Wildfire in peatlands and the effect of smouldering combustion on carbon and mercury emissions

Transcription

1 Wildfire in peatlands and the effect of smouldering combustion on carbon and mercury emissions by Andrew Jamieson Kohlenberg A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Integrative Biology Guelph, Ontario, Canada Andrew J. Kohlenberg, 2015

2 ABSTRACT WILDFIRE IN PEATLANDS AND THE EFFECT OF SMOULDERING COMBUSTION ON CARBON AND MERCURY EMISSIONS Andrew J. Kohlenberg University of Guelph, 2015 Advisor: Dr. Merritt R. Turetsky Warming in the boreal forest region will increase fire size, severity, and frequency. This may result in peatlands making up a larger proportion of future burned area, shifting them from a net sink of atmospheric carbon (C) to a net source. In addition, mercury (Hg) that accumulates in peat is susceptible to re-release to the atmosphere during peat fires. Here I investigated the physical properties driving depth of burn and emissions of C and Hg. I found the interaction between bulk density and water content to be the most important factor in predicting depth of burn, CO 2, and CO emissions. This was not the case for CH 4 and Hg emissions. These results also indicate that previously reported CO:CO 2 and CH 4 :CO 2 emission ratios are underestimating C emissions from smouldering peat. The quantity of C released from burning peat underscores the importance of considering fire in peatland C budgets. ii

3 Acknowledgements First I would like to thank my advisor, Dr. Merritt Turetsky. Thank you for all your guidance and support over these past two years. I cannot express enough how much I value your mentorship. I would also like to thank the members of my advisory committee, Drs. Aaron Berg and Andy Gordon, for their valuable feedback on my project and thesis. Additionally, I would like to thank Dr. Dan Thompson for all the guidance during my field and lab work in Edmonton and for answering all my questions about R. I would like to extend my gratitude to all my friends in Guelph and all the members of the Integrative Biology department for all their help and support during my time in Guelph. There are too many of you to list and I am sure you all know who you are. Thank you very much. I would also like to say thank you to the members of the Turetsky lab group past and present Erin, Jen, Carolyn, and Tobi. I am especially grateful to Dr. David Olefeldt for your encouragement and for hiring me during the final stages of my program. I am very grateful to have the unconditional support of my family, especially my parents, Kim and Monica Kohlenberg. Without you none of this would have been possible. And finally thank you to Angela Brugger. I could not have accomplished this without you. iii

4 Table of contents ABSTRACT... ii Acknowledgements... iii Table of contents... iv List of tables... v List of figures... vi 1.0 Introduction Introduction to peatlands in the boreal forest region Overview of C cycling and peat accumulation in peatlands Smouldering combustion during peat fires Methods Site selection and sample collection Laboratory burn experiments Chemical analysis of peat Statistical Analysis Results Controls on depth of burn Controls on C and Hg emissions Discussion Depth of burn, CO 2, CO, and CH 4 emissions from burning peat Peat fire Hg emissions and relationships with C loss Conclusions Literature cited Tables Figures Appendix iv

5 List of tables Table 1 Location and characteristics of sampling locations Table 2 Results of GLM to test the effect of water content, bulk density, and their interaction on depth of burn Table 3 Correlation matrix for CO 2, CO, CH 4, and THg Table 4 Results of GLMs testing the effect of depth of burn on the total gaseous C, CO 2, CO, and CH 4 emissions Table 5 Statics for the most parsimonious models predicting variation in the total gaseous C, CO 2, CO, and CH 4 emissions Table 6 Comparison of model statics between depth of burn models versus most parsimonious models for C emissions Table 7 Results of GLMs testing the effect of depth of burn on THg, GEM, and PHg Table 8 Statics for the most parsimonious models predicting variation in THg, GEM, and PHg release Table 9 Comparison of model statics between depth of burn models versus most parsimonious models for Hg release Table 10 Comparison of soil C and Hg losses estimated through changes in soil stock versus gas measurements Table 11 Synthesis table of previously reported emission ratios compared to those found in this study Table 12 Synthesis table of soil C and Hg loss and average Hg emissions from previous studies compared to those found in this study v

6 List of figures Figure 1 Photo of peat monolith inside burn box prior to ignition and schematic of in-fuel thermocouple placement Figure 2 Comparison of depth of burn in hollow and hummock peat Figure 3 Comparison of depth of burn in peat blocks with an overlaying vegetation layer of feather moss or Sphagnum Figure 4 Relationships between bulk density, water content, peatland type, surface vegetation and depth of burn Figure 5 Time series of instantaneous emission of CO 2, CO, and CH 4 over the course of a typical burn experiment Figure 6 Relationships between total C, CO 2, CO, and CH 4 production and depth of burn in smouldering peat Figure 7 Plots of the predictors from the most parsimonious models for explaining variation in total C, CO 2, CO, and CH 4 emissions Figure 8 Relationship between depth of burn and THg, GEM, and PHg release Figure 9 Plots of the relationship between THg release and cumulative CO 2, CO, and CH 4 emissions Figure 10 Plots of the predictors from the most parsimonious models for explaining variation in, THg, GEM, and PHg release Figure 11 Change in soil C stock and total gaseous C emissions Figure 12 Change in soil Hg stock and total mass of Hg released Figure 13 Relationship between the percent change in soil C and Hg stock vi

7 1.0 Introduction 1.1. Introduction to peatlands in the boreal forest region The boreal forest region is an important part of the climate system due to its large spatial extent, its role in the global carbon (C) cycle, and its sensitivity to climate change [Chapin III et al., 2000]. The boreal region is the world s largest terrestrial biome, covering 14.7 million km 2, which represents 11% of Earth s terrestrial surface [Bonan, 1989; Bonan and Shugart, 1989]. While climate is an important driver of vegetation patterns and C storage in the boreal forest, the boreal region also exerts strong influences on the climate system [Preston et al., 2006]. Boreal vegetation often reduces surface albedo, leading to a warmer climate than would be observed in un-forested areas [Bonan, et al., 1992; Betts, 2000]. The dominance of conifers makes this more pronounced in boreal forests than in other terrestrial ecosystems [Baldocchi et al., 2000]. Conversely, the ability of the boreal forest to store atmospheric CO 2 acts as a negative feedback to climate warming, resulting in a net cooling effect on climate [Frolking et al., 2011]. Boreal peatlands play a particularly important role in ecosystem-climate feedbacks due to their role as long-term sinks of atmospheric C. Peatlands are a dominant land cover type in some parts of the boreal region, covering approximately 3.5 million km 2 or 25% of the boreal land base [Wieder et al., 2009]. Throughout the Holocene, peatlands have caused net cooling of the climate due to peat accumulation, despite the fact that northern peatlands also serve as net sources of methane (CH 4 ) to the atmosphere [Gorham, 1991; Frolking et al., 2006; Limpens et al., 2008]. Peatlands are defined as wetlands that have accumulated more than 40 cm of peat (organic soil layers) [Zoltai and Martikainen, 1996], which accumulates when soil inputs through net primary production exceeds soil organic matter losses from decomposition, 1

8 disturbance, and leaching [Vitt et al., 1995]. Due to the accumulation of thick peat layers, the majority of the peatland C pool is stored as partially decomposed organic material [Vitt, 1994; Vitt et al., 2000]. While net primary production is similar across forested uplands and peatlands, the slower rate of decomposition in peatlands allows for greater accumulation of soil organic matter [Gower et al., 2001; Kurz et al., 2014]. Peatlands are typically classified as either fens or bogs. Bogs are ombrotrophic, meaning that they receive nutrients and water solely from precipitation and atmospheric deposition. As a result, bogs have acidic and nutrient poor conditions which contribute to slow decomposition rates that increase rates of peat accumulation. Also due to their ombrotrophic nature, the majority of atmospheric inputs are retained in bog peat that vertically accumulate over time [Madsen, 1981]. Bogs are dominated by oligotrophic Sphagnum species and in general can be treed (usually with Picea mariana) or open with no trees, although western Canadian bogs are always treed. In contrast to bogs, fens are minerotrophic ecosystems, receiving water that has come into contact with mineral soils. These surface or ground water inputs can deliver nutrients and as a result, fens are typically more nutrient rich than bogs. Fens can be dominated by a range of vegetation types. In western Canada, fens can be open (moss or graminoid dominated), shrubby (dominated by Salix spp. or Betula pumila), or treed (with Larix laricina or P. mariana) [Wheeler and Giller, 1982; Vitt et al., 1995; Whitehouse and Bayley, 2005]. Fens represent approximately 63% of total peatlands in western Canada, while bogs are relatively rare (<10% of total peatland cover are non-permafrost bogs) [Vitt et al., 2000]. 2

9 1.2. Overview of C cycling and peat accumulation in peatlands Globally, peatlands cover approximately three percent of the global land base and store over 600 Pg of C [Yu et al., 2011]. About 80% of this C, approximately 550 Pg of C, is stored in high latitude peatlands [Yu et al., 2010]. Canadian peatlands store approximately 150 Pg of C [Tarnocai, 2006]. When plant biomass is incorporated into the peat column, it undergoes aerobic decomposition in the acrotelm, or the upper, mostly oxic, layer of peat [Blodau, 2002]. Upwards of 90% of this relatively fresh organic matter is lost as CO 2 and CH 4 as it decomposed in the acrotelm [Gorham, 1991]. The remaining organic matter is transferred to the permanently saturated, anoxic catotelm. These saturated and anoxic conditions, coupled with low temperatures and low redox potential, result in slow organic matter decomposition, and peat accumulation [Clymo, 1984; Clymo et al., 1998]. Only about one percent of the original mass of organic matter remains when this material reaches the catotelm. However, due to slow rates of organic matter transfer into the saturated zone over millennia, this organic matter currently represents the majority of the total peatland C pool [Clymo, 1984; Blodau, 2002]. Peatlands are important long-term C sinks, as C is stored in vertically accumulating layers of peat. However, this accumulation of organic matter also makes peatlands effective biogeochemical sinks of other elements, including nutrients (N and P) [Wang et al., 2014] as well as heavy metals (As, Hg, and Pb) [Shotyk, 1996; Kempter et al., 2010; Outridge et al., 2011]. Like C, Hg originates in the atmosphere and accumulates in vertically aggrading peat layers over time. Unlike C, atmospherically deposited Hg binds to organic matter and is thus incorporated into the peat column [Schuster, 1991; Grigal, 2003]. Over time, large quantities of Hg are able to accumulate peat. Peatland Hg stocks can be as much as 10 times greater than forested upland soil Hg stocks, due mostly to the thickness of the organic soil layer in peatlands 3

10 [Turetsky et al., 2006]. In some areas of the boreal forest, organic soils contain up to 90% of the soil Hg stock [Friedli et al., 2007]. This suggests that, like C, peatlands are a considerable Hg sink. Climate change has the potential to affect the ability of peatlands to accumulate organic matter and thus act as a biogeochemical sink for C and other elements [Belyea and Malmer, 2004]. Decomposition rates increase with temperature, leading to more organic matter decaying in the acrotelm and lower rates of organic matter transfer to the catotelm [Clymo et al., 1998; Preston et al., 2006]. Increases in decomposition will affect soil C fluxes, for example by increasing CO 2 production and release to the atmosphere. Increases in mineralization can also stimulate methanogenesis and CH 4 emissions; although methanotrophy, the conversion of CH 4 to CO 2 in aerobic peat, may also increase [Moosavi and Crill, 1998; Chowdhury and Dick, 2013]. Warmer temperatures will also lead to faster rates of evapotranspiration. In western regions of the North American boreal forest, projected increases in precipitation are likely to be offset by increases in evapotranspiration, leading to overall drier conditions [Chapin III et al., 2000]. This could lower the water table in peatlands and remove anaerobic constraints on decomposition of peat [Fenner et al., 2007; Fenner and Freeman, 2012]. Finally, warmer and drier conditions also have the potential to stimulate plant productivity or alter plant species composition, which could increase the amount and quality of organic matter inputs to soils. While many studies have explored how changes in climate will affect peatland C fluxes, few have examined consequences for other biogeochemical cycles, such as Hg fluxes. Warming may also influence biogeochemical cycles in peatlands by triggering long-term changes in vegetation structure. Warmer and drier conditions are expected to favour more woody shrub plant cover in peatlands [Weltzin et al., 2003; Bunn et al., 2005; Alexander et al., 2012]. 4

11 Such vegetation shifts have implications for albedo, aboveground C pools and fluxes, and fire regimes, all of which will influence how peatlands interact with the climate system [Brassard and Chen, 2006]. Afforestation in peatlands is likely to occur at the expense of moss cover [Turetsky et al., 2010]. Due to the slow decomposition of moss litter relative to vascular plant tissue, decreased moss cover will likely limit the accumulation of C in organic soils [Lang et al., 2009; Turetsky et al., 2010] Given that mosses have high cation and anion exchange and tend to bind strongly to atmospherically-deposited constituents, a reduction in moss cover will likely affect rates of Hg storage in peat. Boreal forest ecosystem function is largely driven by disturbance. The dominant disturbance in the boreal region is wildfire, as an average of 2.3 Mha of land burns annually [Stocks et al., 2002; Rupp et al., 2007]. Fire is a key determinant of patterns of vegetation and forest structure in the boreal, as well as biogeochemical cycling [Rowe and Scotter, 1973; Bonan and Shugart, 1989; Stocks et al., 2002]. Fire frequency in the boreal forest region ranges from years [Bonan and Shugart, 1989; Weir et al., 2000], but in general is expected to decline (i.e., more frequent fires) with climate warming [Flannigan et al., 2005]. While fire is considered to be an important factor in C cycling in boreal upland stands [Harden et al., 2000; Paré et al., 2011; Stephens et al., 2012], its role is not understood well in boreal peatlands. While several studies have examined the role of fire in driving peatland vegetation succession and patterns of microtopography (hummock/hollow patterns) [Benscoter et al., 2005a, 2005b; Benscoter and Vitt, 2008], peatlands in general are viewed to be resistant to burning due to their high moisture levels. However, predicted changes in boreal fire regimes, including greater probability of ignition, increases in the frequency of large fire events (fires covering areas greater than 1000 km 2 ), lengthening of the fire season, and increases in overall 5

12 fire occurrence [Stocks et al., 1998; Flannigan et al., 2005, 2009; Nitschke and Innes, 2007; Wotton et al., 2010], all have the potential to increase burning in peatlands. This underscores the importance of understanding C losses due to fire in peatlands under both current and future climate scenarios. A majority of the peatland C stock is protected from fire, owing to waterlogged conditions that make deeper peat layers non-flammable. Increased rates of evapotranspiration and water table drawdown from a warmer climate will potentially allow large quantities of peat C stored in the soil profile to become vulnerable to burning [Gorham, 1991]. Several studies have found that increases in fire activity increases the amount of C released from organic soils and reduces the overall ability of peatlands to store C [Stocks et al., 1998; Harden et al., 2004; Turetsky et al., 2011]. It was estimated that relatively small increases (< 20%) in both annual burned area and fire intensity would shift peatlands in western Canada from net C sinks to net sources [Turetsky et al., 2002; Wieder et al., 2009] Smouldering combustion during peat fires Although peat usually has high water content, it is still susceptible to combustion. Estimates of the moisture limit of peat combustion range from %, however estimates typically are between % [Frandsen, 1987, 1997; Rein et al., 2008a, 2008b; Garlough and Keyes, 2011]. As a result, peatland fires are dominated by smouldering combustion [Zoltai et al., 1998; Turetsky et al., 2004]. Smouldering combustion is a non-flaming, self-sustaining form of combustion that derives its principal heat from heterogeneous fuel consumption [Aldushin et al., 2006]. During smouldering, the oxidation reaction and heat release occur on the surface of the burning substrate, whereas during flaming these occur in the gas phase above the substrate [Rein 6

13 et al., 2008b]. Ignition and propagation of the smouldering reaction is largely dependent on the moisture content of the fuel [Frandsen, 1987; Rein et al., 2008a; Davies et al., 2013]. Fuels with low moisture content require less energy to propagate the drying front, resulting in faster spread of the smouldering front and greater fuel consumption [Benscoter et al., 2011; Huang and Rein, 2014]. Smouldering can remain latent long after flaming has ceased, emitting combustion products into the atmosphere after a wildfire appears to have ended and can re-establishing flaming elsewhere [Bertschi et al., 2003; Aldushin et al., 2006]. Smouldering is a low temperature process, with surface temperatures peaking at approximately ºC and falling as low as 250ºC, compared to 1500ºC for flaming [Rein et al., 2009]. The spread rate for smouldering is approximately one to 50 mm hr -1, slower than the rate of flaming (~100 mm hr -1 ) [Rein et al., 2008a]. Smouldering produces more C emissions than flaming combustion. In general, smouldering is responsible for a larger fraction of fuel consumption and emissions than flaming combustion [Yokelson et al., 1997; Rein et al., 2008a]. Smouldering is also responsible for 97% of CO emissions and 85% of CO 2 emissions of the entire combustion reaction of burning peat [Hadden, 2011]. Despite its importance to organic C release, little is known about the emission dynamics of smouldering combustion. Because of incomplete combustion, smouldering fires are known to produce more CO and CH 4 than flaming fires [Hamada et al., 2013], though few studies have examined this empirically. Most studies use assumed, standard emission ratios of approximately 12-14% for CO:CO 2 and % for CH4:CO 2 to estimate C emissions from boreal wildfires in modeling activities [Crutzen et al., 1979; Nance et al., 1993; Cahoon et al., 1994; Cofer et al., 1998; van der Werf et al., 2010]. These standard ratios have been generated from studies using airborne sampling or satellite imagery analysis [Cahoon et al., 1994; Cofer et al., 1998; Hamada 7

14 et al., 2013]. Such sampling methods may have bias towards emissions from flaming combustion and as a result may underestimate emissions from smouldering [Andreae and Merlet, 2001]. While emission ratios are likely to vary depending on peatland type, fuel type, and moisture content [Zoltai et al., 1998], no studies to my knowledge have quantified these relationships. The purpose of this study was to build on previous work investigating controls on depth of burn in peat. To this effect, I extended the experimental burn framework of Benscoter et al. [2011] to better understand how these controls influence emissions from smouldering peat. I hypothesised that the interaction between peat bulk density and soil water content would be the most important predictor of depth of burn and CO 2 emissions in burning peat. I predicted that increasing soil moisture would slow the overall loss of C associated with peat combustion, but would lead to increased ratios of CO:CO 2 and CH 4 :CO 2 due to incomplete smouldering combustion. I also hypothesised that soil moisture would be a dominant driver of Hg in peat combustion emissions and that there would be strong correlations between Hg and CO emissions. 2.0 Methods 2.1. Site selection and sample collection Sample collection occurred between June 19-22, 2013 at two bogs and two fens selected in the Slave Lake Athabasca region of northern Alberta, Canada. Sites were selected according to how well they represented western Canadian ombrotrophic bogs and treed fens [Vitt et al., 1995; Table 1]. All sites had a peat depth of at least one metre. Bog one was located 41 km northwest of Slave Lake, AB and was a treed bog located within a larger fen complex. Canopy vegetation 8

15 of Bog one was dominated by P. mariana. Bog two was located approximately five km east of Athabasca, AB and was similar in terms of vegetation composition to Bog one. Both bogs had an even distribution (50/50%) of hummocks and hollows. Fen one was located 10 km west of Bog one and was a treed fen with a canopy of P. mariana and L. laricina. Fen two was also a treed fen located approximately five km east of Bog two within the surrounding fen complex. Both fens were comprised of 65% hummocks and 35% hollows. At each site, I sampled peat within three hollows and two hummocks. Hollows were oversampled relative to hummocks to account for variation seen in burned peatlands, where hollows represent a larger proportion of the burned area in peatlands than hummocks [Shetler et al., 2008] Sampling locations were selected to maximize microtopographic heterogeneity within each site while maintaining vegetation and microtopography homogeneity within each sample. In each sampling location, surface vegetation, including all vascular stems and the upper five cm of moss stems, was removed from a 40x60 cm plot area. I then sampled 40x60x30 cm blocks of peat with a hand saw. Peat blocks were placed into similar sized rubber bins for transportation. All peat blocks represented aerobic peat layers above the water table and were kept at field moisture content during transportation to the lab. Peat blocks were immediately transported to Canadian Forestry Service s (CFS) Northern Forestry Centre (NoFC) in Edmonton, Alberta and stored in a freezer at -14 ºC until I initiated the laboratory burns experiments Laboratory burn experiments Laboratory burn experiments took place between August 22 29, 2013 and December 19, 2013 January 6, Prior to burning, peat samples were moved to a second cooler to thaw at three ºC for one week. To obtain a water content gradient across peat samples, each sample was 9

16 assigned to one of three drying treatments upon thawing: field moisture (no drying), air dried at approximately 23ºC for one week, or dried at 40ºC for one week plus one week at room temperature. Peat was dried in elevated open top rubber containers with 15, four cm diameter holes to allow free water drainage. Air movement above the samples was provided by an oscillating fan operating at one ms -1. A 25x24x20 cm monolith cut from peat and placed in box of the same dimensions as the monolith constructed with 1.3 cm thick ceramic fibreboard (Cotronics Corp, Brooklyn, NY). Samples were cut approximately five cm away from the edge to eliminate any edge drying effects. Prior to burning, a cross section was cut from each monolith and volumetric sub-samples were taken at five cm depth increments and analysed for pre-burn bulk density and water content (see below for methods). These same samples were used for analysis of heat of combustion, all Hg species, C content, and loss on ignition. A tare measurement of the scale and the fibreboard burn box were taken prior to the monolith being placed inside. Inserted in the bottom of the burn box was a heat flux sensor. A fibreboard spacer separated the heat flux sensor from the monolith; this spacer also ensured all monoliths were flush with the top of the burn box to eliminate any influence of edges on the air flow into the box during burning. Once the samples were placed in the burn box, two K-type thermocouple probes were inserted 10 cm into the sample at depths five, 10, 15, and 20 cm (Figure 1). An additional four E-type surface thermocouples were placed five cm from each side of the box. Temperature data were recorded on a Campbell Scientific CR5000 data logger (Campbell Scientific, Logan, USA) at one second intervals. All samples were burned on a scale to record the mass loss every second for the duration of each experiment. Scale recordings were made with a Campbell Scientific CR1000. Surface temperatures were recorded every 30 s with 10

17 an FLIR infrared (IR) camera (FLIR Systems, Wilsonville, OR). Images of each burn experiment were taken with a webcam mounted 70 cm from the peat surface. Images from each were recorded at 30 s intervals for the duration of each experiment. Six fine-wire thermocouples were suspended above the sample to record the temperature of the smoke plume produced by the burning peat. Three thermocouples were hung 15 cm above the peat surface and three were hung 43 cm above the surface. Middle thermocouples were centred over the sample with the remaining thermocouples placed 6 cm to each side. These thermocouples were wired to a Campbell Scientific CR1000 data logger which recorded measurements every second. Each burn was initiated by placing a high output heater over each sample. The heater warmed up for 10 minutes prior to application to allow it to reach the desired heat output, three MJ m -2 to simulate the heat pulse from a crown fire. Due to the rectangular shape of the heater, its orientation was switched after five minutes to provide full coverage of the entire sample. Heat was applied for a total of 10 min to reach the desired heat output. The IR camera and webcam started recording images at the onset of heat application (t = 0). To sample CO, CO 2, and CH 4 a copper pipe was fixed inside the flue of the burn hood at two metres above the peat surface. Gases were drawn through the copper pipe and through 0.675cm diameter vinyl tubing to a Siemens Ultramat 23 and Ultramat 6 (Siemens AG, Berlin, Germany) running in series. CO and CO 2 concentrations were measured with the Ultramat 23 while CH 4 concentration was measured with the Ultramat 6. Concentrations from both instruments were recorded via a Campbell Scientific CR1000 every second along with 1 minute totals of emissions. All concentrations were recorded in ppmv. An exhaust fan in the flue was 11

18 used to draw a constant volume of emissions up at a rate of m 3 s -1. The exhaust fan drew emissions at a rate of m 3 s -1 for experiments 10, 17, and 18. All Hg species were collected though a copper pipe fixed in the flue at the same height as the flue gas intake cm diameter vinyl tubing connected the copper pipe to a Teflon particulate filter pack, to capture particulate Hg (PHg), then to a Tekran quartz gold trap (Tekran Instruments Corp., Toronto, Canada) to capture remaining gaseous element Hg (GEM). All connected to a vacuum pulling air in at ~3.5 L min-1. The vacuum started to draw combustion products into the Hg sampling equipment at the onset of heat application to the peat. Gold traps were sent to Dr. Carl Mitchell at the University of Toronto at Scarborough in Toronto, ON for analysis. Pre-burn peat and post-burn ash samples and Teflon filters were sent to Dr. Brian Branfireun at University of Western Ontario in London, ON. Mercury analyses were conducted by direct mercury analysis and cold vapour atomic fluorescence spectroscopy following US Environmental Protection Agency (USEPA) Methods 7473 and 1631 [USEPA, 2001; USEPA, 2007]. Combustion was judged to have ceased when mass loss had plateaued and fuel thermocouple readings fell below 50ºC. Upon completion, surface ash was collected with a hand held vacuum. Ash samples were used for post-burn Hg, C content, and loss on ignition analyses. Similar to Harden et al. (2004) samples were assessed for total C and Hg before and after burn experiments as another means of quantifying emissions. Prior to burning, sub-samples of peat will be taken to establish pre-burn C and Hg concentrations. Masses of CO 2, CO, and CH 4 emissions in kg were calculated from the recorded concentrations using: 12

19 m gas = V(10 6 ) v fan p gas baseline (Eqn. 1) where V is the observed concentration of the gas recorded in ppmv, v fan is the observed velocity of gases in the flue, p gas is the density of the gas, and baseline is the observed ambient concentration of the gas present. The cumulative mass of each gas emitted over the course of each experiment and emission ratios (CO:CO 2 and CH 4 :CO 2 ) were calculated from these masses Chemical analysis of peat Volumetric sub-samples were weighed immediately following collection and dried at 45ºC (to avoid any Hg loss [Joe Crumbaugh, personal communication, August 8, 2013]) until a constant mass was reached. These weights were used to calculate bulk density (dry fuel mass per sample volume, g cm -3 ), volumetric water content (water volume per sample volume), and percent gravimetric water content (water mass per unit of dry peat). The average bulk density and volumetric water content values used in data analyses (see methods below) were calculated from only those layers consumed by combustion in the experiments. These samples were then homogenised and 0.5 ± 0.05 g of each were placed in ceramic crucibles and heated to 550ºC in a Fisher Scientific IsoTemp muffle furnace (ThermoFisher Scientific, Waltham, MA) for 4 hours. The percent organic matter from each sample was calculated as the weight of combusted matter per unit of pre-combusted sample. These data were used to compare the loss of organic matter through smouldering combustion. 250 ± 5 µg of homogenised pre-burn peat and post-burn ash samples were analysed with an Elementar Vario Max elemental analyzer (Elementar Analysensysteme, Hanau, Germany) to measure the percent C stock in unburned and burned peat Statistical Analysis 13

20 I analysed controls on depth of burn using a series of general linear models (GLMs). The goal of these analyses was to examine the effects bulk density and water content on depth of burn and to determine whether these relationships were dependent on moss species (Sphagnum versus feather moss), microtopography (hummock versus hollow), or peatland type (bog versus fen). I first constructed a GLM using bulk density and water content as fixed effects, and the interaction between these fixed effects. This model is referred to throughout this thesis as the base model. I compared the base model to nine, more complex models created through iterative additions of landscape variables (e.g., peatland type, vegetation type, and microtopography). A list of all models and their parameters can be found in Table A1. A single outlying observation was removed from all models. This point had an unusually high average bulk density. The bulk density of the middle peat layer of this sample was much greater than the layer below. The influence of this observation over analysis was evaluated statistically and was found to have an undue influence over relationships, as judged by a Cook s distance greater than one. Diagnostics of all models were assessed to ensure the assumptions of a GLM were not violated. I selected the most parsimonious model by comparing the corrected Akaike Information Criterion (AICc) across all base and candidate models. AICc was used to account for over-fitting by the addition of extra parameters and the small sample size [Hurvich and Tsai, 1989]. Once the most parsimonious model was selected, a linear regression was used to determine the effect of each parameter on depth of burn. I analysed controls on total gaseous C emissions (CO 2 +CO+CH 4 ) and cumulative CO 2, CO, and CH 4 emissions using a GLM and AICc framework similar to the depth of burn model described above. In these analyses, a base model consisting of only depth of burn was used. A list of parameters from each model and corresponding AICc value can be found in Tables A2-14

21 A6. One observation with undue influence over the percent change in peat C stock analysis was removed. This was the same observation removed from the depth of burn analyses and was removed for the same reasons. Correlations between gases were analyses using Pearson correlations. Emission ratios for each experiment were calculated as a percent from the sum of the mass of CO or CH 4 over the cumulative mass of CO 2 produced [Crutzen et al., 1979]. One tailed t-tests were used to determine if average emission ratios for each experiment was greater and those previously reported in the literature. The effect of water content on emission ratios was analysed using GLMs. A one tailed t-test was used to determine whether Hg concentrations from pre-burn samples were greater than post-burn peat samples. A one tailed t-test was also used to investigate whether the cumulative GEM collected over each burn experiment was greater than the PHg collected. Similar to depth of burn and C emissions, GLM and AICc analyses were used to describe controls on THg, GEM, and PHg release during combustion. Parameters used in each model can be found in Tables A7-A9. All models used in the analyses of Hg data were assessed to ensure they met the assumptions of GLMs through diagnostic plots. Pearson correlation tests were used to investigate the relationships between cumulative CO 2, CO, and CH 4 production with THg release. Pearson correlations were used to examine bias in the change in the soil stock and gaseous emissions of C and Hg. A Pearson correlation was also used to evaluate the relationship between the percent change in soil C and Hg. 15

22 All analyses were conducted using R v3.1.0 statistical programming language [R Core Team, 2014]. AICc calculations were computed with the sme package [Berk, 2013]. Figures were created with the ggplot2 and gridextra packages [Wickham, 2009; Baptiste, 2012]. 3.0 Results 3.1. Controls on depth of burn Across all burn experiments, depth of burn averaged 8 ± 1 cm and ranged from minimal combustion of the surface later (depth of burn of ~1 cm) to complete consumption of the entire peat block (depth of burn of 20 cm). Averaged across soil moisture treatments, depth of burn was greater in hummock than hollow samples (Figure 2; t 8.1 = 3.4, p = ). Many of the hummock samples were associated with feather moss vegetation, which experienced deeper burning than Sphagnum peat (Figure 3; t 11.4 = 3.1, p = ). When parsed into hummock peat only, fuel species influenced depth of burn (F 3,4 = 78.2, p = ), as Sphagnum hummocks burned less severely than feather moss hummocks (Figure 3; β±se = -14.4±2.9, t 4 = -5.0, p = 0.008). Depth of burn did not vary between bog and fen peat (peatland type: p > 0.05). The most parsimonious model predicting variation in depth of burn was the base model, which included volumetric water content, bulk density, and the interaction between volumetric water content and bulk density (AICc = 106.9, F 3,14 = 17.2, R 2 = 0.79, p < ). Models containing additional variables (vegetation type or microtopography effects), or interactions between these variables and water content or bulk density, did not improve model fit (Table A1). In general, depth of burn increased with greater bulk density. However there was an interaction between bulk density and water content (Table 2, Figure 4; p < ), in which drier peat lead 16

23 to greater depth of burn except in several samples with high moisture content, which did not burn deeply despite high bulk density (Figure 4). Independent of bulk density, volumetric water showed a negative relationship with depth of burn as expected, although this relationship was not statistically significant Controls on C and Hg emissions In general, C gas emissions peaked within the first 10 minutes following ignition and diminished until combustion ceased (Figure 5). Emission rates averaged 330 ± 80 mg CO 2 min -1, 120 ± 2 0 mg CO min -1, and 7.1 ± 2.0 g CH 4 min -1. Across all burn experiments, the cumulative emission of each gas averaged ± 15.3 g CO 2, ± 4.5 g CO, and 1.55 ± 0.36 g CH 4. The cumulative emissions of all three gases were correlated with each other (Table 3). As expected, total gaseous C release during combustion increased with depth of burn (F 1,17 = 17.3, R 2 = 0.51, p = ). The cumulative emissions of CO 2, CO, and CH 4 also increased with depth of burn (Table 4, Figure 6). The most parsimonious model predicting variation in total gaseous C, cumulative CO 2, and cumulative CO emissions was the base model, which included volumetric water content, bulk density, and the interaction between these two predictors (Table 5, Tables A3 A5, Figure 7). Emissions of total gaseous C, CO 2, and CO increased with bulk density until the water content became so high (wet) that combustion of peat ceased (Figure 7). The addition of fixed effects such as peatland type, microtopography, and surface vegetation to the base model did not improve model fit for the cumulative emissions of total gaseous C, CO 2, or CO. The base model (bulk density x volumetric water content interaction) explained more variation in emissions of total gaseous C, CO 2, and CO than was explained by depth of burn (Table 6). 17

24 Unlike the other measures of C release, the most parsimonious model for explaining variation in cumulative CH 4 emissions only included volumetric water content as a predictor (Table A6; AICc = 71.6, F 1,16 = 1.1, R 2 = 0.06, p = 0.3). Cumulative CH 4 emissions decreased with increasing volumetric water content, but this relationship was not significant and had little predictive power. Relative to volumetric water content, depth of burn was a stronger predictor of cumulative CH 4 emissions, explaining 36% of variation in CH 4 emissions (Table 6). Ratios of cumulative CO:CO 2 and CH 4 :CO 2 emissions averaged 25 ± 3.1% and 4 ± 0.7% across all burn experiments, respectively. These measured ratios were greater than those previously reported (CO:CO 2 : t 18 = 3.9, P = ; CH 4 :CO 2 : t 18 = 3.7, P = ). The ratio of CO:CO 2 increased with greater depth of burn (β ± SE = 0.85 ± 0.4, t 17 = 2.1, p = 0.05). While CH 4 :CO 2 tended to decrease with greater depth of burn, this relationship was not significant (p > 0.05). Volumetric water content did not affect the ratios of either CO:CO 2 or CH 4 :CO 2 emissions (p > 0.05). Across the burn experiments, more Hg was released as GEM (97.4% if total Hg loss) than PHg (2.6% of Hg loss) (t 10 = 4.4, P = ). Total Hg and GEM release both increased with greater depth of burn (Table 7, Figure 8), while PHg showed no relationship with depth of burn. The most parsimonious model to describe both THg and GEM emissions included bulk density as the only predictor (Tables 7). THg release was correlated with the cumulative emissions of all C gases, but was most strongly correlated with cumulative CO emissions (Table 3, Figure 9). In general, THg and GEM emissions increased with greater density (Tables 8, Figure 10). Relative to the base model, addition of other variables (peatland type, microtopography, and surface vegetation effects) did not improve model fit (Tables A8 and A9). There were no significant predictors of PHg release (Table A10; p > 0.2). Depth of burn 18

25 explained more variation in THg, and GEM release than did bulk density and volumetric water content (Table 9). In addition to quantifying C and Hg emissions, I also estimated the loss of soil C and Hg by quantifying the change in peat C and Hg stock during each burn experiment. I found that C loss estimated by change in soil C stock exceeded the measured gaseous C emissions (Table 10, Figure 11; r = 0.52). Mercury emissions also showed a similar pattern, with greater loss of Hg via change in soil Hg relative to THg captured in the Telfon filters and gold traps (Table 12, Figure 13; r = 0.85). Overall, the change in soil Hg and C stocks were correlated (Figure 13, r = 0.30). 4.0 Discussion 4.1. Depth of burn, CO 2, CO, and CH 4 emissions from burning peat Controls on the combustion of peat and the factors that drive wildfire emissions in peatlands are poorly understood. To improve our understanding of how fire in peatlands contributes to interactions with the atmosphere and climate system, the objective of this research was to identify what properties of peat influence depth of burn and resulting emissions of C and Hg. In general, my results supported some but not all of my predictions. I predicted that the most important predictor of depth of burn would be the interaction between peat bulk density and water content, and this is consistent with the results. The interaction between bulk density and water content were important predictors of depth of burn, and this consistent across microtopography, surface vegetation species, and peatland types. However, due to a lack of 19

26 statistical power, I may not have been able to test the effect of these landscape variable included in the more complicated candidate models. My results also indicate that Sphagnum-derived peat burned less severely (smaller depths of burn) than feather moss-derived peat, particularly in hummocks. This is consistent with previous laboratory [Benscoter et al., 2011] and field studies [Shetler et al., 2008; Terrier et al., 2014] documenting the strong influence of Sphagnum moss on patterns of combustion. As illustrated by my results, in which the addition of vegetation type did not improve model fit, this species effect can likely be explained by the high water content and low bulk density of Sphagnum tissue. The ability for Sphagnum to retain more water than feather moss is due to the large volumes of water that can be stored in hyaline cells, which are large, dead cells that provide a reservoir of water to chloroplasts in the cell walls of Sphagnum species [Rydin and Mcdonald, 1985; Benscoter and Wieder, 2003; Glime, 2007; Shetler et al., 2008]. Sphagnum plants in hummocks also tend to have a tighter, more compact arrangement (greater stem density) than Sphagnum hollows or feather mosses, which contributes to high water holding capacity and limits the ability of fire to propagate [Rydin and Mcdonald, 1985; Miyanishi and Johnson, 2002; Benscoter et al., 2011]. On account of these factors, Sphagnum peat is more resistant to fire than feather moss peat [Terrier et al., 2014]. My results suggest that this moss effect can be explained by universal fuel properties such as bulk density and moisture content. Across my burn experiments, the greatest depth of burn occurred in peat with high bulk density and low moisture. Low bulk density can often limit combustion. The propagation of combustion into deeper peat layers requires sufficient heat transfer to ignite lower soil layers, and combustion of low density fuels like Sphagnum often cannot generate enough energy to ignite wetter, higher density peat below [Frandsen, 1987; Miyanishi and Johnson, 2002; 20

27 Thompson and Waddington, 2014]. In my experiments, increases in bulk density promoted combustion until the peat was too wet to ignite, at a VWC of approximately 0.04 m 3 m -3. Relationships between bulk density and moisture content can be complex in organic soils, as changes in bulk density affect the water storage capacity of peat [Benscoter et al., 2011]. For example, drier peatlands with lower water tables tend to have peat with greater bulk density, as a result of peat subsistence and deformation [Whittington and Price, 2006; Waddington et al., 2010]. Field studies of bulk density variation in the upper 0-30 cm of peat columns show a similar variation in the bulk densities of my samples ( g cm -3 ), indicating that this bulk density effect on burning is applicable in real-life peatfire scenarios [Thompson and Waddington, 2014]. Additionally, the range in depth of burn of peat observed in my experiments are consistent with those seen in laboratory [Benscoter et al., 2011] and field studies [Turetsky and Wieder, 2001; Turetsky et al., 2002]. My results suggest that such changes would increase the vulnerability of peat soils to deeper burning. My results reinforce known controls on depth of burn, but also show that the interaction between bulk density and water content is an important control on aspects of gaseous C emissions from burning peat. These findings suggest that the same general controls on depth of burn can be used to predict total gaseous C, CO 2 and CO emissions. I predicted that CO 2 emissions would decrease and that CO emissions would increase in wetter soils due to greater incomplete combustion. Contrary to this prediction, I found a positive correlation between CO 2 and CO emissions. These findings suggest that increases in the smouldering potential of peat, under conditions of increased bulk density and soil moisture content, causes greater emissions of total C release as both CO 2 and CO. 21

28 The generally accepted emission ratios of CO:CO 2 and CH 4 :CO 2 for smouldering combustion are approximately 12-14% and %, respectively [Crutzen et al., 1979; Nance et al., 1993; Cahoon et al., 1994; Cofer et al., 1998; van der Werf et al., 2010]. These values are used frequently to estimate total CO and CH 4 emissions from wildfires in the boreal forest [Andreae and Merlet, 2001; Kasischke and Bruhwiler, 2002; French et al., 2003] and to estimate the climatic impacts of fire released C [Randerson et al., 2006]. While some laboratory studies have found greater emission ratios (20-50% and 2-7% for CO:CO 2 and CH 4 :CO 2 ), these studies examined burning peat from Indonesian peatlands and are not applicable to estimating C emissions from boreal peatlands [Muraleedharan et al., 2000; Chand, 2005; Hamada et al., 2013]. Emission ratios during my study averaged 25 ± 3.1% for CO:CO 2 and 4 ± 0.7% for CH 4 :CO 2, both greater than those used in previous studies to estimate emissions from burning boreal peat (Table 11). Thus, my results indicate that previous studies estimating boreal wildfire C emissions are likely underestimating trace gas emissions. The emission ratios and factors used to estimate fire C emissions stem from estimates that combine flaming and smouldering [Andreae and Merlet, 2001; Randerson et al., 2006]. Studies tend to make assumptions on the contribution of flaming and smouldering to C release. For example, Kasischke & Bruhwiler [2002] and French et al. [2003] assumed that 20% of belowground C is released through flaming combustion and the rest attributable to smouldering. Using CO 2 and CO concentration measurements from an infrared system Hadden [2011] found that smouldering produced >85% of CO 2 and >95% of CO from burning peat. While distinguishing between flaming and smouldering combustion during my experiments was beyond the scope of this thesis, the relatively low temperatures observed (<600 ºC) suggest that smouldering dominated the combustion reactions [Rein et al., 2009], with a majority of C 22

29 emissions attributable to smouldering. My results, along with the findings of Hadden [2011], show that the 20/80 (flaming/smouldering) ratio of belowground C release may underestimate the amount of C released through smouldering combustion in peatlands. This may explain why trace gas emissions, standardised by CO 2 emissions, produced in my study were higher than previously expected. Due to the greater water content and bulk density, the conditions of belowground organic soils are favourable for supporting smouldering combustion. The positive correlation between CO 2 and CO shows that, not only are trace gas emissions greater during smouldering, but that total C emissions are greater as well. This lends further support to the idea that smouldering combustion in belowground organic matter is responsible for more than 80% of belowground C release Peat fire Hg emissions and relationships with C loss Peat has been found to contain Hg stocks 10 times greater than those in upland soils, due mostly to the thickness of the organic soil layer in peatlands [Turetsky et al., 2006]. This suggests that while peatlands are a considerable Hg sink, these Hg stocks stored in peat could become vulnerable to release from disturbance. Recent studies have pointed to wildfires as major sources of Hg contamination [Obrist et al., 2008; Witt et al., 2009]. Harden et al. [2004] showed that C and Hg losses from soil following burning of the forest floor were correlated, suggesting that Hg is released as soil organic matter is combusted. My results showed a similar correlation between C and Hg emissions (Pearson correlation coefficients; this study: 0.30, Harden et al. [2004]: 0.23). Because Hg is more vulnerable to combustion than C, owing to its much lower boiling point, complete loss of Hg from peat has been observed following wildfire [Friedli et al., 2001; Harden et al., 2004]. Similarly, I measured near-complete Hg loss (>70%) from post-burn ash samples. My results showed that GEM was the most prominent Hg species released from 23

30 smouldering peat, not PHg as was predicted. In fact, PHg release was significantly less than GEM across all of my burn experiments. While previous studies have observed similar proportions of GEM (>95%), wetter soils have been found to produce up to 50% PHg [Obrist et al., 2008]. Obrist et al. [2008] found smouldering combustion to be associated with greater quantities of PHg, however the fuels used in this study was leaves, branches, and pine needles not moss or peat. My results show that this may not be the case for organic soils. Although incomplete combustion is occurring in the smouldering reaction, indicated by the production of CH 4 and CO, temperatures above the boiling point of Hg, 357 ºC, were still reached. Regardless of combustion efficiency, as long as temperatures are high enough to volatilize Hg, little PHg will be released. Bulk density was the only predictor of variation in both THg and GEM, while there were no significant predictors of PHg release. Together, my results suggest that total C emissions and the C emission ratios from burning peat were more sensitive to variation in moisture content than was Hg release. My results supported the prediction that Hg release would be more strongly correlated to CO production relative to other C gas species. This finding supports previous measurements from airborne smoke plume studies [Friedli, 2003; Finley et al., 2009]. Bulk density was an important predictor of variation in both CO and THg release; however this was also the case for CO 2 emissions. While I had originally predicted that Hg release and CO would both increase as products of smouldering combustion, my results showing positive correlations between CO 2 and CO emissions mean that this prediction was overly simple. Previous studies have suggested the correlation between CO and Hg is a result of both sharing a common source [Finley et al., 2009]. A likely explanation for the positive relationship between Hg and CO is that soil layers with high Hg concentrations tend to be highest in denser soil layers with high organic matter content, 24

31 which tend to occur at depth in peatlands [Grigal, 2003; Juillerat et al., 2012]. Limited oxygen available for oxidation in these deeper peat layers causes deeper peat layers will increase the likelihood of smouldering combustion, triggering increases in the proportion of C being emitted as CO [Rein et al., 2009] as well as Hg. To measure C and Hg loss associated with burning, previous studies have quantified changes in soil stocks before and after fire using field collection of pre- and post-burn soil and ash samples [Harden et al., 2004], or have used changes in emission gas concentrations during experimental burning in the laboratory to measure losses (Table 12) [Obrist et al., 2008]. In this study, I estimated C and Hg losses during burning using both approaches. I found that, in general, estimates of both C and Hg loss via changes in soil stock were greater than what was estimated from gas measurements for both C and Hg. This finding suggests that studies using soil stocks to estimate the chemistry of peat fire emissions are overestimating emissions. My calculations of pre- and post-burn C and Hg stocks used a mean bulk density for each 5 cm layer. It is possible that these depth increments were too large to accurately portray changes in bulk density across the peat profile and resulted in over-estimating soil C and Hg losses. I calculated the mass of gas emissions from the recorded concentrations of each gas, measured every second. The frequency of these measurements may lead to an underestimate of the mass of emissions, especially in periods of high gas production. It is possible that these factors led to the discrepancy between the emissions estimates between these two approaches Conclusions My findings indicate that bulk density and water content are the most important factors driving depth of burn as well as total gaseous C, CO 2, and CO emissions from smouldering peat. These 25

32 parameters need to be included in models to accurately predict burn severity and potential C emissions from wildfire in peatlands. These predictions have long been used to understand combustion patterns in boreal forests, suggesting that controls on the burning of thick peat deposits can be understood using traditional fire science tools. However, my study showed no clear controls on CH 4 emissions from burning peat, suggesting that the trace gases produced during smouldering, CO and CH 4, may not be governed by the same fuel properties. Across emissions constituents, I found that the proportion of these trace gases (as indicated by CO:CO 2 and CH 4 :CO 2 ratios) were greater than what has been used in modeling studies. This suggests that the quantity CO and CH 4 from burning peatlands is underestimated in these studies. Overall, this study provides some basic insight into the relationships between C and Hg loss during peat fires. However, more research needs to be conducted on the controls of Hg release in smouldering peat as well as the fate of Hg released from burning peat [Fitzgerald and Mason, 1998]. Some fire-emitted Hg is likely vulnerable to long-range transport, though some may fall out locally, where it could be rebound by soil organic matter or deposited to aquatic where it could be transformed to methylmercury [Zillioux et al., 1993; Grigal, 2003]. Thus, peat fires are likely to serve as important disturbances that could diminish the strength of biogeochemical sinks in peatlands, but also could serve as a mechanism for distributing Hg across the landscape, potentially moving Hg from ecosystems with high preservational environments like peatlands to systems more vulnerable to biomagnification. 26

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43 Table 2. Results of the most parsimonious model for predicting variation in depth of burn during the burn experiments. A comparison of candidate models and their AICc values can be found in Appendix Table A1. VWCxBD represents the interaction between volumetric water content and bulk density. Model parameters β ± SE t-value P-value Volumetric water content ± Bulk density 0.75 ± < VWC:BD ± <

44 Table 3. Results of correlation analyses between of C gases and THg. Values are Pearson correlation coefficients and p values in parentheses. CH 4 CO CO 2 THg CH (0.002) (0.004) (0.02) CO (0.002) (0.001) (0.0002) CO (0.004) (0.001) (0.0009) THg (0.02) (0.0002) (0.0009) - 38

45 Table 4. Results of regression analyses analysing the effect of depth of burn on total C emissions (gc) and cumulative CO 2, CO, and CH 4 emissions during the burn experiments. Model β ± SE Intercept F R 2 p-value Total C 2.5± ± CO 2 5.5± ± CO 2.1± ± CH ± ±

46 Table 5. Results of the most parsimonious model, determined using AICc values across a number of candidate models, predicting variation in total C emissions in grams of C and cumulative CO 2, CO, and CH 4 emissions in grams during the burn experiments. A comparison of candidate models and AICc values across all models can be found in Appendix Tables A2, A3, A4, and A5. VWC corresponds to volumetric water content, BD to bulk density, and VWCxBD to the interaction between volumetric water content and bulk density. Gas Parameters β ± SE t stat p-value Total carbon Intercept -25.0± VWC ± BD 2.6± VWCxBD -13.2± CO 2 Intercept -52.6± VWC -25.6± BD 5.5± VWCxBD -32.3± CO Intercept -23.5± VWC ± BD 2.2± < VWCxBD -8.9± CH 4 Intercept 1.7± VWC -8.2±

47 Table 6. Results of models predicting variation in C emissions, comparing results using depth of burn as a predictor versus the most parsimonious model. The most parsimonious model for total C, CO 2, CO, CH 4 was selected using a comparison of AICc values amongst several candidate models. Parameters of the most parsimonious models and their significance are located in Table 5. A comparison of all candidate models and AICc values across all models can be found in Appendix Tables A2, A3, A4, and A5. VWC corresponds to volumetric water content, BD to bulk density, and VWCxBD to the interaction between volumetric water content and bulk density. Depth of burn only Response variable AICc R 2 p-value AICc Total C CO CO Most parsimonious models Model parameters VWC, BD, VWCxBD VWC, BD, VWCxBD VWC, BD, VWCxBD R 2 p-value CH VWC

48 Table 7. Results of regression analyses using depth of burn as a predictor of THg, GEM, and PHg in ng release during the burn experiments. Model β ± SE Intercept F R 2 p-value THg 0.24± ± GEM 0.24± ± PHg ± ±

49 Table 8. Results of the most parsimonious model, determined using AICc values across a number of candidate models, predicting variation in THg, GEM, and PHg release in ng during the burn experiments. A comparison of candidate models and their AICc values can be found in Appendix Tables A6, A7, A8, and A9. BD represents bulk density and VWC represents volumetric water content. Gas Parameters β ± SE t stat p-value THg Intercept -1.0± BD 0.14± GEM Intercept -1.1± BD 141.6± PHg Intercept ± < VWC -0.69±

50 Table 9. A comparison of models used to analyse variation in Hg release during the burn experiments. The most parsimonious model for THg, GEM, and PHg was selected using a comparison of AICc values amongst several candidate models. Parameters of the most parsimonious models and their significance are located in Table 8. A comparison of all candidate models and their AICc values can be found in Appendix Tables A6, A7, and A8. BD represents bulk density and VWC represents volumetric water content. Depth of burn only Most parsimonious model Response variable AICc R 2 p-value AICc Model Parameters R 2 p-value THg BD GEM BD PHg VWC

51 Table 10. Comparison of estimated soil C and Hg losses with the measured emissions during the experimental burning of peat. Average mass loss C (g) Hg (ng) Estimated soil loss Measured emissions

52 Table 11. A synthesis of previously reported average emission ratios of CO:CO 2 and CH 4 :CO 2 compared against the average emission ratios from this study. CO:CO 2 (%) CH 4 :CO 2 (%) Data collection method This study Laboratory experiments Crutzen et al Synthesis Nance et al Airborne sampling Cahoon et al Satellite imagery analysis Cofer et al Airborne sampling van der Werf et al Satellite imagery analysis Muraleedharan et al. 2000* Laboratory experiments Chand, 2005* 40.6 N/A Laboratory experiments Hamada et al. 2013* Field/ground sampling * Studies measuring emissions from Indonesian wildfires or emissions from Indonesian peat 46

53 Table 12. A comparison of average C and Hg soil losses and average Hg emissions from this study to previous studies. C (% soil loss) Hg (% soil loss) Hg emissions (ng/m 3 ) This study 22.1± ± Harden et al >60 >70 - Obrist et al

54 7.0 Figures Figure 1. A: Photograph of the peat monolith inside the ceramic fibreboard burn box prior to ignition. B: Schematic of the side view diagram of ceramic fibreboard burn box indicating dimensions and in-fuel thermocouple placement. 48

55 Figure 2. Boxplot of depth of burn in hollow and hummock peat following the burn experiments. Boxes represent the range of depth of burn that fall within the first and third quartiles. The solid line represents the median depth of burn for each microtopography class. The single point above the hollow box represents one hollow peat block that experienced greater depth of burn greater than 1.5 IQR above the median. 49

56 Figure 3. The effect of vegetation type on depth of burn across all burn experiments (A) and for combustion of hummock peat only (B). Boxes represent the range of depth of burn that fall within the first and third quartiles. The solid line represents the median depth of burn. 50

57 Figure 4. A: The effect of bulk density on depth of burn in peat during combustion. Point colour corresponds to variation in volumetric water content (VWC) prior to combustion. B: The effect of bulk density on depth of burn in peat parsed by peatland type and fuel species. For visual purposes one data point point with very high bulk density and water content at a depth of burn of 1 cm is not shown; this point was included in all analyses. 51

58 Figure 5. Example of a time series of CO 2, CO, and CH 4 emissions during the experimental burning of peat. This general trend was observed in all experiments. 52

59 Figure 6. The effect of depth of burn on total gaseous C (A), CO 2 (B), CO (C), and CH 4 (D) emissions from the burn experiments. Model and effect statistics are shown in Table 4. 53

60 Figure 7. Plots of the key determinants of variation in C gas emissions from peat during combustion. The models for the total mass of C emitted (panel A), CO 2 (panel B), and CO (panel C) were best predicted by bulk density and the interaction between bulk density and volumetric water content. CH 4 emissions were best predicted by volumetric water content. Parameter statistics for the models predicting variation of each gas are located in Table 5. For visual purposes a point with very high bulk density and water content at a depth of burn of 1 cm is not shown; this point was included in all analyses. 54

61 Figure 8. The relationship between depth of burn and THg (A), GEM (B), and PHg (C). Statistics for these relationships can be found in Table 8. 55

62 Figure 9. The relationships between cumulative emissions of CO 2, CO, and CH 4 and THg release from smouldering peat across. Pearson correlation coefficients for these relationships can be found in Table 3. 56

63 Figure 10. Plots of the parameters included in the most parsimonious models for predicting variation in Hg release from peat during combustion. The models for the THg (A) and GEM (B) were best predicted by bulk density. Particulate Hg (C) release was best predicted by volumetric water content. Parameter statistics for the models predicting variation of each Hg species are located in Table 9. 57