UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

The effects of biochar application on carbon dioxide and methane soil surface fluxes Webster, Cameron 2014

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2014_spring_webster_cameron.pdf [ 2.27MB ]
Metadata
JSON: 24-1.0166863.json
JSON-LD: 24-1.0166863-ld.json
RDF/XML (Pretty): 24-1.0166863-rdf.xml
RDF/JSON: 24-1.0166863-rdf.json
Turtle: 24-1.0166863-turtle.txt
N-Triples: 24-1.0166863-rdf-ntriples.txt
Original Record: 24-1.0166863-source.json
Full Text
24-1.0166863-fulltext.txt
Citation
24-1.0166863.ris

Full Text

  THE EFFECTS OF BIOCHAR APPLICATION ON CARBON DIOXIDE AND METHANE SOIL SURFACE FLUXES  by Cameron Webster B.A.Sc., Mount Royal University, 2011  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Resource Management and Environmental Studies)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2014 ? Cameron Webster, 2014 ii  Abstract Soils contain the largest terrestrial organic carbon (C) stock, representing two-thirds or more of terrestrial C.  Soils can act as a source or sink for carbon dioxide (CO2) and methane (CH4).  One common technique for studying soil surface effluxes of CO2 (FCO2) and of CH4 (FCH4) is the soil chamber.  This involves placing an enclosure over the soil surface and measuring the change in headspace concentration of the gas of interest over time.  Due to the air-filled pore spaces within the near-surface soil, and adsorption of gases of interest onto chamber walls, the effective volume (Veff) of the chamber which contributes to FCO2 and FCH4 measurements is generally higher than the geometric volume (Vg) of the chamber.  It is necessary that Veff be known in order to estimate fluxes accurately.  This study coupled a flow-through non-steady-state automated chamber system to a laser-based cavity ring-down spectrometer (CRDS) to estimate Veff of the chamber system using separate standard additions of CO2 and CH4 calibration gases.   The system was then mounted onto soil cylinders which had been filled with a forest soil from Vancouver Island, British Columbia, Canada.  There has been recent interest in the ability of biochar to provide multiple environmental benefits upon application to soil, including the long-term sequestration of C.  There are conflicting studies as to the effect of biochar on FCO2 and FCH4 and overall greenhouse gas (GHG) emissions. After making background measurements of FCO2 and FCH4 in soil columns, biochar was applied to one of the columns and the resulting FCO2 and FCH4 were measured.  The results from this study showed that the coupling of the CRDS to the automated chamber system proved to be successful.  The estimated Veff during CO2 and CH4 calibration gas injections agreed with past studies as the Veff was 5 to 10% larger than the geometric volume of the chamber.  Following biochar application, the iii  amended soil produced 36.9% more CO2 and consumed 20.4% less CH4 than the control over the four month experiment.   The results showed that soil water content was an important factor in controlling FCO2 and FCH4 following biochar amendment.   iv  Preface   This thesis is original, unpublished, independent work by the author, C.Webster.              v  Table of Contents Abstract ............................................................................................................................................ ii Preface ............................................................................................................................................ iv Table of Contents ............................................................................................................................. v List of Tables ................................................................................................................................. viii List of Figures .................................................................................................................................. ix List of Symbols and Acronyms ....................................................................................................... xii Acknowledgements ...................................................................................................................... xiv Dedication ...................................................................................................................................... xv 1. Introduction............................................................................................................................ 1 1.1 Soil Respiration ............................................................................................................. 3 1.1.1 CO2 Respiration ................................................................................................. 3 1.1.2 Methane Emission and Consumption ............................................................... 4 1.2 Mechanisms for Surface Gas Exchanges....................................................................... 5 1.2.1 Mass Flow ......................................................................................................... 5 1.2.2 Molecular Diffusion ........................................................................................... 6 1.2.3 Influence of Soil Moisture Content and Temperature ..................................... 6 1.3 Chamber Systems ......................................................................................................... 7 1.4 Chamber Types ............................................................................................................. 8 1.5 Effective Volume ......................................................................................................... 10 1.6 Biochar ........................................................................................................................ 11 1.6.1 Biochar and GHG Emissions ............................................................................ 12 1.7 Research Objectives .................................................................................................... 13 2. Materials and Methods ........................................................................................................ 14 2.1 Chamber Design .......................................................................................................... 14 2.1.1 Environmental Conditions .............................................................................. 17 2.2 Chamber Effects on the Environment ........................................................................ 17 2.2.1 Soil Temperature............................................................................................. 18 2.2.2 Air temperature .............................................................................................. 18 vi  2.2.3 Humidity .......................................................................................................... 19 2.2.4 Pressure fluctuations ...................................................................................... 19 2.2.5 Chamber Air Mixing Regime ........................................................................... 20 2.2.6 Leaks and Soil Disturbance ............................................................................. 21 2.3 Testing Chamber Accuracy ......................................................................................... 21 2.4 Soil Cylinder Construction........................................................................................... 22 2.4.1 Soil ................................................................................................................... 23 2.4.2 Ancillary Measurements ................................................................................. 24 2.4.3 External Fans ................................................................................................... 24 2.5 Effective Volume Determination ................................................................................ 25 2.5.1 Calibration Gas Injection ................................................................................. 25 2.5.2 Flux estimation ................................................................................................ 28 2.5.3 Quality Control Procedures............................................................................. 30 2.5.4 Statistical Analyses .......................................................................................... 31 2.6 Effects of Biochar on FCO2 and FCH4 ............................................................................. 31 2.6.1 Biochar Characteristics ................................................................................... 31 2.6.2 Application of Biochar ..................................................................................... 31 2.6.3 Watering System ............................................................................................. 32 2.6.4 Watering Events .............................................................................................. 34 2.6.5 Water Collection ............................................................................................. 36 2.6.6 Automated Chamber Timing ........................................................................... 37 2.6.7 Comparing the Two Chambers ....................................................................... 37 2.6.8 Effect of Soil Moisture Content ...................................................................... 38 3. Results and Discussion ......................................................................................................... 40 3.1 Testing Chamber Accuracy ......................................................................................... 40 3.2 Effective Volume Estimation....................................................................................... 42 3.2.1 CO2 Injection Results ...................................................................................... 42 3.2.2 CH4 injection results ........................................................................................ 45 3.2.3 Issues and Future Research Surrounding Effective Volume Estimation ......... 48 vii  3.3 Effect of ? and Biochar on FCO2 and FCH4 ..................................................................... 49 3.3.1 Effects of ? ...................................................................................................... 49 3.3.2 Chamber 2 as a Model for Chamber 1 ............................................................ 51 3.3.3 FCO2 Spike Following Biochar Application ....................................................... 54 3.3.4 Electrical Conductivity (EC) ............................................................................. 56 3.3.5 Post-Application: Biochar vs. Control Results ................................................. 58 3.3.6 Future Implications ......................................................................................... 67 4. Conclusions........................................................................................................................... 69 References ..................................................................................................................................... 71    viii  List of Tables Table 1.  The watering schedule during background (pre-biochar application) measurements.  The amount of applied water was decreased to 6 L after it was noticed that previous amounts of 10 L were creating some standing water on the soil surface during application.  It was determined that 6 L was a reasonable amount to apply in order to simulate a rain event. ........................................................................ 35 Table 2.  The watering events which took place after biochar application (July 29, 2013).  The application of 6 L generally took three to four hours to complete. ...................... 36 Table 3.  A half-hourly schedule of the chamber timing.  Note that each chamber closed once in a half hour period.  Time between closures allowed the chamber to return to the ambient concentration as well as create well-mixed ambient conditions for the next chamber measurement. ........................................................................................ 37 Table 4.  Results of CO2 flux calibration.  Injections were done with a 20.07% by volume CO2 calibration gas injected at a rate of 15 mL min-1 and the chamber volume assumed to be 65.5 L.  The root-mean-square error (RMSE) for chambers 1 and 2 represent 4.7% and 8.4% of the averages, respectively (n = 9 for chamber 1 and n = 12 for chamber 2). ....................................................................................................................................... 40 Table 5.  Results of CH4 flux calibration injection replications.  Injections were done with a 0.10% by volume CH4 calibration gas injected at a rate of 15 mL min-1 and the chamber volume assumed to be 65.5 L.  The RMSE for chambers 1 and 2 represent 6.1% and 4.9% of the averages, respectively (n = 12 for chamber 1, and n = 9 for chamber 2). ................................................................................................................... 41 Table 6.  The average effective volumes of each chamber calculated using a 20.07% by volume CO2 calibration gas injected at 15 mL min-1.  Each replication consisted of two flux measurements, one with only the soil contributing to the flux measurement, and immediately following that, a second measurement with soil and the injection of the calibration gas contributing to the flux measurement (n=5 for chamber 1 and n=7 for chamber 2).  The effective volumes were larger than the geometric volume of 65.5 L. ....................................................................................................................................... 42 Table 7.  The average effective volumes of each chamber calculated using a 0.10% by volume CH4 calibration gas injected at 15 mL min-1.  Each replication consisted of two flux measurements, one with only the soil contributing to the flux measurement, and immediately following that, a second measurement with soil and the injection of the calibration gas contributing to the flux measurement (n = 3 for both chamber 1 and chamber 2). ................................................................................................................... 45  ix  List of Figures Figure 1.  Close-up of the chamber dome showing the vent tube and PVC collar. ............... 15 Figure 2.  Basic schematic of the chamber system. ................................................................ 16 Figure 3.  The chamber mounted on the aluminum base.  The calibration gas was injected at a flow rate of 15 mL min-1 and the resulting flux was estimated using a linear regression applied to the headspace concentration change over time. ...................... 22 Figure 4.  The chamber system attached to the PVC soil cylinders and the base.  Each cylinder was filled with approximately 146 kg of forest soil which was packed to an average bulk density of approximately 1350 kg m-3. .................................................... 23 Figure 5.  A typical plot of the headspace concentration (mole fraction) of CO2 (ppm or ?mol (mol dry air-1)) over time.  The first 20 seconds were disregarded and the flux calculation zone spanned 100 seconds (e.g. from 20 to 120 seconds).  A linear model was applied and the slope of the headspace concentration change was used to estimate FCO2. ................................................................................................................ 29 Figure 6.  A typical CH4 headspace concentration curve over time.  The negative slope indicates that the activity of methanotrophs outnumbered the activity of methane generating microorganisms (methanogens). ................................................................ 30 Figure 7.  The biochar was evenly spread across the surface of the soil and then incorporated into the top 5 cm using a small garden rake.  Care was taken to ensure even distribution as well as to avoid disturbing the sensors located at the 5 to 10 cm depth. ............................................................................................................................ 32 Figure 8.  The watering system attached to the chambers.  The FCO2 and FCH4 measurements were continuously made during the watering events. ................................................. 33 Figure 9.  The soaker hose released small droplets at a rate of 10 mm per hour.  This allowed for an even distribution of the water and simulated a heavy rain event. ...... 34 Figure 10.  A typical replication of the soil (interval a), a return to baseline ambient concentration levels (interval b) and soil plus calibration gas injection (interval c) CO2 headspace concentration curves.  Note the small increase in headspace CO2 concentration over interval a which has only the soil as a contributor.  The much larger increase in headspace CO2 concentration over interval c has both the soil and the calibration gas injection as contributors (n = 5 and n = 7 for chambers 1 and 2, respectively). ................................................................................................................. 44 Figure 11.  A typical trace during CH4 flux measurement on soil (interval a), a return to background ambient concentration (interval b) and soil plus calibration gas injection (interval c) CH4 headspace concentration curves.  Note the slight headspace concentration CH4 decrease during interval a and the headspace concentration CH4 x  increase during interval c.  An expanded plot showing the decrease can be seen in Figure 12. ....................................................................................................................... 46 Figure 12.  An enlargement of Fig. 11 showing the decrease in CH4 headspace concentration in more detail.  The small increase at interval a can be attributed to pressure and turbulence artifacts during chamber closure which were not included in the flux calculation. .................................................................................................................... 47 Figure 13.  The change in FCO2 as ? changes.  As ? increases, there is a smooth increase and decrease with a maximum FCO2 at a ? of approximately 0.23 m3 m-3. ......................... 50 Figure 14.  The change in FCH4 as ? changes.  There is an increase in consumption with increasing ? to a point, after which the consumption decreases with a maximum CH4 consumption at a ? of approximately 0.26 m3 m-3. ...................................................... 51 Figure 15. The linear relationship between chambers 1 and 2 during FCO2 background measurements.  A strong linear relationship (R2 = 0.93, Syx = 0.13) is present.  The modeled results based on chamber 2 later became the control and chamber 1 received the biochar application and became the biochar chamber. .......................... 52 Figure 16.  The linear relationship between chambers 1 and 2 during FCH4 background measurements.  A strong linear relationship (R2 = 0.80, Syx = 0.0001) is present.  The modeled results based on chamber 2 later became the control and chamber 1 received the biochar application and became the biochar chamber. .......................... 53 Figure 17.  A noticeable spike in FCO2 following biochar application.  The gap before the spike indicates when biochar and then water was being applied and other activity around the chamber.  These measurements were disregarded as they represented effluxes not related to the soil. ................................................................................................... 55 Figure 18.  The FCO2trace of the control chamber followed a normal pattern after the application of water immediately following biochar application. ................................ 56 Figure 19.  Following application of the biochar, a noticeable spike in EC of the soil water was observed................................................................................................................. 57 Figure 20.  A smaller EC spike was present in the control chamber.  This smaller spike mimics the normal EC spike during watering events. ................................................... 58 Figure 21.  The CO2 emitted (g m-2 h-1), CH4 consumed (g m-2 h-1), and the GWP (g CO2 eqv m-2 yr-1) over the post-application period (July 29 to December 21, 2013) by both the biochar amended chamber and the control.  The biochar results show an increase in g CO2 emitted, a decrease in g CH4 consumed and an overall higher mean GWP than the control. .......................................................................................................................... 59 Figure 22.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 h-1) over the wet-interval post-application period by both the biochar amended chamber and the control.  The biochar results show an increase in g CO2 emitted, a xi  decrease in g CH4 consumed and a higher GWP than the control.  The wet interval was designated by ? greater than 0.30 m3 m-3.  This resulted in 818 hourly flux measurements being included in this analysis. ............................................................ 61 Figure 23.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 h-1) over the mid-interval post-application period by both the biochar amended chamber and the control.  The biochar results show a slight increase in g CO2 emitted, a slight decrease in g CH4 consumed and a higher GWP than the control.  The mid interval was designated by ? between 0.22 and 0.30 m3 m-3.  This resulted in 738 hourly flux measurements being included in this analysis. .......................................... 62 Figure 24.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 h-1) over the dry-interval post-application period by both the biochar amended chamber and the control.  The biochar results show an increase in g CO2 emitted an increase in g CH4 consumed and a higher GWP than the control.  The dry interval was designated by ? less than 0.22 m3 m-3.  This resulted in 773 hourly flux measurements being included in this analysis. ...................................................................................... 63               xii  List of Symbols and Acronyms Symbol/Acronym Units Definition A m2 area c mol m-3 concentration of species of interest ca mol mol-1 ambient concentration ccal mol (mol air)-1 concentration of calibration gas c? mol mol-1 concentration of species of interest C  carbon CO2  carbon dioxide CO2 eqv  carbon dioxide equivalents CH4  methane CRDS  cavity ring-down spectrometer Do m2 s-2 binary molecular diffusion coefficient in air EC dS m-1 electrical conductivity F umol m-2 s-1 general soil surface efflux FCO2 umol m-2 s-1 soil CO2 efflux FCH4 umol m-2 s-1 soil CH4 efflux fm mol m-2 s-1 mass flow fd mol m-2 s-1 molecular diffusion FT  flow-through h hour hour Ha % humidity of headspace air IRGA  infrared gas analyzer k m2 air permeability of soil NFT  non-flow-through NSS  non-steady-state O2  oxygen Pa kPa atmospheric pressure RMSE  root-mean-square error Rs  soil respiration SE  standard error SS  steady state sx mol (mol dry air)-1 mixing ratio for species x xiii  Symbol/Acronym Units Definition Syx  standard error of the regression Ta ?C air temperature Ts ?C soil temperature TaK K absolute air temperature in Kelvin V L or m3 volume Veff L or m3 effective volume Vg L or m3 geometric volume yr  year ? m3 m-3 volumetric water content ? mol m-3 molar density of air ?b kg m-3 bulk density of soil ? m3 m-3 soil porosity ? kPa matric soil water potential    xiv  Acknowledgements  I am very grateful to the Ecoyhydro Lab Group for all their support throughout this project.  Many thanks to the Biometerology Group and the Faculty of Land and Food Systems at the University of British Columbia for the technical and academic assistance.  I would like to thank the financial assistance I received from the British Columbia Innovation Council Natural Resources and Applied Sciences Endowment Fund.   I owe a huge amount of gratitude to Iain Hawthorne, who taught me many of the skills and construction techniques that I used in my project.  Sincere thanks to Zoran Nesic, Rick Ketler, Andy Black, and Paul Jassal for huge technical assistance and always being around whenever I had a problem or a question. And a big thank you to Mark Johnson, who helped and encouraged me enormously along the way.    Finally, special thanks to my family and friends.  The support I received throughout this phase of my life was really special to me and I will always remember it.  And a big thank you to Carol Barrio, I couldn?t have done it without you.                xv  Dedication  Para Carol T?, has sido mi apoyo, la alegr?a y la luz que me ha guiado en los momentos dif?ciles. No podr?a haber seguido adelante sin ti. S? que el futuro nos tiene reservados grandes momentos.   Para siempre,  Te Quiero              1  1. Introduction Soils contain the largest terrestrial organic carbon (C) stock (Bolin et al., 2000), representing two-thirds or more of terrestrial C (Schimel et al., 1994; Tarnocai et al., 2009).  Soils can act as a source or sink for carbon dioxide (CO2) and methane (CH4).  The fluxes of these gases are the result of biological processes and therefore have high spatial and temporal variability (Giltrap et al., 2009).   The net rate of CO2 exchange per unit of cross-sectional surface area is known as soil CO2 efflux (FCO2) or soil respiration (Rs).  Globally it is estimated at approximately 75 Pg C annually and is one of the largest fluxes in the carbon cycle (Schlesinger and Andrews, 2000).  FCO2 measurements have a long history and have been measured for over 80 years (Vargas et al., 2010).  The mechanisms influencing FCO2 are complex due to the fact that multiple controlling mechanisms occur over various temporal and spatial scales (Hutchinson & Mosier, 1981).  These temporal scales can range from hours to millennia.  Furthermore, numerous biological and physical factors are responsible for controlling FCO2.  This has led to challenges in predicting future atmospheric CO2 concentrations under different climate scenarios as well as under different local soil conditions (Schlesinger & Andrews, 2000; Heimann & Reichstein, 2008; Vargas et al., 2010).   One common technique for studying FCO2 and the soil surface efflux of CH4 (FCH4) is using soil chambers.  This involves placing an enclosure over the soil surface and measuring the change in headspace concentration of the gas of interest over time (Hutchinson & Livingston, 1995).  When undertaking chamber measurements, it has been noticed that due to the air-filled pore spaces within the near-surface soil, and adsorption of gases of interest onto chamber walls, the volume of the chamber which contributes to FCO2 and FCH4 measurements is higher than that of the geometric 2  volume (Vg) of the chamber (Rayment, 2000; Jassal et al., 2012b).  As the concentration of the species of interest increases in the air-filed pore spaces, these pore spaces essentially act as part of the chamber.  This extra volume, combined with the geometric volume, is known as the effective volume (Veff).  Due to the fact that the volume of the chamber is an essential element in the estimation of FCO2 and FCH4, it is imperative that the volume contributing to the change in headspace concentration be known. There has been recent interest in the ability of biochar, pyrolyzed biomass applied as a soil amendment (International Biochar Initiative, 2011), to provide multiple environmental benefits upon application to soil, including the long-term sequestration of C (Lehmann & Joseph, 2009; Woolf et al., 2010).   There are conflicting studies as to the effect of biochar on FCO2 and FCH4 and overall greenhouse gas (GHG) emissions.  The unknown effects of biochar on FCO2 and FCH4 have created difficulties in assessing the C sequestration potential of biochar as well as the effects of biochar application from a net GHG budgeting perspective. This study aims to accomplish two main goals:  1) To determine the effective volume of a chamber system by coupling the system to a cavity ring-down spectrometer. 2) To determine the effect of biochar amendment on the FCO2 and FCH4 from a forest soil obtained from Vancouver Island, British Columbia, Canada.   3  1.1 Soil Respiration 1.1.1 CO2 Respiration Rs can be defined as the result of two biological processes, autotrophic respiration by plant roots and associated microorganisms, and heterotrophic respiration via microbial decomposition of soil organic matter (Ryan & Law, 2005).  Root respiration can be responsible for anywhere from 10 ? 90 % of total soil respiration.  This often shows a strong diurnal pattern and is regulated by the root biomass as well as the photosynthate transfer from plant leaves (Hanson et al., 2000).  Rs can be used as an indicator of overall biological activity and can also be used as a descriptor of soil quality (Doran & Parkin, 1994).  After soil management practices, Rs may be used to asses early changes in the decomposition rate of soil organic matter (Rochette & Angers, 1999) and is often used to estimate net crop photosynthesis from net ecosystem CO2 exchange (Rochette et al., 1995).   Chamber systems have been used for decades to estimate Rs (Rochette & Hutchinson, 2005).  Principal difficulties when performing chamber analyses are related to changes in: soil and air temperature, CO2 concentration gradients, pressure fluctuations, soil and air moisture, site disturbance, leakage, and air mixing regime (Healy et al., 1996).  Chamber methods are an indirect measure of FCO2 as they rely on headspace gas concentration measurements which only represent the Rs rate under steady state conditions (Hutchinson & Livingston, 1995). Many techniques have been developed to measure the rate of FCO2 including micrometeorological, enclosure, and diffusion theory approaches.  No single approach is applicable to all studies, as each has its own advantages and disadvantages (Norman et al., 1997).  4  Micrometeorological and diffusion theory approaches offer many advantages for quantifying net gas exchange rates, but there are many considerations which must be taken into account when deploying these systems (Healy et al., 1996).   Many research objectives require spatial resolution which is below that of the micrometeorological systems.  Furthermore, diffusion theory applications are also subject to large uncertainties when the sources or sinks of the trace gases are not uniformly distributed, or located too close to the surface for gradients to be measured (Holland et al., 1999).   In the above cases, chamber systems are often applicable.  Chamber systems are often lower cost and easier to set up than alternatives (such as micrometeorological and diffusion theory approaches), which makes them ideal for installing in uneven terrain or locations where access can be a problem (Hutchinson & Livingston, 1995).   1.1.2 Methane Emission and Consumption Estimates of CH4 soil surface fluxes (FCH4) have been obtained in the past (Born, Dorr, & Levin, 1990) where FCH4 was estimated using chamber systems.  Globally, soils are a net sink for CH4, although the uptake capacity of CH4 by soil varies with land use, management practices, and soil conditions (Denman, et al., 2007; Saggar et al., 2007).  Aerobic, well-drained soils are often a sink for CH4.  This is due to the high diffusion rate of CH4 into the soil as well as oxidation by methanotrophic microorganisms (Dalal et al., 2008).  There has been recent interest in the effect of organic amendments including biochar on FCH4 as organic soil amendments have shown promise in decreasing the CH4 emissions in certain soils (Rondon et al., 2005).  Laboratory experiments have resulted in contradictory results depending on volumetric water content (?) (Yanai et al., 2007), soil type, and type of organic amendment used (Spokas & Reicosky, 2009).   5  1.2 Mechanisms for Surface Gas Exchanges When referring to a general soil surface flux, the symbol F will be used.  This will indicate either FCO2 or FCH4.   Flux, F, across the soil-air interface immediately adjacent to the surface can be modelled as the sum of transport due to mass flow (fm), and molecular diffusion (fd) (Equation 1) (Nazaroff, 1992):                   (1) where a positive value of F indicates a net efflux from the soil.  fm can be thought of as the travel down a pressure gradient and fd as the travel down a concentration gradient.  The different components of F vary greatly across different ecosystems and climatic conditions. Mass Flow Mass flow transport in soil (fm, mol m-2 s-1) of gases including CO2 and CH4 occurs due to the pressure difference between soil air and the atmosphere above the soil.  In soils, it can be described by Darcy?s law (Equation 2) (Hutchinson & Livingston, 1995):                        (2) where k is the intrinsic air permeability of the soil (m2), c? (mol mol-1) ,   (mol m3), and ? (Pa s) are the volumetric concentration, density and viscosity, respectively, of the species of interest, respectively, and         is the pressure gradient (Pa m-1).    These pressure gradients can result from air contraction or expansion due to temperature or barometric pressure changes (Schery et al., 1984), through wind flow interactions with the soil surface (Wesley et al., 1989), or through changes in the air-filled pore space from the loss or infiltration of soil water (Ghildyal & Tripathi, 1987).   6  1.2.2 Molecular Diffusion Molecular diffusion is due to random molecular motion down a concentration gradient.  The diffusion flux density (fd, mol m-2 s-1) is described by Fick?s law (Equation 3) (Ghildyal & Tripathi, 1987; Hutchinson & Matson, 1995):                       (3) where Do is the binary molecular diffusion coefficient in air (m2 s-1), c is the concentration of the species of interest (mol m-3) and z is the distance (m).  fd in soils is determined by both the properties of the species of interest and the soil.   The relative importance of the mechanisms for gas exchange (fm and fd) differs with the soil texture.  Soils with low permeability (silts and clays) will most likely have soil gas transport which is dominated by fd whereas in highly permeable, well-drained soils, fm may play a larger role in gas exchange (Hutchinson & Livingston, 1995).   1.2.3 Influence of Soil Moisture Content and Temperature Soil moisture content (?) and soil temperature (Ts) are the major abiotic factors which affect soil respiration (K. A. Smith et al., 2003).  Rain pulses in summer have been shown to not only stimulate soil respiration (Lee et al., 2004), but can also lead to an increase in soil CO2 concentration (Maier et al., 2010).  Furthermore, with an increase in ? from rain, the exchange of gas between soil and atmosphere is reduced, which leads to an accumulation of CO2 in the soil air-filled pores, which in turn leads to a transient CO2 storage (Jassal et al., 2005).  The degree to which soil moisture affects the ability of a soil to transport gas depends on the texture of the soil, with fine-textured soils experiencing a greater reduction in gas transport when compared with a coarser-textured soil.  7  Coarser-textured soils maintain continuity between pore spaces over a larger range of soil moisture contents, which subsequently decreases the reduction in gas transport with increasing soil moisture content (Hutchinson & Livingston, 1995).  1.3 Chamber Systems  Chambers are a simple and reliable method for estimating F by placing an enclosure on the soil surface and measuring the mass balance of a target species within the enclosure.  Chambers are an intrusive method when deployed in the field and it is impossible to install a chamber without some soil disturbance.  Therefore the placement and use of any chamber will modify the flux that was being emitted before the chamber was placed on the soil (Hutchinson & Livingston, 1995).    The biological activity which is responsible for a large portion of F occurs primarily in the top 25 cm of soil (Drewitt et al., 2005).  Soil moisture changes create variations in F due to the impact on the biological gas production rate which strongly influences F (Linn & Doran, 1984).  Changes in air temperature Ta, near surface soil temperature Ts and volumetric water content (?), during deployment affect the value of F in the soil.  Ta and Ts variations are controlled by using short closure times which allow for the thermal inertia of the system to maintain a constant temperature during the F measurement (Rochette & Mcginn, 2005).     8  1.4 Chamber Types Using the system proposed by Livingston and Hutchinson (1995), all soil surface flux chambers fall into one of two categories: steady state (SS) or non-steady state (NSS).  The difference between the two categories is primarily that in a steady state system, F is calculated under a constant headspace concentration and in a non-steady state system, F is calculated under a changing headspace concentration.  Dynamic, static, closed, and open are terms that are also applied to chamber systems (Hutchinson & Livingston, 1995).  These can lead to confusion when used in conjunction with other chamber attributes.  For the remainder of this paper, chamber systems will be described as either SS or NSS.   A SS system assumes that the species of interest concentration gradient across the soil-atmosphere interface is constant, after an initial period of adjustment following chamber deployment.  This steady-state condition is controlled by employing an open-path circulation system to sweep the enclosed volume using a constant flow of ambient air (Hutchinson & Livingston, 1995).  The concentrations of the species of interest in the ambient air entering the chamber and in the air flowing out of the chamber are measured.  Eventually a steady-state is reached, and the difference between the two concentrations is multiplied by the flow rate and divided by the area of soil covered by the chamber to obtain F (Livingston & Hutchinson, 1995).   In contrast, a NSS system causes continual changes to the concentration gradient due to the steady headspace concentration changes within the headspace air.  Following chamber lid-closure the headspace concentration is allowed to increase.  During this increase, samples are taken and the rate of change in the headspace concentration is extrapolated to the pre-lid-closure time by use of a model.  It has been demonstrated that the chamber headspace concentration will not change linearly with time, which is due to a declining difference in concentrations between the soil and the 9  chamber headspace (Healy et al., 1996).  The short closure time of a NSS system, however, can allow for a linear model to be applied as this problem can be assumed to be negligible over short time scales (Jassal et al., 2012b).  Consequently F in a NSS system is determined by multiplying the rate of change in headspace concentration by chamber volume and dividing by the area of soil covered by the chamber.  The NSS system is further broken down into two sub-categories, flow-through (FT) and non-flow-through (NFT).  NFT systems do not have air circulating through the chamber (i.e., air does not leave the chamber, hence it is often referred to as a closed system), and therefore normally rely on discrete samples taken at regular intervals from an extraction port located on the chamber (Rochette & Hutchinson, 2005).  In an FT system, a pump circulates chamber headspace air in tubing that loops from the chamber input and either back into the chamber, or exhausted.  An on-site gas analyzer plumbed into the loop continuously measures the headspace concentration.  A better description of the temporal pattern of the headspace gas concentration results from using this type of system.  Additionally, a shorter closure time is often possible with FT systems as a greater number of measurements are possible.  Other advantages include smaller changes in air and soil temperature, related to the shorter closure time (Livingston & Hutchinson, 1995).   Past studies have shown that there is a difference between the flux estimates from SS and NSS chamber systems.  Norman et al. (1997) showed that there is a consistent difference between SS and NSS chambers with SS chambers consistently recording fluxes which were 10% greater than those recorded by NSS chambers.  A laboratory study conducted by Nay et al. (1994) similarly showed that a NSS chamber system underestimated a simulated F by around 10%.  Rayment (2000) analyzed these differences and concluded that as the headspace concentration changes, so does the concentration within the air-filled pore spaces within the soil.   10  1.5 Effective Volume The volume of the chamber and any associated tubing is easy to determine (this is often referred to as the geometric volume, Vg); however the effective volume (Veff) includes any air-space in the near-surface soil which essentially acts as part of the chamber.  Along with the air-filled pore spaces affecting the volume of the chamber, adsorption of gases of interest onto the chamber walls also contributes to Veff.  As the gas adsorbs to the chamber walls, the surface gas concentration of the walls increases, thus increasing Veff (Goulden & Crill, 1997; Rayment, 2000; Drewitt et al., 2002).  The value of Veff is necessary to calculate F; therefore any error in the estimation of Veff will be directly translated into an error in the calculated F.   Goulden and Crill (1997) developed a method of measuring Veff through standard addition, in which a standard flux measurement was made, and immediately following, a second measurement was made with a calibration gas injected into the chamber at a known flow rate.  The ratio of the rate of addition to the increase in the rate of CO2 rise was used to measure Veff which can be viewed as the product of a ?calibration multiplier? (M) and the geometric volume (Vg), i.e., Veff = MVg.  Veff provided an accurate estimation of the chamber volume which was involved in soil F.  Drewitt et al., (2002) and Jassal et al. (2012a) also used this technique to account for near-surface soil-air spaces as well as the adsorption of CO2 onto the chamber walls.   The present study followed methodologies similar to those in the studies referenced above (e.g., Drewitt et al., 2002; Jassal et al., 2012b) with the added detail of the calculation of the effective volume as it relates to CH4.  This was done because the chamber system used in this experiment measured both CO2 and CH4 simultaneously, so it was necessary to determine Veff using separate CO2 and CH4 injections.  The calculated values of Veff obtained using the CO2 and CH4 injections were then used in the measurement of FCO2 and FCH4, respectively.  A major difference in 11  the experiment outlined in this paper and the studies above is that this experiment took place in a laboratory rather than in an outdoor setting.  1.6 Biochar Biochar is a porous, carbonaceous solid produced during the pyrolysis of biomass (International Biochar Initiative, 2012).  There has been recent interest in the ability of biochar to provide multiple environmental benefits upon application to soil, including the long-term sequestration of C (Lehmann & Joseph, 2009; Woolf et al., 2010).  The predominantly aromatic nature of biochar causes it to be considered a recalcitrant form of C, with residence times ranging from centuries to millennia (Singh et al., 2012).  Some studies have shown that the stability is reduced to the range of a few decades, especially under higher incubation temperatures (? 30?C) (Zimmermann et al., 2012).  Other publications have stated that the residence time is approximately 4000 years (Ameloot et al., 2013).  For comparison, the residence time of below-ground C (non-biochar) increases with depth, with approximate turnover times of: 2 ? 5 years for leaf litter, 5 ? 10 years for root litter, 40 ? 100+ years for humified material, and >100 years for C associated with minerals (Gaudinski et al., 2000).  It has been stated that different feedstocks, pyrolysis temperatures, and environmental conditions all play a large role in the stability of biochar over the long term.  There is currently not a consensus on exactly how biochar degrades over time or the precise mechanisms which control that degradation (Kuzyakov et al., 2014).  Further research is needed to fully determine the potential of biochar as a C sequestration tool and to determine the residence time of biochars made from different feedstocks under different conditions.   12  There has been recent interest in the ability of biochar to increase the activity of methane consuming organisms (methanotrophs) in certain soils.  Sonoki et al. (2013) showed that compost which had been treated with biochar resulted in an increase in methanotrophs during certain phases of the composting process.  The high surface area of biochar has been suggested to increase the microbial activity in biochar-amended soils (Steinbeiss et al., 2009).  In a soil which naturally consumes CH4, this could theoretically increase the quantity of methanotrophs and therefore increase the consumption of CH4 by the soil.  This is of interest from a net greenhouse gases (GHG) perspective as biochar applied to methane consuming soils may have the potential to increase the uptake of methane and therefore sequester more carbon in addition to the recalcitrant C which is applied to the soil (Rondon et al., 2005). 1.6.1 Biochar and GHG Emissions The emission of GHG from soils which have been amended with biochar depend on many conditions such as: biomass type, pyrolysis conditions (temperature, duration), soil type, climatic conditions, and soil physical properties.  (Van Zwieten, et al., 2009; Rogovska et al., 2011; Jones et al., 2011; Bruun et al., 2012).   Recent studies on GHG emissions from biochar-amended soils have resulted in contradictory results.  Some studies  (Zimmerman et al., 2011; Scheer et al., 2011) have shown there to be no change or a slight increase in GHG emissions following biochar application.  Other studies (Spokas & Reicosky, 2009) have shown that the addition of biochar to soils increases both CO2 and CH4 emissions.  An initial increase of CO2 emissions has been shown to have occurred immediately following biochar application.  This has been argued to be a relatively small contribution of C when compared with the overall sequestration quantity (Jones et al., 2011).   13  The mechanisms responsible for governing the effects of biochar on GHG soil emissions are not clearly understood (Castaldi et al., 2011; Case et al., 2012).   These mechanisms may be related to differences in soil properties, feedstocks and/or pyrolysis conditions (Scheer et al., 2011).   There has been recent interest in biochar from a net GHG budget perspective.  Some studies (Sonoki et al., 2013) showed an increase in methanotrophs when certain types of soils were amended with biochar.  As mentioned previously, other studies (Spokas & Reicosky, 2009) have shown an increase in CH4 emissions.  Further research is needed on how CH4 emissions change following biochar amendment, which provided the motivation for the present study.   1.7 Research Objectives  This thesis aims to provide information on the following two topics:  1) To evaluate a method for coupling a laser-based cavity ring-down spectrometer with a flow-through non-steady-state chamber system, including the determination of the effective volume of the chamber system with injections of CO2 and CH4. 2) To evaluate the effects of biochar amendment on FCO2 and FCH4 from a forest soil using the chamber system.   14  2. Materials and MethodsChamber Design An automatic non-steady state chamber system (Jassal et al., 2012b) was installed in the Environmental Interfaces Laboratory in the Earth Systems Building at the University of British Columbia in Vancouver, Canada.  The system consisted of a data logger (Model CR1000, Campbell Scientific Inc., Logan, UT, USA), a cavity ring-down spectrometer with pump (CRDS) (Model G2301-f, Picarro Inc., Santa Clara, CA, USA), two 65-L soil chambers (constructed by the UBC Biometeorology Group), and two 0.75 m long, 0.52 m i.d. (internal diameter) PVC cylinders (525 mm SDR, Wolseley Canada, Langley, BC, Canada).   The chambers were transparent, acrylic domes with an internal volume of 65.5 L (Figure 1).  These chambers and the associated mechanics were identical to those used in Jassal et al. (2012b) with the exception of the system being attached to a CRDS.  Previous versions of this system were connected to an infrared gas analyzer (IRGA).  The system that was used in this experiment is a FT-NSS system.  Livingston and Hutchinson (1995) stated that one of the disadvantages of a FT-NSS system is that the system is rarely able to analyze more than one species at a time.  By coupling the system to a CRDS, the chamber system used in this experiment was able to measure both FCO2 and FCH4 simultaneously.    15   Figure 1.  Close-up of the chamber dome showing the vent tube and PVC collar.  Timing of measurement events and sensor data acquisition was facilitated through the use of the data logger.  Sample tubes connected each chamber to the CRDS with airflow controlled by a solenoid valve, allowing alternation between chambers.  Chamber air was pulled through the CRDS by an external AC vacuum pump and exhausted.  The air was not recirculated as the vacuum pump warms the air.  The warm air would cause an increase in Ta and therefore cause incorrect flux estimations if it was recirculated back into the chamber.  Flow rate through the tubing was 250 mL min-1 giving residence times within the tubing of approximately 30 s.   Each chamber was equipped with two small internal fans to ensure mixing of the headspace air within the chamber.  Comparative tests were performed with one, two, and three fans within each chamber and it was noted that two fans provided good mixing without creating excessive turbulence at the soil surface.  A 30 cm long, 3 mm i.d. Synflex 1300 tube (Saint-Gobain Vent tube 16  Performance Plastics, Wayne, NJ) was installed in the dome to prevent the effects of fluctuations in atmospheric pressure (Figure 2).  Proprietary algorithms used by the CRDS corrected for the water vapour dilution effects on CO2 and CH4 headspace concentrations.     Figure 2.  Basic schematic of the chamber system.  The differential equation describing the change of concentration in the chamber following lid closure is           ?        ?            (4) (Jassal et al.?2012), where c? is the concentration of CO2 or CH4 in the chamber headspace (mol mol-1), ca is the ambient concentration (mol mol-1), ? is the molar density of air (mol m-3), V is the volume (later this will be replaced by the effective volume, Veff) (m3), Q is the flow rate (m3 s-1) and t is the time (s). Integrating from ca at t = 0 to c? at t, we can obtain an equation for F as follows:    (   )(c? ? ca) / (1 ? e-Qt/V)        (5) 17  For small values of Qt/V, i.e., small lid closure times and flow rates, 1 ? e-Qt/V is well approximated by Qt/V.  Substituting this for 1 ? e-Qt/V in Equation (5) gives F = (?V/A) (c? ? ca)/t         (6) which shows that if F remains constant, c changes nearly linearly with t. Since (c? - ca)/t is the slope of the course of concentration following lid closure, Equation (6) can be written as   F = (    )                (7) For Q = 0.25 L min-1 (as in this study ? see below) and lid closure of 4 min, 1 ? e-Qt/V = 0.0153 so the linear approximation is excellent and linear slopes of c? vs. t are expected. 2.1.1 Environmental Conditions The laboratory is controlled for temperature and uncontrolled for humidity as per the building guidelines of the University.  The temperature was maintained at approximately 20 degrees Celsius.  The maximum temperature over the effective volume measurements was 22 degrees Celsius and the minimum was 19 degrees Celsius. 2.2 Chamber Effects on the Environment Chambers are naturally an intrusive object placed on or within a soil matrix which alters the natural environment (Hutchinson & Livingston, 2001).  They affect the environment and therefore the soil surface fluxes in numerous ways, which are described in detail below.  It is important to take the following information into consideration when implementing a chamber system in either the laboratory or the field.    18  2.2.1 Soil Temperature Rs approximately doubles for every 10 ?C rise in Ts between 5 and 30 ?C (Lloyd & Taylor, 1994).  The majority (> 75%) of Rs originate at depths shallower than 20 cm (Jassal et al., 2005).  Chambers therefore need to minimize the temperature differences at the near-surface soil level. The constant environmental conditions in the laboratory where the chamber system was located virtually eliminated all problems associated with increases in soil temperature (Ts) caused by chamber closure.  There was no significant solar radiation which could have increased the Ts to a larger degree than the air temperature (Ta) in the laboratory.  Furthermore, the chambers were closed for a relatively short time span (4 minutes), which was not enough to cause a noticeable rise in Ts.  The Ts was monitored constantly by way of the ancillary sensors and an increase due to chamber closure was not noticed.  2.2.2 Air temperature Ta variations can induce variations in air pressure (Pa) or volume (V).  Changes in Ta cause changes in Pa which can act as a ?piston? and push the air into or pull the air out of the soil beneath a non-vented chamber (Hutchinson & Livingston, 2001).  Addition of a venting tube can prevent changes in Pa, but it needs to be designed in such a way that prevents excessive leakage (Davidson et al., 2002).  The vent tube which was installed on the chamber system was made according to the specifications by Davidson et al. (2002).  Similar to the soil temperature issue, the stable Ta in the laboratory, combined with the relatively short chamber closure time, negated effects of chamber closure on an increase in Ta.  Ta was measured by the CRDS, and there was not a noticeable increase during chamber closure.   19  2.2.3 Humidity As a chamber is closed, the humidity of the headspace air (Ha) often increases and this can have a significant impact on the measurement of FCO2 and FCH4.  Evaporated water which remains enclosed by the chamber lid increases Pa and creates problems similar to those caused by increases in both Ta and Ts (Welles & Mcdermitt, 2005).   This can be minimized in a few ways; the most notable is to use reflective and insulating chambers that decrease the energy available to evaporate water at the soil surface (Rochette & Hutchinson, 2005).  Water vapor pressure effects in vented chambers can be corrected for using simple calculations.  The CRDS which was coupled to the chamber system automatically measured the water vapour concentration in the headspace and proprietary algorithms corrected for the Pa effects caused by the water vapour.   2.2.4 Pressure fluctuations Chamber volume reductions occur when the chamber is closing and during headspace air sampling which cause inaccurate FCO2 and FCH4 measurements (Hutchinson & Mosier, 1981).  Additionally, a closed chamber can isolate the soil surface from changes in atmospheric pressure (Pa) and turbulence due to wind that would normally have an effect on the soil surface fluxes.  The issues of volume (V) change, isolation from Pa, and turbulence effects can both be mitigated by a properly designed vent tube (Hutchinson & Livingston, 2001).  As mentioned previously the vent tube which was installed on the chamber system was made according to the specifications by Davidson et al. (2002) and mitigated the effects of Pa fluctuations.    20  2.2.5 Chamber Air Mixing Regime Adequate mixing of chamber headspace air is required to ensure that the samples are representative of the mean headspace air concentrations.  Previous studies have shown that FCO2 and FCH4 measurements are sensitive to headspace mixing and care should be taken to ensure that the wind speed inside the chamber (due to mixing) mimic outside conditions (Le Dantec et al., 1999; Janssens et al., 2000).  Rochette and Hutchinson (2005) showed that, for a 60 L square chamber without fans, the CO2 flux was highly variable and one small fan was able to provide adequate mixing as there was no noticeable effect between 1, 2, or 4 small fans installed.   In the present study, experiments were carried out in the lab during which one, two or three fans were installed in the chamber system.  A calibration gas was injected following the methods described in Section 3.3 and the resulting observed values of FCO2 and FCH4 were compared with their respective predicted values.   The results were similar to those obtained by Rochette and Hutchinson (2005) as the chamber system without any fans installed gave highly variable flux results.  There was no difference between one, two or three fans, and it was ultimately decided that two fans would provide adequate mixing and also provide a backup system in case of fan failure for periods when the chamber system was operating for extended periods of time without any human monitoring.  These periods were generally weekends and periods of 2 to 5 days where the system was running under complete automation.    21  2.2.6 Leaks and Soil Disturbance Openings through which headspace gases may leak or become influenced by ambient air are important to recognize as these can have an influence on the estimation of FCO2 and FCH4.  Venting tubes and imperfect seals are the most likely areas for leaks.  Rochette and Hutchinson (2005) conducted laboratory tests which concluded that with a properly designed vent tube, following Davidson et al. (2002), leakage through the vent tube should be small in most situations, providing that there are no large Pa differences between the outside and inside of the chamber.   A closed-cell foam gasket was used to provide a good seal between the chamber and the collar.  A close inspection of the gasket provided confidence that the gasket fit tight enough to ensure accurate flux estimations.  2.3 Testing Chamber Accuracy Prior to background measurements being made with the chambers mounted onto cylinders filled with soil, the chamber lids were mounted onto a PVC collar which was fixed to a sealed aluminum base (Figure 3).  The chamber lids attached to the PVC / aluminum base assembly with rubber clamps.  With the chamber closed, a calibration gas of either 20.07% by volume CO2 or 0.10 % by volume CH4 was injected into the chamber at a rate of 15 mL min-1 through a perforated tube that rested on the aluminum base.  The flow meter used was a Kofloc (model RK1200).  Both of the injection gases represented concentrations which were approximately 500 times greater than ambient concentrations.  The approximate ambient concentrations were 400 ppm and 2 ppm for CO2 and CH4, respectively.  22   Figure 3.  The chamber mounted on the aluminum base.  The calibration gas was injected at a flow rate of 15 mL min-1 and the resulting flux was estimated using a linear regression applied to the headspace concentration change over time.  2.4 Soil Cylinder Construction The automated chambers described in the section above were placed onto 525 mm SDR (Standard Dimension Ratio) PVC pipe (often described as 35 Gravity Sewer Pipe) obtained from Wolseley Canada.  These cylinders were cut into 0.75 m lengths.  A metal screen with a 1 cm grid was attached to the bottom of each of the cylinders with potting fabric sandwiched in between the cylinder and the metal screen.  The cylinders were placed on a metal shelf which had been placed on wood supports (Figure 4). 23   Figure 4.  The chamber system attached to the PVC soil cylinders and the base.  Each cylinder was filled with approximately 146 kg of forest soil which was packed to an average bulk density of approximately 1350 kg m-3.   2.4.1 Soil The soil was obtained from a field site which is located about 10 km southwest of Campbell River (49?51?N, 125?19?W) on the east coast of Vancouver Island, Canada. The site was a 62 year-old Douglas-fir stand with an altitude of approximately 300 m above mean sea level.  The site naturally regenerated from a forest fire in 1949 and was an almost homogenous stand. Soil was gathered from a forested area approximately 100 m from Oyster River Main (north side of Oyster River) along the road entering WH017.  The uppermost organic layer (litter ? fermentation ? humified layers or LFH layer) and overlying mineral soil (0 ? 10 cm) were removed to produce a more homogeneous soil and one that more accurately reflected an area which had been previously logged (i.e. with the 24  LFH layer and topsoil stripped off).  The soil was sieved to less than 10 mm in the field and later sieved to less than 4 mm and air dried in the laboratory.  The PVC cylinders were filled with 10 cm of washed gravel (20 mm crushed rock), 15 cm of between 4 mm and 10 mm soil, which had been saved from laboratory sieving,  and 50 cm of sieved <4 mm soil.  The total mass of the forest soil was approximately 146 kg per chamber.  Care was taken when packing the soil to achieve a uniform density of approximately 1350 kg m-3.  For reference, Drewitt et al. (2002) described the soil as a humo-ferric podzol with a bulk density (?b) of 1353 ? 51 kg m-3, soil porosity (?) of 49.0 % at the 10 ? 80 cm depth, and a coarse fraction of 31% greater than 2 mm.  2.4.2 Ancillary Measurements Ts, volumetric water content (?), and electrical conductivity (EC) were measured at depth ranges of 5 to 10 cm and 20 to 25 cm.  The 20 ? 25 cm measurements were made using Decagon Devices Inc. (Pullman, WA, USA) 5TE sensors, and the 5 to 10 cm measurements were made using Decagon Devices Inc. GS3 sensors.  Additionally, soil matric potential (?) was measured at the 5 to 10 cm depth using Decagon Devices Inc. MPS-2 sensors.  These sensors were installed using the manufacturer?s instructions (Decagon Devices Inc., 2012; Decagon Devices Inc., 2013a;b) and recorded by the data logger.   2.4.3 External Fans External fans were mounted above the chambers to ensure that the ambient laboratory air was well mixed and that previous headspace concentrations were cleared out of the chamber after each measurement.  These fans provided good mixing of the air surrounding the chambers, but did not direct a stream of air directly onto the soil surface.   25  2.5 Effective Volume Determination As mentioned previously, Veff is an essential part of flux estimation.  The volume of the chamber is directly related to the calculated flux, and therefore any error present in the calculation of the chamber volume is directly translated into an error in the flux estimate (Rayment, 2000).   The following outlines the steps used to estimate Veff. 2.5.1 Calibration Gas Injection In order to determine Veff, the chambers were mounted on the soil cylinders, and gas flux was measured once from the soil as such and then during gas injection.  Similar to the aluminum base experiment described in section 2.3, a calibration gas was injected at a rate of 15 mL min-1 and at a concentration of 20.07% CO2 or 0.10% CH4 by volume.  The calibration gas used for the injections was supplied by Praxair Canada Inc. (Vancouver, BC, Canada).  The gas was connected to a Kofloc flow meter (model RK1200) and injected into the chamber through a perforated airline which was formed into a circle and was suspended 2 cm above the soil surface.  A pressure gauge was installed between the flow meter and the chamber which allowed the pressure to be maintained at 100 kPa.  The measurement times for both the soil-only and with the gas injection were three minutes, with seven minutes between measurements to allow a return to the ambient concentration.   A small, steady flow of air was supplied by a fan mounted above the chamber.  This aided in the dispersal of the built-up CO2 or CH4 within the chamber and created well-mixed ambient air.  The slope of the change in headspace concentration was calculated using the linear model function lm within the R for statistical computing program (R Version 3.0.2, R core team, 2013).  The same model was used for both the soil surface fluxes and the fluxes with the injected calibration gas.   26  Taking equation 7 and incorporating the dilution effect of water vapour, the flux can be calculated using (Welles et al., 2001) ????????? dtdwwcdtdcAVF eff 1''          (8) where Veff is the effective volume, A is the soil surface area (m2) enclosed by the chamber collar,   is the molar density of air (mol m-3), which is given by Pa/(RTaK) where Pa is the atmospheric pressure (Pa), TaK is the absolute air temperature (K) and R is the universal gas constant (8.31 Pa m3 mol-1 K-1), c? is the concentration of CO2 or CH4 in the chamber headspace (mol mol-1), and dtdc'  is the rate of change in headspace CO2 mole fraction (mol CO2 (mol air)-1 s-1.  The second term on the right hand side of the equation is the correction for the dilution effect resulting from the evaporation of water from the soil surface.   Jassal et al., (2012b) showed that equation (8) can be written as follows ??????? dtdsAVF xeffa           (9) where sx is the mixing ratio (mol (mol dry air)-1 for species x, i.e., CO2 or CH4 and a is the molar density of dry air (mol dry air m-3).  Equation (9) was also used in field experiments conducted by Drewitt et al. (2002), Gaumont-Guay et al. (2006a, b), and Jassal et al. (2012a) during which similar chambers were installed in the field and coupled to an IRGA.  In this experiment, the water vapor correction was not used in the calculations as the CRDS automatically measured the water content of the sample and used proprietary algorithms to correct for the water vapor by converting gas mole fractions into their mixing ratios (mol gas (mol dry air)-1).   27  It is important to note that this equation assumes air circulation back into the chamber.  The system used in this experiment exhausted the chamber headspace air once analysis was complete.  As indicated earlier (page 18), due to the low flow rate of the headspace gas to the CRDS (250 mL min-1) it was determined that this would not affect flux measurements in any meaningful way.  Therefore equation (9) was used for all flux estimations.   During Veff estimations, the total volume extracted from headspace air was only 1.1% of the total chamber volume, which was replaced with ambient air via the vent tube. The injection rate of the calibration gas into the chamber was calculated using calcQI  ?                       (10) (Drewitt et al., 2002; Jassal et al., 2012b) where Q is the flow rate of the calibration gas (mL min-1), and ccal is the concentration of the calibration gas (mol (mol dry air)-1).  During the estimation of Veff, two consecutive readings were obtained, one with inputs from only the soil, and the second with inputs from the soil and the calibration gas.  Following Goulden and Crill (1997), the ratio of the injection rate to the difference between the rate of headspace concentration increase for soil only and soil plus gas injection was used to estimate Veff as follows   ?????? ??dtdcdtdcIVeff12 ''                       (11) where dtdc 1'  is the headspace mole fraction (mol (mol air)-1 s-1 measured from the soil surface before injection and dtdc 2'  is the headspace mole fraction (mol (mol air)-1 s-1 with the injection of the calibration gas.   28  2.5.2 Flux estimation The data from the CRDS and the CR1000 were combined and the fluxes were estimated over each half hour interval.  Following chamber closure, the first 20 seconds of measurements were disregarded to allow any pressure fluctuations due to chamber movement to subside.  The flux was then estimated using 100 seconds of measurements (e.g. from 20 to 120 seconds) (Figure 5).  The CRDS recorded the concentrations of CO2 (ppm), CH4 (ppm) and H2O (%) at a rate of approximately 3 measurements s-1.  A typical plot of the headspace concentration against time showed a steady increase in the concentration of CO2 (Figure 5), and a steady decrease in the headspace concentration of CH4 (Figure 6).  If the chamber were to remain closed for longer (>8 minutes), the effects of the increasing headspace concentration on the concentration gradient begin to become apparent with a decrease in the rate of production or consumption of CO2 or CH4 respectively (data not shown).   29   Figure 5.  A typical plot of the headspace concentration (mole fraction) of CO2 (ppm or ?mol (mol dry air-1)) over time.  The first 20 seconds were disregarded and the flux calculation zone spanned 100 seconds (e.g. from 20 to 120 seconds).  A linear model was applied and the slope of the headspace concentration change was used to estimate FCO2.  As can be seen in Figures 5 and 6, the slope of the concentration increase/decrease over time is very close to linear.  The slope was calculated using the linear model function lm in the R for statistical computing program (R Version 3.0.2, R core team, 2013).  This provided a linear model based on all the data points which fell in the slope calculation zone and provided a concentration change over time.  This slope ??????dtdc'  was then used in Equation 9 (Gaumont-Guay et al., 2009; Jassal et al., 2012b).   Chamber closes 30   Figure 6.  A typical CH4 headspace concentration curve over time.  The negative slope indicates that the activity of methanotrophs outnumbered the activity of methane generating microorganisms (methanogens).    Although the concentration change over time is much smaller for CH4 when compared with CO2, it also exhibited a strong linear relationship.  The same method described above was used for the estimation of FCH4.   2.5.3 Quality Control Procedures In order to ensure that data were properly recorded and instruments were functioning normally, regular checks were made to electrical and air line connections.  When applicable, replications were conducted to ensure that results were consistent.  If the background Chamber closes 31  concentration was abnormal due to disturbances in the laboratory, improper closure, or other system malfunction, the replication or measurement was disregarded.   2.5.4 Statistical Analyses FCO2 and FCH4 estimates were calculated twice per hour for each chamber and subsequently averaged for hourly periods using the open air package in R (Carslaw & Ropkins, 2012).  All replication results were calculated as the mean ? 1 SE and all statistical tests were performed using R project for statistical computing version 3.0.2 (R core team, 2013).   2.6 Effects of Biochar on FCO2 and FCH4 2.6.1 Biochar Characteristics The biochar used in the experiment was produced from 1 cm chipped Douglas-fir woody materials heated in a pyrolysis oven to 420 ? C for 32 min (Diacarbon Energy Inc. Canada).  The resulting biochar had a carbon content of 78.8%, a volatiles content of 18.8% and an ash content of 2.4%.  The biochar was highly hydrophobic and had a wide range of particle sizes, with 32% in the 425 ? 991 ?m range.  Prior to the present experiment, the biochar was stored in a 55 gallon steel drum which was sealed from the atmosphere.  2.6.2 Application of Biochar The biochar was applied on July 29, 2013 at a rate of 20 t ha-1 (15.76 t C ha-1).  This equated to 433 g of biochar (341 g C) applied to the soil within the chamber.  The biochar was evenly spread on the surface of the soil and then incorporated into the top 5 cm using a small garden rake (Figure 32  7).  Care was taken to ensure homogenous mixing as well as to ensure that the sensors installed at the 5 - 10 cm depth were not disturbed.     Figure 7.  The biochar was evenly spread across the surface of the soil and then incorporated into the top 5 cm using a small garden rake.  Care was taken to ensure even distribution as well as to avoid disturbing the sensors located at the 5 to 10 cm depth.    2.6.3 Watering System In order to simulate wetting events, a watering system was installed in the chambers which allowed for a measured amount of distilled water to be dispersed at a rate similar to typical rainfall at the field location where the soil was obtained.  A port was installed on the PVC collar of each chamber and this port was sealed with rubber gaskets.  The outer portion of this connection was attached to a hose which ran vertically to a 10 L container (Figure 8).  The inner portion was 33  attached to a soaker hose (Gardena, product number 59-7448-6) which had been shaped into a spiral using wire.  The soaker hose was suspended 5 cm above the soil surface (Figure 9).    Figure 8.  The watering system attached to the chambers.  The FCO2 and FCH4 measurements were continuously made during the watering events.   As water drained from the container through the soaker hose, small water droplets fell onto the soil (Figure 9).  The water application rate was approximately 2 L per hour, or 10 mm per hour.  This simulated ?rain? evenly distributed the water onto the surface of the soil without creating substantial disturbance.  The watering system allowed for continual FCO2 and FCH4 measurements during the watering events.  The water applied to the soil was analyzed following the procedure described in section 2.6.5.  The applied water remained very similar to distilled water after moving through the tubing and soaker hose.  The dissolved organic content was similar to that of distilled water.   34   Figure 9.  The soaker hose released small droplets at a rate of 10 mm per hour.  This allowed for an even distribution of the water and simulated a heavy rain event.   2.6.4 Watering Events During background measurements (pre-biochar application), a total of eight watering events took place.  Table 1 outlines the date and amounts of applied water at each watering event during background measurements.  Care was taken to ensure that the mounting of the watering system did not create substantial disturbance to the soil.  FCO2 and FCH4 measurements were temporarily suspended during the setup of the system to avoid any incorrect flux estimates due to human interference.  The measurements resumed immediately following installation.  Installation of the watering system normally took approximately 5 minutes.      35  Table 1.  The watering schedule during background (pre-biochar application) measurements.  The amount of applied water was decreased to 6 L after it was noticed that previous amounts of 10 L were creating some standing water on the soil surface during application.  It was determined that 6 L was a reasonable amount to apply in order to simulate a rain event.  Date Amount (L) Depth Equivalent (mm) April 8, 2013 10 46 April 10, 2013 10 46 April 12, 2013 10 46 April 16, 2013 10 46 April 18, 2013 10 46 April 23, 2013 10 46 July 22, 2013 6 28 July 24, 2013 6 28  Following biochar application, watering events took place twice a week (every 3 or four days), beginning immediately following biochar application (July 29, 2013) (Table 2).  There was no delay between applying the biochar and beginning the first watering event.      36  Table 2.  The watering events which took place after biochar application (July 29, 2013).  The application of 6 L generally took three to four hours to complete.   Date Amount (L) Depth Equivalent (mm) July 29, 2013 6 28 August 1, 2013 6 28 August 5, 2013 6 28 August 8, 2013 6 28 August 12, 2013 6 28 August 15, 2013 6 28 August 19, 2013 6 28 August 22, 2013 6 28 September 14, 2013 6 28  2.6.5 Water Collection A water collection system was installed under the chambers to collect leachate.  The collection system was not installed during the background measurements; however water was collected following biochar application.  The collection system consisted of a plastic container which underlay the entire cylinder.  The water was collected and stored in glass beakers, and later analyzed on a spectrometer (model Spectro::lyzer) (Jollymore et al., 2012) following the manufacturer?s instructions.  The analysis of water was deemed outside the scope of this thesis, as the purpose of this thesis was to explore the effects of biochar amendment on FCO2 and FCH4.  Further analysis of the water results will be interesting to explore in the near future.  37  2.6.6 Automated Chamber Timing The chambers were closed for 4 minutes with each chamber alternating every 15 minutes (Table 3). Flux measurements were made during closed periods. Table 3.  A half-hourly schedule of the chamber timing.  Note that each chamber closed once in a half hour period.  Time between closures allowed the chamber to return to the ambient concentration as well as create well-mixed ambient conditions for the next chamber measurement.  Time (mins) Chamber 1 state Chamber 2 state 0 ? 4 Closed Open 4 ? 15 Open Open 15 ? 19 Open Closed 19 ? 30 Open Open  Each chamber was therefore closed for 13.3 % of the total time.  There was enough time in between measurements for the accumulated headspace air to dissipate.  The residence time inside the tubing between the chambers and the CRDS was 25 s and 30 s for chambers 1 and 2 respectively. During the chamber measurements, 1.5 % of the headspace air was removed for each 4 min closure and replaced by ambient air via the vent tube.  As discussed earlier (page 18), this percentage was deemed to be small enough to not require using a flow parameter in the flux calculation.   2.6.7 Comparing the Two Chambers During the background flux measurements, the two chambers showed a strong linear correlation with each other (R2 = 0.93 for FCO2 and R2 = 0.80 for FCH4).  Due to the strong correlation, a linear model was used to predict the resulting FCO2 and FCH4 in the absence of biochar amendment 38  following a before-after control-intervention approach (E. P. Smith, 2006).  Chamber 1 received biochar application and chamber 2 was the control.  A linear model was used to predict the response of chamber 1 in the absence of biochar application.  This was compared to the observed results.  For the remainder of this thesis, the post-application results from the biochar applied chamber are referred to as ?biochar chamber? and the post-application modelled results are referred to as ?control?.  Studies (e.g., Davidson et al., 1998) have shown that Ts plays an important role in FCO2 and FCH4.  The role that Ts plays in the experimental system was not considered, as the controlled temperature of the laboratory allowed for nearly constant Ts to be maintained.  The half-hour flux estimations were averaged to one hour.  These hourly flux estimations were then converted into g CO2 emitted (g CO2 m-2 h-1) and g CH4 consumed (g CH4 m-2 h-1).  These were combined to calculate a net greenhouse-warming potential (GWP).  This gave an overall effect of how the biochar treatment affected the net global warming potential (GWP) compared to the control.  The GWP value for CH4 of 34 for a 100-year time frame was used based on the IPCC 5th Assessment Report (IPCC, 2013). 2.6.8 Effect of Soil Moisture Content In order to look into the data in more detail, the results were analyzed over three moisture levels.  These included periods when the ? was increasing, and periods when ? was decreasing.  The responses were predicted over three moisture intervals which were given the labels: wet, mid, and dry.  This allowed for the results to show how the biochar chamber was responding compared to the control at different points in the wetting cycle.  It is important to note that ? was not included as a parameter in the model as there was no discernible correlation between FCO2 or FCH4 with ?, and the linear correlation of  ? measurements between the two chambers was very strong (R2 = 0.98).  The 39  wet interval spanned moisture levels with a ? greater than 0.30 m3 m-3; the mid interval spanned moisture levels with a ? between 0.22 and 0.30 m3 m-3 ; the dry interval spanned moisture levels with a ? less than 0.22 m3 m-3.    In order to avoid bias, similar time slots were given to each of the three intervals.  The wet interval included 818 hourly flux estimations, the mid interval included 738 hourly flux estimations and the dry interval included 773 hourly flux estimations.     40  3. Results and DiscussionTesting Chamber Accuracy The observed flux during CO2 injection with the aluminum base attached was 10.01 and 9.24 umol m-2 s-1 for chambers 1 and 2 respectively, which compared to the injected flux of 9.63 umol m-2 s-1.  The root-mean-square error (RMSE) was 0.47 and 0.78 for chambers 1 and 2, respectively (Table 4).    Table 4.  Results of CO2 flux calibration.  Injections were done with a 20.07% by volume CO2 calibration gas injected at a rate of 15 mL min-1 and the chamber volume assumed to be 65.5 L.  The root-mean-square error (RMSE) for chambers 1 and 2 represent 4.7% and 8.4% of the averages, respectively (n = 9 for chamber 1 and n = 12 for chamber 2). Chamber Average Measured Flux (?mol m-2 s-1) Actual (injected) Flux (?mol m-2 s-1) RMSE (?mol m-2 s-1) 1 10.01 9.63 0.47 (n = 9) 2 9.25 9.63 0.78 (n = 12)  The average measured flux for chamber 1 was above the injected flux, and for chamber 2 was below the injected flux.  This was most likely due to the fact that the injection replications were done on different days, and therefore the flow meter was possibly not set at exactly the same mark.  During experimentation with the system, a small change of the dial of the flow meter produced no visual difference in the flow rate as viewed on the flow meter, but did produce a difference in the measured FCO2.  Another source of error which was not measured was the amount of leakage present in each chamber.  It is possible that the tube connections had small amounts of leakage which would cause small variations in FCO2 estimation and these would vary between the chambers.  The error in both the flow meter and the calibration gas were ? 2 %.  Taking these errors into account, the injected FCO2 had a range of 9.25 to 10.02 umol m-2 s-1.  The FCO2 measurements, therefore, fell within the error associated with the instruments.   41  The average estimated FCH4 for chamber 1 was above the injected flux and the average estimated FCH4 for chamber 2 was below the injected flux (Table 5).  Similar to the CO2 injection replications, this could be due to a variety of factors, most likely the ?2% error on both the flow meter and the calibration tank, and small, unknown leakages.  Taking the error on the flow meter and calibration tank into account, the predicted flux had a range of 0.046 to 0.050 umol m-2 s-1.     Table 5.  Results of CH4 flux calibration injection replications.  Injections were done with a 0.10% by volume CH4 calibration gas injected at a rate of 15 mL min-1 and the chamber volume assumed to be 65.5 L.  The RMSE for chambers 1 and 2 represent 6.1% and 4.9% of the averages, respectively (n = 12 for chamber 1, and n = 9 for chamber 2). Chamber Average Measured Flux (?mol m-2 s-1) Actual (injected) Flux (?mol m-2 s-1) RMSE (?mol m-2 s-1) 1 0.051 0.048 0.0031 (n = 12) 2 0.047 0.048 0.0023 (n = 9)  The small RMSE values for both the CO2 and CH4 replications and the fact that the measured fluxes fell within the percent error range of the system gave confidence that the system was performing within reasonable expectations and could accurately estimate soil surface fluxes.     42  3.2 Effective Volume Estimation The chambers were mounted onto the soil cylinder during the Veff estimations.  During the estimation of Veff, ? of both chambers was at 0.21 m3 m-3.  This ? was close to the point at which both chambers produced the maximum effluxes of CO2.  The maximum effluxes of CO2 occurred at a ? of approximately 0.23 m3 m-3.  The maximum consumption of CH4 occurred at a ? of approximately 0.26 m3 m-3. Lower water contents reduce microbial decomposer activity by slowing the diffusion of substrates and products, while higher water contents restrict O2 supply.  Plots showing the effect of ? on FCO2 and FCH4 can be found in section 3.3.1.  3.2.1 CO2 Injection Results Table 6 shows the average Veff and standard error of the mean (SE) calculated for each chamber during the CO2 effective volume measurements.  The average effective volume calculated using CO2 injection was 68.6 ? 1.35 L for chamber 1 and 67.5 ? 0.60 L for chamber 2. Table 6.  The average effective volumes of each chamber calculated using a 20.07% by volume CO2 calibration gas injected at 15 mL min-1.  Each replication consisted of two flux measurements, one with only the soil contributing to the flux measurement, and immediately following that, a second measurement with soil and the injection of the calibration gas contributing to the flux measurement (n=5 for chamber 1 and n=7 for chamber 2).  The effective volumes were larger than the geometric volume of 65.5 L.   Chamber Average Effective Volume (L) Standard Error of the Mean (L) 1 68.6 1.35 2 67.5 0.60  43  These results agree with past studies (Nay et al. 1994; Norman et al., 1997; Rayment, 2000) which show Veff to be in the range of 5 to 10% greater than Vg, which for these chambers was 65.5 L.  As mentioned previously, this is likely partly due to the inclusion of air-filled pore spaces within the top few centimeters of the soil.  These spaces develop an increasing concentration of headspace gas, and therefore can be considered an effective addition to the chamber.  Using the average porosity of 0.49, the depth of soil which contributed to the volume of the chamber was approximately 4.3 cm and 3.0 cm for chambers 1 and 2 respectively.  This is an estimate, as it assumes no CO2 adsorption onto the chamber walls as stated by Rayment (2000).  Drewitt et al. (2002) found Veff was about 11% and 20% higher in the laboratory and in a forest floor CO2 flux study, respectively.  The exact amount of CO2 adsorption onto the chamber walls was not tested.  During Veff estimation, a stark contrast can be seen between the headspace concentration increase from the soil only and the headspace concentration increase with the addition of the calibration gas (Figure 10).    44   Figure 10.  A typical replication of the soil (interval a), a return to baseline ambient concentration levels (interval b) and soil plus calibration gas injection (interval c) CO2 headspace concentration curves.  Note the small increase in headspace CO2 concentration over interval a which has only the soil as a contributor.  The much larger increase in headspace CO2 concentration over interval c has both the soil and the calibration gas injection as contributors (n = 5 and n = 7 for chambers 1 and 2, respectively).     a b c 45  3.2.2 CH4 injection results The average effective volume calculated using CH4 injection was 67.2 ? 0.30 L for chamber 1 and 66.1 ? 0.12 L for chamber 2 (Table 7).    Table 7.  The average effective volumes of each chamber calculated using a 0.10% by volume CH4 calibration gas injected at 15 mL min-1.  Each replication consisted of two flux measurements, one with only the soil contributing to the flux measurement, and immediately following that, a second measurement with soil and the injection of the calibration gas contributing to the flux measurement (n = 3 for both chamber 1 and chamber 2).  Chamber Average Effective Volume (L) Standard Error of the Mean (L) 1 67.2 0.30 2 66.1 0.12    The difference between the headspace concentration CH4 decrease due to consumption and the headspace concentration increase due to calibration gas injection was very noticeable (Figure 11).  The CH4 consumption was hard to see visually, however it is noticeable and appears to be quite linear when the consumption is enlarged (Figure 12). 46   Figure 11.  A typical trace during CH4 flux measurement on soil (interval a), a return to background ambient concentration (interval b) and soil plus calibration gas injection (interval c) CH4 headspace concentration curves.  Note the slight headspace CH4 concentration decrease during interval a and the headspace CH4 concentration increase during interval c.  An expanded plot showing the decrease can be seen in Figure 12.  a b c 47   Figure 12.  An enlargement of Fig. 11 showing the decrease in CH4 headspace concentration in more detail.  The small increase at interval a can be attributed to pressure and turbulence artifacts during chamber closure which were not included in the flux calculation.     The runs were completed concurrently and timed so that the chamber system had adequate time to return to the baseline value.  Small artifacts from turbulence were present during chamber closure; however these were not used in the flux calculation.  a Chamber closes  Chamber opens  48  The results of the Veff estimation replications agreed with past studies which find the effective volume to be larger than the Vg (Rayment, 2000; Jassal et al., 2012b).  The degree to which Veff is larger than Vg likely depends on a variety of factors including soil type, chamber material, and moisture content of the soil at the time of measurement.  The estimation of the effective volume allowed for increased confidence in the flux estimations, as a better idea of the true volume contributing to the soil surface fluxes was obtained.   Veff estimations were consistently larger during CO2 injections than they were during CH4 injections.  The average Veff for chamber 1 was 68.6 L and 67.2 L for CO2 and CH4 respectively.  The average Veff for chamber 2 was 67.5 L and 66.1 L for CO2 and CH4 respectively.  The reasons behind this are not clear at this time, but it could be argued that the adsorption of the CO2 onto the chamber walls was greater than the adsorption of CH4 onto the chamber walls; however it is important to note that these differences were determined to be not statistically significant using a two-tailed t-test (p values of 0.47 and 0.18 for chambers 1 and 2 respectively).   3.2.3 Issues and Future Research Surrounding Effective Volume Estimation The error on the flow meter meant that the flow of the calibration gas into the chamber system had an error of approximately (? 2 %).  This, when combined with the error on the calibration gas tank (? 2 %), contributed to the RMSE for measurements of both FCO2 and FCH4. Determining the effective volume at different soil moisture levels and using different calibration gas concentrations could have given more data with regards to how the effective volume changes as these parameters change; however time did not permit such a detailed investigation into the effective volume.  A mass flow controller would have given a more accurate flow rate of the calibration gas injection; however time and budget constraints limited the analysis to a flow meter.  49  More detailed probes into how Veff changes in response to different soil parameters and different calibration gas concentrations would provide interesting results and allow a more accurate depiction of how Veff would change during field-deployment of the chamber system.   3.3 Effect of ? and Biochar on FCO2 and FCH4  3.3.1 Effects of ?  Before biochar application, there was a noticeable increase in FCO2 as ? increased, followed by a decrease as ? increased above 0.23 m3 m-3 (Figure 13).  As the soil becomes too moist, there is not enough aeration for the microbial community, and perhaps some areas of the soil are anoxic, which hinders CO2 production.  As the soil dries, microbial processes increase to a maximum.  After which the soil becomes too dry for microorganisms to proliferate.    50   Figure 13.  The change in FCO2 as ? changes.  As ? increases, there is a smooth increase and decrease with a maximum FCO2 at a ? of approximately 0.23 m3 m-3.  FCH4 results showed a similar pattern.  There is a general increase in the consumption of CH4 as ? increases; however as ? increases above 0.28 m3 m-3, consumption appears to sharply decrease.  The value of ? at which CH4 consumption is the highest is approximately 0.26 m3 m-3 (Figure 14).   51   Figure 14.  The change in FCH4 as ? changes.  There is an increase in consumption with increasing ? to a point, after which the consumption decreases with a maximum CH4 consumption at a ? of approximately 0.26 m3 m-3. 3.3.2 Chamber 2 as a Model for Chamber 1 As mentioned in section 2.6.7, the two chambers showed a strong linear relationship between each other for both CO2 and CH4 fluxes based on the background measurements made before biochar application to the soil.  The R2 was 0.93 and 0.80 for FCO2 and FCH4, respectively.  The linear relationship between the chambers is noticeable (Figures 15 & 16).  After treatment application chamber 1 was the biochar chamber, and, for comparison, chamber 2 was used to estimate the 52  fluxes/effluxes expected from chamber 1 assuming it had no biochar applied.  The linear relationships between the chambers for both CO2 and CH4 are shown in Figures 15 and 16.   Figure 15. The linear relationship between chambers 1 and 2 during FCO2 background measurements.  A strong linear relationship (R2 = 0.93, Syx = 0.13) is present.  The modeled results based on chamber 2 later became the control and chamber 1 received the biochar application and became the biochar chamber.    R2 = 0.93 y = 0.82x + 0.14 53   Figure 16.  The linear relationship between chambers 1 and 2 during FCH4 background measurements.  A strong linear relationship (R2 = 0.80, Syx = 0.0001) is present.  The modeled results based on chamber 2 later became the control and chamber 1 received the biochar application and became the biochar chamber.    The strong relationship presented above allowed for the modelling outlined in section 2.6.7.  For the remainder of this thesis, chamber 1 will be referred to as the biochar chamber, and the predicted results based on the model will be referred to as the control chamber.  If chamber 2 fluxes were assumed to be equal to the control fluxes (i.e., the regression equation was not applied), the control fluxes would have been skewed. Chamber 2 was consistently lower than chamber 1; therefore without using the regression equation, the results would have underestimated the control fluxes.  R2 = 0.80 y = 0.98x ? 1.78 54  3.3.3 FCO2 Spike Following Biochar Application Immediately following the biochar application and subsequent watering event, a noticeable spike occurred in the FCO2 (Figure 17) which was not present in the control chamber (Figure 18) over the same time interval.  This was most likely due to decomposition of the labile carbon fraction in the biochar.  This phenomenon has been observed in the past (e.g., Lehmann & Joseph, 2009).  As mentioned previously, this spike is relatively small when compared with the total CO2 emitted as soil respiration and therefore does not play a large role in net GHG emissions.  Having said that, it was an expected event during the experiment, and the noticeable spike provided increased confidence that the system was functioning properly.  However, no spike was noticed in the control where an initial decrease in FCO2 was due to the water application, which temporarily limited gas exchange.  Also, as the water infiltrated into the soil, a rise in FCO2 can be seen as the pore spaces become available for gas exchange once more. 55   Figure 17.  A noticeable spike in FCO2 following biochar and water application.  The gap before the spike indicates when biochar and then water was being applied and other activity around the chamber.  These measurements were disregarded as they represented effluxes not related to the soil.   biochar and water  application  July 29, 2013 56   Figure 18.  The FCO2trace of the control chamber followed a normal pattern after the application of water.   3.3.4 Electrical Conductivity (EC) The EC of the soil solution in the chamber amended with biochar showed a spike immediately following application and the subsequent watering event (Figure 19).  A smaller EC spike was present in the control chamber immediately following biochar application.  This smaller spike mimics the normal EC spike during watering events.  The EC spike in the soil water from biochar-treated soil can be attributed to the high EC of the biochar.  It has been shown previously that water application  July 29, 2013 57  biochars made at temperatures above 400 ?C may have a higher EC than biochars made at a lower temperature.  This is not true for all biochars, as the feedstock and pyrolysis conditions can produce different results (Bagreev et al., 2001).  The EC returned to approximately the baseline values, which would indicate that most of the EC increasing materials were flushed through.    Figure 19.  Following application of the biochar, a noticeable spike in EC of the soil water was observed.  biochar and water application  58   Figure 20.  A smaller EC spike was present in the control chamber.  This smaller spike mimics the normal EC spike during watering events.   3.3.5 Post-Application: Biochar vs. Control Results There appeared to be a treatment effect with the biochar amended chamber showing higher CO2 emissions than the control as well as lower CH4 consumption than the control (Figures 21-24).  These results are the average effluxes (?mol m-2 s-1) over the four month timeframe converted into g CO2 m-2 h-1 and ?g CH4 m-2 h-1.  This conversion allows for a comparison and averages the results of the experiment over the four month timeframe.  The average g CO2 m-2 h-1 was 0.152 for the biochar chamber and 0.112 for the control.  The average ?g CH4 m-2 h-1 was -78.0 for the biochar chamber and -97.9 for the control.  The biochar amended chamber also had an overall higher GWP than the water application  59  control with the GWP having the values of both the CO2 and CH4 factored in.  The average GWP in g CO2 eqv m-2 h-1 was 0.150 for the biochar chamber and 0.108 for the control.    Figure 21.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 yr-1) over the post-application period (July 29 to December 21, 2013) by both the biochar amended chamber and the control.  The biochar results show an increase in g CO2 emitted, a decrease in ?g CH4 consumed and an overall higher mean GWP than the control.      60  Exploring the data further, the secondary modelling showed interesting results.  The different moisture intervals revealed that the biochar chamber behaved differently from the control under varying ?.  During the wet interval, which was designated as ? greater than 0.30 m3 m-3, the g CO2 m-2 h-1 was 0.061 for the biochar chamber and 0.054 for the control.  The consumed ?g CH4 m-2 h-1 was -64.8 for the biochar chamber and -76.3 for the control.  The GWP in g CO2 eqv m-2 h-1 was 0.059 for the biochar chamber and .051 for the control (Figure 22).   During the mid interval, which was designated by ? between 0.22 and 0.30 m3 m-3, the g CO2 m-2 h-1 was 0.26 for the biochar chamber and 0.25 for the control.  The consumed ?g CH4 m-2 h-1 was -89 for the biochar chamber and -95 for the control.  The GWP in g CO2 eqv m-2 h-1 was 0.258 for the biochar chamber and 0.245 for the control (Figure 23).   During the dry interval, which was designated by ? less than 0.22 m3 m-3, the g CO2 m-2 h-1 was 0.146 for the biochar chamber and 0.129 for the control.  The consumed ?g CH4 m-2 h-1 was -81.3 for the biochar chamber and -77.6 for the control.  This was the only interval at which the biochar chamber had an increased ?g CH4 m-2 h-1 consumed when compared with the control.  The GWP in g CO2 eqv m-2 h-1 was 0.144 for the biochar chamber and 0.126 for the control (Figure 24).   61   Figure 22.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 h-1) over the wet-interval post-application period by both the biochar amended chamber and the control.  The biochar results show an increase in g CO2 emitted, a decrease in ?g CH4 consumed and a higher GWP than the control.  The wet interval was designated by ? greater than 0.30 m3 m-3.  This resulted in 818 hourly flux measurements being included in this analysis.       62   Figure 23.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 h-1) over the mid-interval post-application period by both the biochar amended chamber and the control.  The biochar results show a slight increase in g CO2 emitted, a slight decrease in ?g CH4 consumed and a higher GWP than the control.  The mid interval was designated by ? between 0.22 and 0.30 m3 m-3.  This resulted in 738 hourly flux measurements being included in this analysis.      63   Figure 24.  The CO2 emitted (g m-2 h-1), CH4 consumed (?g m-2 h-1), and the GWP (g CO2 eqv m-2 h-1) over the dry-interval post-application period by both the biochar amended chamber and the control.  The biochar results show an increase in g CO2 emitted an increase in ?g CH4 consumed and a higher GWP than the control.  The dry interval was designated by ? less than 0.22 m3 m-3.  This resulted in 773 hourly flux measurements being included in this analysis.    3.3.5.1 Effect of Biochar on FCO2 From these results, it is clear that the biochar chamber did emit more CO2 than the control chamber.  The increase in CO2 emissions when compared with the control was noticeable.  Using the overall averages presented in Figure 21, the amended soil emitted 0.041 g CO2 m-2 hr-1 more than the control.  This represents a 36.9% increase over the control.  It is difficult to state exactly what caused this, as both biotic and abiotic factors were involved.  Spokas et al., (2009) conducted 64  incubation studies using a forest soil and varying amounts of biochar and the results also showed an increase in the CO2 emitted; however this could have been released directly from the biochar itself, and not as a result of the biochar influencing the soil.  This lends value to the hypothesis that the biochar was releasing the CO2 in one of two ways: either as the labile fraction was consumed by microorganisms, or through reactions involving O2 and water.    Ameloot et al. (2013) reported that an increased release of CO2 following biochar application is most likely the result of three factors: priming of the native soil organic content, biodegradation of biochar components by soil microorganisms, or the abiotic release of biochar C.   It is interesting to note that during the mid-interval, the biochar chamber and the control chamber were more similar than the other intervals, with the biochar chamber producing 0.261 g CO2 m-2 h-1 compared with the control chamber producing 0.248 g CO2 m-2 h-1.  As of now, no explanation is apparent; however changing biological activity is likely the key factor.   The findings in this biochar experiment, therefore, cannot conclusively state that the FCO2 of the soil increased only from the application of biochar, as it is not known how much the abiotic release of C contributed to the increase in CO2 emissions.  Furthermore, the findings cannot conclusively state what would occur if the soil was maintained for a longer period of time at any one of the moisture intervals. 3.3.5.2 Effect of Biochar on FCH4 The results show a decrease in the consumption of CH4 for the biochar chamber compared to the control.  Using the overall results from Figure 21, the amended soil consumed, on average, 19.9 ?g CH4 m-2 h-1 less than the control.  This represents a decrease of 20.4% compared with the control consumption of CH4.  As mentioned previously, the overall net consumption or emission of CH4 is a 65  result of the sum of the consumption by methanotrophs and the production by methanogens.  Therefore the resulting ?g CH4 m-2 h-1 is the result of net CH4 oxidation by methanotrophs and CH4 production by methanogens (Baldocchi et al., 2012).  The fact that the CH4 consumption decreased in the biochar amended soil when compared to the control does not necessarily mean that consumption decreased per se.  There could have been an increase in the production of CH4 by methanogens which caused the net consumption of CH4 to decrease (Itoh, Ohte, & Koba, 2009).  It has been shown that methanotrophs need well-aerated soil to proliferate (Mikkela, Sundh, & Svensson, 1995).  This could explain the resulting decrease in CH4 consumption as the chambers experienced wet periods, which may have led to a proliferation of methanogens over certain periods of time.   The wet interval produced the largest difference between the biochar chamber and the control chamber in regards to CH4 consumption.  The biochar chamber consumed 15.1% less CH4 than the control.  These results agree with the abovementioned results which show that methanotrophs need well aerated soil to proliferate.  During the wet interval, the soil may have been too moist for the methanotrophs to proliferate to their full potential due to anaerobic conditions within areas of the soil matrix. Similarly, the dry interval was the only interval in which the biochar chamber consumed more CH4 than the control.  This also agrees with past data.  As the chambers dried out, there was more aeration and the methanotrophs were able to proliferate.  These results suggest that biochar as a soil amendment could increase CH4 consumption under certain moisture conditions.  The overall decrease suggests that the biochar amended soil either consumed less CH4 or emitted more CH4 thereby resulting in an overall reduction in CH4 consumption when compared with the control.   Wang et al., (2012) suggested that soil level microbial processes drive the overall 66  dynamics of FCH4. From this, one can conclude that the biochar amended chamber had a greater proportion of methanogens than methanotrophs, when compared to the control.   3.3.5.3 Effect of Biochar on Global-Warming Potential The average GWP of the biochar amended soil was 0.042 g CO2 eqv m-2 h-1 higher than the control.  This shows that the CH4 consumption was not enough to offset the increase in the CO2 emissions.  What is not taken into account with the calculation of the GWP is the C sequestered in the soil from biochar application.  It is not known what percentage of the labile portion of the biochar was decomposed during the experiment, but a rough comparison of the total C sequestered compared to the total C emitted can give some indication as to the sequestration potential of this specific biochar in this soil, under these conditions.   Using the mean GWP results, the difference between the biochar chamber and the control was 0.042 g CO2 eqv m-2 h-1 which equates to 0.011 g C m-2 hr-1.  The g C applied to the soil surface was 341 g C which equates to 1575 g C m-2.  The meta-analysis of biochar stability conducted by Ameloot et al. (2013) outlined numerous stability studies.  The majority of these were conducted with grass as a feedstock for the biochar; however there were five studies which used wood as feedstocks at temperatures similar to the conditions under which this experiment?s biochar was produced.  Keith et al. (2011) used wood pyrolyzed at 450? and 500?; Major et al. (2010) used mango wood pyrolyzed at 500?; and Santos et al. (2012) used Ponderosa pine pyrolyzed at 450?.  Taking the average of these five studies, a suitable mineralization rate for our biochar was estimated at 0.0031 % biochar C mineralized day-1.  Over the four month timeframe (145 days) of the experiment, the total % which was estimated to have been mineralized was 0.45%.  Taking this into account, the C which remained in the amended soil was approximately 339 g C per chamber area or 1566 g C m-2.  The difference in the 67  total carbon emitted by the biochar chamber when compared to the control chamber over the same time period was 39.75 g C m-2.  Therefore the biochar chamber had an overall C sequestration of 1526.25 g C m-2 more than the control, over the four month experiment.   With the conflicting results surrounding the mineralization of biochar (Ameloot et al., 2013), this is only a rough approximation; however it showed that even with an increase in CO2 production and a decrease in CH4 consumption, the biochar amended soil resulted in a net storage of C in the soil, during the timeframe of this experiment.   Many questions remain, especially the issue of accurately estimating the stability of biochar within a soil matrix, as well as the effect of biochar on FCO2 and FCH4 under different soil and atmospheric conditions.  The results presented here show that a biochar amended soil produced more CO2 and consumed less CH4 than was predicted by a model which assumed no biochar application, and provided a positive net C sequestration to the soil.  Furthermore, the biochar amended soil did consume more CH4 than the control under dry moisture conditions (specified by a ? less than 0.22 m3 m-3).   3.3.6 Future Implications The use of the automated chambers coupled to a CRDS was a novel way of exploring GHG emissions from biochar amended soil, and hopefully this same system can be modified and used in a field setting.  Powering the system will take some ingenuity, as the CRDS does consume significant amounts of power (145 W during analysis); however if the CRDS was located at a site which was already dealing with high-energy devices, perhaps modifications could be made to incorporate a CRDS and chamber system into the existing instrumentation.   68  As mentioned previously, the measurement of Veff under varying soil type, soil moisture conditions, and calibration gases could provide interesting results.  Due to the fact Veff is such an integral part of flux estimation, it is important that the factors governing Veff be researched as thoroughly as possible.   The emissions of the CO2 were indistinguishable as being from either just the biochar, or a product of the biochar and soil matrix.  Further studies may provide more information on how much biochar alone contributes to the FCO2 in the absence of soil.  This would give a clearer picture of how biochar affects FCO2.   Future studies should consider the moisture content of soils which are amended with biochar.  From the results of this experiment, it appears moisture may play a large role in the biotic (and potentially) abiotic mechanisms which control FCO2 and FCH4.  More research is needed on the effects of varying moisture levels with regards to FCO2 and FCH4 from biochar amended soils.        69  4. Conclusions 1. The coupling of a CRDS to an automated FT-NSS chamber system proved to be successful.  The experiments conducted on the accuracy of the system while the chamber was mounted on an aluminum base, with an artificial flux introduced into the chamber, provided results which fell within the error of the instrument, flow meter, and calibration gases.   2. The estimated Veff of the chambers were 68.6 L and 67.5 L for chamber 1 and 2, respectively when using a 20.07% by volume CO2 calibration gas.  The estimated Veff of the chambers were 67.2 L and 66.1 L for chamber 1 and 2, respectively when using a 0.10% by volume CH4 calibration gas.  The Vg of the chambers was 65.5 L.  These effective volumes agree with past literature in which Veff was often 5 ? 15% larger than Vg.   3. The soil which was amended with biochar produced on average 36.9% more CO2 than the control over the experimental time period.  The following three factors most likely caused this increase: priming the decomposition of native soil organic matter content by soil microorganisms, biodegradation of labile carbon in the biochar by soil microorganisms, and the abiotic release of biochar C, due to the release of the labile portion of the biochar to the atmosphere.  4. The soil which was amended with biochar consumed on average 20.4% less CH4 than the control over the experimental time period.  This could have been due to an increase in the activity of methanogens, a decrease in the activity of methanotrophs, or a combination of the two.  The wet moisture interval showed the largest difference between the biochar chamber and the control, with the biochar chamber consuming less CH4 than the control.  The dry moisture interval showed that the biochar chamber consumed more CH4 than the control.  This is likely due to anaerobic conditions being present during the wet interval, 70  which favoured the proliferation of methanogens, and the dry conditions providing enough aeration for the methanotrophs to proliferate in the soil.  5. After taking the increased CO2 emissions and the decreased CH4 consumption into consideration, combined with a theoretical degradation rate of biochar, it was estimated that this biochar applied to this soil had a net positive sequestration of 1526.25 g C m-2 over the experimental time period of four months or 145 days.    71  References Ameloot, N., Graber, E. R., Verheijen, F. G. a., & De Neve, S. (2013). Interactions between biochar stability and soil organisms: review and research needs. European Journal of Soil Science, 64, 379?390. doi:10.1111/ejss.12064 Bagreev, A., Bandosz, T. J., & Locke, D. C. (2001). Pore structure and surface chemistry of adsorbents obtained by pyrolysis of sewage sludge-derived fertilizer. Carbon, 39, 1971?1979. doi:10.1016/S0008-6223(01)00026-4 Baldocchi, D., Detto, M., Sonnentag, O., Verfaillie, J., Teh, Y. A., Silver, W., & Kelly, N. M. (2012). The challenges of measuring methane fluxes and concentrations over a peatland pasture. Agricultural and Forest Meteorology, 153, 177?187. doi:10.1016/j.agrformet.2011.04.013 Born, M., Dorr, H., & Levin, I. (1990). Methane consumption in aerated soils of the temperate zone. Tellus, 42B, 2?8. Bruun, E. W., Ambus, P., Egsgaard, H., & Hauggaard-Nielsen, H. (2012). Effects of slow and fast pyrolysis biochar on soil C and N turnover dynamics. Soil Biology and Biochemistry, 46, 73?79. doi:10.1016/j.soilbio.2011.11.019 Carslaw, D.C. & Ropkins, K. (2012). Openair ? an R package for air quality and data analysis. Environmental Modelling & Software. Volume 27-28, pp. 52-61.  Case, S. D. C., McNamara, N. P., Reay, D. S., & Whitaker, J. (2012). The effect of biochar addition on N2O and CO2 emissions from a sandy loam soil ? The role of soil aeration. Soil Biology and Biochemistry, 51, 125?134. doi:10.1016/j.soilbio.2012.03.017 Castaldi, S., Riondino, M., Baronti, S., Esposito, F. R., Marzaioli, R., Rutigliano, F. a, ? Miglietta, F. (2011). Impact of biochar application to a Mediterranean wheat crop on soil microbial activity and greenhouse gas fluxes. Chemosphere, 85, 1464?71. doi:10.1016/j.chemosphere.2011.08.031 Dalal, R. C., Allen, D. E., Livesley, S. J., & Richards, G. (2008). Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes: a review. Plant and Soil (Vol. 309, pp. 43?76). doi:10.1007/s11104-007-9446-7 Davidson, E. A., Belk, E., & Boone, R. D. (1998). Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest. Global Change Biology, 4, 217?227. Decagon Devices Inc. (2012). GS3 Water Content, EC and Temperature Sensors Operator's Manual. Version 1. Pullman, WA, USA. Decagon Devices Inc. (2013a). 5TE Water content, EC, and Temperature Sensor Operator's Manual. Version: January 2013. Pullman, WA, USA. 72  Decagon Devices Inc. (2013b). MPS-2 Dielectric Water Potential Sensor Operator's Manual. Version: January 2013. Pullman, WA, USA. Denman, K. L., Brasseur, G., Chidthaison, A., Ciais, P., Cox, P. M., Dickinson, R., ?  Zhang, X. (2007). Coupling between changes in teh climate system and biogeochemistry. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, H.L. Miller (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assesment Report of the Intergovernmental Panel on Climate Change (pp. 499--587). Cambridge, U.K.: Cambridge University Press. Doran, J. W., & Parkin, T. B. (1994). Defining and assessing soil quality. In J.E. Doran, Defining soil quality for a sustainable environment (pp. 3-32). Madison, WI, USA: SSSA Spec. Publ. Drewitt, G.B., Black, T.A., Nesic, Z., Humphreys, E.R., Jork, E.M., Swanson, R., Ethier, G.J.,  Griffis, T.J., Morgenstern, K. 2002. Measuring forest floor CO2 exchange in a coastal temperate rainforest. Agric. For. Meteorol. 110: 299-317. Drewitt, G. B., Black, T. A., & Jassal, R. S. (2005). Using measurements of soil CO2 efflux and concentrations to infer the depth distribution of CO2 production in a forest soil. Gaudinski, J. B., Trumbore, S. E., Eric, A., & Zheng, S. (2000). Soil carbon cycling in a temperate forest?: radiocarbon-based estimates of residence times , sequestration rates and partitioning of fluxes, 33?69. Gaumont-Guay, D., Black, T.A., Griffis, T.J., Barr, A.G., Jassal, R.S., Nesic, Z. (2006a). Interpreting the dependence of soil respiration on soil temperature and water content in a boreal aspen stand. Agric. For. Meteorol. 140: 220-235. Gaumont-Guay, D., Black, T.A., Griffis, T.J., Barr, A.G., Morgenstern, K., Jassal, R.S., Nesic, Z.  (2006b). Influence of temperature, drought and growth on seasonal and interannual variations of soil, bole and ecosystem respiration in a boreal aspen stand. Agric. For. Meteorol. 140: 203-219. Gaumont-Guay, D., Black, T.A., McCaughey, H., Barr, A.G., Jassal, R.S., Nesic, Z. (2008). Soil CO2 efflux in contrasting boreal deciduous and coniferous stands and its contribution to the ecosystem carbon balance. Global Change Biol., doi: 10.1111/j.1365-2486.2008.01830.x Gaumont-Guay, D., Black, T. A., Mccaughey, H., Barr, A. G., Krishnan, P., Jassal, R. S., & Nesic, Z. (2009). Soil CO2 efflux in contrasting boreal deciduous and coniferous stands and its contribution to the ecosystem carbon balance. Global Change Biology, 15, 1302?1319. doi:10.1111/j.1365-2486.2008.01830.x Goulden, M. L., & Crill, P. M. (1997). Automated measurements of CO2 exchange at the moss surface of a black spruce forest. Tree physiology, 17, 537?542. Ghildyal, B. P., & Tripathi, R. P. (1987). Soil Physics. New York: John Wiley & Sons. 73  Hanson, P. J., Edwatds, C. T., Garten, C. J., & Andrews, J. A. (2000). Separating root and soil microbial contributions to soil respiration: A review of methods and observations. Biogeochemistry, 48, 115-146. Healy, R. W., Striegl, R. G., Russell, T. F., Hutchinson, G. L., & Livingston, G. P. (1996). Numerical evaluation of static-chamber measurements of soil-atmosphere gas exchange: Identification of physical processes. Soil Science Society of America Journal, 60, 740-747. Heimann, M., & Reichstein, M. (2008). Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature, 451(7176), 289?92. doi:10.1038/nature06591 Holland, E. A., Robertson, G. P., Greenberg, J., Groffman, P. M., Boone, R. D., & Gosz, J. R. (1999). Soil CO2, N2O, and CH4 Exchange. In G. P. Robertson, D. C. Coleman, & C. Bledsoe (Eds.), Standard Soil Methods for Long-Term Ecological Research (pp. 185?201). Oxford: Oxford University Press. Hutchinson, G. L., & Livingston, G. P. (1995). Enclosure-based measurements of trace gas exchange: applications and sources of error. In Biogenic Trace Gases: Measuring Emissions from Soil and Water (pp. 28?65). Oxford, U.K.: Blackwell Science Ltd. Hutchinson, G. L., & Livingston, G. P. (2001). Vents and seals in non-steady-state chambers used for measuring gas exchange between soil and the atmosphere. European Journal of Soil Science, 52, 675?682. Hutchinson, G. L., & Mosier, A. R. (1981). Improved Soil Cover Method for Field Measurement of Nitrous Oxide Fluxes. Soil Sci. Soc. Am. J., 45, 311?316. International Biochar Initiative. (2011). About Us | International Biochar Initiative. About Us Webpage. Retrieved December 08, 2011, from http://www.biochar-international.org/about International Biochar Initiative. (2012). Standardized Product Definition and Product Testing Guidelines for Biochar That Is Used in Soil. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Itoh, M., Ohte, N., & Koba, K. (2009). Methane flux characteristics in forest soils under an East Asian monsoon climate. Soil Biology and Biochemistry, 41, 388?395. doi:10.1016/j.soilbio.2008.12.003 Janssens, I. a., Kowalski, A. S., Longdoz, B., & Ceulemans, R. (2000). Assessing forest soil CO2 efflux: an in situ comparison of four techniques. Tree physiology, 20, 23?32. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12651523  74  Jassal, R., Black, A., Novak, M., Morgenstern, K., Nesic, Z., & Gaumont-Guay, D. (2005). Relationship between soil CO2 concentrations and forest-floor CO2 effluxes. Agricultural and Forest Meteorology, 130(3-4), 176?192. doi:10.1016/j.agrformet.2005.03.005 Jassal, R.S., Black, T.A., Nesic, Z. 2012a. Biophysical controls of soil CO2 efflux in two coastal Douglas-fir stands at different temporal scales Agric. For. Meteorol.153: 134?143. Jassal, R.S., Black, T.A., Nesic, Z., Gaumont-Guay, D. 2012b. Using automated non-steady-state chamber systems for making continuous long-term measurements of soil CO2 efflux in forest ecosystems. Agric. For. Meteorol. 161: 57?65. Jollymore, A., Johnson, M. S., & Hawthorne, I. (2012). Submersible UV-Vis Spectroscopy for Quantifying Streamwater Organic Carbon Dynamics: Implementation and Challenges before and after Forest Harvest in a Headwater Stream, 3798?3813. doi:10.3390/s120403798 Jones, D. L., Murphy, D. V., Khalid, M., Ahmad, W., Edwards-Jones, G., & DeLuca, T. H. (2011). Short-term biochar-induced increase in soil CO2 release is both biotically and abiotically mediated. Soil Biology and Biochemistry, 43, 1723?1731. doi:10.1016/j.soilbio.2011.04.018 Kuzyakov, Y., Bogomolova, I., & Glaser, B. (2014). Biochar stability in soil: Decomposition during eight years and transformation as assessed by compound-specific 14C analysis. Soil Biology and Biochemistry, 70, 229?236. doi:10.1016/j.soilbio.2013.12.021 Le Dantec, V., Epron, D., & Dufr?ne, E. (1999). Soil CO2 efflux in a beech forest?: comparison of two closed dynamic systems. Plant and Soil, 214, 125?132. Lee, X., Wu, H., Sigler, J., Oishi, C., & Siccama, T. (2004). Rapid and transient response of soil respiration to rain. Global Change Biology, 10, 1017?1026. doi:10.1111/j.1365-2486.2004.00787.x Lehmann, J.; Joseph, S. (Eds.). (2009). Biochar for Environmental Management. London, U.K.: Earthscan. Linn, D. M., & Doran, J. W. (1984). Effect of water-filled pore space on carbon dioxide and nitrous oxide production in tilled and non-tilled soils. Soil Science Society of America Journal, 48, 1267-1272. Livingston, G. P., & Hutchinson, G. L. (1995). Enclosure-based measurement of trace gas exchange: applications and sources of error. In P. A. Matson, & R. C. Harriss, Biogenic Trace Gases: Measureing Emissions from Soil and Water (pp. 14-51). Oxford: Blackwell Scientific Publications. Lloyd, J., & Taylor, J. A. (1994). On the temperature dependence of soil respiration. Functional Ecology, 8, 315?323. Maier, M., Schack-Kirchner, H., Hildebrand, E. E., & Holst, J. (2010). Pore-space CO2 dynamics in a deep, well-aerated soil. European Journal of Soil Science, 61(6), 877?887. doi:10.1111/j.1365-2389.2010.01287.x 75  Mikkela, C., Sundh, I., & Svensson, B. H. (1995). Diurnal variation in methane emission in relation to the water table , soil temperature , climate and vegetation cover in a Swedish acid mire. Biogeochemistry, 28, 93?114. Nazaroff, W. (1992). Radon transport from soil to air. Reviews of Geophysics, 137-160. Norman, J. M., Kucharik, C. J., Gower, S. T., Baldocchi, D. D., & Crill, P. M. (1997). A comparison of six methods for measuring soil-surface carbon dioxide fluxes. Journal of Geophysical Research, 102, 28771 ? 28777. R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: http://www.R-project.org/. Rayment, M. B. (2000). Closed chamber systems underestimate soil CO2 efflux. Europea, 51, 107?110. Rochette, P., & Angers, D. A. (1999). Soil-surface CO2 fluxes induced by spring, sumer and fall moldboard plowing in a sandy loam. Soil Science Society of America Journal,63, 621-628. Rochette, P., & Hutchinson, G. L. (2005). Measurement of Soil Respiration in situ: Chamber Techniques. In M. K. Viney, J. L. Hatfield, & J. M. Baker, Micrometeorology in Agricultural Systems (pp. 247-286). Madison, WI, USA: Amercan Society of Agronomy, Inc. Rochette, P., & Mcginn, S. M. (2005). Soil-Surface Gas Fluxes. Rogovska, N., Laird, D., Cruse, R., Fleming, P., Parkin, T., & Meek, D. (2011). Impact of Biochar on Manure Carbon Stabilization and Greenhouse Gas Emissions. Soil Science Society of America Journal, 75(3), 871. doi:10.2136/sssaj2010.0270 Rondon, M., Ramirez, J. A., & Lehmann, J. (2005). Greenhouse gas emissions decrease with charcoal additions to tropical soils. Retrieved January 15, 2014, from International Biochar Initiative: http://www.biochar-international.org/node/924 Ryan, G., & Law, E. (2005). Interpreting , Measuring , and Modeling Soil Respiration Author (s): Michael G . Ryan and Beverly E . Law. Biogeochemistry, 73, 3?27. Saggar, S., Tate, K. R., Giltrap, D. L., & Singh, J. (2007). Soil-atmosphere exchange of nitrous oxide and methane in New Zealand terrestrial ecosystems and their mitigation options: a review. Plant and Soil, 309, 25?42. doi:10.1007/s11104-007-9421-3 Scheer, C., Grace, P. R., Rowlings, D. W., Kimber, S., & Zwieten, L. (2011). Effect of biochar amendment on the soil-atmosphere exchange of greenhouse gases from an intensive subtropical pasture in northern New South Wales, Australia. Plant and Soil, 345, 47?58. doi:10.1007/s11104-011-0759-1 Schery, S. D., Gaeddert, D. H., & Wilkening, M. H. (1984). Factors affecting exhalation of radon from a gravelly sandy loam. Journal of Geophysical Research, 89, 7299-7309. 76  Schlesinger, W., & Andrews, J. (2000). Soil Respiration and the Global Carbon Cycle. Biogeochemistry, 48, 7?20. Smith, E. P. (2006). BACI design. In A. H. El-shaarawi & W. W. Piegorsch (Eds.), Encyclopedia of Environmetrics (Vol. 1, pp. 141?148). John Wiley & Sons, Ltd. Smith, K. A., Ball, T., Conen, F., Dobbie, K. E., Massheder, J., & Rey, A. (2003). Exchange of greenhouse gases between soil and atmosphere?: interactions of soil physical factors and biological processes. European Journal of Soil Science, 54, 779?791. doi:10.1046/j.1365-2389.2003.00567.x Spokas, K. A., & Reicosky, D. C. (2009). Impacts of Sixteen Different Biochars on Soil Greenhouse Gas Production. Annals of Environmental Science, 3, 179?193. Suarez, D. (1999). Impact of agriculture on CO2 fluxes as affected by changes in inorganic carbon. In R.E. Lal, Global climate change and pedogenic carbonates (pp. 357-272). Boca Raton, FL, USA: CRC Press. Van Zwieten, L., Singh, B., Joseph, S., Kimber, S., Cowie, A., & Yin Chan, K. (2009). Biochar and Emissions of Non-CO2 Greenhouse Gases from Soil. In J. Lehmann, S. Joseph, (Eds.), Biochar for Environmental Management (pp. 227-249). Sterling, VA, USA: Earthscan: London. Vargas, R., Carbone, M. S., Reichstein, M., & Baldocchi, D. D. (2010). Frontiers and challenges in soil respiration research: from measurements to model-data integration. Biogeochemistry, 102(1-3), 1?13. doi:10.1007/s10533-010-9462-1 Welles, J. M., & Mcdermitt, D. K. (2005). Measuring carbon dioxide in the atmosphere. In J. L. Hatfield & J. M. Baker (Eds.), Micrometeorology in agricultural systems (pp. 267?300). Madison, WI: ASA, CSSA, and SSSA. Wesley, M. L., Lenschow, D. H., & Denmead, O. T. (1989). Flux Measurements Techniques. In D. H. Lenschow, & B. B. Hicks, Global Tropospheric Chemistry: Chemical Fluxes in the Global Atmosphere (pp. 31-46). Boulder, CO, USA: National Center for Atmospheric Research. Yanai, Y., Toyota, K., & Okazaki, M. (2007). Effects of charcoal addition on N2O emissions from soil resulting from rewetting air-dried soil in short-term laboratory experiments. Soil Science and Plant Nutrition, 53, 181?188. doi:10.1111/j.1747-0765.2007.00123.x Zimmerman, A. R., Gao, B., & Ahn, M.-Y. (2011). Positive and negative carbon mineralization priming effects among a variety of biochar-amended soils. Soil Biology and Biochemistry, 43, 1169?1179. doi:10.1016/j.soilbio.2011.02.005 Zimmermann, M., Bird, M. I., Wurster, C., Saiz, G., Goodrick, I., Barta, J., ? Smernik, R. (2012). Rapid degradation of pyrogenic carbon. Global Change Biology, 18, 3306?3316. doi:10.1111/j.1365-2486.2012.02796.x  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0166863/manifest

Comment

Related Items