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"MEASUREMENTS AND MODELING OF G A S FLUXES IN U N S A T U R A T E D MINE W A S T E M A T E R I A L S  by LOUIS K A T E L E K A B W E Dipl., Institut d e R e c h e r c h e Scientifique, 1977 B . S c , Universite du Q u e b e c a Montreal, 1983 M . S c , M . S c , T h e University of S a s k a t c h e w a n , 1994, 2001  A T H E S I S S U B M I T T E D IN P A R T I A L F U L F I L M E N T O F THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in  THE FACULTY OF GRADUATESTUDIES (Mining Engineering)  T H E UNIVERSITY O F BRITISH C O L U M B I A June 2007  \u00A9 Louis Katele Kabwe, 2007  \u000CAbstract A c c u r a t e m e a s u r e m e n t s a n d predictions of surface CO2 fluxes are n e e d e d to quantify However,  b i o g e o c h e m i c a l reaction no  standard  rates  appears  to  in unsaturated  exist  for  geologic media and  establishing the  soils.  a c c u r a c y of  field  m e a s u r e m e n t s of soil respiration rates. A s a result, a technique to m e a s u r e CO2 fluxes from the soil surface to the a t m o s p h e r e w a s recently d e v e l o p e d a n d verified m e s o c o s m s o v e r the range of C0  2  in  fluxes reported for field conditions. T h e m e t h o d ,  termed the d y n a m i c c l o s e d c h a m b e r ( D C C ) , w a s s h o w n to accurately m e a s u r e CO2 fluxes from ground surface to the a t m o s p h e r e in m e s o c o s m s . T h e main a d v a n t a g e of this direct technique is the almost instantaneous estimation of the CO2 flux. Although the D C C is a promising t e c h n i q u e , its ability to accurately quantify surface C0  2  flux  under field conditions remains to be verified. T h e field application of the D C C is investigated in this thesis with a particular f o c u s on quantifying reaction rates in waste-rock piles at the K e y L a k e uranium mine in northern S a s k a t c h e w a n , C a n a d a . It s h o u l d , however, be noted that the  dominant  g e o c h e m i c a l reactions in the two w a s t e - r o c k piles at the K e y L a k e mine w e r e not typical of acid rock d r a i n a g e ( A R D ) w a s t e - r o c k piles. T h e CO2 fluxes m e a s u r e d in this study o c c u r in the organic material underlying the w a s t e rocks, in contrast to A R D waste-rock piles w h e r e O2 c o n s u m p t i o n a n d CO2 production are the results of sulphide oxidation a n d carbonate buffering.  T h i s work provided a c o m p l e t e suite of  measurements  required to characterize spatial distribution of CO2 fluxes o n larger-scale studies of w a s t e - r o c k piles. T h e r e h a s b e e n no previous field-scale study to quantify C0 a c r o s s a waste-rock pile.  ii  2  fluxes  \u000CThe ability of the D C C method to accurately quantify field soil respiration was demonstrated by comparing the D C C fluxes to those obtained using two other C 0 flux 2  measurement techniques: the static closed chamber ( S C C ) and eddy covariance (EC) methods. The D C C yielded comparable data but had distinct advantages over the two other methods in terms of speed and repeatability. The D C C was also used to investigate C 0 fluxes under the climatic variables 2  (e.g., rainfall and evaporation) that affect soil water content at the Deilmann north (DNWR) and Deilmann south (DSWR) waste-rock piles, at the Key Lake uranium mine. The effects of rainfall events on waste-rock surface-water conditions and C 0 fluxes 2  were of short duration. A simple model for predicting the effects of soil water content on C 0  2  diffusion  coefficient and concentration profiles was developed. The model was verified with measured C 0  2  fluxes obtained from meso-scale columns of unsaturated  sand.  Verification of the model showed good agreement between predicted and measured data. The model was subsequently used to predict C 0  2  diffusion and concentration  profiles in response to changes in soil water contents in the piles and also to predict surface C 0 fluxes from the D N W R and D S W R for a 6-d test period [August 1 (day 3) to 2  August 6 (day 8) 2002] following a 72.9 mm precipitation event over the initial 48-h [July 30 (day 1) to July 31 (day 2) 2002]. The model predicted surface C 0 fluxes trends that 2  were very similar to the measured surface C 0 fluxes from the D N W R and D S W R piles 2  during the test period Based on the tests conducted in this thesis the D C C method has shown to be suitable for field applications to quantify C 0 fluxes and to characterize the spatial and 2  temporal dynamics of C 0 fluxes from unsaturated C-horizon soils and waste-rock piles. 2  iii  \u000CTable of Contents Page  Abstract  ii  Table of Contents  iv  List of Tables  ix  List of Figures  x  Abbreviations and Symbols  xxiii  Acknowledgements  xxvii  Dedication  xxviii  CHAPTER 1 : Introduction  1  1.1  Introduction  1  1.2  Research Objectives  3  1.3  Organization of the Thesis  5  CHAPTER 2:  Literature Review  7  2.1  Introduction  7  2.2  Waste-Rock Piles  8  2.3  Sources of C 0 in Subsurface Soils and Waste-Rock Piles  19  2.3.1  C 0 Production by Microbial Respiration (Biotic)  20  2.3.2  C 0 Production by Pyrite Oxidation-Carbonate Buffering (Abiotic)  24  2.4  2  2  2  Studies of C 0 in Subsurface Pore Gas and Associated Surface Gas 2  Fluxes from Waste Rock and non Waste-Rock Systems 2.4.1  2.4.2  27  Studies of Subsurface C 0 2 G a s and Surface C 0 2 Fluxes from Waste Rock Piles  28  Studies of Subsurface C 0 2 from Non-Waste-Rock Material  31  iv  \u000C2.5  Climatic Variables Affecting Subsurface and Surface G a s Fluxes: Precipitation and Evaporation  39  2.5.1  Evaporation  40  2.5.2  Methods of Predicting Evaporation  44  2.5.3  SoilCover Program  47  2.5.4  Chapter Summary  50  C H A P T E R 3:  Materials and Methods  53  3.1  Introduction  53  3.2  Laboratory Program  53  3.2.1  Sample Collection  53  3.2.2  Grain-Size Analysis  54  3.2.3 Water Retention Curve  56  3.2.4  58  Saturated Hydraulic Conductivity  3.3  Laboratory Mesocosm and Minicosms (sand columns)  62  3.4  Field Program  65  3.4.1  Site Location and Description  66  3.4.2  Field C 0 Flux Measurements Methods  72  2  3.4.2.1 Measuring C 0 Fluxes using Dynamic Closed Chamber (DCC) 2  Method  74  3.4.2.2 Measuring C 0 Fluxes using Static Closed Chamber (SCC) 2  Method  77  3.4.2.3 Measuring C 0 Fluxes using Eddy Covariance (EC) Method  80  3.4.2.4 Gravimetric Water Content Measurement  83  3.4.2.5 Meteorological Weather Station  83  3.4.2.6 Chapter Summary  84  2  \u000CC H A P T E R 4: 4.1  4.2  Results and Data Interpretation  86  Laboratory Tests Program  86  4.1.1  Grain-Size Distribution  86  4.1.2 Water Retention Curve  91  4.1.3  99  Hydraulic Conductivity  Field Tests Program  104  4.2.1  Diurnal Variation In C 0 Flux  104  4.2.2  Spatial and Temporal Variation in C 0 Flux Measured using the  2  2  Dynamic Closed Chamber at the Deilmann South Waste-Rock Pile 4.2.3  Spatial and Temporal Variations in C 0 Flux Measured using the 2  Dynamic Closed Chamber at the Deilmann North Waste-Rock Pile 4.2.4  107  112  Cross-Statistical Comparison Between C 0 Fluxes Measured from 2  across the DNWR and D S W R 4.3.  115  Comparison of C 0 Fluxes Measured using the D C C to those Measured 2  using Static Closed Chamber (SCC) and Eddy Covariance (EC) Methods on the Deilmann South Waste Rock Pile (DSWR)  117  4.3.1  Introduction  117  4.3.2  D C C Fluxes  118  4.3.3. S C C Fluxes  118  4.3.4  E C Fluxes  123  4.3.4  Summary of the advantages and disadvantages of the dynamic closed chamber (DCC) method  C H A P T E R 5: 5.1.  129  A n a l y s i s and D i s c u s s i o n  131  Introduction  131  vi  \u000C5.2  Effects of Rainfall Events on Waste-Rock Surface Water Conditions and C 0  2  Fluxes Across the Surfaces of the Deilmann North (DNWR) and Deilmann South (DSWR) Waste Rock Piles 5.2.1  131  Short Term Effects of Rainfall Events on Near Surface-Water Conditions  132  5.2.2 Short Term Effects of Rainfall Events on C 0 Fluxes 2  5.3  5.4  138  Predictions of Evaporative Fluxes and Near-Surface Water Contents Profiles  142  5.3.1  144  Short Term Predictions of Evaporative Fluxes  5.3.2 Short Term Predictions of Near Surface Water Contents Profiles  150  C 0 Diffusion Prediction and Model Proposed  153  5.4.1  153  2  C 0 Diffusion 2  5.4.2 Biotic C 0 Production Rate  159  5.4.3 Development of the Partial Differential Equation  163  5.4.4 Finite Difference Formulation  166  5.5  Computer Code Program  169  5.6  Application of the \"C02\" Model Using Measured Values in Sand Minicosms ..173  2  5.6.1  Prediction of C 0 Concentration Profiles in Response to Changes in 2  Water Contents Profiles  173  5.6.2 Simulations of C 0 Concentrations Profiles using Sand Minicosms 2  Measured Data 5.7  176  Prediction of C 0 Diffusion and Concentration-Depth Profiles in Response to 2  Changes in Water-Depth Profiles in the DSWR  vii  181  \u000C5.8  5.9  Predictions of C 0 Diffusion and Surface C 0 Flux from the D N W R and 2  2  D S W R Piles Following Rainfall Events  188  Chapter Summary  192  C H A P T E R 6.  S u m m a r y and C o n c l u s i o n s  193  References  197  Appendices  219  Appendix A:  Measuring 0 2 Fluxes Using the Dynamic Closed Chamber (DCC) System  Appendix B:  219  Eddy Correlation Method: a Brief Theory  227  Appendix C : Computer Code for C 0 Diffusion Model  231  Appendix D: Waste-Rock Samples Analyses Results  244  2  Appendix E: C 0 Flux Measurements Results obtained at the Deilmann south 2  (DSWR) and Deilmann north (DNWR) Waste-rock Piles using the Dynamic Closed Chamber (DCC), Static Closed Chamber (SCC) and Eddy Covariance (EC) Methods  252  Appendix F: Data for Measurements of Water Contents and C 0 Fluxes Across 2  the Surfaces of the D S W R and D N W R Piles after Rainfall Events Appendix G : Minicosms Data used for Validation of the C 0 Diffusion Model 2  257 259  Appendix H: Climatic Parameters used in Simulations with SoilCover and recorded at the weather station installed on the Deilmann south waste-rock (DSWR) pile Appendix I:  262  SoilCover Run Summary Pages for Simulations of Evaporative Fluxes At the D S W R and D N W R Piles during the Field Tests  Vlll  254  \u000CList of Tables Page  Table 2.1  In-situ thermal conductivity measurements in waste-rock dumps material  I  Table 2.2  In-situ air permeability measurements in waste-rock dump material...  Table 2.3  In-situ oxygen diffusion coefficient measurements in waste-rock dumps  5  15  15  Table 2.4  Typical physical properties of A R D waste-rock piles (Ritchie, 1994a).  Table 2.5  Physicochemical properties of the Doyon and Nordhalde waste rock piles (Lefebvre et al., 2001)  1 6  1 7  Table 2.6  Typical characteristics of A R D (Ritchie, 1994a)  18  Table 2.7  Summary of CO2 concentrations for waste-rock material  28  Table 2.8  Summary of C 0 concentrations for non-waste-rock material  31  Table 2.9  Summary of surface C 0 fluxes for non-waste-rock material  33  Table 4.1  Nature, origin, and basic geotechnical properties of various granular  2  2  materials Table 4.2  87  Nature and origin of data for the hydraulic conductivity \"k\" value of various granular materials  Table 4.3  103  Summary of results of C 0 flux measurements using the dynamic 2  closed chamber system (DCC) for the test period of 2000-2002 at Deilmann south waste-rock pile (DSWR) Table 4.4  112  Summary of results of C 0 flux measurements using the dynamic 2  closed chamber system (DCC) for the test period of 2000-2002 at Deilmann north waste-rock pile (DNWR)  113  \u000CList of Figures Page  Figure 2.1  Diagram describing the complex flow system develop within the waste-rock  dump  in  response to  precipitation  and  climatic  conditions Figure 2.2  9  Conceptual model of water flow and vapour transport in a wasterock dumps  Figure 2.3  11  (A) Depth geologic profile for Deilmann south waste-rock (DSWR) pile (Adapted from Birkham et al., 2003 and (B) Map showing the Deilmann north (DNWR), Deilmann south (DSWR) and outline of former lake bed at the Key Lake mine, Saskatchean, Canada  Figure 2.4  22  0 2 consumption rates vs C 0 2 production rates for forest soils, lake  bottom  sediments,  bottom  gneissic  waste  rocks  units:  x r e p r e s e n t s g n e i s s i c w a s t e r o c k s (DNWR); A r e p r e s e n t s  u.mol/kg/week; lake  and  sediments  c o l l e c t e d from  beneath  the  waste-rock  pile  (DNWR): \u2022 r e p r e s e n t s forest soils (natural forest site a d j a c e n t to the DNWR) (Lee et al., 2003b)  Figure 2.5  (A)  Relation  of  23  evaporation  (flux)  to  time  under  different  evaporativities. (B) Relation of relative evaporation rate (actual rate as a function of the potential rate) to time, indicating the three stages of the drying process Figure 3.1  Photographs  showing  the  sedimentation process setups  42 mechanical  sieve  machine  and 55  \u000CFigure 3.2  Schematic diagram and photograph of Tempe cell setup for measurement of soil water characteristic curve  Figure 3.3  57  Schematic diagram and photograph of permeameter cell setup for measurement of saturated hydraulic conductivity  Figure 3.4  60  Schematic diagram and photographs of the columns (mesocosm and minicosms) used to calibrate and verifify the dynamic closed chamber method  Figure 3.5  63  Map of Saskatchewan showing the location of the Key Lake uranium mine, Saskatchewan, Canada  Figure 3.6  67  Photograph showing the Deilmann pit, the Deilmann north wasterock (DNWR) and Deilmann south waste-rock (DSWR) piles at the Key Lake uranium mine, Saskatchewan, Canada  Figure 3.7  69  Depth geologic profile for Deilmann south waste-rock (DSWR pile at the Key Lake mine, Saskatchewan, Canada (Adapted from Birkham etal., 2003)  Figure 3.8  71  Map of the Deilmann north waste-rock (DNWR) and Deilmann south  waste-rock  Saskatchewan,  (DSWR)  Canada,  piles  at  showing  the  the  Key  Lake  chambers  mine,  and  the  meteorological weather station locations Figure 3.9  Schematic diagram  and  photograph  73 of the  dynamic  chamber (DCC) setup for surface C 0 flux measurements 2  XI  closed 75  \u000CFigure 3.10  (A) Typical slopes of direct measurement of concentration versus time using the dynamic closed chamber (DCC) method  (B)  schematic diagram and (C) photograph of the D C C method setup for measuring surface C 0 gas fluxes  78  2  Figure 3.11  Photograph showing the meteorological weather station and the eddy covariance (EC) sensors for measuring C 0 flux installed on 2  Deilmann south waste-rock (DSWR) Figure 3.12  81  (A) S c h e m a t i c d i a g r a m a n d (B) photograph of station installed  meteorological weather  on Deilmann south waste-rock (DSWR) pile at the  Key Lake mine, Saskatchewan, Canada Figure 4.1  85  Particle size distribution curves (without gravel and boulder-sized) for the samples of waste-rock from Deilmann south waste-rock pile (DSWR) for ground surface sand (curve with symbols) and core sand/sandstone (curves with full lines). Symbols represent the measured data from this thesis. The full lines show the one standard deviation range of grain-size data obtained by Birkhman et al. (2002)  Figure 4.2  88  Particle size (without gravel and boulder-sized) distribution curves for the samples of waste-rock from Deilmann north waste-rock pile (DNWR) for ground surface sand (curve with broken line and symbols)  and  core  basement-rock  (curves  with solid  lines).  Symbols represent the measured data from this thesis. The full lines show the one standard deviation range of grain-size data obtained by Birkhman et al. (2002)  xii  89  \u000CFigure 4.3  Water retention curve (WRC) of the sample of waste-rock (with fine fraction only) from the Deilmann north waste-rock (DNWR) pile. Symbols represent the measured data and the solid line the best fit curve generated with SoilCover (SoilCover, 1997)  Figure 4.4  92  Water retention curve (WRC) of the sample of waste-rock (with fine fraction only) from the Deilmann south waste-rock (DSWR) pile. Symbols represent the measured data and the solid line the best fit curve generated with SoilCover (SoilCover, 997)  Figure 4.5  93  Water Retention Curve (WRC) of the sample of waste-rock from the Deilmann north (DNWR) pile: Symbols represent the measured data and the solid line represents the best fit curve generated with SoilCover(SoilCover, 1997)  Figure 4.6  95  Water Retention Curve (WRC) of the sample of waste-rock from the Deilmann south (DSWR) pile: Symbols represent the measured data for and the solid line represens the best fit curve generated with SoilCover (SoilCover, 1997)  Figure 4.7  96  Characteristic of the sample of the waste-rock from the Deilmann north waste-rock pile (DNWR): hydraulic conductivity curve (K). The value of saturated hydraulic conductivity (K ) was measured sat  in the laboratory but the unsaturated hydraulic conductivity (K) was derived from the Brooks and Corey mode (Brooks and Corey, 1964)  101  xiii  \u000CFigure 4.8  Characteristic of the sample of the waste-rock from the Deilmann north waste-rock pile (DNWR): hydraulic conductivity curve (K). The value of saturated hydraulic conductivity (K ) was measured sat  in the laboratory but the unsaturated hydraulic conductivity (K) was derived from the Brooks and Corey mode (Brooks and Corey, 1964) Figure 4.9  102  Short-term (hourly) variations in the C 0 flux measured at DSF1 on 2  August 6, 2000. Fluxes were determined using the dynamic closed chamber (DCC) method and averaging a series of four to eight measurement cycles, with each cycle lasting from 2- to 8-min (depending on the magnitude of the flux). The shaded box represents the 95% confidence interval (\u00B117 mg C 0  2  rrf  2  h\" ) 1  around the calculated daily mean (235 mg C 0 nrf h\" ) 2  106  1  2  Figure 4.10  (A) C 0 fluxes measured using the dynamic closed chamber (DCC) 2  at twenty selected sampling stations (DSF1 -  DSF20) at the  Deilmann south waste-rock pile (DSWR) (Figure 3.8) during the summers of 2000 and 2002 (B) average flux values (mg C 0 m\" h\" 2  2  1  ) measured from sampling locations (\u2022) on the D S W R  xiv  108  \u000CFigure 4.11  Daily variations in the C 0 flux measured at the Deilmann south 2  waste rock (DSWR) pile in (A) July, (B) August, and (C) September 2000. Flux measurements were obtained at three to four locations on each sampling date. At each location, the flux was determined using the dynamic closed chamber (DCC) method and averaging a series of four to eight measurement cycles, with each cycle lasting from 2- to 8-min (depending on the magnitude of the flux). The overall mean for each monthly sampling period is represented by the dashed lines (  ). Within months, symbols labeled with the  same letter are not significantly different at the P &lt; 0.05 level of probability Figure 4.12  109  Box &amp; Whisker plot characterizing the spatial and long-term temporal variability in the C 0 flux measured using the dynamic 2  closed chamber (DCC) method at the Deilmann south waste-rock (DSWR) pile in 2000 and 2002. The estimated, time-averaged flux = 170 ( \u00B1 5 1 ) mg C 0 rrf h\" . The minimum and maximum flux 2  1  2  values are marked by asterisks (*). Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance Figure 4.13  1  1  1  1  1  C 0 fluxes measured using the dynamic closed chamber (DDC) at 2  nine sampling stations (DNF1 - DNF9) at the Deilmann north waste-rock pile (Figure 3.6) during the summers of 2000 and 2002: (A) Data points presented on a X Y (scatter) and (B) average flux values (mg C 0 rrf h\" ) from samplings locations on the DNWR. ... 2  1  2  X V  4  \u000CFigure 4.14  Spatial and temporal variations in C 0 fluxes measured during the 2  summer of 2000 and summer 2002: box-and-wisker plots showing the mean, standard deviation, and extreme values for Deilmann north waste-rock (DNWR) pile data Figure 4.15  116  Box-and-wisker plot for flux measurements obtained using the D C C method at the Deilmann south waste-rock (DSWR) pile during the period from August 2 4  th  to August 25 , 2002 (set of data for th  comparison with the other two methods: S C C and EC). The minimum and maximum flux values are marked by asterisks (*). Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance Figure 4.16  119  (A) C 0 flux values (mg nrT h\" ) obtained using the static closed 2  1  2  chamber (SCC) at eleven selected sampling stations (\u2022) at the Deilmann south waste-rock (DSWR) pile in the morning (AM) (between 10:00 and 11:00) and afternoon (PM) between 16:30 and 17:30) on August 24, 2002 (B) averages fluxes (mg C 0 m 2  2  h\" ) 1  from the sampling locations Figure 4.17  120  Box-and-wisker plots for C 0 flux measurements obtained from the 2  Deilmann south waste rock pile on August/24/2002. Measurements were obtained using the static closed chamber method between the hours of 10:00 and 11:00 A M and 16:30 and 17:30 P M . The minimum and maximum flux values are marked by asterisks *. Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance  XVI  121  \u000CFigure 4.18  Comparison of the dynamic closed chamber (DCC) and static closed chamber (SCC) methods for measuring C 0  2  fluxes. Flux  measurements were obtained at the Deilmann south waste-rock (DSWR) pile site during the period from August 2 4 to August 25 , th  th  2002. The minimum and maximum flux values are marked by asterisks (*). Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance Figure 4.19  122  Diurnal variations in the C 0 flux measured from 10:00 to 17:00 on 2  August 25, 2002 using the E C method at the Deilmann south waste-rock (DSWR) pile. The shaded box represents the 95% confidence interval (\u00B1 24 mg C 0  nrf h\" ) around the calculated 2  2  1  daily mean (150 mg C 0 m\" h\" ) 2  124  1  2  Figure 4.20  Measured C 0 fluxes using Eddy covariance (EC) at the Deilmann 2  south waste-rock (DSWR) pile. Measurements were obtained on a continuous basis during the period from June 2 5  th  to August 2 5  th  2002. Each data point represents the daily mean value averaged over the period from 10:00 to 17:00 hours: The shaded box (B) represents the 95% confidence interval (\u00B110 mg C 0  m~  2  2  h\" ) 1  around the overall mean (150 mg C 0 m~ h\" ). Note: gaps in the 2  1  2  data set represent precipitation events during which no useful data were collected by the E C system  XVII  1  2  5  \u000CFigure 4.21  Comparison of the eddy covariance (EC) and chamber-based methods for measuring the C 0  flux from the Deilmann south  2  waste-rock (DSWR) pile Figure 5.1  127  Rainfall and volumetric water contents measured over an 8-day test period [July 30 (day 1) to August 6 (day 8), 2002] at station DNF1 with time at the Deilmann north waste-rock (DNWR) pile  Figure 5.2  133  Rainfall and volumetric water contents measured over an 8-day test period [July 30 (day 1) to August 6 (day 8), 2002] at sampling station DSF1 with time at the Deilmann south waste-rock pile (DSWR)  Figure 5.3  134  Volumetric water content profiles measured over an 8-d test period (30 July (day 1) to 6 August (day 8) 2002 at station: (A) DSF1 at the Deilmann south waste-rock (DSWR) pile and (B) DNF1 at the Deilmann north waste-rock (DNWR) pile with time  Figure 5.4  Rainfall, water contents, and C 0  2  137  fluxes measured at sampling  station DNF1 over an 8-d test period (30 July (day 1) to 6 August (day 8) 2002) at the Deilmann north waste-rock (DNWR) pile with time Figure 5.5  139  Rainfall, water contents, and C 0  2  fluxes measured at sampling  station DSF1 over an 8-d test period (30 July (day 1) to 6 August (day 8) 2002) at the Deilmann south waste-rock (DSWR) pile with time  140  xviii  \u000CFigure 5.6  Variations in C 0 flux measurements with surface-water saturation 2  (S=8/n) measured over an 8-d test period [July 30 (day 1) to August 6 (day 8), 2002] at DSF1 and DNF1 at the Deilmann south waste-rock pile (DSWR) and Deilmann north waste-rock  pile  (DNWR) respectively Figure 5.7  (A)  143  Rainfall, water contents measured, and SoilCover predicted  evaporative fluxes at the Deilmann north waste-rock (DNWR) pile (B) ratio of actual (AE) and  potential (PE) evaporation as a  function of time over an 8-d test period [30 July (day 1) to 6 August (day 8) 2002] Figure 5.8  (A)  145  Rainfall, water contents measured, and SoilCover predicted  evaporative fluxes at the Deilmann north waste-rock (DSWR) pile (B) ratio of actual (AE) and  potential (PE) evaporation as a  function of time over an 8-d test period [30 July (day 1) to 6 August (day 8) 2002] Figure 5.9  146  SoilCover predicted evaporative fluxes (actual A E and potential (PE) and (B) ratio of A E / P E at the Deilmann south waste-rock pile (DSWR) over a 27-d test period (29 July (day 1) to 24 August (day 27) 2002) with time  Figure 5.10  148  SoilCover predicted evaporative fluxes (actual A E and potential (PE) and (B) ratio of A E / P E at the Deilmann north waste-rock pile (DNWR) over a 27-d test period (29 July (day 1) to 24 August (day 27) 2002) with time  149  xix  \u000CFigure 5.11  Comparison of (A) measured and (B) SoilCover simulated water content profiles and (B) SoilCover predicted water contents for a 6d test period [30 July (day 3) to 4 August (day 8), 2002] at the Deilmann north waste-rock (DNWR) pile  Figure 5.12  151  Comparison of (A) measured and (B) SoilCover simulated water content profiles and (B) SoilCover predicted water contents for a 6d test period [30 July (day 3) to 4 August (day 8), 2002] at the Deilmann south waste-rock (DSWR) pile  Figure 5.13  ;  152  (A) Hypothetical water content profiles obtained by reducing the initial water profile (d1) by a factor of 0.8 consecutively (B) Simulated effective diffusion coefficient (D ) of C 0 as a function of e  2  water content using artificial data presented above Figure 5.14  Simulated microbial respiration rates as a function of temperature and water content using Equation 5.21  Figure 5.15  163  Representative elementary volume, R E V , for derivation of partial differential equation  Figure 5.16  158  164  Three nodes and the mass fluxes entering and exiting node 1 for development of the finite difference formulation  167  Figure 5.17  Flowchart for Visual Basic program  170  Figure 5.18  Stability curves generated by the model for different iterations using time steps of 0.05 day  172  X X  \u000CFigure 5.19  (A)  Hypothetical water  contents profiles in a sand material  described in Figure 5.13A (B) Model predicted C 0 concentrations 2  profiles in a HT sand column obtained with hypothetical simulated water contents profiles (Figure 5.19A) and an initial measured C 0  2  concentrations profiles (dl) in HT column (Kabwe et al., 2002) igure 5.20  Measured volumetric  water  content  temperature (LT) (thermostat  profiles in the  175  low  (A)  set at 5 \u00B0C) and (B) high  (21  temperature (HT) (room tem[perature) minicosms. V represent the water table Figure 5.21  177  Measured C 0 concentration profiles in the (A) high temperature 2  (HT) (21 - 23 \u00B0C) and, low temperature (LT) (5 \u00B0C) minicosms (Richards, 1998; Kabwe, 2001) Figure 5.22  Model predicted C 0  2  179  concentration profiles in the  (A)  high  temperature (HT) (21 - 23 \u00B0C) and (B) low temperature (LT) (5\u00B0C) minicosms Figure 5.23  180  Relationship between measured and simulated C 0 concentrations 2  from (A) low temperature (LT) and (B) high temperature (HT) minicosms plotted on a 1:1 scale Figure 5.24  182  Depth profiles for Deilmann south waste-rock (DSWR) pile (A) Geologic profile (B) mean C 0 concentration (Vol.) and (C) mean 2  volumetric water contents values (Adapted from Birkham et al, 2003)  184  XXI  \u000CFigure 5.25  (A) Hypothetical water-depth profiles in D S W R pile and (B) model predicted effective  diffusion  coefficients  (D )  in  e  response to  changes in water contents (Figure 5.32A). Curve d1 represents the actual measured mean water- depth profile (Birkham et al., 2003). The subsequent profiles were generated by  reducing the initial  measured water-depth profile by a factor of 0.1 consecutively Figure 5.26  186  Model predicted changes in: (A) effective diffusion coefficient (D ) e  and (B) C 0  2  concentrations profiles in response to changes in  water contents profiles described in Figure 5.32A Figure 5.27  Rainfall, measured surface water content and  187 C0  2  flux and  predicted effective diffusion coefficient (D ) and surface C 0 e  2  flux at  the Deil;mann North was;te-rock (DNWR) pile over an 8-day test period [30 July (day 1) to 6 August (day 8) 2002] with time Figure 5.28  Rainfall, measured surface water content and  C0  2  189 flux and  predicted effective diffusion coefficient (D ) and surface C 0 e  2  flux at  the Deilmann South waste-rock (DSWR) pile over an 8-day test period [30 July (day 1) to 6 August (day 8) 2002] with time  XXll  190  \u000CAbbreviation and Symbols 1.  Abbreviations  Meaning  A  Area  Arh  Inverse of relative humidity of air  AE  Actual evaporation  AEV B  Air entry value Inverse of relative humidity of soil surface  R N  C  Concentration  C  p  Specific heat capacity  C  u  Uniformity coefficient  C  v  Specific heat of the soil  C  h  Volumetric specific heat of the soil  C  w  Coefficient of consolidation with respect to water phase  CV  Coefficient of variation  D* D  e  D  a  Bulk diffusion coefficient Effective diffusion coefficient Diffusion coefficient through air phase Equivalent particle diameter  DH D  w  Diffusion coefficient through water phase  D  v  Diffusion coefficient of water vapor  Dvap  Molecular diffusion of water vapor  DAS  Data acquisition system  DCC  Dynamic closed chamber  DNF#  Collar location # at Deilmann north  DSF#  Collar location # at Deilmann south  DNWR  Deilmann north waste-rock  DSWR  Deilmann south waste-rock  E  Vertical Evaporatve flux  E  a  E  p  Actual evaporation Potential evaporation  Eh  Sensible heat flux  XXlll  \u000CEC  Eddy covariance  e  Void ratio  e  s  Vapour pressure at the soil surface  e  a  Vapour pressure of the air above the evaporating surface  f(u)  Wind mixing function  Fco2  Flux of carbon dioxide  g  Acceleration due to gravity  G  Production rate  Go G  r  G  s  Reference production rate Ground heat flux Groung heat flux Height  h h  Relative humidity of the soil surface  r  \"w  Total head  Hh  Pressure head  H  Henry's Law coefficient  HT  High temperature  k  Hydraulic conductivity  k  r  Constant in Arrhenius equation  k  s  Saturated hydraulic conductivity  L  Pore-size distribution index  U  Latent heat of evaporation of water  m  Total mass of gas  P  Total Pressure  PE P  Potential evaporation Saturation vapour pressure of the soil  s  Pv  Saturation vapour pressure of soil  S  P  Vapor pressure within the soil  v  q Qn  Humidity Net radiant energy available at the surface  R  Universal gas constant  REV  Representative elementary volume  Rc  Concentration ratio of two isotopes xxiv  \u000CS  Degree of saturation  S  r  Residual saturation  S  e  Effective saturation  SCC  Static closed chamber  SWCC  Soil water characteristic curve  t  time  T  Temperature  T  a  T  s  Air temperature Surface temperature  Tra  Tortuosity coefficient for air  Trw U  Tortuosity coefficient for water phase wind speed  a  U*  Friction velocity.  V  Volume  V  s  Volume of voids  V  v  Volume of solids  W  Molecular weight of water  v  Vertical wind  w  XXV  \u000C2.  Symbols  Meaning  a  Tortuosity factor of the soil  (3  Cross-sectional area of the soil available for vapor transfer  A  Slope of the saturation vapor pressure versus temperature curve  y  Psych rometric constant Mass density of water  p  w  p  s  Mass density of soil  p  a  Density of dry air  X  Thermal conductivity of the soil  v|/  Total suction  \\|/  Residual suction  r  Total porosity  6 0q  Equivalent porosity  e  6  a  8  w  8  Air porosity Water porosity Factor representing the ratios of  xxvi  1 3  C and  1 2  C  \u000CAcknowledgements I would like to express my sincere gratitude to my supervisor Dr. Ward Wilson for the time and energy he has expended, along with his willingness to share his expertise, experience, guidance, encouragement and support throughout the course of my M S c and this PhD thesis works. His constant enthusiasm, positive attitude and Christian faith were a source of inspiration for me.  My thanks are also extended to all members of my Advisory Committee: Dr Scott Dunbar, Dr. Bern Klein and Dr Marek Pawlik for their valuable suggestions and constructive feedbacks.  I gratefully acknowledge the assistance and guidance of my former M S c thesis cosupervisor and co-author, Dr. Jim Hendry from the University of Saskatchewan.  Cogema Resources Ltd, Cameco Corporation, and Natural Sciences and Engineering Research Council of Canada are acknowledged for providing financial support through an Industrial Research Council (IRC).  The assistance by the mine staff of Cameco at the Key Lake uranium mine, and Ray Kirkland and Tyler Birkham from the  University of Saskatchewan, is  gratefully  acknowledged. My sincere thanks are also extended to Dr Henrique Rubio and Dr Bruno Bussiere who reviewed my thesis draft.  May those who have contributed either directly or indirectly to this work, especially, my sister Bernadette Kapembe and Dr Musangu Ngeleka, and whom I have failed to mention individually, find in this work the fruit of their conjugated efforts (none mentioned, none forgotten).  Finally I would like to thank my wife Florence Chabu Kabwe for her patience, understanding and for the support throughout the course my studies. My son Fiston, my daughter Berthe, and my grand-daughter Joy Makayla were a source of joy and inspiration for me. xxvii  \u000CDedication To my late mother Saya Kasuba and my late father Jonas Mumba Katele: their dreams have been fulfilled.  (Romans 5: 1-5) ...knowing that suffering produces endurance, and endurance produces character, and character produces hope, and hope does not disappoint us, because God's love has been poured into our hearts through the Holy Spirit that has been given to us.  To my Creator and Saviour, Jesus Christ.  xxviii  \u000CPage  Chapter I: Introduction  1  CHAPTER I INTRODUCTION  1.1  Introduction Accurate measurements and predictions of surface gas fluxes such as CO2 and  O2 are of great importance to the mining engineers and/or researchers in the development of a long-term management plan for reactive mine waste dumps. Measurements of C 0 fluxes are needed to quantify biogeochemical reaction 2  rates in unsaturated geologic media and soils (Hendry et al., 1993, 1999, 2001; Wood et al., 1993; Wood and Petraitis, 1984; Affek et al., 1998; Keller and Bacon, 1998; Lee et al., 2003, Birkham et al., 2003). These flux measurements can also provide needed input for global warming models (Hanson et al., 1993; Sundquist, 1993; Holland et al., 1995; Sellers et al., 1995; Thierron et al., 1996; Wickland and Striegl, 1997; Buchmann et al., 1999). Quantification of gas (e.g., O2) diffusion rates can be used to determine the extent of sulfide oxidation in unsaturated waste-rock piles (e.g., Harries and Ritchie, 1985; Davis and Ritchie, 1987; Hockley et al., 2000; Timms and Bennett, 2000; Bennett et al., 2003; Molson et al., 2005) and mine tailings impoundments (Elberling and Nicholson, 1996; Wunderly et al., 1996; Elberling et al., 2000; Elberling and Damgaard, 2001). Oxygen gas diffusion rates can also be used to establish how effective soil cover systems would be at reducing gas diffusion into the waste rock and tailing profile (Harries and Ritchie, 1985; Yanful et al., 1993a; O'Kane et al., 1995; Smolensky and Hockley, 1999; Aubertin et al., 2000; Timms and Bennett, 2000; Mbonimpa et al., 2002,  \u000CChapter I: Introduction  Page  2  2003; Bussiere et al., 2002, 2003; Cook et al., 2004; Martin et al., 2006; Aubertin et al., 2006). Soil C 0  2  flux is a complex process controlled by biotic and abiotic factors  (Buchmann, 2000). The presence of C 0 also exerts an important control on the pH of 2  the pore water in unsaturated zones (Lowson et al., 1982; Neal and Whitehead, 1988). The C 0  2  dissolved in water has a major influence on water chemistry (Neal and  Whitehead, 1988) and soil acidification (Elberling and Jakobsen, 2000) and it drives carbonic acid weathering of silicate and carbonate minerals (Reardon et al., 1979). Over the past two decades considerable attention has focused on radiatively important biogenic trace gas such C 0  2  because of the concern of global warming  (Blake and Rowland, 1988; Matson and Harris, 1995; Trumbore et al., 1996, Brooks et al., 1997; Fahnestock et al., 1998; Hobbie et al., 2000; Burkins et al., 2001). Similarly, because of the concern over acid rock drainage (ARD), most studies involving pore gases in mine wastes (including waste-rock piles) have focused on 0  2  consumption  rates as an indication of the rate of sulfide mineral oxidation. To date, there has been comparatively little attention paid to the C 0 side of biotic and abiotic gas production 2  and fluxes from the subsurface C-horizon soils and mine waste dumps. Monitoring of 0 - C 0 2  2  fluxes may provide a practical tool for identifying and  understand the different important mechanisms in the waste dumps such as the zones of microbial respiration and pyrite oxidation-carbonate buffering in mine waste dumps (Lee et al., 2003) as well as providing an indication as to the extent of microbial activity in the waste dumps. Fluxes of C 0  2  from waste-rock piles, though important in  determining geochemical reactions rates, are poorly characterized and standards establishing the accuracy of field measurements of C 0  2  surface fluxes are lacking  \u000CPage  Chapter I: Introduction  3  (Nakayama, 1990; Norman et al., 1992; Rayment and Jarvis, 1997; Janssens et al., 2000; Scott-Denton et al., 2003). In previous work, the author (Kabwe, 2001, and Kabwe et al., 2002) developed and verified in mesocosms a technique to measure C 0 fluxes from the soil surface to 2  the atmosphere. The technique termed the dynamic closed chamber (DCC) method is based on direct measurement of the change in C 0  2  concentration with time in the  headspace of a chamber installed on ground surface over a relatively short period of time. The D C C method was shown to accurately measure C 0  2  surface fluxes from  ground surface to the atmosphere in mesocosms. This is a direct technique of measurements and it provides an almost instantaneous indication of the reaction rate under field conditions, regardless of climatic or moisture conditions in the waste dumps. This laboratory-verified technique provided the opportunity to quantify temporal and spatial C 0 fluxes under field conditions and at the same time, compare these fluxes 2  measurements to those obtained from two other methods: the static closed chamber (SCC) and eddy covariance (EC) methods. This thesis work presents the results of the field applications of the D C C to quantify reaction rates and other processes at work in mine waste-rock piles at the Key Lake uranium mine in northern Saskatchewan. The work provided a complete suite of measurements required to characterize spatial distribution of C 0 fluxes on waste rock. 2  The author is not aware of any larger-scale studies that quantify C 0 fluxes across a 2  waste-rock pile.  \u000CChapter I: Introduction  1.2  Page  4  Research Objectives The main objective of this thesis was to extend the application of a novel and  laboratory-verified device (the dynamic closed chamber D C C system) designed and developed by the author (Kabwe, 2001; Kabwe et al., 2002) for CO2 flux measurements under field conditions on the D S W R and D N W R mine dumps at the Key Lake mine in northern Saskatchewan. It should be noted that this thesis represents the Phase II work of a 'Collaborative Research Program in the Mining Industry for Waste-Rock Hydrology', between the University of Saskatchewan and the University of British Columbia and funded by Cameco Mining and Cogema Resources. Phase I of the research work (Kabwe, 2001, and Kabwe et al., 2002) involved the design and testing of a dynamic closed chamber method for measurements of COaflux in mesocosms.  The specific objectives of this thesis were: (1)  To compare the D C C field CO2 fluxes data with those obtained using two other field soil respiration techniques: static closed chamber ( S C C ) and eddy covariance (EC) methods.  (2)  To measure the drying rate on surfaces of waste-rock piles after rainfall events  (3)  To predict evaporation on the surfaces of waste-rock piles using the SoilCover (Unsaturated Soils Group, 1997) computer model.  (4)  To predict changes in water content profiles on waste-rock piles after rainfall events using SoilCover computer model.  \u000CPage  Chapter I: Introduction  (5)  5  To design and develop a numerical model for CO2 gas production and diffusion in unsaturated materials.  (6)  To validate the CO2 model using measured data.  (7)  To use the CO2 model to predict C 0  2  diffusion and concentration-  depth profiles in the waste-rock piles in response to changes in waterdepth profiles at the Key Lake mine. (8)  To use the CO2 model to predict the effects of rainfall events on the surface effective diffusion coefficient and surface CO2 flux on the waste-rock piles at the Key Lake mine.  The work presented in this thesis applies the D C C method that was previously developed and verified in mesocosms to measure C 0 flux from ground surface to the 2  atmosphere (Kabwe et al., 2002) and was shown to accurately measure CO2 surface fluxes. In this study the D C C will be tested under field conditions to quantify and determine biogeochemical reaction rates in waste-rock piles. The method can be of great value to the mine engineer in the development of closure designs for mine wasterock piles at the Key Lake mine. The method can also be extended to other mine waste dumps to quantify biogeochemical reaction rates in unsaturated geologic media and soils at other mine sites in Canada and world wide.  1.3  Organization of the Thesis Chapter 2 of the thesis presents a literature review for studies of subsurface C 0  2  and O2 production and consumption rates and the associated fluxes in waste-rock  \u000CChapter I: Introduction  Page  6  systems and other soil ecosystems. The chapter ends with a brief review of climatic variables affecting the gas fluxes: precipitation and evaporation. Chapter 3 presents material characterizations. The objective was to determine the soil properties and characteristics of near-surface waste rock which influence the CO2 gas flux. The tests conducted include: grain size analysis, water retention curve (WRC) (or soil water characteristic curve), and saturated hydraulic conductivity. Chapter 4 discusses results of field application of the dynamic closed chamber (DCC) method for measurements of CO2 fluxes. The chapter ends with a comparison of the D C C fluxes to those obtained from two other methods: static closed chamber (SCC) and eddy covariance (EC) methods. Chapter 5 presents results of the investigations for the climatic variables affecting CO2 fluxes (e.g., effects of rainfall and evaporation on soil moisture and CO2 fluxes) precipitation and evaporation. The \" C 0 2 \" diffusion model developed in this work is discussed at the end of Chapter 5. The theoretical background, development of the partial differential equation including verification of the model developed are also presented. Final conclusions are summarized in Chapter 6.  \u000CChapter II: Literature Review  Page  7  CHAPTER II Literature Review  2.1  Introduction This chapter presents a literature review for studies of subsurface CO2 and O2  production, consumption rates and the associated surface fluxes in waste-rock systems and establishes the need for the research. Section 2.2 presents a physical description of a waste-rock pile to help conceptualize the complex flow developed within the waste rock in response to climatic conditions. Section 2.3 discusses the sources of CO2 in waste-rock piles, more specifically on biotic and abiotic reactions in waste-rock piles. Section 2.4 presents results of studies for C 0 production and O2 consumption rates and surface fluxes in 2  waste-rock and non-waste-rock systems reported in literature. Section 2.5 provides a discussion on the two important climatic variables that affect subsurface and surface gas fluxes namely, precipitation and evaporation. The chapter summary is presented in section 2.6.  \u000CPage  Chapter II: Literature Review  2.2  8  Waste-Rock Piles Waste-rock piles from mining operations are constructed from the excavation  and surface deposit of overburden rock, which commonly contain sulfide minerals. These above-ground, coarse-grained deposits tend to be heterogeneous in structure as a result of placement methods (e.g., end dumping, lift placement and compaction). The volume of a waste rock dump can be as large as hundreds of millions of cubic meters, making its size several kilometers in width and hundreds of meters in depth. These unsaturated and exposed to atmospheric precipitation, energy (e.g., solar energy) and gases (oxygen, carbon dioxide) and have the potential for generating sulfuric acid ( H S 0 ) in the presence of sulfide minerals (e.g., F e S , Fe-|. S) (Nordstrom and Alpers, 2  4  2  x  1999; Keit and Vaughan, 2000; Rimstidt and Vaughan, 2003; Elberling, 2005) (see Figure 2.1). This acid can dissolve heavy metals in the mine waste and produce acid rock drainage (ARD), which is potentially toxic to plants, animals and humans. The oxidation rate of sulphide minerals (e.g., pyrite, F e S ) depends on a number of factors 2  which define the environment within the waste-rock dumps, including temperature, pH, oxygen concentration, chemical composition of the pore water, and microbial population (Ritchie, 1994). Oxidation is an exothermic process that produces a large amount of heat (Elberling, 2005). Field measurements show that the temperature can be as much as 63 K warmer than the atmosphere in waste-rock piles with heights of 20-30 m (Harries and Ritchie, 1981; Gelinas and Choquette, 1992). Another factor that complicates the oxidation process is the presence of bacteria. Certain species of bacteria (e.g., Thiobacillus ferrooxidans) were found to increase the rate of oxidation by two orders of magnitude (Lorenz and Tarpley, 1963; Brierley, 1978).  \u000CPage  Chapter II: Literature Review  Energy  FLUXES  Water  l  1  CH 0 + 0 2  FeS  2 ( s )  2  ISSUES  Figure 2.1  Geochemistry  2  + 7/20 + H 0 = F e 2  S  Measurement  3  2 +  2  2(g)  Scale  = Fe  2  1  = H 0 + C02(  FeS ( ) + C a C 0 ( s ) + 7 / 2 0 2  2  Reaction Rates  Hydrology  PROCESSES  Gas 0 &amp; C 0  2 +  B)  + 2SO4\" + 2 H 4  , 2/ ++  + Ca  +  + 2SO4\" + C O ^ 4  \u00BB Environmental Loadings Time Evolution  conceptual models of the complex flow system and pollutant generation  and transport processes developed within a pyritic waste-rock dump in response to precipitation and climatic conditions.  9  \u000CChapter II: Literature Review  Page  10  In short, the physico-chemical-microbial environment within a waste-rock dump determines the sulfide mineral oxidation rate, which in turn determines the physicochemical-microbiological environment (Ritchie, 1994). Figure 2.2 shows a conceptual model of water and gas flow, and vapour transport in a waste-rock dump and internal structure and material segregation of a waste rock pile (Herasymuik, 1995; Fala et al., 2005). M E N D (2001) proposed four hydrostratigraphic types to characterize waste-rock piles. The types differ depending upon the materials and construction methods, and the characteristics of flow. The four types are: i.  Non-segregated coarse-grained rock piles that transmit water rapidly to the base of the pile;  ii.  Non-segregated fine-grained rock piles that are likely to contain a basal saturated zone;  iii.  The segregated rock piles that contain a fine-grained crest zone that may not permit the passage of significant quantities of water; and  iv.  Layered, segregated dumps that contain a finer-grained crest and sandy gravel layers to the face of the rock pile.  Segregated waste-rock dumps exhibit a graded stratigraphy caused by the segregation that occurs as materials roll down the pile at the angle-of-repose (Figure 2.2A). Finer sandy gravels are present at the crest, while coarser materials accumulate further down-slope. Dawson and Morgenstern (1995) have shown that when materials consisting mostly of finer sandy gravel are end-dumped, little segregation occurs and a finer grained layer is formed in the dump. A layer of finer material is typically found on the surface of the rock and a layer of coarser material is found at the base (Fala et al.,  \u000CC h a p t e r II: Literature R e v i e w  Page  natural soil or rock  Figure 2.2.  C o n c e p t u a l model of internal structure a n d material segregation of a  w a s t e rock pile.  11  \u000CChapter II: Literature Review  Page  12  2005). The bulk grain size distribution then includes alternating fine and coarse-grained material layers (see Figure 2.2) (Morin et al., 1991). When the grain size distribution is more variable, the vertical pile profile can be irregular and the structure of alternating fine and coarse layers less distinct. Layering inside the pile can be locally enhanced by construction traffic (heavy equipment), which tends to crush and compact the surface material, creating layers that can be up to 1 m thick (Aubertin et al., 2002; Martin, 2004; Fala et al.,2005). The grain and pore size distribution within a waste rock piles affects its hydraulic properties, which in turn control internal flow. Preferential flow can be caused by continuous macropores or by vertical, horizontal, or inclined layers of relatively high hydraulic conductivity that often control the movement of water within a pile. When the layering occurs as a fine-grained unit above a coarse-grained zone, a capillary barrier is formed in which water is preferentially retained in the fine grained material due to capillary forces (Nicholoson et al., 1989; Buissiere et al., 2003; Fala et al., 2005). Capilarry barriers have ben proposed for use in waste rock piles to control air and water flow (Poulin et al., 1996; Lefebvre et al., 2001b). The unsaturated condition and heterogeneous, coarse-grained nature of the waste rock deposits often make geochemical and geotechnical parameters difficult to measure (Pantelis et al., 2002). The problem is exacerbated when the dump is &gt; 10 m high, as this is as high as a column of water can be supported by atmospheric pressure. For these reasons, few data on the chemical composition of pore water within waste-rock dumps are in the literature. Monitoring the chemistry of the gas phase in unsaturated environments is a 1  relatively easy process. The gas can be easily drawn from sampling ports within the pile and the technology to measure chemically important components (i.e. 0 and CO2) is 2  \u000CChapter II: Literature Review  Page  13  readily accessible (Russell and Appleyard, 1915; Enock and Dasberg, 1971; de Jong and Schappert, 1972; Rightmire and Hanshaw, 1973; Rightmire, 1978; Hass et al., 1983; Jaynes et al., 1983a; Wallick, 1983; Wood and Petraitis, 1984; Harries and Ritchie, 1985; Ceding et al., 1991; Gelinas et al., 1992; Elberling et al., 1993; Hendry et al., 1993, 2001; Ritchie, 1994a, 1994b; Elberling and Nicholson,1996; Lee, 1997; Keller and Bacon, 1998; Russell and Voroney, 1998; Helgen et al., 2000; Hockley et al., 2000; Kabwe etal., 2002). The O2 and CO2 concentrations in the pore gas can be expected to vary because there are sinks and sources for these within the waste-rock dumps. The source of 0 a waste-rock dump is at the outer surface of the dump. 0  2  2  in  concentrations vary with  increasing distance into a dump in a manner that depends on the prevailing O2 transport mechanisms and on the oxidation rates. It should be noted that 0  2  concentrations less  than 0.2% mole fraction have been measured (Bennett et al., 1999), and values as low as 0.01% mole fraction have been reported (Goodman et al., 1992). C 0 concentrations 2  in the pore space of a waste-rock dump can range up to about 20% mole fraction and are frequently in the range 1-10% (Harries and Ritchie, 1983; Schuman et al., 1992). This is much higher than atmospheric levels of 0.03%. Elevated concentrations of C 0  2  increase the oxidation rate of pyrite by moderate thermophiles (Norris, 1989), some workers have reported increased growth rate of thiobacillus ferrooxidans with increasing levels of CO2 (Holuigue et al., 1987; Beyer et al., 1990), but others have reported little change up to 7% (Kelly and Jones, 1978; Norris, 1989). Haddadin et al. (1993) observed that increased C 0 concentrations increased the pyrite oxidation rates, but 2  that concentrations of 4 % were inhibitory to all three of the microbial populations involved in pyrite oxidation in the system studied.  \u000CChapter II: Literature Review  Page  14  The monitoring of O 2 and C 0 concentrations in pore gas is commonly used as 2  an indication of the occurrence, location, rate and type of chemical reactions occurring in subsurface environments (Ritchie, 1994). 0  2  consumption by sulphide mineral  oxidation is of particular importance in assessing the impact of waste-rock piles on the environment because of the associated acid generation (Molson et al., 2005). A decrease in 0  2  concentrations in waste-rock piles does not necessarily indicate the  occurrence of sulphide mineral oxidation. Organic oxidation is another common, but less environmentally harmful, process during which 0  2  is consumed. C 0 in pore gas is 2  an indicator of the types of oxidation processes occurring. For example, for a given amount of 0  2  consumed, carbonate buffering of acid generated from sulphide mineral  oxidation will typically produce less C 0 than organic oxidation (discussed further in this 2  Chapter). In addition, carbonate minerals and organic molecules have different ratios of stable carbon isotopes (discussed briefly in section 2.3): consequently, the stable carbon isotope signature of pore gas C 0 may be used to trace the C 0 source (Hendry 2  2  et al., 2002). Ritchie (1994a) and Lefebvre et al. (2001) provided a summary of typical physical properties and typical characteristics of A R D waste-rock with data on bulk properties of waste-rock dumps (Tables 2.1, 2.2, 2.3, 2.4 and 2.5). The gas concentration and temperature profiles in a waste-rock dump depend on the magnitude of a number of physical properties of the dump material, including air permeability, the gas diffusion coefficient, and the thermal conductivity. Ritchie (1994a) pointed out that the set of data in these Tables indicate that, at least for the parameters measured  \u000CPage  Chapter. 11: Literature Review  15  Table 2.1. In-situ thermal conductivity measurements in waste-rock dumps material # of measurement  Mine site location  |  points in waste dump |  (Wm'  1  P  (Wm  K\")  K\")  -1  1  1  0.71-1.63  1.2 +_0.4  3  1.04-1.22  1.2 + 0.1  7  1.57-3.31  2.1 +0.6  Rum Jungle, Australia\"  6  1.77-3.12  2.2 + 0.5  Doyen mine, Canada  y  6  2.5  Nordhalde, Germany  y  8  1.0  Aitik mine, Sweden  8  x  Heath Steele, Canada Kelian, Kalimantan  Table 2.2. I  Average  Range  x  x  In-situ air permeability measurements in waste-rock dumps material  Mine site location  j  Range  # of measurement  B points in waste dump  (m ); 2  Aitik mine, Sweden\"  27  (0.6 +0.2) x 1fJ - (1.4 +0.1) x 10\"  Heath Steele, Canada\"  24  (1.6+ 0.15) x 10\" - ( 4 . 7 + 0.5) x 10\"  Kelian, Kalimantan\"  18  (3.9 + 0.1)6) x 10\"  144  (8.89 + 0.19) x 10\" - (1.49 +_0.21) x 10\"  Rum Jungle, Australia Doyen mine, Canada  y  J Nordhalde, Germany  y  y  11  a  11  9  j  9  13  9  8.1 x10\"  10  2.5 x 10\"  I  12  Table 2.3. In-situ oxygen diffusion coefficient measurements in waste-rock dumps # of measurement points in  Mine site location  waste dump  Range nrrV^xlG- ) , 6  Aitik mine, Sweden\"  2  (2.25 + 1.04)-(6.85-1.02)  Heath Steele, Canada\"  3  (2.65+ 0.55)-(3.35-0.25)  Woodlawn, Australia\"  2  (3.49+ 1.64)-(5.07-0.39)  j  6  2.85  j  8  5.70  Doyen mine, Canada Nordhalde, Germany X = Ritchie, 1994;  y  y  y = Lefebvre et al., 2001  \u000CChapter II: Literature Review  Page  16  Table 2.4. Typical physical properties of A R D waste-rock piles (Ritchie, 1994a) Approximate Property Height j Area Density  Unit  Typical value  Range of values  m  20  2 to 150  ha  30  0.1 to 150  1500  1300 to 1900  2  0.5 to 30  Kg/m  a  Sulfur content as pyrite  Wt. %  Tropical to polar  Climate type Rainfall  m/yr  0.1 to 5 10  Water content within  Vol. %  5 to 25  of 0.5 m yr\" )  dump I Porosity  (at infiltration 1  %  40 0.6 kg m\"  Carbonate density  a  0.04 % O 2 diffusion  m /s 2  coefficient  5 x 10'  Air permeability  m  Temperature within  \u00B0C  J dump  2  2 x 1 0 to 6x10\" -6  6  |  1x10\"  12  6  to 1x10\"  -7 to 65  9  \u000CPage  Chapter II: Literature Review  Table 2.5. Physicochemical properties of the Doyon and Nordhalde waste rock piles (Lefebvre etal., 2001) Nordhalde  Unit  Doyen  Volume of waste rocks  m*  11.5x10  Maximum thickness  m  35  80  Sericite  Slates  Properties  Main Rock Type  27.0x10  b  b  schists Solid density Porosity Average water  [  2740  2751  0.00  0.30  %  42  63  m  8.1x10-  Kg/m  a  Dim.  |  saturation Effective vertical air  2  2.5x10\"  1u  12  permeability Water infiltration rate J Average thermal  0.350  0.166  2.5  1.0  IT^/S  2.13x10^  2.13x10\"  \u00B0C  1 -65  3-16  m/year W/m \u00B0C  |  j conductivity Effective oxygen diffusivity J Range of Temperature within dumps Sulfur content as pyrite  %  j  1 - 2%  b  17  \u000CPage  Chapter II: Literature Review  18  Table 2.6. Typical characteristics of A R D (Ritchie, 1994a). I Property  Range  Impact  2 to 4  Mobilization of metal ions  Concentration  Discoloration and turbidity  Ferrous and ferric  100 to 3000  in receiving waters as pH  ions; ferric oxides,  mg L\"  increases and ferric salts  Typical associated chemical species  Acidity (pH)  Iron  Sulfuric acid  1  precipitate  hydroxides; jarosites  j  Reduction in aquatic flora and fauna; Heavy  C u , Mn, Zn, C d , Hg,  metals  Pb, A s , R a  1 to 200 mg L\"  1  bioaccumulation; reduction in quality of potable groundwater supplies | Reduction in quality of | potable groundwater  Total dissolved salts  C a , Mg, A l , S 0 \" 2  4  100 to 30000 mg L\"  1  | supplies; reduction in J quality of water supplies | for livestock  I  \u000CChapter II: Literature Review  Page  (though small data-set) the heterogeneity of the dump material and  19  layering  consequent on the method of dump formation do not carry through in any marked way. These bulk properties are required both to predict the environmental conditions within a dump, and to quantify oxidation rates. The extents to which these properties vary from place to place in a dump and from dump to dump provide some insight on the impact that dump-construction methods and details of dump composition have on these bulk properties (Fala et al., 2005). The gas concentration and temperature profiles in a waste-rock dump depend on the magnitude of a number of physical properties of the dump material, including air permeability, the gas diffusion coefficient, and thermal conductivity. It was noted that the variation from dump to dump (from this data set) was about the same as that within a dump and that gas transport in a dump was dominated by diffusion when the air permeability was 10\"  10  m  2  or less (Bennett et al., 1989;  Pantelis and Ritchie, 1991a).  2.3  Sources of C 0  2  in Subsurface Soils and Waste-Rock Piles  The generation of soil C 0 flux is a complex process controlled by biotic and 2  abiotic factors (Buchmann, 2000; Shi et al., 2006). Gas-filled pores in soil typically contain 10-100 times higher concentrations of C 0 than the atmosphere (Welles et al., 2  2001), primarily due to soil C 0  2  production from respiration in living roots and  heterotrophic soil microorganisms (Elberling, 2003). C 0 in pore gas may be used to 2  identify its source. The ratio of stable carbon isotopes ( C / C ) in C 0 from pore gas 1 3  1 2  2  indicates if the C 0 source is organic, inorganic, or a combination of both. It should be 2  noted that the carbon isotopes technique was used by Birkham et al. (2003) to determine the source of C 0 2 in the waste-rock piles (DNWR &amp; D S W R ) and beneath at  \u000CPage  Chapter II: Literature Review the Key Lake mine.  20  Birkham et al. (2003) presented measured values of carbon  isotopes ratio for the waste-rock piles at the Key Lake mine and concluded that pore gas CO2 in the D N W R pile (see Figure 2.3) likely originated from a combination of organic (biotic) and inorganic (abiotic) sources (Birkham et al., 2003). Carbon isotopes ratio values for the D S W R indicated the majority of pore gas CO2 from D S W R originated from an organic source underlying the waste rock (e.g., Figure 2.3, dewatered lake) (Birkham et al., 2003).  2.3.1 C 0 production by microbial respiration (biotic) 2  Microbial aerobic respiration and oxidation of organic matter are generally considered to be the primary sinks for O2 and the main sources of elevated biogenic C 0 concentrations in the subsurface. Rates of aerobic microbial degradation of organic 2  matter and contaminants in the subsurface are greater than anaerobic degradation (Hendry et al., 2002). An understanding of the physical transport mechanisms and the biochemical processes that control C 0 and O2 concentrations and fluxes to and within 2  the subsurface are needed (Bennet and Ritchie, 1990; Hendry et al., 1999; Hendry et al., 2002; Pantelis et al., 2002; Lee et al., 2003b). O2 consumption and CO2 production by microbial respiration in unsaturated media can be represented by the general biotic reaction (Stumm and Morgan, 1981; Lee et al., 2003b):  CH 0 +0 2  2  -&gt; C 0  2 ( g )  +H 0 2  [2.2]  where C H 0 represents a simple carbohydrate. In this simple case of organic oxidation 2  \u000CChapter II: Literature Review o n e m o l e of 0  2  Page  21  c o n s u m e d results in the production of o n e mole of C O 2 . M o r e c o m p l e x  organic m o l e c u l e s (e.g.,  C106H263O-110N16P)  m a y have molar ratios of 0  2  c o n s u m p t i o n to  C O 2 production of c l o s e r to 1:0.77 (Drever, 1997):  Cio6H2630 oN P + 1 3 8 0 11  106CO ( ) 2  g  16  2 ( g )  -&gt;  2  + I 6 N O 3  [2-3]  +  +HPOJ\" +122H 0 + 18H  +  2  B a s e d o n the E q u a t i o n s 2.2 a n d 2.3, respiratory c o n s u m p t i o n of 1 m o l of 0  2  should  p r o d u c e 0.8 o r 1 mol of C 0 . 2  It w a s noted in the literature review that mine w a s t e - r o c k piles a r e , in s o m e c a s e s , constructed upon organic carbon-rich d e w a t e r e d lake bottoms (Birkham et a l . , 2 0 0 3 ; L e e et a l . , 2 0 0 3 b ) ( s e e F i g u r e 2.3). Microbial respiration in t h e s e buried d e p o s i t s can also consume 0  2  a n d produce C 0 . L e e a n d co-workers ( L e e et a l . , 2 0 0 3 ) found 2  that t h e s e stiochiometric ratios a r e very similar to t h o s e o b s e r v e d for microbial respiration in forest soils ( 1 O : 0 . 7 C O ) (see Figure 2 . 3 B ) a n d in buried lake s e d i m e n t s 2  2  b e n e a t h mine w a s t e - r o c k piles ( 1 O : 0 . 5 C O ) . T h e y found a positive correlation b e t w e e n 2  the rates of 0  2  2  c o n s u m p t i o n a n d C 0 production a n d organic c a r b o n content (i.e.,  higher o r g a n i c c a r b o n contents s u g g e s t e d that the difference  2  in forest in 0 / C 0 2  2  soil than  lake  ratios w e r e  bottom sediments) a n d  d u e to differences  in the  stoichiometry of the organic c a r b o n . Other r e s e a r c h e r s ( A m u n d s o n et a l . , 1988; W a n g et a l . , 1999) reported positive correlation between respiration rates a n d o r g a n i c c a r b o n content in unsaturated z o n e s . M e a s u r e m e n t s of 0 - C 0 2  2  fluxes, therefore, m a y provide  a n indication of the z o n e s of respiration a n d the extent of microbial activity in the w a s t e rock pile.  \u000CChapter II: Literature Review  Figure 2.3.  Page  22  (A) Depth geologic profile for Deilmann south waste-rock (DSWR) pile.  (Adapted from Birkham et al., 2003) (B) Map showing of the Deilmann north (DNWR), Deilmann south (DSWR) and outline of former lake bed at the Key Lake mine, Saskatchewan, Canada.  \u000CPage  Chapter II: Literature Review  Figure 2.4.  0  2  consumption rates vs C 0  2  23  production rates for forest soils, lake bottom  sediments, and gneissic waste rocks (units: umol/kg/week; x represents gneissic waste rocks (DNWR); A represents lake bottom sediments collected from beneath the waste-rock pile (DNWR): \u2022 represents forest soils (natural forest site adjacent to the D N W R ) (Lee et al., 2003b).  \u000CPage  Chapter II: Literature Review  2.3.2  C0  2  24  production by pyrite oxidation-carbonate buffering (abiotic)  Soil C 0 derived from unsaturated mine waste-rock piles can also be produced 2  in abiotic (e.g. sulfide minerals) reactions in situ (Elberling and Nicholson, 1996; Timms and Bennett, 2000; Birkham et al., 2003; Lee et al., 2003b). If gaseous 0  2  is present in  the unsaturated waste-rock piles, the oxygen can be consumed by microorganisms in the chemical oxidation of minerals (e.g. pyrite) and can lead to formation of acid and sulfate (Stumm and Morgan, 1981; Ritchie, 1994b; Lee et al., 2003b):  F e S ( ) + 3.50 ( ) + H 0 2  s  2  g  Fe(ll)+ 2 S O ^ + 2 H  2  [2.4]  +  The resulting Fe(ll) in Equation 2.4 can be oxidized to Fe(lll) by : 2Fe(ll) + 0.5O ( ) + 2 H 2  2Fe(lll) + H 0  +  g  [2.5]  2  Combining Equations 2.4 and 2.5 we obtain: 2FeS ( ) + 7 . 5 0 2  s  In a solution with pH &gt; 3, F e  2(g  3 +  ) +H 0 2  2Fe(lll) + 4 S O j ~ + 2 H  +  [2.6]  can precipitate from solution to produce additional acid  (Dubrovsky et al., 1984; Janzen et al., 2000) by: Fe(lll) + 3 H 0  Fe(OH) + 3 H  2  +  3  [2.7]  Precipitation of other Fe(lll)-bearing phases, such as goethite (a-FeOOH) or schwertmannite (Fe808(OH) S04), may occur in acid mine waters (Bigham et al., 1990). 6  Alternatively, Fe(lll) can be consumed through further oxidation of sulphide minerals in acidic water (Wiersma and Rimstidt, 1984; Blowes et al., 1995) by:  \u000CPage  Chapter II: Literature Review  FeS + 14Fe  + 8 H 0 -&gt; 1 5 F e  3 +  2  3 +  2  + 2 S 0 . +16H 2  -  25  [2.8]  +  Carbonate minerals are often present in natural subsurface environments and have a buffering effect on the pH of subsurface pore water (Freeze and Cherry, 1979). The acid generated by pyrite oxidation can dissolve available carbonates to produce C 0  2  gas by: C a C 0 ( ) + 2 H -&gt; C a +  3  +H 0 +C0  2 +  s  2  [2.9]  2 ( g )  Combining Equations 2.4 and 2.9 yields Equation 2.10: F e S + C a C 0 +3.50 ( ) - &gt; F e ( l l ) + C a 2  3  2  g  2+  + 2S0 . +C0 2  [2.10]  -  2 ( g )  In addition, combining Equations 2.6 and 2.9 yields Equation 2.11 2FeS + C a C 0 +7.50 ( 2  3  2  g)  -&gt;2Fe(lll)+Ca  + 4S0 T +C0  2+  [2.11]  2  2 ( g )  Furthermore, combining Equations 2.6, 2.7, and 2.9 yields Equation 2.13 for near neutral pH solution: F e S ( ) + 2 C a C Q + 3 . 7 5 0 ) + 1 . 5 H 0 -&gt; Fe(OH) + 2 C a 2  s  3  2(g  2  3  2 +  + 2S0 . + 2C0 2  Based on Equations 2 . 1 0 - 2 . 1 2 , consumption of 1 mol of 0  2  -  2 ( g )  [2.12]  by pyrite oxidation with  carbonate buffering may produce 0.1, 0.3, or 0.5 mol of C 0 and between 0.5 and 0.6 2  mol of sulphate. The C 0 produced is thus an indirect measure for the carbonate buffering and an 2  indicator of the types of oxidation processes occurring. It is therefore suggested that both sulfide oxidation-carbonate buffering and microbial respiration may control 0  2  and  C 0 gas concentrations in the unsaturated waste-rock piles (Lee et al., 2003b). 2  C0  2  produced in unsaturated soils and waste-rock piles undergoes redistribution  via gas transport and geochemical reactions with water and various mineral phases  \u000CChapter II: Literature Review  Page  26  (Hendry et al., 1993) and will diffuse upward to the atmosphere (soil respiration) and downward to the water table under concentration gradients. Microbially produced CO2 can dissolve in the recharging water and react to produce bicarbonate (HCO3). These species are then transported to the water table in the dissolved state. In addition to sinks attributed to CC&gt;2-carbonate mineral reactions, CO2 also dissolves in water to produce carbonic acid. Although this latter flux is small compared to the soil C 0 efflux, 2  it has a major influence on water chemistry (Neal and Whitehead, 1988) and soil acidification (Elberling and Jakobsen, 2000) and it drives carbonic acid weathering of silicate and carbonate minerals (Reardon et al., 1979; Elberling, 2003). Many studies have shown that the dominant sink for CO2 from unsaturated zones is the atmosphere (Solomon and Cerling, 1987; Hendry et al., 2001). For example, Hendry et al. (1993) showed that 2 % of the C 0  2  produced in a 3.2 m thick sandy  unsaturated zone under high recharge conditions was removed by the recharging ground water. Solomon and Cerling (1987) determined that about 4 % of the CO2 produced in an unsaturated zone was removed by the recharging ground water. The dominant mechanism for gas transport in soil pores is generally accepted to be concentration controlled molecular diffusion through air-filled pores (Keen, 1931; Grable, 1966; Weeks et al., 1982; Elberling, 2003). Variations in pore gas composition due to thermal convection and atmospheric pressure variations were observed by Harries and Ritchie (1985), Bell et al. (1991), Hockley et al. (2000), Lefebre et al. (2001), and Molson et al. (2005). While diffusion is typically limited to a near-surface zone of a few meters depth, advection (due to a thermal gradient and/or wind pressure gradients or barometric pumping) and barometric pumping have the potential to move air (and oxygen) to much greater depths into the pile. In general, the more permeable  \u000CChapter II: Literature Review  Page  27  general, the more permeable the waste-rock material, and the greater the height-towidth ratio of the waste-rock pile, the greater is the potential for advective air movement. The reactivity of the waste-rock material as well as the coarsenes (hence air permeability), and the spatial variability of these properties within a pile, have a strong influence on the magnitude of thermally induced advection (Wels and Robertson, 2003). In contrast, air movement due to barometric pumping is controlled by the waste rock porosity, changes in ambient air pressure and the heterogeneity of air permeability of the waste-rock dump.  2.4  Studies of C 0  2  in Subsurface Pore Gas and Associated Surface Gas  Fluxes from Waste-Rock and non Waste-Rock Systems The following sections present literature review of studies of CO2 in subsurface pore gas and associated surface gas fluxes from waste-rock and non waste-rock systems. It should be noted that this literature review serves to establish the need for surface CO2 fluxes measurements on both mine waste-rock and non-waste-rock systems. This is because the dominant oxidation reactions and associated C 0 fluxes 2  measured at the D S W R occur in the organic material underlying the waste-rock pile (Birkham et al., 2003). Birkham et al. (2003) also suggested that pyrite oxidationcarbonate buffering and the resulting O2 consumption and C 0  2  production are more  likely to be observed in the gneissic waste rocks at the DNWR. Many studies have investigated C 0 in pore gas in subsurface and surface gas 2  fluxes for non waste-rock material and only few attempts have been made to quantify C0  2  production or surface flux (Birkham et al., 2003) in waste-rock piles.  \u000CChapter II: Literature Review  Page  28  2.4.1 Studies of subsurface C 0 gas and surface gas fluxes from waste2  rock piles The following section along with Tables provide the literature review results for typical studies of C 0  2  production and consumption rates and surface fluxes for waste-  rock piles.  Table 2.7. Summary of C 0 concentrations for waste-rock material. 2  Sources  Locations  Harries and Ritchie (1985)  Rum Jungle Australia  Gelinas et al. (1992)  La Mine Doyen, Quebec  Hockley et al. (2000)  Birham et al. (2003)  Germany  Key Lake mine Saskatchewan Canada  Waste Rock: size and geologic material 15 to 25 m high waste-rock piles silty sand to rocks, 1 to 3% pyrite 30 to 35 m high waste-rock pile, 3.5 to 4.5% pyrite 1 to 3% sulphides, high cone, of carbonates 20 to 28 m high, sand/sandstone  C 0 maximum concentration 2  (%)  &gt; 20  7  60  8  Harries and Ritchie (1985) measured pore gas C 0 and 0 concentrations in the 2  2  within a pyritic waste-rock pile (average height 25 m) at the Rum Jungle uranium mine in Australia to identify oxidation zones and measure rates of oxidation. The waste-rock pile consisted mainly of pyritic, graphitic shale. C 0 concentrations varied from near 2  \u000CChapter II: Literature Review  Page  atmospheric levels to greater than 20 %. 0  2  29  concentrations varied from near  atmospheric levels to 0 %. Important conclusions of this study were that 0  2  supply was  a rate-limiting factor for oxidation and that both diffusion and advection (due to thermal and atmospheric effects) resulted in gas migration through the pile. Advection due to thermal effects (buoyancy forces) was significant in regions where temperatures were elevated (&gt;50 \u00B0C) relative to monthly mean temperatures (25 to 30 \u00B0C). Advection due to changes in atmospheric pressure was observed at depths of up to 7.5 m where 0  2  concentration fluctuations matched the semidiurnal changes in atmospheric pressure. Gelinas et al. (1992) studied the physico-chemical conditions for La Mine Doyon in Quebec. A R D from the waste-rock piles had pH values around 2 and total dissolved solids (mainly sulfates, Fe, Al and Mg) values up to 200 000 mg/L; pyrite concentrations in the waste-rock piles were approximately 3.5 to 4.5 % (by mass). Porosity of the piles and the dry bulk density were estimated at 35 % and 1850 kg/m , respectively. 3  Maximum temperatures typically ranged from 40 to 50\u00B0C, C 0 pore gas concentrations 2  typically increased to about 7 % at a depth of 30 m and 0  2  pore gas concentrations  typically decreased to about 2.5 % at a depth of 30 m. Air convection (due to thermal effects) was identified as a key gas transport process as air venting from the waste piles was observed during cold weather. Hockley  et  al.  (2000)  measured  temperature,  C0  2  and  0  2  pore-gas  concentrations, and air pressure in a waste-rock pile at a uranium mine in Germany. Typical seepage from the waste-rock pile had a pH of 2.7 and sulfate concentrations above 10 000 mg/L.  Acid generating material had sulfide mineral concentrations  ranging from 1 to 3 % (by mass). Thermal convection of pore gas was observed at some sites during winter months as stable temperatures deeper in the pile were  \u000CChapter II: Literature Review  Page  between the summer and winter ambient temperatures. barometric pressure fluctuations was also observed. C 0 increased (up to 60%) with increasing depth. concentrations of carbonate material. 0  2  30  G a s transport due to 2  concentrations typically  C 0 production was attributed to high 2  concentrations typically decreased (down to  0%) with increasing depth; Birkham et al. (2003) measured CO2 concentrations profiles, C 0 consumption 2  and production rates, and C 0 fluxes from two waste-rock (the Deilmann south waste2  rock (DSWR) and Gaertner (GWR) piles at the Key Lake Uranium Mine in northern Saskatchewan. The concentrations exhibited a linear increase for C 0 in concentrations 2  with depth through the piles and suggested that the dominant sites of reactions occurred below the piles. Mean C 0 concentrations at the D S W R changed little with 2  depth (change in C 0 concentrations less than 1 % from atmospheric concentrations). 2  C 0 concentration increased from 10 to 20 m, decrease from 20 to 30 m, and increased 2  from 30 to 40 m. They found that oxidation of organic matter beneath the waste-rock pile dominated the pore-gas chemistry and that significant changes in 0  2  and C 0  2  concentrations within mine waste piles may not be the result of sulfide mineral oxidation/carbonate buffering. The gas consumption and production values ranged between 0.04 and 0.15 ug C 0 / g soil/day C 0 . 2  C0  2  concentration depth profiles at the Key Lake mine were similar to those  2  presented in other waste-rock studies (Harries and Ritchie, 1985; Hockley et al., 2000) in that C 0 concentrations were negatively correlated to 0 2  C0  2  2  concentrations. Although  concentrations increased to a maximum of 20 % at the Rum Jungle mine in  Australia (Harries and Ritchie, 1985), 0 + C 0 values were usually less than 15 %. CO2 2  2  concentration depth profiles presented by Hockley et al. (2000) had CO2 concentrations  \u000CChapter II: Literature Review  Page  31  that usually increased to much greater than 20 % (up to 60 %) at depths below 20 m; 0 2 + C 0 values ranged from approximately 5 % to approximately 60 %. 2  In summary, waste-rock studies in the literature indicated that only one attempts have been made to quantify C 0  2  production and C 0  2  concentration depth profiles  (Birkham et al., 2003) and surface flux. A need, therefore, exists for measurements of surface C 0 fluxes for large waste-rock piles. 2  2.4.2 Studies of subsurface C 0 from non waste-rock material 2  The following section along with Tables provide the literature review results for typical studies of C 0  2  production and consumption rates and surface fluxes for non  waste-rock piles. It should be noted that the range of non-waste-rock systems is wide, and only selected literature review results will be presented.  Table 2.8. Summary of C 0 concentrations for non-waste-rock 2  material.  Source  Location  Waste rock size and geologic material  Max. C0 cone. 2  (%)  De Jong and Schappert (1972) Rightmire and Hanshaw (1973) Atkinson (1977)  Canadian prairie  (1.5 m of unsaturated heavy clay)  2.26  Florida  England  (sand, forest and grassland) (limestone soils, depths to 130 m)  1.8  \u000CPage  C h a p t e r II: Literature R e v i e w  Table 2.8 Continued.  Source  Location  R e a r d o n et a l . (1979)  Ontario  J a y n e s et a l . (1983b)  Eastern States  H a a s et a l . (1983)  Waste rock size and geologic material (up to 11 unsaturated, calcareous forest region)  United  North D a k o t a )  m  (reclaimed coal mine)  Max. C0 cone. (%) 2  of 0.8  sand, strip  Great Plains(greater than 13 m of calcareous claystone a n d siltstone, lignite present, v e g e t a t e d . (less than 13 m thick unsaturated zone, reclaimed coal mine a r e a , high c a r b o n a t e content in some areas)  Wallick(1983)  Alberta  W o o d and Petraitis (1984)  Southern High Plains, Texas  (51 to 77 m of calcareous geologic material)  Solomon and C e r l i n g (1987)  Utah  (approximately 2 m of unsaturated m o n t a n e soil, vegetated)  W o o d et a l . (1993)  Southern Saskatchewan  (7 m of unsaturated silt loam/till, vegetated)  T r u m b o r e et al. (1995)  Eastern Amazonia  (45 m of unsaturated clay, forest and pastureland)  L e e (1997)  Massachusetts  (0.5 to 12 m unsaturated s a n d )  of  18.7  19 to 2 0  24  3.02  1.24  3  7 5  32  \u000CPage  Chapter II: Literature Review  T a b l e 2.9.  Summary of surface C 0 fluxes for non waste-rock 2  material. C 0 surface flux (mmol C/m /day) 2  Source  Location/geologic material  2  De Jong and Schappert(1972)  Canadian prairie (at least 1.5 m of unsaturated heavy clay), (d=1710 kg/m ) Southern High Plains, Texas (51 to 77 m of calcareous geologic material), (d=1710 kg/m3) Utah (approximately 2 m of unsaturated montane soil, vegetated) (d=2070 kg/m3) Washington state (loess, vegetated) d=1869 kg/m3)  Up to 241  3  Wood and Petraitis (1984) Solomon and Cerling (1987) Wood et al. (1993)  2.5x10^ to 1.2x10\" 2  7.48x10\" to 0.64  2  9.63x10\" to 8.18x10\" 0 to 2.58x10\"  4  2  Wood et al. (1993)  Trumbore etal. (1995)  Southern Saskatchewan (7 m of unsaturated silt loam/till, vegetated)(d=2056 kg/m3) Eastern Amazonia (45 m unsatuareted clay, forest and pastureland)  Lee (1997)  Massachusetts (0.5 to 12 m of unsaturated sand)  Russell and Voroney (1998)  central Saskatchewan (calcareous till, forest)  Hendry etal. (1999) Southern  Saskatchewan (5.75 m of unsaturated sand  3  220 to 580 19.6 (low veg.) 372 (golf course) 50 (woodland) 123 (grassy area) 53 to 807 30.8 (average)  \u000CChapter II: Literature Review  Page  34  de Jong and Schappert (1972) calculated CO2 production rates and CO2 surface flux in the Canadian prairies using a Fickian approach (using measured CO2 concentration depth profiles and an estimated diffusion coefficient). The maximum CO2 concentration measurement within the top 1.5 m was 2.26 %.  CO2 production rates  ranged up to 241 /jg C/g/day and the surface flux of CO2 ranged up to 1 473 mmol C/m /day. The geologic material was heavy clay. 2  Russell and Voroney (1973) measured CO2 surface fluxes ranging from 53 to 807 mmol C/m /day in central Saskatchewan (forest region). 2  Root respiration was  estimated to contribute 60 % of the C 0 production and a strong correlation was found 2  between CO2 surface flux and temperature, pore-gas C 0 concentration in the humus 2  layer (surficial organics) and moisture content. The geologic material was medium to fine-textured, medium to strongly calcareous, glacial till.  Volumetric soil moisture  content ranged from about 10 to 35 %. Atkinson (1977)  measured pore gas CO2 concentrations  in a  limestone  environment in England. CO2 concentrations increased up to 1.8 % with measurements taken at depths as great as 130 m.  Oxidation of down-washed organic matter was  noted as a possible source of C 0 at depth. Maximum total carbon content was 11 % in 2  the limestone soils. Reardon et al. (1979) measured pore gas CO2 concentrations values in a forest area in Ontario. CO2 % generally increased with depth (up to between 0.3 and 0.8 %). The water table was 6 to 11 m deep and the total porosity was approximately 0.38. At one site a C 0 concentration gradient at the water table was observed indicating that 2  the groundwater was a source of CO2 (degassing). A C 0 concentration gradient at the 2  water table was not observed at the other site.  \u000CChapter II: Literature Review  Page  35  Jaynes et al. (1983a) measured pore-gas CO2 concentrations at a reclaimed coal strip mine in eastern United States. CO2 concentrations ranged from near atmospheric to greater than 15 %.  O2+CO2 values were less than 20.9 %, and O2 and CO2  concentrations were negatively correlated.  0  2  and CO2 concentrations were weakly  correlated to temperature. The average bulk dry density was 1560 kg/m . The average 3  pyritic S concentration was 0.18 % (by mass, ranging from 0.02 to 2.0 %); the average C concentration was 4.4 % (by mass, ranging from 0.4 to 22.8 %). Haas et al. (1983) measured pore-gas CO2 concentrations, at 8 sites in North Dakota (Great Plains region).  C0  2  concentrations fluctuated seasonally indicating a  relationship to root respiration. Organic and lignite (coal) oxidation were dominant sources of C 0 in the pore gas. 2  Wallick (1983) measured pore-gas C 0 concentrations in the Battle River Mine 2  area in Alberta to indicate when a reclaimed mined area had reached geochemical equilibrium with unmined landscapes. C 0 concentrations ranging from atmospheric to 2  24 % and originated from both carbonate dissolution ( C 0  2  degassing) and organic  oxidation. Wood and Petraitis (1984) calculated O2 consumption rates, CO2 production rates and C 0 surface fluxes at two sites on the Southern High Plains of Texas. 2  O2  consumption rates varied from approximately 2.3 x 10\" to 2.5 x 10~ \\xg 0 /g/day 3  2  2  (assuming bulk dry density of 1735 kg/m ); CO2 production 3  rates varied from  approximately 2.5 x 10\" to 1.2 x 10~ p,g C/g/day; and surface fluxes were 2.2 mmol 4  2  C/m /day and 10.5 mmol C/m /day. CO2 production was calculated as a function of 2  2  depth and gas migration was attributed to diffusion. C 0 concentrations increased with 2  depth (up to approximately 1 and 2.5 %); O2 concentrations decreased with depth  \u000CChapter II: Literature Review  Page  36  (down to approximately 19 and 16 %). Although the sites contained significant amounts of calcium carbonate (up to 80 % in many zones), C 0 production was attributed to 2  oxidation of organic particles deep in the unsaturated zone. An important conclusion of this study was that a very small amount of C 0  2  production at depth had a more  profound effect on the geochemistry of the system than a similar production rate near the surface. The unsaturated zones studied were 51 m (gas probes to 36 m) and 77 m (gas probes to 21 m) thick. Solomon and Cerling (1987) calculated C 0 production rates ranging from 7.48 x 2  10\" to 0.64 JJQ C/g/day in a montane soil in Utah using the concentration-gradient 2  approach.  Pore gas C 0  2  increased to 1.24 % during the growing season and also  during the winter due to the capping effect of the snowpack. C 0 in the pore gas was 2  noted as an important factor in the weathering of certain minerals (i.e. albite). Also, C 0  2  removal by dissolution in infiltrating water was determined to be minimal (less than 4 % of annual budget of C 0 in pore gas); as much as 15 % of the C 0 in pore gas could be 2  2  removed during periods of low C 0 production. 2  Wood et al. (1993) calculated C 0 production rates in Washington state (9.63 x 2  10\" to 8.18 x 10\" fjg C/g/day) and south central Saskatchewan (0 to 2.58 x 10\" /jg 4  2  3  C/g/day) considering diffusive fluxes and partitioning of C 0 into infiltrating water; C 0 2  sorption onto solid phase was considered negligible.  C0  2  2  production rates were  reasonably correlated with temperature and microbial abundance at the Washington site; no correlation was observed at the Saskatchewan site.  Production at the  Saskatchewan site was found to be isolated within 1 m above the water table (approximately 6.5 m below ground surface) and to within the top 2 m during the growing season (root respiration). The water table at the Washington site was 5 to 6 m  \u000CChapter II: Literature Review  Page  below the ground surface.  37  CO2 and O2 pore gas concentrations were negatively  correlated at both sites; C 0 % generally increased with depth (up to approximately 2 % 2  in Washington and 3 % in Saskatchewan). CO2 production was attributed to root respiration and the oxidation of soil organic carbon. Total organic carbon (TOC) of the loess at the Washington site ranged from 1.5 % near the surface to 0.03 % deeper in the unsaturated zone; T O C of the till at the Saskatchewan site ranged from 1.3 % near the surface to 0.2 % deeper in the unsaturated zone. Volumetric moisture contents at the Saskatchewan site ranged from 11.78 to 21.88 %. Trumbore et al. (1995) measured CO2 surface fluxes (220 to 580 mmol C/m /day) from clay soils in Eastern Amazonia by collecting C 0 from the subsurface as 2  2  it was released. The study sites ranged from pastureland to forest and CO2 production was attributed to both root respiration and oxidation of soil organic matter. Total soil C concentrations ranged from 0.10 to 0.20 % below 3 m to 2.52 to 3.18 % near the surface. Dry bulk density ranged from 960 to 1 220 kg/m . CO2 concentrations in the 3  pore gas increased (up to about 8 %) with depth (down to 8 m). Lee (1997) calculated the surface flux of CO2 at four different sites (gravel-pit area, woodland, golf course, and grassy area using a Fickian approach. CO2 surface fluxes and maximum CO2 concentrations were: 19.6 mmol C/m /day and 0.7 % (gravel2  pit area), 372 mmol C/m /day and 5 % (golf course), 50 mmol C7m /day and 1.1 % 2  2  (woodland), and 123 mmol C/m /day and 3.2 % (grassy area). 2  C0  2  concentrations  were measured to a depth of 3.5 m and generally increased with depth. The geologic material in the study area was sandy (quartz and Na feldspar) with no carbonate minerals present and low organic C content (&lt; 0.1 %).  \u000CChapter II: Literature Review  Page  38  Hendry et al. (2001) measured pore gas C 0 concentrations, and calculated C 0 2  2  production rates and surface fluxes, and measured field C 0 surface fluxes for a 5.7 m 2  thick sandy, unsaturated zone (in central Saskatchewan). Volumetric moisture content ranged from 3 % to over 6 %; mean density and porosity values were 1510 kg/m and 3  0.43,  respectively.  C0  2  concentrations  fluctuated  seasonally with  maximum  concentrations occurring in the summer (0.85 to 1.22 %) and minimum concentrations occurring in the winter (0.04 to 0.24 %). A numerical model constrained by measured CO2 concentrations and fluxes, temperature and moisture contents and assuming gas migration due to diffusion was used to calculate C 0 production rates between 5 jjg C/g 2  dry soil/day (summer respiration in the soil horizon) and less than 10\" mg C/g dry 4  soil/day in unsaturated sections of the C horizon.  It was also noted that microbial  activity ( C 0 production) might be very low despite the presence of microorganisms in 2  the unsaturated zone. In summary, studies indicated that C 0  2  pore gas concentrations typically  increased (absolute maximum of 24%) with increasing depth. Reardon et al., 1979, Wood and Petraitis, 1984, Solomon and Cerling, 1987, Hendry et al., 1993, Wood et al., 1993, Trumbore et al., 1995, Lee 1997, Hendry et al., 1999, and Birkham et al.2003, all presented C 0  2  concentration depth profiles in which C 0  2  concentrations generally  increased with depth. Readon et al., 1979, Wood et al., 1993, Lee 1977, and Hendry et al., 1999 presented concentration depth profiles in which C 0 concentrations at shallow 2  depths were elevated due to seasonal root respiration. C 0  2  concentrations were  elevated at shallow depths in Hendry et al., 1993 due to increased concentrations of organic matter in the top 0.3 m.  \u000CChapter II: Literature Review  2.5  Climatic Variables Affecting  Subsurface  and  Page  39  Surface  Gas  Fluxes: Precipitation and Evaporation Climate has the potential to enhance or reduce soil CO2 fluxes. Precipitation can create changes in soil water content and gases (e.g., C 0 ) profiles within unsaturated 2  zones; the extent of the effect depends on the intensity and duration of rainfall (Freeze 1969; Capehart and Carlson 1997). Heavy rainfall events, which close the air pathways to the atmosphere in the upper layers of the soils, may results in an inverted C 0 profile 2  for a short period and in lower surface C 0 flux (Osozawa and Hasegawa, 1995). Soil 2  C0  2  flux decreases as the soil moisture decreases (Davidson et al.,1998). The  influence of the soil water content on gas flux measurements and diffusion is important only when the soil is at a high water content (Davidson and Trumbore, 1995; Moncrieff and Fan, 1999; Conen and Smith, 2000; Hutchinson et al., 2000). Moreover, Moncrieff and Fan (1999) pointed out that no available theory completely describes the influence of high water content on the C 0 flux from each soil layer. 2  Evaporation from mine wastes (tailings and waste rocks) is a crucial component of the water balance, partitioning incoming precipitation into water losses back to the atmosphere and controlling water available for soil moisture storage and deep drainage (Carey et al., 2005). Soil covers are widely used in mine waste (tailings and waste rocks) to prevent the generation of acid. To assess the long-term performance of a cover, it is necessary to study the total water balance, including evaporation (Wilson et al., 1994; Wilson et al., 1997; Yanful et al., 2003a; Carey et al., 2005). For an effective soil cover, the soil must maintain a high degree of saturation (Yanful, et al., 2003a; Aubertin et al., 2006). Soil water evaporation significantly affects water content, and as  \u000CChapter II: Literature Review  Page  40  a result the degree of saturation of the soil. Therefore, knowledge of the rate of evaporation at the soil-atmosphere interface is required to estimate the water content of candidate cover soils (Wilson et al., 1994; Yanful et al., 2003a; Carey et al., 2005). The following sections briefly present the theory and methods of estimating evaporation, including SoilCover (Unsaturated Soils Group, 1997) computer model. SoilCover was used to estimate evaporative fluxes for comparison with direct measurements of evaporation using eddy covariance (EC) method (Carey et al., 2005). SoilCover model is well-established in the literature, and has been shown by several research to give reasonable accuracy solutions to real-world problems (Rykaart et al., 2001; Scanlon et al., 2002; Noel and Rykaart, 2003; Yanful and Mousavi, 2003a; Yanful et al., 2003b; Vermaak and Beznuidenhout, 2003);.  2.5.1  Evaporation Evaporation involves the change in state of water from a liquid to a vapour. The  process occurs when water molecules, which are in constant motion, possess sufficient energy to overcome the surface tension at the liquid surface and escape into the atmosphere (Gray, 1995).  The evaporation demand is governed by environmental  conditions, such as air temperature, relative humidity, net radiation and wind speed (Wilson, 1990; Unsaturated Soils Group, 1997). Evaporation from soil surfaces is strongly controlled by the water content and water transmission properties of the soil. The rate of movement of water from soil to air depends on the energy gradient and the resistance offered by each pathway through which water moves. Under the same climatological conditions, the evaporation rate can be expected to differ from the rate of evaporation from a free water surface because of the influence of the soil on the mass  \u000CChapter II: Literature Review  Page  41  and energy exchange processes (Hillel, 1980). For example: 1.  The  surfaces of  soil in a  unsaturated, therefore  the  natural vapor  environment  usually are  pressure is less than  the  saturation vapor pressure at the surface temperature. 2.  The capillary conductivity, which controls the rate of capillary flow of water in an unsaturated soil under a specific energy gradient, is largely a function of the soil moisture content, the size, shape and distribution of the soil pores, and fluid properties.  There is a distinct difference between potential (PE) and actual evaporation (AE). The actual rate of evaporation from a soil surface depends on the availability of water (Thornthwaite, 1948; Penman, 1948; Holmes, 1961; Bouchet, 1963; Priestley and Tayor, 1972; Brutsaert, 1982; Morton, 1983; Wilson et al., 1994 and 1997). The maximum potential rate occurs only when the soil surface is fully saturated and water is present on the ground surface. The actual rate of evaporation begins to decline once the soil surface becomes unsaturated. The rate of evaporation continues to decline as the soil surface continues to desiccate.  Hillel (1980) showed typical curves for  evaporation rates versus drying time for soil (Figure 2.4). The soil drying process has been observed to occur in three recognizable stages (Fisher, 1923; Pearce et al., 1949; Hillel, 1980): (1)  An initial constant-rate stage, which occurs early in the process, while the soil is wet and conductive enough to supply water to the site of evaporation at a rate commensurate with the evaporative demand. During this stage, the evaporation rate is limited by, and hence also controlled by, external meteorological conditions (i.e., radiation, wind, air humidity, etc.)  \u000CPage  Chapter II: Literature Review  42  (A)  Time Evaporation at Potential (B)  /  rim** Figure 2.5.  (A) Relation of evaporation (flux) to time under different  evaporativities (curves 1 - 4 are in order of decreasing initial evaporation rate). (B) Relation of relative evaporation rate (actual rate as a function of the potential rate) to time, indicating the three stages of the drying process.  \u000CChapter II: Literature Review  Page  43  rather than by the properties of the soil profile. A s such, this stage, being weather controlled, is analogous to the fluxcontrolled stage of infiltration in contrast with the profile-controlled stage. The evaporation rate during this stage might also be influenced by soil surface conditions. In a dry climate, this stage of evaporation is generally brief and may last only a few hours to a few days. (2)  An intermediate falling-rate stage, during which the evaporation rate falls progressively below the potential rate (the evaporativity). At this stage, the evaporation rate is limited or dictated by the rate at which the gradually drying soil profile can deliver moisture toward the evaporation zone. Hence, it can also be called the soil profile-controlled stage.  (3)  A residual slow-rate stage, which is established eventually and which may persist at a nearly steady rate for many days, weeks, or even months. This stage apparently comes about after the surface-zone has become so desiccated that further liquid-water conduction through  it  effectively  ceases. Water transmission through the desiccated layer thereafter occurs primarily by the slow process of vapor diffusion, and it is affected by the vapor diffusivity of the dried surface zone and by the adsorptive forces acting over molecular distances at the particle surfaces (Hillel, 1980). This stage is often called the vapor diffusion stage and can be important where the surface layer is such that it becomes quickly desiccated. The transition from the first to second stage is generally a sharp one, but the second stage generally blends into the third stage so gradually that the last two cannot be  \u000CChapter II: Literature Review  Page  44  separated so easily. This can be explained by the fact that during the initial stage, the soil surface gradually dries out and soil moisture is drawn upward in response to steepening evaporation-induces gradients (Hillel, 1980). The rate of evaporation can remain nearly constant as long as the moisture gradients toward the surface compensate for the decreasing hydraulic conductivity (resulting from the decrease in water content). Hillel (1980) noted that since, as the evaporation process continues, both the gradients and the conductivities at each depth near the surface are decreasing at the same time, it follows that the flux toward the surface and the evaporation rate inevitably decreases as well. A s shown in Figure 2.4, the end of the first, i.e., the beginning of the second stage of drying can occur rather abruptly.  2.5.2  Methods of predicting, evaporation Evaporation can be calculated with a formulation of Dalton Equation (Wilson et  al., 1994):  [2.13]  Where: f(u) = a wind mixing function e = vapour pressure at the soil surface s  e = vapour pressure of the air above the evaporating surface. a  The actual evaporation rate is governed by the vapor pressure difference (e - e ) and s  a  the potential evaporation rate by the vapor pressure difference ( e - e ) (for a specific a  a  \u000CChapter II: Literature Review  Page  45  set of conditions of net available energy, Q, drying power, E , surface temperature, T , A  s  and surface vapor pressure, e ). s  Penman (1948) formulated an equation for evaporation from a well-watered, short-grass surface by incorporating net radiation and energy balance into Dalton equation:  \u2022  E=  A  Q  n  +  Y  E  [2.14]  a  A+y  where;  E  = Vertical evaporative flux (mm day\" ),  A  = Slope of the saturation vapor pressure versus,  1  temperature curve at the mean temperature of the air (mmHg/\u00B0C), Q  = Psychrometric constant,  y E  = Net radiant energy available at the surface (mm day\" ), 1  n  a  =f(u)(e -e ) a  a  Penman equation is well-known, and many variations on it have been developed over the years (Burman and Pochop, 1994). There are several other commonly used methods for the calculation of potential evaporation, including the  Thornthwaite  (Thornthwaite and Mather, 1955) for montly calculations, and Priestley-Taylor method (Priestley and Taylor, 1972; Wilson, 1990). Wilson (1990) and Burnman and Pochop (1994) provide a more detailed review of potential evaporation calculation methods. Another analytical approach to the  prediction  of actual evaporation  was  presented by Granger (1989), who suggested that the actual rate of evaporation from the soil could be determined through the Dalton equation, and the actual vapour pressure at the soil surface. Granger did not present a method for calculation of vapour  \u000CChapter II: Literature Review  Page  46  pressure at the soil surface. Wilson et al. (1994, 1997) expanded on the work of Penman (1948) and developed a coupled thermal, vapor, and liquid water flow model for predicting actual evaporation from a bare soil. Dalton's Law was utilized to calculate evaporation rate based on the suction at the soil surface:  E =  where;  E E  A  \" A + yA Q  +  y  E  [2.15]  a  r h  = Vertical evaporative flux (mm day\" ), 1  a  = f(u)e (B -A h) where, a  f(u)  rh  r  = Function dependent on wind speed, surface  roughness, and eddy diffusion, 0.35(1+0.1 U ), a  U e  =Wind speed (km hr\" ), 1  a  a  =Vapor pressure in the air above the evaporating  surface, A  r h  Bh r  =lnverse of the relative humidity of the air, =lnverse of the relative humidity at the soil surface.  In this equation, the parameter A  r h  (inverse of relative humidity) at the soil  surface becomes unit in the case of saturated vapour pressure in the soil surface, and the equation simplifies to the original Penman equation. In order to predict the actual evaporation with this equation, it is necessary to solve for the vapour pressure at the soil surface. The solution for evaporation events is equally complex because the rate of  \u000CChapter II: Literature Review  Page  47  potential evaporation is determined by both the rate of potential evaporation established by climatic conditions and the suction at the soil surface.  2.5.3  SoilCover program SoilCover is a one-dimensional finite-element package that models transient  liquid and water vapor flow, based on a theoretical model for predicting th rate of evaporation from soil surfaces presented by Wilson et al. (1994). The model is based on a system of equations for couple heat and mass transfer in soil (Yanful et al., 2003). The flow of water vapor and liquid water are described on the basis of Fick's Law and Darcy's as follows:  x 5t  where:  w  5h  5y  'y  w  +  J  D  cf  [2.16]  .  5  y  ;  h = Total head (m) w  t = Time (s) C j y = Coefficient of consolidation with respect to the liquid water phase C  1  = J Pw9  p = Mass density of water (kg m\" ) 3  w  g = Acceleration due to gravity (m s\" ) 2  y = Position (m) K = Hydraulic conductivity (m s\" ) 1  w  \u000CChapter II: Literature Review  Page  48  = Coefficient of consolidation with respect to the water vapour phase  P(Pw) gm 2  w 2  = Slope of the moisture retention curve (1/kPa) P = Total pressure in the air P h  D =aP v  t  V  3  P  R T J  = diffusion coefficient of water vapor through the soil (kg m kl\\T  a  = p  273  is the tortuosity factor of the soil; and p is the cross-  sectional area of the soil available for vapor transfer  D  ,1.75  f  = 0.229 x10\" 1+  T  4  vap  V  27315j  = is the molecular diffusivity of water vapor in air ( m s\" ) 2  T W  1  = temperature (K) = the molecular weight of water (0.18 kg kmol\" ) 1  v  R  = the universal gas constant (8.314 J mol\" K\" ). 1  1  Temperature is evaluated on the basis of conductive and latent heat transfer as follows:  5T  5  8t \" 5y V =  _| 8  vJ  r(p+p )i 8  I  J  v  p  5  y  f S P v l  8  y  J  [2.17]  1  \u000CChapter II: Literature Review  Page  49  where: T  = temperature (\u00B0C)  Ch  =  C ps v  = the volumetric specific heat of the soil as a function of water content (J rrf \u00B0C- ) 3  C p  = the specific heat of the soil (J kg\" \u00B0C) 1  v  = the mass density of the soil (kg m\" ) 3  s  X L  1  = the thermal conductivity of the soil (W m\" \u00B0C\" ) 1  1  = the latent heat of vaporization of water (J kg\" ). 1  v  SoilCover calculates the vapor pressure in the soil using the  relationship  provided by Edlefsen and Anderson (1943), in which vapor pressure is calculated on the basis of the total suction in the liquid phase:  P =Psvh v  Where:  [2.18]  r  P = Actual vapour pressure within the soil v  P h  s v  r  = Saturation vapour pressure of the soil at its temperature, T = relative  humidity  of the  soil surface as a function  of  temperature fygWv  h= r  e^  R T  v|/ = Total suction in the total suction in the unsaturated soil (m). Atmospheric coupling is achieved by calculating the soil evaporative flux. Soil evaporative flux is a function of the vapor pressure gradient between the cover surface  \u000CChapter II: Literature Review  Page  50  and the atmosphere. A modified Penman formulation proposed by Wilson (1990) in Equation 2.14 is used. The surface temperature may be estimated using the following  relationship  (Wilson, 1990):  T  s  =  T  a  +  ^  (  Q  \"  E  _  G  s  )  [  Z  1  9  ]  where: T  s  = the temperature at the soil surface (\u00B0C)  T  a  = the temperature of the air above the soil surface  (\u00B0C)  G  = the ground heat flux (mm day\" of equivalent latent 1  s  heat). y  = Psychrometric constant,  2.5.4 Chapter Summary In summary, from the literature review, it was noted that many studies have investigated 0  2  and C 0  2  in subsurface pore gas and surface 0  2  and C 0 fluxes for 2  natural ground profiles. Very few studies have focused on quantifying surface C 0  2  fluxes and C 0 production rates for waste-rock systems. The literature review for waste2  rock studies also indicated that quantification of 0  2  consumption rates has been  completed almost exclusively by Australian researchers using using  temperature  profiles. A need, therefore, exists for measurements of surface C 0 fluxes and C 0 2  production rates in waste-rock piles.  2  \u000CChapter II: Literature Review  Page  51  O2 is geochemically important and active because it is a strong oxidizing agent (has strong affinity for electrons). Examples will be outlined later in the thesis to illustrate the role of O2 as an oxidant in a waste-rock environment (e.g., oxidation of sulphide minerals and oxidation of organic matter). The production of CO2 gas is important because it dissolves in the pore water and produces an increase in the activity of H (increase acidity). It is also important to note that carbonate minerals are +  often present in natural subsurface environments and have a buffering effect on the pH of subsurface pore water (Freeze and Cherry, 1979). This effect will be discussed later in the thesis. Pore gas migration in this study will be attributed to diffusion. This assumption is consistent with previous sub-surface gas studies (de Jong and Schappert, 1972; Elberling and Nicholson, 1996; Harries and Ritchie, 1985; Solomon and Cerling, 1987). Different diffusion models describe the interaction between gas molecules and the porous media through which the gas is diffusing. The Knudsen model depends upon the molecular weight and temperature of the gas as well as the pore size through which it is diffusing, but it is not influence by the presence of other gas molecules. The molecular diffusion process assumes that gas molecules collide only with other gas molecules. Molecular diffusion depends upon the molecular weights and temperatures of all the gases in a particular system and does not consider the physical nature of the porous media. A third diffusion model assumes that gas molecules collide with each other and with the porous media. Diffusion is dependent upon pore size, molecular weights and temperatures of the gases, and the physical nature of the porous media. This gas diffusion model will be used in the present work and the development of related equations will be described later in the thesis.  \u000CChapter II: Literature Review  Page  52  The influence of soil water content on gas fluxes measurements and diffusion is important when the soil is at high water content (Davidson and Trumbore, 1995; Moncrieff and Fan, 1999). The water content of soil depends on several factors: soil texture, temperature, soil respiration rate, environmental conditions of adjacent layers. These factors, which control C 0  2  fluxes, vary in different ecosystems and under  different climatic conditions. A s pointed above, climate has the potential to enhance or reduce soil C 0 fluxes. The total water balance, including evaporation is necessary, 2  example to assess the long-term performance of a cover (Wilson et., 1994, and 1997; Aubertin et al., 2006). The dependency of the effective diffusion coefficient on soil water content for different textured soils is well documented (Klute and Letey, 1958; Rowell et al., 1967; Mbonimpa et al., 2003). The diffusion coefficient of C 0 in water is about four 2  orders of magnitude slower than that in the air-filled voids. The knowledge of the rate of evaporation at the soil-atmosphere interface is therefore required to estimate the water content of candidate cover soils (Wilson et al., 1994; Yanful et al., 2003a) In conclusion, the work described in the subsequent chapters is primarily directed at the measurement of C 0  2  fluxes from a waste rock surface. A review of  literature shows there is need for further study in this important area of mine waste management. A new instrument is developed and tested using other methods. In addition, the new instrument is used to measure C 0 from a waste rock surface under 2  natural field conditions. The influence of surface water conditions with respect to the diffusion coefficient of C 0 and associated fluxes is also investigated. 2  \u000CChapter III:  Materials and Methods  Page  53  and  field  CHAPTER III Materials and Methods  3.1  Introduction The  methods  used  in this  thesis  consist of  laboratory  tests  measurements. The objective of the laboratory program was to determine the hydraulic properties and characteristics of the soil that influence the C 0 gas surface fluxes. The 2  tests were conducted in the geotechnical laboratory of the department of mining engineering at the University of British Columbia. The tests conducted include: grain size analysis, water retention curve (WRC) measurements, and saturated hydraulic conductivity tests. The field program consisted of (i) measuring the C 0 surface fluxes 2  at the Deilmann north (DNWR) and Deilmann south (DSWR) waste-rock piles using the dynamic closed chamber (DCC) method during the summers of 2000 and 2002 and compare the results with those obtained using the static closed chamber (SCC) and eddy covariance (EC) methods and (ii) investigating the effects of climatic variables (e.g., rainfall and evaporation) which affect the gas fluxes.  3.2  Laboratory Program The laboratory program consisted of sample collection and testing for hydraulic  properties.  3.2.1 Sample collection A 5-kg sample of waste-rock material from the ground and near ground surface (0-0.15 m) was collected at three different locations around DNF1 and DSF1 (Figure  \u000CChapter III:  Materials and Methods  Page  54  3.8) at the D N W R and D S W R using a sampling scoop. The triplicates samples from each of the waste-rock piles were combined and placed in zippered airtight plastic bags. The samples were shipped to the department of mining engineering of the University of British Columbia for laboratory tests. All samples were stored in the laboratory at room temperature. It should be noted that the waste-rock samples were not representative of the entire D N W R and D S W R piles because physical weathering of the waste rock would have occurred at the surface and near surface of each pile over the years. The grain size of the samples collected was &lt; than 5 cm. It should be noted that the D N W R and D S W R piles consisted of sand and sandstone, and basement gneiss rock, respectively, and that, after physical weathering, had broken down to soil with texture of a medium sand. Therefore, the samples of waste-rock material collected were not representative of the entire waste-rock piles.  3.2.2 Grain-size analysis The particle-size analysis of the soil samples was determined by sieve analysis according to A S T M Designation: D 422-63. Two tests were performed on each sample: (i) the distribution of particle sizes larger than 75 pm (retained on the No. 200 sieve) was determined by sieving (ii) the distribution of particle smaller than 75 pm was determined by a sedimentation process, using a hydrometer to secure the necessary data (Figure 3.1).  \u000CChapter III:  Materials and Methods  Page  55  \u000CChapter III:  Materials and Methods  Page  56  (i) Particles larger than 75 um: Approximately 200 g of each waste-rock sample larger than 75 pm (retained on the No. 200 mesh) was dried at 110 \u00B0C for 24 h. The oven dried sample was sieved through sieves with mesh sizes of 4, 10, 20, 40, 60, 80, 100,140, 200 and 270 on a shaker for 10 minutes. The mass and percent of waste-rock retained on each sieve were determined by weighing and plotted against the size of the sieves openings. (ii) Sedimentation process: Approximately 70 g of each sample passing through 200 mesh opening sieve was oven dried for 24 h and the mass was subsequently recorded. The sedimentation process was done in a 1-L glass cylinder using Sodium hexametaphosphate as a dispersing agent dissolved in distilled water (Figure 3.1). The solution was adjusted to a pH of 9.5 using sodium carbonate. After agitation of the slurry hydrometer readings were taken at specified time intervals up to 24 h. A sieve analysis was then performed on the material after the suspension was washed with tap after and oven dry at 110 \u00B0C. The percentage of soil remaining in suspension at the level at which the hydrometer was measuring the density of the suspension was calculate using a formula (ASTM Standard D 422-63, 1998).  3.2.3  Water retention curve Water retention curves (WRCs) (or soil water characteristic curves) for the two  waste-rock samples were determined in a Plexiglas Tempe cell apparatus (0.1 m dia. x 0.14 m height) using standard methods (Fredlund and Rahardjo, 1993) (Figure 3.2). In this test approximately 75 percent of the cell volume was filled with the waste-rock sample. The samples were tested using a 1 bar ceramic stone conducted at room temperature of approximately 20 \u00B0C. Atmospheric pressure was maintained at the  \u000CChapter III:  Materials and Methods  Page  (A)  J5L II  Top  I  Confining Ring  SAMPLE  0-Rn[-  But  Water Dbckirp -*\u2022 Collection  Air Flush Port W\u00ABt\u00ABr FiUed Grooves  Figure 3.2.  Ceramic Stole  (Ur-\u00BB)  (A) Schematic diagram of Tempe cell and (b) water  retention curve ( S W C C ) measurement setup.  57  \u000CChapter ill: Materials and Methods  Page  58  discharge face of the porous stone. Air did not flow through the cell unless the air pressure exceeded the air entry value of the ceramic disk. Small amounts of the air diffused through the water in the pores of the high air entry disk and were subsequently flushed from the base of the cell. However, the test was not affected as the air pressure in the cell was maintained by the inlet pressure. The high air entry disk at the base of the apparatus must be saturated prior to the start of the test. The sample was slowly saturated from the base upwards with distilled water until the sample surface was flooded. The sample was left saturated over night prior to the measurements. After saturation of the waste-rock specimen, increasing pressures of 0.2, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 80 and 100 kPa were applied to the air phase within the cell. The total mass of the waste-rock filled Tempe cell was continually monitored during the drainage phase of each pressure increment. Equilibrium was achieved when zero discharge (measured as change in mass) was observed over a 24 to 72 hour period. Upon reaching equilibrium at 100 kPa of applied suction, the sample was removed from the Tempe cell. The water content corresponding to the highest matric suction (100 kPa) was measured by oven-drying the waste-rock sample. This water content together with the previous changes in weight were used to back-calculate the water contents corresponding to the other suction values. The matric suctions were then  plotted  against their corresponding water contents to yield the S W C C . Fredlund and Rahardjo (1993) provide a discussion on the measurement of the matric suction.  3.2.4. Saturated hydraulic conductivity The saturated hydraulic conductivity (K t) of the samples was determined by sa  performing a falling-head hydraulic conductivity test in a stainless steel permeameter  \u000CChapter III:  Materials and Methods  Page  59  cell (0.101 m dia. x 0.116 m height) using an A S T M Standard Test Method, D 5856, 1995 (Figure 3.3). It should be noted that the falling-head test is usually recommended for soil having a K t &gt; 10\" - 10\" m s\" and the constant head test is recommended for 4  5  1  sa  coarse-grained soils. The base and top plates of the permeameter were sealed using rubber O-rings. The top plate was connected to a 100 ml standing pipe burette (0.015 m dia. x 0.70 m height). The base plate was connected to a constant head reservoir. Oven-dried waste-rock samples were uniformly and loosely poured into the cell to about 95 percent of the cell volume^ The weight of the dry sample was determined by the difference between the weight of the waste-rock-filled cell and the empty cell. The sample was saturated downward with distilled water flowing from the burette through All air bubbles were removed from the apparatus system by downward flushing of the system with distilled water. Water from the standing pipe burette was allowed to flow through the waste-rock sample using a regulated valve The time for water to fall between two defined elevations on the standing pipe burette was recorded for each test. The test was repeated until a constant time for water to fall a given height was achieved. The final sample height was then measured before removing the sample from the permeameter cell. The K t was calculated using the following equation (ASTM sa  Standard Test Method, D 5856, 1995):  aL  where:  * Log-9-  a = cross-sectional area of the burette, L= Length of the waste-rock sample in the permeameter,  [3.1]  \u000CPage  Chapter 111: Materials and Methods  (A)  Graduated cylinder.  LA  Figure 3.3.  (A) Schematic diagram and (B) experimental  setup of the saturated hydraulic conductivity measurement.  60  \u000CChapter III:  Materials and Methods  Page  61  A= cross-sectional area of the permeameter, T = time when water in the standing pipe i s a t H , 0  0  T-i= time when water in the standing pipe is at H i ,  H and Hi= are the heads from the stand pipe to the bottom 0  constant head. The hydraulic conductivity (K) of an unsaturated soil is a function of the degree of saturation (or the volumetric water content) or soil matric suction (y) (Huang et al. 1998). A number of empirical relationships have been proposed to determine K as a function of volumetric water content, or matric suction or \\\\i (Richards 1931; Wind 1955; Gardner 1956; Davidson et al. 1969; Philip 1986; Ahuja et al. 1988, and Frudlund and Rahardjo, 1993). However, the models proposed by Brooks and Corey (1964) and Mualem (1978), appear to be of wider applicability than other models. W e used the Brooks and Corey (1964) relation to calculate the unsaturated hydraulic conductivity (K):  K ( ) = K sat  [3.2]  &gt; V &gt; MMEV  V  in which the measured K t is defined as above at s a  \\\\i  &lt;  V|/ EV, A  M/ EV A  is the suction  corresponding to air-entry value (AEV) and,  n = 2 + 3L  L = the pore-size distribution index,  [3.3]  \u000CChapter III:  Materials and Methods  Page  L = - Aiog(se) Alog(^)  62  [3.4]  S = effective saturation, e  S = degree of saturation at  3.3  S-S  r  1-S  r  [3.5]  and S = residual saturation. r  Laboratory Mesocosm and Minicosms (sand columns) Previously, Kabwe et al. (2002) tested the dynamic closed chamber (DCC)  method in the laboratory in well-constrained mesocosm (2.4 m dia. x 3.2 m thick) and two minicosms (0.58 m dia. x 1.2 m thick) that was shown to accurately measure C 0  2  fluxes from ground surface to the atmosphere (Figure 3.4). One minicosm was maintained at 18-23 \u00B0C (HT) and the other at 5 \u00B0C (LT). Data measured from these columns were used to validate the numerical model developed in this thesis [see also Appendix E). The data include: the water contents, C 0  2  and 0  2  concentrations and  temperature profiles measured in the columns. These large physical models, referred to as mesocosms, are considered better surrogates for the natural ecosystem because they have biogeochemical cycles and gradients representative of the natural system. They are of sufficient size that relevant physical, chemical, and biological processes are active and thus permit natural behavior under controlled conditions and can provide an important link in the validation and extrapolation of results to ecosystems (Hendry et al., 2001; Lawrence and Hendry, 1995). The description, construction and filling of the mesocosm are presented in Lawrence et al. (1993), Hendry and Lawrence et al. (1993),  \u000CChapter III:  Materials and Methods  Page  63  1  F i g u r e 3.4.  Figures showing (A) schematic diagram and (B) photograph of the  mesocosm column (Lawrence et al., 1993), and (C) minicosms columns, C 0  2  gas  analyzer and small chamber setup used in the calibration and verification of the D C C method (Kabwe, 2001; Kabwe et al., 2002; Richards, 1998)..  \u000CChapter III:  Materials and Methods  Page  64  Lawrence and Hendry (1995), and Hendry et al. (2001). The following section briefly presents the physical descriptions of the mesocosm and minicosms. Mesocosm:  (Figure 3.4A) Fine-grained, poorly graded sand was excavated  from the C-horizon of an unsaturated zone at a field site located 10 km south of Saskatoon, Saskatchewan (Hendry et al., 2001). The sand was excavated to a depth of 6 m using a 2 m diameter solid-stem auger in November 1992. A s it was excavated, the 65 tons of sand were placed on plastic sheets and transported to the laboratory. The bottom 0.5 m of the cylinder mesocosm (2.4 diameter x 4.6 m high) was filled with 6 to 12 diameter gravel to facilitate control of the water table. The sand excavated from the field site was placed on top of the gravel in the order opposite to which it was removed from the field site. The volume of the excavated material yielded a final sand thickness of 3.6 m. and minicosms shown in Figure 3.4. Biologically produced CO2, was considered to be the primary source for the CO2. Studies (Hendry et al., 2001) on microbial aspects of the mesocosm indicated the presence of microbial activity throughout the unsaturated zone. These results indicated that biological activity within the mesocosm was likely sufficient to account for the generation of C 0 throughout the 2  profile. Minicosms: (Figure 3.4B) Two minicosms were constructed from a 0.58 m ID polyvinylchoride (PVC) tube, 1.3 m in height, fitted with removable airtight lids. The minicosms were filled with about 634 kg of sand excavated from an unsaturated C horizon (no A or B horizons) at a field site located 10 km south of Saskatoon, Saskatchewan. The texture and chemistry of the C-horizon sand are described in Hendry et al. (1999, 2000). The methods of filling the minicosms and installation of the instrumentation are described in Richards (1998). On day 1 of the study (August 06,  \u000CChapter III:  Materials and Methods  Page  65  1995), the water tables in the minicosms were lowered from ground surface to a depth of 0.95 m. One minicosm was maintained at room temperature (21-23\u00B0C) (HT -High Temperature) and the other minicosm at 5 + 2 \u00B0C (LT - Low Temperature). Results of studies of microbial populations also indicated that biological activity within the minicosms was likely sufficient to account for the generation of C 0  2  throughout the  profile (Hendry et al., 1999; Richards, 1998).  3.4  Field Program The field investigations were carried out at the Key Lake uranium mine, northern  Saskatchewan, Canada (57\u00B0 12' latitude, 105\u00B0 35' longitude) during the summers of 2000 (June to September) and 2002 (August to September). Two waste-rock piles were selected for study: the Deilmann north waste-rock (DNWR) and the Deilmann south waste-rock (DSWR) piles. The D S R W was selected because of its overall simplicity. Specifically, (1) it is texturally uniform with a grain size similar to that of the sand used in mesocosms to verify the D C C method (Kabwe et al., 2002); (2) the surface of the pile was devoid of plant cover and there was no soil development, hence any spatial or temporal variability associated with surficial respiration was minimal. A weather station was installed at the D S W R in April 2000 to characterize basic climatic variables. C 0 flux collars for the dynamic close chambers (DCC) method were 2  installed on the DNWR and D S W R in April 2000 by the author of this thesis. C 0 flux 2  collars for the static closed chamber (SCC) method were installed on the D N W R and D S W R in Summer 2002 by the author of this thesis. Sensors for measuring C 0 flux 2  using the eddy covariance (EC) technique were installed on the tripod of weather station on D S W R in Summer 2002.  \u000CChapter III:  Materials and Methods  Page  66  The following sections describe materials and methods of the field tests along with the results and interpretation.  3.4.1 S i t e l o c a t i o n a n d d e s c r i p t i o n The Key Lake uranium mine is located at the southern rim of the Athabasca Basin in north-central Saskatchewan, approximately 750 km north of Saskatoon, Canada (57\u00B0 12' latitude, 105\u00B0 35' longitude) (Figure 3.5). The average mean annual temperature at the mine site from 1977 to 1998 was -1.33 \u00B0C. From 1977 to 1998, average winter precipitation (October to April inclusive; predominantly snow) was 163.6 mm, average summer precipitation (May to September inclusive; predominantly rain) was 294.8 mm and average total precipitation was 457.4 mm (Birkham et al., 2003). The average annual evaporation (potential) for this time period was 652.9 mm (data obtained at the Key Lake mine site). Basement gneiss rock is unconformably overlain by Athabasca Group sandstone (Key Lake Mining Corporation, 1979). The Deilmann ore body was mined from 1984 to 1997. Predominant uranium-bearing minerals were coffinite ( U S i 0 ) and pitchblende 4  ( U 0 ) (Key Lake Mining Corporation, 1979). 2  Arsenide, nickel and sulfide minerals  associated with the Key Lake deposits included niccolite (NiAs), gersdorffite (NiAsS) and millerite (NiS) (Key Lake Mining Corporation, 1979). Dissolution of these minerals would potentially increase the concentrations of Ni and A s in infiltrating waters. Minor amounts of pyrite (FeS ) and cobaltite ((Co, Fe)AsS) were also present (Key Lake 2  Mining Corporation, 1979)  \u000CF i g u r e 3.5.  Map of Saskatchewan showing the location of the Key Lake  uranium mine., Saskatchewan, Canada.  \u000CChapter III:  Materials and Methods  Page  Gangue materials consisted of sandstone and basement gneiss. comprising  the  sandstone  included  quartz  (Si0 ),  chlorite  2  ((Mg,  68  The minerals Fe, AI) (AI, 6  Si) Oio(OH) ), kaolinite (AI Si 05(OH)4), calcite ( C a C 0 ) and siderite ( F e C 0 ) (Key 4  8  2  2  3  3  Lake mining Corporation, 1979. The basement gneiss was typically composed of quartz, muscovite (KAI (AISi30io)(OH) ), chlorite and feldspars (Key Lake Mining 3  2  Corporation, 1979). Graphitic gneiss was also present. Acid base accounting results for sand/outwash till, sandstone and basement rock indicated that both the sulfur and carbonate contents were very low and that the wasterock piles were not clearly acid generating or consuming (Steffen Robertson and Kirsten (Canada) Inc., 1993). The ratio of neutralization potential to acid generation potential (NP/AP) for sand/outwash till was 1.6 with a standard deviation (s.d.) of 1.30 and a sample size (n) of 29. The mean total sulfur content was 0.03 % (s.d. = 0.02, n = 29). The N P / A P for sandstone was 0.8 (s.d. = 1.93, n = 68) and the total sulfur content was 0.04 % (s.d. = 0.02, n = 68). The N P / A P for basement rock was 1.7 (s.d. = 1.5, n = 27) and the total sulfur content was 0.11 % (s.d. = 0.05, n = 27). Temperatures within the piles ranged from 0 to 2\u00B0C. The excavation of the Deilmann pit resulted in the concurrent construction of two main waste-rock piles from 1984 to 1997, the Deilmann north waste-rock (DNWR) and the Deilmann south waste-rock (DSWR) piles (Figure 3.6). The waste-rock piles were constructed in lifts approximately 8 m in height.  Haul ramps were used to transport  material to each new lift pad where the waste rock was then dumped and pushed off the edge of the pad to maintain a flat top. Compaction and physical weathering of the waste rock would have occurred at the surface of each lift as a result of machinery traffic.  \u000CChapter III:  F i g u r e 3.6.  Materials and Methods  Page  Photograph showing the Deilmann pit, the Deilmann north waste-rock  (DNWR) and Deilmann south waste-rock (DSWR) piles at the Key Lake uranium mine, Saskatchewan, Canada.  69  \u000CChapter III:  Materials and Methods  Page  70  The degree of compaction and weathering could be expected to be the greatest nearest the haul ramp and to decrease further from the haul ramp. The D S W R was constructed between 1984 and 1995 and consists exclusively of sand and sandstone (Key Lake Mining Corporation, 1979) (Figure 3.7). The maximum height of the D S W R pile is 28 to 31 m above the original ground surface. The bottom of the pile has elevated concentrations of organic matter derived from both lake bottom sediments and forest soils (e.g., Figure 3.7).  The original ground surface was not  scraped of organic material before construction creating a layer of organic-rich sand at the bottom of a large area of the waste-rock pile (Figure 3.7) The D N W R pile was constructed from 1984 to 1997 and consists of a mixture of sand, sandstone and basement rock. The maximum height of the D N W R pile is approximately 42 m above the original ground surface. Several lakes near the pits were drained, exposing lake bottom sediments. The bottom of the pile has also elevated concentrations of organic matter derived from both lake bottom sediments and forest soils. Similarly, the original ground surface of the D N W R was also not scraped of organic material before construction creating a layer of organic-rich sand at the bottom of a large area of the waste-rock pile (Birkham et al., 2003). The geochemical conditions of the waste-rock piles at the Key Lake mine were unique because sulfur contents &lt; 0.11 % S (Steffen Robertson and Kirsten (Canada) Inc., 1993) were low compared to most other geochemical studies (Jaynes et al., 1983a; Gelinas et al., 1992; Elberling et al., 1993; Ritchie, 1994a; Elberling and Nicholson, 1996; Keller and Bacon, 1998; Hockley et al., 2000).  Geochemical  conditions are also unique because of the cold climate and, as previously mentioned, the piles are constructed of overburden sand, sandstone and gneissic basement rock  \u000CChapter III:  Materials and Methods  Figure 3.7.  Page  71  Depth geologic profile for Deilmann south waste-rock (DSWR) pile at the  Key Lake uranium mine, Saskatchewan, Canada (Adapted from Birkham et al., 2003).  \u000CChapter III:  Materials and Methods  Page  72  that were dumped on the original ground surface. Approximately 44 million cubic meters of waste rock produced at this site is more than most waste-rock volumes reported in the literature (Gelinas et al., 1992; Ritchie, 1994a; Hockley et al., 2000). Geochemical conditions are also unique because of the cold climate.  3.4.2  Field C 0 flux measurement methods 2  This section presents the three methods for C 0  2  flux measurements: the  dynamic closed chamber (DCC), the static closed chamber (SCC), and eddy covariance (EC) methods. It should be noted again that the D C C was designed, verified and applied on field by the author of this thesis. The S C C was designed by the Department of Soil Science of the University of Saskatchewan (U of S), however, the field measurements were carried out by the author of this thesis, but the gas analysis for C0  2  were done at the Department of Soil Science of the U of S (Farrell et al., 2002).  Sensors for measuring C 0 fluxes using E C method were installed by the Department 2  of Geography of the U of S on the tripod of the weather station installed on the D S W R by the author of this thesis on April 28, 2000 (see later in this section). Data analysis was also done at the same Department of the U of S. Twenty collars were installed on the DNWR and nine on the D S W R , between April 27 and 29, 2000 (Figure 3.8). The collars were manually driven into the waste-rock piles, leaving the top 0.01 rrrof the collar above the waste-rock surface. The ground surface was leveled by rotating a straight edge template (0.01 m thick) on top of the collar. The collars were allowed to stabilize in the sand for about 60 days prior to the start of the C 0 flux measurements. 2  \u000CC h a p t e r III:  Materials a n d M e t h o d s  Page  Deilmann South Waste Rock Pile  Figure 3.8. M a p of the D e i l m a n n north w a s t e - r o c k ( D N W R ) a n d D e i l m a n n south w a s t e - r o c k ( D S W R ) piles at the K e y L a k e m i n e , S a s k a t c h e w a n , C a n a d a , s h o w i n g the c h a m b e r s a n d the m e t e o r o l o g i c a l w e a t h e r station locations.  73  \u000CChapter III: Materials and Methods  3.4.2.1  Measuring C 0  Page  74  fluxes using dynamic closed chamber (DCC)  2  method A technique to measure C 0 fluxes from the soil surface to the atmosphere was 2  recently developed and verified in mesocosms over the range of C 0 fluxes reported for 2  field conditions by the author of this thesis (Kabwe, 2001 and Kabwe et al., 2002). The technique termed the dynamic closed chamber (DCC) method, is based on direct measurement of the change in C 0 concentration with time in the headspace of a 2  chamber installed on ground surface. Carbon dioxide concentrations were directly measured using a portable C 0 gas analyzer (ADC 2250, BioScientic Ltd). 2  The work of this thesis focused on the field application of the DCC method to quantify reaction rates in waste-rock piles. Full details of the design, construction, and operation of the DCC are presented in Kabwe (2001) and Kabwe et al. (2002). The following section briefly described the DCC method. Chamber collars for the DCC were fabricated from fiberglass rims (0.76m dia.x 0.15m height); the chamber lid (0.76m dia. x 0.05m thick) was fabricated from Plexiglas (Figure 3.9). A rubber O-ring was installed into a groove on the underside of the lid to provide an air-tight seal between the lid and the collar. Inlet and outlet brass-fittings were installed in the lid. A perforated tube (1.20 m long) with one end connected to the inlet fitting was installed into a groove on the underside of the lid to provide air dispersion in the chamber headspace. The lid was attached to the collars with nuts and bolts. Carbon dioxide analyses were performed using an ADC 2250 differential infrared C 0  2  gas analyzer (ADC BioScientific Ltd). The analyzer provided simultaneous absolute and differential gas measurements. All CO2 measurements were corrected for pressure  \u000CChapter III:  Page  Materials and Methods  75  Figure 3.9. (A) Typical slopes of direct measurement of concentration versus time using the dynamic closed chamber (DCC) method (B) schematic diagram and (C) photograph of the D C C method setup for measuring surface C 0  2  gas fluxes.  \u000CChapter III:  Materials and Methods  Page  76  broadening and dilution effects caused by water vapor; single bench (CO2) peak-topeak noise was typically &lt;0.2 ppmV at 350 ppmV CO2. Measurements were made by sealing the lid onto the collar and continuously circulating air from the chamber (top, center) through the A D C 2250 C 0 analyzer and back into the chamber through the 2  perforated air-dispersion ring on the underside of the lid (see Figure 3.9). Prior to measuring a flux, the ambient C 0 concentration was measured at the 2  collar. The C 0 was then scrubbed from the air in the sealed chamber (using soda lime 2  in an on-line trap) to lower the C 0 concentration to below ambient (to yield improved 2  accuracy at low flux levels). In the measurement mode, the analyzer measured the C 0  2  concentrations in the chambers as they increased from sub-ambient to ambient and higher concentrations (Figure 3.9). During this period, the CO2 concentration in the chamber was measured at 1 s intervals, with mean concentrations recorded every 10 s. The flow rate through the chambers was maintained at approximately 39 L h\" . The flux 1  of C 0  2  from the soil surface was calculated from the rate of change in C 0  2  concentrations in the chambers as follows:  Fco  dC = 2  dt  [3.6]  xh  where F c o ' the C 0 flux from the soil surface, C is the concentration (mg m\" ) in the s  2  3  2  chamber at ambient temperature and pressure, t is time, h is chamber height (m), and dC/dt is the slope of the best fit of the time series as time approaches zero. Fluxes were determined by averaging a series of four to eight measurement cycles. The final flux reported here equals the flux observed at the ambient air CO2 concentration, which was determined prior to measurements. The time required to determine one series of flux  \u000CChapter III:  Materials and Methods  Page  77  measurements ranged from 2 to 8 min, depending on the magnitude of the flux. To minimize temperature variation within the chamber, it was shielded from the sun during the measurement period. Note: in all cases, actual C O 2 concentrations were measured as mixing ratios (i.e., volume per unit volume of air) and were converted to a mass basis as described by Hutchinson and Livingston (2000).  3.4.2.2  Measuring C 0  2  fluxes using static closed chamber (SCC)  method Ambient fluxes of C 0 also were measured using a static closed chamber ( S C C ) 2  consisting of a P V C cap fitted with a vent tube and Swagelok\u2122 sampling port (see Figure 3.10). Collars (15 cm x 20.3 cm i.d.) for the chambers were manually driven into the collar. To minimize the effects of soil disturbance on the C 0 flux, the collars were 2  inserted into the waste rock about one week prior to the start of the measurement period. Each chamber had a volume-to-surface area ratio of about 9:1, with an internal headspace volume (including the above-ground portion of the collar) of 1750 c m and a 3  surface area of 201 c m . Once the chamber was sealed to the collar, gas samples were 2  collected at 20-min intervals. G a s samples were collected from the enclosed headspace using a disposable, 20-cc syringe equipped with a 25-gauge, / -inch needle. G a s 5  8  samples were withdrawn through the sampling port (sealed with a gray butyl rubber septum) in the top of each chamber; injected into pre-evacuated (ca. 5 x 10\" atm), 123  cc Exotainers\u2122, and analyzed using gas chromatography (Farrell et al., 2002). The gas samples were stored under a positive pressure of approximately 2-atm (i.e., 20-cc of headspace gas was injected into each 12-cc collection tube) to minimize any gaseous exchange with atmospheric air. The gas samples were then shipped to the Department  \u000CChapter III:  Materials and Methods  Figure 3.10.  (A) Schematic diagram and (B) photograph of the  static closed chamber ( S C C ) setup (collar and cap) installed on the Deilmann south waste-rock ( D S W R ) pile.  Page  78  \u000CChapter III:  Materials and Methods  Page  79  of Soil Science of U of S for C 0 gas analysis. 2  Carbon dioxide concentrations were determined using a Varian Model CP2003 Micro-GC equipped with a micro-TCD and Poraplot U column (injector temperature = 110\u00B0C, column and detector temperature = 50\u00B0C). Ultra-high purity (UHP) helium was used as the carrier gas. The vertical flux density for C 0  above the soil surface (mg C 0  2  m\" h\" ) was 2  2  1  determined by measuring the change in gas concentration beneath the sealed chamber at set (equally spaced) time intervals. The vertical flux was then calculated using the diffusion-based estimation model proposed by Hutchinson and Mosier (1981) (see also Appendix I): V^-Cp) 0 0 2  , ^ - C Q )  2  At (2C -C -C ) 1  1  and  2  t = 2t and 2  1  (Cj-CO  0  ( C l  ~  C  q  )  &gt;1  [3.8]  where: V is the volume (m ) of enclosed chamber air, A is the area (m ) of soil that is 3  2  covered by the chamber, C is the initial C 0 concentration (mg m\" ), and C i and C are 3  0  2  2  the C 0 concentrations (mg m\" ) at times U (0.33 h) and t (0.67 h). The 20 min time 3  2  2  interval between samples was long enough for the C 0 concentration in the chamber 2  headspace to increase to a measurable level, yet short enough that the C 0  2  concentration in the chamber neither leveled off nor declined during the interval from ti to t . Carbon dioxide concentrations were converted to a mass basis after correcting for 2  variations in temperature (i.e., 15\u00B0C), vapor-pressure (to correct for wet gas), and atmospheric pressure.  \u000CChapter III:  3.4.2.3  Page  Materials and Methods  80  Measuring C 0 fluxes using eddy covariance (EC) method 2  Sensors for measuring C 0  2  flux using eddy covariance (EC) technique were  installed on the same tripod of weather station in 2002 between 2 July and 25 August (Figures 3.11 and 3.12). A three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT) was mounted on a 1.5 m boom with the mid-point of the sonic head approximately 1.7 m above the ground surface within the constant flux layer. The instrument height is justified considering the surface is vegetation-free without wake elements and above the height of the roughness sub-layer. An open-path gas analyzer (LI-7500, LI-COR Inc., Lincoln, NB) was placed on the boom adjacent to the sonic anemometer at the same height with approximately 0.2 m separating the mid-point of each sensor. Various wind components were recorded every half hour using a C R - 2 3 X data-logger (Campbell Scientific Inc., Logan, UT). Although the D S W R site was flat, it had a fetch of only 150 to 300 m. The 'flux footprint' of a tower (which is a function of wind speed and direction and the height of the tower) was calculated as described by Schuepp et al. (1990). The peak for the flux footprint ranged from 35 to 50 m upwind, with approximately 90% of the cumulative flux footprint within 150 m upwind of the Both tower. The E C method was used to measure the C 0  2  flux on a continuous basis  (Baldocchi et al., 1988). Changes in C 0 concentration were measured using the LI2  7500 open-path C 0 / H 0 gas analyzer. Latent and sensible heat fluxes were measured 2  2  concurrently. Wind speed and gas concentration measurements were obtained at a frequency of 10 Hz. The C 0 flux (Fco ) was calculated as the product of the mean 2  2  covariance of the vertical wind speed fluctuations (w'j and the scalar fluctuations in C0  2  \u000CChapter III:  Materials and Methods  Page  Figure 3.11. Photograph showing the meteorological weatherstation and the eddy covariance (EC) sensors for measuring C 0 flux installed 2  on Deilmann south waste-rock pile (DSWR).  81  \u000CChapter III:  Materials and Methods  Page  F  C 0 2 = P a  w'C0  82  [3.9]  2  where p is the density of the dry air and the prime (') denotes the deviation from the a  mean (see also Appendix A). The running mean was based on a 300-s time constant; the resultant mean fluxes and various wind components were recorded every 30 minutes. Corrections and adjustments to the Fco  a r 2  \u00A9 summarized as follows. First,  fluxes were corrected for changes in air density (Webb et al., 1980) and were removed when u* &lt; 0.1 m s\" (Note: u* is the friction velocity as measured by E C ) due to poor 1  energy balance closure at low wind speeds (Twine et al., 2000; Barr et al., 2002). Second, F o was corrected for underestimation by eddy covariance by adjusting for C  2  energy-balance closure, assuming that eddy covariance underestimated F c o by the 2  same fraction that it underestimated sensible and latent heat fluxes (Black et al., 2000, Twine et al. 2000; Barr et al., 2002), i.e., by 24% for all measurement periods (r = 0.86, 2  n = 3035). Additionally, as a check on the energy balance closure method, a power spectral density function was computed using high-frequency (20 Hz) data to determine if sampling interval was sufficient to capture low and high frequency eddies (i.e., Moore, 1986). This analysis indicates that the tower captured 79% of the energy being transferred (sampling at 10 Hz and integrating over 30 minutes). However, this highfrequency data was collected during one 6-hour period only, and as such the energybalance method of correction was used as it was considered more representative over all stability conditions.  \u000CChapter III:  3.4.2.4  Materials and Methods  Page  83  Gravimetric water content measurement Waste rock samples were retrieved in triplicates from selected locations  around DNF and D S F (Figure 3.8) at four different depths (0, 0.05, 0.10 and 0.15 m) at the D N W R and D S W R , from the period of July 29 to August 06, 2002. The samples weighing between 200 to 250 g were immediately placed in zippered plastic bags to preserve the in situ moisture in the samples. The samples were transported to the on site Key Lake metallurgical laboratory and kept in the refrigerator. The samples were tested within 24 h. The gravimetric water content for waste rock samples was measured according to the A S T M Standard Test Methods for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass (D2216-05). The method consisted of taking the waste rock samples weighing between 100 and 200g. The samples were put in a container of known weight, weighed (subtracting the weight of the container gives the weight of the wet soil). The samples were then put in an oven, and dried at 105 \u00B0C for about 24 h until all the water had evaporated. After drying the samples, they were weighed again (subtracting the weight of the container gives the weight of the dry soil). The moisture content on a weight basis was the difference between the wet and dry weights divided by the dry eight. The gravimetric water contents were converted to volumetric water contents using data from the W R C and specific gravity.  3.4.2.5  Meteorological weather station  A meteorological weather station (Campbell Scientific, Inc.) was installed on D S W R (Figure 3.12) approximately 5 m southwest of the collar located at DSF1 (Figure 3.8) on April 28, 2000 by the author of this thesis. The meteorological station sensors  \u000CChapter III:  Materials and Methods  Page  84  and data acquisition systems (DAS) were installed on a tripod. The wind monitor and net radiometer were mounted on a steel cross-arm at a height of approximately 3 m above the ground surface. The air temperature and relative humidity probe was housed in a radiation shield (approximately 1.8 m above the ground surface) to minimize the effects of solar radiation. The tipping bucket rain gauge was installed on a wooden plank near the tripod (approximately 1 m above the ground surface). The D A S consisted of a C R 1 0 data- logger (Campbell Scientific Inc.), a storage module and a solar panel with 12 volt battery system. The station collected hourly average air temperatures.  3.4.2.6  Chapter S u m m a r y In summary, in a previous study using large-scale, laboratory mesocosms filled  with sand [Kabwe et al., 2002], the D C C method was shown to accurately measure CO2 fluxes from ground surface to the atmosphere. This laboratory-verified  technique,  therefore, provided the opportunity to quantify CO2 fluxes under field conditions. The following chapter 4 presents results of the field application of the D C C method. The D C C method was used to determine the magnitude of spatial and, to a lesser degree, temporal variations in the C 0 efflux on the D N W R and D S W R piles. In addition, fluxes 2  measured using the D C C method were compared to those obtained from two other methods: static closed chamber (SCC) and eddy covariance (EC) methods.  \u000CChapter III:  Materials and Methods  Figure 3.12. (A) Schematic diagram and (B) photograph of  Page  meteorological weather  station installed on Deilmann south waste-rock (DSWR) pile at the Key Lake mine, Saskatchewan, Canada.  85  \u000CChapter I V : Results and Data Interpretation  Page  86  CHAPTER IV Results and Data Interpretation  4.1  Laboratory Tests Program The laboratory program consisted of testing for hydraulic properties for samples  from the Deilmann north (DNWR) and Deilmann south (DSWR) waste-rock piles .  4.1.1 Grain-size distribution The laboratory tests results for the near-ground surface ( 0 - 0 . 1 5 m) samples from D S W R and DNWR for grain-size distribution are plotted respectively in Figures 4.1 and 4.2. The detailed tests results are presented in Appendix C . The grain-size distribution curves from D S W R sample (Figure 4.1, curve with symbols) indicated that 90% of the material was sand size with 10% silt- and clay-size particles. The sand sizes ranged from coarse (6%), medium (32%), and fine (52%). The uniformity coefficient (C ) u  of the sample ( C = D o/Dio) was found to be about 3.6 (e.g., D i = 0.015 cm is the size u  6  0  such that 10% of the particles are smaller than that size). For comparison, a washed beach sand would have a C of about 2 to 6 whereas a sample with a C &lt; 4 is u  u  considered well sorted while a sample with a C &gt; 6 is considered poorly sorted. The u  void ratio (e) of the sample (e = V A / ) was found to be 0.560 (e.g., V is the volume of V  S  v  voids and V is the volume of solids). The C , e and D-io values of the sample are typical s  u  of the values for granular non-consolidated sand materials reported in Table 4.1.  \u000CPage  Chapter IV: Results and Data Interpretation  87  Table 4.1. Nature, origin, and basic geotechnical properties of various granular materials.  e  Dio(cm)  C  . Coarse sand  0.05800  1.3  0.750  . Borden sand  0.00910  1.7  0.590  . Modifield Borden sand..  0.00800  1.8  0.640  Kissiova (1996)  . Secrete sand  0.01450  3.5  0.570  MacKay (1997)  . Ottawa sand  0.00937  1.7  0.634  0.00930  2.6  0.269  0.00930  2.6  0.267  0.00930  2.6  0.618  Source  Sydor (1992)  Material  u  . Beaver Creek sand consolidated at 5 kPa.... Bruch (1993)  . Beaver Creek sand consolidated at 10 kP.... . Beaver Creek sand  Lim e t a l . (1998)  consolidated at 5 kPa....  The mean \u00B1 one standard deviation grain-size distributions (curves with solid lines) obtained from 106 core samples from D S W R (Birkham, 2002) are also presented in Figure 4.1 for comparison. The partial grain-size (without gravel and boulder-sized) distribution for the material evaluated in this thesis (curve with symbols) was within the envelope of the core samples up to the grain size &gt; 0.3 mm)..  \u000CChapter IV:  Page  Results and Data Interpretation  U.S Standard Sieve numbers  U.S. Sieve openings in inches  10  1/2  20  50 '  100 I  140 I  270 I  at  c  \"w CO  a. c &lt;u  o  03  Gravel Fine Coarse  Figure 4 . 1 .  0.01  0.1  10  100  Coarse  Sand Medium  Fine  Silt or Clay  Particle size distribution curves (without gravel and boulder-sized) for the  samples of waste-rock from Deilmann south waste-rock pile (DSWR) for ground surface sand (curve with symbols) and core sand/sandstone (curves with full lines). Symbols represent the measured data from this thesis. The full lines show the one standard deviation range of grain-size data obtained by Birkhman et al. (2002).  88  \u000CChapter IV:  Results and Data Interpretation  Figure 4.2.  Page  89  Particle size (without gravel and boulder-sized) distribution curves for the  samples of waste-rock from Deilmann north waste-rock pile (DNWR) for ground surface sand (curve with broken line and symbols) and core basement-rock (curves with solid lines). Symbols represent the measured data from this thesis. The full lines show the one standard deviation range of grain-size data obtained by Birkhman et al. (2002).  \u000CChapter IV: Results and Data Interpretation  Page  90  The C of the mean grain-distribution (not presented) for the core samples was u  about 3.3 and was typical of the value obtained for this study. Birkham (2002) noted, however, that the grain-size distributions of waste-rock samples were not representative of the entire D S W R pile as gravel and boulder-sized particles were excluded from the analysis The grain-size distribution for the near-surface sample collected from D N W R of (Figure 4.2, curve with symbols) indicated that 8 3 % of the material was sand size with 17% silt- and clay-size particles. The sand sizes ranged from coarse (16%), medium (42%), and fine (25%). The C of the sample was found to be about 6.3 (e.g., D-m = u  0.018 cm). The D N W R sample is considered to be poorly sorted than that from the D S W R . The e of the sample was found to be 0.591 and was within the range of the granular sand materials reported in Table 4.1. The mean \u00B1 one standard deviation of 26 grain-size distributions (curves with solid lines) obtained from core samples from D N W R (Birkham, 2002) are also presented in Figure 4.2. The grain-size distribution curve measured in this study was outside the mean \u00B1 one standard deviation envelope for the basement-rock core samples. The C of the mean grain-size distributions (not shown) u  for the basement-rock was determined to be about 30. Birkham (2002) found that 4 0 % of the D N W R basement-rock bulk sample was coble-sized. This was consistent with the visual observation that the basement-rock in the D N W R generally had larger particles compared with the sand-sandstone material. A s was the case for the grain-size distributions from the D S W R , the grain-size distributions of samples from the D N W R could not be considered representative of the entire D N W R pile because boulder-sized particles were excluded from the analysis (Birkham, 2002).  \u000CPage  Chapter IV: Results and Data Interpretation  91  4.1.2 Water retention curve For a given porous media, the relationship between the soil water content (9) and the soil suction (vj/) or matric potential is known as either  the water retention curve  (WRC) (Marshall et al., 1996; Aubertin et al., 1998; Delleur, 1999), the soil water characteristic curve ( S W C C ) (Fredlund and Xing, 1994; Barbour, 1998), the soil suction curve (Yong, 2001), or the soil moisture-retention curve (Kovacs, 1993; Hillel, 1980; Looney and Falta, 2000). In this thesis, the W R C will be used to represent the relationship between 6 and  Figures 4.3 and 4.4 show the W R C s for the D N W R and  D S W R samples respectively measured in the laboratory using Tempe cells. The solid symbols are measured data (from 0.2 to 100 kPa suction) and the solid lines (0 to 1 million kPa suction) represent the best fit curves generated with SoilCover model using an equation developed by Fredlund and Xing (1994). The water content (6) can also be expressed in terms of saturation (S ) (S = 0/n), where n is the soil porosity (Figures 4.5 and 4.6). It should be noted that 9 at zero suction is equivalent n. The n for the samples for D N W R and D S W R were found to be 0.36 and 0.38, respectively. The W R C describes the soil's ability to store and release water (Fredlund and Rahardjo, 1993; Barbour, 1998). It also represents the drying curve for the soil material and provides useful information on the water retention and water transmission behavior of a waste-rock pile and helps to describe the effects of waste-rock texture and void ratio (e) on the distribution of the water phase in the waste-rock pile, and thus, the gas diffusion in this pile (Barbour, 1998). The W R C can be seen as a representation of the pore-size distribution function with assumption based on the capillary model (Mualem, 1986). Aubertin et al. (2003) also developed a model to predict the S W C C from basic  \u000CChapter I V : Results and Data Interpretation  F i g u r e 4.3.  Page  92  Water retention curve (WRC) of the sample of waste-rock (with fine fraction  only) from the Deilmann north waste-rock (DNWR) pile. Symbols represent the measured data and the solid line the best fit curve generated with SoilCover (SoilCover, 1997).  \u000CChapter I V : Results and Data Interpretation  Page  1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 Matric s u c t i o n (kPa)  Figure 4.4  Water retention curve (WRC) of the sample of waste-rock (with fine  fraction only) from the Deilmann south waste-rock (DSWR) pile. Symbols represent the measured data and the solid line-the best fit curve generated with SoilCover (SoilCover, 1997).  93  \u000CChapter IV: Results and Data Interpretation  Page  94  geotechnical properties. In general, the W R C is described as having three parts: (i), the upper horizontal line of the curve represents approximately 100% saturation of the sample (ii) the fast decreasing slope, and (ii) the slow decreasing slope represents the residual water content. The W R C s show that the soil samples remain saturated when suctions are lower than the air-entry values (AEVs). The A E V corresponds to the suction at which the soil sample will begin to ciesaturate and, depending on the soil type, may or not be well defined. SoilCover model calculations yielded values of A E V s of 2.4 and 1.3 kPa for the D N W R and D S W R , respectively. The capillarity of the soil allows it to remain saturated at suction less than the A E V (Aubertin et al., 2003). The slope of the curve defines the volume of water taken on or released by a change in pore-water pressure. The W R C s show that the A E V of the sample from the D S W R is better defined (with steep slope) than that from the D N W R (with smooth slope) (Figures 4.3 and 4.4). The A E V can be defined graphically as the intersection of the best-fit lines of the two linear segments of the W R C (as shown in Figures 4.3 and 4.4) (Fredlund and Xing, 1994 and Barbour, 1998). The tangent (graphical) method yielded values of approximately 1.5 and 1 kPa for the D N W R and D S W R piles respectively. SoilCover model simulations yielded values of A E V s of 2.4 and 1.3 kPa for the D N W R and D S W R , respectively. The slight difference in the A E V s values is due to slight variations in the waste rock textures and porosity. Fine grained soils tend to have flat (or smooth) functions with high A E V s , whereas coarse grained soils tend to have steep functions with low A E V s . For example, the D S W R sample contained less fine-grained (e.g., 10% silt- and clay-size particles), than the D N W R (e.g., 17% silt- and clay-size particles) and the rest of the material was sand-size.  \u000CChapter I V : Results and Data Interpretation  Figure 4.5.  Page  Water retention curve (WRC) of the sample of waste-rock from the  Deilmann north (DNWR) pile. Symbols represent the measured data and the solid line the best fit curve generated with SoilCover (SoilCover, 1997).  95  \u000CChapter I V : Results and Data Interpretation  Page  1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 Matric S u c t i o n (kPa)  Figure 4.6.  Water retention curve (WRC) of the sample of waste-rock from the  Deilmann south (DSWR) pile. Symbols represent the measured data and the solid line the best fit curve generated with SoilCover (SoilCover, 1997).  96  \u000CChapter IV: Results and Data Interpretation  Page  97  Yanful et al. (2003a) measured the S W C C for fine sand and found an A E V value 3 kPa. This value is close to the value of the D S W R sand sample measured in this thesis (i.e., 2.4 kPa). Wilson et al. (1994) and Newman (1999) also measured the WRCs for fine-grained materials (Beaver Creek sand) and both found an A E V of approximately 3 kPa. Since the soils are close to saturation up to 2.4 and 1.3 kPa suctions for the D N W R and D S W R , respectively, almost all the pore spaces are filled with water and thus the CO2 flux is expected to be significantly reduced. It should be noted that the free diffusion coefficient of CO2 is about four orders of magnitude larger in air than in water, diffusive transport in the water-filled pores is much slower than that in the airfilled voids. The results show that above the A E V s , the water contents (or saturation) decrease rapidly with matric suction. The W R C s show the two samples drain rapidly between values of matric suctions of 2.4 and 10 kPa and 1 and 10 k P a for the D N W R and D S W R , respectively. At 10 kPa suction, the samples retained about 20% and 10% water for the D N W R and D S W R samples, respectively. This behavior is characteristic of uniform sand and sand/silt materials and has been also described by others (Wilson et al., 1994; Barbour, 1998). A s the matric suction increased the samples reach slow residual values. The residual water content is controlled primarily by the fine fraction and the surface area of the sample. The residual suctions (\u00A5,-) (suction at residual water content)  were  determined using the tangent method applied to the W R C s as described by Fredlund and Xing (1994) and were found to be 11 and 6 kPa for the D N W R and D S W R , respectively (Figures 4.5 and 4.6).  \u000CPage  Chapter I V : Results and Data Interpretation  98  Aubertin et al. (2003) provides also the following expression to evaluate  0-42  \"\u2022-&lt;^r and also  TAEV  []  r 4 1 l  (suction at AEV):  (eD )  x  H  where e is the void ratio, and D is an equivalent particle diameter for a heterogeneous H  mixture and b and x are fitting parameters. For practical geotechnical applications, the value of D can also be approximated using the following function (Aubertin et al., 1998; H  Mbonimpa et al., 2000, and 2002): D = [ l + 1.17log(C )]D H  u  [4.3]  10  where D 1 0 is the diameter corresponding to 10% passing on the cumulative grain-size distribution curve, and C  u  is the coefficient of uniformity ( C  u  = D 6 0 / D 1 0 ) . For the  equivalent capillary rise in granular soils b can be approximated using the following function (Aubertin etal., 1998): ? \u2014 x \u2014  b =  [4.4]  1.17log(C )+1 u  Using the values of C and D u  1 0  for the DNWR and D S W R samples (see Section 4.1.1)  of this thesis), the D for the D S W R and D N W R were found to be 0.02478 cm and H  0.03488 cm, and b for the D S W R and D N W R were found to be 0.143 and 0.388, respectively. Equation 4.2 yielded values of  of 22.07 cm (9.21 kPa) and 55.90 cm  (5.59 kPa) for the D N W R and D S W R , respectively. These values are close to those determined graphically using the tangent method (11 and 6 kPa for the D N W R and D S W R , respectively). Similarly, Equation 4.2 yielded values of  ^PAEV  of 18.8 cm (1.88  \u000CChapter IV:  Results and Data Interpretation  Page  99  kPa) and 10.36 cm (1.04 kPa) for the D N W R and D S W R , respectively. These values are very close to those determined graphically using the tangent method (1.5 and 1 kPa) for the D N W R and D S W R , respectively. It should be noted that Equation 4.1 is frequently quite practical for fine-grained soils because D-io and C are often unknown. u  4.1.3 H y d r a u l i c c o n d u c t i v i t y The hydraulic conductivity is a measure of the ability of the soil to transmit water and depends upon both the properties of the soil and the fluid (Klute and Dirksen, 1986). Total porosity, pore-size distribution,  and pore continuity  are the important soil  characteristics affecting hydraulic conductivity and S W C C . The hydraulic conductivity at or above the saturation  point (e.g., A E V ) is referred to as saturated  hydraulic  conductivity (K t), and for water contents below saturation, it is called the unsaturated sa  hydraulic conductivity 'K' (Figures 4.7 and 4.8). Laboratory tests described in the previous section were conducted to determine the saturated hydraulic conductivity 'k t'. sa  using the falling-head permeability tests. The tests yielded values of ' K t ' of 1.20 x 10\"  6  sa  m s\" and 1.49 x 10\" m s\" and for the D N W R and D S W R near-ground surface ( 0 - 0 . 1 5 1  5  1  m) samples, respectively. These values are characteristic of sand and sand/silt materials. The nature and origin of various data of K are given in Table 4.1. Wilson et al. (1994) measured a value of 3.9 x 10\" m s\" for Beaver Creek sand using the falling6  1  head permeability tests. Newman (1999) also measured value of saturated hydraulic conductivity of 6.2 x 10\" m s\" for Beaver Creek sand. Yanful et al. (2003) obtained 5  1  values of K t of 1.9 x 10\" m s\" and 7.3 x 10\" m s\" for fine sand and coarse sand, 6  1  6  1  sa  respectively. Hatanaka et al. (1997) measured values of 1.5 x 10\" - 4.3 x 10\" m s\" for 5  4  1  undisturbed sands (12 results). Mbonimpa (1998) determined values of 8.2 x 10\" - 1.1 5  \u000CChapter I V : Results and Data Interpretation  Page  100  x 10\" m s\" for uniform sand (30 results). These values are very similar to those 3  1  obtained in this work for D N W R and D S W R . The D N W R contained more fine sand (52%) than the D S W R (25%) and had a comparatively lower K t. The hydraulic sa  conductivity 'K' of an unsaturated soil is a function of matric suction y . Laboratory testing was not conducted to measure 'K' at different values of matric suction. Various methods of calculating the hydraulic conductivity 'K' were described above. The relation between the 'K' and y  derived from the Brooks and Corey (1964) model for the  samples is shown in Figures 4.7 and 4.8. The 'K' of the samples from D N W R and D S W R decreased rapidly with increasing \\\\&gt; past the A E V s at 1.3 and 2.4 kPa suctions, respectively. A s suction was increased by two orders of magnitude, the 'K ' are s  predicted to decrease by more than 10 orders of magnitude. At \\\\i = 100 kPa, both K values decreased to &lt;10\" m s\" . 15  1  In summary, the W R C s and associated 'Ksat' of the samples from D S W R and D N W R showed that the near-ground surface (0 - 0.15 m) sample on D N W R retained more water at saturation associated with increasing matric suction than that on D S W R . This behavior is due to slight variations in the waste-rock textures that control soil water.  \u000CChapter I V : Results and Data Interpretation  Page  1.0E-04  Figure 4.7.  Characteristic of the sample of the waste-rock from the Deilmann north  waste-rock pile (DNWR): hydraulic conductivity curve (K). The value of saturated hydraulic conductivity (K t) was measured in the laboratory but the unsaturated sa  hydraulic  conductivity (K) was derived from the Brooks and Corey mode (Brooks and Corey, 1964).  101  \u000CPage  Chapter I V : Results and Data Interpretation  102  1.0E-04  1.0E-06 +  1.0E-16 4 0.1  10  100  M a t r i c s u c t i o n (kPa)  Figure 4.8.  Characteristic of the sample of the waste-rock from the Deilmann south waste-  rock pile (DSWR): hydraulic conductivity curve (K). The value of saturated hydraulic conductivity (K ) was measured in the laboratory but the unsaturated hydraulic conductivity sat  (K) was derived from the Brooks and Corey mode (Brooks and Corey, 1964).  \u000CPage  Chapter I V : Results and Data Interpretation  Table 4.2.  103  Nature and origin of data for the K value of various granular materials  Source of results Wilson et al. (1993) Hatanaka e t a l . (1997) Mbonimpa (1998) Newman (1999)  Type of material (number of results) Beaver Creek sand Undisturbed sands (12 results) Uniform sand (30 results) Beaver Creek sand Fine sand  Yahful et al. (2003) . Coarse sand  Range of K values measurd (m s' ) 3.9 x 10\" 1  b  1.5 x 1 0 \" - 4 . 3 x 10\"  4  8.2 x 1 0 ~ - 1.1 x 10\" 6.2 x 10\" 1.9 x 10\"  3  5  5  b  b  7.3 x 10-  6  \u000CChapter IV: Results and Data Interpretation  4.2  Page  104  Field Tests Program This section presents the results of the field tests described in the previous  sections. The tests were conducted at the Deilmann north waste-rock (DNWR) and Deilmann south waste-rock (DSWR) piles at the Key Lake uranium mine, northern Saskatchewan, over a period of two years (summers of 2000 and 2002). The CO2 flux results were obtained using the D C C and compared to those obtained using two other methods: dynamic closed chamber (DCC), static closed chamber (SCC), and eddy covariance (EC) methods. The data presented include the results of: 1.  Diurnal variation in CO2 flux measured with the D C C at the Deilmann south wasterock (DSWR) pile.  2.  Quantification of spatial and temporal variations in CO2 flux using the D C C at the Deilmann north waste-rock (DNWR) and D S W R piles.  3.  Measurements of C 0 flux using S C C at the D S W R .  4.  Measurements of C 0 flux using E C at the D S W R .  5.  Measurements of near- and surface-water contents and associated CO2 fluxes  2  2  after heavy rainfall events at the D N W R and D S W R piles.  4.2.1  Diurnal variation in C 0  2  flux  Temporal variability was addressed on a diurnal and long-term basis. The shortterm (hourly) variations in the C 0  2  flux was measured using the D C C at a single  sampling station (DSF1) (Figure 3.8) over a 9-h period (09:00 to 17:00) on August 6, 2000 at the D S W R . The corresponding average hourly air temperature was recorded from the weather station installed on D S W R . The measurements were repeated two to  \u000CChapter IV: Results and Data Interpretation  Page  105  three times during the test period to reflect the diurnal variation in C 0 flux due to 2  perturbations in daily weather conditions such as cloudy and rainy days. Representative results of both the C 0 flux measurements and air temperature are presented in Figure 2  4.9. The C 0 flux ranged from 219 to 250 mg C 0 m\" h\" (Figure 4.9), with a mean 2  2  1  2  value of 235 (\u00B1 14) mg C 0  m\" h\" . Coefficients of variation (CV) for the individual 2  2  1  sampling periods ranged from 4.6 to 6.5% and were comparable to those reported under more controlled conditions in laboratory mesocosms (Kabwe et al., 2002). Shortterm (hourly) variations in the flux were not significant (P &lt; 0.05) (e.g., P is test statistic on which a decision rule is based for a test of hypotheses). At D S F 1 , both the magnitude of the C 0 flux and the daily variation in the flux were smaller than the 2  values generally reported for agricultural or forest soils (Brumme &amp; Beese, 1992; Loftfield et al., 1992; Rochette et al., 1992; Ambus &amp; Robertson, 1998; Frank et al., 2002). There was only a weak diurnal pattern to the flux and no correlation between the C0  2  flux and air temperature (r = 0.548) (e.g., r is a coefficient of correlation). Parkin  and Kaspar (2003) reported that diurnal changes in the soil-to-atmosphere C 0  2  flux  were strongly correlated with air temperature (more so than with soil temperature) when C0  2  production at the surface was a major component of the total measured C 0 flux 2  (Wohlfahrt et al., 2005; Shi et al., 2006). This, together with the results of Birkham et al. (2003) and Lee et al. (2003a, 2003b), suggests that the C 0 flux from the surface 2  waste-rock pile may be a result of the upward migration of gas produced during organic matter oxidation at depth and its subsequent transport to, and diffusion across the waste rock/air interface (see Figure 3.7).  \u000CChapter IV: Results and Data Interpretation  Figure 4.9.  Page  Short-term (hourly) variations in the C 0  2  106  flux measured at DSF1 on  August 6, 2000. Fluxes were determined using the dynamic closed chamber (DCC) method and averaging a series of four to eight measurement cycles, with each cycle lasting from 2- to 8-min (depending on the magnitude of the flux). The shaded box represents the 95% confidence interval (+17 mg C 0 m\" h\" ) around the calculated daily 2  2  mean (235 mg C 0 m\" h\" ). 2  2  1  1  \u000CPage  Chapter IV: Results and Data Interpretation  4.2.2  Spatial and temporal variation in C 0  2  107  flux measured using the D C C at  the Deilmann south waste rock (DSWR) pile The D C C was used to quantify spatial and temporal variations in the C 0 flux at 2  20 sampling  stations  (DSF1 -  DSF20)  (Figure  3.8)  at the  DSWR  pile. The  measurements were over three periods during summer 2000 (July 1-11; August 1-11, and September 8-16) and twice during summer 2002 (July 13-22 and August 21-26). Results of the C 0 flux measurements are presented in Figures 4.1 OA and Figure 2  4.10B. Table 4.1 and Figures 4.11 A, 4.11B, and 4.11C present results of statistical analysis of the C 0 fluxes measured in July, August, and September 2000. During each 2  4 to 6 day sampling period, the C 0 flux was measured at a minimum of 12 sampling 2  stations, with three to four stations sampled each day. Differences between sampling stations were generally small (average C V = 24%), indicating that the degree of spatial variability was relatively low. Moreover, the analysis of variance (ANOVA) indicated that within each sampling period differences between the daily C 0 significant ( F i = 2.87; F g = 1.17; F ju  Au  S e p  2  fluxes were not  = 0.60).  The F distribution is the ratio of the variances of two independent samples from normal populations and is given by:  , and  F =  x = 2  [4.5]  X2 \u00BB2 l  where S is the variance associated with samples of size n from a normal distribution 2  with variance a and x is the chi-square distribution with u = n - 1 degrees of freedom. 2  2  \u000CChapter IV:  Results and Data Interpretation  Page  108  (A) ' 400 v  _  350  \"\u2022c  300  H  CN  Ui  250 \u2022  200 \u2022  X ^  150  O  100  A  \u2022 \u2022  A  \u2022  50  0  0 1  2  3  4  5  6  7  8  9  101112131415161718192021  C h a m b e r location  \u2022 July-00  BAugust-00  \u2022 Sept- 00  (DSF#)  OJuly-02  \u2022 02-Aug  (B)  Figure 4.10.  (A) C 0 fluxes measured using the dynamic closed chamber (DCC) at 2  twenty selected sampling stations (DSF1 - DSF20) at the Deilmann south waste-rock pile (DSWR) (Figure 3.8) during the summers of 2000 and 2002 (B) average flux values (mg C 0 m\" h\" ) measured from sampling locations (\u2022) on the D S W R . 2  2  1  \u000CC h a p t e r IV:  R e s u l t s a n d D a t a Interpretation  400  \u2022  300 -  CM  E  (A)  (B)  X 3  o o  300  CM  ab  E  -  a  -  Ui  \u00A3  200 -  180  ,  a i  3  100  CM  o o  July 2000  182  184  186  188  August 2000  190  212  213  Julian Day  .c  1  i  215  216  217  1  r  (C)  September 2000  300  E  214  Julian Day  400  CM  ii i  H -  100 -  r  ii  200  X  -  CM  ;  ab  Ui  E  109  400  ab  a  Page  -  Ui  E  200  -  O  X 3  1&gt;  o  245  246  -  o o  100 -  0 243  244  247  248  Julian Day Figure 4.11. Daily variations in the C 0 flux m e a s u r e d at the D e i l m a n n s o u t h w a s t e rock 2  ( D S W R ) pile in (A) J u l y , (B) A u g u s t , a n d (C) S e p t e m b e r 2 0 0 0 . F l u x m e a s u r e m e n t s w e r e o b t a i n e d at three to four locations o n e a c h s a m p l i n g date. At e a c h location, the flux w a s d e t e r m i n e d using the d y n a m i c c l o s e d c h a m b e r ( D C C ) m e t h o d a n d a v e r a g i n g a s e r i e s of four to eight m e a s u r e m e n t c y c l e s , with e a c h c y c l e lasting from 2 - to 8-min ( d e p e n d i n g o n the m a g n i t u d e of the flux). T h e overall m e a n for e a c h monthly s a m p l i n g period is r e p r e s e n t e d by the d a s h e d lines (  ). Within m o n t h s , s y m b o l s l a b e l e d with the s a m e letter a r e not  significantly different at the P &lt; 0.05 level of probability.  \u000CChapter IV: Results and Data Interpretation  Page  110  Working under the assumption that both samples are from the same normal population (as was in this case) then Equation becomes:  [4.6]  F = | |  The F ratio was then compared to the expected value of F(ui = n-i - 1, u = n - 1) 2  2  using the nearest table entry. With the exception of the July 2000 data set, differences between sampling periods were not significant (average\u2014yielding a long-term average flux of 194 (\u00B1 75) mg C 0 m\" h\" . Again, these results suggest a the laterally extensive source of C 0 at 2  1  2  2  the base of the pile (see Figure 3.7) and indicate that the production and upward migration of C 0  2  through the waste-rock pile is relatively uniform both spatially and  temporally. As noted above, the July 2000 data set yielded a mean C 0 flux (238 mg C 0 m\" 2  2  2  h\" ) that was significantly greater (P &lt; 0.05) than the mean flux calculated for any of the 1  other sampling periods. The flux data collected in July 2000 also exhibited a much wider range in values as indicated by the size of the 'box' and length of the 'whiskers' (Figure 4.12) suggesting a greater degree of spatial and temporal variability during this measurement period. Flux measurements from across the site also were obtained during the summer of 2002. Differences among sampling stations obtained during the summer 2002 at the D S W R were also relatively small (overall C V = 31 %) and were not significant\u2014yielding an overall average flux 174 (\u00B1 31) mg C 0 m\" h\" . Both calculated mean C 0 fluxes for 2  2  1  2  the summer 2002 periods were not significantly different from those obtained during the summer 2000 periods at the D S W R , with the exception of the July 2000 data set.  \u000CChapter IV:  Page  Results and Data Interpretation  111  500  f  400  CM  i  E,  300  *  200  8  CM  I LJ  100  * Jul'00  Aug'OO  Sep'00  Jul'02  Aug'02  Sampling Date  Figure 4.12. Box &amp; Whisker plot characterizing the spatial and long-term temporal variability in the C 0 flux measured using the dynamic closed chamber (DCC) method at the Deilmann 2  south waste-rock (DSWR) pile in 2000 and 2002. The estimated, time-averaged flux = 170 (\u00B1 51) mg C 0 m\" to\" , The minimum and maximum flux values are marked by asterisks (*). 2  1  2  Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance.  \u000CPage  Chapter IV: Results and Data Interpretation  112  Table 4.3. Summary of results of C 0 flux measurements using the dynamic closed 2  chamber system (DCC) for the test period of 2000-2002 at Deilmann south waste-rock pile (DSWR). Summer 2002  Summer 2000 Std  Mean  CV  a  b  Mean  CV  Std  mg m\" h\"  mg m\" h\"  %  mg m\" h\"  mg m\" h\"  July  238(n=19)  86  36  185(n=15)  51  28  August  175(n=18)  51  29  162(n=10)  58  36  Sept.  150(n=10)  44  29  Overall  194  75  39  174  53  31  2  1  2  1  2  2  1  1  %  Overall average (summer 2000 and summer 2002): (188 \u00B1 68 mg m\" h\" ) 2  a b  1  S t d : standard deviation C V : coefficient of variation  Consequently, the data from each sampling period were pooled and replotted as Boxand Whisker plots (Figure 4.12) to better illustrate the relative consistency of the spatial and short-term temporal variability associated with C 0  flux measurements at the  2  DSWR.  4.2.3  Spatial and temporal variations in C 0  2  flux measured using the D C C  at the Deilmann north waste-rock (DNWR) pile The spatial and temporal variations in the C 0  2  flux were measured at the  Deilmann north waste-rock (DNWR) pile using the D C C at 9 sampling stations (DNF1 DNF9) (Figure 3.8). The measurements were assessed three times during summer 2000 (July 1-11; August 1-11, and September 8-16) and twice during summer 2002  \u000CC h a p t e r IV:  R e s u l t s a n d D a t a Interpretation  Page  113  Table 4.4. Summary of results of C 0 flux measurements using the dynamic closed 2  chamber system (DCC) for the test period of 2000-2002 at Deilmann north waste-rock pile (DNWR). Summer 2002  Summer 2000 Mean  a  m g m\" h\" 2  Std  b  m g m\" h\"  1  2  1  CV  Mean  Std  %  m g m\" h\" 2  CV  m g m- h\" 2  1  1  %  July  159(n=9)  41  25  302(n=9)  83  27  August  203(n=9)  50  18  249(n=9)  91  37  Sept.  169(n=9)  52  31  Overall  177  50  28  89  32  276  O v e r a l l a v e r a g e ( s u m m e r 2 0 0 0 a n d s u m m e r 2 0 0 2 ) : (217 + 8 3 m g m\" a b  2  h\" ) 1  S t d : standard deviation C V : coefficient of variation  Data of the C 0 f l u x measurements are presented in Figures 4.13A and 4.13B. 2  Table 4.4 and Figure 4.14 summarize the results of statistical analysis of the C 0  2  fluxes measured during the summer of 2000 (July to September) and during the summer of 2002 (July to August 2002) at the DNWR. Differences among sampling stations obtained during the summer of 2000 were small (overall C V = 28%) and were not significant, yielding an overall average flux of 177 (+ 50) mg m\" h\" . Similarly, 2  1  differences among sampling stations obtained during the summer of 2002 were also relatively small (overall C V = 32%) and were not significant, yielding an overall average flux of 276 (\u00B189) mg m\" h\" . However, both calculated mean C 0 fluxes for the summer 2  1  2  of 2002 were significantly different from the mean flux calculated for other sampling periods in the summer of 2000 at the DNWR. To better illustrate the relative consistency of the spatial and short-term temporal variability associated with C 0 flux measurements at the DNWR, the data from each 2  \u000CA  400 i  9  350 300 250 150  \u00A3  100  \u00B0  50  8  \u2022  A  O  O  O \u2022  \u2022  \u2022  114  o  \u2022  \u2022  \u2022 \u2022  E 200 g  Page  \u2022  **  ( )  Results and Data Interpretation  \u2022  \u2022  *  A  \u2022 O  Chapter IV:  7  8  t  0 2  1  3  4  5  6  9  10  Chamber location (DNF#) \u2022 July-02 \u2022 Aug-02 ASept-02 OJul-02 \u2022 Aug-02  (B)  D N W R 2000 \\  146 * 238 184 186 \\  I  0m  Figure 4.13.  SOO m  # 1 2 5  261  \\  \\  \\  I  | 0m  D N W R 2002  228 \\  \\  238*  ' \\  \\  \\  t  \\  \u2014 \u2014  302  \\  313\u00BBV 1 \\  500 m  \\  5  \\ |  \\  ^\"\u2122\\  \\  \\  $\u2122  230,  !j  5  \\  C 0 fluxes measured using the dynamic closed chamber (DDC) at nine 2  sampling stations (DNF1 - DNF9) at the Deilmann north waste-rock (DNWR) pile (Figure 3.6) during the summers of 2000 and 2002: (A) Data points presented on a X Y (scatter) and (B) average flux values (mg C 0 m\" h\" ) from samplings locations on the D N W R . 2  2  1  \u000CChapter IV: Results and Data Interpretation  Page  115  sampling period were also pooled and re-plotted as Box-and-Whisker plots (Figure 4.14). A s indicated by the size of the \"boxes\" and length of the \"whiskers\" the fluxes data obtained in July and August 2002 also exhibited much wider ranges in v a l u e s suggesting  a  greater  degree  of  spatial  and  temporal  variability  during  these  measurement periods.  4.2.4  Cross-statistical  comparison  between C 0  2  fluxes measured from  across the DNWR and DSWR piles Results of cross-statistical comparison tests between the overall averages C 0  2  fluxes calculated for the summers of 2000 and 2002 at the D S W R and the D N W R (Tables 4.1 and 4.2) showed that only the summer 2002 data set for the D N W R yielded an overall average C 0 flux that was significantly different from other summer sampling 2  periods. Differences among the remaining sampling periods were not significant. The degree of spatial variability at both sites was generally small (average C V is 28%-39%). These minor differences were attributed to slight variations in the waste-rock textures that control soil water. The overall averages of C 0 fluxes at the D N W R and the D S W R 2  over the 2-year test period (summer 2000 and summer 2002) were 217 \u00B1 83 mg rrf h\" 2  1  and 188 + 68, respectively. In summary, the above results appear to reflect the laterally extensive source of C0  2  determined to originate in the dewatered organic-rich lake-bottom sediments at the  base of the piles (Figures 3.7 and 3.8) (Birkham et al.,2003; Lee et al., 2003) and suggest that the production and upward migration of C 0 through the waste-rock piles 2  is relatively uniform both spatially and temporally. At the D S W R the dominant oxidation reactions occur in the organic material underlying the waste rock, the more minor gas reactions in the waste rock are masked (Birkham et al., 2003). However, it should be noted that the acid-base accounting and humidity test results (Cameco, unpublished)  \u000CChapter IV:  Results and Data Interpretation  Page  116  _  500  400  &lt;V E ut E  I  h  300  1  1&lt;  200  1  I  _3  **CM  O O  100  T Jul'OO  Aug'OO  Sep'OO  Jul'02  Aug'02  Sampling Date  Figure 4.14. Spatial and temporal variations in C 0 fluxes measured during the summer of 2  2000 and summer 2002: box-and-wisker plots showing the mean, standard deviation, and extreme values for Deilmann north waste-rock (DNWR) pile data.  \u000CChapter IV: Results and Data Interpretation  Page  117  suggested that pyrite oxidation-carbonate buffering and the resulting O2 consumption and C 0 production are more likely to be observed in the gneissic waste rocks at the 2  D N W R that yielded much greater acid generating potential (AP) (3-11.2 kg C a C 0  3  eq./tonne) and acid neutralizing potential (NP) (3.1-4.2 kg C a C 0 eq./tonne) than sandy 3  waste rocks (AP: 0.9-1.2 kg C a C 0 eq./tonne; N P : 0.9-1.1 kg C a C 0 eq./tonne). It may 3  3  be concluded that the difference between the overall averages C 0 fluxes calculated for 2  the summer of 2000 and 2002 at the D N W R could be the result of sulphide oxidation and carbonate buffering. Birkham et al. (2003) also concluded that the sulphide oxidation and carbonate buffering may have been dominant reactions within the DNWR.  4.3  Comparison  of  C0  2  Fluxes  Measured  using  the  DCC  Measured using Static Closed Chamber (SCC) and Eddy  to  those  Covariance  (EC) Methods on the Deilmann South Waste-Rock Pile (DSWR)  4.3.1  Introduction In a previous study using large-scale laboratory mesocosms filled with sand the  D C C method was shown to accurately measure C 0 fluxes from ground surface to the 2  atmosphere over the range of CO2 fluxes reported for field conditions (Kabwe et al., 2002). However, to ascertain whether the D C C best approximated field CO2 fluxes, the D C C measurements were compared to those obtained from across the D S W R using static closed chamber (SCC) and Eddy covariance (EC) methods. The comparison was based on flux data measured on the same period (August 24 to 25, 2002).  \u000CChapter IV: Results and Data Interpretation  4.3.2  Page  118  D C C fluxes Subsets of the D C C data obtained during the period from August 24 to 25, 2002,  were pooled and replotted as Box-and-Whisker plot (Figure 4.15) for comparison with those obtained using S C C and E C methods. The A N O V A indicated that differences between the daily C 0 fluxes measure using the D C C from August 24 and 2 5  2002  th  2  were not significant (P &lt; 0.05)\u2014yielded an average flux of 162 + 58 mg CO2 m\" h\" 2  1  (n=12)forthe 2-d period.  4.3.3  SCC fluxes Flux measurements using S C C were conducted on August 24, 2002. Headspace  gas samples were collected in both the morning (between 10:00 and 11:00) and afternoon (between 16:30 and 17:30). Flux measurements obtained in both the morning (between 10:00 and 11:00) and afternoon (between 16:30 and 17:30) on August 24, 2002 are presented in Figures 4.16 and 4.17. Results of statistical analysis are presented as box-and-whisker plots in Figure 4.18. Temporal variations were generally small and there was no significant difference between the average C 0 f l u x measured in the morning (181 \u00B1 60 mg C 0 m\" h~ ) and 2  2  1  2  that measured in the afternoon (173 \u00B1 62 mg C 0 m\" h\" ). Presumably, this reflects the 2  1  2  fact that the morning and afternoon measurement periods occurred at the beginning and end of the diurnal cycle. Nevertheless, these results support those obtained earlier using the D C C method, which indicated that the spatial and short-term  temporal  variability associated with the C 0 flux was relatively small. Flux measurements of both 2  \u000CChapter IV:  Page  Results and Data Interpretation  119  Figure 4.15. Box-and-wisker plot for flux measurements obtained using the D C C method at the Deilmann south waste-rock (DSWR) pile during the period from August 2 4 to August 2 5 , th  th  2002 (set of data for comparison with the other two methods: S C C and E C ) . The minimum and maximum flux values are marked by asterisks (*).  Note: values occurring beyond the  \"whiskers\" were identified as outiiers and were not included in the analysis of variance.  \u000CChapter IV:  () A  Page  Results and Data Interpretation  120  350 300 250 H  s 200 x  r5 o  O  o  \u2022  H  150  O O  100 50 2  4  6  8  10  12  14  16  18  20  22  Sampling location (DSF#) \u2022  AM1  Figure 4.16.  AM2  \u2022  PM1  O  PM2  (A) C 0 flux values (mg m\" h\" ) obtained using the static closed chamber 2  1  2  (SCC) at eleven selected sampling stations (\u2022) at the Deilmann south waste-rock (DSWR) pile in the morning (AM) (between 10:00 and 11:00) and afternoon (PM) between 16:30 and 17:30) on August 24, 2002 (B) averages fluxes (mg C 0 m 2  2  h\" ) from the sampling locations. 1  \u000CChapter IV: Results and Data Interpretation  Page  121  Figure 4.17. Box-and-wisker plots for C 0 flux measurements obtained from the Deilmann 2  south waste rock (DSWR) pile on August 24, 2002. Measurements were obtained using the static closed chamber ( S C C ) method between the hours of 10:00 and 11:00 (AM) and 16:30 and 17:30 (PM). The minimum and maximum flux values are marked by asterisks (*). Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance.  \u000CPage  Chapter IV: Results and Data Interpretation  122  (A)  g X 3  O  o Chamber position J DCCS \u2022 Static chambers  r  (B)  Figure 4.18. Comparison of the dynamic closed chamber (DCC) and static closed chamber (SCC) methods for measuring C 0 fluxes. Flux measurements were obtained using the two 2  methods at the Deilmann south waste-rock (DSWR) pile site during the period from August 24  th  to August 25 , 2002. The minimum and maximum flux values are marked by asterisks (*). th  Note: values occurring beyond the \"whiskers\" were identified as outliers and were not included in the analysis of variance.  \u000CChapter IV: Results and Data Interpretation  Page  123  the S C C and D C C methods yielded comparable results (Figure 4.19) during the August 2002 test period, with no significant difference ( P &lt; 0.05) between the mean CO2 fluxes obtained using the two methods. These results demonstrate that both the S C C and D C C methods are equally applicable to the measurement of C 0  2  fluxes from the  surface of the waste-rock pile.  4.3.4 E C f l u x e s Near-continuous measurements of the C 0 method during the period from June 2 5  th  2  flux were obtained using the E C  to August 2 5  th  2002. Measurements were  recorded on a data logger installed on the weather station on D S W R . At the end of the test period the data logger was shipped to the Department of Geography of the University of Saskatchewan for data analysis. It should be noted that data collected during precipitation events by the E C system are no useful (Carey et al., 2005). Subsets of the data were then used to assess the magnitude of the diurnal cycle and compare the E C and chamber-based methods (DCC and S C C ) . Figure 4.20 shows the diurnal variation in C 0 flux measured from 2  10:00 to 17:00 on August 25, 2002 using the E C at the D S W R The greater temporal resolution provided by the E C system revealed that the CO2 flux exhibited a distinct diurnal pattern in Figure 4.20. A s was the case in August 2000, there was no correlation between the magnitude of the C 0  2  flux and air  temperature\u2014again suggesting that temperature\u2014induced changes in C 0  2  production  at the surface of the waste-rock pile were not a major contributor to the total measured C0  2  flux. Compared to the diurnal variations in the C 0 flux (average s.d. = \u00B148 mg 2  C 0 rrf h\" ), day-to-day variations in the average flux were generally small (average 2  2  1  \u000CChapter IV:  Page  Results and Data Interpretation  124  300-^  250  E o E  15f&gt;  2 3  X  3  CM  o o  ioa  i  .  i  i  i_  09:0010:0011:0012:0013:0014:0015:0016:0017:00  Time  Figure 4.19.  Diurnal variations in the C 0 flux measured from 10:00 to 17:00 on August 25, 2  2002 using the E C method at the Deilmann south waste-rock (DSWR) pile. The shaded box represents the 95% confidence interval (\u00B1 24 mg C 0 mean (150 mg C 0 m\" h\" ). 2  2  1  m* h\" ) around the calculated daily 2  2  1  \u000CC h a p t e r IV:  F i g u r e 4.20.  Page  R e s u l t s a n d D a t a Interpretation  Measured C 0  2  125  fluxes using E d d y c o v a r i a n c e ( E C ) at the D e i l m a n n s o u t h w a s t e -  rock ( D S W R ) pile. M e a s u r e m e n t s w e r e o b t a i n e d o n a c o n t i n u o u s b a s i s during the period f r o m June 25  t h  to A u g u s t 2 5  t h  2 0 0 2 . E a c h d a t a point r e p r e s e n t s the daily m e a n v a l u e a v e r a g e d o v e r  the period f r o m 1 0 : 0 0 to 1 7 : 0 0 hours: T h e s h a d e d b o x (B) r e p r e s e n t s the 9 5 % c o n f i d e n c e interval ( \u00B1 1 0 m g C 0 m\" h\" ) a r o u n d the overall m e a n (150 m g C 0 2  2  1  rrf h\" ). N o t e : g a p s in the 2  2  1  d a t a s e t r e p r e s e n t precipitation e v e n t s during w h i c h n o useful d a t a w e r e c o l l e c t e d by the E C system.  \u000CChapter IV: Results and Data Interpretation s.d. = \u00B135 mg CO2 m\"  126  h\" ). The near-continuous measurements of the CO2 flux  2  1  obtained during the period from June 2 5 4.21 and 4.22.  Page  to August 2 5  th  th  2002 are shown in Figures  Despite a slight downward trend in the daily CO2 flux with time, the  A N O V A revealed that there was no significant difference (P &lt; 0.05) between the averages for July (160 \u00B1 36 mg C 0 m\" h\" ) and August (136 \u00B1 32 mg C 0 m\" h\" ). A 2  1  2  2  1  2  monthly similar trend was observed in 2000 (using the D C C method), which suggests that there may be a small, but distinct seasonal fluctuation in the C 0 flux. 2  Flux measurements obtained using both the E C and chamber-based methods occurred on six occasions during July and August 2002 are presented in Figure 4.23 Differences in the C 0 flux measured on individual sampling dates, though sometimes 2  large, were not significant (P &lt; 0.05). There was no consistent trend; i.e., the chamber-based methods yielded daily flux values that were less than those obtained using the E C method on the first three sampling dates and greater than those obtained using the E C method on the last three sampling dates. A s a result, the time-averaged C 0 flux calculated from the E C data 2  (171 \u00B1 39 mg CO2 m\" h\" ) was comparable to that calculated from the corresponding 2  1  chamber data (178 \u00B1 31 mg C 0 m\" h\" ). 2  1  2  In summary, the D C C results showed that the flux of CO2 from the surface of the waste-rock pile to the atmosphere as relatively uniform, both spatially and temporally. Presumably, this reflects the combined influence of a relatively constant rate of C 0 2 production in the organic-rich zone at the base of the waste-rock pile (Birkham et al., 2003) and the textural uniformity of the overburden material (sand) used to construct the pile (Birkham, 2002). That is, these factors combine to exert a controlling influence on the composition and upward migration of pore gases and, in turn, the efflux of gases  \u000CPage  Chapter IV: Results and Data Interpretation  127  300 r  250  *_  2001  D) \u00A3,  3  \u2022 150[  0  x 3 CM  100  o  o  50  \u2022 \u2022  Eddy covariance Chan.ber-based  194  196  197 Julian Date  Figure 4.21.  199  236  237  (2002)  Comparison of the eddy covariance (EC) and chamber-based methods for  measuring the C 0 flux from the Deilmann south waste-rock (DSWR) pile. 2  \u000CChapter IV: Results and Data Interpretation  Page  128  from the surface to the atmosphere. Whereas the chamber-based (DCC and S C C ) methods yielded comparable data, with an overall time-averaged C 0 2 f l u x of 171 \u00B1 54 mg CO2ITI\" h\" ; the E C method 2  1  than that calculated from the chamber data. Underestimation of the F c o associated 2  with soil respiration by EC-based methods relative to chamber-based methods has been reported widely in the literature (e.g., Goulden et al., 1996; Norman et al., 1997; Law et al., 1999; Janssens et al., 2000; Davidson et al., 2002). Though not excessively large, these differences presumably reflect the different processes measured by the two methods. The chamber data exhibited slightly greater standard deviations than the E C data (i.e., D C C = \u00B158 mg C 0 m\" h\" ; S C C = \u00B159 mg C 0 m\" h\" ; E C = \u00B132 mg C 0 m\" h\" ). 2  1  2  2  1  2  2  1  2  This most likely reflects the fact that the variability associated with the chamber-based measurements includes both a spatial and temporal component, whereas the variability associated with the E C method is primarily temporal in nature. Thus, it was concluded that both chamber types were suited to the quantification and spatial resolution of C 0  2  fluxes associated with waste-rock piles at the Key Lake mine and that the E C method provided the best estimate of the temporal variability in the C O 2 flux. It is important to note that no single method of measuring soil-atmosphere gas exchanges can meet all objectives. Thus, the choice of method will depend on the type of information required and the characteristics of the site being investigated. Chamberbased methods are especially useful for characterizing spatial variability as well as providing more detailed information regarding local-scale processes. Though more expensive  and  technically  more  complex,  the  eddy  covariance method (and other micrometeorological techniques) provides a powerful tool  \u000CChapter IV: Results and Data Interpretation  Page  129  that allows for spatial integration and near-continuous, long-term monitoring of the s o i l atmosphere flux. Finally, the presence of oxygen in the waste-rock atmosphere is critical for the determination of reaction rates within waste-rock piles. Measurements of surface O2 fluxes is required to complement the field C 0 flux data. 2  4.3.4 Summary of the  advantages and  disadvantages of the  dynamic  closed chamber method (DCC) Some of the advantages of the D C C method can be summarized as follow: 1.  The D C C method presents a relatively fast method of measuring field C 0 fluxes 2  (2 to 10 min. depending on the magnitude of the fluxes). 2.  It is a direct method and provides an almost instantaneous indication of the flux  measurements regardless of climatic or moisture conditions in the waste dumps. 3.  The D C C S uses a portable C 0 analyzer and can be used to measure the CO2 2  fluxes in situ at the same locations using the same chambers with minimal disturbance of the soil. The disadvantages of the D C C method can be summarized as follow: 1.  The method requires a very sensitive CO2 gas analyzer, thus high initial capital  investment ($19,000 CDN). 2. wind,  The C 0 and  to  2  flux measurements can be influenced by solar radiation and strong changes  in  chamber  pressure  and  temperature  during  longer  measurement cycles (&gt; 1 h). 3.  Although the actual measurement time of change in concentrations in the  chamber headspace was short in most cases, the set up of the experiment that include  \u000CChapter IV: Results and Data Interpretation  Page  130  the attachment of the lid to the chamber prior to measurement was labour intensive, taking several minutes to attach the lid. This long time period was due to the size of the chamber and large number of bolts (n=24) required to ensure a gas-tight seal between the collar and the lid.  \u000CPage  Chapter V: Analysis and Discussion  131  CHAPTER V Analysis and Discussion  5.1  Introduction The previous chapter was directed at the primary objective of the present study  with respect to the development of a reliable apparatus (i.e., the dynamic closed chamber (DCC) method) for measuring C 0  2  fluxes from waste rock. This chapter  extends the application of the D C C method and presents the results of the investigation for the influence of a short-term, multi-day (29 July to 5 August 2002) heavy rainfall event on waste-rock water conditions and associated C 0 fluxes from Deilmann north 2  (DNWR) and Deilmann south (DSWR) waste-rock piles at the Key Lake uranium mine in northern Saskatchewan. The partial differential equation used for the C 0  2  model  used in this thesis to quantify C 0 production and diffusion through unsaturated soils is 2  also described. Results of the model validation and its application on mine waste-rock piles are presented. The main objectives of this chapter were to predict the influence of soil water on C 0 fluxes from mine waste-rock piles and to validate and apply the \" C 0 2 \" 2  model to predict concentration-depth profiles and surface C 0  2  fluxes in the waste-rock  piles.  5.2  Effects of Rainfall Events on Waste-Rock Surface Water Conditions and C 0  2  Fluxes A c r o s s the Surfaces of the Deilmann North (DNWR)  and Deilmann South (DSWR) Waste-Rock Piles  \u000CChapter V: Analysis and Discussion C0  2  Page  132  fluxes from both the D N W R and the D S W R were measured three times  during the summer of 2000 (July to September 2000) and twice during the summer of 2002 (July to August 2002) as described in the previous sections. The total precipitation recorded for each year from 2000, 2001 and 2002 were 483.4, 524.6, and 548.9 mm, respectively. During this period, the year 2002 recorded the greatest  precipitation  (approximately 1.13 times higher) than that of 2000. From 1977 to 1998, average winter precipitation (October to April inclusive; predominantly as snow) was 163.6 mm, average summer precipitation (May to September inclusive; predominantly rain) was 294.8 mm (data obtained at Key Lake mine site). In general, rainfall accounts for approximately 64% of the average total precipitation at the Key Lake mine site. The effects of the rainfall events on the D N W R and the D S W R surface-water conditions and surface C 0 fluxes are discussed in the following sections. 2  5.2.1  Short-term effects of rainfall events on near surface-water conditions Figures 5.1 and 5.2 show the changes of measured volumetric (6) water contents  at near ground surfaces (0 - 0.15 m) with time, following the cessation of 75.9 mm rainfall over an initial 48-h period [July 30 (day 1) to July 31 (day 2)] with a gradual decrease in rainfall from August 1 (day 3) to August 3 (day 5), at the D N W R and the D S W R . Results show that the ground surfaces of the piles (0 m, open circles) dried rapidly, whereas the drying rates at greater depths (0.05 m and below) decreased slowly with time (Figures 5.1 and 5.2) (see data in Appendix E). The ground surface of the D S W R (Figure 5.2) drains more rapidly than that at the DNWR. For example, on 31 July 2002 (day 3) the ground surface water content on the D S W R was about 0.06 compared with 0.23 on the DNWR. Both ground surfaces continued to dry rapidly with  \u000CChapter V: Analysis and Discussion  Page  40  0.30 Stage III  Stage II  35 +  30 +  n = 0.36 S = 69.5%  r  + 0.25  + 0.20 25 +  ^  CD  i  E E  75  133  c  \u2022  20 +  + 0.15  15 +  o a&gt;  \\  DC  CD C  CO  \"\u2022EL  \\  + 0.10  10 +  \\  + 0.05  \\  5 +  o J Z 1  4  0.00  +\u2022  5  6  8  Day # o \u2022 A O  Figure 5.1.  3 Rainfall Water cont.: 0 m 0.05 m 0.10 m 0.15 m  Rainfall a n d volumetric water contents (0) m e a s u r e d over a n 8-d test p e r i o d !  (30 J u l y (day 1) to 6 A u g u s t (day 8) 2002) at station DNF1 with time a t . t h e D e i l m a n n north; w a s t e - r o c k ( D N W R ) pile.  \u000CChapter V: Analysis and Discussion  Page  40  134  0.30 Slage  Stage III  Stage II  35 + + 0.25 30 + + 0.20 25 +  0  ;  f  E  =  20 +  0.15  CO  c \u20225 rx  n = 0.38 S = 31.5%  15 +  *\u2022* a\" \u2014\u2022 . 10 +  5 +  + 0.10  \u2022  H  \u2022  o A  \\  Figure 5.2.  + 0.05  o  4 \u2022 Rainfall Water cont.: 0 m 0.05 m 0.10 m 0.15 m  o o a&gt; co  J U L +\u2022  o \u2022 o  c ~  I-  5  0.00  8  Day#  Rainfall a n d water contents m e a s u r e d over a n 8-d test period (30 J u l y (day  1  1) to 6 A u g u s t (day 8) 2002) at s a m p l i n g station D S F 1 with time at the D e i l m a n n s o u t h w a s t e - . rock ( D S W R ) pile. T h e porosity n=0.38.  I  .;  \u000CChapter V: Analysis and Discussion  Page  135  time to water content values of about 0.10 and 0.004 (day 8), respectively, on the D N W R and the D S W R . The drying rates eventually diminished with time, although at greater depths (0.05 m and below), water contents remained elevated at the end of the test period (day 8). This behavior is caused by the reduction of the unsaturated hydraulic conductivity due to the decrease in surface-water content as evaporation continues (Shuttleworth, 1993; Wilson et al., 1994; Capehart and Carlson 1994; Ek and Cuenca 1994; Capehrt and Carlson 1997). The soil surface curves can be described as having three stages of drying as described in Hillel (1980) and Wilson et al. (1994). Stage I drying occurred during the wet period from day 1 to day 3 when the soil surfaces were nearly saturated. Stage II drying starts from day 3 after the cessation of heavy rainfall events. The beginning of the second stage of drying occurs rather abruptly (see Figures 5.1 and 5.2) as the soil surfaces rapidly dried out. The length of time for the second stage of drying lasts depends upon the intensity of the meteorological factors that determine atmospheric evaporativity, as well as upon the conductive properties of the waste-rock itself. The soil surface at the D N W R (Figure 5.1) dried out more slowly than that at the D S W R (Figure 5.2). For example, on day 3 (August 1, 2002) the surface-water content on the D N W R was about 0.23 as compared to 0.06 on the D S W R . The empirical rate of the decrease of ground soil surface-water (0 m) content (d8 /dt) can be described by (Gray, 1995): w  [5.1]  \u000CPage  Chapter V: Analysis and Discussion where 6  W  136  is the volumetric water content (cubic metre of water per c u b i c meter of air), t  is the time, a n d a a n d b are parameters related to the b o u n d a r y conditions a n d c o n d u c t a n c e properties of the soil, respectively. T h e e x p o n e n t b, w h i c h is related to soil diffusivity,  is obviously most important, a n d the greater its v a l u e , the greater the  d e c r e a s e in water content. T h e u s e of this m o d e l to d e v e l o p descriptive equations for the rate of drying of the ground s u r f a c e at the  dfi  0.948) a n d the DNWR ( - - ^ -  DSWR ( - =  28.67 * r - , R = 5  0 8  2  = 7.19 * t ~ - , R = 0.826) yielded high correlation 3  30  2  coefficients (using Microsoft E x c e l ) . T h e drying equations indicate that the drying rate at the DSWR is greater than that at the DNWR (e.g., the e x p o n e n t b for DSWR is greater than that for the Gray  DNWR).  (1995) pointed out that, if the drying rates w e r e limited only by a diffusion-  limited p r o c e s s (i.e. v a p o r diffusion a c r o s s the drying z o n e ) , the e x p o n e n t s in the drying rate functions w o u l d be 0.5. E q u a t i o n 5.1 is purely empirical a n d d o e s not attempt to a c c o u n t for flow m e c h a n i s m s . F o r e x a m p l e , during drying, the water is s i m u l t a n e o u s l y redistributing a w a y from the w a s t e - r o c k ground s u r f a c e s (e.g., F i g u r e s 5.3) b e c a u s e of both upward flow d u e to evaporation a n d d o w n w a r d d r a i n a g e d u e to gravity; thereby s p e e d i n g d e c a y of the s u r f a c e drying rates. T h e redistribution t e n d s to persist longer in the w a s t e rock at the  DNWR than that at the DSWR. T h e time-variable rate of  redistribution d e p e n d s not only on the hydraulic properties of the w a s t e r o c k s , but a l s o on the initial wetting depth, a s well a s on the relative d r y n e s s of the bottom layers (Hillel,  1980).  \u000CChapter V: Analysis and Discussion  Figure 5.3.  Page  V o l u m e t r i c w a t e r content (6) profiles m e a s u r e d o v e r a n 8-d test period [30  J u l y (day 1) to 6 A u g u s t (day 8) 2002] at (A) station D N F 1 at the D e i l m a n n north w a s t e - r o c k ( D N W R ) pile a n d (B) station D S F 1 at the D e i l m a n n s o u t h w a s t e - r o c k ( D S W R ) pile with time.  137  \u000CPage  Chapter V: Analysis and Discussion  5.2.2  Short-term effects of rainfall events on C 0 The changes in measured C 0  2  2  138  fluxes  fluxes from ground surface following  the  cessation of 75.9 mm rainfall over the initial 48-h period [July 30 (day 1) to July 31 (day 2)] are presented in Figures 5.4 and 5.5 (solid circles). On 31 July 2002 (day 3), C 0  2  fluxes measured from the D N W R and the D S W R were 3% and 36 % of their initial average values of 217 and 188 mg m\" h\" , respectively. The figures showed that the 2  changes of surface C 0  2  1  fluxes with time were negatively correlated with measured  surface water contents for the-waste rock sand. As the water contents at ground surfaces decreased exponentially, the surface C0  2  fluxes increased exponentially from day 3 to day 8. These inverse linear  relationships yielded correlation coefficients of R = -0.997 and R = -0.820 (using 2  2  Microsoft Excel) for the D N W R and the D S W R , respectively. By the end of the 8-d test period, the surface C 0 fluxes had increased by factors of 4 and 45 while the ground 2  surface water contents had decreased from 6.7% to 0.04% and from 25.0% to 1.5% at the  DNWR  and the  DSWR,  respectively, and the  measured C 0  2  gas  fluxes  approximated their initial mean flux values. This observation suggested that it takes about 5 to 6 d after a heavy rainfall event for the gas fluxes to approach pre-rainfall values. In addition it is further suggested that the impact of rainfall events on C 0 fluxes 2  from the waste-rock piles is of relative short duration.  \u000CChapter V: Analysis and Discussion  Figure 5.4.  Page  139  Rainfall, water contents, and C 0 fluxes measured at station DNF1 over an 2  8-d test period [30 July (day 1) to 6 August (day 8) 2002] at the Deilmann north waste-rock (DNWR) pile with time.  \u000CPage  Chapter V: Analysis and Discussion  140  Day# mmmA Rainfall o Surf, water cont. (0 m) \u2022 CQ2  F i g u r e 5.5.  Rainfall, water contents, and C 0 fluxes measured at the DSF1 over an 8-d 2  test period [30 July (day 1) to 6 August (day 8) 2002] at the Deilmann south waste-rock (DSWR) pile with time.  \u000CChapter V: Analysis and Discussion  Page  141  The functional relationship between the measured surface CO2 flux and the surface-water content is also shown in Figure 5.6. Results showed that the surface CO2 flux is sensitive to changes of waste-rock surface-water content after heavy rainfall event, exhibiting a power decrease with surface-water content of the form:  F  C 0 2  =a*e-  [5.2]  b  where Fco2 is the surface C 0 flux (milligrams per square meter per hour), 0 2  W  is the  volumetric water content (cubic metre of water per cubic metre of air), and a and b are parameters related to the boundary conditions and conductance properties of the porous media, respectively. The use of this model to develop descriptive equations showed that a good relationship between the surface C 0 flux and the ground surface 2  water content of the waste rock at the D N W R DSWR  (F 2(S) C0  = 53.50 *0~  a21  (F 2(N) C0  = 4.71*9~  115  , R = 0.790) and 2  , R = 0.846) (Figure 5.6). The difference in the coefficients 2  a and b between the two piles is attributed to textural variability that affects the water content and the diffusivity of CO2, which is also a function of water content. In summary, measurements showed that the gas-flow conditions at the ground surfaces of the DNWR and D S W R were transient after a heavy rainfall. The transient effects were attributed to rapid drainage and evaporation. The effect of heavy rainfall on water-content profiles and C 0 fluxes was of a relatively short duration. 2  \u000CChapter V: Analysis and Discussion  5.3  Page  142  Predictions of Evaporative Fluxes and Near-Surface Water Contents Profiles It was noted from the previous sections that rainfall events can create changes in  soil water content and CO2 gas profiles within unsaturated zones and that the extent of the effect depends on the intensity and duration of the rainfall. Evaporation from mine wastes is a crucial component of the water balance (Carey et al., 2005). Similarly, soil water evaporation significantly affects water content, and as a results, the degree of saturation of the soil and the gas diffusion. Knowledge of the rate of evaporation at the soil-atmosphere interface is required to estimate the water content of candidate cover soils. The one-dimensional SoilCover computer model (Unsaturated Soils Group, 1997) was used to estimate evaporative fluxes at the D N W R and the D S W R over the 8-d test period [(30 July (day 1) to 6 August, 2002 (day 8)]. The model calculates daily evaporation on a site-specific basis, using weather data collected at the site as a boundary condition for the calculation of actual evaporation. The weather data (radiation, air temperature, humidity, and wind speed, etc.) is used in combination with soil characteristics and the calculated changes in soil moisture (details data input, output and results are also presented in Appendix H). The SoilCover predicted evaporative fluxes at the D S W R were compared to published measured values obtained by Carey et al. (2005) from the D S W R during the same test period.  \u000CPage  Chapter V: Analysis and Discussion  143  400 DNWR  350  DSWR -  -  1  300 -.f E  Power (DNWR) Row er (DSWR)  250 --  E. 200 x  f  3  \u00AB O  150  O  F  C 0 2  100 --  = 53.50X  0 2 1 0 1  R = 0.846 2  F  50  =4.71x-\"^ R = 0.790  c c e  2  0.2  0.0  +  0.4  0.6  0.8  Saturation (S)  Figure 5.6.  V a r i a t i o n s in C 0  2  flux m e a s u r e m e n t s with s u r f a c e - w a t e r  saturation  (S=8/n)  m e a s u r e d o v e r a n 8-d test period [30 J u l y (day 1) to 6 A u g u s t (day 8) 2002] at stations D N F 1 a n d D S F 1 of the D N W R (n=0.36) a n d D S W R (0.38) piles, respectively.  \u000CPage  Chapter V: Analysis and Discussion  5.3.1  144  Short-term predictions of evaporative fluxes Figures 5.7 and 5.8 show the SoilCover model predictions of cumulative  evaporative fluxes and the ratio of actual (AE) and potential (PE) evaporation (AE/PE) as a function of time for the 8-d test period at the D N W R and the D S W R , respectively. Simulations results indicated that during the period of heavy rainfall events from day 1 to day 3 the evaporation rate was relatively low. During this period the cumulative P E and A E were equal at both the D N W R and the D S W R . This stage is being referred as stage I drying (Wilson et al., 1994). Wilson et al. (1997) noted that the A E is approximately equal the P E rate of evaporation until the value of matric suction reaches approximately 3000 kPa. During this period the evaporation is controlled by external meteorological conditions (Hilled, 1980; Wilson et al., 1994). As the ground surfaces continued to dry from day 3 to day 5 the rate of evaporation started increasing rapidly. The cumulative evaporation was slightly higher at the D S W R (PE = 5.3 mm) than at the D N W R (PE = 4.5 mm) on day 5. After day 5, the values of Actual rate of evaporation and Potential rate of evaporation started to progressively diverge with A E less than P E , but slight faster at the D S W R than at the D N W R (Figures 5.7B and 5.8B). Moreover, results showed that during the separation of the A E and P E , the water contents had dropped dramatically from 0.25 on day 3 to about 0.003 on day 5 at the D N W R and from 0.07 on day 3 to about 0.001 on day 5 at the D S W R , respectively. At the end of the 8-d test period the model simulation results indicated 9.5 and 10.9 mm cumulative P E s for the D N W R and the D S W R . These values represent averages daily evaporation rate of 1.2 and 1.4 mm d\" for the 8-d test period 1  for the D N W R and D S W R respectively. Carey and co-workers (Carey et al., 2005) directly measured summer evaporation (6 June to 25 August, 2002) using eddy (EC)  \u000CChapter V : Analysis and Discussion  Page  145  -12  (A)  E  -10 +  x -8  o CL  ro &gt; a&gt; &gt;  +  -6 + -4  +  04-  I Rainfall  Figure 5 . 7 .  (A)  \u2022PE  AE - H -Vol.water  R a i n f a l l , water contents m e a s u r e d , a n d S o i l C o v e r predicted e v a p o r a t i v e  fluxes at the D e i l m a n n north w a s t e - r o c k ( D N W R ) pile (B) ratio of actual ( A E ) a n d  potential  ( P E ) e v a p o r a t i o n ( A E / P E ) a s a function of time o v e r a n 8-d test period [30 J u l y (day 1) to 6 A u g u s t (day 8) 2002].  \u000CPage  Chapter V: Analysis and Discussion  146  Day* Rain  PE  AE - H - Vol. water  (B)  0.4 -0.2 -0.0  -I  1  1  1  1  r  1  2  3  4  5  6  7  8  Day* Figure 5.8.  ( A ) R a i n f a l l , w a t e r contents m e a s u r e d , a n d S o i l C o v e r predicted e v a p o r a t i v e  f l u x e s at the D e i l m a n n south w a s t e - r o c k ( D S W R ) pile a n d (B) ratio of actual ( A E ) a n d  potential  ( P E ) ( A E / P E ) e v a p o r a t i o n a s a function of time o v e r a n 8-d test period [30 J u l y (day 1) to 6 A u g u s t (day 8) 2 0 0 2 ] .  \u000CChapter V: Analysis and Discussion  Page  147  covariance method at the D S W R : They measured the cumulative A E s (data not shown here) for the 8-day test period and found the cumulative A E of 8.0 mm with an average evaporation of 1 mm d\" at the D S W R (Carey et al., 2005). The results showed good 1  agreement between SoilCover model predicted and E C measured A E s data for the 8-d test period at the D S W R . Carey et al. (2005) also noted that the measured A E was significantly less than the P E at the D S W R due to high surface albedo that reduce available energy for evaporation. Figures 5.9 and 5.10, respectively, show SoilCover model simulation results of cumulative P E and A E for a 27-d period (July 29 to August 24, 2002) for the D N W R and the D S W R . During this period subsequent rainfall events occurred between day 16 and day 19 where a total of 26.9 mm fell at the sites. These rainfall events are depicted in Figures 5.9B and 5.10B where the ratios A E / P E equal to unit (AE/PE=1). At the end of the 27-d simulation period the model results yielded cumulative A E s of 32 and 35.6 mm with ratio of P E / A E of 1.44 and 1.37 for the D N W R and the D S W R , respectively. These results represent averages A E evaporation of 1.2 and 1.3 mm d\" at the D N W R and D S W R respectively. 1  Carey et al. (2005) field-measured data indicated cumulative A E of 37 mm with an average of 1.4 mm d\" for the 27-d test period at the D S W R . These results show 1  good agreement between model (SoilCover) simulations and measured A E s values for the 27-d test period at the DSWR. In summary, the comparison between the SoilCover predicted A E evaporation and the E C measured A E (Carey et al., 2005) indicated the ability of the SoilCover model to predict, with sufficient accuracy the A E at the surfaces of the waste-rock materials.  \u000CC h a p t e r V: A n a l y s i s a n d D i s c u s s i o n  F i g u r e 5.9.  Page  148  S o i l C o v e r predicted e v a p o r a t i v e fluxes (A) actual A E a n d potential P E a n d (B)  the ratio of A E / P E at the D e i l m a n n north w a s t e - r o c k ( D N W R ) pile o v e r a 2 7 - d test period [29 J u l y (day 1) to 2 4 A u g u s t (day 27) 2002] with time.  \u000CC h a p t e r V: A n a l y s i s a n d D i s c u s s i o n  Page  -50 A  1  1  1  1  1  1  1  1  1  1  1  r  1  3  5  7  9  11  13  15  17  19  21  23  25  149  27  Day#  (B)  0.2 -0.0  -I  1  1  1  1  1  1  1  1  1  1  1  1  1  3  5  7  9  11  13  15  17  19  21  23  25  27  Day#  Figure 5.10.  S o i l C o v e r predicted e v a p o r a t i v e fluxes (A) actual A E a n d potential P E a n d (B)  the ratio of A E / P E at the D e i l m a n n south w a s t e - r o c k ( D S W R ) pile o v e r a 2 7 - d test period [29 J u l y (day 1) to 2 4 A u g u s t (day 27) 2002] with time.  \u000CChapter V: Analysis and Discussion  Page  150  5.3.2 Short-term predictions of near-surface water contents profiles SoilCover was also used to predict the changes in water content profiles after the rainfall events over the 8-d test period [(30 July (day 1) to 6 August, 2002 (day 8)] at the D N W R and D S W R . In the simulations, the initial water content profile was required in order to predict the subsequent water profiles. The initial measured water contents profiles and the soil properties of the D N W R and D S W R were used as inputs data for the model simulations. The upper boundary value of water content of 0.03 (3%) was also specified in the model as initial water conditions during the simulations. Figures 5.11A and 5.12A present the measured water contents profiles and Figures 5.11B and 5.12B present the SoilCover predicted water contents profiles at the D N W R and D S W R piles. Both the measured and SoilCover predicted data show that the water contents conditions at the ground surfaces of the D N W R and the D S W R were transient after the heavy rainfall events. The transient conditions at both the ground surfaces were attributed to the sandy texture of the waste-rock piles and their associated high saturated hydraulic conductivities. The water redistribution appears to persist longer in the waste rock at the D N W R than at the D S W R and this was attributed to slight variations in the waste-rock textures that control soil water.  \u000CC h a p t e r V: A n a l y s i s a n d D i s c u s s i o n  Page  151  Figure 5.11. C o m p a r i s o n of (A) m e a s u r e d a n d (B) S o i l C o v e r predicted w a t e r content profiles for the 8 - d a y test period [July 3 0 (day 1) to A u g u s t 6 (day 8), 2002] at the D e i l m a n n north w a s t e - r o c k ( D N W R ) pile.  \u000CChapter V: Analysis and Discussion  Figure 5.12.  Page  152  C o m p a r i s o n of (A) m e a s u r e d a n d (B) S o i l C o v e r predicted w a t e r content profiles  for the 8-day test period [July 3 0 (day 1) to A u g u s t 6 (day 8), 2002] at the D e i l m a n n s o u t h w a s t e - r o c k ( D S W R ) pile.  \u000CPage  Chapter V: Analysis and Discussion  5.4  C0  153  Diffusion Prediction and Model Proposed  2  This section presents the important theoretical relationships used to quantify CO2 production and diffusion through unsaturated geologic media. The theoretical of the partial differential equation describing the change in C 0 concentration with depth and 2  time as a function of C 0  2  production and diffusion is also presented. The section is  concluded with the description of a relatively simple computer code program used to solve the finite difference formulation of the governing equation of the model developed in this work. The processes of diffusion, production of CO2, the equations describing one-dimensional transient C 0 diffusion and production are described below. 2  5.4.1  C0  2  diffusion  G a s dynamics in most soil systems is a three-dimensional problem, but the use of one-dimensional models is generally accepted (de Jong and Schappert, 1972; Collin and Rasmuson, 1988). One-dimensional CO2 diffusion in a gaseous environment is commonly described by Fick's First Law that defines the mass flux of C 0 in a given 2  direction as directly proportional to the negative of the concentration gradient in that direction (Fetter, 1993):  [5.3]  where:  F o2 = mass flux of C 0 (kg m\" s\" ), 2  1  2  C  D = the free air diffusion coefficient (m s\" ), 2  C = C 0 concentration (kg m\" ), and 3  2  1  \u000CPage  Chapter V: Analysis and Discussion  154  Z = depth (m).  The use of Equation 5.3 assumes that Fick's law adequately describes the diffusive gas flux. For gases such as C 0 , which have sources or sinks in the system 2  and constitute a small fraction of the total system pressure, this appears to be true (Thorstenson and Pollock, 1989). The diffusion coefficient in air (similar to atmospheric composition) for C 0 is 1.39x10\" m s \" (at 0\u00B0C) (Weast and Astle, 1981). The diffusion 5  2  1  2  coefficient increases with increasing temperature and decreasing molecular weight (Fuller etal., 1966). Aubertin et al. (2000) described the use of an equivalent porosity to represent the effective porosity available for the diffusion of oxygen. Applying this relationship for C 0 2 diffusion transforms the water porosity into an equivalent air porosity by portioning it with Henry's Law coefficient as follows:  0eq=e +e H  where:  [5.4]  w  a  9 = equivalent porosity (m m\" ), 3  3  e q  6  a  = air porosity (m m\" ),  9  W  = water porosity (m m\" ), and  3  3  3  3  H = Henry's Law coefficient (approximated as 0.03 for C 0 in air 2  and water at 25 \u00B0C) (Hendry et al., 993). Increasing water saturation decreases the equivalent and effective porosity and reduces C 0 diffusion. Using Henry's law to represent phase partitioning of a reactive 2  gas, such as C 0 , is an approximation of the true process (Hendry et al., 1993). 2  \u000CChapter V: Analysis and Discussion  Page  155  Fick's First Law defining C 0 diffusion through porous media as a function of the 2  equivalent porosity is defined by:  Fco =-e 2  where:  e q  D^  [5.5]  F = mass flux of C 0 (Kg m\" s\" ), 2  1  2  B  = equivalent porosity (m m\" ), 3  e q  3  D* = bulk diffusion coefficient (m s\" ), 2  1  C = C 0 concentration (Kg m\" ), and 3  2  Z = depth (m). The equivalent porosity and the bulk diffusion coefficient (D*) are often combined into a variable D , the effective diffusion coefficient, to give: e  D  e  =9 D*  [5.6]  e q  Then Equation 5.5 can be written in terms of the effective diffusion coefficient as:  Fco2=- D  e  \u00A7  E5-7]  In soils both diffusion and chemical reactions will determine the C 0 gradient as 2  described by Fick's second law. Assuming steady state conditions, this law can be written as (Hendry et al., 1999):  \u000CPage  Chapter V: Analysis and Discussion  dC  156  2  D  [5.8]  = -G  dz  e  2  where G is a reaction rate (pg C O 2 *g dry soil\" *d\" ). 1  1  Aubertin et al. (2000) and Mbonimpa et al. (2003) also defined the effective diffusion coefficient (D ) from Equation 5.6 as a function of the components of the e  diffusion in the air and water phase as represented in Equation 5.9.  D =D +HD e  Where:  a  [5.9]  w  D = diffusion coefficient component through air phase (m s\" ), 2  1  a  D = diffusion coefficient component through water phase (m s\" ), 2  1  w  H  = Henry's coefficient as defined above.  =0 D\u00B0T a  where:  a  [5.10]  and D w = \"9 w ^Dw\u00B0 Tw w  \u2014  W  1  D\u00B0 = diffusion coefficient of C 0 through air (m s\" ), 2  1  2  D\u00B0 = diffusion coefficient of C 0 through water (m s\" ), 2  1  2  T = tortuosity coefficient for air phase, and a  T = tortuosity coefficient for water phase. w  The tortuosity coefficients are related to the properties of the material through the following equations (Collin and Rasmuson, 1988; Mboninpa et al., 2003):  \u000CPage  Chapter V: Analysis and Discussion  157  2x+1  [5.11]  0  2y+  [5.12]  e| +(i-e ) =1  [5.13]  e  [5.14]  x  x  a  where:  2  y w  +(i-e ) =i y  w  9 = total porosity.  Mbonimpa et al. (2003) noted that a reasonable estimation of the value of the variables x and y is 0.75. Using this value for x and y and combining Equations 5.9 5.12, the diffusion coefficient equation can be simplified to the Equation 5.15 (Aachib et al., 2002, 2004).  De=4P\u00B0a a  e  0  5+HD  w0w  [5.15]  5  Equation 5.15 was used in the model adopted in this thesis for the evaluation of CO2 diffusion. An example of the variation of the effective diffusion coefficient D as a function e  of water porosity 6 is illustrated in Figure 5.13B. The water contents profiles in Figure W  5.13A represent hypothetical drying forcing conditions generated in unsaturated sand material. The initial water-depth profile (curve d1, Figure 5.13A), however, represents  \u000CPage  Chapter V: Analysis and Discussion  1.0  -I 0  1 10  1 20  1 30  1 40  Vol. water content (%) Figure 5.13A. Hypothetical water content profiles in unsaturated sand Material. Curve d1 is an actual measured water profile in the HT minicosm.  4.0E-06 --  A d1 \u2022 d2 \u2022 d3 \u2022 d4  A  d5 \u00AB d 6  o  d6  o  d7 o d8  X  d9  3.5E-06 - 3.0E-06 -2.5E-06 -2.0E-06 -d  1.5E-06 -1.0E-06 - 5.0E-07 --  +  +  10 20 30 Volumetric water content (%)  40  \u2022fir  Figure 5.13B. Simulated effective diffusion coefficient (D ) of C 0 as a e  2  function of water content using hypothetical data presented above.  158  \u000CChapter V: Analysis and Discussion  Page  159  the measured mean water-depth profile in the HT minicosm column described in Section 3.3 of Chapter 3 of this thesis and in Kabwe et al. (2002).  The subsequent  profiles were generated by reducing the initial water-depth profile (curve d1) by a factor of 0.1 consecutively. The corresponding simulated changes of the effective diffusion coefficient D of C 0 was computed using Equation 5.15 with the parameters: 6 = 0.40 e  and  2  = 1.39x10\" m s \" and H = 0.03. The general trend shows an increase in the D 5  2  1  e  with a decrease in water content or vice versa, as shown in Figure 5.13B. The dependency of the D  e  on soil water content for different textured soils is well  documented (Klute and Letey, 1958; Rowell et al., 1967; Collin and Rasmuson, 1988; Mbonimpa et al., 2003). The diffusion coefficient of C 0 in water is about four orders of 2  magnitude slower than that in the air-filled voids.  5.4.2  Biotic C0  2  production rate  As discussed in the previous chapter C 0  2  can be produced in biotic reaction  (e.g., microbial respiration) or in abiotic (e.g., carbonate buffering) reactions. However the model developed in this thesis will only be focused on biotic reaction. It should be noted that the D C C method was tested and verified in mesocosms filled with finegrained sand excavated from the C-horizon of an unsaturated zone at a local field site located near Saskatoon, Saskatchewan (Hendry et al., 1993; Kabwe et al., 2002). Studies on microbial aspects of the mesocosm indicated that biological activity within the mesocosm was likely sufficient to account for the generation of C 0 throughout the 2  profile (Hendry et al., 2001; Lawrence et al., 1993). The \" C 0 2 \" model described in this thesis will be also tested and validated with mesocosms measured data.  \u000CChapter V: Analysis and Discussion  Page  160  The CO2 production (microbia) rate (G) can be described by a function similar to that used by Hendry et al. (1999):  G = 6^G [g(T)g(e )g(z)] 0  Where:  [5.16]  w  G = CO2 production rate (kg C kg\" dry soil day\" ), 1  1  G = reference production rate ( kg C kg\" dry soil day\" ), 1  1  0  g(T) = the production contribution based on temperature, g(9 ) = the production contribution based on soil moisture content, w  g(z) = the production contribution based on depth. The function provides the option of determining the G term as a function of temperature, soil moisture content, and/or depth. The functional dependence of production upon temperature, soil moisture content and depth are (Hendry et al., 1999):  g(T)=e &lt; - &gt; k  when:  T&gt; T  m i n  T  and k is arbitrary  The lowest temperature at which CO2 production occurs is  g(T) = 0  When:  [5.17]  T  T &lt; I min  T j . m  n  [5.18]  \u000CPage  Chapter V: Analysis and Discussion  161  [5.19]  g(e )=0w w  where:  a = arbitrary  g(z)=e  where:  -bz  [5.20]  b = arbitrary.  These functions represent the influence of the primary independent variables (Hendry et al., 1999):  g(T) is the Arrhenius equation, where a Q i o ( = e  10k  ) value  determines the degree to which respiration increases with a 10\u00B0C increase in temperature; g(z) represents the commonly observed (e.g., Simunek and Suarez, 1993) exponential decrease in productivity with depth; and the combination of O g(0 ) serves a  w  to reflect the reduction in activity which typically occurs at high and low water contents (Ekpete and Cornfield, 1965; Rixon, 1968; Grant and Rochette, 1994; Hendry et al., 1999). The biotic production rate used in the model developed in this thesis was represented by a function similar to that used by Hendry et al., 1999 (the Equation 5.21):  [5.21]  G = G e!e e r k  0  where:  w  k = constant in the Arrhenius equation ( \u00B0 C ) (k =0.044\u00B0C\" ), 1  r  T = measured temperature (\u00B0C), and  1  r  \u000CPage  C h a p t e r V: A n a l y s i s a n d D i s c u s s i o n  162  T = reference temperatures (\u00B0C). The parameters a and b are fitting parameters. Note that the coefficient Qio(=e ) is often used to represent the relative increase in respiration intensity per 10kr  10\u00B0C increase in temperature. The value of k=0.044\u00B0C indicates an average Q10 of 1.6. The variability in Q10 is most likely attributed to differences in microbial community structure. It is acknowledge that sensitivity analysis demonstrated that G is only weakly dependent on 1 &lt; b&lt; 3 (Hendry et al., 1999). Figure 5.14 illustrates the simulated microbial respiration rates as a function of temperature and water content using Equation 5.21. Values of the parameters in Equation 5.14 used in the simulations were specified by Hendry et al. (2001): a = 2; b =1.25, k = 0.044 \u00B0C; G = 207 ^g C.g\" .d\" when T = 6.17 \u00B0C. The production of C 0 was 1  1  0  2  attributed to microbial activity in the C-horizon sand (Hendry et al., 2000). The simulated results show that at low water content (9 ), C 0 production W  2  decreases because of a lack of water; at high 6 , production also decrease because of W  excess water filling the pore spaces. The model simulation shows that the maximum microbial CO2 production occurred at a water content of 0.25 that corresponds to a water saturation of 70% (e.g., for 6 = 0.40). This is within values reported in the literature. Linn and Doran (1984) observed that soil incubated with 60% soil pore space filled with water, supported maximum aerobic microbial activities.  \u000CPage  Chapter V: Analysis and Discussion  163  3.0E-11  Soil water content Figure 5.14.  Simulated microbial respiration rates as a function of tempe-  rature and water content using Equation 5.21.  5.4.3  Development of the partial differential equation The following section presents the development of the partial differential equation  describing the change in CO2 concentration with depth and time as a function of CO2 diffusion and production. Let's consider a representative elementary volume (REV) for derivation of the partial differential equation as illustrated in Figure 5.15.  \u000CPage  Chapter V: Analysis and Discussion  164  \"out REV  F i g u r e 5.15.  Representative elementary volume, R E V , for derivation of  partial differential equation.  By applying the conservation of mass principle to a porous, cubic volume (dimensions: dx, dy, dz) through which a gaseous species is diffusing (the mass flux entering the volume (F ) minus the mass flux exiting the volume (F t) must equal the in  ou  change in storage (Sm/dt): where F = mass of gaseous species per unit of area per unit of time in the z direction.  (F - Fout )dxdy + G(dxdydz) = ^  [5.22]  in  at  where:  G = production rate of gaseous species within cubic volume (kg C kg\" dry 1  soil day\" ), 1  m = total mass of gaseous species within cubic volume (kg m\" ), and 3  t = time (s).  \u000C165  Page  Chapter V: Analysis and Discussion Equation 5.22 can be rewritten as:  f  f  Fin\" F + in , n  V  V  SF \u2014  dz  dz  ^  dxdy + G(dxdydz) =  am  [5.23]  The mass of gaseous species found in the pore gas and pore water of the cubic volume can be represented by:  m = (C 6 dxdydz)+(HC e dxdydz) a  where:  a  a  [5.24]  w  C = mass of gaseous species /volume of pore gas (kg m\" ), 3  a  H = Henry's law coefficient, 6 = air porosity (m rrf ), and 3  3  a  9  = water porosity (m m\" ). 3  W  3  Considering Equations 5.8 ( F = - D \u2014 ) and 5.24, Equation 5.23 can be rewritten: dz e  D  ~ dxdydz  e dz  G(dxdydz) = ( C 9 d x d y d z + H C 9 d x d y d z ) g  \u2022 +  a  a  a  [5.25]  w  Assuming that 9 and D do not vary over the depth interval dz and that 9 , 9 a  e  a  W)  H,  dx, dy, and dz do not vary over time interval dt, Equation 5.25 can be simplified to Equation 5.26 similar to Hendry et al. (2001).:  \u000CPage  Chapter V: Analysis and Discussion  D  e  ^ - +G= (e dz  a +  H6 ) w  8C  a  dt  166  [5.26]  Equation 5.26 is the governing equation used in the model.  5.4.4 Finite difference formulation The simple model developed in this thesis used a finite difference numerical method to solve the partial differential equation given by Equation 5.26. The numerical method offers a discrete approximation to problems with complex physical properties and geometry, but requires numerous calculations, which are lessened by the use of digital computers capable of performing numerous calculations quickly. The finite difference formulation of Equation 5.26 is a simple calculation based on approximating the derivatives of the function, resulting in a solution only at the discrete points (Lin et al., 1997). For a number of nodes there will be n linear equations, hence the problem may be solved (Freeze and Cherry, 1979). The finite difference method has many advantages, including: simple problems are easily solved, abundance of literature, successful algorithms are available to solve the system of equations and the accuracy is good. To develop the finite difference formulation defining the change in concentration at a given node, three nodes were defined as shown in Figure 5.16. The nodes represent the center of the finite difference element. The three mass fluxes (Fin, F t. ou  and Fp d) are defined in Equations 5.27, 5.28, and 5.29: ra  \u000CPage  Chapter V: Analysis and Discussion  167  0 .  I prod  out  Figure 5.16. Three nodes and the mass fluxes entering and exiting nodel for development of the finite difference formulation.  F  F F  _  dC  o u t  .n-  = \"D \u2014 =-D e  8z~  (Ca-CO  [5.27]  \" ^\\(z -zi] e  2  [5.28]  --D ^ - - D \u00B0 e  F  a  e o , 1 | (  D  z  prod =  Z  i  _  Z  o  j  [5.29]  0,1,2  G A Z  Substituting these three equations and Equation 5.23 into Equation 3.19 results in Equation 5.30  fo-Cp) 'eo,1  i( - r Zl  Zo  e1 2  ( C  -  2  C  l  )  i(z - ii 2  Z  9  A C e q  iKi.2| At  [5.30]  \u000CPage  Chapter V: Analysis and Discussion  168  a n d solving E q u a t i o n 5.30 for the c h a n g e in concentration ( A C i ) g i v e s E q u a t i o n 5 . 3 1 , w h i c h d e f i n e s the c h a n g e in concentration at n o d e 1 o v e r a given time-step (At):  AC  At 1  [  n  (Cj-Cp)  (Cz-CQ  [5.31]  + G  =  w h e r e : Co(t), Ci(t) a n d C ( t ) are the concentrations of C 0 2  2  at time (t) at three  adjacent n o d e s of increasing depth n u m b e r e d 0, 1 a n d 2; z , Z i a n d z 0  2  a r e the depths  below ground s u r f a c e of the three n o d e s ; D o,i(t) a n d D i , ( t ) are the effective diffusion e  e  2  coefficients b e t w e e n n o d e s 0 a n d 1, a n d 1 a n d 2 respectively, determined at time (t); 0 i ( t ) is the equivalent porosity at n o d e 1 at time t a n d ; A z , i , e q  0  2  is the d i s t a n c e b e t w e e n  the midpoint b e t w e e n n o d e s 0 a n d 1 a n d the midpoint b e t w e e n n o d e s 1 a n d 2. D , n + i ( t ) en  is calculated from the m e a n of D ( t ) a n d D + i ( t ) . T h e m a x i m u m length of e a c h time step en  en  within the diffusion model w a s determined by:  ^ 4 AZ  = 0.5  [5.32]  2  T h e variable defined a s A z , i , in E q u a t i o n 5.31 is the a v e r a g e of the two s p a c e s 0  2  on either s i d e of the n o d e 1. A v a l u e of At w a s c a l c u l a t e d at every n o d e in the profile, a n d the s m a l l e s t time step w a s u s e d . F o r all the v a r i a b l e s , the s u b s e t  numbers  s e p a r a t e d by c o m m a s indicate the node(s) from w h i c h the variable must be c a l c u l a t e d . V a l u e s of T a n d 0  W  w e r e interpolated onto the grid in both time a n d s p a c e .  \u000CChapter V: Analysis and Discussion The  boundary  condition  Page for  the  finite  difference  solution  is  a  169 constant  concentration at the top. A t m o s p h e r i c concentration is the constant v a l u e for the s u r f a c e node (e.g., 0 . 0 3 6 % CO2 a t m o s p h e r i c concentration).  5.5  Computer Code Program A s i m p l e c o m p u t e r program called \" C 0 2 \" w a s written using M a c r o V i s u a l B a s i c  of Microsoft E x c e l to s o l v e Equation 5.30. T h e full V i s u a l B a s i c c o d e s for the model is provided in A p p e n d i x B.  A flow chart for the program is s h o w n in Figure 5.17. T h e  model u s e s water content matrix (depth a n d time) a s the input for the diffusion a n d production calculations. T h e model is therefore, able to u s e the S o i l C o v e r water content (or saturation) output a s input  to the \" C 0 2 \" model to calculate the c h a n g e in CO2  concentration with depth a n d time a s a function of CO2 diffusion a n d production. T h e upper boundary condition (depth of 0 m) of the m o d e l w a s constrained to volumetric concentration of 0.036 % for C 0 . T h i s represents the relative concentration 2  of CO2 in the a t m o s p h e r e . T h e model required v a l u e s for soil porosity (bulk), volumetric water contents profile a n d temperature. C 0 concentration profiles w e r e a l s o required to 2  run the m o d e l . Initial concentration depth profiles provided a starting point for the model while concentration profiles at a later time provide the m o d e l with v a l u e s it c o u l d attempt to match. C 0  2  production w a s determined a s a function of G , soil air porosity (6 ), a n d 0  soil moisture content (0 ) W  with G  0  a  being the only fitted parameter. T h e  parameter \" a \" w a s set at 2 w h i c h m a x i m i z e d the product of of a n d 0  W  arbitrary  at a d e g r e e of  saturation of 0.70 (where d e g r e e of saturation is the ratio of the v o l u m e of water-filled v o i d s to the v o l u m e of total void s p a c e ) . M a x i m u m reactivity at a d e g r e e of saturation of 0.70 w a s r e a s o n a b l e b e c a u s e a m p l e a m o u n t s of both water a n d CO2 (from pore g a s )  \u000CPage  Chapter V: Analysis and Discussion  Set-up Major Matrices Write spreadsheet of water contents matrix Write values of constants  User Input Input # of nodes Input initial cone. Ifiput porosity Input G o value  Variable Assignments Interpolate a saturation values for each node Calculate the time-step for each node and compare the minimum value to max/min time-step. Calculate the diffusion coefficient for each node. Calculate the average value of the diffusion coefficient and nodal spacing  Finite Difference Calculation Calculate change in concentration Calculate new concentration profile.  1 Output -  Figure 5.17.  Write concentration matrix Write Diffusion coefficient matrix  Flowchart for theJVisual B a s i c program  170  \u000CChapter V: Analysis and Discussion  Page  171  for reactions would be available. A d e g r e e of saturation of 0.70 a l s o a g r e e d with literature v a l u e s of m a x i m u m respiration rates. T h e program structure c o n s i s t e d of two nested loops: the innermost loop a n d the outer loop (Figure 5.17). T h e innermost loop o c c u r s for e a c h time-step a n d is w h e r e the finite difference calculation t a k e s place. T h e outer loop o c c u r s for e a c h \"day\" w h e r e the program starts by creating all major matrices. T h e u s e r u s e s the input s p r e a d s h e e t (described later in this section) to input the initial concentration profile a n d the total porosity ( a s s u m e to be the s a m e throughout the profile). T h e m i n i m u m a n d m a x i m u m time-step v a l u e is specified but c a n be c h a n g e d if d e s i r e d . T h e s e v a l u e s limit how s m a l l or how large the time-step v a l u e get. T h e time-step is calculated a s a function of the coefficients in the finite difference equation. T h e formula u s e d to calculate the time-step is given in Equation 5.30 that defines the time-step required for mathematical stability (Zill a n d C u l l e n , 1992). It w a s determined from trial simulations that for most modeling s c e n a r i o s , 3 5 0 iterations w e r e required to r e a c h stability ( S e e F i g u r e 5.18). T h e model calculates the time-step for e a c h n o d e then t a k e s the m i n i m u m v a l u e a n d c o m p a r e s it to the m a x i m u m a n d m i n i m u m time-step specified by the program or the user. A s noted in the F i g u r e 5.18, c o n v e r g e n c e w a s a c h i e v e d after 3 5 0 iterations. T h e stability w a s poor below 2 0 0 iterations. H e n c e , 3 5 0 iterations w e r e performed for e a c h simulation. T h e output of the model is a s p r e a d s h e e t file containing the: d a y #, iteration #, nodes,  new  concentration, concentration  c h a n g e s , diffusion,  difference calculated v a l u e s are presented in A p p e n d i x B.  saturation  and  time  \u000CChapter V : Analysis and Discussion  Page  Ui  c o (0  g  8.0E-09 +  |  6.0E-09 H  O  4.0E-09 2.0E-09 0.0E+00  F i g u r e 5.18  Stability curves generated by the model for different  iterations using time steps of 0.05 day.  172  \u000CChapter V: Analysis and  5.6  Page  Discussion  Application of the C 0  2  173  Model Using Measured Values in Sand  Minicosms T h e theory  and development  of a numerical  model for C 0  2  diffusion  and  transport w e r e presented in the previous s e c t i o n s . T h e model w a s b a s e d o n the finite difference method to s o l v e the o n e - d i m e n s i o n a l diffusion equation using the program Macro Visual Basic. T h e experiments for the d y n a m i c c l o s e d c h a m b e r ( D C C ) method w e r e d e s i g n e d a n d carried out to evaluate the ability a n d a c c u r a c y of the D C C to m e a s u r e C 0  2  fluxes  under actual field conditions o n the s u r f a c e s of the D N W R a n d D S W R . H o w e v e r , no instrumentation w a s installed to m e a s u r e d C 0  2  concentrations a n d gradients in the  s h a l l o w profile within the upper meter of the w a s t e rock piles. T h e r e f o r e , it is not possible to rigorously test the full utility of the C 0  2  model following the h e a v y rainfall  event similar to the S o i l C o v e r modeling that w a s c o n d u c t e d to predict c h a n g e s in soil water content. T h e \" C 0 2 \" model is evaluated in this section for the prediction of C 0  2  concentrations m e a s u r e d in the m i n i c o s m s experiments previously d e s c r i b e d in section 3.3 ( K a b w e 2001 a n d K a b w e et a l . , 2002). T h e simulation results are interpreted a n d d i s c u s s e d in the following s e c t i o n s .  5.6.1 Prediction of C 0 concentration profiles in response to changes in 2  water contents profiles It w a s s h o w n in the previous s e c t i o n s that c h a n g e s in microbial respiration c a n result from c h a n g e s in temperature a n d water content. In the following hypothetical water content profiles for the s a n d c o l u m n  simulations  w e r e u s e d to illustrate the  \u000CChapter V: Analysis and Discussion  Page  174  effects of water content o n the effective diffusion coefficient a n d C O 2 g a s concentration depth- profiles in the s a n d c o l u m n . Figure 5 . 1 9 A s h o w s the hypothetical water profiles in a s a n d c o l u m n . T h e s e profiles w e r e g e n e r a t e d by progressively reducing the initial water content profile (curve d1) by a factor of 0.8 consecutively. It should be noted that the initial water content profile (curve d1) is a real m e a s u r e d water profile of a s a n d c o l u m n (HT) d e s c r i b e d in K a b w e (2001) a n d K a b w e et al.( 2002). In this e x a m p l e , the initial water contents at 0, 0.45 a n d 0.9 m depths (curve d1) w e r e 12, 2 0 a n d 3 4 % respectively. T h e final hypothetical water contents at 0, 0.45 a n d 0.9 m depths (curve d10) w e r e 3, 4 a n d 8 % respectively. T h e c o r r e s p o n d i n g starting CO2 concentration profile (Figure 5 . 1 9 B , c u r v e d1) represented the actual m e a s u r e d CO2 concentrations for the s a n d c o l u m n d e s c r i b e d in K a b w e et a l . (2002).  In this e x a m p l e , the CO2 concentrations at 0.15, 0 3 0 a n d 0.60 m  depths w e r e 0.082, 0.14 a n d 0 . 1 5 % respectively. T h e s u b s e q u e n t simulated c h a n g e s in C0  2  profiles in the c o l u m n in r e s p o n s e to c h a n g e s in the water contents profiles  p r e s e n t e d in F i g u r e 5 . 1 9 A a r e s h o w n in F i g u r e 5 . 1 9 B (curves from d1 to d l O ) . A s the soil water content c h a n g e s from wet to dry conditions (Figure 5.19, c u r v e s from right to the left) the CO2 concentration profiles a l s o i n c r e a s e proportionally (Figure 5 . 1 9 B , c u r v e s from left to the right). At the e n d of the simulation the CO2 concentration at 0.15, 0.30 a n d 0.60 m depths w e r e 0.13, 0.17 a n d 0.20 %, respectively. S i n c e a constant C 0  2  flux w a s applied to the b a s e of the H T c o l u m n during the simulation, the c h a n g e in the C0  2  concentrations profiles w a s d u e to the c h a n g e in the effective diffusion coefficient  (D )  (Equation 5.15) for C 0 , w h i c h is a function of water content. T h e g e n e r a l trend  e  2  s h o w e d a d e c r e a s e in the D with a n i n c r e a s e in water content. e  \u000CPage  Chapter V: Analysis and Discussion  1.0 -I  1  1  &lt;  0  10  20  30  1 40  V o l . water content (%) Figure 5.19A.  Hypothetical water contents profiles in a sand material described in  Figure 5.13A.  1.0  -I 0  1  1  0.1  0.05  C0  2  1  0.15  1  0.2  1  0.25  c o n c e n t r a t i o n (% b y vol.)  F i g u r e 5 . 1 9 B . Model predicted C 0 concentrations profiles in a HT sand column 2  obtained with hypothetical simulated water contents profiles (Figure 5.19A) and an initial measured C 0 concentrations profile (d1) in HT column (Kabwe et al., 2002). 2  175  \u000CPage  Chapter V: Analysis and Discussion  5.6.2  Simulations of C 0  2  Concentration Profiles using Sand  176  Minicosm-  Measured Data In order to test the ability of the \" C 0 2 \" model to predict the C 0  2  diffusion in s a n d  material, simulations w e r e performed using the m i n i c o s m - m e a s u r e d data d e s c r i b e d in section 3.3 of C h a p t e r 3 of this thesis a n d in K a b w e (2001), K a b w e et a l . (2002) a n d R i c h a r d s (1998). O n e m i n i c o s m w a s kept at r o o m temperature (18 - 2 3 \u00B0C) (HT) a n d another at 5 \u00B0C (LT) (see A p p e n d i x F). T h e m i n i c o s m experiments started after the m i n i c o s m s w e r e filled with about 6 3 4 kg of s a n d e x c a v a t e d from an unsaturated C horizon at a field d e s c r i b e d in K a b w e et a l . (2002) a n d R i c h a r d s (1998). A constant application rate of water (2 L l/week) w a s applied to the m i n i c o s m s from  the  beginning  of  the  experiments.  However,  each  minicosm  demonstrated  relatively high water r e l e a s e rates during the first 70 d a y s of experiments ( R i c h a r d s , 1998). Effluent rates stabilized after 60 d a y s from the beginning of the experiment. T h e water contents profiles s h o w n in F i g u r e s 5 . 2 0 A a n d 5 . 2 0 B represent m e a n v a l u e s of m e a s u r e d water profiles in the m i n i c o s m s for the period of 100 d a y s from the start of experiments. A s e x p e c t e d , the water content i n c r e a s e s with increasing depth to the water  table.  T h e s e water  \"profiles  were  concentrations profiles in high (HT) (21 -  u s e d to  predict  the  changes  23 \u00B0C) a n d low (LT) (5 \u00B0C)  of  C0  2  temperature  m i n i c o s m s ( K a b w e et a l . , 2 0 0 2 ; R i c h a r d s , 1998). T h e m e a s u r e d temperature profiles in the H T (18 - 2 3 \u00B0C) a n d L T (5 \u00B0C) m i n i c o s m s over the first 100 d a y s after filling are presented in A p p e n d i x X . T e m p e r a t u r e s r e m a i n e d near constant a n d the deviation w a s &lt; 1.0 \u00B0C ( R i c h a r d s , 1998).  standard  \u000CChapter V: Analysis and Discussion  F i g u r e 5.20.  Page  177  Measured volumetric water content profiles in the (A) low temperature (LT)  (thermostat set at 5 \u00B0C and (B) high temperature (room temperature) minicosms. V represent the water table.  \u000CChapter V: Analysis and Discussion Responses  to  short-term  Page  fluctuations  in room temperature  m i n i c o s m s w e r e o b s e r v e d only at the 0.02 m depth.  for  both  LT and  178 HT  F o r simulation p u r p o s e s , the  a v e r a g e temperatures profiles w e r e u s e d for e a c h m i n i c o s m . Figures 5.21 A a n d 5.21 B s h o w the C 0  concentration profiles for the H T a n d L T  2  m i n i c o s m s respectively, m e a s u r e d during the first 100-d period from the beginning of the  experiments  (Kabwe  (2001)  and  R i c h a r d s (1998).  The  C0  concentrations  2  i n c r e a s e d with depth, reaching the greatest concentrations at the capillary fringes. During approximately the first 60 d a y s of the experiment, C 0  2  concentrations w e r e not  yet stable. T h e generally higher concentrations during the first 60 d a y s w e r e attributed to the disturbance of the soil at the time of e x c a v a t i o n a n d m i n i c o s m s fillings ( L a w r e n c e et a l . , 1993; C h a p p e l l e , 1996).  S t a b l e concentration profiles w e r e r e a c h e d after 80  d a y s at 0.75 m depth ( R i c h a r d s , 1998) but after the initial period of stabilization, C 0 concentrations at all positions tended to d e c r e a s e at a low constant rate.  2  The \" C 0 2 \"  model w a s u s e d to predict the m i n i c o s m s concentrations profiles d u e to c h a n g e s in the water content profiles d e s c r i b e d a b o v e (Figure 5.20). T o simulate the C 0  2  concentrations profiles, the starting C 0  2  concentrations  profiles a n d the reference production rate ( G ) w e r e required for the m i n i c o s m s . T h e 0  initial concentrations profiles on d a y 12 (Figures 5.21 A a n d 5.21 B) for the L T and H T w e r e u s e d a s inputs to predict s u b s e q u e n t concentrations profiles d u e to c h a n g e s in water contents profiles (Figure 5.20). Figures 5 . 2 2 A a n d 5.22B s h o w model simulated C 0  2  profiles within the L T a n d  H T m i n i c o s m s respectively, for the c a s e w h e r e G * w a s c h a r a c t e r i z e d by constraining a = 2, b = 1.25, k = 0.04 \u00B0C in Equation 5.21, a n d G 1999).  = 2 7 0 u g C g d \" . (Hendry et a l . , 1  0  \u000CPage  Chapter V: Analysis and Discussion  179  0.0  0.1  +  0.2 4-  0.3 +  0.4 + 0.6  + 0.5  0.8  0.6 4-  +  H  0.9  0  1  0.2 C0  F i g u r e 5.21.  +  1  1  0.4 0.6 0.8 2  h  1  c o n e . (% vol.)  1.2  0.7  0.00  0.05 C0  2  0.10  0.15  0.20  c o n e . (% v o l )  Measured C 0 concentration profiles in the (A) high temperature (HT) (21 - 23 2  \u00B0C) and (B) low temperature (LT) (5 \u00B0C) minicosms (Richards, 1998; Kabwe, 2001).  \u000CChapter V: Analysis and Discussion  Page  F i g u r e 5.22. Model predicted C 0 concentration profiles in the (A) high temperature 2  (HT) (21 - 23 \u00B0C) and (B) low temperature (LT) (5 \u00B0C) minicosms.  180  \u000CChapter V: Analysis and Discussion  Page  181  C o m p a r i s o n b e t w e e n the m e a s u r e d (Figures 5 . 2 1 A a n d 5.22A) a n d predicted C0  2  concentration profiles (Figures 5.21 B a n d 5.22B) s h o w s that the model c l o s e l y  a p p r o x i m a t e s the  m e a s u r e d CO2  concentration  profiles  in both the  LT and  m i n i c o s m s , e x c e p t in the region between 0.2 a n d 0.4 m depth. T h e  HT  relationship  between m e a s u r e d a n d model prediction is s h o w n in Figure 5.23 for the L T a n d H T m i n i c o s m s , respectively. D a t a for the L T m i n i c o s m yield a g o o d correlation ( R  2  =0.98)  b e t w e e n m e a s u r e d a n d model prediction a s c o m p a r e to R = 0.74 for the H T m i n i c o s m . 2  In s u m m a r y , a simple o n e - d i m e n s i o n a l numerical model for the prediction of c h a n g e s in the effective diffusion coefficient of C 0  2  a n d its redistribution in s u b s u r f a c e  s a n d material d u e to c h a n g i n g water contents w a s d e v e l o p e d a n d validated using m i n i c o s m - m e a s u r e d data for unsaturated s a n d c o l u m n s . T h e match between  the  simulated a n d the m e a s u r e d concentration profiles for the two m i n i c o s m s w a s g o o d . T h e L T m i n i c o s m yielded the best fit (R =0.98) between the m e a s u r e d a n d simulated 2  profiles a s c o m p a r e d to R = 0 . 7 9 for the H T m i n i c o s m . It s h o u l d a l s o be noted that the 2  c h a n g e in CO2 concentration profile in the L T m i n i c o s m w a s s m a l l e r than that in the H T m i n i c o s m o v e r the 100-day test period.  5.7  Prediction of C 0  2  Diffusion and  Concentration-Depth Profiles  in  Response to Changes in Water-Depth Profiles in the DSWR T h e \" C 0 2 \" m o d e l w a s a l s o u s e d to predict CO2 diffusion a n d concentration-depth profiles in the D S W R pile. T h e D S W R w a s selected, for simulations b e c a u s e it h a s a grain s i z e similar to that of the s a n d u s e d in m i n i c o s m s a n d m e s o c o s m to verify the D C C method ( K a b w e et a l . , 2002). M o r e o v e r , the model w a s a l s o constrained to biotic production rate a n d that the D S W R pile w a s controlled by the oxidation of o r g a n i c  \u000CPage  Chapter V: Analysis and Discussion  0.20  c o  0.15  j +  \u2022  dl2  \u2022 d19  \u2022  ^/\u2022  Ad26 + d34  co5 3 (A  Xd47 \u2022 0.05  R = 2  +  0.98  i  0.00 0.00  0.05  0.10  Simulated C 0  2  d75  Ad96  1  0.15  concentration  0.20  (%)  c o  c o c o o  6  o  \u2022a 3 (/&gt;  CO  0.2 Simulated C 0  F i g u r e 5.23.  0.4 2  0.8  0.6  concentration  (%)  (A) Relationship between measured and simulated C 0  in the low temperature (LT) (5 \u00B0C) minicosm plotted on a 1:1 scale.  2  concentrations  182  \u000CChapter V: Analysis and Discussion  Page  183  c a r b o n present in the lake-bottom s e d i m e n t s (Birkham et a l . , 2 0 0 3 ; L e e et a l . , 2003) w h i c h are at a constant temperature (0 - 1 \u00B0C) a n d moisture content (25 V o l . %) ( B i r k h a m et a l . , 2 0 0 3 ) (see a l s o Figure 5.24). Figure 5.24 s h o w s the g e o l o g i c profile, a n d the m e a n CO2 concentration- and water content-depth profiles for D S W R (Birkham et a l . , 2 0 0 3 ) . B i r k h a m et a l . (2003) reported that the trends of C 0 - d e p t h profile w a s stable o v e r time, s u g g e s t i n g near 2  steady-state conditions with respect to g a s concentrations, a n d thus, reaction rates. T h e CO2 concentration i n c r e a s e s with increasing depth up to the o r g a n i c layer of the pile (Figure 5 . 2 4 B ) a n d s u g g e s t e d that the dominant sites of reaction o c c u r r e d below the pile. B e l o w the o r g a n i c layer the more vertical C 0  2  concentration-depth  profile is  o b s e r v e d a n d that supported the interpretation that the dominant site of production w a s from the o r g a n i c - r i c h material at the b a s e of the pile (Birkham et a l . , 2003). T h e volumetric water content (Figure 5 . 2 4 C ) v a l u e s generally ranged from 2 to 3 0 % , with standard deviations at e a c h depth generally l e s s than 2 % , s u g g e s t i n g n e a r steady-state water conditions (Birkham et a l . , 2003). Z o n e of i n c r e a s e d water contents (&gt;20%) w e r e m e a s u r e d at the natural ground surface between 18 a n d 20 m, a n d near the original ground s u r f a c e . T h e d e e p e s t z o n e s of elevated water content c o r r e s p o n d e d to the underlying o r g a n i c layer (Figure 5 . 2 4 C ) . S i n c e the C 0 - a n d water-depth profiles w e r e stable o v e r time ( B i r k h a m et a l . , 2  2 0 0 3 ) with small variations in m e a s u r e d v a l u e s , it w a s not p o s s i b l e to simulate or predict s u b s e q u e n t c h a n g e s in C C V d e p t h profiles a s s o c i a t e d with c h a n g e s in water content. F o r illustration p u r p o s e s , hypothetical drying forcing conditions w e r e g e n e r a t e d in the pile by reducing the initial m e a s u r e d m e a n water-depth profile (Figure 5 . 2 4 B a n d Figure 5.25(A), curve dO) by a factor of 0.1 c o n s e c u t i v e l y a s illustrated in Figure 5.25A.  \u000CPage  Chapter V: Analysis and Discussion  (B)  (A) Depth  0 0 \u2022 5 \u2022 Sand  et layer with ice crystal  t t \u2022  2  4  6  *  \u2022  8  10  10  20  30  40  0 5  10  15 \u2022  15  \u00A3 25H  Water content (%) 0  10 \u2022  \u00A3. 20 \u2022 Sand  (C)  C02conc.(%)  184  A  20  \u2022  25 H  \u2022  Original ground  30  30 \u2022  30 4  Organic/sand \u2022  35 \u2022 40 45  F i g u r e 5.24.  \u2022  I  35 40 45  Depth profiles for Deilmann south waste-rock (DSWR) pile (A) Geologic profile  (B) mean C 0 concentration (Vol.) and (C) mean volumetric Water contents values (Adapted 2  from Birkham et al, 2003).  \u000CChapter V: Analysis and Discussion  Page  185  B e c a u s e of the unsaturated condition in the w a s t e - r o c k pile, the variations in water contents w e r e relatively s m a l l , except near the original ground a n d b e t w e e n 18 a n d 2 0 m depth. Figure 5 . 2 5 B s h o w s the model predicted effective diffusion coefficients De-profiles in r e s p o n s e to c h a n g e s in water-depth profiles (hypothetical) (Figure 5.25A) within the D S W R pile. T h e modeling a p p r o a c h incorporated oxidation reactions limited to the organic-rich material at the b a s e of the pile (up to 3 0 m depth) (Birkham et a l . , 2003). T h e water content-depth profiles v a l u e s w e r e a l s o limited to the o r g a n i c layer. S i n c e the production rate of the w a s t e - r o c k material w a s not known or d e t e r m i n e d , a n arbitrary n u m b e r w a s u s e d instead. T h i s m a y c a u s e s o m e errors in the v a l u e s . T h e plots in Figure 5 . 2 5 B s h o w a d e c r e a s e in D - d e p t h profiles with d e c r e a s i n g watere  depth profiles through the pile. T h e s e profiles trends are a l s o illustrated in F i g u r e 5 . 2 6 A for the plots of the D a s a function of water content. A s e x p e c t e d the D d e c r e a s e s with e  e  increasing water content. T h e model predicted c h a n g e s in CO2 concentration-depth profiles are s h o w n in Figure 5 . 2 6 B . T h e c h a n g e s in CO2 concentration-depth profiles in r e s p o n s e to c h a n g e s in hypothetical water-depth profiles w e r e not significant d u e to the low initial starting water-depth profile v a l u e s in the w a s t e - r o c k pile. T h i s interpretation is supported by the m e a s u r e d standard deviations of less than 2 % (Birkham et a l . , 2003), s u g g e s t i n g n e a r steady-state water conditions in the pile o v e r time. H o w e v e r ,  the  trends s h o w e d that a s the soil water-depth profiles c h a n g e s from wet to dry conditions (Figure 5 . 2 5 A , c u r v e s from right to the left) the CO2 concentration-depth profiles a l s o i n c r e a s e proportionally (Figure 5 . 2 6 B , c u r v e s from left to the right). In s u m m a r y , the model w a s u s e d to estimate C 0  2  diffusion a n d concentration-  depth profiles in D S W R in r e s p o n s e to c h a n g e s in water-depth profiles. B e c a u s e of the unsaturated condition of the waste-rock pile a n d the near steady-state conditions with  \u000CChapter V: Analysis and Discussion  F i g u r e 5.25.  Page  186  (A) Hypothetical water-depth profiles in D S W R pile and (B) model predicted  effective diffusion coefficients (D ) in response to changes in water contents in Figure 5.32A e  Curve dO in Figure 5.32A represents the actualmeasured mean water-depth profile in Figure 5.31 C in the D S W R (Birkham et al.,2003). The subsequent profiles (hypothetical water profile) were generated byreducing the initial measured water-depth profile by a factor of 0.1 consecutively.  \u000CPage  Chapter V: Analysis and Discussion  |^  F i g u r e 5.26.  Water content (%, Vol.)  187  ||_  Model predicted changes in: (A) effective diffusion coefficient (D ) as a function e  of water content and (B) C 0 concentrations depth-profiles in response to changes in water 2  contents profiles described in Figure 5.32A.  \u000CChapter V: Analysis and Discussion  Page  188  respect to gas concentrations and water content profiles in the D S W R simulations results showed relatively small variations in predicted values.  5.8  Predictions of C 0  2  Diffusion and Surface C 0  2  Flux from the DNWR and  DSWR Piles Following Rainfall Events A simplified form of the \" C 0 2 \" model was also used to predict the effects of rainfall events on the surface effective diffusion coefficient (D ) and surface C 0 flux at e  2  the D N W R and D S W R during the 6-day test period [August 1 (day 3) to August 6 (day 8) 2002] following 75.9 mm rainfall event over the initial 48-h period [July 30 (day 1) to July 31 (day 2) 2002]. It should be noted that the effects of rainfall events on surface water conditions and C 0 fluxes at the D N W R and D S W R were discussed in details in 2  Section 5.2 of Chapter 5 of this thesis. Figures 5.27 and 5.28 show the measured surface water contents, C 0  2  fluxes  and rainfall events during the 8-d test period as discussed in Chapter 5, along with the model predicted surface D  e  and C 0  2  fluxes from the D N W R and D S W R piles,  respectively. The model was simulated using the measured surface water contents (curves with broken lines and open marks) for each day of the 6-d (day 3 to day 8) test period. Figure 5.27 shows that as the D N W R ground surface continued to dry gradually from day 3 to day 8, the model predicted surface D (curve with solid line with triangle e  marks) also continued to gradually increase with time. It should be noted that the predicted surface D and measured surface C 0 flux exhibit very similar trends. They e  2  both increased initially at a fast rate from day 3 to day 5, then at a slow rate and eventually reached a plateau on day 7. At the end of the test period on day 8 the surface D at the DNWR was found to be 4.25x10\" m s\" .The similar trends were also 6  e  2  1  \u000CPage  Chapter V: Analysis and Discussion  Figure 5.27.  189  Rainfall, measured surface water content and C 0 flux and predicted 2  effective diffusion coefficient (D ) and surface C 0 e  2  flux at the Deilmann North waste-rock  (DSWR) pile over an 8-day test period [30 July (day 1) to 6 August (day 8) 2002] with time.  \u000CPage  Chapter V: Analysis and Discussion  45  190  0.50  Rainfall Predicted surfacevDe  +  0.45  +  0.40  +  0.35  j\u00BB  0.30  &lt;P  f ~\u00A3 0.25  |  ~ o  0.20  |  Q\u00B0  o  5 +  0.15  +  0.10  0.05  0.00  F i g u r e 5.28.  \u2014 -  C02flux De  I Rain \u2022Water\"  -  - C 0 2 flux (Pred) - P o l y . (De)  Rainfall, measured surface water content and  effective diffusion coefficient (D ) and surface C 0 e  2  C0  2  flux and predicted  flux at the Deil;mann North waste-rock  (DNWR) pile over an 8-day test period [30 July (day 1) to 6 August (day 8) 2002] with time.  \u000CChapter V: Analysis and Discussion  Page  191  o b s e r v e d at the D S W R in Figure 5.28. Similarly, both the m e a s u r e d surface C 0 a n d predicted surface D  e  2  flux  initially i n c r e a s e d at a fast rate from d a y 3 to d a y 5 a n d  eventually r e a c h e d a plateau on d a y 7. At the e n d of the test period the surface D  e  at  the D S W R w a s found to be 4.40 x 10\" m s\" . 6  T o predict the surface C 0  2  1  flux using F i c k ' s 1st a n d 2  2  n d  law (e.g., E q u a t i o n 5.3  u s e d in the model) the concentration gradient (e.g., d C / d z ) must b e k n o w n . T h e concentration gradients, however, w e r e not m e a s u r e d during the test period. But b a s e d o n the m e a s u r e d a v e r a g e surface C 0 surface  D  estimated  e  2  flux a n d the c o r r e s p o n d i n g m o d e l predicted  from the D N W R a n d D S W R piles, the concentration gradients c a n be  using F i c k ' s first law (e.g., Equation 5.3). T h e concentration gradients w e r e  found to be:  ^ = dz  2.03x10- U^2  lm *mj 3  DC  -  \" kg  f  and ^ = 1.24x10Vm 3z  w  2  3  A  for the D N W R a n d  DSWR  *rrv  pilesrespectively. It w a s a s s u m e d in the model simulations that the C 0 w a s p r o d u c e d 2  at a s t e a d y rate below the piles (e.g., the dominant sites of reactions) (Birkham et a l . , 2 0 0 3 ) a n d that the shallow C 0 gradient near the ground surface would remain constant 2  during the  relatively  short-term  wetting event.  It should be pointed out that this  a s s u m p t i o n is not completely valid; h o w e v e r actual C 0  2  m e a s u r e m e n t s in the s a n d  profiles immediately b e l o w the ground s u r f a c e s of the w a s t e - r o c k piles w e r e  not  obtained a n d thus it is not p o s s i b l e to accurately constrain the lower boundary of the transient m o d e l . B a s e d o n this simplifying a s s u m p t i o n , the calculated surface C 0  2  fluxes during the 6-d test period at the D N W R a n d D S W R are a l s o s h o w n respectively in F i g u r e s 5.27 a n d 5.28 (curves with solid lines with circle marks) along with the predicted surface D s . R e s u l t s s h o w e d that the calculated (predicted) a n d m e a s u r e d e  \u000CChapter V: Analysis and Discussion C0  2  Page  192  fluxes exhibited very similar trends. T h e s e trends supported the interpretation that  the flux is proportional to the D times the concentration gradient. e  5.9  Chapter Summary In s u m m a r y , results s h o w e d that the w a t e r content at g r o u n d s u r f a c e is transient  after a h e a v y rainfall a n d is a n important factor in controlling CO2 fluxes. Both the E C m e a s u r e d A E a n d P E ( C a r e y et a l . , 2 0 0 5 ) a n d S o i l C o v e r predicted A E and P E values showed good agreement. T h e \" C 0 2 \" m o d e l predicted, a s e x p e c t e d , a d e c r e a s e in D - d e p t h profiles a n d e  i n c r e a s e in the C 0  2  concentration-depth profiles with d e c r e a s i n g water-depth profiles  through the D S W R pile. T h e m o d e l a l s o predicted s u r f a c e CO2 fluxes trends that w e r e very similar to the m e a s u r e d surface CO2 fluxes during the 6-d test period from the D N W R a n d D S W R following h e a v y rainfall events.  \u000CChapter VI: Summary and Conclusions  Page  193  CHAPTER VI Summary and Conclusions  A recently d e v e l o p e d a n d laboratory-verified d y n a m i c c l o s e d c h a m b e r ( D C C ) method has b e e n tested under field conditions o n w a s t e - r o c k piles at the K e y L a k e uranium mine. T h e method has b e e n u s e d to quantify the magnitude of spatial a n d temporal variations in the C 0 flux on the D e i l m a n n north ( D N W R ) a n d D e i l m a n n south 2  ( D S W R ) waste-rock piles o v e r a period of two y e a r s ( s u m m e r 2 0 0 0 - s u m m e r 2002). T h e ability of the D C C to accurately quantify field respiration w a s d e m o n s t r a t e d by comparing  the  D C C fluxes  to  those  obtained  using  two  other  field  C0  2  flux  m e a s u r e m e n t t e c h n i q u e s : the static c l o s e d c h a m b e r ( S C C ) a n d e d d y c o v a r i a n c e ( E C ) methods. T h e main a d v a n t a g e of this direct technique is that it provides a n almost instantaneous indication of the reaction rate under field conditions, regardless of climatic or moisture conditions in the w a s t e d u m p s . T h e D C C w a s a l s o u s e d to investigate the effects of climatic variables (e.g., rainfall a n d evaporation) on near-surface waste-rock-water conditions w h i c h a l s o affect surface C 0 g a s fluxes. 2  A relatively s i m p l e \" C 0 2 \" model w a s d e v e l o p e d to predict the c h a n g e s in the effective  diffusion  coefficient  of C 0 , s u r f a c e C 0 2  flux a n d  2  its  redistribution  in  s u b s u r f a c e material in r e s p o n s e to c h a n g e s in soil water contents. At the D S W R site the D C C w a s u s e d to demonstrate that the C 0  2  flux w a s  relatively uniform a c r o s s the pile (with a C V of only about 3 0 % ) . T h i s C V reflects the c o m b i n e d influence of a relatively constant rate of C 0  2  production in the organic-rich  \u000CChapter VI: Summary and Conclusions  Page  194  z o n e at the b a s e of the waste-rock pile a n d the textural uniformity of the overburden material (sand) u s e d to construct the D S W R pile ( B i r k h a m , 2002). That is, t h e s e factors c o m b i n e to exert a controlling influence o n the composition a n d upward migration of pore g a s e s a n d , in turn, the flux of g a s e s from the surface to the a t m o s p h e r e . C o m p a r i s o n between the D C C a n d the static c l o s e d c h a m b e r ( S C C ) s h o w e d that there w a s no significant difference (p &lt; 0.05) b e t w e e n the m e a n C 0  2  fluxes obtained  using the two m e t h o d s at the D S W R . W h e r e a s the c h a m b e r - b a s e d ( D C C a n d S C C ) m e t h o d s yielded c o m p a r a b l e data from the D S W R , with a n overall t i m e - a v e r a g e d C 0 flux of 171 + 54 m g C 0 averaged C 0  2  m\" h\" ; the e d d y c o v a r i a n c e ( E C ) method yielded a time2  2  2  1  flux (150 + 3 5 m g C 0  2  m\"  2  h\" ) that w a s about 1 2 % lower than that 1  calculated from the c h a m b e r d a t a . Underestimation of the C 0 flux a s s o c i a t e d with soil 2  respiration  by  EC-based  methods  relative to  chamber-based methods  has  been  reported widely in the literature [e.g., G o u l d e n et a l . , 1996; N o r m a n et a l . , 1997; L a w et al., 1999; J a n s s e n s et a l . , 2 0 0 0 ; D a v i d s o n et a l . , 2002]. T h o u g h not e x c e s s i v e l y large, t h e s e differences p r e s u m a b l y reflect the different p r o c e s s e s m e a s u r e d by the two m e t h o d s . T h e c h a m b e r data exhibited slightly greater standard deviations than the E C d a t a (i.e., D C C = + 58 mg C 0  m\" h\" ; S C C = + 59 m g C 0 2  2  1  m\" h\" ; E C = + 32 m g C 0 2  2  1  2  m\" h~ ). It is believed that this likely reflects the fact that the variability a s s o c i a t e d with 2  1  the c h a m b e r - b a s e d m e a s u r e m e n t s includes both a spatial a n d temporal c o m p o n e n t , w h e r e a s the variability a s s o c i a t e d with the E C method is primarily temporal in nature. T h e overall a v e r a g e s of C 0 fluxes at the D N W R a n d D S W R m e a s u r e d with the 2  D C C o v e r the 2 - y e a r test period ( s u m m e r 2 0 0 0 a n d s u m m e r 2002) w e r e 188 + 6 8 a n d 2 1 7 + 8 3 m\" h\" , respectively. 2  1  \u000CChapter VI: Summary and Conclusions  Page  195  B a s e d o n t h e s e results, it w a s c o n c l u d e d that the D C C is well-suited to the quantification a n d spatial resolution of C 0 fluxes a s s o c i a t e d with w a s t e - r o c k piles. 2  T h i s work s h o w e d that the effects of h e a v y rainfall events on the C 0  2  flux a n d  near-surface water conditions w e r e of short duration. T h e short-term effects of rainfall events w e r e reflected in the lack of long-term spatial a n d temporal variations in C 0  2  fluxes (average C V is 2 8 % - 3 9 % ) at both sites o v e r a 2-year test period ( s u m m e r 2 0 0 0 a n d s u m m e r 2002). B e c a u s e of lack of temporal a n d spatial variation in C 0 fluxes, it is 2  c o n c l u d e d that rainfall e v e n t s had little long-term effects o n C 0  2  flux from w a s t e - r o c k  piles. During the test period, S o i l C o v e r w a s u s e d to predict the rate of evaporation on the D S W R a n d results w e r e c o m p a r e d to published f i e l d - m e a s u r e d evaporation using e d d y c o v a r i a n c e ( E C ) method o n the D S W R ( C a r e y et a l . , 2 0 0 5 ) . R e s u l t s s h o w e d very g o o d a g r e e m e n t between the model predicted a n d E C m e a s u r e d v a l u e s . Both the fieldm e a s u r e d a n d predicted data indicated a n a v e r a g e evaporation rate of approximately 1.1 m m per d a y at the D S W R for the 8-day test period. Verification of the \" C 0 2 \" model d e v e l o p e d s h o w e d g o o d a g r e e m e n t  between  predicted a n d s a n d c o l u m n - m e a s u r e d data. S i m u l a t i o n s results for the d e e p profile in D S W R s h o w e d relatively small variations in predicted C 0  2  concentration-depth profiles  a s s o c i a t e d with c h a n g e in water content during a simulated drying event. A simplified model w a s a l s o u s e d to predict surface C 0 fluxes on the D N W R a n d D S W R at the K e y 2  L a k e mine following rainfall events. R e s u l t s s h o w e d the model predicted surface D C0  2  e  and  fluxes that exhibited very similar trends with m e a s u r e d d a t a . In s u m m a r y , the C 0  2  m o d e l , along with others c a p a b l e of predicting c h a n g e s in water content profiles with time  such  as  SoilCover,  can  be  of  value  in the  prediction  and  monitoring  of  \u000CChapter VI: Summary and Conclusions  Page  196  b i o g e o c h e m i c a l p r o c e s s e s occurring in the unsaturated g e o l o g i c material a n d natural soils. Finally, the field results s h o w e d that the D C C method is e s p e c i a l l y useful for characterizing spatial variability a s well a s identifying z o n e s of sulphide oxidation a n d c a r b o n a t e buffering in the w a s t e - r o c k piles. T h e method h a s distinct a d v a n t a g e s o v e r the traditional methods in terms of a c c u r a c y , s p e e d , a n d repeatability a n d it c a n be u s e d to m e a s u r e the CO2 fluxes in situ at the s a m e locations using the s a m e c h a m b e r s with minimal disturbance of the soil. T h e method c a n be e x t e n d e d to a n y other mine w a s t e d u m p s to quantify b i o g e o c h e m i c a l reaction rates in unsaturated g e o l o g i c m e d i a a n d soils e l s e w h e r e in C a n a d a . In c o n c l u s i o n , it is believed the objectives of this t h e s i s work w e r e met a s stated in the introduction.  \u000CReferences  Page  197  References  Aachib, M., Aubertin, M., Mbonimpa, M. 2002. 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G a s transport in unsaturated  zones:  multicomponent s y s t e m s and the a d e q u a c y of F i c k ' s law. W a t e r R e s o u r . R e s .  \u000CPage  References  216  25: 477-507. T i m m s , G . P . , and Bennet, J . W . 2 0 0 0 . In P r o c e e d i n g of the International C o n f e r e n c e on A c i d R o c k Drainge ( I C A R D ) , Denver, C o l o r a d o 2 0 0 0 : 8 4 1 - 8 4 9 . T r u m b o r e , S . E . , D a v i d s o n E . A . , d e C a m a r g o , P. B., N e p s t a d D. C , and Martinelli L. A . 1995.  Belowground  cycling  of  carbon  in forests  and  pastures  of  Eastern  A m a z o n i a . G l o b a l B i o g e o c h e m i c a l C y c l e s 9(4), 5 1 5 - 5 2 8 . T w i n e , T . E . , K u s t a s , W . P . , N o r m a n , J . M . , C o o k , D.R., H o u s e r , P . R . , M e y e r s , T . P . , P r u e g e r , J . H . , S t a r k s , P . J . , a n d W e s e l y , M.L. 2 0 0 0 . 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J o u r n a l of the R o y a l Meteorological S o c i e t y 106: 8 5 - 1 0 0 . W e e k s , E . P . , E a r p , D . E . , a n d T h o m p s o n , G . M . 1982. U s e of a t m o s p h e r i c fluorocarbons F-11 a n d F-12 to determine the diffusion parameters of the unsaturated z o n e in the southern High P l a i n s of T e x a s , W a t e r R e s o u r . R e s . 18: 1 3 6 5 - 1 3 7 8 . W e l l e s , J . M . , D e m e t r i a d e s - S h a h , T . H . , a n d McDermitt, D.K., 2 0 0 1 . C o n s i d e r a t i o n s for m e a s u r i n g ground C 0 2 effluxes with c h a m b e r s . C h e m i c a l G e o l o g y 177: 3-13. W i c k l a n d , K. P . , Striegl, R. G . , 1997. M e a s u r e m e n t s of soil c a r b o n dioxide a n d m e t h a n e concentrations a n d fluxes, a n d soil properties at four a g e s of jack pine forest in the S o u t h e r n Study, S a s k a t c h e w a n , C a n a d a , 1 9 9 3 - 1 9 9 5 . U . S . G e o l . S u r v . O p e n File R e p . , 9 7 - 4 9 . W i e r s m a , C . L . , Rimstidt, J . D . , 1984. R a t e s of reaction of pyrite a n d marcasite with ferric iron at p H 2. G e o c h i m . C o s m o c h i m . A c t a 4 8 : 8 5 - 9 2 . W i l s o n , M . R. P e d o g e n e s i s of fluids a s s o c i a t e d with unconformity-type  1987.  uranium d e p o s i t s in  S a s k a t c h e w a n , a n d with the Z o r t m a n - L a n d u s k y A u - A g d e p o s i t s in M o n t a n a . P h . D . , University of S a s k a t c h e w a n . W i l s o n , G . W . 1990. S o i l Evaporative fluxes for G e o t e c h n i c a l E n g i n e e r i n g problems. P h . D . thesis, University of S a s k a t c h e w a n , S a s k a t o o n , S a s k a t c h e w a n , C a n a d a . W i l s o n , G . W . , F r e d l u n d , D . G . , a n d Barbour, S . L . 1994. C o u p l e d  soil-atmosphere  modeling for soil evaporation. C a n . G e o t e c h . J . 3 1 : 1 5 1 - 1 6 1 . W i l s o n , G . W . , F r e d l u n d , D . G . , a n d Barbour, S . L . 1997. T h e effect of soil suction on evaporative fluxes from soil s u r f a c e s . C . G e o t e c h . J . , 34: 1 4 5 - 1 5 5 . W i n d , G . P . 1 9 5 5 . Field experiment c o n c e r n i n g capillary rise of moisture in h e a v y clay soil. Netherlands Journal of Agricultural S c i e n c e 3: 6 0 - 6 9 . Wohlfahrt, G . , A n f a n g , C , B a h n , M . , Haslanter, A . , N e e s e l y , C , Schmitt, M . , Drosler,  \u000CPage  References  218  M . , P f d e n h a u e r , a n d J . , C e r n u s c a , A . 2 0 0 5 . Quanifying nighttime e c o s y s t e m respiration of a m e a d o w using e d d y c o v a r i a n c e , c h a m b e r s a n d modeling. A g r i . For. Meteorol. 128: 1 4 1 - 1 6 2 . W o o d , B . D . , a n d Petraitis, M . J . 1984. Origin a n d distribution of c a r b o n dioxide in the unsaturated z o n e of the southern High P l a i n s of T e x a s . W a t e r R e s o u r . R e s . 20 (9): 1 1 9 3 - 1 2 0 8 . W o o d , B. D., Keller K. C , a n d J o h n s t o n e , D. L. 1 9 9 3 . In situ m e a s u r e m e n t of microbial activity a n d controls o n microbial C 0  2  production in the unsaturated z o n e . W a t e r  R e s o u r . R e s . 29(3), 6 4 7 - 6 5 9 . Y a n f u l , E . K . , a n d A u b e , B . C . 1 9 9 3 a . M o d e l l i n g moisture  retaining soil c o v e r s . In  P r o c e e d i n g s of the 1993 Joint C S C E - A S C E N a - \u00A9 2 0 0 5 N R C C a n a d a 1198 C a n . Geotech. J . Vol. 42, 2005 \u00A9 2005 N R C Canada. Y a n f u l , E . K . , M o u s a v i , S . M . , a n d Y a n g , M . 2 0 0 3 a . M o d e l i n g a n d m e a s u r e m e n t of evaporation  in  moisture-retaining  soil  covers. A d v a n c e s  in  Environmental  R e s e a r c h , 7: 7 8 3 - 8 0 1 . Y a n f u l , E . K . a n d M o u s a v i , S . M . 2 0 0 3 b . Estimating falling rate of evaporation from finite soil c o l u m n s . T h e S c i e n c e of the Total Environment 3 1 3 : 151-152 Y o n g , R . N . 2 0 0 1 . G e o e n v i r o n m e n t a l engineering - C o n t a m i n a t e d soils, pollutant fate, a n d mitigation. C R C P r e s s , B o c a R a t o n , F l a . Zill, D . G . a n d C u l l e n , M . R . 1992. A d v a n c e d Enginering M a t h e m a t i c s . P W S P u b l i s h i n g Company, Boston, MA.  \u000CPage  Appendices  219  APPENDIX A  Measuring 0  2  Fluxes Using the Dynamic Closed Chamber (DCC) System  NOTE: T h i s section is out of s c o p e of this thesis. T h i s is a n on-going r e s e a r c h work being carried out by the author of this thesis in the Department of Mining E n g i n e e r i n g at U B C , under the supervision of Prof. W a r d W i l s o n .  A1.  Design of the dynamic closed chamber (DCC) Full details of the d e s i g n , fabrication, a n d description of the D C C c h a m b e r are  presented in section 3.4.2.1 of this thesis, a n d in K a b w e (2001) a n d K a b w e et a l . (2002). T h e d y n a m i c c l o s e d c h a m b e r ( D C C ) u s e d in this work w a s initially d e s i g n e d for measuring C 0  2  fluxes ( K a b w e , 2001 a n d K a b w e et a l . , 2002). It c o n s i s t s of a n o p e n -  e n d e d rim (collar) with a lid. Full details of the d e s i g n , construction, a n d operation are presented in K a b w e et a l . (2002). C h a m b e r collars w e r e fabricated from fiberglass rims (0.76m dia. x 0.15m height); the c h a m b e r lid (0.76m d i a . x 0 . 0 5 m thick) w a s fabricated from P l e x i g l a s . T h e lid w a s attached to the collars with nuts a n d bolts. T h e c h a n g e s of m a s s of 0  2  within the c h a m b e r w a s m e a s u r e d using a n O x y m a x  E R - 1 0 o x y g e n g a s analyzer.  A2.  Principle of operation T h e rate of c h a n g e s of the m a s s of 0  2  within the c h a m b e r p l a c e d on the surface  of the w a s t e - r o c k d u m p c a n be d e s c r i b e d by:  (A1)  \u000CAppendices  Page  w h e r e F is the flux of 0  2  220  through the surface of the w a s t e - r o c k a n d A is the a r e a of the  b a s e of the c h a m b e r . T h e rate of c h a n g e of 0  2  concentration within the c h a m b e r , d C / d t ,  is given by ( T i m m s a n d Bennett, 2001):  dC J_drn =  dt  V dt  w h e r e V is the v o l u m e of the c h a m b e r . T h e 0 of c h a n g e of 0  2  v  2  ;  flux c a n then be calculated from the rate  concentration within the c h a m b e r . C o m b i n i n g E q u a t i o n s 1 a n d 2 g i v e s :  ^= F  dt  h  v  (A3) '  w h e r e h = V / A , the height of the c h a m b e r . T h e critical a s p e c t of the flux c h a m b e r d e s i g n is the n e e d to be able to m e a s u r e the low fluxes typical of c o v e r e d s y s t e m s .  A3.  Description and measurement principle of the Oxymax ER-10 oxygen gas  analyzer 0  was  2  Columbus  analyzed  Instruments,  using  an  0 -Gas 2  Respirometer  (Micro-Oxymax  ER-10,  O h i o U S A ) . T h e E R - 1 0 is a c o m p u t e r i z e d a p p a r a t u s  for  m e a s u r i n g very low levels of g a s e o u s o x y g e n uptake. A n I B M - P C c o m p a t i b l e c o m p u t e r maintains a n d d i s p l a y s the operation of the M i c r o - O x y m a x instrument. B e f o r e starting the m e a s u r e m e n t , the s y s t e m n e e d s only the time interval b e t w e e n s a m p l e s to be specified a n d the c h a m b e r v o l u m e w h i c h is c o m p u t e d automatically during s y s t e m calibration. W h e n the experiment is started, the software a s s u m e s control of the acquisition of information a n d storage of results a n d / o r presentation to the printer. O x y m a x E R - 1 0 c a n m e a s u r e liquid or solid s a m p l e s from 50 m L to 10 L in volume.  T h e principle of m e a s u r e m e n t , involves air s a m p l i n g from the h e a d s p a c e of  the c h a m b e r , circulating it through the g a s a n a l y z e r a n d returning b a c k to the s a m p l e c h a m b e r without a n y contact with the s a m p l e . S a m p l e s are continuously a e r a t e d with adjustable airflow (100 m L / m i n . to 1,500 mL/min.), e x c e p t for the short time interval w h e n a particular s a m p l e is being m e a s u r e d by the g a s a n a l y z e r . Calibration of 0  2  gas  a n a l y z e r is performed automatically at specific time interval with ambient air, thus,  \u000CAppendices  Page  221  removing the n e e d for the mixed g a s bottle. R e s u l t s of m e a s u r e m e n t s are presented in I^ICVmin or a s a n a c c u m u l a t e d (total in u.1) v a l u e of 0  2  c o n s u m e d from the beginning of  the experiment. T h e O x y m a x E R 1 0 operates on the principle of using g a s s e n s o r to m e a s u r e the c h a n g e in the o x y g e n in the h e a d s p a c e of a m e a s u r i n g cell a n d using this information to calculate how m u c h o x y g e n the s a m p l e is c o n s u m i n g (oxygen uptake). T o c o m p u t e the o x y g e n c o n s u m p t i o n requires two m e a s u r e m e n t s of the h e a d s p a c e s e p a r a t e d by a s p a n of time. T h e o x y g e n s e n s o r o p e r a t e s a s a n o x y g e n battery (fuel cell), a n d m e a s u r e s o x y g e n p e r c e n t a g e directly. T h e sensitivity of the s y s t e m to o x y g e n c o n s u m p t i o n (uptake) is d e p e n d e n t on two factors: the v o l u m e of the h e a d s p a c e g a s in the m e a s u r i n g cell, a n d the s p a n of time between m e a s u r e m e n t s .  In g e n e r a l , the s m a l l e r the h e a d s p a c e v o l u m e , the  higher the sensitivity. A l s o , the longer the time b e t w e e n m e a s u r e m e n t s , the higher the sensitivity. T h e v o l u m e of the h e a d s p a c e in the cell is automatically m e a s u r e d by the apparatus. T h e apparatus u s e s a direct method to detect a n d correct errors in the sensor  outputs  (resulting  from  environmental  p r e s s u r e c h a n g e s , or c h a n g e s in the  temperature  s e n s o r ) , a n d thereby  changes, r a i s e s the  barometric system's  measuring accuracy. T h e M i c r o - O x y m a x instrument a l s o contains a feature called 'automatic refresh\" w h i c h allows the g a s in the h e a d s p a c e of the m e a s u r i n g cell to be r e p l a c e d periodically with fresh air or other g a s mixtures.  T h i s feature is important if the level of o x y g e n  c o n s u m p t i o n by the s a m p l e is high e n o u g h that the o x y g e n b e c o m e s depleted in the headspace gas.  A4.  Preliminary Results and Discussion  A4.1  Site Location T h e S y n c r u d e C a n a d a Ltd ( S C L ) mine is located 30 km north of Fort M c M u r r a y ,  A l b e r t a , C a n a d a . T h e regional climate is continental. T h e m e a n annual precipitation is approximately 4 4 0 m m of which 3 1 0 m m is rain (Meiers et a l . , 2006). T h e m e a n annual potential evaporation ( P e n m a n ) is in the range of 600 to 700 m m / y e a r ( B o e s e , 2 0 0 3 ; B a b o u r et a l . , 2 0 0 1 ; E l s h o r b a g y et a l . , 2005).  \u000CAppendices  Page  Mildre L a k e M i n e surface d u m p a n d  222  other referred to a s S W 3 0 d u m p s  constructed with marine s a l i n e - s o d i c s h a l e overburden r e m o v e d  were  during mining of soil  s a n d s . T h e S C L mine p r o d u c e s o v e r 2 0 0 , 0 0 0 barrels of oil per d a y (Meiers et a l . , 2006). U p to 14 t o n n e s of o v e r b u r d e n is e x c a v a t e d for e a c h c u b i c meter of oil p r o d u c e d . T h e s e overburden  deposits are salt rich (saline) a n d s o d i c . T h e glacial soil c o n s i s t s of  approximately 2 % gravel, 3 8 % s a n d , and 6 0 % silt a n d clay s i z e d particles while the s h a l e c o n s i s t s of approximately 0 . 5 % gravel, 1 4 . 5 % s a n d , a n d 8 5 % silt a n d clay s i z e d particles (Meiers et a l . , 2006).  A4.2  Selection of the height of the DCC chamber T h e height of the c h a m b e r w a s s e l e c t e d b a s e d on the test results c o n d u c t e d on  S W D 3 0 dump  at S y n c r u d e on A u g u s t  12, 2 0 0 1 . T h e rate of c h a n g e in  oxygen  concentration within the c h a m b e r d e s c r i b e d by E q u a t i o n 3 A s h o w s that the smaller the height, the greater the rate of c h a n g e of o x y g e n concentration.  However, a smaller  height a l s o results in a smaller g a s v o l u m e and a greater relative uncertainty o n the v o l u m e , d u e to the irregular profile of the c o v e r surface (Timms a n d Bennett, 2001). F i g . 1 s h o w s the c h a n g e s in o x y g e n concentration m e a s u r e d using three different c h a m b e r v o l u m e s of 2.5, 4 . 5 , and 6.3 L with the corresponding c h a m b e r heights of 0 . 0 1 , 0.015 a n d 0.02 m, respectively. R e s u l t s clearly indicated that the s m a l l e r v o l u m e of 2.5 L (e.g., h = 0.01 m) yielded the s m a l l e r rate of c h a n g e of concentration (e.g., a nearly flat slope). H o w e v e r , the c h a m b e r v o l u m e of 4 . 5 L (h = 0.015 m) yielded the greater rate of c h a n g e of o x y g e n concentration (e.g., s t e e p e r slope) than the c h a m b e r v o l u m e of 6.3 L (h = 0.02 m). T h e height (h = 0.015 m) for the h e a d s p a c e of the c h a m b e r presented here, w a s therefore s e l e c t e d a s a c o m p r o m i s e between maximizing the rate of c h a n g e of o x y g e n concentration a n d minimizing the uncertainty o n the g a s v o l u m e of the chamber.  \u000CAppendices  Page  223  21.0  (2) -G  \u2022e  1\u2014  10  30  40  \u2022 \u2022  50  60  70  80  90  Time (min)  4.5 L 2.5 L 6.3 L  O  Fig. 1.  20  C h a n g e s in o x y g e n concentrations in c h a m b e r s with v o l u m e of (1) 2.5 L  a n d (2) 4.5 L a n d (3) 6.3 L a s a function of time. M e a s u r e m e n t s w e r e d o n e at the S W D 3 0 d u m p at the S y n c r u d e mine on 19 July, 2 0 0 1 .  A4.3  Effect of Relative Humidity Fig.  2  showed  the  oxygen  concentration  and  oxygen  consumption  rate  m e a s u r e d on 12 A u g u s t , 2001 at the D S W R a s a function of time. T h e m e a s u r e m e n t s results yielded a linear d e c r e a s e in o x y g e n concentration of the form: y = - 0 . 0 0 1 2 x + 20.905  (R  2  = 0.9934).  The  plot  revealed a  slight  large  initial  drop  in  oxygen  concentration within the c h a m b e r during the first 10 min time-interval followed by a more gradual d e c r e a s e in O 2 concentration. T h e initial larger drop in O 2 concentration is likely d u e to the effect of relative humidity. T h e effect of relative humidity o n o x y g e n concentration within the c h a m b e r is well d o c u m e n t e d in literature. T i m m s a n d Bennett (2001) indicated that early m e a s u r e m e n t s with the surface c h a m b e r d e v i c e revealed a large initial drop in o x y g e n concentration within the c h a m b e r w h e n it w a s p l a c e d o n the g r o u n d , followed  by a  more  gradual  decrease.  E R - 1 0 e m p l o y s a drying  agent  \u000CPage  Appendices  224  (Anhydrous M a g n e s i u m Perchlorate) in port in w h i c h g a s is drawn to r e m o v e water vapor.  20.92  1000  20.80 0  10  20  30  40  50  60  70  80  T i m e (min) \u2022 Cone.  \u2022  Rate  F i g . 2. C h a n g e in o x y g e n concentration a n d o x y g e n c o n s u m p t i o n rate in a c h a m b e r installed  at the D e i l m a n n  south  waste-rock  (DSWR)  a s a function  of time.  M e a s u r e m e n t s w e r e d o n e o n A u g u s t 9, 2 0 0 1 .  W a t e r removal capacity d e p e n d s o n t h e type a n d s i z e of the drying employed.  agent  F i g . 2 s h o w e d the o x y g e n c o n s u m p t i o n rate (open s y m b o l s ) m e a s u r e d  within the c h a m b e r . T h e d e g r e e o f variation w a s s m a l l ( c o v a r i a n c e , C V = 17.6) with a m e a n rate of 133 + 23.4 \\x min\" . During the m e a s u r e m e n t s the c h a m b e r temperature 1  d e c r e a s e d from  17.1 to 15.5 \u00B0 C . R e s u l t s a l s o s h o w e d that the s e n s o r ' s pressure  r e m a i n e d constant (797.9) throughout the duration of the m e a s u r e m e n t s . M o r e o v e r the E R - 1 0 detects a n d corrects errors in the s e n s o r s outputs resulting from environmental temperature  c h a n g e s a n d barometric  p r e s s u r e c h a n g e s . T h e effect  concentration w a s eliminated by including s o d a lime in the drying c o l u m n .  of high CO2  \u000CPage  Appendices  A.5  225  Effect of soil cover system on 0 diffusion 2  Fig. 3 s h o w s the m e a s u r e d o x y g e n concentrations in the c h a m b e r s installed o n a c o v e r (curve with broken line) a n d non-cover (curve with solid line) s e c t i o n s of the D 3 0 D u m p s using the E R - 1 0 R e s p i r o m e t e r . T h e m e a s u r e m e n t s w e r e d o n e o n J u l y 18, 2 0 0 1 . R e s u l t s s h o w e d that the o x y g e n concentration in the h e a d s p a c e of the c h a m b e r installed o n the c o v e r section did not c h a n g e significantly throughout the duration of 8 6 min test-period. T h e plot, however, revealed a slight drop in o x y g e n concentration from 20.91 to 2 0 . 8 7 % during the first 10 min time-interval.  20.95 20.90 o &gt; 20.85 + c o  20.80 +  2  20.75 +  \u00A7 20.70 + c \u00B0o 20.65 +  6 20.60 + 20.55  10  20  30  40  50  60  70  80  time (min)  -a- - With cover \u2022 Without cover  Fig. 3 . C h a n g e s in o x y g e n concentrations in the c h a m b e r s installed o n (1) a c o v e r a n d (2) u n c o v e r portions of the D 3 0 d u m p at S y n c r u d e o n J u l y 8, 2 0 0 1 .  T h e slight d e c r e a s e in concentration is likely related to the effect of relative humidity o n o x y g e n concentration within the c h a m b e r . T h e o x y g e n concentration in the c h a m b e r installed o n u n c o v e r section of the D u m p s d e c r e a s e d initially at a faster rate followed by a g r a d u a l d e c r e a s e d with time. T h e d e c r e a s e in o x y g e n concentration w a s represented by the function y = - 1 0 \" * X + 6  3  0 . 0 0 0 3 X - 0 . 0 2 4 4 X + 2 0 . 9 4 8 ( R = 0.9948). A t the e n d of the 86-min test period the 2  2  concentration d e c r e a s e d from 20.91 to 2 0 . 5 8 % .  \u000CAppendices  Page  226  Finally, the a b o v e results indicated that the D C C c h a m b e r with the E R - 1 0 R e s p i r o m e t e r c a n be suitable for a s s e s s i n g the performance of the c o v e r p l a c e d on mine w a s t e d u m p s .  \u000CPage  Appendices  227  APPENDIX B Eddy Correlation (EC) Method  B.1  Introduction T h i s section presents a brief theory a n d derivation of b a s i c equations describing  the E d d y correlation ( E C ) m e t h o d .  B.2  Theory and basic equations The eddy correlation flux, is expressed as  Flux (kg m s ) = p.w.c 2  [A1]  1  where: p is the density of the air (kg m\" ); w is the vertical wind (m s\" ); c is the mass 3  1  concentration of substance (kg kg\" ) i.e., molecular weights for C 0 and air (mjvr\\ ). 1  2  a  Eddy correlation when standard micrometeorogical criteria are met [Hicks et al. 1989] will provide absolute evaluations of  vertical fluxes in natural environments without making  assumptions associated with diffusivities or the nature of the surface cover.  In addition, the  exchange rate measured represents a spatially integrated flux and the technique is unobtrusive, therefore not disturbing the environment under study.  If we write each term of the right-  hand side of the above equation [A1] as the sum of a mean value and an instantaneous departure from that mean, i.e.  p-p  + p'\\ w=w + w';  c-c+c  [A2]  then the equation becomes:  flux = pwc + pwc' + pw'c + pwc' + [A3]  p'wc + p'wc' + p'w'c + p'w'c' since  \u000CPage  Appendices  228  [A4] the average value of the flux reduces to:  flux = pwc+ pwc'+ w p' c' + c p' w' + p'w'c'  [A5]  If one ignores density fluctuations (small near the surface) and puts p = p:  [A6]  flux= pwc+ pw'c'  where the first term is the flux due to the mean vertical flow and the second is that due to the eddies. Over uniform surfaces this further reduces to:  [A7]  F = pw'c'  Application of this method requires measurement of the vertical wind and the substance concentration (i.e. temperature for the heat flux, vapor pressure for the water vapor flux, C 0  2  concentration for the carbon dioxide flux) with sensors of time response short enough to respond to all eddies (fluctuations)  (typically a fraction of a second or less). The two  instantaneous measurements must be multiplied and their products summed to give flux totals over a period. This is most efficiently done by a measurement system incorporating a small computer. The eddy correlation method derived the latent and sensible heat fluxes using the following relationships:  H = pC w'T'  [AS]  E = L w'q'  [A9]  p  v  where w, T, q, and C are the vertical velocity, temperature, humidity, and specific heat p  capacity of air respectively.  B.3.  Wind profile and the transfer of momentum The wind profiles above a stand can be represented by the simple logarithmic equation:  \u000CPage  Appendices \"* lini|z^ =\u2014 k \\ o  229  (  u  7  Z  [A10] J  where u is the velocity at height z, u. is the friction velocity, ZQ is the roughness parameter and z  k is Von Karman's constant (k = 0.4, average size of the eddy). Since momentum equals to mass times velocity, a decrease in wind speed represents a decrease of momentum. This decrease or loss of momentum may be thought of as a downward flux of momentum from the air towards the surface. The momentum flux (x) is expressed as: du  = pK M  T  [A11]  dz  where, K is eddy transfer for momentum and x is also called dynamic viscosity. M  The kinematic viscosity is expressed as: = KM  P Assume: K  M  du  [A12]  dz  = ku (z-Z))and  \u2014 = u  2  t  P where D is zero-plane displacement, Equation [A12] becomes: du  u.  dz  [A13]  k(z-D)  This expression represents the wind shear at height z over an aerodynamically rough surface Rearranging and integrating the above equation U-U  2=2  J  ^du =k \u00AB=0  z=j  [A14]  z-D  gives u, . z-D u= \u2014 In \\  = ^ln(z-2&gt;)-ln(z ) 0  o  J  K  [A15]  \u000CPage  Appendices  230  If one measure the wind speed u, at several heights, z, and plotting ln(z - D) as a function of u, one can calculate u* and ZQ Equation [14] could also be reported as:  z -D^  k Au  r  Au = \u2014 In  2  v  z,-Dj  u. =  [A16]  z -D^  f  In  2  z -D  K  x  The sensible heat and latent heat fluxes can also be expressed as  H =  dT  -pC K p  h  [A17]  dz de  LE = -pAK  v  [A18]  dz  where, p is air density, C is the specific heat capacity of air, K is the eddy diffusivity for heat, p  h  and A 6 / A z is the potential temperature gradient, K is the eddy diffusivity for water vapour, and v  Ae/Az is the vapor pressure gradient. Similar expressions can be written for gradients of temperature and vapour pressure as follows  dT  H  dz  C u,k{z - d)  de  yXE  [A19]  and  dz  pC u k(z-d) p  t  where y is the psychrometric constant.  [A20]  \u000CAppendices  Page  231  APPENDIX C Computer code for C 0 diffusion model 2  This section  presents the  computer  codes for the  \"C02\"  diffusion  model  and  spreadsheets describing different function of the program including: 1.  Input spreadsheet  2.  Water content spreadsheet  3.  Temperature spreadsheet  4.  Output spreadsheet  5.  Results spreadsheet  1. Input spreadsheet The user uses this spreadsheet to enter the # of nodes and # of simulation days. All constants values are also entered on this spreadsheet.  The elevation (in m), porosity, and  initial concentrations profile (in Kg/m3) is required to run the program and should be entered on this spradsheet. The program run button [C02] is also located on this spreadsheet.  2. Water content spreadsheet This spreadsheet contains a table of water content (as %). The Y axis represent the # of node (Elevation) and X axis represents # number of days of simulations. Note that the number of nodes and days are determined in the input spreadsheet. 3.  Temperature spreadsheet This spreadsheet is used to enter the temperature profiles for each day if known.  However, the average temperature for the profile can be entered throughout the table. 4.  Output spreadsheet  \u000CAppendices The  Page output spreadsheet presents the  complete  results of  calculations of  232 new  concentration and diffusion coefficient for each iteration. The spreadsheet displays the: day #, iteration*, Nodes, new concentration, concentration changes, diffusion coefficient, saturation and time difference calculated values.  5.  Results spreadsheet The new concentrations (in %) and diffusion coefficients (in m/s) for each profile and for  each day are printed on this spreadsheet.  \u000CPage  Appendices  C.1.  C o m p u t e r c o d e for \" C Q 2 \" model  P u b l i c elev(), por(), oldconc(), daysv() A s D o u b l e P u b l i c watcont(), temp(), G(), Temperature(), allsatu(), G O , k, a , b A s D o u b l e P u b l i c N o d e s , d a y s , maxdelt, mindelt, starow A s Integer  S u b read_input() N o d e s = W o r k s h e e t s ( \" l n p u t \" ) . C e l l s ( 5 , 3).Value d a y s = Worksheets(\"lnput\").Cells(6, 3).Value maxdelt = W o r k s h e e t s ( \" l n p u t ) . C e l l s ( 7 , 3).Value n  mindelt = Worksheets(\"lnput\").Cells(8, 3 ) . V a l u e starow = W o r k s h e e t s ( \" l n p u t \" ) . C e l l s ( 9 , 3).Value GO = W o r k s h e e t s ( \" l n p u t ) . C e l l s ( 1 0 , 3).Value ,,  k = Worksheets(\"lnput ).Cells(11, 3).Value ,,  a = W o r k s h e e t s ( \" l n p u t \" ) . C e l l s ( 1 2 , 3).Value b = W o r k s h e e t s ( \" l n p u t \" ) . C e l l s ( 1 3 , 3).Value Tref = W o r k s h e e t s ( l n p u t \" ) . C e l l s ( 1 4 , 3).Value ,,  R e D i m elev(Nodes), por(Nodes), oldconc(Nodes), daysv(days) R e D i m watcont(Nodes, days), allsatu(Nodes, days), G ( N o d e s , days), T e m p e r a t u r e ( N o d e s , days), t e m p ( N o d e s , d a y s ) Worksheets(\"results\").Cells.ClearContents Worksheets(\"output\").Cells.ClearContents Worksheets(\"TempDiff').Cells.ClearContents  For i = 1 To Nodes elev(i) = Worksheets(\"lnput\").Cells(starow + i - 1 , 2 ) . V a l u e por(i) = Worksheets(\"lnput\").Cells(starow + i - 1 , 3).Value oldconc(i) = Worksheets(\"lnput\").Cells(starow + i - 1 , 4 ) . V a l u e Next i  'populates the vector containning saturation, G a n d d a y s v e c t o r matrixes  233  \u000CAppendices  Page  234  For i = 1 T o Nodes For d = 1 To days daysv(d) = Worksheets(\"watercont\").Cells(4, d + 1).Value watcont(i, d) = Worksheets(\"watercont\").Cells(i + 4 , d + 1).Value allsatu(i, d) = watcont(i, d) / 1 0 0 Temperature(i, d) = W o r k s h e e t s ( \" T e m p e r a t u r e \" ) . C e l l s ( i + 3, d + 1).Value W o r k s h e e t s ( \" T e m p D i f f ' ) . C e l l s ( i , d).Value = Temperature(i, d) - Tref temp(i, d) = W o r k s h e e t s ( ' T e m p D i f f ' ) . C e l l s ( i , d ) . V a l u e Nextd Worksheets(\"results\").Cells(i + 2, 2 ) . V a l u e = oldconc(i)  Next i End Sub  Sub CoCON() D i m deltax(), sumdelt() A s D o u b l e D i m diffusion(), timesteps(), waterpor(), airpor() A s D o u b l e D i m eqpor(), avgdiffusion(), c o n c c h a n g e ( ) , sctime() A s D o u b l e D i m difffluxin(), satu(), difffluxout(), coflux(), newconc() A s D o u b l e  diffcoefair = 0 . 0 0 0 0 1 8 diffcoefwater = 0 . 0 0 0 0 0 0 0 0 2 5  'diffusion coeficients (De)in m 2 / s 'diffusion coeficients (De)in m 2 / s  henry = 0.03 readjnput ' a v e r a g e of the s i z e of the s p a c e s o n either side of a n o d e D e l e v R e D i m d e l t a x ( N o d e s ) , diffusion(Nodes), t i m e s t e p s ( N o d e s ) R e D i m waterpor(Nodes), airpor(Nodes), e q p o r ( N o d e s ) , avgdiffusion(Nodes) R e D i m c o n c c h a n g e ( N o d e s ) , diffluxin(Nodes) R e D i m difffluxout(Nodes), coflux(Nodes), n e w c o n c ( N o d e s ) , s a t u ( N o d e s ) , sctime(days)  For i = 1 To Nodes - 2 deltax(i + 1) = Abs(((elev(i + 2) - elev(i + 1)) + (elev(i + 1) - elev(i))) / 2) Next i  \u000CPage  Appendices deltax(1) = deltax(2) deltax(Nodes) = deltax(Nodes - 1 )  'calculate time interval in s e c o n d s for timesteps  For d = 1 T o days -1 sctime(d) = (daysv(d + 1) - daysv(d)) * 8 6 4 0 0 Nextd  'main loop to c o m p u t e C o 2 concentration per d a y d = 1 q=2 W h i l e d &lt;= d a y s For i = 1 To Nodes satu(i) = allsatu(i, d) Next i countb = 1 difference = maxdelt + 1 sumdeltat = 0  W h i l e sumdeltat &lt; sctime(d) 'statement to a v o i d e x t r e m e v a l u e s For i = 1 To Nodes If satu(i) &lt;= 0 T h e n satu(i) = 0.00001 E n d If If satu(i) &gt;= 1 T h e n satu(i) = 0.9999 E n d If Next i 'compute D w , D a , E q p o r , D e For i = 1 To Nodes waterpor(i) = satu(i) * por(i)  235  \u000CPage  Appendices  236  airpor(i) = por(i) - waterpor(i) eqpor(i) = airpor(i) + henry * waterpor(i) diffusion(i) = (1 / por(i) waterpor(i)  A  A  2) * (diffcoefair * airpor(i)  A  3.5 + henry * diffcoefwater *  3.5)  timesteps(i) = eqpor(i) * 0.5 * (deltax(i) G ( i , d) = GO * (airpor(i)  A  A  a) * (waterpor(i)  2) / diffusion(i) A  b) * Exp(k * (temp(i, d)))  Next i deltat = A p p l i c a t i o n . W o r k s h e e t F u n c t i o n . M i n ( t i m e s t e p s ) If deltat &gt; maxdelt T h e n deltat = maxdelt E n d If If deltat &lt; mindelt T h e n deltat = mindelt E n d If If deltat &gt; difference T h e n deltat = difference E n d If sumdeltat = sumdeltat + deltat  ' s o l v e finite difference equation avgdiffusion(l) = 0 For i = 1 To Nodes -1 dlev = (elev(i) - elev(i + 1)) avgdiffusion(i + 1) = dlev / ((dlev / (2* diffusion(i))) + (dlev / (2 * diffusion^ + 1)))) Next i ' c o m p u t e c o n c c h a n g e vector to s o l v e the differential equation concchange(l) = 0 For i = 2 To Nodes -1 d1 = (oldconc(i) - oldconc(i - 1 ) ) / (Abs(elev(i) - elev(i - 1 ) ) ) d 2 = (oldconc(i + 1) - oldconc(i)) / (Abs(elev(i + 1) - elev(i))) c o n c c h a n g e ( i ) = (deltat / (deltax(i) * eqpor(i)) * (-avgdiffusion(i) * d1 + avgdiffusion(i + 1) * d 2 + G ( i , d))) Next i  \u000CAppendices  Page  concchange(Nodes) = concchange(Nodes - 1 ) For i = 1 To Nodes newconc(i) = oldconc(i) + c o n c c h a n g e ( i ) If newconc(i) &lt; 0 T h e n newconc(i) = 0 E n d If oldconc(i) = (newconc(i)) Next i For i = 1 To Nodes -1 ' Interpolate new saturation v a l u e s for the next iteration satu(i) = allsatu(i + 1, d) + (allsatu(i + 1, d + 1) - allsatu(i + 1, d)) * deltat / sctime(d) Next i countb = countb + 1 difference = sctime(d) - sumdeltat  For i = 1 To Nodes Worksheets(\"output\").Cells(q, 1).Value = d Worksheets(\"output\").Cells(q, 2).Value = countb Worksheets(\"output\").Cells(q, 3).Value = i Worksheets(\"output\").Cells(q, 4 ) . V a l u e = newconc(i) Worksheets(\"output\").Cells(q, 5).Value = c o n c c h a n g e ( i ) Worksheets(\"output\").Cells(q, 6 ) . V a l u e = diffusion(i) Worksheets(\"output\").Cells(q, 7).Value = satu(i) Worksheets(\"output\").Cells(q, 8).Value = difference q = q + 1 Next i q = q + 1 Wend W o r k s h e e t s ( \" r e s u l t s \" ) . C e l l s ( 1 , 1). V a l u e = \" N e w C o n c e n t r a t i o n \" Worksheets(\"results\").Cells(4 + N o d e s , 1).Value = \"Diffusion\" For i = 1 To Nodes W o r k s h e e t s ( \" r e s u l t s \" ) . C e l l s ( 2 , d + 1).Value = daysv(d)  237  \u000CPage  Appendices  Worksheets(\"results\").Cells(i + 2, d + 1).Value = newconc(i) * 1 0 0 0 0 0 0 0 0 Worksheets(\"results\").Cells(i + 2, 1). V a l u e = elev(i) Worksheets(\"results\").Cells(5 + N o d e s , d + 1).Value = daysv(d) Worksheets(\"results\").Cells(i + N o d e s + 5, d + 1).Value = diffusion(i) Worksheets(\"results\").Cells(i + N o d e s + 5, 1). V a l u e = elev(i) Next i d = d + 1  Wend Worksheets(\"output ).Cells(1, 1).Value = \" D a y \" M  Worksheets(\"output\").Cells(1, 2).Value = \"Iteration*\" Worksheets(\"output\").Cells(1, 3).Value = \" N o d e \" Worksheets(\"output\").Cells(1  4).Value = \"Newconc\"  Worksheets(\"output\").Cells(1  5).Value = \" C o n c c h a n g e \"  Worksheets(\"output\").Cells(1  6).Value = \"Diffusion\"  Worksheets(\"output\").Cells(1  7).Value = \"Saturation\"  Worksheets(\"output\").Cells(1  8).Value = \" T i m e Difference\"  End Sub  S u b U p d a t e E m b e d d e d C h a r t ( ) '\\ attach m a c r o to chart object ActiveSheet.DrawingObjects(Application.Caller).Select UserForml .Show  End Sub  S u b U p d a t e C h a r t S h e e t O '\\ attach m a c r o to rectangle drawn o v e r chart UserForml.Show  End Sub  238  \u000CPage  Appendices  C.2.  Typical  Table C1.  239  spreadsheets Typical input  spreadsheet  L' Microsoft Excel - Co2b_v2 |1)UpdatedlK2b 1] FJe Edit View Insert Format lools Data  A  Window Help Acrobat  C  B  1  D  E  F  G  H  J  I  K  LT  2 3 Constants Value  4 Description  Constant  5  Nodes  0  6  Days  11  7  Maxdelt at  8600  8  Mindelt at  50  9  Data starts  10  GO  0  CQ2  18 1.00E-15  11  k  0,044  12  a  2  13  b  1,25  14  Tref  5,17  _  15 16 17 i-nodes  Elevation(i)  Porosity (i)  OldCone (i)  n an U.3U nU.oonU 0,70  18  1  0,00  4.00E-01  3.60E-09  19  2  0,02  4.00E-01  3.80E-09  20  3  0.15  4.00E-01  5.70E-09  21  4  0,30  4.00E-01  7.40E-09  22  5  0.45  4.00E-01  9.20E-09  23  6  0,60  4.00E-01  1.10E-08  010  24  7  0,75  4.00E-01  1.30E-08  0.00 \u2014 * ~ T * ^  25  8  0.9C  4.00E-01  1.60E-08  1 2  &gt; Hjjnput/ water \u2022nt / Temperature / TempDiff / out ]ut / Bheetl, results /  N  Draw* k Auto5hapes' \\ ^ \u2022 0 i  4 QIQ  \u2022  060 050 040 \u2014 0.20  Q C02Programl...  rosoftExc...  j Microsoft Visu...  3 PhDthesisApp  i  1  1  4  3  1  l\u00AB1  I . J T A .  1 Q PhDThesisApp...  -\u00AB\u2014  /  030  Ready  Start  1i \u2014 \u2022 \u2014  r~  1  5  6  1  In  mm  1:02 PM  3 w *  Friday \u2022  6V.  i; /Cl/2007  \u000CPage  Appendices  Table C2.  Typical Water content spreadsheet  I.  2 Microsoft Excel - Co2b_v2 |1)UpdatedlK2b I]  Rle  o  Edit  View  Insert  Fymat  Tools  t  i  d  s  o  240  Data  Window  -  Help  \u2022\u2022  IT  Acrobat  t z - w i i  Format Painter  F23  B  A  C  E  D  ||d ffi  F  G  1  H  J  K  M  L  *  1 2 3  Water concentration  4  Nodes\\days  3  4  5  7  6  8  9  10  11  l:  1  2  1  12.8000  10,88  9.248  6  . 2  15.2000  12.92  10.982  7  3  171500  14,5775  12,391 10.5322 8,952407 7,609546 6,468114 5.497897 4.673213 3.972231 3.376396 4.38931!  15.725  13.366 11.3613 9.657116 8,208548 6,977266 5.930676 5,041075 4.284914 3,542175 4,73482!  5  7.8608 6,68168 5,679428 4.827514 4,103387 3,487879 2.964697 2.519992  3.2759'  9.3347 7,934495 6,744321 5.732673 4272772 4.141856 3 520578 2.992 491 3.89023!  8  4  18.5000  9  5  19,0200  16,167  13,742 11,6807 9,928559 8,439275 7,173384 6.097376  20.028 17,0235 14.47001 12,29951 10,45458 8,886397 7,553437 6,420422 5,457358 7.09456! 24,189 20,5609 17.47677 14,85525 12,62697 10,73292 9,122983 7,754535 6,591355 8,56876!  10  6  27,7200  23.562  11  7  33,4800  28.458  12  8  34,0000  28.9  5,18277 4.405354 3,744551 4.86791'  24.565 20,8803 17.74821 15,08598 12,82308 10,89962 9.264678 7S74976  6,69373 8.70184'  13  14 15  !  16 17 18 19  20 21 22  23 24 25 26 27  no  i&lt; &lt;  \u2022 w / Input \\watercont/ Temperature /  Draw  AytoShapes'X  \\ \u2022  0  TempDiff / output / Sheetl / results /  | 4O 1H  *\"/'i*BSgi  i  |&lt;  \u00A7 .  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T y p i c a l model simulation  243  results spreadsheet  \u2022 Microsoft Excel Co2b_v2 (1 )UpdatedlK2b -J File Edit View Insert Format Tools Data Window Help Acrobat  0\u00A3Sy\u00A7  \u00A7\\k7  I I M -  \u2022--Infill  B|ioo% ^ ,  J43 A B C 1 New Concentration 2 1 2 3 0.36 0.36 0 4 0.02 0.392039 0.368416 0.15 0.526496 0.547786 5 0.3 0.B69966 0.713908 B 0.45 0.852024 0.912509 7 5 0.6 1.106526 1.173726 9 0.75 1.40201 1.469232 10 ' 0.9 1.70201 1.7B9232 11 12 Diffusion 13 1 2 14 0 2.56E-06 2.81E-06 15 0.02 2.3BE-06 2.62E-06 16 0.15 2.23E-06 2.5E-06 17 0.3 2.18E-06 2.4EE-06 18 0.45 1.46E-06 1.78E-06 19 0.6 1.09E-06 1.41E-06 20 0.75 1.06E-06 1.38E-06 21 0.9 1.06E-06 1.38E-06 22  D  E  F  G  H  1  J  K  L  3 0.36 0.497119 0.5B4218 0.75623B 0.97024 1,238455 1.534936 1.834936  4 0.36 0 0.624813 0.790302 1.020683 1.294453 1.591763 1.891763  5 0,36 0.104148 0.626809 0.B22791 1.063397 1.341775 1.639664 1.939664  6 0.36 0.729202 0.593987 0.852761 1.099365 1.381568 1.679847 1.979847  7 0.36 2.00E-01 0.654541 0.872405 1.130432 1.41537 1.713982 2.013982  B 0.3B 0.198531 0.66548 0.891883 1.156605 1.443958 1.742813 2.042813  9 0.36 0.219773 0.672806 0.908654 1.178888 1.468257 1.76731 2.06731  10 0.36 0.431767 0.655853 0.924551 1.197704 1.488863 1.788067 2.088067  11 0.36 0.431767 0.655853 0.924551 1.197704 1.488863 1.788067 2.088067  3 4 5 3.03E-06 3.23E-06 3.41 E-06 2.87E-06 3.09E-06 3.28E-06 2.76E-06 2.99E-06 3.19E-06 2.72E-06 2.95E-06 3.16E-06 2.08E-06 2.:7E-0E 2.64E-06 1.73E-06 2.04E-06 2.33E-06 1.7E-06 2.01 E-06 2.3E-06 1.7E-05 1.01 E-06 2.3E-06  6 3.57E-06 3.45E-06 3.37E-06 3.35E-06 2.88E-06 2.59E-06 2.57E-06 2.57E-06  7 3.7E-06 3.6E-06 3.54E-06 3.51 E-06 3.09E-06 2.84E-06 2.82E-06 2.82E-06  6 3.82E-06 3.74E-06 3.68E-06 3.65E-06 3.29E-0B 3.06E-0B 3.04E-0B 3.04E-0B  9 3.93E-06 3.85E-06 3.8E-06 3.78E-06 3.46E-06 3.26E-06 3.24E-06 3.24E-06  10 4.02E-06 3.95E-06 3.91 E-06 3.89E-06 3.61 E-06 3.43E-06 3.42E-06 3.42E-06  11 4.02E-06 3.95E-06 3.91 E-06 3.89E-06 3.61E-06 3.43E-06 3.42E-06 3.42E-06  2.5  0.0000045 0.000004  -Seriesl  0.0000035  - Series2 Series3  non/  Xi  4 0.0000025 input I watercont / Temperature / TempDiff / output / Sheetl \\results  Draw  AutoShapes- \\  \\ DO|  4  Ready 'i  Sfflft  3 PhDThesisApp... 3 C02Programl...  ^ PhDtheasApp...  3 Document3-...  j Microsoft Visu...  j j Microsoft be...  - Series4  15  U  N  0  \u000CPage  Appendices  244  APPENDIX D Waste-rock Sample Analyses Results  D.1  Introduction T h i s chapter presents the laboratory results of the waste-rock s a m p l e s d e s c r i b e d  in section 2.2.6 of chapter 2 . T h e d a t a presented includes the results of: 1.  G r a i n s i z e distribution c u r v e s for the waste-rock s a m p l e s from the D S W R a n d DNWR.  2.  S o i l water characteristic c u r v e s ( S W C C s ) for the s a m p l e s from D S W R a n d DNWR.  3.  Saturated hydraulic conductivities for the s a m p l e s from the D S W R a n d DNWR.  D.2.  Grain-size distribution  Table D1.  Grain-size test with dispersing agent  The particle-size analysis of the waste-rock samples was determined according to modified A S T M Designation: D 422-63. Results of the laboratory analysis are presented below:  University of British Columbia Department of Mining Engineering CO-MIX Laboratory  GRAIN SIZE TEST WITH DISPERSING AGENT Sample:  TP01  Depth:  0.3 - 0.4 m Hygroscopic moisture  Place: Date:  10-Dec03  Specific gravity # 2 mm  Soil (g) =  \u000CPage  Appendices Tare N\u00B0 Tare (g) Tare + W S  Pycnometer N\u00B0 Temperature (\u00B0C)  (fl)  Pyc. + water (g)  245  Tare + D S Pyc.+water+soil(g)  (g) Moisture (%)  G(g/cm ) 3  w(%):  90.0  Mass of air dried soil M (g) = t  G .  1.09  s  Total mass of dried sample Mdt (g)  2.74 89.0  Coarse screening Sieve 2\" 1 1/2\" 1\" 3/4\" 3/8\" 4  Opening (mm) 50.8 38.1 25.4 19.1 9.5 4.76  Retained (g) 0.00 0.00 0.00 0.00 0.00 0.00  Total retained (g) 0.00 0.00 0.00 0.00 0.00 0.00  10  2.00  0.00  0.00  0.25 0.149  Retained (%) 0.00 0.00 0.30 7.80 36.50  Total retained (g) 0.00 0.00 0.30 8.10 44.60  0.074  30.40  75.00  % passing 100.0 100.0 100.0 100.0 100.0 100.0 100.0  Fine Screening Sieve 16 30 40 60 100 200  Opening (mm) 1.19 0.59 0.42  % passing 100.0 100.0 99.7 90.9 49.9 15.8  HYDROMETER 90.01  Mass of wet soil submited to sedimentation Msw (g) Time 30 s 1 min. 2 min. 4 min. 8 min. 15 min. 30 min. 1 h 2 h  time (s) 30 60 120 240 480 900 1800 3600 7200  temp. (\u00B0C) 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.8 18.8  R (g/cm 3) 1.014 1.013 1.013 1.013 1.012 1.012 1.012 1.012 1.011 A  Rh (g/cm 3) 1.0053 1.0053 1.0053 1.0053 1.0053 1.0053 1.0053 1.0051 1.0051 A  a (cm) 13.8 14.1 14.1 12.8 13.1 13.1 13.1 13.2 13.3  Hydrometer N \u00B0 : 863 Q S (%) 15.4 13.6 13.6 13.6 11.9 11.9 11.9 11.3 10.4  d (mm) 0.0719 0.0513 0.0363 0.0245 0.0175 0.0128 0.0090 0.0063 0.0045  \u000CPage  Appendices  246  4h  14400  18.8  1.011  1.0051  13.3  10.4  0.0032  8h  28800  18.8  1.011  1.0051  13.3  10.4  0.0023  18.8  1.010  1.0051  13.6  8.6  0.0013  24 h  D (mm)  86400  % mat. Pass  % mat.ret.  50.80 38.10  100.0 100.0  0.0 0.0  25.40  100.0  0.0  19.10  100.0  0.0  9.52  100.0  0.0  4.76  100.0  0.0  2.00  100.0  0.0  1.190  100.0  0.0  0.590  100.0  0.0  0.420  99.7  0.3  0.250  90.9  9.1  0.149  49.9  50.1  0.074  15.8  84.2  0.0719  15.4  0.0513  13.6  84.6 86.4  0.0363  13.6 13.6  86.4 86.4  11.9  88.1  1.1.9 11.9  88.1 88.7  0.0045  11.3 10.4  0.0032  10.4  89.6  0.0023  10.4  89.6  0.0013  8.6  91.4  0.0245 0.0175 0.0128 0.0090 0.0063  Cu = 300  MATERIAL* 20&lt;Coarse gravel&lt;60 6,0&lt;Median gravel&lt;20,0  % of material 0.0  0.0  2,0&lt;Fine gravel&lt;6,0  0.0  0,60&lt;Coarse sand&lt;2,0  0.0  0,20&lt;Median sand&lt;0,6  29.4  0,06&lt; Fine s a n d &lt;0,20  57.3  0,002 &lt; Silt &lt; 0,06  4.0  88.1 89.6  C l a y &lt; 0,002  9.3  \u000CPage  Appendices  247  Grain Size (with dispersing agent) IUU.U -  OA  n  o n  n  yu.u OU.U -  a n  C C3 3 3  % pa;.sing  C  (U.U -  OA  A  OA  A  OU.U -  /  zu.u -4  10.0 0.0 0.0010  &gt;-  \u2014 \u2014\u2022- \u2014 i  \u2022  4  0.1 000  0.0 100  1.0000  Particles diameter (mm)  Table D2.  Grain-size test without dispersing agent  1 \u2022 Sample from DNWR:  4 10 20 40 60 80 100  Mm 12.5 4.75 2 0.85 0.417 0.25 0.177 0.15  140 200 270  0.105 0.075 0.053  -270 Total  -0.053  mesh  9  %  %retained  20 25 53.1 53.1 54.3 14.9 6.9 6.4 0.8 5.6  8.00 10.00 21.25 21.25 21.73 5.96 2.76  8.9 250  3.56 100.04  8 18.00 39.25 60.50 82.23 88.19 90.95 93.51 93.83 95.76 99.32  2.56 0.32 2.24  Cum. Pas % 100 92.04 82.04 60.79 39.54 17.81 11.85 9.09 6.53 6.21 4.28  \u000CPage  Appendices  248  2. Sample from DSWR:  D.4  mesh  Mm 12.5  g  %  %retained  4 10 20 40 60 80 100 140 200 -200 Total  4.75 2 0.85 0.417 0.25 0.177 0.15 0.105 0.075 -0.075  3.1 14.6 34.6 56.2 60.1 33.8 15.9 13.8 5 12.9 250  1.24 5.84 13.85 22.49 24.05 13.53 6.36 5.52  1.24 7.08 20.93 43.42 67.47 80.99 87.35 92.88 94.88 100.04  2.00 5.16 100.04  Cum. Pas % 100 98.80 92.96 79.11 56.62 32.57 19.05 12.69 7.16 5.16  Soil Water Characteristic Curve (SWCC) Test Results  1 \u2022 Sample from DSWR:  Sample cell cell+sample sample water soil w.c.  DSWR 1660.9 1977.6 316.7 9.4 307.3 0.030589  Tare Tare+wet  4.1 53.6  diameter ht  Tare+dry  51.8  ini. vol  Water Soil w.c.  6.9 4.64  final ht Dia  173.5026  volume  4.460 6.900 166.7719  Tare tare+wet tare+dry  7.9000 328.4800 314.2000  water  14.2800 306.3000 0.0466  1.8 47.7 0.0377  vol. w.c. 0.3716 0.3699 0.3676  %vol. W.c. 37.1637 36.9908 36.7603  2026.4 2007.4  suction 0.2 0.5 1  Weight 2027.1 2026.8 2026.4  w.c. 0.1917 0.1907 0.1894  final w.c. 0.2105 0.2095 0.2082  1997.3 1992.6 1989.7 1987.7  2 3 4 5  2007.4 1997.3 1992.6 1989.7  0.1276 0.0947 0.0794 0.0700  0.1462 0.1132 0.0979 0.0884  0.2581 0.1999 0.1728 0.1561  25.8094 19.9882 17.2793 15.6078  1986.2  6  1987.7  0.0635  0.0819  0.1446  14.4551  Soil w.c. vol. Water vol. Soil porosity void ratio  64.4800 109.0226 0.3866 0.5914  \u000CPage  Appendices 1985.2 1983.1 1980 1978.8 1977.6 1976.9  7 8 10 30 50 80 100  0.0586 0.0553 0.0485 0.0384 0.0345 0.0306 0.0283  1986.2 1985.2 1983.1 1980 1978.8 1977.6 1976.9  0.0770 0.0737 0.0669 0.0567 0.0528 0.0489 0.0466  0.1359 0.1301 0.1180 0.1002 0.0933 0.0863 0.0823  13.5906 13.0142 11.8039 10.0171 9.3255 8.6339 8.2304  Gs  249  2.8095  D . S a m p l e from D N W R :  Sample Cell cell+sample Sample Water Soil w.c.  DNWR Tare Tare+wet Tare+dry  1669.2 1960.8 291.6 21.9 269.7 0.081201  Water Soil w.c.  4.1 53.6 51.8  5 6.9 186.964  diameter Ht ini. vol  1.8 47.7 0.0378  6.9 4.64 173.5026  final ht dia volume  Tare tare+wet tare+dry  6.9000 303.5000 276.5000  Water Soil w.c.  27.0000 269.6000 0.1001  suction 0.2 0.5 1  weight 1990.4 1989.5 1987.6  w.c. 0.1910 0.1876 0.1806  final w.c. 0.2311 0.2277 0.2207  vol. w.c. 0.3591 0.3539 0.3429  % vol. W . c . 35.9072 35.3885 34.2934  2  0.1605  0.2007  0.3118  31.1811  3 4 5  1982.2 1975.4 1972.3 1970.5  0.1353 0.1238 0.1172  0.1754 0.1639 0.1573  0.2726 0.2548 0.2444  27.2618 25.4751 24.4377  6 7 8 10 30 50 80  1968.5 1967.4 1966.4 1964.9 1960 1957.8 1956.1  0.1098 0.1057 0.1020 0.0964 0.0782 0.0701 0.0638  0.1499 0.1458 0.1421 0.1365 0.1183 0.1102 0.1039  0.2328 0.2265 0.2207 0.2121 0.1839 0.1712 0.1614  100  1955.1  0.0601  0.1001  0.1556  23.2850 22.6510 22.0746 21.2101 18.3859 17.1179 16.1381 15.5617  vol. Water  62.3000  vol. Soil  111.2026  porosity void ratio  0.3332  Gs  2.4244 2.2700  0.5602  \u000CPage  Appendices  D.5  250  Saturated Hydraulic Conductivity Test Results 1 \u2022 Sample from DSWR:  UBC University of British C o l u m b i a Department of Mining Engineering CO-MIX Laboratory Falling Head Permeability T e s t  FLUX UPWARD Golden Sunlight - Tailings Place:  PermDSWRI State: 31\" - 4 3 \"  Sample: Depth:  Mold No.  Height (cm) 11.60  3  Mold + Sample (g) = Across (Tl2) -  0.00801  Lsamole ( ) Vsample (cm3)  0.098  m  =  Area  Loose - Hig Moist. %  Diameter (cm) 10.10 5122.00  Volumme (cm3)  Weight (g)  929.38  3850.7  Gsample =  1.62 Temp, C:  28-Apr-03  Date:  Cylinder Small g/cm.3 23.0  785.0 Telaosed (min)  h (cm)  At (min)  0.000  96.0  -  0.257  51.0  0.257  0.000  96.0  -  0.259  51.0  0.259  0.000  96.0  -  0.257  51.0  0.257  0.000  96.0  -  0.257  51.0  0.257  A;(%)  K (m/s)  k  ava  (m/s)  1.01 E-04 0.8 1.00 E-04 1.01 E-04 1.01 E-04 0.0 1.01 E-04  \u000CPage  Appendices  251  2, Sample from DNWR:  UBC University of British C o l u m b i a Department of Mining Engineering CO-MIX Laboratory Falling Head Permeability T e s t  FLUX UPWARD Golden Sunlight - Tailings Place:  PermDNWRI State: 31\"-43\"  Sample: Depth:  Mold No.  Height (cm) 11.60  3 Across (m2) -  0.00801  Lsamole (m) Vsample (cm3)  0.089  =  Loose - Hig Moist. %  Diameter (cm)  Volumme (cm3)  Weight (g)  10.10  929.38  3850.7  5083.50  Mold + Sample (fl) =  Area  Gsample =  1.73 Temp, C:  28-Apr-03  Date:  Cylinder Small g/cm3 23.0  712.9 Teiaosed  (min)  h  (cm)  At  (min)  0.000  96.0  -  0.233  51.0  0.233  96.0  -  0.256  51.0  0.256  0.000  96.0  -  0.256  51.0  0.256  0.000  96.0  -  0.256  51.0  0.256  0.000  At(%)  K (m/s)  kavc  (mis)  1.01 E-04 9.9 9.19E-05 9.41 E-05 9.19E-05 0.0 9.19E-05  \u000CPage  Appendices  252  APPENDIX E C 0 flux measurement results obtained at the Deilmann south 2  (DSWR) and Deilmann north (DNWR) waste-rock piles using the dynamic closed chamber (DCC), static closed chamber (SCC) and eddy covariance (EC) methods  Table E1.  C 0  2  fluxes m e a s u r e m e n t s obtained using the  dynamic c l o s e d c h a m b e r ( D C C ) at the Deilmann south w a s t e rock ( D S W R ) pile.  Loc. #  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  Year  2000  July  August  215 290 284 292 300 350 356 274 182 192 250 247 234 121 368 224  225 202 143 291 154 246 173 180 202 182 169  58 91 185  Year  2002  Sept.  July  August  204 182 102  162  132  218 191 142 123 136 204 131 190 203  187  134 213 297 254  209  144  89  178 137 200 104 121 179  116 189 164 203 96 113 106  89  190  132 115 145 129  288  \u000CPage  Appendices  Table E2.  C 0  2  fluxes measurements obtained using the  d y n a m i c c l o s e d c h a m b e r ( D C C ) at t h e D e i l m a n n n o r t h w a s t e r o c k ( D N W R ) pile. Year  2000  Loc. #  July  Aug.  1 2 3 4 5 6 7 8 9  164 191 111 103 197 219 135 183 136  231 274 205 136 178 266 132 204 198  Table E3  Year  2002  Sept  July  Aug.  158 248 122 104 183 228 107 204 164  450 298 228 245 373 381 410 141 318  254 317 294 211 384 246 305 89 142  C 0 flux measurements obtained using the static 2  closed chamber (SCC) in the morning (between 10:00 and 11:00 on August 24, 2002 at nine selected sampling stations (DSF1 DSF9).at the Deilmann south waste-rock (DSWR) pile (Figure 4.8A).  Loc.  AM1  # 1 3 4 5 6 7 8 9 10 11 13 20  Avg  AM2  mg m\" h\" 2  226 185 369  174 248 260 125  139 151 146 129 164 175  139  1  200 185 248 314 125 139 151 146 134 164 175  253  \u000CPage  Appendices Table E4  C 0 flux m e a s u r e m e n t s obtained using the static 2  c l o s e d c h a m b e r ( S C C ) in the afternoon (between 16:30 a n d 17:30) o n A u g u s t 2 4 , 2 0 0 2 at six s e l e c t e d s a m p l i n g stations ( D S F 1 - D S F 9 ) at the D e i l m a n n south w a s t e - r o c k ( D S W R ) pile. Loc.  PM2  PM1  #  1 3 4 5 6 7 8 9 10 11 13 20  Table E5.  rn\"  Mg  2  Avg.  h\"  1  164 94.5  261 269  212.5 181.75  128  75  101.5  125  125  145 270  145 270  Summary of C 0 f l u x m e a s u r e m e n t s obtained using 2  the static c l o s e d c h a m b e r ( S C C ) in the morning (between 10:00 a n d 11:00) a n d afternoon (between 16:30 a n d 17:30) at nine s e l e c t e d s a m p l i n g stations ( D S F 1 - D S F 9 ) at the D e i l m a n n south w a s t e - r o c k ( D S W R ) pile o n A u g u s t 2 4 , 2 0 0 2 . Loc.  # 1 3 4 5 6 7 8 9 10 11 13 20  PM  AM mg  m\"  2  h\"  1  200 185 248 315 125  213 182  139 151 146 134 164  102  175  125 145 270  254  \u000CPage  Appendices  Table E6.  T e m p o r a l variations in the C 0 flux obtained at the Deilmann 2  south w a s t e - r o c k ( D S W R ) pile using the E d d y c o v a r i a n c e ( E C ) method M e a s u r e m e n t s w e r e obtained during the period from J u n e 2 5 25  t h  t h  to A u g u s t  2 0 0 2 . E a c h data point represents the daily m e a n v a l u e a v e r a g e d  over the period from 10:00 to 17:00 hours. Mean C0 Flux 2  C0  2  Flux Standard Deviation  Julian Day  Day  mg m' hr'  mg m\" hr'  176  25-Jun-02  122  51  177  26-Jun-02  143  67  178  27-Jun-02  134  63  179  28-Jun-02  157  53  180  29-Jun-02  103  50  181  30-Jun-02  182  01-Jul-02  183  02-Jul-02  106  71  184  03-Jul-02  132  73  185  04-Jul-02  186  05-Jul-02  104  37  187  06-Jul-02  100  43  188  07-Jul-02  176  74  189  08-Jul-02  96  49  190  09-Jul-02  118  45  191  IO-Jul-02  133  62  192  11-Jul-02  112  52  193  12-Jul-02  154  23  194  13-Jul-02  160  37  195  14-Jul-02  111  29  196  15-Jul-02  178  56  197  16-Jul-02  156  43  198  17-Jul-02  146  43  199  18-Jul-02  130  84  200  19-Jul-02  87  25  201  20-Jul-02  202  21-Jul-02  181  46  203  22-Jul-02  163  90  204  23-Jul-02  100  48  205  24-Jul-02  146  27  z  1  i  1  255  \u000CPage  Appendices 206  25-Jul-02  107  20  207  26-Jul-02  101  48  208  27-Jul-02  96  25  209  28-Jul-02  132  46  210  29-Jul-02  211  30-Jul-02  212  31-Jul-02  213  01-Aug-02  214  02-Aug-02  107  44  215  03-Aug-02  118  36  216  04-Aug-02  119  50  217  05-Aug-02  149  42  218  06-Aug-02  145  17  219  07-Aug-02  102  42  220  08-Aug-02  78  40  221  09-Aug-02  111  75  222  10-Aug-02  105  55  223  11-Aug-02  85  29  224  12-Aug-02  151  48  225  13-Aug-02  226  14-Aug-02  227  15-Aug-02  228  16-Aug-02  79  22  229  17-Aug-02  139  63  230  18-Aug-02  113  55  231  19-Aug-02  152  92  232  20-Aug-02  103  34  233  21-Aug-02 .  93  53  234  22-Aug-02  77  52  235  23-Aug-02  67  23  236  24-Aug-02  104  60  237  25-Aug-02  101  44  256  \u000CPage  Appendices  257  APPENDIX F Data for measurements of near- and surface-water contents and C 0  2  fluxes across the surfaces of the DSWR and DNWR after heavy rainfall events  This section presents results of measurements of near- and surface-water contents (0 - 0.15 m) and associated C 0 fluxes from the DSWR and DNWR piles over 2  an 8-d test period [30 July (day 1) to 6 August (day 8) 2002] after rainfall events. Wasterock samples were collected each day during the test period at sampling stations DSF1 and DNF1 (Figure 4.8A) and analyzed for water contents within 24 hours. The gravimetric water contents were measured at 0, 0.05, 0.10, and 0.15 m depths. The gravimetric water contents were converted to volumetric water contents using the waste-rock properties measured in the laboratory (e.g., SWCC, soil specific density and porosity). The climatic parameters for the test site (e.g., rainfall and temperature) were recorded from the weather station installed on DSWR. Results of volumetric water contents, C 0 fluxes, rainfall events, and average 2  daily temperatures for the DSWR and DNWR are presented in the Tables below..  \u000CAppendices  Table F1.  Page  '258  W a t e r c o n t e n t s a n d CO2 fluxes m e a s u r e d o v e r a n 8-d test period [30 July  (day 1) to 6 A u g u s t (day 8) 2002] at station D N F 1 with time at the D e i l m a n n north w a s t e - r o c k ( D N W R ) pile. Day  Temp.  Rainfall  co  #  \u00B0C  (mm)  Flux  Jul. 30  1  12.6  39.2  Jul. 31  2  10.0  36.6  mg m\"* h'  Aug. 01  3  11.8  7  Aug. 02  4  7.5  Aug. 03  5  Aug. 04  Date  2  Depth  0.15 m  0 m  0.05 m  0.10 m  Water  content  (vol.)  7  0.2187  0.1267  0.1432  0.1463  1  17  0.060  0.1237  0.1191  0.116131  6.5  0.4  264  0.0211  0.1342  0.0950  0.0980  6  8.4  0  268  0.0256  0.1010  0.0950  0.0950  Aug. 05  7  10.5  0  306  0.0045  0.0980  0.1161  0.1176  Aug. 06  8  13.2  0  316  0.0131  0.1110  0.0794  0.0829  Table F2.  W a t e r contents a n d C 0  2  1  fluxes m e a s u r e d o v e r a n 8-d test period [30 July  (day 1) to 6 A u g u s t (day 8) 2002] at station D S F 1 with time at the D e i l m a n n south w a s t e - r o c k ( D S W R ) pile. Date  Day #  Temp.  Rainfall  \u00B0C  (mm)  Jul. 30  1  12.6  39.2  Jul. 31  2  10.0  36.6  Aug. 01  3  11.8  Aug. 02  4  Aug. 03  C0  2  Depth  flux  15 c m  0 cm  5 cm  10 c m  Mg m\" h\"'  Water  content  (vol.)  7  67  0.0571  0.0970  0.0913  0.0870  7.5  1  97  0.0313  0.0785  0.0770  0.0870  5  6.5  0.4  153  0.0010  0.0728  0.0699  0.0685  Aug. 04  6  8.4  0  138  0.0029  0.0599  0.0542  0.0613  Aug. 05  6  10.5  0  144  0.0017  0.0770  0.0285  0.0514  Aug. 06  8  13.2  0  241  0.00036  0.0728  0.0585  0.0499  z  \u000CPage  Appendices  259  APPENDIX G Minicosms data used for simulations with C 0 diffusion model 2  This section presents measured column data (Kabwe et al., 2002) used for simulation with C 0 diffusion model developed in this thesis. The data were obtained from two 2  minicosms: one kept at low temperature (LT) at about 5 \u00B0C and another one at room temperature  (HT).  The data presented  include: water  contents  profiles,  concentrations profiles, and temperatures profiles.  G.1.  HT Minicosm (column kept at room temperature) Table F 1 . Temperature data from HT minicosm measured from Day 12 to Day 96 after filling the column with sand material. Day # Depth (m)  D-12  19  26  34  47  75  96  0  9.00  9.02  9.05  9.01  9.02  8.86  8.11  0.02  7.57  7.71  7.71  7.62  7.42  7.21  7.49  0.3  8.34  8.47  8.42  8.35  8.13  8.34  7.74  0.6  8.80  8.91  8.88  8.80  8.65  8.90  8.08  0.9  9.37  9.46  9.44  9.35  9.24  -9.52  8.69  C02  \u000CPage  Appendices  Table  Volumetric water contents from HT minicosm  G2.  measured from Day 12 to Day 96 after filling the column with sand material Depth  Day#  (m)  11  18  25  32  46  73  95  0  18.29  18.29  18.29  18.29  18.29  18.29  18.29  0.15  22.56  21.03  21.39  21.85  21.32  22.91  24.12  0.30  24.87  23.74  21.43  25.04  21.99  25.63  26.65  0.45  25.89  23.53  24.93  26.61  23.55  29.16  29.69  0.60  27.59  26.38  25.93  28.35  25.12  28.44  29.52  0.7  36.60  39.85  36.65  40.91  25.73  39.98  43.14  0.90  49.80  43.99  44.63  47.12  46.82  48.53  51.31  105  49.52  47.87  46.93  47.58  47.57  48.06  52.46  Table G3. C 0 Concentration from HT minicosm measured from Day 1 2  to Day 96 after filling the column with sand material Day #  Depth  (m)  D12  d19  d26  d33  d47  d75  d96  0  0.036  0.036  0.036  0.036  0.036  0.036  0.036  0.02  0.048  0.04  0.038  0.041  0.041  0.044  0.04  0.15  0.29  0.288  0.279  0.271  0.263  0.225  0.203  0.3  0.323  0.345  0.343  0.329  0.322  0.269  0.254  0.45  0.422  0.437  0.51  0.452  0.424  0.371  0.337  0.6  0.458  0.495  0.5  0.538  0.556  0.494  0.48  0.75  0.605  0.573  0.63  0.738  0.86  1.041  1.051  260  \u000CPage  Appendices  G..2. LT Minicosm (sand column kept at low temperature ~5 \u00B0C)  Table G4.  Volumetric water contents from L T minicosm measured from  Day 12 to Day 96 after filling the column with sand material  depth  Day #  (m)  11  18  25  32  46  73  95  0  10.07  10.07  10.07  10.07  10.07  10.07  10.07  0.15  17.79  14.64  15.50  19.81  19.50  18.59  20.19  0.30  15.63  13.66  11.91  17.53  16.49  17.36  16.19  0.45  20.22  17.78  16.70  17.98  18.52  18.99  19.98  0.60  21.46  20.71  17.69  20.88  20.56  21.65  23.37  0.75  18.27  17.14  21.66  19.84  20.18  22.74  20.22  0.90  28.04  28.29  28.84  29.94  30.04  28.76  30.66  1.05  32.93  32.55  30.47  33.98  34.96  35.05  38.42  Table G5.  C 0 2 concentrations from L T minicosm measured from Day 12  to Day 96 after filling the column with sand material  depth  Day#  (m)  Day 12  Day 19  Day 26  Day 34  Day 47  Day 75  Day 96  0  0.036  0.036  0.036  0.036  0.036  0.036  0.036  0.02  0.051  0.043  0.041  0.047  0.041  0.047  0.043  0.15  0.099  0.098  0.106  0.082  0.099  0.108  0.097  0.30  0.138  0.144  0.147  0.153  \u2022 0.149  0.158  0.143  0.45  0.144  0.153  0.164  0.171  0.173  0.184  0.169  0.60  0.147  0.158  0.173  0.182  0.193  0.196  0.191  0.75  0.000  0.000  0.000  0.000  0.000  0.000  0  261  \u000CPage  Appendices  262  APPENDIX H Climatic parameters used in simulations with SoilCover and recorded at the weather station installed on the Deilmann south waste-rock (DSWR) pile  Simulation of evaporative fluxes [potential ( P E ) a n d actual (AE)] using S o i l C o v e r numerical  model  required  the  site weather  parameters  as  inputs.  The  weather  parameters u s e d in simulations w e r e recorded at a w e a t h e r station installed on D S W R . T h e description of the w e a t h e r station w a s p r e s e n t e d in chapter 4. T h e weather parameters u s e d in the m o d e l simulations are presented in T a b l e G1  \u000CPage  Appendices T a b l e H1. W e a t h e r  parameters  recorded  at  the  weather  station  263  installed  D e i l m a n n south w a s t e - r o c k ( D S W R ) pile. Key Lake, 2002 T Air oC  RH MAX Pet.  205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232  Jul-24 Jul-25 Jul-26 Jul-27 Jul-28 Jul-29 Jul-30 Jul-31 Aug-01 Aug-02 Aug-03 Aug-04 Aug-05 Aug-06 Aug-07 Aug-08 Aug-09 Aug-10 Aug-11 Aug-12 Aug-13 Aug-14  21.28583 21.22417 22.51708 17.78146 17.15208 13.12063 11.20646 12.05354 8.072521 7.386729 10.02648 12.50308 14.59542 14.94688 19.24146 21.89958 22.04375 19.66188 14.67708 16.47563 14.16771 14.02771  Aug-15 Aug-16 Aug-17 Aug-18 Aug-19 Aug-20  12.06688 11.3225 11.96729 13.00967 11.39683 9.112875  233 234 235  Aug-21 Aug-22 Aug-23 Aug-24  15.07235 20.53417 20.70354  88.7 91.8 96.1 98.4 100 97.7 98.9 100 97.4 96 91.8 88.8 95.5 99.8 100 98.1 84.8 77 97.3 90.5 98.5 99.7 98 99.1 98.9 95.7 95.6 87.3 85.4 72.6 94.1  21.9425  76.8  236  R H MIN Pet.  Net R a d W/m2  Net Rad MJ  39.01 39.93 39.06 78.7 58.27 70.6  91.9805 46.53471 60.10498 24.0446 70.62138 21.79919 31.37233 58.67254 44.57667 66.0985 50.56073 50.86933 61.26404 20.31873 93.0589 86.83577 70.22992 65.12854 30.10281 66.93844 19.09198 48.55794 44.98344 22.15146 73.07313 54.86875 13.02202 59.66702  7.95 4.02 5.19 2.08 6.10 1.88 2.71 5.07 3.85 5.71 4.37 4.40 5.29 1.76 8.04 7.50 6.07 5.63 2.60 5.78 1.65 4.20 3.89 1.91 6.31 4.74 1.13 5.16  4.289375 2.788542 2.620271 3.058292 3.287375 5.67475 6.197479 6.021208 8.522083 7.299021 5.679396 3.107708 5.039354 3.40325 3.4625 2.132104 3.077438 4.502417 3.830146 3.648688 2.913188 3.579667 2.106208 2.743583 3.738729 2.488333 4.382563 5.10575  0 1.8 0 5.4 7.7 39.2 36.6 7 1 0.4 0 0 2.6 13.3 0 0 0 0 0 0 8.8 2.9 7.9 7.3 0 0 2.2 0  62.63533 56.01819 56.71417  5.41 4.84 4.90  60.14081  5.20  3.510646 5.302063 2.354833 5.660375  0 0 0 0  .91.5 83 68.36 66.55 51.57 44.88 47.1 87.8 41.11 31.3 35.37 36.82 51.91 38.84 65.73 62.58 69.85 81.9 48.51 28.92 50.55 37.3 38.32 38.08 34.59 25.21  WIND m/s  Rain Mm  on  \u000CAppendices  Page  264  APPENDIX I SoilCover run summary page for simulations of evaporative fluxes at the DSWR and DNWR piles during the field tests  T h e following p a g e s present the daily input a n d output data a n d s u m m a r y p a g e s of S o i l C o v e r m o d e l simulations results of evaporative fluxes from the D S W R  and  D N W R piles obtained during the 8-d and 27-d test periods. T h e climatic parameters for input data w e r e obtained from the weather station installed at the D S W R . T h e soil properties of the w a s t e rocks w e r e obtained from laboratory tests.  \u000CPage  Appendices T a b l e 1.1  265  Daily input data for S o i l C o v e r simulations for evaporative fluxes during the  8-d test period at the D e i l m a n n south waste-rock ( D S W R ) pile.  Moisture boundaries section  Weather data section  Run .. .  Max AilTfciT'D  V ArTemp  Mm RH  Max RH  Net R=1G  (MJ/m2-, dayi  4  .  (krtvriri  '(h'rs)  '.hrsj  1.41  3  39.2  0  24  10.00  1.8834  0.98  15.50  11.00  2.7110  0.99  0.92  0.5918  3  36.6  0  24  11.00  5.00  5.069  1.00  0.83  0.793  3  7  0  24  1.7391  3  1  0  24  3.8514  0.97  0.68  3.50  5.7109  0.96  0.66  2.565  3  0.4  0  24  3.00  4.3685  0.92  0.52  4.4193  3  0  0  24  18.50  5.00  4.3951  0.88  0.45  5.6997  3  0  0  24  18.50  5.00  4.3951  0.88  0.45  5.6997  3  0  0  24  13.00 17.00  7 ' 8 '  Other daily data section  Moisture boundaries section Bot \u2022 Temp  .. ', s. hjlii (Type)  Start time  18.00  3.00  Bo; BC  (dec) ,  \"lop BC  0.71  10.00  6  '-; '.Windl - * :- :  (Vaiue)  1  0.11  1  0.087  1  0.087 0.068  \\  0.061  1  0.051  1  0.05  1  0.05  1111118 \u2022  HB  Bot Temp  Pan tvfip  Write Day  Root  irrT'day;  Out  \u2022 on.  :  I  1  13  I  1  65  1  1  8.25  1  1  HlSPil  \u2022 H HB i  1  1  Roo: Bo;  (CT/,  1 ' ~  \u2022SSI  1  ?' \":T.Ti  1  BBH :  1  10 11.7  \u2022  1  11.7  1  1  \u2022 \u2022 \u2022 \u2022 M g n M H  iliHijH  \u2022  \u000CT a b l e 12.  2 6 6  Page  Appendices  Daily output data for S o i l C o v e r simulations for evaporative fluxes during  the 8-d test period at the D e i l m a n n south w a s t e rock ( D S W R ) pile. Elapsed  Act  Pot  Time days  . Pot ' \u2022 \\  Evap  Evap  Tran  (mm)  (mm)\"  (mm)  Water  Spec  Bottom  Tran \" '\u2022\"-'lET.  Bal  Flux  Flux  (mni)  (%)  Act  \u2022^\u00ABTot (mm)  (mm) '  (mm)  2  0 -0.72 -0.745  0 -0.72 -0.745  0 0 0  0 0 0  0 -0.72 -0.745  0 -23.775 -81.124  0 39.2 36.6  0 0.28 -0.068  3  -1.239  -1.239  0  0  -1.239  192.386  7  -0.13  4  -1.057  -1.057  0  0  -1.057  212.662  1  -0.137  5  -1.615  -1.615  0  0  -1.615  222.733  0.4  -0.266  6  -1.638  -1.468  0  0  -1.468  222.885  0  -0.747  7  -1.922  -1.189  0  0  -1.189  222.831  0  -0.909  8  -1.933  -0.981  0  0  -0.981  223.142  0  -0.92  Cum  Cum.  Cum.  0 1  . Runoff  (mm)  Selected ' Node Fix ,  f'  5  Cum. .  Net  (mm) r  Infiltration . (mni)  0 0 0 0 0 0 0 0 0  0 15.873 35.855 5.761 -0.057 -1.215 -1.468 -1.189 -0.981  0 22.606 0 0 0 0 0 0 0  - Cum.  Cum.'  PE  AE  PT.  AT  ET  Precip:  (mm)  (mm)  (mm)  (ram'  (mm)  (mm),  0 -0.72 -1.465 -2.705 -3.762 -5.377 -7.014 -8.936  0 -0.72 -1.465 -2.704 -3.762 -5.377 -6.844 -8.034  -10.869  -9 015  Cum. /  Cum.  Cum.  Runoff  Infil.  Bott F l .  int fix  (mm)  (mm)  (mm)  (mm)  0 22.606 22.606 22.606 22.606 22.606 22.606 22.606 22.606  0 15.873 51.728 57.489 57.432 56.217 54.749 53.56 52.579  0 0.28 0.212 0.082 -0.056 -0.322 -1.068 -1.978 -2.897  Cum.  0 0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0 0  0 -0.72 -1.465 -2.704 -3.762 -5.377 -6.844 -8.034 -9.015  1  0 39.2 75.8 82.8 83.8 84.2 84.2 84.2 84.2  \u000CAppendices  Page  T a b l e 13.  .26.7  S o i l C o v e r simulations s u m m a r y for evaporative fluxes during the 8-d test  period at the D e i l m a n n south w a s t e rock ( D S W R ) pile. .  SoilCover. V. 4.0] Run Summary Page  0SWR2b  i. .Project Name: 7. fhl&amp;i Direct tin :  .c;\\scv4\\  1  J. Rim hinuM'.ten*:  4.  :Mesli  liifnrmaiimi:  ' no  5.  Soil  Prcpt  \\[\\  H  i  H&lt;  \u2022i  I  I'M  1  if &gt; \\  i '  n't i l l s  ' 'c' \"\"\"\"\"\"\"  |  Oi  1 ,. ii',  ~ ii  T. Vi\"'ii'i(iiiii v-stii'\u2014'-v i  , i ',  i f  !' i !  8. R. .r '.H*,'!' v 32606  \u000CPage  Appendices T a b l e 14.  268  Daily input d a t a for S o i l C o v e r simulations for evaporative fluxes during the  27-d test period at the D e i l m a n n south w a s t e rock ( D S W R ) pile. Moisture boundaries section  Weather data section  Ri.i Dry  Max A'lTenir .  1 .  2  4  \"  \\  5  V ArTemr  ,c,  Max RH  RH  W-no Speed  : :.  :\u2022\u2022  (dec)  (krr'nri  Net  v.. -.  TOD  FJC  BC ;Va.uej  ITS'  11.50  9.00  1.8834  0.98  0.71  1.41  3  39.2  0  15.50  11.00  2.711  0.99  0.92  0.5918  3  36.6  0  24  11.00  5.00  5.069  1  0.83  0.793  3  7  0  24  10.00  3.00  3.8514  0.97  0.68  1.7391  3  1  0  24  13.00  3.50  5.7109  0.96  0.66  2.565  3  0.4  0  24  17.00  3.00  4.3685  0.92  0.52  4.4193  3  0  0  24  0  0  24  18.50  5.00  4.3951  0.88  0.45  5.6997  3  8  18.50  5.00  5.29  0.96  0.47  5  3  2.6  0  24  9  17.00  11.00  1.76  1  0.88  3.4  3  13.3  0  24  24.00  12.00  8.04  1  0.41  3.5  3  0  0  24  11  26.00  13.50  7.5  0.98  0.31  2.1  3  0  0  24  12  28.00  8.00  6.07  0.85  0.35  3.1  3  0  0  24  13  26.50  11.00  5.63  0.77  0.37  4.5  3  0  0  24  21.00  7.00  2.6  0.97  0.52  3.8  3  0  0  24  22.00  3.50  5.78  0.91  0.39  3.6  3  0  0  24  20.00  6.00  1.65  0.99  0.66  2.9  3  0  0  24  17  17.50  10.50  4.2  0.1  0.63  3.6  3  8.8  0  24  18  15.00  8.50'  3.89  0.98  0.7  2.1  3  2.9  0  24  12.50  9.00  1.91  0.99  0.82  2.7  3  7.9  0  24  20  15.50  6.00  6.31  0.99  0.49  3.7  3  7.3  0  24  21  17.00  1.00  4.74  0.96  0.3  2.5  3  0  0  24  22  15.00  7.00  1.13  0.96  0.51  4.4  3  0  0  24  23  14.00  0.00  5.16  0.87  0.37  5.1  3  2.2  0  24  ..:  21.00  0.00  5.41  0.85  0.38  3.5  3  0  0  24  25  24.50  16.00  4.84  0.73  0.38  5.3  3 \u2022  0  0  24  26  26.50  10.00  4.9  0.94  0.35  2.3  3  0  0  24  27  26.00  15.00  5.2  0.77  0.25  5.7  3  0  0  24  16  --i  \u000CAppendices  Page Other daily data section  Moisture boundaries section Bot HC ' .  Bot BC  \u2022  .  BoI imp  fenp  0.11  1  14  1  0.087  1  10  1  0.087  0.051  IHI BIS llBi \u2022gill  8.25  0.05  \u2022  10  0.068 0.061  . -i  1 1 1 1 1 1  .  i  15  i -  15  1 1 1  iiiiiii Biiiit  0.05  0.05 0.05  illlli  0.05  1  \u2022\u2022SB! llMBBi lliilllllll  15  '-'.'\";r?'.'i  \" -' 1 ' B I B  '- 1 \u2022 -' .1 I-'!  \u2022KM  15 15  0  15  1  15  0  0.05  mmmm  15  0.05  i  15  1  0.05  ilSiili  15  1  0.05  15  1  0.05  15  T a b l e 15.  \u2022 H  \u2022  15  0.05  1 1  1 \u2022  15  111111 \u2022Bill IBllIillB  0.05  1  1  15  \u2022\u20221  0.05  1  1 1 \u2022\u2022\u2022HI  i  Bllllll  15  0.05  1 1  1  HON  15  0.05  1  (HnRlil  15  0.05  -  15  0.05  1  i cm)  \u2022  8 6.5  1  0.05  \u2022  w^SBIiliillllllf  11.7  0.05  Pan \u201E'\u2022\u2022 Write\" ' Root; , Day  13  0.05 0.05  \u2022  .'(mrri/Sayi  \u2022  \u2022  1  1 1  269  \u2022 H I ^filllll  o  o  iiiiiii iiiiiii UB1IB M M iS&amp;9Ni llNi  o  Daily output data for S o i l C o v e r simulations for e v a p o r a t i v e fluxes during  the 27-d test period at the D e i l m a n n south w a s t e rock ( D S W R ) pile. Elapsed Time  Pot  Act  Act  Evap  Evap \u2022  Trail  ET  (mm)  (mm) .  (min)  (mm)  J  .Water  Tot  Spec v Bottom  Bal :  \u2022 (%) \u2022  (mm)  (mm) 0  1  -0.633  -0.633  0  0  -0.633  -20.61  39.2  0.291  2  -0.745  -0.745  0  0  -0.745  -75.6  36.6  -0.084  3  -1.239  -1.239  0  0  -1.239  -186.932  7  -0.094  4  -1.057  -1.057  0  0  -1.057  -207.207  1  -0.14  5  -1.616  -1.616  0  0  -1.616  -217.28  0.4  -0.255  6  -1.638  -1.464  0  0  -1.464  -217.44  0  -0.686  0  -1.174  -217.384  0  -0.836  0  -1.987  -221.791  2.6  -0.901  7  -1.913  -1.174  0  8  -1.99  -1.987  0  \u000CAppendices  Page  270  -0.571 -2.927  -0.571  0  0  -0.571  0  0  -2.814  -225.3 -225.433  13.3 0  -1.084  -2.814  11  -2.91  -2.271  0  0  -2.271  -224.448  0  -1.192  12  -2.717  0  0  -1.294  -224.843  -2.781  0  0  -1.197  -1.282  -0.42  0  0  -0.42  -224.213 -223.966  0 0  -1.177  13 14  -1.294 M.197  0  15 16  -2.294 -0.862  0 0  0 0  -0.731 -0.291  -224.154 -224.128  0 0  -1.107 -1.127  17 18 19 20 21 22 23 24  -2.536 -1.307 -0.609 -2.046 -1.788 -0.827 -1.968 -2.22  0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0  -2.522 -1.307 -0.609 -2.046. -1.788 -0.827 -1.968 -1.74  -235.63 -239.589 -243.004 -246.594 -246.637  8.8 2.9 7.9 7.3 0 0 2.2  -1.076 -1.085 -1.058 -1.068 -1.02 -1.021 -1.004  0  -1.056  25 26  -2.748 -2.248 -3.084  -0.731 -0.291 -2.522 -1.307 -0.609 -2.046 -1.788 -0.827 -1.968 -1.74 -1.347 -1.022  0 0  0 0  -1.347 -1.022  -250.551 -249.817  0 0  -0.903  0  0  -0.903  -249.701  0  -1.169 -1.178 -1.187  -Net '  Cum.  Cum.  Cum.  Cum.  Cum.  Cum.  Infiltration  PE  AE  PT  AT  ET  Precip.  (mm)  (mm)  9 10  27  Selected  Runoff  Node Fix (mm)  (nun)  HHH3 23.438  (mm)  -246.737 -250.723 -250.604  (mm)  -1.155  -1.17  -1.085  (mm),  (mm)  (mm)  0  0  0  0  0  0  0  0 0 0 0 0  15.129 35.855 5.761 -0.057  -0.633 -1.378 -2.617 -3.674  -0.633 -1.378 -2.617 -3.674  0 0 0 0  0 0 0 0  -0.633 -1.378 -2.617 -3.674  0 39.2 75:8 82.8 83.8  -1.216  -5.29  -5.29  0  0  -5.29  84.2  0 0  -1.464 -1.174  -6.928 -8.841  0 0  84.2 84.2  0.613  -10.831  0 0 0  -6.754  0  -6.754 -7.928 -9.914  0  0  12.729 -2.814  0 0  -10.485  0  -10.485 -13.299  0  0  -11.402 -14.329  0  -13.299  100.1 100.1  0 0  0  -2.271  -17.239  -1.294  -19.957  0 0  0  0  -15.57 -16.864  -15.57 -16.864  100.1 100.1  0  0 0 0  -1.197  -22.738 -24.02. -26.314  -18.062  0 0 0  0 0 0  -18.062 -18.481 -19.213  100.1 100.1 100.1  0  100.1  -22.026 -23.333  0 0  7.291 5.254  -29.713 -31.02 -31.629 -33.675  0 0  -1.788 -0.827  -35.463 -36.29  0 0 0 0 0 0  0 0  -19.504  0 0  -0.291 5.142 1.593  -18.481 -19.213 -19.504  108.9 111.8 119.7 127 127 127  0 0  0.232 -1.74  -38.259 -40.479  -22.026 -23.333 -23.942 -25.988 -27.776 -28.603 -30.571 -32.312  0 0 0 0 0 0 0  0 0 0 1.136 0 0 0 0 0 0 0  -0.42 -0.731  -27.177  -23.942 -25.988 -27.776 -28.603 -30.571 -32.312  0  0  0 0  0  0 0 0 0 0 0 0  -7.928 -9.914  86.8  129.2 129.2  \u000CAppendices  Page  0  0  -1.347  -43:227  -33.659  - 0  0  -33.659  129.2  0 0  0 0  -1.022  -45.475 -48.559  -34.681 -35.584  0  0  129.2  0  0  -34.681 -35.584  Cum.  ' Cum.  -0.903  C u m . ',  Runoff  Infil.  Bott FI.  (mm)  (mm)  (mm)  0  0  23.438 23.438 23.438 23.438  15.129 50.985 56.745 56.688  23.438 23.438 23.438  - Cum. int fix  '  (mm)  0 0.291 0.207 0.113 -0.027  0  55.473 54.008 52.835 53.448 66.177 63.363 61.092  -0.282  0 0 0 0 0 0 0  23.438 24.573 24.573 24.573  59.798 58.601 58.181 57.45 57.158 62.301 63.894 71.185  -7.313 -8.483 -9.59 -10.716 -11.802 -12.877 -13.962 -15.02  24.573 24.573  76.439 74.651  -16.088 -17.109  24.573  73.824  24.573 24.573  74.055 72.315  -18.129 -19.133  0 0  24.573  70.967 69.946  -20.189 -21.357  0 0  -22.536 -23.722  0 0  23.438 23.438 23.438 23.438 23.438 23.438 23.438 23.438  24.573 24.573  69.043  -0.968 -1.805 -2.705 -3.789 -4.944 -6.136  0 0 0 0  0 0 0 0 0 0 0 0 0 0  129.2  271  \u000CPage  Appendices T a b l e 16.  S o i l C o v e r simulations s u m m a r y for evaporative fluxes during the 27-d  test period at the D e i l m a n n south w a s t e rock ( D S W R ) pile.  SoilCover V.- 4:0! Hun Summary P a a  f !''!l|lvl Nil!)  t &gt;scv4\\  1 I ' m i a l D i m in \\ i. Run I ' l t n i m e s m :  4. Mesli ]iii'iii'm;ilio\u00BB:  5, Soil I'liilM I i N \" M M  6, ROHIMIHI'V Ciiiiflisioiis ' '  1  \u2022 &gt;  ,:/'  \u2022  C ' '  i ii  OP  \u2022\u2022  &gt; -  -- Vtffl! Ill*)*' M l l I M  M'  H.- Run O i ' i i ii' \u00AB i ' i t'.ti s  :  1  n  &gt; &lt;  _  272  \u000CAppendices T a b l e 17.  Page  273  Daily input d a t a for S o i l C o v e r simulations for e v a p o r a t i v e fluxes during the  8-d test period at the D e i l m a n n north w a s t e rock ( D S W R ) pile.  Moisture boundaries section  Weather data section  Run Day  AirTsrrp  N&amp;t -  Max RH  GiT/}  (dec)  AiiTemp  Speed  \u2022  I 03 BC  Top  Start \u2022  \u2022  V (C) 1  1.8834  0.98  0.71  1.41  3  39.2  0  24  18.00  10.00  1.8834  0.98  0.71  1.41  3  36.6  0  24  0.5918  3  7  0  24  9.00  2.7106  4  15.50  9.00  5  11.00  5.00  10.00  7 8  0.99  0.91  5.0693  1  0.83  0.793  3  1  0  24  3.8514  0.97  0.68  1.7391  3  0.4  0  24  3.00  5.7109  0.96  0.67  2.565  3  0  0  24  13.00  3.50  4.3685  0.92  0.52  4.4193  3  0  0  24  17.00  3.00  4.3951  0.89  0.45  5.6997  3  0  0  24  Moisture boundaries section Bet BC  Other daily data section  Toe Temp  Blllllii!llll 1  \u2022  10.00 \u2022  11.50  Got BC  (his.  18.00  3  '6'\"\"''  \u2022  \u2022.0.3alue) i&gt;-  r=n j  Bot TtniD  lC)  &lt;C:  \u2022  14  1  0.3  13  1  0.23  1  0.146  1  0.116  1  0.098  1'  1  0.095  1- -  .1  0.118  '\u2022\"ill  \u2022\u20221.-' . ;  8  Evap  \u2022\u2022wilt  ! (mm/day.)'.';  1  IllilB  \u2022\u2022\u2022\u2022  6.5  A  11.7 10  Root  Out  ccrri  1 1 1 1  1  8.25 10  Day  IBilll lllllBI  \u2022  H  1  \u2022\u2022 \u2022  ;m)3  Wamm flHllll IllISlBI \u2022 H i l l 1118111\u2022 \u2022 M i l iRllllllllllll  ~.J&amp;\\ *'  1  ' .1  1  1  1  Root  '  illfllit  \u000CAppendices  Page  T a b l e 18.  274  Daily output d a t a for S o i l C o v e r s i m u l a t i o n s for e v a p o r a t i v e fluxes during  the 8-d test period at the D e i l m a n n north w a s t e rock ( D S W R ) pile. Elapsed  ;  Pot,  , Act  POt  '  Act  Time  Evap  Evap  Tran  Tran  days  (mm)  (mm)  (mm)  (mm).  1  \"'ET  -. (mm)  Water  Spec  Bal  Flux  Bottom , Flux  (%)  (mm)  (mm)  0  0  0  0  0  0  0  0  0  1.  -0.72  -0.72  2  -0.745  -0.745  0 0  0 0  -0.72 -0.745  -23.775 -81.124  39.2 36.6  0.28 -0.068  3  -1.239  -1.239  0  0  -1.239  192.386  7  -0.13  4  -1.057  -1.057  0  0  -1.057  212.662  1  -0.137  5  -1.615  -1.615  0  0  -1.615  222.733  0.4  -0.266  6  -1.638  -1.468  0  0  -1.468  222.885  0  -0.747  7  -1.922  -1.189  0  0  -1.189  222.831  0  -0.909  8  -1.933  -0.981  0  0  -0.981  223.142  0  -0.92  Cum.  Cum.  Cum.  \u2022AT  ET  Precip.  (mm)  (mm)  Selected  Runoff  Net  Cum.  Cum. *  Cum. '  Node Fix (mm)  (mm)  ,  Infiltration  PE  (mm)  (mm)  0 22.606 0  0 0 0  0 15.873 35.855  0 -0.72 -1.465  0 0  0 0  5.761 -0.057  0 0. 0  0 0  0 Cum.  Infil.  (mm) .  (mm)  (mm)  :S  .  pt-  \u2022  (mm)  (mm) '  0 -0.72  0 0  0 0  0 -0.72  0 39.2  -2.705 -3.762  -1.465 -2.704 -3.762  0 0 0  0 0 0  -1.465 -2.704 -3.762  75.8 82.8 83.8  -1.215 -1.468  -5.377 -7.014  -5.377 -6.844  0 0  -5.377 -6.844  0  -1.189  -8 936  -8.034  0 0 0  0  -8.034  84.2 84.2 84.2  0  -0.981  -10.869  -9.015  0  0  -9.015  84.2  \u2022 Cum. v , / Cum.  Runoff  . AE  ,  Cum.  Bott.FI.  int fix  (mm)  (mm)  0  0  0  0  22.606  15.873  0.28  0  22.606  51.728  0.212  0  22.606 22.606  57.489  0.082  0  57.432  -0.056  0  22.606 22.606  56.217 54.749  -0.322 -1.068  0 0  22.606 22.606  53.56 52.579  -1.978 -2.897  0 0  \u000CAppendices T a b l e 19.  Page S o i l C o v e r simulations s u m m a r y for evaporative fluxes during the 8-d  test period at the D e i l m a n n south w a s t e rock ( D N W R ) pile.  \\  n,\\AKP c  1; Hnjjw.t Name:  1  2, i ' r o j i x ! U i r i ' d o r y : I  Rui) I' ,i  i  ; &lt;&gt; Run Swumaryfage\u2022  c:\u00BBscv4i  H'ti  4, MuMi Immm m \u00AB \u00BB  5,\".Soil Pi i| n% Niii'in ii  i -i I  _  1  \\  - r  B&lt; \" i! i n (-it ii&gt;tiitih 1  i  \\ \u2022JXfA.m  v~  U  P, '  \u2022 iJt  )1 ,  f  1 T _ ,4 i i i  1  user  ! 1  1 1.SGE-03 !! I1  OS  h.. Hiiii.Out|Hii stimman':  &gt;' '.0'.' I .,&gt;  !  i l ( I lM  !  ~  275  \u000CAppendices  Page  Table 110.  276  Daily input d a t a for S o i l C o v e r simulations for e v a p o r a t i v e fluxes during the  27-d test period at the D e i l m a n n nortth w a s t e rock ( D N W R ) pile.  Weather data section  '  Max A.rlerr'B  Aulen-.p  \u2022  !C&gt;  1  18.00  2  Net  Moisture boundaries section  Max RH  RH  Speed  Top BC  aay;  idee)  idee\"'  ikir-'ii)  Hype)  10.00  1.8834  0.98  0.71  1.41  3  39.2  0  24  18.00  10.00  1.8834  0.98  0.71  1.41  3  36.6  0  24  11.50  9.00  2.7106  0.99  0.91  0.5918  3  7  0  24  15.50  9.00  5.0693  1  0.83  0.793  3  1  0  24  \u2022  11.00  5.00  3.8514  0.97  0.68  1.7391  3  0.4  0  24  6  10.00  3.00  5.7109  0.96  0.67  2.565  3  0  0  24  7  13.00  3.50  4.3685  0.92  0.52  4.4193  3  0  0  24  8  17.00  3.00  4.3951  0.89  0.45  5.6997  3  2.6  0  24  Top \u2022\u2022\u2022  S:a-1 lire  time  (Value)' ' (hrs)  17.00  11.00  1.76  1  0.88  3.4  3  13.3  0  24  . 10  18.50  12.00  8.04  1  0.41  3.5  3  0  0  24  11  17.00  13.50  7.5  0.98  0.31  2.1  3  0  0  24  12  24.00  8.00  6.07  0.85  0.35  3.1  3  0  0  24  13  26.00  11.00  5.63  0.77  0.37  4.5  3  0  0  24  14  28.00  7.00  2.6  0.97  0.52  3.8  3  0  0  24  15  26.50  3.50  5.78  0.91  0.39  3.6  3  0  0  24  16  21.00  6.00  1.65  0.99  0.66  2.9  3  0  0  24  17  22.00  10.50  4.2  0.1  0.63  3.6  3  8.8  0  24  20.00  8.50  3.89  0.98  0.7  2.1  3  2.9  0  24  17.50  9.00  1.91  0.99  0.82  2.7  3  7.9  0  24  6.00  6.31  0.99  0.49  3.7  3  7.3  0  24  12.50  1.00  4.74  0.96  0.3  2.5  3  0  0  24  22  15.5  7.00  1.13  0.96  0.51  4.4  3  0  0  24  23  17.00  0.00  5.16  0.87  0.37  5.1  3  2.2  0  24  24  15.00  0.00  5.41  0.85  0.38  3.5  3  0  0  24  25  14.00  16.00  4.84  0.73  0.38  5.3  3  0  0  23  26. ' .  21.00  10.00  4.9  0.94  0.35  2.3  3  0  0  24  24.50  15.00  5.2  0.77  0.25  5.7  3  0  0  24  9  \u2022  '  \u2022  19  15  20' ' ' ;21  Moisture boundaries section Bot BC Type  Bot BC  Other daily data section  I IsP  Bot I enp  1 1 1 1 1  0.3  \u2022  n  i  l  Roo:  koct  ililiiiliillilBl  ;C) \u2022  Par Lvac  PSIMBMI Bot.(cm)  imm'dayi  14  '1  \u2022  1  0.3  1  13  \u2022  1  0.23  1  8  1  1  0.146  1  6.5  1  0.116  1  8.25  1  ,  IlilB  \u2022  - iV\"- . !  :  .!..',t. I H l l l l \u2022  S M I B I M  \u000CAppendices 1  0.098  1  1  0.095 0.118  1\u2022 :  0.05  -.  0.05  1  1  \u2022  1  11.7  '? 1  0.05 -  1  -  10  \u2022  ' I 1'';'  1 1  Page  10  llltllttil  1  15  '  1  \u2022  1  i  1&gt;.  15  t  15  i  1  ffijjjfjlllj  1  \u201E  0.05  15  1  0.05  15  1  mBmni IHHRII  1  0.05  1  15  1  0.05  '  .1-''  15  1  0.05  \u2022  -1'.  15  0  1  0.05  :.  15  0  1  0.05  1  0.05  1 \"\u2022  15  0 1  1  1  ,  . 1  15  1  1  0  0.05  \u00A3?:1 :\u2022  15  0.05  HH9B  15  IRiiSBi  0  . 1  0.05  15  KSHM  0  1  0.05  15  M H I  0  1  0.05  15  1  0.05  15  1  0.05  \u2022 1  15  1  0.05  1  15  T a b l e 111.  \u2022\u2022\u2022\u2022 \u2022Bslli \"\"v.T-.t'  HHHH \u2022  1I111S1 BMm  llllli  lllfliliBilliplli  IBityili  1  1  n m  ililfi  277  0 1  BlBill  B11IBI JilillBl  lllllilill 1 -  . -  ''-1  iliiiiiij  \u2022lilt HEfli  HHB I N H i i lliillli 1 8 H B 1  1  ,  1  , \u20221  ...  i  Daily output d a t a for S o i l C o v e r s i m u l a t i o n s for e v a p o r a t i v e fluxes during  the 27-d test period at the D e i l m a n n north w a s t e rock ( D N W R ) pile. Pot  Act  Pot  Act  Time  Evap  Evap  Tran  Tran  days  (mm)-  ' v (mm)  (mm)  (mm)  ' Elapsed  Tot , ET  '  (mm) \u2022-'  Water  Spec  Bal  Flux  (%)  * (mm)  0  0  0  0  0  0  0  1  -0.735 -0.727  -0.735 -0.727  0  0  -0.691  -0.691  0 0  0. 0  -0.735 -0.727  -7.323 -9.52 -12.549  -1.38 -1.096  0 0  0  5  -1.38 -1.096  6 7  -1.496 -1.525  -1.496 -1.069  0  8  -1.825  9  -1.825 -0.579  10 11 12  2 3  -0.691  Bottom , Flux (mm)  0 39.2 36.6 7  0 -11.85 -35.624 -16.112  -.1.38 -1.096  -19.872  1  -22.03  0.4  -8.056 -3.075  0 0  0 0 0  -1.496 -1.069  -22.851 -23.114  0 0  -1.683 -0.854  -1.825  -23.299  2.6  -0.579  0  0  -0.579  -33.409  13.3  0.603 -0.072  -2.708  -2.708  -2.708  0  -1.681  -47.271 -59.167  0  -1.681  0 0  0  -2.553  0  -0.163 -0.177  -2.573 -2.774  \u2022 -1.254 -0.687  0  0 0  -64.431 -67.107  -0.152  0  -1.254 -0.687  0  13  0  -0.122  14 15  -1.419 -2.447  -0.536 -0.594  0 0  0 0  -0.536 -0.594  -68.718 -69.565  0 0  -0.095 -0.074  16 17  -0.877  -0.295 -2.664  0 0  0 0  -0.295 -2.664  -70.247  0 8.8  -0.055  -1.415 -0.668 -2.027  0 0 0  0 0 0  -1.415 -0.668 -2.027  -72.585 -78.405 -98.202  2.9 7.9  -0.005 -0.063 -0.1  4  18 19 20  -2.676 -1.415 -0.669 -2.027  0  -70.985  7.3  -0.008  \u000CAppendices  Page  21  -1.629  -1.629  0  0  -1.629  110.986  0  -0.165  22 23  -0.836 -2.087  -0.835 -2.087  0 0  0 0  -0.835 -2.087  120.726 -127.48  0 2.2  -0.168 -0.084  24  -1.991  -1.313  0  0  -1.313  129.965  0  -0.107  25  -2.363  -0.971  0  , o  -0.971  131.853  0  -0.105  26  -2.084  -0.561  0  0  -0.561  132.993  0  -0.086  27  -3.03  -0.498  0  0  -0.498  133.788  0  -0.067  Runoff  (mm)  Selected  Net  Cum.  Cum.  Cum.  Cum.  Node Fix  Infiltration  PE  AE  PT  AT  ET  Precip.  (mm)  (mm)  (mm)  (mm)  (mm)  (mm)  (mm) \u2022\u201E..;  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  (mm) .  38.465 35.873 6.309 -0.38 -0.696 . -1.496 -1.069 0.775 12.721 -2.708 -1.681 -1.254 -0.687 -0.536 -0.594 -0.295 6.136 1.485 7.232 5.273 -1.629 -0.835 0.113 -1.313 -0.971 -0.561 -0.498  -0.735 -1.461 -2.152 -3.532 -4.628 -6.124 -7.649 -9.474 -10.053 -12.761 -15.314 -17.887 -20.662 -22.081 -24.528 -25.405 -28.081 -29.496 -30.165 -32.191 -33.821 -34.656 -36.743 -38.735 -41.098 -43.181  -0.735 -1.461 -2.152 -3.532 -4.628 -6.124 -7.193 -9.018 -9.597 -12.304 -13.985 -15.239 -15.926 -16.461 -17.056 -17.351 -20.015 -21.43 -22.098 -24.125 -25.754 -26.59 -28.677 -29.99 -30.961 -31.522  -46.211  -32.02  - Cum.  \u2022 Cum. .  Cum:  Runoff  Bott Fl.  int fix  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  . Cum.  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 -0.735 -1.461 -2.152 -3.532 -4.628 -6.124 -7.193 -9.018 -9.597 -12.304 -13.985 -15.239 -15.926 -16.461 -17.056 -17.351 -20.015 -21.43 -22.098 -24.125 -25.754 -26.59 -28.677 -29.99 -30.961 -31.522 -32.02  Cum.  0 39.2 75.8 82.8 83.8 84.2 84.2 84.2 86.8 100.1 '100.1 100.1 100.1 100.1 100.1 100.1 100.1 108.9 111.8 119.7 127 127 127 129.2 129.2 129.2 129.2 129.2  278  \u000CAppendices (mm)  Page (mm)  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 38.465 74.339 80.648 80.268 79.572 78.076 77.007 77.782 90.503 87.796 86.115 84.861 84.174 83.639 83.044 82.749 88.885 90.37 97.602 102.875 101.246 100.41 100.523 99.21 98.239 97.678 97.18  (mm)''.' 0 -11.85 -47.474 -63.586 -71.643 -74.718 -76.4 -77.254 -76.652 -76.724 -76.887 -77.064 -77.216 -77.337 -77.432 -77.506 -77.561 -77.569 -77.575 -77.637 -77.738 -77.903 -78.071 -78.155 -78.262 -78.367 -78.453 -78.52  r(mm)-,C  .... o.. . o \u2022 0' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  279  \u000CAppendices T a b l e 112.  Page S o i l C o v e r simulations s u m m a r y for evaporative fluxes during the 27-d  test period at the D e i l m a n n north w a s t e rock ( D N W R ) pile.  SoilCover V. 101  l Pmj&lt;:d;iV;iiii!-:  Run Summary Pace  DNWRP4x  :  2. Project Directory:  c:\\scv4\\  3. : Run Pitrifmeters:  4. IVteh Infill ot.!\u00BB'  5. .Soil Pn'ihiu suiin .  |  \u201E,  ,,,,  6. Bfluiiilarv (.imdiiimi* M M  ll  Oi-Apr-88  '  Ussr  1, Vfiill Hit \" Ii \" Ii*  X. Run Out &gt;ii mi * ii \u2022 s  s i l l  | .i,  i  I i &gt;i  11.  iifeUiiimiiiiliveE'  I I  ..!) Nftf-uniBtavcAFiiiiiiiJ'... 'K  i i  v  in  i i i i  i i i ) '  User Kcste  ii  II  280  "@en
<https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0081099>