Assessment of Global Mercury Deposition through ... - ACS Publications

Subscriber access provided by the University of Exeter

Article

Assessment of global mercury deposition through litterfall Xun Wang, Zhengduo Bao, Che-Jen Lin, Wei Yuan, and Xinbin Feng Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b06351 • Publication Date (Web): 15 Jul 2016 Downloaded from http://pubs.acs.org on July 17, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 27

Environmental Science & Technology

1

Assessment of global mercury deposition through litterfall

2 3 4 5 6 7 8 9

Xun Wang1,2, Zhengduo Bao3,4, Che-Jen Lin1,3,4,*, Wei Yuan1,2, Xinbin Feng1,*

10 11 12 13 14

1

State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences,

Guiyang, China 2

University of Chinese Academy of Sciences, Beijing, China

3

Center for Advances in Water and Air Quality, Lamar University, Beaumont, TX, USA

4

Department of Civil and Environmental Engineering, Lamar University, Beaumont, TX, USA

* Corresponding Authors: Xinbin Feng Phone: +86-851-5895728 Fax:+ 86-851-5891609 Email: [email protected]

Che-Jen Lin Phone: (409) 880-8761 Fax: (409) 880-8121 E-mail: [email protected]

ACS Paragon Plus Environment


15

ABSTRACT

16

There is a large uncertainty in the estimate of global dry deposition of atmospheric mercury (Hg). Hg

17

deposition through litterfall represents an important input to terrestrial forest ecosystems via cumulative

18

uptake of atmospheric Hg (most Hg0) to foliage. In this study, we estimate the quantity of global Hg

19

deposition through litterfall using statistical modeling (Monte Carlo simulation) of published datasets of

20

litterfall biomass production, tree density and Hg concentration in litter samples. Based on the model

21

results, the global annual Hg deposition through litterfall is estimated to be 1180±710 Mg yr-1, more than

22

two times greater than the estimate by GEOS-Chem. Spatial distribution of Hg deposition through

23

litterfall suggests that deposition flux decreases spatially from tropical to temperate and boreal regions.

24

Approximately 70% of global Hg0 dry deposition occurs in the tropical and subtropical regions. A major

25

source of uncertainty in this study is the heterogeneous geospatial distribution of available data, more

26

observational data in regions (Southeast Asia, Africa, and South America) where few data exist will

27

greatly improve the accuracy of the current estimate. Given that the quantity of global Hg deposition via

28

litterfall is typically 2-6 times higher than Hg0 evasion from forest floor, global forest ecosystems

29

represent a strong Hg0 sink.


Page 2 of 27

Page 3 of 27


30



31

Page 4 of 27

Introduction

32

Mercury (Hg) is a global health concern due to its toxicity and ubiquitous presence in the

33

environment. There is a contradicting trend between the declining atmospheric Hg concentration observed

34

by global monitoring network and the flat or slightly increasing Hg release from anthropogenic emission

35

sources over the last 20 years

36

commercial products, biases on the emission estimates of artisanal and small-scale gold mining activities,

37

and ignorance on the modifications of Hg emission speciation profiles driven by air pollution control

38

technologies 7. Uncertainties on the global estimates of Hg emissions from natural sources

39

depositions

40

the linkage between sources and sink terms of atmospheric Hg. Present estimate for global Hg emission to

41

the atmosphere is 7500-8000 Mg yr-1, including 1900-2400 Mg yr-1 from anthropogenic sources (most in

42

the form of Hg0 vapor), 1800-2800 Mg yr-1 from oceanic gross evasion (Hg0 vapor), and 1700-2800 Mg

43

yr-1gross evasion (Hg0 vapor) from terrestrial sources 2, 3, 12. Atmospheric Hg is primarily removed through

44

deposition of Hg0 from atmosphere (1500-3800 Mg yr-1) and HgII deposition (4300-5400 Mg yr-1)

45

following oxidation of Hg0 by atmospheric oxidants

46

depositions have been made based on model simulation or approximate scale-up of limited measurements

47

16, 17

10, 11

1-6

. Possible reasons include the underestimation of Hg releases from

3, 8, 9

and Hg

have limited our understanding of Hg cycling, and underscore the need for reassessing

11, 13-15

. Estimates on global natural emission and

, and remain uncertain due to a lack of observational data and process understanding. Terrestrial forest has been regarded as an underestimated sink for atmospheric Hg on a global scale 10,

48 49

18, 19

. Forest vegetation predominately removes atmospheric Hg0 through uptake by stomata

50

deposition on cuticle in foliage

51

transported to forest floor through litterfall/throughfall, and then sequestrated in soil

22

20, 21

or HgII

. These fixed Hg can be stored in live biomass (e.g., stem), or


8, 10

. Hg deposition

Page 5 of 27


52

through litterfall has been considered a lower bound of Hg dry deposition to forest ecosystem 15. Direct

53

measurements and stable isotope studies have suggested that Hg deposition through litterfall is

54

significantly greater than wet deposition

55

soil/peats

56

by atmospheric models

57

mainly because the whole-ecosystem fluxes over the canopy are largely missing in literatures

58

systematic evaluation on the role of Hg deposition through litterfall provides new insights into the role of

59

forest ecosystems in the global biogeochemical cycling of Hg.

26, 27

23-25

and strongly influence the size of Hg storage in forest

. Such deposition data have also been applied for verifying dry deposition fluxes predicted 15, 28

. To date, the source-sink characteristics of global forest is still undefined, 9, 16, 19

.A

60

In this study, the quantity and geospatial distribution of global Hg deposition through litterfall are

61

assessed through statistical modeling using published datasets of litterfall biomass production, tree

62

density and Hg concentration in litterfall. The statistical estimate using Monte Carlo simulation in this

63

work and global Hg modeling results are compared. The implications in terms of the role of the Hg input

64

through litterfall in global Hg cycling are discussed.

65 66

2 Materials and methods

67

2.1 Data collection. To ensure data comparability, the field data obtained at 186 remote/rural forest sites

68

were collected from peer-reviewed literature published during 1995-2015 (Figure 1.1). Most sites

69

included in this study are background sites and only 5 rural sites in China have an atmospheric Hg

70

concentration of 3.0-5.0 ng m-3 29. The dataset includes Hg concentrations in litterfall (referred to leaf

71

litter), and Hg deposition through litterfall, Hg deposition through open-field rainfall, and Hg deposition

72

through throughfall. The Hg concentrations and biomass (and therefore the calculated fluxes) are based



Page 6 of 27

73

on the dry weight. An unpublished Hg concentration dataset collected in China in autumn of 2013 and

74

2014 was also included in Table S1 (described in details in the supplemental information, SI). Based on

75

the spatial distribution of total gaseous Hg concentrations and forest types, the literature data were

76

classified into three site groups for data analysis: North-America and Europe (NAE, 1.0 to 2.0 ng m-3

77

atmospheric Hg concentration

78

atmospheric Hg concentration, and subtropical evergreen broadleaf/temperate forests) and Brazil (BRA,

79

1.0 to 2.0 ng m-3 atmospheric Hg concentration 31, 32, and tropical/subtropical evergreen broadleaf forests).

80

A global database of litterfall biomass production at 575 forest sites is available on-line from the Oak National

30, 31

Laboratory

, and temperate/boreal forests), China (CHI, 1.5 to 5.0 ng m-3

81

Ridge

Distributed

Active

Archive

82

http://dx.doi.org/10.3334/ORNLDAAC/1244). In the database, few sites are located in subtropical regions.

83

Since 59% of forests in China are locate in subtropical regions 33, we setup a database that includes 283

84

mature forest sites (158 sites in subtropical regions) in China using the quality-assured data (number of

85

replicates≥3, collector size=1 m2) obtained from China National Knowledge Infrastructure

86

(CNKI).Combining the two databases, the site distribution comprehensively covers a broad climate zone

87

with latitudes from 70°N to 60°S (Figure 1.2).

88

2.2 Estimation of global Hg deposition through litterfall. Monte Carlo simulation is a modeling

89

technique that relies on random sampling and statistical data analysis 34. In this study, we chose to use

90

Monte Carlo simulation of a sufficiently large size of random samples to re-construct the sampling

91

distribution in an attempt to reduce the bias caused by the limited but acceptable sample sizes (e.g., 858

92

sites for litter biomass production), and to offset the uncertainties associated with Hg concentration

93

caused by sampling methodology (e.g., multi-year sampling versus single grab sampling). It is noted that

94

the values statistics presented in the discussion represent the mean and standard deviation (SD) derived

95

from the probability distribution functions of the observational data.


Center

(ORNLDAAC,

Page 7 of 27


96

Briefly, Monte Carlo simulation was applied to integrate the datasets of Hg concentration in litterfall

97

and litterfall biomass to produce the probabilistic Hg flux through litterfall (details in SI). The Monte

98

Carlo simulation and Hg flux calculation was performed using MATLAB 2013a and ArcGIS 10.3. The

99

model-estimate of Hg deposition (Mi, Mg yr-1) was classified for the 14 WWF (World Wildlife Fund for

100 101

Nature) biomes:

= × , ×

(1)

,

102

Where Li is Hg deposition flux caused by litterfall; is the ratio for unit conversion; td,i is tree density

103

(stems ha-1); tn,i is total number of trees in the ith biomes type compiled from a best estimate in a recent

104

study 35(http://elischolar.library.yale.edu/yale_fes_data/1). The global spatial distribution of Hg deposition

105

through litterfall was then calculated based on the spatial distribution of tree density.

106

To obtain data representativeness, a data quality control procedure was utilized to remove data noise

107

contributed by extreme observations. From Table S2, datasets have a normal distribution or similar

108

distributions (e.g., Weibull, t Location-Scale, etc.) with a sample size in each dataset being >10. Hence,

109

we removed the data falling out of the range of Mean±3SD based on the Pauta Criterion (99.7%

110

confidence interval), and data from 18 sites were removed. Another issue is the potential data variability

111

over the entire time frame (1995-2015) that the data were collected. Comparison of the literature data was

112

performed to identify the data difference over the sampling period. The Hg concentration in litterfall

113

collected in 1999 (Amazonian rainforest) is not significantly different from the concentration measured in

114

the 2013 samples ((Mean±SD, 55±11 vs. 57±10 ng g-1, P > 0.05)

115

litterfall collected in 1999 (Mt. Gongga, China) is not significantly different from the concentration in the

116

2014 samples (77±9 vs. 80±4 ng g-1, P > 0.05, unpublished data). In addition, Hg concentrations measured

32

. Similarly, Hg concentration in fir



117

in 3 years of litterfall samples from 23 Mercury Deposition Network sites in eastern USA do not show

118

significant annual variations 15. Based on these observations, we assume that the Hg concentration in litter

119

samples at a given site remains relatively unchanged from 1995 to 2015. A Chi-Square Goodness-of-Fit

120

Test

121

MATLAB 2013a.

36

was applied to obtain a best fit for a probability density function at 95% confidence level using

122 123

3 Results and discussion

124

3.1 Hg concentration in litterfall. Hg concentration in the litter samples of 168 global sites ranges from

125

17 to 238 ng g-1 with a mean (±SD) of 54±22 ng g-1 and a median of 46 ng g-1 (Figure 2). The highest Hg

126

concentration is found in BRA (mean= 92±49 ng g-1, range=40-238 ng g-1, median=76 ng g-1),

127

significantly higher (P < 0.05) than the concentrations observed in CHI (mean= 46±25 ng g-1,

128

range=17-135 ng g-1, median=40 ng g-1) and NAE (mean= 43±12 ng g-1, range=21-78 ng g-1, median=42

129

ng g-1).

130

Although Hg concentration in foliage is correlated with atmospheric Hg0 concentration based on

131

experiments of plotted plants 37, the difference in litter Hg concentration in the three regions cannot be

132

solely explained by the disparity in atmospheric Hg0 concentration. Using the data collected at

133

comparable atmospheric Hg0 concentration (1.0-2.0 ng m-3)

134

Amazon rainforest is 70% higher than the value in NAE. Atmospheric Hg concentration in CHI is

135

generally higher than in NAE

136

elevated. Under the 3.0-5.0 ng m-3 atmospheric Hg in CHI, the Hg concentration in litter samples can be

137

statistically equal to those found at NAE sites with 1.0-2.0 ng m-3 atmospheric Hg concentration (Figure

10, 29

32

, mean litter Hg concentration in remote

, however, the Hg concentration in litter in CHI is not comparatively


Page 8 of 27

Page 9 of 27

138


S1) 29, 38, 39.

139

In addition to atmospheric Hg concentration, environmental factors (e.g., solar irradiation, air

140

temperature, altitude, etc.) and biological factors (e.g., plant species, leaf age, leaf placement etc.) also

141

significantly influence Hg uptake by foliage

142

deciduous species can be different from the value for coniferous species

143

accumulated rate (slower rate in coniferous foliage) 43, 44 and leaf life span (1-5 years in coniferous foliage

144

versus several months for deciduous foliage)

145

statistically significant. Figure 3 shows that Hg concentrations in the litter samples of deciduous species

146

are comparable to those of coniferous species in both CHI and NAE (P =0.68 in CHI and P =0.57 in NAE,

147

by T test). This is likely a result of the combined climate/orographic/biological effects that dilute the

148

individual difference of vegetation species. In addition, evergreen broadleaf species have the highest litter

149

Hg concentration (Figure 3). Hg uptake by foliage has been suggested to occur along with plant

150

metabolism 16 that incorporates Hg into leaf biomass. The combined effects of 1-2 years of leaf lifespan 45

151

and stronger foliage assimilation

152

enhanced Hg accumulation in the litter of evergreen broadleaf forests.

153

3.2 Hg deposition flux through litterfall. Measured Hg deposition flux via litterfall ranges from 2.7 to

154

219.9 μg m-2 yr-1, with a mean of 27.3±36.2 µg m-2 yr-1 and a median of 15.3 µg m-2 yr-1 based on the data

155

collected at 90 sites globally (Figure 4.1-4.3). BRA has the highest flux (mean=83.6±47.6 µg m-2 yr-1,

156

range =43.0-184.0 µg m-2 yr-1, median=60.0 µg m-2 yr-1), followed by CHI (mean=78.4±72.6 µg m-2 yr-1,

157

range =26.0-219.9 µg m-2 yr-1, median=55.4 µg m-2 yr-1), and then NAE (mean=15.2±8.4 µg m-2 yr-1,

158

range =2.7-59.5 µg m-2 yr-1, median=14.1 µg m-2 yr-1). The litterfall biomass productions in BRA (821±83

46, 47

16, 20, 37, 40, 41

41

. Hg concentration in the litter samples of 41, 42

due to the difference in Hg

. At a regional scale, however, the difference is not

for evergreen broadleaf species are the likely causes for the



Page 10 of 27

159

Mg km-2 yr-1) and in CHI (1192±576 Mg km-2 yr-1) are significantly higher than those observed in NAE

160

(200±145 Mg km-2 yr-1). The difference in litterfall Hg fluxes of different regions is primarily caused by

161

the difference of litter biomass production rather than by that of Hg concentration in litter, as evidenced

162

by the much stronger correlation between litter biomass production and litterfall Hg flux (Figure

163

S2.1-S2.2). This indicates that biomes of the same type in different regions have a similar atmospheric Hg

164

uptake quantity, which provides a basis for estimating of global Hg deposition through litterfall.

165

Hg deposition through litterfall is substantially greater than the wet deposition at forest sites. The dry

166

to wet deposition ratios range from 4.1 in BRA and 4.3 in CHI to 1.6 in NAE. Considering total Hg dry

167

deposition in a forest ecosystem as the sum of Hg deposition by litterfall and the difference in Hg

168

deposition between throughfall and rainfall (i.e., dry deposition flux = deposition flux through litterfall +

169

deposition flux through throughfall – deposition flux through rainfall)

170

dominates the Hg loading to forest floor, constituting 70% of total (dry + wet) deposition in NAE, 84% in

171

CHI and 85% in BRA(Figure 4.4). Of the dry deposition, ~75% is contributed by litterfall in NAE and

172

CHI. Using the available data 32, Hg deposition through litterfall accounts for 47% total dry deposition in

173

BRA due to the high precipitation in the rainforest region. The Hg deposition data in tropical forest (e.g.,

174

BRA region) are scarce and more data for this forest type are needed for a more thorough assessment.

175

3.3 Global spatial distribution of Hg deposition through litterfall. Global Hg deposition flux through

176

litterfall is estimated using Eqs. 1-6 in SI. Since Hg concentrations in the litterfall samples from

177

coniferous and deciduous species are not significantly different (P =0.69, Figure 3), the concentration data

178

for the two forest types are classified into a same category to increase the sample size for reducing the

179

uncertainty potentially associated with a small sample size in the Monte Carlo simulation. The random


48, 49

, dry deposition of Hg

Page 11 of 27


180

samples of Hg concentration for biome types C2-C13 in Figure 5.1 are generated from the probability

181

density distribution of Hg concentration data including coniferous and deciduous species. Similarly, Hg

182

concentration data for evergreen broadleaf species is utilized for biome types C1 and C14 representing the

183

evergreen species.

184

The flux estimated by the statistical modeling ranges from 1.2 from 48.2 µg m-2 yr-1 depending on the

185

biome types (Figure 5.1). Evergreen broadleaf species yielded the highest flux for C1 (48.2 µg m-2 yr-1).

186

The rank of flux values for C2-C13 biome types is shown in Figure 5.1. This flux distribution is

187

controlled by the litterfall biomass production (Table S2 and Figure S2.3-S2.4). The subplot in Figure 5.1

188

also shows a comparison of the model-predicted fluxes with the field-observed values (slope=0.78,

189

R2=0.83), suggesting that the modeling results agree well with field measurements. The estimated Hg

190

mass input through litterfall is shown in Table S3 and Figure 5.2. Summing the contribution from all

191

biome types, global Hg deposition through litterfall is estimated to be 1180±710 Mg yr-1 (median of the

192

model-constructed probability density distribution =1150 Mg yr-1). The deposition flux decreases spatially

193

from tropical to temperate/boreal regions (Figure 5.3): 30% of total deposition is in the temperate/boreal

194

regions and 70% of total deposition is in the tropical/subtropical regions.

195

Global Hg deposition through litterfall is equivalent to 50-60% of global anthropogenic emissions 3.

196

However, the spatial distribution of Hg deposition caused by litterfall is distinct from the distribution of

197

global anthropogenic emissions (Figure S3). Specifically, in central Africa (47-79˚W and 4-11˚S) and

198

Amazon Basin of South America, anthropogenic Hg emission is typically < 0.5 g km-2 yr-1 (Figure S3),

199

compared to the large Hg removal through litterfall at 65.0±30.0 g km-2 yr-1 (ranging at 10.0-200.0 g

200

km-2yr-1, Figure 5.3). Based on previous mass balance studies in temperate/boreal forests, the quantity of



Page 12 of 27

201

soil Hg evasion in background forest is 2-6 times lower than the quantity of Hg deposition through

202

litterfall on an annual basis 24, 48. Available data show that Hg0 evasion in rainforest soil in Brazil is 6.0 g

203

km-2 yr-1 50. Therefore, the quantity of atmospheric Hg removal through litterfall is much larger than the

204

sum of anthropogenic and natural emissions in these regions. Given that (1) two-thirds of global Hg

205

deposition is contributed by anthropogenic activities 3, 11, (2) atmospheric Hg is relatively well mixed, and

206

(3) vegetative uptake does not discriminate the source origins of Hg, remote forest ecosystems capture a

207

large quantity of anthropogenic release of Hg into the atmosphere.

208

3.4 Uncertainties for global upscaling methodology. Ideally, global estimates are best made if there is a

209

feasible approach to collect representative samples globally. Unfortunately, such global sampling has not

210

been possible due to limited research resources particularly in the remote regions. Specifically, the limited

211

observational datasets available in the tropical/subtropical regions introduce uncertainty to the present

212

estimates. The forest area in tropical/subtropical regions (19.5 million km2) is comparable to the area in

213

the temperate/boreal regions (19.0 million km2)

214

regions is 4 times smaller than that for the temperate/boreal regions and yields a larger uncertainty. As

215

shown in Figure 5.1 & 5.2, C1 has the highest Hg deposition through litterfall yet exhibits the greatest

216

data variability (95% confidence interval = 7.0-112.0μg m-2 yr-1). For grasslands savannas and shrublands,

217

the 95% confidence interval for C7 (rank second in the quantity of Hg deposition via litterfall, Figure 5.2)

218

in tropical/subtropical regions is 40-70% larger than the range for C8-C10 in temperate/boreal regions.

219

More data for evergreen broadleaf forests in Southeast Asia and Africa, as well as for tropical and

51

; while the sample sizes for the tropical/subtropical


Page 13 of 27


220

subtropical grasslands savannas and shrublands in Africa and South America, will allow a better

221

quantification of global Hg sink caused by forest ecosystems.

222

It is assumed in this study that biomes of the same type lead to a similar Hg uptake quantity. Based

223

on the data collected at the global forest sites and the model verification in Figure 5.1, the assumption

224

appears to be scientifically sound but remains to be further verified by more measurements. Since the Hg

225

concentration data were collected at background sites and not targeted to investigate any specific source

226

region, the litterfall data are regarded as random data. Given the fact that the calculated fluxes for similar

227

biomes using the data measured by diverse research groups at different sites appear to be consistent with

228

each other, the random sampling assumption also seems to be conceivable. Finally, more data for the tree

229

density and biomass production would also reduce the propagation of uncertainties 52, 53.

230 231

4 Implications

232

The large deposition through litterfall suggests a strong uptake of Hg by vegetation that was

233

previously underestimated by global modeling. For example, GEOS-Chem estimates the mass of Hg

234

uptake by foliage for the whole terrestrial ecosystems using a fraction (from 25%×LAI/1.25 at LAI ≤ 1.25

235

to 25 % at LAI > 1.25, LAI is the leaf area index) of simulated dry deposition (~2500 Mg yr-1) based on

236

the LAI of vegetation coverage

237

only ~30% of the area of terrestrial ecosystems 55, the uptake of Hg by foliage in forest ecosystems by

238

GEOS-Chem is < 625 Mg yr-1 (25% of 2500 Mg yr-1), a large underestimation compared to the

239

observation-based estimate in this study (1180±710 Mg yr-1). Based on the finding that forest type

17, 54

. Given that forest area has LAI values greater than 1.25 and covers



Page 14 of 27

240

influences the quantity of Hg uptake by foliage, a modeling approach incorporating landuse data and plant

241

physiology is recommended for future modeling efforts. Since field observations have established that the

242

air-foliage exchange of Hg0 is bi-directional

243

nature 56-58 would probably better represent the Hg0 exchange process.

8, 9, 16

, a mechanistic model representing the bi-directional

244

Hg stored in litters and topsoil by legacy Hg deposition can be quickly released into the atmosphere

245

as Hg0 vapor. There have been conflicting reports regarding the role of forest ecosystems as a Hg0 sink or

246

source

247

Extrapolating the median of global database of laboratory and field measured fluxes, forest ecosystems

248

appear to be a Hg0 sink of 59 Mg yr-1, although the estimated sink has a large uncertainty (37.5th to 62.5th

249

percentiles range from a deposition of 727 Mg yr-1 to an emission of 703 Mg yr-1) 9. Such uncertainty is

250

mainly caused by the variation of reported Hg0 uptake by foliage (median = -210 Mg yr-1, 37.5th-62.5th

251

percentile = -824 to 456 Mg yr-1) and the limited geospatial representation of available data

252

litterfall flux to represent the Hg0 uptake by foliage reduces the uncertainty of dry deposition estimate.

253

Based on the estimate in this study, global forest ecosystems clearly represent a net Hg0 sink with a

254

median value of ~1000 Mg yr-1.

3, 8, 59, 60

. An earlier estimate of net Hg0 evasion from forest ecosystems was 340-2000 Mg yr-1 3, 59.

9, 16

. Using

255

The deposition by litterfall has incorporated the re-emission process, although the actual re-emission

256

quantity remains unknown because Hg in litters is a result of multiple processes including uptake (most

257

Hg0 and small amount of deposited HgII), oxidation, re-volatilization of chemically bound Hg, etc. One

258

uncertainty is that Hg uptake from the atmosphere can be translocated to branches, stems, and roots 61, 62,

259

which is not accounted for in the estimate using litterfall data. An earlier study estimated that ~139 Mg

260

yr-1 Hg is stored in forest woods 19, and it remains difficult to quantify the amount of Hg translocation


Page 15 of 27


261

after Hg uptake by leaves. Further studies focusing on the transformation and translocation processes after

262

plant uptake will help constrain Hg0 sink in forest ecosystems.

263

To better assess the role of forest ecosystems in the global Hg cycling, it is also essential to

264

understand the fate of deposited Hg on forest floor. Field data showed that total Hg mass in litters can

265

increase by 37%-147% after 1-2 years of decomposition in temperate/boreal forest ecosystems due to Hg

266

adsorption from throughfall or fungal translocation from surface soils

267

accumulation on forest floor. However, there is no study reporting the fate of litterfall Hg in

268

tropical/subtropical forests. The processes of Hg accumulation and sequestration in tropical/subtropical

269

forests may be different from those in temperate/boreal forests because of the shorter timescale of carbon

270

and nutrient cycle 65. In addition, observations of Hg deposition from throughfall in tropical/subtropical

271

forests are scarce and need more data. We highlight the importance of the role of tropical/subtropical

272

forests in global Hg cycling, which requires further assessment when more data become available.

42, 63, 64

, leading to enhanced Hg

273 274

ASSOCIATED CONTENT

275

Supporting Information

276

Litterfall sample collection andmeasurement for unpublished Hg dataset, methodology about Monte Carlo

277

simulation, figures (Figures S1-S3) and tables (Table S1-S3).

278 279

AUTHOR INFORMATION

280

Corresponding Authors

281 282 283 284

*(X. F.) Phone: +86-851-5895728E-mail: [email protected] *(C. L.) Phone: (409) 880-8761 E-mail: [email protected] Notes The authors declare no competing financial interest

285



Page 16 of 27

286

ACKNOWLEDGMENTS

287

This work was funded by Natural Science Foundation of China (41430754), National "973" Program of

288

China (2013CB430003), and State Key Laboratory of Environmental Geochemistry, IGCAS. The funding

289

support is gratefully acknowledged. Four anonymous reviewers are acknowledged for their thoughtfull

290

comments.


Page 17 of 27


291

REFERENCES

292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330

1.

Gustin, M. S.; Amos, H. M.; Huang, J.; Miller, M. B.; Heidecorn, K., Measuring and modeling mercury in the

atmosphere: a critical review. Atmos Chem Phys 2015, 15, (10), 5697-5713. 2.

Streets, D. G.; Devane, M. K.; Lu, Z. F.; Bond, T. C.; Sunderland, E. M.; Jacob, D. J., All-Time Releases of Mercury

to the Atmosphere from Human Activities. Environ Sci Technol 2011, 45, (24), 10485-10491. 3.

Pirrone, N.; Cinnirella, S.; Feng, X.; Finkelman, R. B.; Friedli, H. R.; Leaner, J.; Mason, R.; Mukherjee, A. B.;

Stracher, G. B.; Streets, D. G.; Telmer, K., Global mercury emissions to the atmosphere from anthropogenic and natural sources. Atmos Chem Phys 2010, 10, (13), 5951-5964. 4.

Temme, C.; Blanchard, P.; Steffen, A.; Banic, C.; Beauchamp, S.; Poissant, L.; Tordon, R.; Wiens, B., Trend,

seasonal and multivariate analysis study of total gaseous mercury data from the Canadian atmospheric mercury measurement network (CAMNet). Atmospheric Environment 2007, 41, (26), 5423-5441. 5.

Cole, A. S.; Steffen, A.; Pfaffhuber, K. A.; Berg, T.; Pilote, M.; Poissant, L.; Tordon, R.; Hung, H., Ten-year trends

of atmospheric mercury in the high Arctic compared to Canadian sub-Arctic and mid-latitude sites. Atmospheric Chemistry and Physics 2013, 13, (3), 1535-1545. 6.

Slemr, F.; Ebinghaus, R.; Simmonds, P. G.; Jennings, S. G., European emissions of mercury derived from

long-term observations at Mace Head, on the western Irish coast. Atmos Environ 2006, 40, (36), 6966-6974. 7.

Zhang, Y. X.; Jacob, D. J.; Horowitz, H. M.; Chen, L.; Amos, H. M.; Krabbenhoft, D. P.; Slemr, F.; St Louis, V. L.;

Sunderland, E. M., Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions. P Natl Acad Sci USA 2016, 113, (3), 526-531. 8.

Gustin, M. S.; Lindberg, S. E.; Weisberg, P. J., An update on the natural sources and sinks of atmospheric

mercury. Appl Geochem 2008, 23, (3), 482-493. 9.

Agnan, Y.; Le Dantec, T.; Moore, C. W.; Edwards, G. C.; Obrist, D., New Constraints on Terrestrial Surface

Atmosphere Fluxes of Gaseous Elemental Mercury Using a Global Database. Environ Sci Technol 2016, 50, (2), 507-524. 10. Lindberg, S.; Bullock, R.; Ebinghaus, R.; Engstrom, D.; Feng, X.; Fitzgerald, W.; Pirrone, N.; Prestbo, E.; Seigneur, C., A synthesis of progress and uncertainties in attributing the sources of mercury in deposition. Ambio 2007, 36, (1), 19-32. 11. Amos, H. M.; Jacob, D. J.; Streets, D. G.; Sunderland, E. M., Legacy impacts of all-time anthropogenic emissions on the global mercury cycle. Global Biogeochem Cy 2013, 27, (2), 410-421. 12. AMAP/UNEP Technical Background Report for the Global Mercury Assessment 2013. Arctic Monitoring and Assessment Programme, Oslo, Norway/UNEP ChemicalsBranch, Geneva, Switzerland. vi + 263 pp; 2013. 13. Amos, H. M.; Jacob, D. J.; Kocman, D.; Horowitz, H. M.; Zhang, Y. X.; Dutkiewicz, S.; Horvat, M.; Corbitt, E. S.; Krabbenhoft, D. P.; Sunderland, E. M., Global Biogeochemical Implications of Mercury Discharges from Rivers and Sediment Burial. Environmental Science & Technology 2014, 48, (16), 9514-9522. 14. Holmes, C. D.; Jacob, D. J.; Corbitt, E. S.; Mao, J.; Yang, X.; Talbot, R.; Slemr, F., Global atmospheric model for mercury including oxidation by bromine atoms. Atmos Chem Phys 2010, 10, (24), 12037-12057. 15. Risch, M. R.; DeWild, J. F.; Krabbenhoft, D. P.; Kolka, R. K.; Zhang, L. M., Litterfall mercury dry deposition in the eastern USA. Environ Pollut 2012, 161, 284-290. 16. Zhu, W.; Lin, C. J.; Wang, X.; Sommar, J.; Fu, X.; Feng, X., Global observations and modeling of atmosphere–surface exchange of elemental mercury: a critical review. Atmos. Chem. Phys. 2016, 16, (7),



331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371

Page 18 of 27

4451-4480. 17. Song, S.; Selin, N. E.; Soerensen, A. L.; Angot, H.; Artz, R.; Brooks, S.; Brunke, E. G.; Conley, G.; Dommergue, A.; Ebinghaus, R.; Holsen, T. M.; Jaffe, D. A.; Kang, S.; Kelley, P.; Luke, W. T.; Magand, O.; Marumoto, K.; Pfaffhuber, K. A.; Ren, X.; Sheu, G. R.; Slemr, F.; Warneke, T.; Weigelt, A.; Weiss-Penzias, P.; Wip, D. C.; Zhang, Q., Top-down constraints on atmospheric mercury emissions and implications for global biogeochemical cycling. Atmos Chem Phys 2015, 15, (12), 7103-7125. 18. Gustin, M. S.; Engle, M.; Ericksen, J.; Xin, M.; Krabbenhoft, D.; Lindberg, S.; Olund, S.; Rytuba, J., New insights into mercury exchange between air and substrate. Geochim Cosmochim Ac 2005, 69, (10), A700-A700. 19. Obrist, D., Atmospheric mercury pollution due to losses of terrestrial carbon pools? Biogeochemistry 2007, 85, (2), 119-123. 20. Laacouri, A.; Nater, E. A.; Kolka, R. K., Distribution and Uptake Dynamics of Mercury in Leaves of Common Deciduous Tree Species in Minnesota, USA. Environ Sci Technol 2013, 47, (18), 10462-10470. 21. Rutter, A. P.; Schauer, J. J.; Shafer, M. M.; Creswell, J. E.; Olson, M. R.; Robinson, M.; Collins, R. M.; Parman, A. M.; Katzman, T. L.; Mallek, J. L., Dry deposition of gaseous elemental mercury to plants and soils using mercury stable isotopes in a controlled environment. Atmos Environ 2011, 45, (4), 848-855. 22. Stamenkovic, J.; Gustin, M. S., Nonstomatal versus Stomatal Uptake of Atmospheric Mercury. Environ Sci Technol 2009, 43, (5), 1367-1372. 23. Jiskra, M.; Wiederhold, J. G.; Skyllberg, U.; Kronberg, R. M.; Hajdas, I.; Kretzschmar, R., Mercury Deposition and Re-emission Pathways in Boreal Forest Soils Investigated with Hg Isotope Signatures. Environmental Science & Technology 2015, 49, (12), 7188-7196. 24. Grigal, D. F., Inputs and outputs of mercury from terrestrial watersheds: A review. Environmental Reviews 2002, 10, (1), 1-39. 25. Demers, J. D.; Blum, J. D.; Zak, D. R., Mercury isotopes in a forested ecosystem: Implications for air-surface exchange dynamics and the global mercury cycle. Global Biogeochem Cy 2013, 27, (1), 222-238. 26. Enrico, M.; Le Roux, G.; Marusczak, N.; Heimburger, L. E.; Claustres, A.; Fu, X. W.; Sun, R. Y.; Sonke, J. E., Atmospheric Mercury Transfer to Peat Bogs Dominated by Gaseous Elemental Mercury Dry Deposition. Environ Sci Technol 2016, 50, (5), 2405-2412. 27. Zhang, H.; Yin, R. S.; Feng, X. B.; Sommar, J.; Anderson, C. W.; Sapkota, A.; Fu, X. W.; Larssen, T., Atmospheric mercury inputs in montane soils increase with elevation: evidence from mercury isotope signatures. Sci Rep-Uk 2013, 3, (11), 3322-3322. 28. Zhang, L.; Blanchard, P.; Gay, D. A.; Prestbo, E. M.; Risch, M. R.; Johnson, D.; Narayan, J.; Zsolway, R.; Holsen, T. M.; Miller, E. K.; Castro, M. S.; Graydon, J. A.; St Louis, V. L.; Dalziel, J., Estimation of speciated and total mercury dry deposition at monitoring locations in eastern and central North America. Atmos Chem Phys 2012, 12, (9), 4327-4340. 29. Fu, X. W.; Zhang, H.; Wang, X.; Yu, B.; Lin, C.-J.; Feng, X. B., Observations of atmospheric mercury in China: a critical review. Atmos. Chem. Phys. Discuss. 2015, (15), 11925-11983. 30. Kos, G.; Ryzhkov, A.; Dastoor, A.; Narayan, J.; Steffen, A.; Ariya, P. A.; Zhang, L., Evaluation of discrepancy between measured and modelled oxidized mercury species. Atmos Chem Phys 2013, 13, (9), 4839-4863. 31. Sprovieri, F.; Pirrone, N.; Bencardino, M.; D’Amore, F.; Carbone, F.; Cinnirella, S.; Mannarino, V.; Landis, M.; Ebinghaus, R.; Weigelt, A.; Brunke, E. G.; Labuschagne, C.; Martin, L.; Munthe, J.; Wängberg, I.; Artaxo, P.; Morais, F.; Cairns, W.; Barbante, C.; Diéguez, M. D. C.; Garcia, P. E.; Dommergue, A.; Angot, H.; Magand, O.; Skov, H.; Horvat,


Page 19 of 27

372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412


M.; Kotnik, J.; Read, K. A.; Neves, L. M.; Gawlik, B. M.; Sena, F.; Mashyanov, N.; Obolkin, V. A.; Wip, D.; Feng, X. B.; Zhang, H.; Fu, X.; Ramachandran, R.; Cossa, D.; Knoery, J.; Marusczak, N.; Nerentorp, M.; Norstrom, C., Atmospheric Mercury Concentrations observed at ground-based monitoring sites globally distributed in the framework of the GMOS network. Atmos. Chem. Phys. Discuss. 2016, 2016, 1-32. 32. Fostier, A. H.; Melendez-Perez, J. J.; Richter, L., Litter mercury deposition in the Amazonian rainforest. Environmental Pollution 2015, 206, 605-610. 33. Ran, Y. H.; Li, X.; Lu, L.; Li, Z. Y., Large-scale land cover mapping with the integration of multi-source information based on the Dempster-Shafer theory. International Journal of Geographical Information Science 2012, 26, (1), 169-191. 34. Raychaudhuri, S., INTRODUCTION TO MONTE CARLO SIMULATION. Proceedings of the 2008 Winter Simulation Conference 2008, 91-100. 35. Crowther, T. W.; Glick, H. B.; Covey, K. R.; Bettigole, C.; Maynard, D. S.; Thomas, S. M.; Smith, J. R.; Hintler, G.; Duguid, M. C.; Amatulli, G.; Tuanmu, M. N.; Jetz, W.; Salas, C.; Stam, C.; Piotto, D.; Tavani, R.; Green, S.; Bruce, G.; Williams, S. J.; Wiser, S. K.; Huber, M. O.; Hengeveld, G. M.; Nabuurs, G. J.; Tikhonova, E.; Borchardt, P.; Li, C. F.; Powrie, L. W.; Fischer, M.; Hemp, A.; Homeier, J.; Cho, P.; Vibrans, A. C.; Umunay, P. M.; Piao, S. L.; Rowe, C. W.; Ashton, M. S.; Crane, P. R.; Bradford, M. A., Mapping tree density at a global scale. Nature 2015, 525, (7568), 201-205. 36. Snedecor, G. W.; Cochran, W. G., Statistical methods. Iowa State University Press: 1989; p 163. 37. Ericksen, J. A.; Gustin, M. S.; Schorran, D. E.; Johnson, D. W.; Lindberg, S. E.; Coleman, J. S., Accumulation of atmospheric mercury in forest foliage. Atmos Environ 2003, 37, (12), 1613-1622. 38. Wang, Z. W.; Zhang, X. S.; Xiao, J. S.; Zhijia, C.; Yu, P. Z., Mercury fluxes and pools in three subtropical forested catchments, southwest China. Environ Pollut 2009, 157, (3), 801-808. 39. Fu, X. W.; Feng, X. B.; Zhu, W. Z.; Rothenberg, S.; Yao, H.; Zhang, H., Elevated atmospheric deposition and dynamics of mercury in a remote upland forest of southwestern China. Environ Pollut 2010, 158, (6), 2324-2333. 40. Ericksen, J. A.; Gustin, M. S., Foliar exchange of mercury as a function of soil and air mercury concentrations. Sci Total Environ 2004, 324, (1-3), 271-279. 41. Blackwell, B. D.; Driscoll, C. T., Deposition of Mercury in Forests along a Montane Elevation Gradient. Environ Sci Technol 2015, 49, (9), 5363-5370. 42. Demers, J. D.; Driscoll, C. T.; Fahey, T. J.; Yavitt, J. B., Mercury cycling in litter and soil in different forest types in the Adirondack region, New York, USA. Ecol Appl 2007, 17, (5), 1341-1351. 43. Graydon, J. A.; St Louis, V. L.; Lindberg, S. E.; Hintelmann, H.; Krabbenhoft, D. P., Investigation of mercury exchange between forest canopy vegetation and the atmosphere using a new dynamic chamber. Environ Sci Technol 2006, 40, (15), 4680-4688. 44. Poissant, L.; Pilote, M.; Yumvihoze, E.; Lean, D., Mercury concentrations and foliage/atmosphere fluxes in a maple forest ecosystem in Quebec, Canada. Journal of Geophysical Research-Atmospheres 2008, 113, (D10). 45. Luo, T. X.; Neilson, R. P.; Tian, H. Q.; Vorosmarty, C. J.; Zhu, H. Z.; Liu, S. R., A model for seasonality and distribution of leaf area index of forests and its application to China. J Veg Sci 2002, 13, (6), 817-830. 46. Zhang; Yongjiang, Water and carbon balances of deciduous and evergreen broadleaf trees from a subtropical cloud forest in southwest China. Dissertations & Theses - Gradworks 2012, p 20-65. 47. Seyoum, Y.; Fetene, M.; Strobl, S.; Beck, E., Foliage dynamics, leaf traits, and growth of coexisting evergreen and deciduous trees in a tropical montane forest in Ethiopia. Trees-Struct Funct 2012, 26, (5), 1495-1512.



413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453

48. St Louis, V. L.; Rudd, J. W. M.; Kelly, C. A.; Hall, B. D.; Rolfhus, K. R.; Scott, K. J.; Lindberg, S. E.; Dong, W., Importance of the forest canopy to fluxes of methyl mercury and total mercury to boreal ecosystems. Environ Sci Technol 2001, 35, (15), 3089-3098. 49. Rea, A. W.; Lindberg, S. E.; Keeler, G. J., Dry deposition and foliar leaching of mercury and selected trace elements in deciduous forest throughfall. Atmos Environ 2001, 35, (20), 3453-3462. 50. Carpi, A.; Fostier, A. H.; Orta, O. R.; dos Santos, J. C.; Gittings, M., Gaseous mercury emissions from soil following forest loss and land use changes: Field experiments in the United States and Brazil. Atmos Environ 2014, 96, 423-429. 51. Pan, Y.; Birdsey, R. A.; Fang, J.; Houghton, R.; Kauppi, P. E.; Kurz, W. A.; Phillips, O. L.; Shvidenko, A.; Lewis, S. L.; Canadell, J. G.; Ciais, P.; Jackson, R. B.; Pacala, S. W.; McGuire, A. D.; Piao, S.; Rautiainen, A.; Sitch, S.; Hayes, D., A Large and Persistent Carbon Sink in the World's Forests. Science 2011, 333, (6045), 988-993. 52. Botkin, D. B.; Simpson, L. G., Biomass of the North-American Boreal Forest - a Step toward Accurate Global Measures. Biogeochemistry 1990, 9, (2), 161-174. 53. Brown, S.; Lugo, A. E., Biomass of Tropical Forests - a New Estimate Based on Forest Volumes. Science 1984, 223, (4642), 1290-1293. 54. Smith-Downey, N. V.; Sunderl, E. M.; Jacob, D. J., Anthropogenic impacts on global storage and emissions of mercury from terrestrial soils: Insights from a new global model. Journal of Geophysical Research Atmospheres 2010, 115, (G3), 227-235. 55. Lindquist; Gerrand; MacDicken; Achard; Beuchle; Brink; Mayaux; San-Miguel-Ayanz, Global forest land-use change 1990-2005. Fao Forestry Paper 2012, p 20. 56. Wright, L. P.; Zhang, L. M., An approach estimating bidirectional air-surface exchange for gaseous elemental mercury at AMNet sites. J Adv Model Earth Sy 2015, 7, (1), 35-49. 57. Zhang, L. M.; Wright, L. P.; Blanchard, P., A review of current knowledge concerning dry deposition of atmospheric mercury. Atmos Environ 2009, 43, (37), 5853-5864. 58. Wang, X.; Lin, C. J.; Yuan, W.; Sommar, J.; Zhu, W.; Feng, X., Emission-dominated gas exchange of elemental mercury vapor over natural surfaces in China. Atmos. Chem. Phys. Discuss. 2016, 2016, 1-40. 59. Lindberg, S. E.; Hanson, P. J.; Meyers, T. P.; Kim, K. H., Air/surface exchange of mercury vapor over forests - The need for a reassessment of continental biogenic emissions. Atmos Environ 1998, 32, (5), 895-908. 60. Lindqvist, O.; Johansson, K.; Aastrup, M.; Andersson, A.; Bringmark, L.; Hovsenius, G.; Hakanson, L.; Iverfeldt, A.; Meili, M.; Timm, B., Mercury in the Swedish Environment - Recent Research on Causes, Consequences and Corrective Methods. Water Air Soil Poll 1991, 55, (1-2), 1-261. 61. Yin, R. S.; Feng, X. B.; Meng, B., Stable Mercury Isotope Variation in Rice Plants (Oryza sativa L.) from the Wanshan Mercury Mining District, SW China. Environ Sci Technol 2013, 47, (5), 2238-2245. 62. Siwik, E. I. H.; Campbell, L. M.; Mierle, G., Distribution and trends of mercury in deciduous tree cores. Environmental Pollution 2010, 158, (6), 2067-2073. 63. Hall, B. D.; Louis, V. L. S., Methylmercury and total mercury in plant litter decomposing in upland forests and flooded landscapes. Environmental Science & Technology 2004, 38, (19), 5010-5021. 64. Pokharel, A. K.; Obrist, D., Fate of mercury in tree litter during decomposition. Biogeosciences 2011, 8, (9), 2507-2521. 65. Vitousek, P. M., Litterfall, Nutrient Cycling, and Nutrient Limitation in Tropical Forests. 1984, 65, (1), 285-298. 66. Zhou, J.; Feng, X. B.; Liu, H. Y.; Zhang, H.; Fu, X. W.; Bao, Z. D.; Wang, X.; Zhang, Y. P., Examination of total


Page 20 of 27

Page 21 of 27

454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494


mercury inputs by precipitation and litterfall in a remote upland forest of Southwestern China. Atmos Environ 2013, 81, 364-372. 67. Grigal, D. F.; Kolka, R. K.; Fleck, J. A.; Nater, E. A., Mercury budget of an upland-peatland watershed. Biogeochemistry 2000, 50, (1), 95-109. 68. Hultberg, H.; Munthe, J.; Iverfeldt, A., Cycling of Methyl Mercury and Mercury - Responses in the Forest Roof Catchment to 3 Years of Decreased Atmospheric Deposition. Water Air Soil Poll 1995, 80, (1-4), 415-424. 69. Rea, A. W.; Keeler, G. J.; Scherbatskoy, T., The deposition of mercury in throughfall and litterfall in the lake champlain watershed: A short-term study. Atmos Environ 1996, 30, (19), 3257-3263. 70. Rea, A. W.; Lindberg, S. E.; Scherbatskoy, T.; Keeler, G. J., Mercury accumulation in foliage over time in two northern mixed-hardwood forests. Water Air Soil Poll 2002, 133, (1-4), 49-67. 71. Sheehan, K. D.; Fernandez, I. J.; Kahl, J. S.; Amirbahman, A., Litterfall mercury in two forested watersheds at Acadia National Park, Maine, USA. Water Air Soil Poll 2006, 170, (1-4), 249-265. 72. Fisher, L. S.; Wolfe, M. H., Examination of mercury inputs by throughfall and litterfall in the Great Smoky Mountains National Park. Atmos Environ 2012, 47, 554-559. 73. Schwesig, D.; Matzner, E., Pools and fluxes of mercury and methylmercury in two forested catchments in Germany. Sci Total Environ 2000, 260, (1-3), 213-223. 74. Graydon, J. A.; Louis, V. L. S.; Hintelmann, H.; Lindberg, S. E.; Sandilands, K. A.; Rudd, J. W. M.; Kelly, C. A.; Hall, B. D.; Mowat, L. D., Long-Term Wet and Dry Deposition of Total and Methyl Mercury in the Remote Boreal Ecoregion of Canada. Environ Sci Technol 2008, 42, (22), 8345-8351. 75. Fostier, A. H.; Forti, M. C.; Guimaraes, J. R. D.; Melfi, A. J.; Boulet, R.; Santo, C. M. E.; Krug, F. J., Mercury fluxes in a natural forested Amazonian catchment (Serra do Navio, Amapa State, Brazil). Sci Total Environ 2000, 260, (1-3), 201-211. 76. Munthe, J.; Hultberg, H.; Iverfeldt, A., Mechanisms of Deposition of Methylmercury and Mercury to Coniferous Forests. Water Air Soil Poll 1995, 80, (1-4), 363-371. 77. Lee, Y. H.; Bishop, K. H.; Munthe, J., Do concepts about catchment cycling of methylmercury and mercury in boreal catchments stand the test of time? Six years of atmospheric inputs and runoff export at Svartberget, northern Sweden. Sci Total Environ 2000, 260, (1-3), 11-20. 78. Munthe, J.; Hultberg, H., Mercury and Methylmercury in Runoff from a Forested Catchment — Concentrations, Fluxes, and Their Response to Manipulations. Springer Netherlands: 2004; p 207-212. 79. Larssen, T.; de Wit, H. A.; Wiker, M.; Halse, K., Mercury budget of a small forested boreal catchment in southeast Norway. Sci Total Environ 2008, 404, (2-3), 290-296. 80. Bringmark, L.; Lundin, L.; Augustaitis, A.; Beudert, B.; Dieffenbach-Fries, H.; Dirnbock, T.; Grabner, M. T.; Hutchins, M.; Kram, P.; Lyulko, I.; Ruoho-Airola, T.; Vana, M., Trace Metal Budgets for Forested Catchments in Europe-Pb, Cd, Hg, Cu and Zn. Water Air Soil Poll 2013, 224, (4). 81. Johnson, K. B.; Haines, T. A.; Kahl, J. S.; Norton, S. A.; Amirbahman, A.; Sheehan, K. D., Controls on mercury and methylmercury deposition for two watersheds in Acadia National Park, Maine. Environ Monit Assess 2007, 126, (1-3), 55-67. 82. Lee, Y. H.; Bishop, K. H.; Munthe, J.; Iverfeldt, A.; Verta, M.; Parkman, H.; Hultberg, H., An examination of current Hg deposition and export in Fenno-Scandian catchments. Biogeochemistry 1998, 40, (2-3), 125-135. 83. Selvendiran, P.; Driscoll, C. T.; Montesdeoca, M. R.; Bushey, J. T., Inputs, storage, and transport of total and methyl mercury in two temperate forest wetlands. J Geophys Res-Biogeo 2008, 113.



495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522

Page 22 of 27

84. Schwesig, D.; Matzner, E., Dynamics of mercury and methylmercury in forest floor and runoff of a forested watershed in Central Europe. Biogeochemistry 2001, 53, (2), 181-200. 85. Silva, E. V.; Machado, W.; Oliveira, R. R.; Sella, S. M.; Lacerda, L. D., Mercury deposition through litterfall in an Atlantic Forest at Ilha Grande, southeast Brazil. Chemosphere 2006, 65, (11), 2477-2484. 86. Blackwell, B. D.; Driscoll, C. T.; Maxwell, J. A.; Holsen, T. M., Changing climate alters inputs and pathways of mercury deposition to forested ecosystems. Biogeochemistry 2014, 119, (1-3), 215-228. 87. Teixeira, D. C.; Montezuma, R. C.; Oliveira, R. R.; Silva, E. V., Litterfall mercury deposition in Atlantic forest ecosystem from SE - Brazil. Environ Pollut 2012, 164, 11-15. 88. Juillerat, J. I.; Ross, D. S.; Bank, M. S., Mercury in litterfall and upper soil horizons in forested ecosystems in Vermont, USA. Environ Toxicol Chem 2012, 31, (8), 1720-1729. 89. Obrist, D.; Johnson, D. W.; Lindberg, S. E., Mercury concentrations and pools in four Sierra Nevada forest sites, and relationships to organic carbon and nitrogen. Biogeosciences 2009, 6, (5), 765-777. 90. Obrist, D.; Johnson, D. W.; Lindberg, S. E.; Luo, Y.; Hararuk, O.; Bracho, R.; Battles, J. J.; Dail, D. B.; Edmonds, R. L.; Monson, R. K.; Ollinger, S. V.; Pallardy, S. G.; Pregitzer, K. S.; Todd, D. E., Mercury Distribution Across 14 US Forests. Part I: Spatial Patterns of Concentrations in Biomass, Litter, and Soils. Environ Sci Technol 2011, 45, (9), 3974-3981. 91. Niu, Z. C.; Zhang, X. S.; Wang, Z. W.; Ci, Z. J., Mercury in leaf litter in typical suburban and urban broadleaf forests in China. J Environ Sci-China 2011, 23, (12), 2042-2048. 92. Ma, M.; Wang, D. Y.; Du, H. X.; Sun, T.; Zhao, Z.; Wei, S. Q., Atmospheric mercury deposition and its contribution of the regional atmospheric transport to mercury pollution at a national forest nature reserve, southwest China. Environ Sci Pollut R 2015, 22, (24), 20007-20018. 93. Fu, X. W.; Zhang, H.; Yu, B.; Wang, X.; Lin, C. J.; Feng, X. B., Observations of atmospheric mercury in China: a critical review. Atmos Chem Phys 2015, 15, (16), 9455-9476. 94. Ma, M.; Wang, D. Y.; Du, H. X.; Sun, T.; Zhao, Z.; Wang, Y. M.; Wei, S. Q., Mercury dynamics and mass balance in a subtropical forest, southwestern China. Atmos Chem Phys 2016, 16, (7), 4529-4537. 95. Tang, R.; Wang, H.; Luo, J.; Sun, S.; Gong, Y.; She, J.; Chen, Y.; Yang, D.; Zhou, J., Spatial distribution and temporal trends of mercury and arsenic in remote timberline coniferous forests, eastern of the Tibet Plateau, China. Environmental Science & Pollution Research 2015, 22, (15), 1-11.

523 524


Page 23 of 27


525 526

Figure 1: (1) The documented 162 sites where litterfall Hg concentration/flux data are available. Another

527

24 sites are not shown due to lack of accurate latitude and longitude measurements. (2) The documented

528

858 global sites where litterfall biomass production data are available.



Page 24 of 27

529 530

Figure2: Histograms of measured Hg concentration in litterfall samples collected globally. NAE is

531

North-America-Europe, CHI is China and BRA is Brazil. The data are obtained from earlier studies

532

32, 38, 39, 41, 42, 48, 49, 66-94

.


15, 25,

Page 25 of 27


533 534

Figure 3: Box chart for Hg concentrations in litters for different forest types. “Dec” is the deciduous forest;

535

“Mix” is the mixed forest; Con is the coniferous forest; and Eve is the evergreen broadleaf forest. The

536

post hoc tests (Tukey’s HSD) were performed at 5% significance level. The data are obtained from earlier

537

studies 15, 32, 38, 39, 41, 42, 48, 49, 66-95.



Page 26 of 27

538 539

Figure4: (1)-(3) Histogram of measured Hg deposition through rainfall, throughfall and litterfall globally.

540

(4) Pie charts showing Hg deposition in forest ecosystems of the three regions. Evergreen broadleaf forest

541

is the predominant forest type for the sites in CHI. The data are obtained from earlier studies 15, 32, 38, 39, 41,

542

42, 48, 49, 66-94

.


Page 27 of 27


543 544 545 546 547

Figure5: (1) Hg deposition flux through litterfall for different biomes with the standard deviation (SD) and confidence interval (CI). (2) Global Hg deposition budget through litterfall for different biomes. (3) Gridded Hg deposition through litterfall. The observed fluxes are obtained from earlier studies 15, 32, 38, 39, 41, 42, 48, 49, 66-94 .


Assessment of Global Mercury Deposition through ... - ACS Publications

Recommend Documents