Atmospheric Environment 173 (2018) 6-15
ELSEVIER
Contents lists available at ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Mercury from wildfires: Global emission inventories and sensitivity to r®
2000-2050 global change I
Aditya Kumar''’’^, Shiliang Yaoxian Huang''’‘^, Hong Liao^ Jed O. Kaplan^
^ Department of Geolo^cal and Mining Engineering and Sciences, Michigan Technological University, Hoi^hton, MI, USA
^ Department of Civil and Envirorunental Engineering Michigan Technological University, Houghton, MI, USA
School of Environment and Energy, Peking University Shenzhen Graduate School, Shertzhen, China
Now at Department of Climate and Space Sciences and Engineering University of Michigan, Ann Arbor, MI 48109, USA
® School of Environmental Science and Engineering Nanjing University of Information Science & Technology, Nanjit^ China
^ ARVE Research SARL, Fully, Switzerland
GRAPHICAL ABSTRACT
2000-2050:
A (climate)
A (land us^and cover)
A (mercury anthropogenic
emissions)
■=>
A (mercury
wildfire emissions)
ARTICLE INFO
ABSTRACT
Keywords:
Biomass burning
Modeling
Climate change
Land use
Land cover
We estimate the global Hg wildfire emissions for the 2000s and the potential impacts from the 2000-2050
changes in climate, land use and land cover and Hg anthropogenic emissions by combining statistical analysis
with global data on vegetation type and coverage as well as fire activities. Global Hg wildfire emissions are
estimated to be 612 Mg year“^. Africa is the dominant source region (43.8% of global emissions), followed by
Eurasia (31%) and South America (16.6%). We find significant perturbations to wildfire emissions of Hg in the
context of global change, driven by the projected changes in climate, land use and land cover and Hg anthro¬
pogenic emissions. 2000-2050 climate change could increase Hg emissions by 14% globally and regionally by
18% for South America, 14% for Africa and 13% for Eurasia. Projected changes in land use by 2050 could
decrease the global Hg emissions from wildfires by 13% mainly driven by a decline in African emissions due to
significant agricultural land expansion. Future land cover changes could lead to significant increases in Hg
emissions over some regions (-1-32% North America, -1-14% Africa, -1-13% Eurasia). Potential enrichment of
terrestrial ecosystems in 2050 in response to changes in Hg anthropogenic emissions could increase Hg wildfire
emissions globally (-1-28%) and regionally (-1-19% North America, -1-20% South America, -1-24% Africa, -1-41%
Eurasia). Our results indicate that the future evolution of climate, land use and land cover and Hg anthropogenic
emissions are all important factors affecting Hg wildfire emissions in the coming decades.
* Corresponding author. Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, USA.
E-mail address: slwu(a)mtu.edu (S. Wu).
http://dx.doi.Org/10.1016/j.atmosenv.2017.10.061
Received 29 Jime 2017; Received in revised form 11 October 2017; Accepted 28 October 2017
Available online 31 October 2017
1352-2310/ © 2017 Elsevier Ltd. All rights reserved.
A. Kumar et al.
Atmospheric Envirortment 173 (2018) 6~15
1. Introduction
Mercury (Hg) is a toxic and persistent pollutant in the global en¬
vironment. Hg emitted to the atmosphere can be transported long dis¬
tances before depositing to terrestrial and aquatic systems. The atmo¬
spheric emissions of Hg include both anthropogenic sources such as
fossil fuel combustion, smelting of ores, cement production, waste in¬
cineration, and artisanal gold mining (Chen et ah, 2014; Pacyna et ah,
2010; Pirrone et ah, 2010; Streets et ah, 2011; Veiga et ah, 2006),
natural emissions from erupting and passively degassing volcanoes,
geothermal hot springs, topsoil enriched in Hg (Ferrara et ah, 2000;
Nimick et ah, 2013; Nriagu and Becker, 2003; Pyle and Mather, 2003;
Varekamp and Buseck, 1986), and biomass burntng/wildfires (Friedli
et ah, 2003, 2009; Sigler et ah, 2003; Turetsky et ah, 2006; Veiga et ah,
1994; Weiss-Penzias et ah, 2007; Wiedinmyer and Friedli, 2007).
Terrestrial vegetation plays an important role in the biogeochemical
cycling of Hg, and is considered a significant reservoir for atmospheric
Hg. Obrist (2007) estimated the global uptake of atmospheric Hg by
vegetation could be more than 1 000 Mg per year. Retention of atmo¬
spheric Hg by vegetation delays its transport to soils and aquatic sys¬
tems (Graydon et ah, 2012). Hence, vegetation plays an important role
in Hg sequestration by terrestrial ecosystems. Wildfires, through the
burning of biomass, can effectively mobilize the Hg stored in terrestrial
ecosystems and lead to massive emissions of Hg and other pollutants
into the atmosphere (Andreae and Merlet, 2001; Biswas et ah, 2007; Ito
and Penner, 2004; Turetsky et ah, 2006; van der Werf et ah, 2006;
Veiga et ah, 1994). Multiple studies have estimated Hg wildfire emis¬
sions at the global and regional scales (Table 2) with global total
emissions in the range of 104-1 330 Mg Hg year^^ indicating large
uncertainty in the estimated Hg emissions from wildfires. A number of
studies have developed wildfire emission inventories for Hg based on
CO or CO 2 emission inventories by applying fixed emission ratios be¬
tween Hg and CO/CO 2 [e.g (Brunke et ah, 2001; Ebinghaus et ah, 2007;
Sigler et ah, 2003).]. To our knowledge, Friedli et ah (2009) is the only
study so far that have compiled a multi-year (1997-2006) emission
inventory for Hg from wildfires at the global scale by accounting for the
variations in Hg emission factors across geographical regions and eco¬
systems. The emission factor (EF) for a given species (Hg in this case)
can be expressed as the mass of that species emitted per unit dry bio¬
mass burned (Andreae and Merlet, 2001). It is affected by both the
vegetation type and geographical region.
Significant changes in global vegetation coverage are expected in
the coming decades driven by either climate change (referred to as land
cover change hereon) or anthropogenic land use change (referred to as
land use change hereon) (Bachelet et ah, 2001, 2003; Cox et ah, 2000;
Falloon et ah, 2012; Notaro et ah, 2007; O'ishi and Abe-Ouchi, 2009;
Tilman et ah, 2001; Wu et ah, 2012). Wu et ah (2012) predicted sig¬
nificant changes in forests and grassland coverage by 2050 with the
northern mid-latitudes being affected the most. Notaro et ah (2007)
predicted reduced forest cover over the Amazon, South Africa and
Australia due to the radiative effect of rising CO 2 and poleward shift of
the boreal forest due to both radiative and physiological effects. In
addition, continued anthropogenic conversion of forested land for
agriculture in the future to support a growing global human population
and the resulting food and energy demand could result in significant
degradation of natural vegetation. Tilman et ah (2001) projected an
18% increase in global agricultural (cropland & pastureland) area in
2050 relative to 2000. By 2050 (IPCC AIB scenario), significant de¬
gradation of natural vegetation for agricultural purposes is predicted in
Eastern US, Central Africa, Southern and Equatorial Asia and Western
Europe while agricultural land area could decrease in South America,
East Asia, Western parts of Australia and Russia (IPCC, 2001; MNP,
2006).
The changes in vegetation type and density associated with future
land use/land cover change could have a direct influence on future
wildfire activity. Huang et ah (2015) predicted a 15% increase in global
wildfire frequency in 2050 due to land cover change alone with major
increases in Africa and North America. On the contrary, land use
change could result in significant declines in fire occurrence in Africa
and Western US whereas increases are predicted in Australia and South
America (Huang et ah, 2015). Furthermore, Hg emissions from wild¬
fires are affected by the Hg content in the vegetation (biomass) (Biswas
et ah, 2007) making Hg emissions particularly sensitive to future al¬
terations in land use and land cover. In addition to land use/land cover
change, future changes in climate would also influence global fire ac¬
tivity. Predicted warmer temperatures in the future together with al¬
terations in precipitation patterns driven by climate change could result
in significantly different fire regimes from the 2000s (Cochrane and
Barber, 2009; Flannigan et ah, 2009; Yue et ah, 2013). Huang et ah
(2015) predicted a 19% increase in global fire frequency in 2050 caused
by changes in meteorology due to climate change. Yue et ah (2013)
predicted that summertime surface aerosol concentrations resulting
from wildfire emissions over the Western US could increase by 46-70%
for organic carbon and 20-27% for black carbon by 2050 relative to the
2000s driven by climate change. Finally, the changes in anthropogenic
emissions of Hg can significantly affect Hg emissions from wildfires by
affecting the atmospheric deposition of Hg. Because atmospheric de¬
position is a major source of Hg enrichment in terrestrial vegetation and
Fig. 1. Definition of global regions used in this
study. BONA: Boreal North America, TENA:
Temperate North America, CEAM: Central North
America, NHSA: Northern Hemisphere South
America, SHSA: Southern Hemisphere South
America, EURO: Europe, MIDE: Middle East,
NHAF: Northern Hemisphere Africa, SHAF:
Southern Hemisphere Africa, BOAS: Boreal Asia,
CEAS: Central Asia, SEAS: South East Asia, EQAS:
Equatorial Asia, AUST: Australia.
7
A. Kumar et al.
Atmospheric Enviroriment 173 (2018) 6-15
soils, future alterations in Hg atmospheric deposition to terrestrial en¬
vironments could play a key role in determining the emission factors of
Hg from wildfires. In this study, we estimate the atmospheric Hg
emissions from global wildfires by accounting for the regional varia¬
tions in both fire activities and Hg emission factors and examining the
impacts from 2000 to 2050 changes in climate, land use/land cover and
anthropogenic emissions.
2. Methodology
Hg emissions from wildfires are calculated based on the classical
equation for biomass burning emissions (Seiler and Crutzen, 1980) and
accounting for various factors including vegetation type and density,
Hg content in biomass, and fire characteristics. Considering the sig¬
nificant variation in fire characteristics, land cover and climate across
geographical regions, the analyses for various regions is carried out
separately. We use geographical region definitions that have been
commonly used in the literature (Aldersley et ah, 2011; Friedli et ah,
2009; Giglio et ah, 2010, 2013, 2006b; hi et ah, 2012; Van der Werf
et ah, 2010; van der Werf et ah, 2006).
Fig. 1 shows a map of the geographical regions. The Hg emissions
model is developed at a spatial resolution of 4° x 5° (latitude x long¬
itude). The monthly mean Hg emissions from wildfires at a grid cell (i,
j), E(ij), are calculated as:
9
Eaj) = Yj
k=l
2k=i i)
*Ai
(i,j)
*CF(
(ij)
Where:
EFkgjy Hg emission factors for land cover type k at grid cell (i, j)
Burned area at (i, j)
fk(ijy Fractional areal coverage for land cover type k at (i, j)
Mkjijy Available biomass density for vegetation type k at grid cell (i,
j)
CF(ij): Combustion fraction at (i, j)
For Hg emission factors for the 2000s, we follow Friedli et ah (2009)
to assign region-specific EFs for various regions but update the tropical
forests EFs with data from Melendez-Perez et ah (2014). The final EF
(122 ng Hg/g biomass burned) for tropical forests is the average of
values reported by Friedli et ah (2009) (198 ng Hg/g biomass burned)
and Melendez-Perez et ah (2014) (47 ng Hg/g biomass burned). Boreal
regions (BONA and BOAS) and Equatorial Asia (EQAS) have the highest
EF values (315 ng Hg/g biomass burned) due to high soil mercury pools
present there (Friedli et ah, 2009) followed by temperate forests
(242 ng Hg/g biomass burned), tropical forests (122 ng Hg/g biomass
burned) and grasslands (41 ng Hg/g biomass burned).
Available biomass density (M) is the amount of dead/live plant
material available for consumption by fires per unit area. The available
biomass in a grid cell includes herbaceous vegetation, non-woody and
woody tree parts and plant litter (decomposable and resistant).
Available biomass estimates for forest and non-forest ecosystems from
Jain et ah (2006), generated using the terrestrial component of the
Integrated Science Assessment Model (ISAM) (Jain and Yang, 2005) are
used here. These estimates are provided for nine geographical regions
and four land cover types (tropical, temperate, boreal forests and non-
forested ecosystems). Combustion fraction (CF) represents the fraction
of available biomass that burns during a fire. It is a function of vege¬
tation type, its spatial arrangement and moisture content (Ito and
Penner, 2004). We follow the scheme used by Wiedinmyer et ah (2006)
to estimate the CF. This scheme, based on the percentage of tree cover
(TC) in a grid cell, classifies the vegetation into different types and
assigns corresponding CF estimates for trees and herbaceous vegetation
therein, as:
CF
030for trees and 0.90 for herbs if %TC > 60%
_ 0.30 for trees and herbs if 40% < %TC
< 60%
0.00 for trees and 0.98 for herbs %TC < 40%
( 2 )
hand cover data used in this work consists of fractional areal ve¬
getation coverage output (2000 and 2050) from the Lund-Potsdam Jena
Dynamic Global Vegetation (LPJ DGVM) model (Gerten et ah, 2004;
Hickler et ah, 2006; Schaphoff et ah, 2006; Sitch et ah, 2003; Thonicke
et ah, 2001; Wu et ah, 2012). The LPJ model is a process based global
model of vegetation dynamics. It simulates the production and loss of
plant biomass, competition amongst different plant species for re¬
sources, vegetation structural properties and soil biogeochemistry
(Gerten et ah, 2004; Thonicke et ah, 2001) based on the inputs of
meteorology, soil type and atmospheric CO 2 concentration. Natural
vegetation in each grid cell is described in terms of fractional coverage
of 9 plant functional types (PFTs), which include tropical broadleaved
evergreen tree (TrBE), tropical broadleaved raingreen tree (TrBR), tempe¬
rate needleleaved evergreen tree (TeNE), temperate broadleaved evergreen
tree (TeBE), temperate broadleaved summergreen tree (TeBS), boreal nee-
dleleaved evergreen tree (BNE), boreal needleleaved summergreen tree
(BNS), C3 and C4 herbs. Each woody PFT is either evergreen, sum¬
mergreen or raingreen depending on water availability and tempera¬
ture whereas herbaceous PFTs are C3 or C4 based on the type of pho¬
tosynthesis activity associated with them. The fractional natural
vegetation coverage output used here was obtained by driving the LPJ
DGVM with meteorology fields (2000 and 2050) generated from the
GISS Global Climate Model Version 3 (GISS GCM v3) (Rind et ah, 2007;
Wu et ah, 2007, 2008a, 2008b) following the IPCC AIB scenario for
future greenhouse gas concentrations. Further simulation details are
provided in Wu et ah (2012). Here 10-year average vegetation data
(1991-2000 vs 2041-2050) is used to examine the long-term changes in
vegetation.
Data for anthropogenic land use consists of cropland areal coverage
(2000 and 2050) from the IMAGE model following the IPCC AIB sce¬
nario (IMAGE Team, 2001; MNP, 2006). The data was regridded from a
spatial resolution of 1° x l°-4° x 5° in this work. For each grid cell,
fractional coverage of each of the LPJ PFTs was uniformly reduced in
proportion to the crop fraction to accommodate for cropland coverage.
For the 2000s, the LPJ PFT and crop fractions are at the 2000s level.
The 2000-2050 land use and land cover scenario includes both natural
vegetation and crop coverage as predicted for 2050. The land cover
change scenario has natural vegetation changing in response to climate
change but the cropland coverage is kept fixed at the 2000s level
whereas in the land use change scenario natural vegetation is kept fixed
at the 2000s level and cropland coverage following land use trends for
2050 is used. The climate change scenario involves changes in me¬
teorology only (using GISS GCM v3 meteorology fields for 2000 and
2050).
The burned area A(ij) was estimated as:
A(i,j) = a(ij)(Tf(-,, (3)
where the proportionality factor aqj) is a function of land cover type
and coverage (represented by tree cover (Tf^jj), herb cover (Hf^yj) and
barren land Bf^jj in grid cell (i, j)) and climate (represented by surface
temperature T^^yj and precipitation Ps^yj in grid cell (i, j)). N,. is the fire
frequency which is affected by fire ignition sources such as lightning
strikes and anthropogenic ignitions, meteorological conditions, vege¬
tation density and human population density. Huang et al. (2015) ac¬
counted for all these factors and estimated the changes in fire frequency
(at a spatial resolution of 4° x 5° and monthly temporal resolution) in
response to changes in climate, land cover, land use and population
density considering a suite of scenarios (land use/land cover change.
8
A. Kumar et al.
Atmospheric Envirortment 173 (2018) 6-15
climate change, changes in ignition agents and anthropogenic sup¬
pression of fires). Their results for the 2050 climate change, land use
and land cover change scenarios are used here.
Our burned area estimation methodology involves building a sta¬
tistical model relating fire activity (independent variable) and burned
area (dependent variable), which was subsequently used with the fire
model generated fire frequencies to predict burned area (for 2000 and
2050). We use available global fire frequencies and burned area data¬
sets from satellite observations and consider land cover (% tree cover
(% TC), % herb cover (% HC) and % barren land (% BL)) and me¬
teorology (Ts and P^) as the major factors influencing the relationship
between fire activity and burned area. In order to account for this de¬
pendence, regression tree models are employed. A regression tree is a
decision tree based statistical model (Breiman et al., 1984; Breiman and
Meisel, 1976; Loh, 2008, 2011). It recursively partitions the input data
space {y, Xi, ...Xn}(y is the dependent variable and x's are the in¬
dependent variables) consisting of training data for the dependent and
independent variables into subsets and fits separate linear regression
models to each subset. The independent variables can be used as
splitting variables (used to make univariate splits at each node of the
tree), predietive variables (used to predict the dependent variable) or
both. The splitting of input data space into subsets allows application of
regression relations to a homogeneous data space with respect to the
splitting variables and thus improve the applicability of the relations to
the data space. The dependenee of burned area on land cover has been
highlighted by Van Der Werf et al. (2003) and Giglio et al. (2006b) and
regression trees for predicting global burned area from fire frequencies
have been previously used [e.g (Giglio et al., 2006b, 2010).]. Separate
regression trees were developed for each of the 14 geographical regions
in Fig. 1. This approach allows modeling the effects of fuel type (tree/
herbaceous), configuration and availability and favorable/unfavorable
weather conditions on wildfire spread.
The splitting variables include %TC, %HC, %BL, Tj and P^. Each
terminal node of the tree consists of a linear regression model with fire
frequencies as the independent variable and burned area as the de¬
pendent variable. Training data used for constructing the regression
trees is described below. Observed fire frequencies (2001-2015) are
from the Collection 5 Terra MODIS Climate Modeling Grid (CMG) fire
product (data at ftp;//fuoco.geog.umd.edu/modis/C5/cmg/) available
at a 0.5° X 0.5° spatial and monthly temporal resolution (Giglio et al.,
2006a; Justice et al., 2002). For burned area, estimates from the Global
Fire Emissions Database Version 4 (GFEDv4) (data at ftp://fuoco.geog.
umd.edu/gfed4/monthly/) ((2001-2015)) (Giglio et al., 2013) are
used. The spatial resolution of this product is 0.5° x 0.5° (monthly
temporal resolution). Both datasets were regridded to a 4° x 5° spatial
resolution for use in this work. LPJ land cover data (accounting for
cropland coverage as described earlier) was used for the 2000s land
cover. The PFTs were combined to obtain %TC (sum of all tree PFTs), %
HC (sum of all herbaceous PFTs) and the remainder after accounting for
natural vegetation and cropland yielded the %BL (non-vegetated land).
Surface temperature and precipitation datasets consisted of monthly
means generated from 3 hourly averaged (A-3) fields from the GEOS-4
(2001-2003) (Suarez et al., 2005), GEOS-5 (2004-2012) (Rienecker,
2008) and hourly average (A-1) fields from the GEOS-FP (2013-2015)
(Lucchesi, 2013; Molod et al., 2012) meteorology products (4° x 5°
spatial resolution). The datasets for MODIS fire frequency, GFEDv4
burned area, LPJ land cover and GEOS meteorology at 4° x 5° were
prepared for each of the 14 geographical regions as defined in Fig. 1.
Thus, training data for each regional regression tree consisted of a 15-
year (2001-2015) time series of MODIS Terra fire frequencies, GFEDv4
burned area estimates, LPJ land cover data for the 2000s and GEOS
meteorology fields. These data were used to construct regression trees
for each of the 14 geographical regions. Subsequently, these regression
tree models were applied to estimate the monthly-burned area for 2000
and 2050 scenarios based on fire frequencies estimated from the fire
model along with land cover data from the LPJ model and surface
temperature and precipitation fields (monthly means from 3 hourly
averaged (A-3) fields) from the GISS GCM v3 model. With all the input
data available, the Hg emissions (with monthly and 4° x 5° resolution)
from wildfires for various regions and scenarios are calculated using the
fire emissions model (i.e. Eq. (1)).
To examine the perturbations to wildfire emissions of Hg from
changes in anthropogenic emissions of Hg, we assume a linear re¬
lationship between the Hg emission factors from wildfires and the at¬
mospheric deposition of mercury (Dep): EF 2000 /DEP 2000 = EF 2050 /
DEP 2050 - The actual response of Hg enrichment in vegetation and soils
to changes in atmospheric deposition could be very complicated and
non-linear, but without detailed data available, the linear simplification
allows us to estimate the sensitivity of wildfire emissions of Hg to
changes in anthropogenic emissions. The atmospheric deposition of
mercury for the 2000s and 2050 were estimated using the global
mercury simulation in the GEOS-Chem model (Bey et al., 2001). The
GEOS-Chem mercury simulation (Corbitt et al., 2011; Giang et al.,
2015; Holmes et al., 2010; Jaegle et al., 2009; Selin and Jacob, 2008;
Selin et al., 2007, 2008; Smith-Downey et al., 2010; Strode et al., 2007;
Zhang et al., 2016) includes three Hg species (elemental (Hg (0)), di¬
valent (Hg (II)) and particulate bound Hg (Hg (P)). It includes coupled
land-ocean-atmosphere cycling of Hg. Hg emissions include anthro¬
pogenic and natural sources and re-emission of previously deposited
mercury from terrestrial and aquatic systems. Sinks for Hg inelude dry
deposition, wet deposition (for Hg (II) and Hg (P)) including sea salt
uptake (for Hg (II)). This work uses v9-02 of the model and years
2005-2011 were simulated with the GEOS-5 meteorology. The years
2005-2007 were used to initialize the model and the results presented
are averages for 2008-2011. The model was driven by anthropogenic
emissions for 2050 following the IPCC AIB scenario (Corbitt et al.,
2011; Streets et al., 2009). Hg GDEP was calculated as the sum of de¬
position for all Hg species (Hg (0), Hg (II) and Hg (P) dry deposi¬
tion -H Hg (II) and Hg (P) wet deposition).
3. Results
3.1. Burned area estimates
Using the regression tree models described above, the calculated 2000s
(1998-2002 average) annual burned area estimates for various regions,
are shown in Table la. The regression tree models were able to explain
most of the variability in burned area (based on GFEE)v4 data) for each
region (R^ = 0.81 (BONA), 0.72 (TENA), 0.68 (CEAM), 0.75 (NHSA), 0.86
(SHSA), 0.82 (EURO), 0.81 (MIDE), 0.91 (NHAF), 0.94 (SHAF), 0.81
(BOAS), 0.85 (CEAS), 0.73 (SEAS), 0.77 (EQAS), 0.96 (AUST)). Global
annual burned area for the 2000s is estimated at —334 Mha year^^ with
maximum (51%) contribution from the African continent
(MIDE + NHAF + SHAF —170 Mha year^^) followed by Eurasia (23%)
(EURO -H BOAS -H CEAS -H SEAS -H EQAS - 78 Mha year^^), South
America (14%) (NHSA + SHSA - 47 Mha year^^), Australia (9%)
(AUST — 29 Mha year^^) and North Tknerica (3%)
(BONA + TENA + CEAM —10 Mha year^^). Table la contains burned
area estimates from this work together with available literature estimates
for comparison. There is considerable variability in the global and con¬
tinental burned area estimates in the literature due to different approaches
used (e.g., process based fire modeling vs. satellite observations) and dif¬
ferent year/time period examined. However, our results reproduce the
major burned area patterns common to most of the studies (e.g. maximum-
burned area in Africa). Overall, estimates of both global and regional
wildfire burned area in this work are in reasonable agreement with the
literature and represent the spatial distribution of burned area well.
Our calculated changes in wildfire burned area driven by changes in
climate, land use, land cover are shown in Table lb. We find that the
2000-2050 climate change could increase the global fire frequencies by
19% (Huang et al., 2015), which would cause significant increases in
burned area at both global (1-22%) and regional (Africa (1-28%),
9
A. Kumar et ai
Atmospheric Environment 173 (2018) 6-15
Table la
Estimated regional® and global wildfire burned area (in Mha year^^) for the 2000s.
Study
Study period
NA
SA
AFR
EURAS
AUS
GLOB
This work
1998-2002
10
47
170
78
29
334
GFEDv4
1996-2015
6
21
238
28
49
342
(Giglio et al.,
2013)
GFEDv3
1997-2011
5
23
253
30
52
363
(Giglio et al.,
2010)
GLOBSCAR 2000
2000
11.1
121
18
200
(Simon et al.,
2004)
GBA 2000'=,
2000
7
10.5
224
52.5
56
350
(Tansey et al.,
2004a)
(Tansey et al.,
2004b)
L3JRC
2000-2007
392
(Tansey et al.,
2008)
(Randerson et al.,
2000-2010
8.7
33.8
323.7
49.5
48.5
464.3
2012)
Li et al. (2012)
1997-2004
180
330
MCD45A1
2002-2010
338
(Roy et al.,
2008)
FINNvl
2005-2010
18.4
74.5
302.9
64.7
16.6
477.1
(Wiedinmyer
et al., 2011)
® Regional definitions: NA = North America (BONA + TENA + CEAM); SA = South
America (NHSA + SHSA); AFR = Africa (MIDE + NHAF + SHAE); EURAS = Eurasia
(Europe (EURO) + Asia (BOAS + CEAS + SEAS + EQAS)); AUS = Australia (AUST);
GLOB = global total. The subcontinental regions such as BONA etc. are as defined in
Fig. 1.
Continental estimates calculated from percent distribution in Tansey et al. (2004b)
[North America: 2%, South America: 3%, Eurasia (Europe + Asia (including Russia)):
15%, Africa: 64%, Australia + Papua New Guinea: 16%].
Table lb
Projected 2000-2050 changes in wildfire burned area driven by changes in climate, land
use, and land cover at global and regional® scales. Estimated burned area for the 2000s is
shown in Mha year^^.
Scenario
NA
SA
AER
EURAS
AUS
GLOB
2000s
10
47
170
78
29
334
climate change
+ 23%
+ 16%
+ 28%
+ 6%
+ 32%
+ 22%
land use change
-13%
+ 12%
-7%
-13%
+ 31%
-3%
land cover change
+ 28%
+ 2%
+ 16%
-3%
-5%
+ 8%
land use and land cover
+ 16%
+ 18%
+ 6%
-11%
+ 32%
+ 6%
change
® The regional definitions are the same as in Table la.
Australia ( + 32%), North America ( + 23%), South America ( + 16%)
and Eurasia ( + 6%)) scales. An increase of 6% ( + 8% (land cover
change), —3% (land use change)) in global burned area is predicted
due to the 2000-2050 changes in land use/land cover. However, at the
continental scale, more pronounced changes are observed. Burned area
in North America and Africa is predicted to decline by 13% and 7%
respectively due to reduction in natural vegetation coverage caused by
agricultural land expansion. Lesser natural vegetation coverage could
significantly reduce wildfire activity and limit wildfire spread. On the
other hand, greater vegetation density and the resulting higher fire
frequencies due to land cover change could increase burned area in
both continents ( + 28% North America, +16% Africa). Agricultural
land expansion is predicted to decline in South America and Australia
by 2050 resulting in more natural vegetation and greater fire fre¬
quencies increasing the burned area by 12% and 31% respectively. In
Eurasia, burned area could decline by 13% due to increase in agri¬
cultural land coverage. The combined effects of 2000-2050 land use/
land cover change contribute to greater burned area in all continents
(North America ( + 16%), South America ( + 18%), Africa ( + 6%),
Australia ( + 32%)) except Eurasia ( — 11%).
3.2. Hg emissions from wildfires for the 2000s
Our calculated Hg emissions from wildfires for the 2000s are shown
in Table 2. Results from previous literature on Hg emissions from bio¬
mass burning are included in the table for comparison. Our best esti¬
mated global total Hg wildfire emissions for the 2000s is 612 Mg year^^
with 43.8% emissions from the African continent
(MIDE + NHAF + SHjAF = 268 Mg year^^) followed by Eurasia
(EURO + BOAS + CEAS + SEAS + EQAS = 31%, 190 Mg year^^).
South America (NHSA + SHSA = 16.6%, 102 Mg year^^). North
America (BONA + TENA + CEAM = 7.9%, 48 Mg year^^) and Aus¬
tralia (AUST = 0.7%, 4 Mg year^^). Africa and Eurasia are the domi¬
nant source regions for Hg emissions from wildfires. High emissions
from Africa can be attributed to the high fire activity, which results in
more than half of the global burned area occurring in the continent. The
significant contribution from Eurasia primarily reflects the high Hg
pools present over the boreal parts of the continent (Friedli et al.,
2009). Globally, tropical (43.3%) and boreal (33%) forest burning
contribute the most to wildfire emissions of Hg followed by temperate
forests (16.4%) and grasslands (7.3%).
The calculated global total wildfire emissions of Hg in this work
compare very well with the climatological values reported in the lit¬
erature. However, at the continental scale, there are some significant
differences. Equatorial Asia is not the major source of wildfire emissions
of Hg, as found by Friedli et al. (2009). In addition, the calculated
emissions for Africa are much higher, although we follow the same
regional emission factor assignment methodology used by them. This
could be due to differences in other inputs to the fire emissions model
(e.g. predicted burned area from simulated fire frequencies, available
biomass density and different combustion fraction schemes used). The
calculated global Hg emissions and the source distribution show much
better agreement with Streets et al. (2009) with Africa being the most
important source region followed by Eurasia + Oceania and South
America.
Our fire emissions model uses inputs from a large number of data
sources. For example, the values for Hg emission factors are average
values that have been compiled from several studies in the literature;
estimates of the burned area involves regression tree models, which
were developed based on data from MODIS-Terra, GFEDv4, the LPJ
model as well as the GEOS meteorology. Thus, it is very difficult to
quantify the uncertainty in our final results associated with all the
model parameters. Nevertheless, we have carried out some simple
analyses to examine the sensitivities of our results to various model
parameters. We first use the bootstrap methods (Efron, 1979; Efron and
Tibshirani, 1993) to evaluate the uncertainties in our calculated Hg
emissions from wildfires associated with the inter-annual variability in
meteorology. Bootstrap methods belong to the class of nonparametric
Monte Carlo methods. In this work, nonparametric bootstrapping is
used which regards the data sample as the pseudo-population dis¬
tribution with similar characteristics as the true population. It involves
estimating the sampling distribution of a statistic (e.g. mean of the
sample) by repeated sampling (with replacement) from the data sample
and subsequent determination of the properties of the statistic (e.g.
standard error of the mean). We apply bootstrap methods to the 5-year
(1998-2002) emissions sample (global and continental) to determine
the standard error of the mean as a measure of the uncertainty in the
emissions. In order to provide a range for the mean, the 95% better
bootstrap confidence intervals (BCa) (Efron and Tibshirani, 1993) are
reported. Random samples (size n = 5) were selected from the original
emissions sample and the mean was calculated. This procedure was
repeated 10,000 times to create a sample of 10,000 means. The stan¬
dard deviation of these 10,000 means (standard error (SE) of the mean)
10
A. Kumar et ai
Atmospheric Environment 173 (2018) 6-15
Table 2
Model calculated 2000s wildfire emissions of Hg (in Mg year^^) for the global total and various regions.'
Study
Study Period
NA
SA
AFR
EURAS
AUS
GLOB
This work
1998-2002
48
102
268
190
4
612
Brunke et al. (2001)
510-1 140"
380-1 330'*
Chen et al. (2013)^
2000-2010
6.20
Cinnirella and Pirrone (2006)^
4.3-28.4
Delacerda (1995)^
17
De Simone et al. (2015)
2006-2010
600-678
Ebinghaus et al. (2007)
1996-2000
210-750
Friedli et al. (2009)
1997-2006
50
108
141
357
19
675 (435-915)
Huang et al. (2011)®
2000-2007
27
Michelazzo et al. (2010)^
2000-2008
7
Nelson et al. (2012)
2006
21-63
Roulet et al. (1999)®
(range for the 1980s)
6-9
Sigler et al. (2003)
250-430
Streets et al. (2009)
(for 1996 and 2006)
28.6'’-28.7'
146.9'-156.5‘’
252.7''-229‘
176.3'’-181.4'
586'-614'’
Veiga et al. (1994)®
1988
88
Wiedinmyer and Friedli (2007)*
2002-2006
44 (20-65)
Weiss-Penzias et al. (2007)
Late 1990s
670 (340-1 000)
^ The regional definitions are the same as in Table la.
The specific values shown for this work represent our best estimates; refer to Table 3 and the corresponding discussion in the text for uncertainty analyses.
" Based on Hg/CO emission ratio.
Based on Hg/C02 emission ratio.
® Emission estimates only for China.
^ Emissions for Europe (1990-2004) and Russian federation (1996-2002) only.
^ For Amazon only.
'* For 1996.
‘ For 2006.
^ Emissions for lower 48 states of North America & Alaska only.
Table 3
Sensitivity of calculated wildfire Hg emissions (in Mg year^^) to various model para¬
meters used in this study.
Region
Emission
factors
Burned area
Available
biomass
density
Meteorology^^
(SE/Mean)
North America
44-50
41-52
44-54
46-51 (2.08%)
South America
95-120
86-116
91-109
95-111 (4.04%)
Africa
251-311
238-279
230-293
255-281 (2.51%)
Eurasia
163-197
175-203
182-202
180-199 (2.58%)
Australia
3.8-4.4
3.9-4.2
3.4-4.8
3.4-4.5 (6.75%)
Global
556-683
544-654
551-662
598-630 (1.32%)
^ Ranges of Hg emissions represent the 95% confidence intervals.
was computed to estimate the uncertainty. The standard errors range
from 1.3% to 7% (SE/mean; Table 3), reflecting the relatively small
inter-annual variability in the simulated meteorology from the GISS
GCM.
We then examine the sensitivities of our calculated Hg emissions
from wildfires to the uncertainties in model parameters including
burned area, Hg emission factors and available biomass density. Based
on literature studies (e.g. Andreae and Merlet, 2001; Brunke et al.,
2001; Friedli et al., 2003, 2009; Ebinghaus et al., 2007; Weiss-Penzias
et al., 2007), it appears an uncertainty of 20-30% is typical for these
model parameters. Therefore, we have performed two additional si¬
mulations for each model parameter by assuming a 20% uncertainty
(case 1: —20 to 0% change in the parameter, case 2: 0-20% change in
the parameter). For each case, a sequence of random numbers between
the range [-20, 0] or [0, 20]) were generated with a uniform distribu¬
tion function. These numbers represent the percentage changes to be
applied to the model parameters. Therefore, for case 1, all the random
numbers would be between —20 and 0 and 0 to 20 for case 2. For
burned area, the random number sequence length equaled to one
number representing a particular region (1: BONA, 2: TENA ... 14:
AUST). For emission factors and biomass density, the sequence con¬
sisted of different numbers for each vegetation type in a region. Based
on these perturbation tests, we have summarized the sensitivities of our
final results to various model parameters, as shown in Table 3. We find
that the sensitivities of calculated Hg emissions to the three model
parameters (Hg emission factors, available biomass density, and the
burned area) are similar and a 20% uncertainty in each of the para¬
meter would lead to around 20% uncertainty in our final results.
3.3. Changes in Hg emissions driven by climate, land use/land cover and
anthropogenic emissions change
The perturbations to wildfire emissions of Hg due to 2000-2050
changes in climate, land use and land cover and anthropogenic emis¬
sions are shown in Fig. 2(a), Fig. 2(b) (Supplementary material) and
Table 4. We find significant increases in wildfire emissions of Hg due to
2000-2050 changes in climate. Global emissions increase by 14%
mainly driven by increases in Africa ( + 14%), South America ( + 18%),
and Eurasia (4-13%). In Africa, emissions increase mainly in the
southern and northern parts, which could experience significantly
warmer and drier conditions than the 2000s resulting in greater and
more severe wildfires. Greater precipitation in Central Africa on the
other hand causes a decline in wildfire activity and emissions. Eurasian
emissions increase is primarily due to higher wildfire activity caused by
warmer conditions in the boreal parts of the region (14% increase in
BOAS). However, emissions in Equatorial Asia could decline due to
suppression of wildfire activity caused by greater precipitation. Sig¬
nificant increases in wildfires is predicted over the boreal parts of North
America due to warmer temperatures and over Western US due to
warmer and drier conditions than the 2000s. These changes in climate
result in increasing Hg emissions from North America by 8%. Australia
(34% increase in emissions) and South America could mainly experi¬
ence greater wildfire activity in the eastern parts due to higher tem¬
peratures and less precipitation in 2050.
In response to the projected changes in land use and land cover by
2050, we find that land use change would be a major driving force in
Africa. Substantial conversion of forests to croplands causes wildfire
emissions to decline by 36% in the continent, outweighing a 14%
11
A. Kumar et al.
Atmospheric Enviroriment 173 (2018) 6-15
Hg emissions (2000)
Hg emissions change CC (2050-2000)
180* ISO^W i20“W 90“W 60“W 30‘W
30*E 60°E gp^E t20°E ISO^E 180“
0.00 0.29 0.57 0.66 1.14 143 1.71 2.00 > ug/m2V
Hg emissions change LU (2050-2000)
180* 150“W 120* W 90°W 60*W 30'W 0* 30‘E 60*E 90*E 1 20*E 150“E 180*
< -1.00 -O-TI -0.43 -0.14 0.14 0 43 0.71 1.00 > ug/iTi2/y
Hg emissions change LC (2050-2000)
60'N
30‘N
60*S
60"N
30*N
0“
W W'''
30“S
60*S
if , . : '
180* ISOV 120*W 90*W 60*W 30*W
< <1.00 -0.71 >0.43 4.14 0.14 0.43 0.71 1.00 >
Hg emissions change LU/LC (2050-2000)
ug/m2/y
< -1.00 -0.71 -0.43 4.14 0.14 0.43 0.71 1.00 > ug;in2/y
Hg emissions change EF (2050-2000)
180" I50°W ISO“W 90"w 60“W 30“W
< .1.00 -0.71 ^>.43 .0.14 0.14 0.43 0.71 1.00 >
t 60°E 00"E 120'E 150°E 180‘
ug/mo'y
ISO" 150"W 120"W 90°W 60‘W 30"W
30"E 60"E 00"E 1 20*E 150"E ISO"
< .1.00 .0.71 41.43 41.14 0.14 0.43 0.71 1.00 > llo/nl2V
Fig. 2. (a): Wildfire emissions of Hg for 2000 C[ig/mVyear) (top left), and projected changes by 2050 Cpg/mVyear) due to climate change (CC) (top right), land use change (LU) (middle
left), land cover change (LC) (middle right), land use/land cover change (bottom left) and anthropogenic emissions change (EF change) (bottom right).
Table 4
Projected 2000-2050 changes in wildfire emissions of Hg driven by changes in climate,
land use, and land cover. Estimated 2000s Hg emissions are shown in Mg year^^.
Scenario/Region^
NA
SA
AFR
EURAS
AUS
GLOB
2000s
48
102
268
190
4
612
climate change
+ 8%
+ 18%
+ 14%
+ 13%
+ 34%
+ 14%
land use change
-12%
+ 19%
-36%
-1%
+ 58%
-13%
land cover change
+ 32%
-5%
+ 14%
+ 13%
-11%
+ 12%
land use and land cover
change
+ 19%
+ 21%
- 24%
+17%
+182%
+ 0.8%
anthropogenic emissions
+ 19%
+ 20%
+ 24%
+ 41%
+ 18%
+ 28%
^ The regional definitions are the same as in Table la.
increase in emissions due to greater forest coverage and resulting fire
frequencies caused by land cover change. Overall, the combined effects
of land use and land cover result in a 24% decline in Hg wildfire
emissions from Africa. Land use change could also be the dominant
factor influencing emissions in South America and Australia. Land use is
projected to decline in both the continents resulting in more forest
coverage and wildfires than the 2000s. Hence, emissions increase sig¬
nificantly in both continents (-1-19% (South America), -1- 58%
(Australia)). However, land cover change could cause a decline in
emissions in both continents ( — 5% (South America), —11%
(Australia)), primarily due to reduction in temperate forest (high Hg
emission factors) coverage in sub-tropical South America and decrease
in wildfire frequency in Australia. Overall, 2000-2050 land use/land
cover change results in increasing Hg wildfire emissions by 21% in
South America and 182% in Australia.
On the contrary, future changes in land cover in North America are
found to have a greater influence on Hg emissions from wildfires (32%
emissions increase) than the changes in anthropogenic land use (12%
emissions decrease). Increase in boreal and temperate forest coverage
in the high and mid-latitudes of the continent and the resulting greater
fire frequencies lead to increases in Hg emissions over boreal North
America (1-29%) and the US (1-35%). Land use change would have
negligible impacts on emissions in boreal North America; however, it
could result in a significant decrease in emissions from the US (— 23%).
12
A. Kumar et al.
Atmospheric Envirortment 173 (2018) 6-15
In Eurasia as well, land cover change acts as a major factor causing a
13% increase in emissions, primarily caused by boreal forest expansion
and the resulting increase in wildfires. Land use change would have
negligible overall effects partly reflecting the diverging trends in an¬
thropogenic land use in this region (e.g. decreases in Eastern Asia and
parts of Russia but increases in Equatorial Asia).
Following the IPCC AIB scenario, the global anthropogenic emis¬
sions are predicted to increase with Hg(II) being the dominant emis¬
sions constituent in 2050 (Corbitt et al., 2011; Streets et al., 2009). As a
consequence, we find that the Hg enrichment of terrestrial ecosystems
driven by changes in atmospheric Hg deposition by 2050 would lead to
increases in global wildfire emissions of Hg by 28%. The most sig¬
nificant increases are calculated over Eurasia (1-41%) and Africa
(1- 24%) which together account for about 75% of global Hg wildfire
emissions for the 2000s. Wildfire emissions of Hg from South East,
Central and East Asian countries increase significantly in response to
greater Hg anthropogenic emissions in these countries and the resulting
deposition to terrestrial environments. South America (1-20%), North
America ( + 19%) and Australia ( + 18%) experience significant in¬
creases in emissions as well. Greater Hg deposition to the boreal regions
results in an increase of 19% in wildfire emissions from boreal North
America and 35% from boreal Asia. Emissions from tropical peatlands
in Equatorial Asia also increase due to greater Hg deposition in 2050. It
should be noted that new developments in technology (such as the
mercury control technology being used by coal-fired power plants) and
policy (such as the Minamata Convention) can significantly affect the
future trends of anthropogenic emissions of Hg, but these factors are not
accounted for in this study.
4. Conclusions
We investigate the Hg emissions from wildfires in this study. We
first develop the global and regional emission inventories for the 2000s
and then examine the perturbations from the projected 2000-2050
changes in climate, land use, land cover and anthropogenic Hg emis¬
sions. Africa (43.8%), Eurasia (31%) and South America (16.6%) are
found to be the major sources of Hg wildfire emissions in the 2000s.
Following the IPCC AIB scenario, 2000-2050 climate change would
lead to more frequent and severe wildfires in most regions around the
world resulting in significant increases in wildfire emissions of Hg at
both the global and continental scales. Climate change driven altera¬
tions in natural vegetation could also increase global emissions parti¬
cularly in the boreal regions, the US and Africa. However, these impacts
of a future favorable climate for fires and land cover change on global
emissions are suppressed by continued anthropogenic destruction of
natural vegetation in order to support agricultural development. As a
result, emissions in Africa, which is a major source of wildfire emis¬
sions, decline in 2050 due to reduced forest cover. In addition, de¬
struction of forests in Equatorial Asia and the Western US reduces Hg
wildfire emissions from these regions. On the other hand, a projected
rise in anthropogenic emissions in 2050 and the resulting greater Hg
contamination of terrestrial environments contributes to increasing
emissions globally and regionally. Wildfire emissions of Hg in the
boreal regions are predicted to increase in response to the 2000-2050
changes in climate, land cover and anthropogenic Hg emissions which
could have significant implications for Hg deposition to the Arctic.
Conflicts of interest
The authors declare that they have no conflict of interest.
Acknowledgements
This study is supported by NSF (grant #1313755) and U.S. EPA
(grant # 83518901). J.O. Kaplan was further supported by the
European Research Council (COEVOLVE 313797). S. Wu acknowledges
the sabbatical fellowship from Peking University. Superior, a high
performance computing cluster at Michigan Technological University,
was used in obtaining results presented in this publication.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://dx.
doi.org/10.1016/j.atmosenv.2017.10.061.
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