The gray
shaded region shows the 1σ error of Hgnor0, which is composed
of the observational error, mismatch error, and regression error.
Emission inversion: reference emissions
For our reference emissions, we use parameterizations in GEOS-Chem with
improvements from recent literature. As shown in Table 2, the global mercury
emission is estimated as 6.0 Gg yr-1, with an uncertainty range of
0.4–12.2 Gg yr-1. Mercury released via natural processes is assumed to
be entirely Hg0 (Stein et al., 1996), while a small fraction of
anthropogenic mercury is in oxidized forms. Anthropogenic emission is
unidirectional, but air–surface exchange is bi-directional (emission and
deposition) (Xu et al., 1999; Gustin et al., 2008). A positive net emission
from a surface means it is a net source of Hg0, whereas a negative
value means it is a net sink. We describe below our reference emissions for
individual sources.
Global mercury emissions into the atmosphere (Mg yr-1).a
Source
Included in
Reference emission
Optimized emission
inversion?b
Anthropogenicc
1960 (420–3510)
2250 (1150–3360)
Asia
Y
770 ± 390
1060 ± 110
Other regions
N
760
760
Contaminated sites
N
80 (70–100)
80 (70–100)
Oxidized Hg
N
350
350
Net ocean
2990 (470–5510)
3160 (1160–5160)
Net NH ocean
Y
1230 ± 630
1670 ± 530
Net SH ocean
Y
1760 ± 880
1490 ± 680
Net terrestriald
1070 (-510 to 3130)
340 (-590 to 1750)
Soil
Y
1680 ± 840
860 ± 440
Prompt re-emission
N
520
500
Hg0 dry deposition
N
-1430
-1320
Geogenic
N
90 (60–600)
90 (60–600)
Biomass burning
N
210
210
TOTALe
6020 (380–12150)
5750 (1720–10270)
a Flux values in parentheses indicate estimated uncertainty ranges. For
sources included in the inversion, “average ± SD” is shown. The
uncertainty ranges of contaminated sites and geogenic emissions are from
AMAP/UNEP (2013) and Mason (2009), respectively. If the uncertainty range of
a source is not available, we assume that its SD is a half of its best
estimate.
b Only selected mercury emission sources are included in the inversion,
see Sect. 2.3.4.
c Oxidized Hg emissions from anthropogenic sources are not included in
the inversion. “Asia” and “Other regions” (except Asia) refer to
emissions of Hg0.
d Because air–terrestrial interactions are bi-directional, we assume
that uncertainties of prompt re-emission and Hg0 deposition have been
covered by that of soil emission.
e Total mercury emissions are the sum of anthropogenic, net ocean, and
net terrestrial emissions.
Anthropogenic sources
We use the anthropogenic emission inventory based on activity data for year
2010, developed by AMAP/UNEP (2013). As shown in Table 2, the total
anthropogenic emission is 1960 Mg yr-1, with an uncertainty range of
1010–4070 Mg yr-1 (AMAP/UNEP, 2013). We do not optimize oxidized
mercury emissions (accounting for 19 % of the total anthropogenic sources)
because this form has a short atmospheric lifetime (days to weeks) and may
not significantly contribute to observed TGM concentrations. The geospatial
distribution for emissions from contaminated sites (Kocman et al., 2013) is
not available for this inventory, and we distribute this small source (80 Mg yr-1) based on the locations of mercury mines (Selin et al., 2007). We
do not consider in-plume reduction of oxidized Hg emitted from coal-fired
power plants (Y. Zhang, et al., 2012). About 50 % of global emissions are
from Asia (defined as 65–146∘ E, 9∘ S–60∘ N), and a small fraction are from Europe and North America
(together < 10 %). For other regions like Africa and South
America, there is no effective observational site to constrain emissions
(Fig. 1). Thus, only anthropogenic emissions from Asia are optimized in the
inversion, but we still include other regions' anthropogenic emissions in
the GEOS-Chem simulations.
Ocean
The mixed layer (ML) slab ocean model in GEOS-Chem is described in Soerensen
et al. (2010b). Net Hg0 emission from ocean surfaces is determined by
the supersaturation of Hgaq0 in the ML relative to the atmosphere
and the air–sea exchange rate. Hgaq0 in the ML is mainly produced
by the net photolytic and biotic reduction of Hgaq2+. Atmospheric
deposition accounts for most Hgaq2+ inputs into the ML, but
subsurface waters also contribute a considerable fraction. The ML interacts
with subsurface waters through entrainment/detrainment of the ML and
wind-driven Ekman pumping.
We improve several parameterizations in GEOS-Chem based on recent findings.
(1) Basin-specific subsurface water mercury concentrations are updated
according to new measurements (Lamborg et al., 2012; Munson, 2014), as shown
in the Supplement, Fig. S4. (2) Soerensen et al. (2010b) used the
Wilke–Chang method for estimating the Hgaq0 diffusion coefficient
(DHg) (Wilke and Chang, 1955), but this estimate was believed to be too
high (Loux, 2004). We adopt a revised DHg derived by molecular dynamics
(MD) simulation (Kuss et al., 2009). As shown in the Supplement, Fig. S5,
compared to the Wilke–Chang method, the MD simulation obtains a DHg that
agrees much better with laboratory results (Kuss, 2014). (3) Particulate
mercury (HgaqP) sinking from the ML is estimated by linking the
organic carbon export (biological pump) and HgaqP : C ratios.
Soerensen et al. (2010b) used the model of Antia et al. (2001) for
estimating carbon export fluxes, giving a global total of 23 Gt C yr-1.
However, this estimate is mainly based on the flux measurement data from
much deeper depths and may not well represent carbon export from the ML.
Different models suggest global carbon export fluxes ranging from
5 to 20 Gt C yr-1 with a best estimate of 11 Gt C yr-1 (Sanders et al., 2014;
Henson et al., 2011). Thus, we multiply carbon export fluxes in GEOS-Chem by
a factor of 0.47 (11 Gt C yr-1/23 Gt C yr-1) to match this best
estimate.
Net global ocean emission of 2990 Mg yr-1 from the improved GEOS-Chem
(considered as reference emission, shown in Table 2) compares favorably with
best estimates of 2680 Mg yr-1 using a bottom-up approach (Pirrone et
al., 2010; Mason, 2009). Due to their different seasonal characteristics, we
divide the global ocean into the NH (Northern Hemisphere) and SH (Southern
Hemisphere) oceans and optimize their emissions separately.
Terrestrial ecosystem
Although atmosphere–terrestrial Hg0 exchange is bi-directional, only
recently developed exchange models have coupled deposition (downward) and
emission (upward) fluxes and dynamically estimated net fluxes by gradients
between air Hg0 and “compensation points” inferred from surface
characteristics (Bash, 2010; Bash et al., 2007). Because their complex
parameterizations lack field data for verification (X. Wang et al., 2014),
such exchange models have not been incorporated into current global CTMs. As
described in Selin et al. (2008) and Holmes et al. (2010), GEOS-Chem treats
emission and deposition fluxes of Hg0 separately. Only dry deposition
is considered for Hg0 due to its low Henry's law constant (Lin and
Pehkonen, 1999). Net emission from terrestrial surfaces (Enet)
represents the sum of these processes: volatilization from soil
(Esoil), prompt re-emission of deposited Hg (Epr), geogenic activity
(Egg), biomass burning (Ebb), and dry deposition to surfaces
(EddHg0).
Enet=Esoil+Epr+Egg+Ebb-EddHg0
Soil emission (Esoil) is specified as a function of solar radiation and
soil Hg concentration:
Esoil(ngm-2h-1)=βCsoilexp(1.1×10-3×Rg),
where Csoil is soil Hg concentration (ng g-1) and Rg is the
solar radiation flux at the ground (W m-2). GEOS-Chem assumes a global
average soil concentration of 43 ng g-1 for preindustrial conditions
and derives its spatial distribution from the local equilibrium between
emission and deposition. The scaling factor β (1.2 × 10-2 g m-2 h-1) is obtained from the global mass balance of
the preindustrial simulation. Selin et al. (2008) assumed that present-day
soil mercury reservoir and emission have both increased by 15 % compared
to the preindustrial period and distributed this global average increase
according to the present-day deposition pattern of anthropogenic emission.
However, by linking soil mercury with organic carbon pools, Smith-Downey et
al. (2010) estimated that present-day Hg storage in organic soils has
increased by 20 % while soil emission by 190 %. Mason and Sheu (2002)
suggested doubled soil emissions compared to preindustrial times. Thus,
following Smith-Downey et al. (2010), we assume a 190 % global increase in
the present day, and distribute this increase according to the anthropogenic
emission deposition pattern. The present-day reference soil emission is
calculated to be 1680 Mg yr-1.
An additional 520 Mg yr-1 is emitted from the soil, vegetation, and
snow (Epr) through rapid photoreduction of recently deposited oxidized
Hg (Fisher et al., 2012). Geogenic emission (Egg) is set as 90 Mg yr-1, consistent with its best bottom-up estimate (Mason, 2009; Bagnato
et al., 2014). Biomass burning (Ebb) of 210 Mg yr-1 is estimated
using the Global Fire Emissions Database version 3 of CO (van der Werf et
al., 2010) and a Hg : CO ratio of 100 nmol mol-1 (Holmes et al., 2010).
This amount falls at the lower end of bottom-up estimates (Friedli et al.,
2009). Dry deposition of Hg0 is estimated using a resistance-in-series
scheme (Wesely, 1989) and has a downward flux of 1430 Mg yr-1. Using
Eq. (1), net emission of Hg0 from terrestrial surfaces is calculated to
be 1070 Mg yr-1 in GEOS-Chem (Table 2), at the lower end of the
bottom-up estimates (1140–5280 Mg yr-1) (Mason, 2009; Pirrone et al.,
2010) and also lower than 1910 Mg yr-1 by Kikuchi et al. (2013) using
a different empirical mechanism (Lin et al., 2010).
Sources included in emission inversion
Because of limitations in both observations and the CTM, only anthropogenic
emission from Asia, ocean evasion (separated into the NH and SH), and soil
emission are optimized in the emission inversion (see Table 2). The
remaining sources are still included in the simulation but not inverted
because they are too diffusely distributed, their magnitude is small, and/or
observations are not sensitive to them (Chen and Prinn, 2006). The seasonal
sources (the NH ocean, SH ocean, and soil) usually have strong
spatiotemporal variations and the inversion optimizes their monthly
magnitudes and uncertainties. For the aseasonal Asian anthropogenic
emission, the inversion optimizes its annual magnitude and uncertainty.
Bayesian inversion method
We use a Bayesian method to invert emissions and parameters with a weighted
least-squares technique (Ulrych et al., 2001). The unknowns (correction
factors for reference emissions and parameters) are contained in a state
vector x and their a priori errors (uncertainties in reference
emissions and parameters) in a matrix P. In the emission inversion,
as we include one aseasonal source (Asian anthropogenic emission) and three
monthly sources (the NH ocean, SH ocean, and soil), the vector
x contains 37 elements. P is a 37 × 37 diagonal
matrix with each diagonal element equal to the square of 1σ a priori error of
the corresponding element in x (see Sect. 2.6.1).
Our inversion method assumes a linear relationship between the observation
vector yobs and x, as
shown in the measurement equation:
yobs=yref+Hx+ε,
where yref contains monthly Hg0
concentrations modeled by GEOS-Chem using the reference emissions and
parameters. The vectors yobs and
yref both have 12 (number of months per
year) × 27 (number of observational sites) = 324 elements.
ε represents the model and observational errors which will be discussed
in detail in Sect. 2.6.
The state vector x is related to monthly Hg0
concentrations by the sensitivity matrix H, in which the elements
are written as
hij=yi-yirefxj-xjref≈∂yi∂xj,
where i and j are indices for the observational and state
vectors, respectively. H describes how monthly Hg0
concentrations at different observational sites respond to changes in the
state vector x (for examples see the Supplement, Fig. S6).
The GEOS-Chem CTM acts as a mathematical operator relating the
emissions/parameters to monthly Hg0 concentrations. For the emission
inversion, sensitivities for the seasonal and aseasonal sources are
generated by two different types of simulations. The aseasonal Asian
anthropogenic emission is perturbed above the reference level by 50 %, and
we run the GEOS-Chem CTM until steady state is reached. For the seasonal
sources (e.g., the NH ocean emission from March), a 1-month pulse of
Hg0 is emitted, and we track modeled Hg0 concentrations by
GEOS-Chem for the next 3 years. After this, we assume that the perturbed
concentrations at all observational sites will exponentially decrease
(Saikawa et al., 2012).
The objective function J with respect to x is
J(x)=xTP-1x+(Hx-yobs+yref)TR-1(Hx-yobs+yref),
where R, a diagonal 324 × 324 matrix, represents errors
related to observations and the CTM and will be described in detail in Sect. 2.6. By minimizing J, we obtain the expression for the optimal
estimate of the state x:
x=(HTR-1H+P-1)-1HTR-1(yobs-yref),
Q=(HTR-1H+P-1)-1,
where the matrix Q contains the a posteriori errors of x. The
size of Q is the same as the matrix P. Each diagonal
element in Q is the square of 1σ a posteriori error of the corresponding
element in x. A detailed mathematical derivation of the
above equations can be found in Wunsch (2006). As shown in Eqs. (6) and (7),
several vectors and matrices need to be calculated during the optimization
procedure, including the observational vector
yobs and its error matrix R, the
error matrix P of the a priori state, the sensitivity matrix H,
and the vector yref which is obtained from
the reference simulation of the GEOS-Chem CTM.
Parameter inversion
As described in Sect. 3.2.1, based on results of ocean evasion in our
emission inversion and sensitivity tests of model parameters, we identify
two ocean parameters in GEOS-Chem for improvement: the rate constant of dark
oxidation of Hgaq0 (denoted as KOX2, following notations in
Soerensen et al., 2010b) and the partition coefficient between
Hgaq2+ and HgaqP (denoted as KD). For simplicity
they are expressed in decimal logarithms (-log KOX2 and log KD).
A -log KOX2 (s-1) of 7.0 is specified in GEOS-Chem (Soerensen et
al., 2010b). From a survey of laboratory studies (see details in the
Supplement) (Amyot et al., 1997; Lalonde et al., 2001, 2004; Qureshi et al.,
2010), we suggest that this value is too low and that a more appropriate
range of -log KOX2 is 4.0–6.0. The chemical mechanisms for dark
oxidation of Hgaq0 remain unclear. OH generated from
photochemically produced H2O2 via the Fenton reaction may oxidize
Hgaq0 in dark conditions (Zhang and Lindberg, 2001; Zepp et al.,
1992). Light irradiation before a dark period is needed, and dark oxidation
kinetics depend on intensity and duration of light (Qureshi et al., 2010;
Batrakova et al., 2014). Future work could include a more mechanistic
representation of this process as laboratory studies become available.
KD (=Cs/CdCSPM) describes the affinity of aqueous
Hg2+ for suspended particulate matter (SPM), where Cs, Cd, and
CSPM are the concentrations of HgaqP, Hgaq2+, and
SPM, respectively. GEOS-Chem uses a log KD (L kg-1) of 5.5 based
on measurements in the North Pacific and North Atlantic oceans (Mason and
Fitzgerald, 1993; Mason et al., 1998).
In the parameter inversion, we attempt to constrain these two ocean model
parameters using the Bayesian approach described in Sect. 2.4. For
consistency with sources in the emission inversion, two other parameters are
included, i.e., emission ratios for soil (ERSoil) and Asian
anthropogenic sources (ERAsia). It is noted that the emission inversion
and parameter inversion are carried out separately. Because the responses of
Hg0 concentrations to changes in ocean parameters are nonlinear, as
shown in the Supplement Fig. S7, we use a two-step iterative inversion
method (Prinn et al., 2011). At each iteration step, the sensitivity matrix
H is estimated by linearizing the nonlinear function around the
current parameter estimate. In the parameter inversion, the state vector
x contains four elements (corresponding to the four parameters),
and P and Q are 4×4 matrices.
Error representation
Successful estimation of x (Eq. 6) and its uncertainty
Q (Eq. 7) depends on reasonable representations of all relevant
errors, including the a priori errors associated with reference emissions/parameters
(contained in P) and errors related to Hg0 observations and
the CTM (contained in R). R consists of three parts:
observational errors, model–observation mismatch errors, and model errors.
Errors in reference emission and parameters
For the emission inversion, we set the 1σ errors in reference
emissions as 50 % in order to match uncertainties in their estimates using
bottom-up approaches (see Table 2). For example, the reference emissions and
1σ errors for the NH and SH oceans are 1230 ± 630 and 1760 ± 880 Mg yr-1, respectively. The uncertainty range of reference
emission from the global ocean is estimated as 470–5510 Mg yr-1,
comparing very well with 780–5280 Mg yr-1 from bottom-up estimates
(Mason, 2009; Pirrone et al., 2010). For the parameter inversion, the a priori
estimates of two ocean model parameters are taken from literature reviews
(Batrakova et al., 2014): -log KOX2 (5.0 ± 1.0) and log KD
(5.3 ± 0.4). The a priori uncertainties of ERSoil and ERAsia are
chosen as 50 %, the same as in the emission inversion.
Observational errors
Observational errors for ground-based sites determine their relative
importance in deriving the optimized state. As shown in Eq. (8), the total
observational errors (σTOT) contain instrumental precision
(σIP), intercomparison (σIC), and sampling
frequency errors (σSF) (Rigby et al., 2012; Chen and Prinn,
2006).
σTOT=σIP2+σIC2+σSF2
The instrumental precision (σIP) of high-frequency Hg0
measurements using the Tekran instrument is ∼ 2 % (Poissant
et al., 2005). Here an intercomparison error (σIC) is used to
represent the comparability of Hg0 concentrations measured by different
research groups using the Tekran instrument. In principle, it includes several
inaccuracies during the measurement process (e.g., the instrument's flow
control and the permeation source rate for the automated calibration) and
also arises from the different data management and quality control protocols
taken by different research groups (Steffen et al., 2012). Its value has
been assessed during several field intercomparisons (Temme et al., 2006;
Aspmo et al., 2005; Munthe et al., 2001; Ebinghaus et al., 1999; Schroeder
et al., 1995). Hg0 concentrations measured by different groups have a
relative SD of reproducibility of 1–9 %, and we choose a generous uniform
intercomparison error of 10 %. Sampling frequency error (σSF)
reflects the ability of each site to capture the overall variability of
Hg0 concentration in 1 month and is calculated as the monthly SD
divided by the square root of the number of valid hourly data points in this
month (Rigby et al., 2012). Table 1 shows observational errors at each site,
averaged over 2009–2011. The total observational errors are dominated by
intercomparison errors. The other two types of errors have small
contributions.
Model–observation mismatch errors
The mismatch error (σMM) exists because an observation is made
at a single point in space, but its corresponding grid box in model
represents a large volume of air. We estimate σMM as the SD of
monthly Hg0 concentrations in the eight surrounding grid boxes (at the
same vertical layer) from the reference simulation (Chen and Prinn, 2006).
As shown in Table 1, σMM values are larger over strongly emitting
continental areas (e.g., SGR and WLG) and smaller over remote marine areas
(e.g., CPT and AMS).
Model errors
All existing CTMs including GEOS-Chem are imperfect, due to both errors in
meteorological data driving the CTMs and errors induced by their
parameterizations of physical and chemical processes. The former type of
model errors is termed “forcing errors” and the latter “process errors”
(Locatelli et al., 2013). Physical processes consist of horizontal/vertical
resolution, advection/convection, turbulence, planetary boundary layer
mixing, etc. The CTM for Hg is subject to large process errors due to highly
uncertain atmospheric chemistry. Recent studies have shown that Br
concentration may be significantly underestimated in GEOS-Chem (Parrella et
al., 2012; Gratz et al., 2015) and that current Br-initiated oxidation
mechanisms are incomplete in describing all possible radical reactions
(Dibble et al., 2012; F. Wang, et al., 2014). In order to provide a
preliminary assessment of the effect of Br oxidation chemistry on our
inversion, we perform an additional parameter inversion including six new
elements in the state vector x, and each of them
represents Br columns in a 30∘ latitudinal band (see results in
Sect. 3.3 and Fig. S8 in the Supplement).
Quantifying model errors requires incorporating many CTMs which are driven
by different meteorology and which contain different parameterizations
(Prinn, 2000). Multi-CTM intercomparison studies have been performed for
CO2 and CH4 (Gurney et al., 2002; Baker et al., 2006; Locatelli et
al., 2013), suggesting that model errors can impact inverted emissions. Few
other global CTMs exist for Hg (Bullock et al., 2008, 2009). Due to our
inability to quantify model errors using a single CTM, model errors are not
incorporated in our inversion, like many other inverse studies (Huang et
al., 2008; Xiao et al., 2010; Rigby et al., 2012). As a result, R
in Eq. (5) only includes observational errors and model–observation mismatch
errors.
Monthly Hg0 concentrations for all ground-based
observational sites. Note different scales on vertical axes. Error bars
correspond to the total errors described in Sect. 2.6. The two numbers in
parentheses after the name of each site are its latitude and longitude. For
polar sites (ALT, ZEP, ADY, and TRS), the gray color shows the observed
Hg0 concentrations that are not used in our inversions due to AMDEs, as
shown in Sect. 2.1.
Results and discussion
Emission inversion: model–observation comparison
We first test whether the comparison between ground-based Hg0
observations and model outputs improves when using optimized emissions,
compared to reference emissions. Figure 3 shows the modeled and observed
Hg0 concentrations at all 27 sites. To quantify model performance, we
calculate the normalized root mean square error (NRMSE) for each site:
NRMSE=1n∑i=1nXobs,i-Xmod,i21n∑i=1nXobs,i,
where Xobs,i and Xmod,i are the observed and modeled
Hg0 concentrations at the ith month (n in total), respectively. As shown
in Table 1, an average NRMSE of 0.13 is obtained for the emission inversion,
smaller than that of 0.16 for the reference simulation, indicating that the
emission inversion can better reproduce ground-based observations. While
this is a relatively small uncertainty reduction (-0.03), we do not expect
better performance for our inversion. This is because errors in Hg0
observations (as described above, and in Table 1) are roughly 13 %, which
constrain the optimization. Our inversion brings the average NRMSE within
the observation error.
The NRMSEs are not reduced for all 27 sites (see Table 1). For three Nordic
sites (ZEP, ADY, and BKN) and four Asia-Pacific sites (WLG, SGR, LUL, and
MLO), the NRMSEs increase. Hg0 concentrations are ∼ 1.8 ng m-3 at the three
Nordic sites, higher than the modeled values (Fig. 3) from both reference simulation and emission inversion, and also higher
than those measured at many background sites in Europe (Ebinghaus et al.,
2011; Kentisbeer et al., 2014; Weigelt et al., 2013). Part of the
differences may be explained by a positive bias in the instrumentation of
these Nordic observations when compared to other laboratories (Temme et al.,
2006). It is also possible that GEOS-Chem cannot sufficiently capture local
meteorology and/or emissions at these sites. For the Asia-Pacific sites, the
reference simulation underestimates Hg0 at SGR (-32 %, calculated as
(yref/yobs-1) × 100 %, hereinafter the same) and WLG (-19 %) and predicts comparable
values at MLO (+2 %) and LUL (+0 %). Such discrepancies likely arise
from unknown intercomparison errors and are influenced by local emission and
meteorology factors not captured by the CTM (Fu et al., 2012b; Wan et al.,
2009). These sites are operated by three different laboratories but, to the
best of our knowledge, no field intercomparisons have been conducted among
these laboratories.
Figure 4 compares monthly Hg0 observations with model simulations for
sites aggregated into four regions: Asia-Pacific, North America, Europe, and
Southern Hemisphere. The emission inversion significantly improves the
comparison for the SH sites (CPT, AMS, and TRS, see Table 1). In the
reference simulation, Hg0 concentrations at the SH sites vary
seasonally, with a high in austral winter (∼ 1.3 ng m-3)
and a low in austral summer (∼ 0.9 ng m-3). However,
observed Hg0 shows little seasonal variation with monthly
concentrations of ∼ 1.0 ng m-3. The emission inversion
reduces the Hg0 concentration in austral winter and fits the observations
much better (the average NRMSE decreases from 0.19 to 0.10). As shown in
Fig. 3, all three SH sites show improvement after optimization.
The emission inversion also improves the comparison for sites in North
America (the average NRMSE decreases from 0.13 to 0.08). Hg0 data at a
total of 11 sites are available, including five coastal sites (ALT, SAT,
KEJ, SCZ, and GRB), five inland sites (BRL, EGB, HTW, ATS, and YKV), and one
mountaintop site (MBO) (see Fig. 1 and Table 1). Hg0 at the coastal
and inland sites are observed to be 1.41 ± 0.04 and 1.29 ± 0.06 ng m-3, respectively. This coastal–inland difference in observation is
consistent with results of Cheng et al. (2014), who found that air masses
from open ocean at the site KEJ had 0.06 ng m-3 higher Hg0
concentrations than those originating over land. The reference simulation
and emission inversion both obtain comparable Hg0 concentrations at the
coastal sites (1.43 ± 0.06 and 1.38 ± 0.07 ng m-3). At the
inland sites, the emission inversion predicts Hg0 concentrations (1.38 ± 0.03 ng m-3) closer to observations than the reference
simulation (1.50 ± 0.06 ng m-3).
Averaged monthly observations and model simulations of
Hg0 concentrations for the ground-based observational sites in the four
regions (Asia-Pacific: 45∘ E–140∘ W, 0–90∘ N; North America:
140–45∘ W, 15–90∘ N; Europe:
15∘ W–45∘ E, 15–90∘ N, and the Southern Hemisphere). Note different
scales on vertical axes. Hg0 observations are shown with total errors
as described in Sect. 2.6.
Over-water Hg0 observations serve as an independent test of the
emission inversion. As shown in Fig. 2, Hg0 concentrations over the
North Atlantic Ocean from both the reference simulation and the emission
inversion fall within 1σ uncertainty ranges of Hgnor0. The
NRMSEs for the reference simulation and the emission inversion are 0.09 and
0.10, respectively. Thus, using Hg0 emissions constrained by
ground-based observations, GEOS-Chem still matches these regional over-water
observations.
We additionally test the performance of the inversion by comparison with
regional wet deposition data. Since most oxidized Hg is formed from the
oxidation of Hg0, changing Hg0 emissions may have an effect on
modeled oxidized Hg and its subsequent deposition. We compare model results
to the observed wet deposition fluxes from NADP/MDN (2012), as shown in the
Supplement, Fig. S9. We use the monitoring sites active in 2009–2011 (n=126). Both the reference simulation and the emission inversion fit
observations well (R≈0.7, NRMSE ≈0.3). Accordingly, the
effect of the inversion on the NADP/MDN (National Atmospheric Deposition Program/Mercury Deposition Program) wet deposition fluxes is
insignificant.
Emission inversion: optimized emissions
The annual reference and optimized emissions of mercury are shown in Table 2. The relationship σ¯=n∑i=1nσt2, where n=12 months and σt is monthly error,
is used to compute the annual uncertainty for seasonal processes (Chen and
Prinn, 2006). The uncertainty of the aseasonal source (annual Asian
anthropogenic emission) is obtained directly from Eq. (7). The global
optimized mercury emission is ∼ 5.8 Gg yr-1, with an
uncertainty range of 1.7–10.3 Gg yr-1. Compared to our reference
emission of ∼ 6.0 Gg yr-1 (uncertainty range: 0.4–12.2 Gg yr-1), the emission inversion results in a slightly smaller value and
also reduces its uncertainty range. The optimized value is smaller than
previous estimates of 7.5 Gg yr-1 by Pirrone et al. (2010) using a
bottom-up approach. The emission inversion increases emissions from
anthropogenic sources and ocean surfaces but decreases those from
terrestrial surfaces. The ocean accounts for more than half (55 %) of the
total, while the terrestrial surface contributes only a small fraction
(6 %).
Ocean
Net Hg0 evasion from the global ocean is optimized by the emission
inversion as 3160 Mg yr-1, with an uncertainty range of 1160–5160 Mg yr-1 (Table 2). The NH and SH oceans contribute similar amounts to the
total but, on an area basis, evasion from the NH ocean is higher since it is
30 % smaller. We are able to reduce ocean evasion uncertainty from 50
to 40 % by using top-down constraints.
Figure 5 shows the monthly reference and optimized emissions of seasonal
sources. We find, for both hemispheres, that the emission inversion
generally results in increased ocean emissions in summer and decreased
emissions in winter, compared to the reference simulation. As a result, we
hypothesize that one or more ocean processes that affect the seasonal
behavior of aqueous mercury and its evasion are not well-represented in
GEOS-Chem. We therefore conduct a series of sensitivity studies of model
parameters to test their potential effects on the seasonal pattern of ocean
emission. We also compare the parameter values used in GEOS-Chem with their
possible ranges in a recent review (Batrakova et al., 2014). The tested
model parameters in GEOS-Chem include rates of redox chemical reactions and
physical processes in the ML and subsurface mercury concentrations affecting
physical exchange between the ML and subsurface waters. Through these
sensitivity tests and literature review, we identify two processes as
candidates for improvement, the rate constant of dark oxidation of
Hgaq0 (KOX2) and the partition coefficient between
Hgaq2+ and HgaqP (KD). We optimize these two ocean
model parameters in the parameter inversion, as described in Sect. 2.5.
Terrestrial ecosystem
As shown in Table 2, the emission inversion reduces soil emissions of
Hg0 by about 50 %, from 1680 ± 840 to 860 ± 440 Mg yr-1. Using Eq. (1), the optimized net emission flux from terrestrial
surfaces (Enet) is 340 Mg yr-1. If we do not consider geogenic
activities (90 Mg yr-1) and biomass burning (210 Mg yr-1), the
Enet2 (calculated as Esoil+Epr-EddHg0 and
representing net emissions from soils/vegetation) is almost zero after
optimization. Thus, terrestrial surfaces are neither a net source nor a net
sink of Hg0. This is in contrast to bottom-up estimates that the
terrestrial surface is a net source of about 2000 Mg yr-1 (Pirrone et
al., 2010; Mason, 2009).
Monthly emissions for the three seasonal sources (NH ocean,
SH ocean, and soil) from the reference simulation (blue solid lines),
emission inversion (red solid lines), and parameter inversion (green dashed
lines). The gray shaded regions and red error bars indicate 1σ
uncertainties for the reference emissions and emission inversion,
respectively.
Vegetation is now believed to serve as a net sink of atmospheric Hg0
through foliar uptake and sequestration (Gustin et al., 2008; Stamenkovic
and Gustin, 2009; X. Wang et al., 2014). Although its size has not been
well quantified, we suggest that this sink is important in global mass
balance since litterfall transfers 2400–6000 Mg Hg yr-1 to terrestrial
surfaces (Gustin et al., 2008). Air–soil flux measurements show that
Hg0 emissions from background soils generally dominate over dry
deposition (Obrist et al., 2014; Edwards and Howard, 2013; Park et al.,
2013; Denkenberger et al., 2012; Ericksen et al., 2006). Our result of a
smaller soil Hg source is consistent with a study by Obrist et al. (2014),
which suggested that Hg was unlikely to be re-emitted once incorporated into
soils and that terrestrial Hg emission was restricted to surface layers
(Demers et al., 2013). Our result is also in agreement with estimates of
terrestrial fluxes of southern Africa using Hg0 correlations with
222Rn, a radioactive gas of predominantly terrestrial origin (Slemr et
al., 2013). Considering that soil is a smaller source while vegetation a
sink of Hg0, our result that the terrestrial ecosystem is neither a net
source nor a net sink of Hg0 is reasonable, implying that the
magnitudes of soil emission and dry deposition of Hg0 (primarily to
vegetation) are similar. We evaluate dry deposition fluxes modeled by
GEOS-Chem against data in L. Zhang et al. (2012), which estimated fluxes at
sites in North America and obtained good agreements with surrogate surface
and litterfall measurements (Graydon et al., 2008; Lyman et al., 2007). As
shown in the Supplement, Fig. S10, there is no bias in the average dry
deposition flux at eight background sites, indicating that ∼ 1400 Mg yr-1 (modeled by GEOS-Chem) may be reasonable estimates for
both emission and dry deposition of Hg0.
Comparison of Asian Hg0 emissions (Mg yr-1) from
recent studies.a
Reference
Base year
Anthropogenic
Net
Net
Total
terrestrialb
oceanb
Emission inventories
Streets et al. (2009)c
2006
800
Streets et al. (2011)c
2008
700
Muntean et al. (2014)
2008
580
AMAP/UNEP (2013)
2010
770
Rafaj et al. (2013)c
2010
550–750
Other studies
Pan et al. (2007)d
1999
420
2270
Shetty et al. (2008)d
2001
710
120
Strode et al. (2008)
2004
890–990
1260–1450
Fu et al. (2015)e
2007–2010
1590–1870
This study
Reference emission
2009–2011
770 ± 390
360
230
1360
Emission inversion
2009–2011
1060 ± 110
130
300
1490
Inversion using different
2009–2011
650–1770
0–230
260–300
1180–2030
Asian sites
a Here Hg0 only refers to gaseous elemental mercury.
b Net terrestrial and ocean emissions are from the Asian domain.
c Estimated values from tables and figures in the references.
d An east Asian domain is used in these studies. Their terrestrial and
ocean surfaces are smaller than those of the Asia domain.
e The Asian domain includes mainland China, southern Asia, Indochinese
Peninsula, and central Asia, and does not include ocean surfaces.
Anthropogenic emission from Asia
Table 3 summarizes Asian emissions of Hg0 (only GEM) estimated by
several recent bottom-up emission inventories and modeling studies. These
inventories reported Asian anthropogenic emissions ranging from 550 to 800 Mg yr-1.
In our model simulations, the reference emission of 770 Mg yr-1 follows AMAP/UNEP (2013).
The emission inversion using all 27 sites increases this value to 1060 ± 110 Mg yr-1. Uncertainty in
Asian anthropogenic emission should be larger than that obtained using our
inversion method, because emission estimates are sensitive to the
Asia-Pacific sites used in the inversion. As discussed above, model
performance at several Asia-Pacific sites is affected by unknown
intercomparison errors and local emission and meteorological factors not
captured by GEOS-Chem. To obtain a more accurate estimate of uncertainty, we
perform seven emission inversions, each including only one Asia-Pacific
site.
As shown in Table 3, these inversions result in Asian anthropogenic
emissions of Hg0 ranging from 650 to 1770 Mg yr-1. Comparing this
range to its bottom-up inventory estimates of 550–800 Mg yr-1, we
suggest that it is very likely to be underestimated. We estimate total
(anthropogenic + natural + legacy) Hg0 emission in Asia as
1180–2030 Mg yr-1. Our uncertainty ranges cover those in Strode et al. (2008), which estimated total Asian emission of 1260–1450 Mg yr-1 with
890–990 Mg yr-1 from anthropogenic sources, by comparing GEOS-Chem to
the observed Hg : CO ratio at sites OKN and MBO. Pan et al. (2007) assimilated
aircraft observations into a regional CTM and estimated total Hg0
emission in east Asia as 2270 Mg yr-1, at the upper end of our range.
Fu et al. (2015) obtained a total Hg0 emission in Asia of 1590–1870 Mg
yr-1, which compared well with our range, using the Hg0 : CO and
Hg0 : CO2 slopes observed at ground-based sites and inventories of
CO and CO2. Shetty et al. (2008) estimated natural terrestrial emission
in east Asia was about 710 Mg yr-1, much higher than our 0–230 Mg yr-1 in a larger domain. The difference is due to their larger
estimation of vegetation evapotranspiration (630 Mg yr-1).
Parameter inversion
Results of the parameter inversion are presented in Table 4. The a posteriori KOX2
of 6 × 10-6 s-1 is much larger than its current value (1 × 10-7 s-1) in GEOS-Chem, suggesting that Hgaq0
dark oxidation in the ML is more important than previously thought. The a posteriori log
KD of 4.2 is lower than seawater values in the literature (Fitzgerald
et al., 2007; Batrakova et al., 2014) but agrees with the lower end of freshwater measurements (Amos et al., 2014). We attribute this discrepancy to
several simplifying assumptions in GEOS-Chem. KD is linked to the
estimates of SPM concentrations in the ML and organic carbon export. As
described above, the amount of organic carbon export is very uncertain (5–20 Gt C yr-1). A smaller organic carbon export may correspond to a larger
log KD. The uncertain spatial and seasonal variations of carbon export
may also affect the estimate of log KD. In addition, there are no
available global data sets of SPM in the ML. GEOS-Chem derives SPM
concentrations from MODIS satellite chlorophyll a and C : Chl a ratios
(Soerensen et al., 2010b). Thus, the uncertain SPM fields may also affect
log KD. As for the other two parameters (ERSoil and ERAsia),
the parameter inversion decreases soil emission but increases Asian
anthropogenic emission, consistent with the emission inversion (see Table 4).
Evolution of the parameters' estimates in the parameter
inversion.
Parameter
A priori
First iteration
Before second
A posteriori
iteration∗
-log KOX2
5.0 ± 1.0
5.1 ± 0.1
5.1 ± 1.0
5.2 ± 0.1 (KOX2=6×10-6 s-1)
log KD
5.3 ± 0.4
4.4 ± 0.2
4.4 ± 0.2
4.2 ± 0.2 (KD=1.6×104 L kg-1)
ERSoil
1.0 ± 0.5
0.37 ± 0.08
0.37 ± 0.19
0.24 ± 0.1 (soil emission decreases by 76 %)
ERAsia
1.0 ± 0.5
1.7 ± 0.1
1.7 ± 0.9
1.9 ± 0.1 (Asian anthropogenic emission increases by 90 %)
∗ For the second iteration, we use the best estimates derived from the
first
iteration, but larger parameter uncertainties. The uncertainty of 1.0 for
-log KOX2 is the same as that for the a priori estimate. The uncertainties for
ERSoil and ERAsia are chosen as 50 % of their best
estimates, consistent with the emission inversion. The uncertainty for log KD is
chosen as 0.2 because it is approaching the lower end (4.2) of the possible
values in the literature survey.
Similar to our model–observation comparison for the emission inversion, we
run GEOS-Chem using optimized parameters and calculate the NRMSEs for all
ground-based sites (Table 1). A smaller average NRMSE of 0.14 for the
parameter inversion than that of 0.16 for the reference simulation shows
improvement in model performance. GEOS-Chem simulations using optimized
parameters also match regional over-water Hg0 (NRMSE = 0.10, Fig. 2)
and wet deposition measurements (Fig. S9 in the Supplement). In addition, we
evaluate the optimized model against recent surface ocean measurements of
total aqueous mercury (HgaqT), Hgaq0, and
HgaqP (Table 5). For HgaqT, 50 and 75 % (6 and 8
out of 12) of the modeled data from the reference and optimized simulations,
respectively, are within measurement ranges. For Hgaq0, 60 % (6
out of 10) of the modeled data from both simulations are within measurement ranges.
For HgaqP, the reference simulation predicts a higher value while the
parameter inversion predicts a lower value than the only measurement data.
These results suggest that the parameter inversion is comparable or
potentially better than the reference simulation with regard to modeling
surface ocean mercury.
Optimizing the two ocean model parameters, -log KOX2 and log KD,
changes the global ocean Hg budget in GEOS-Chem, as shown in Fig. 6. Sources
of Hgaq in the ML include deposition of oxidized Hg and physical
transport from subsurface waters. They are balanced by Hg0 evasion and
HgaqP sinking. In the reference simulation, although deposition
(20.2 Mmol yr-1) accounts for most ML Hgaq inputs, the two
physical transport processes, entrainment/detrainment of the ML and Ekman
pumping, together supply a considerable amount (FINT: 6.1 Mmol yr-1) from subsurface waters. This upward flux is a result of the
gradient in HgaqT between the ML (0.8 pM) and subsurface waters
(1.1 pM). Hg0 evasion and HgaqP sinking remove 14.9 and 11.4 Mmol yr-1 from the ML, respectively. The combined effect of the larger
KOX2 and smaller KD in the parameter inversion is, in the ML, that
Hgaq2+ increases from 0.69 to 0.95 pM, HgaqP decreases
from 0.05 to 0.004 pM, and Hgaq0 remains 0.06 pM.
HgaqP sinking becomes a smaller sink (1.7 Mmol yr-1) due to
the lower KD. Physical transport contributes a downward flux (-1.5 Mmol yr-1) since the gradient of HgaqT between the ML (1.0 pM) and
subsurface waters (1.1 pM) is diminished.
Global ocean mercury budget modeled by GEOS-Chem. Blue
color indicates the reference simulation and red color the parameter
inversion. Fluxes are in megamoles per year. Notations in this figure follow
Soerensen et al. (2010b). FINT denotes net fluxes from subsurface
waters through entrainment/detrainment of the mixed layer and Ekman pumping.
Physical transport and HgaqP sinking affect seasonal variations of
simulated Hg0 evasion from the ocean (Soerensen et al., 2010b). In
summer, enhanced biological productivity increases HgaqP sinking
and decreases Hg0 evasion by shifting speciated Hgaq equilibrium
in the ML towards Hgaq0 loss. During winter months, the ML deepens
and Hgaq in subsurface waters invade the ML by entrainment; additionally,
Hg0 evasion will be enhanced if subsurface waters contain higher
HgaqT. In the parameter inversion, physical transport and
HgaqP sinking are both weakened, as described above. As a result,
the parameter inversion overturns seasonality of simulated ocean evasions in
both hemispheres (Fig. 5), agreeing with results from the emission
inversion.
Recent surface ocean mercury measurements and simulated
concentrations.a
Location
Date
Latitude, longitude
Measurement
Reference
Parameter
Ref.c
simulationb
inversionb
HgaqT (pM)
Atlantic Ocean
Nov 2008
15–50∘ N, 20–5∘ W
0.8–3.0
0.64
0.89
(1)
30–15∘ S, 0–15∘ E
0.4–2.8
0.48
0.97
(1)
Apr–May 2009
15–50∘ N, 25–5∘ W
0.4–2.3
0.34
0.82
(1)
50–15∘ S, 65–20∘ W
0.5–1.5
0.68
0.89
(1)
Oct–Nov 2005
20∘ S–35∘ N, 25∘ W–10∘ E
0.5–4.5
0.63
1.2
(2)
Jun 2008
32∘ N, 64∘ W
0.6–1.0
0.65
1.2
(3)
Sep 2008–2009
25–35∘ N, 65–60∘ W
0.6–0.9
0.95
1.2
(4)
Aug 2010
30–32∘ N, 65–60∘ W
1.2–1.6
0.91
1.2
(4)
Pacific Ocean
Mar 2006
20–50∘ N, 152∘ W
0.5–1.9
0.96
1.2
(5)
May 2009
30∘ N, 140∘ W
0.2–0.4
0.80
1.1
(6)
Oct 2011
15∘ S–17∘ N, 175–155∘ W
< 0.5
0.83
1.1
(7)
Southern Ocean
Mar–Apr 2008
66–44∘ S, 140–147∘ E
0.6–2.8
0.85
1.1
(8)
Hgaq0 (fM)
Atlantic Ocean
Nov 2008
15–50∘ N, 20–5∘ W
30–140
52
51
(1)
30–15∘ S, 0–15∘ E
15–30
38
68
(1)
Apr–May 2009
15–50∘ N, 25–5∘ W
15–40
27
55
(1)
50–15∘ S, 65–20∘ W
10–70
54
59
(1)
Jul 2005
60∘ N, 40∘ W–5∘ E
30–90
22
83
(9)
Sep 2008–2009
25–35∘ N, 65–60∘ W
80–170
80
87
(4)
Jun 2009
32∘ N, 64∘ W
105–135
55
90
(4)
Aug 2010
30–32∘ N, 65–60∘ W
130–260
77
94
(4)
Pacific Ocean
Oct 2011
15S–17∘ N, 175–155∘ W
< 100
71
81
(7)
Southern Ocean
Mar–Apr 2008
66–44∘ S, 140–147∘ E
< 280
72
58
(8)
HgaqP (fM)
Pacific Ocean
Oct 2011
15∘ S–17∘ N, 175–155∘ W
20–50
70
5
(7)
a 1 pM = 10-9 mol m-3; 1 fM = 10-12 mol m-3.
b Numbers in bold represent the modeled concentrations that are out of the
corresponding measurement ranges.
c References: (1) Kuss et al. (2011), (2) Pohl et al. (2011), (3) Lamborg et
al. (2012),
(4) Soerensen et al. (2013), (5) Sunderland et al. (2009), (6) Hammerschmidt and Bowman (2012), (7) Munson (2014), (8) Cossa et
al. (2011), and (9) Andersson et al. (2011).
As described in Sect. 2.6.4, we conduct an additional parameter inversion
including six new elements representing Br columns in different latitudinal
bands. As shown in the Supplement, Fig. S8, -log KOX2 is found to be
strongly correlated with Br columns at 30–60∘ N,
30∘ S–0∘, and 60–30∘ S. The other
three factors, log KD, ERSoil, and ERAsia, have no or weak
correlations with Br columns. Thus, we suggest that the inversion results of
smaller terrestrial emissions and larger Asian anthropogenic emissions are
not likely to be affected by the uncertainty in atmospheric chemistry, but
the poor understanding of atmospheric chemistry may limit our ability to
further constrain specific ocean model parameters.
Implications for the Hg biogeochemical cycle
We use the box model developed by Amos et al. (2013, 2014) to explore the
long-term impact of our inverted emissions and parameters on the global
biogeochemical cycling of mercury. This seven-box model dynamically couples
the atmosphere, three terrestrial reservoirs (fast, slow, and armored), and
three ocean reservoirs (surface, subsurface, and deep). All rate
coefficients of Hg mass between reservoirs are assumed to be of the first order.
The simulation is initialized with geogenic emissions to represent the natural
mercury cycle and, after reaching steady state, it is driven by historical
anthropogenic emissions (Streets et al., 2011; Horowitz et al., 2014).
Two box-model simulations are performed. The first uses rate coefficients
from the present-day global budget in the reference simulation. The second
uses those from our emission and parameter inversions and has higher
anthropogenic emissions, lower re-emission from terrestrial surfaces, and
less sinking out of the surface ocean than the first one does (Table S4 in the
Supplement). The second simulation obtains larger terrestrial mercury
reservoirs, highlighting their important role in sequestering legacy
mercury. The oceans are a smaller mercury reservoir of ∼ 1700 Mmol in the second simulation, compared to that of ∼ 2000 Mmol
in the first simulation. The former number is more consistent with the
estimates of about 1300–1400 Mmol by Lamborg et al. (2014) and Zhang et al. (2014). The first box-model simulation shows that 18 % of present-day
atmospheric deposition is from primary anthropogenic emissions, 76 % is
legacy, and 6 % is natural (i.e., geogenic emissions). Applying our
inversion results into the box model, the second simulation suggests that
primary anthropogenic emissions account for a larger fraction (18–23 %) of
present-day atmospheric deposition. Legacy releases of mercury contribute a
smaller proportion (72–76 %) but still play a major role.
Summary and conclusion
Here, we perform global-scale inverse modeling combining ground-based
Hg0 observations and GEOS-Chem mercury simulations. Using Bayesian
inversion methods, we are able to constrain present-day mercury emission
fluxes from major sources (emission inversion) and relevant key parameters
in GEOS-Chem (parameter inversion), and reduce uncertainties associated with
these fluxes and parameters.
The emission inversion better reproduces the ground-based Hg0 observations
(particularly for sites in the Southern Hemisphere and North America) than
the reference simulation and also matches measured Hg0 over the North
Atlantic Ocean and wet deposition fluxes in North America. We obtain a
global Hg emission of 5.8 Gg yr-1 (uncertainty range: 1.7–10.3 Gg yr-1), smaller than the estimate of 7.5 Gg yr-1 using a bottom-up
approach (Pirrone et al., 2010). The global ocean accounts for 3.2 Gg yr-1 Hg (55 % of the total). The terrestrial ecosystem is neither a
net source nor a net sink of atmospheric Hg0, in contrast to its
bottom-up estimate as a significant source (Pirrone et al., 2010). The
optimized Asian anthropogenic emissions range from 650 to 1770 Mg yr-1,
suggesting that bottom-up inventories (550–800 Mg yr-1) may have
underestimated their value. The total Asian Hg0 emission (including
anthropogenic, natural and legacy sources) is estimated as 1180–2030 Mg yr-1, consistent with recent studies (Fu et al., 2015; Strode et al.,
2008; Pan et al., 2007).
The emission inversion changes seasonal patterns of ocean emissions in both
hemispheres. We identify and constrain two ocean model parameters in
GEOS-Chem that can explain this seasonal pattern, the rate constant of dark
oxidation of Hgaq0 (KOX2) and the partition coefficient
between Hgaq2+ and HgaqP (KD). The a posteriori KOX2 (6 × 10-6 s-1) is larger than its current value in GEOS-Chem
(1 × 10-7 s-1), suggesting that dark oxidation of
Hgaq0 is more important than previously thought. The a posteriori log KD
(4.2) is smaller than its a priori (5.3), leading to less HgaqP sinking
out of the mixed layer. These changes in parameters affect the simulated
global ocean mercury budget, especially mass exchange between the mixed
layer and subsurface waters. The parameter inversion changes seasonality of
ocean emissions in both hemispheres, agreeing with results from the emission
inversion.
Our inversion results suggest changes in our understanding of the timescales
of cycling between different mercury reservoirs. Based on these changes, the
long-term biogeochemical box-model simulations result in larger estimated
terrestrial mercury pools and smaller ocean mercury pools. Legacy mercury
accounts for a smaller fraction of present-day atmospheric deposition than
previous estimates, whereas the contribution of primary anthropogenic
emissions becomes larger (up to 23 %).
Our inversion results identify specific knowledge gaps in mercury
observation and modeling that currently limit our ability to constrain the
biogeochemical cycle of mercury. First, and most important, effective
inversions are hampered by the uncertain atmospheric Hg measurements,
particularly the large intercomparison errors in measured GEM. Only a few
experiments have been made to evaluate the comparability of mercury
measurements (Gustin et al., 2013). Our results show that intercomparison
errors (about 10 %) dominate the total observational errors and thus
limit the uncertainty reduction possible by our inverse approach. Our
inversions only lead to moderate reductions of the average NRMSE (Sect. 3.1). Therefore, research aimed at quantifying and reducing the
intercomparison errors should be given high priority by the mercury
measurement community. Second, observational sites are sparse in some
regions (e.g., the Southern Hemisphere). More sites in these regions are
necessary to further constrain emissions. Third, the uncertainty in
atmospheric mercury chemistry also affects our inversion results
(specifically, in constraining ocean model parameters). Improving our
understanding of atmospheric mercury chemistry at both global and regional
scales (e.g., the polar regions) requires a combination of both measurement
and modeling advances.