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Impacts of Land Cover Change on Biomass Burning Emissions of MercuryAditya Kumar1,2, Shiliang Wu1,2,3,Yaoxian Huang1

1Department of Geological & Mining Engineering & Sciences, 2Department of Civil & Environmental Engineering, 3Atmospheric Sciences Program Michigan Technological University, 1400 Townsend Drive, Houghton, MI-49931, USA

• Mercury (Hg) is a toxic and persistent pollutant in the global environment.• It can undergo deposition from the atmosphere to water bodies where conversion to

methyl mercury (CH3Hg) (a potential neurotoxin) occurs.• Biomass burning is a significant source of atmospheric Hg, resulting in the release of

previously deposited Hg from terrestrial vegetation and soil.• Hg emissions from biomass burning depend on vegetation type and hence would

change with alterations in land cover.• We estimate changes in Hg biomass burning emissions due to land cover changes in the

future (2050). The emissions can be calculated as:𝐄𝐄 = 𝐄𝐄𝐄𝐄 ∗ 𝐍𝐍𝐟𝐟 ∗ 𝐀𝐀 ∗ 𝐌𝐌𝐟𝐟

Where:E: Hg emissionsEF: Emission Factors (Hg emitted per unit biomass burned)Nf: Number of firesA: Burned AreaMf: Biomass(fuel) burned per unit area

Introduction

Estimation of Emission Factors

Funding for this project is provided by the NSF CNH project. We would like to thank the projectmembers at Michigan Tech and other institutions. (award#ICER-1313755).

Future Work

• Hg emission factors (EF) are a function of vegetation type and proximity to emissionsources.

• We archive Hg emission ratios (ER) (moles of Hg emitted per unit mole of CO emitted)available in literature and compute vegetation and region dependent effective emissionratios (EER).

𝐄𝐄𝐄𝐄𝐄𝐄 = �𝐢𝐢=𝟏𝟏

𝐧𝐧

𝐟𝐟𝐢𝐢𝐄𝐄𝐄𝐄

Where:EER: Effective emission ratio at a locationfi: Fraction of ith vegetation type at a locationER: Emission ratio for ith vegetation at that location• Land cover data for 9 plant functional types (PFTs) based on the Lund-Potsdam-Jena

Dynamic Global Vegetation (LPJ) model [Sitch et. al., 2003; Wu et. al., 2012] was used anddegraded from a 1° x 1° (latitude x longitude) spatial resolution to 4° x 5°

Vegetation Emission Ratio (mol Hg/mol CO)

Reference

Tropical BroadleavedRaingreen Tree

1.78e − 07 (Global) Ebinghaus et. al., 2007

Tropical Broadleaved Evergreen Tree

1.78e − 07 (Global) Ebinghaus et. al., 2007

Temperate NeedleleavedEvergreen Tree

0.85e − 07 N Amer (ave ,0.37e − 07 (Europe, N Afr)

Cinirella & Pirrone 2006

Temperate BroadleavedEvergreen Tree

1.13e − 07 N Amer ,0.37e − 07 (Europe, N Afr)

Friedli et. al., 2003,Cinirella & Pirrone 2006

Temperate BroadleavedSummergreen Tree

0.56e − 07 N Amer ,0.37e − 07 (Europe, N Afr)

Woodruff et. al., 2001, Cinirella & Pirrone 2006

Boreal NeedleleavedEvergreen Tree & Boreal

NeedleleavedSummergreen Tree

1.57e − 07 Alaska ,1.45e − 07 Canada, N Amer ,

1.19e − 07 (Europe, Russ)

Weiss-Penzias et. al., 2007,Friedli et. al., 2003; Sigler et. al., 2003,Cinirella &

Pirrone 2006

C3 & C4 Herbaceous 1.35e − 07 (N Amer),0.37e − 07 Eur, N Afr ,

2.1e − 07 (S Afr)

Engel et.al., 2006, Friedli et. al., 2003, Biswas et.al.,2006 , Cinirella & Pirrone 2006

Table 1: Emission ratios archived from literature for each LPJ PFT for different globalregions. When no data was available for a region, average ER computed from available datafor each vegetation type was assigned

Estimation of Burned Area• We use regression trees to generate burned area estimates for present and future.• A Regression tree [Breiman et. al., 1984] is a decision tree based predictive model which involves fitting a linear

regression model to each of the terminal nodes of the tree.• We follow Giglio et. al., 2006 in developing regression trees for this work with % tree cover, % herb cover, %

barren land used as splitting variables whereas number of fire counts as both a splitting and explanatoryvariable.

• Each terminal node is fitted with the following regression model:𝐁𝐁𝐁𝐁𝐁𝐁𝐧𝐧𝐁𝐁𝐁𝐁 𝐀𝐀𝐁𝐁𝐁𝐁𝐀𝐀 = 𝛂𝛂 ∗ 𝐍𝐍𝐁𝐁𝐍𝐍𝐍𝐍𝐁𝐁𝐁𝐁 𝐨𝐨𝐟𝐟 𝐄𝐄𝐢𝐢𝐁𝐁𝐁𝐁𝐅𝐅

• In order to account for the regional variability of land cover, we develop separate regression trees for 14different regions following Giglio et. al. 2006, Giglio et. al., 2010.

Figure 1: Defined regions for regression tree construction.

• Training data for the regression trees consisted of:1.) MODIS Terra fire counts data (monthly means) from 2001-2011 at a spatial resolution of 1° x 1°2.) GFEDv3 burned area estimates from 2001-2011 (monthly means) at 0.5° x 0.5°3.) MODIS Terra + Aqua land cover data at 0.05° x 0.05°[All the above datasets were degraded to a resolution of 4°x 5°]

• Emission factors can be obtained from EERs assuming a constant ratio between them for all fires [Friedli et. al., 2009]:

𝐄𝐄𝐄𝐄(𝛍𝛍𝛍𝛍/𝐤𝐤𝛍𝛍)𝐄𝐄𝐄𝐄 (𝐩𝐩𝐩𝐩𝐍𝐍𝐩𝐩𝛍𝛍/𝐩𝐩𝐩𝐩𝐍𝐍𝐩𝐩𝐩𝐩)

= 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏

Region Description Region DescriptionBONA Boreal North

AmericaCEAS Central Asia

TENA Temperate North America

SEAS South East Asia

CEAM Central America NHAF Northern Hemisphere Africa

NHSA Northern Hemisphere South

America

SHAF Southern Hemisphere Africa

SHSA Southern Hemisphere South

America

EQAS Equatorial Asia

EURO Europe AUST AustraliaBOAS Boreal Asia MIDE Middle East

Table 2 (right): Description of regions in Figure 1.

Figure 2: Regression trees for some of the regions.

Region Sample Size (N) R2 Region Sample Size (N) R2

AUST 5546 0.88 MIDE 4193 0.67

BOAS 3898 0.67 NHAF 4927 0.86

BONA 2124 0.80 NHSA 1988 0.62

CEAM 2792 0.66 SEAS 3920 0.49

CEAS 6983 0.73 SHAF 5449 0.86

EQAS 3709 0.66 SHSA 7209 0.76

EURO 2654 0.82 TENA 3944 0.74

Table 3: R2 for the regression trees constructed.

Changes in Effective Emission Ratios • Effective emission ratios show significant changes in some regions (Figure 3) indicating that changes

in EERs due to future land cover change will result in significant changes in emissions on theregional scale.

Figure 3: Effective emission ratios for present (top left), future (top right), difference (bottom left) andpercentage difference (bottom right) between future and present.

Changes in Number of Fires• We use present day and future fire counts generated by Huang et. al., (submitted for publication).• Changes in number of fires due to land cover change results in a 14% global emission increase in

the future indicating that land cover change in the future will increase the Hg biomass burningemissions.

Figure 4: Number of fires for present (top left), future (top right), difference (bottom left) andpercentage difference (bottom right) between future and present.

• Generate present and future burned area estimates using the constructed regression trees.• Estimate the biomass burned per unit area for present and future.• Estimate the present and future Hg biomass burning emissions and the changes in the future.

References

Acknowledgements

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