Jun 1, 2017 - (2011) and described by the equation (Zumdahl & DeCoaste (2010); ...... folk.uio.no/rvogt/KJM_MEF_4010/Alkalinity.pdf Accessed on 20 th.
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI - GHANA
Arsenic Remediation in Mine Drainage at the AngloGoldAshanti Obuasi Gold Mine in Ghana
by
Gordon Foli (B Sc., MPhil Geology, University of Ghana, Legon)
A Thesis submitted to the Department of Geological Engineering (Faculty of Civil and Geo-Engineering) College of Engineering in partial fulfilment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
JUNE, 2017
DECLARATION I hereby declare that this submission is my own work towards the PhD and that, to the best of my knowledge, it contains no material previously published by another person, nor material which has been accepted for the award of any other degree of the University, except where due acknowledgement has been made in the text. Gordon Foli (PG 20295423) …………………………… Candidates' Signature ……….......………… Date Certified by: Professor S.K.Y. Gawu (PhD) Geological Engineering. Dept. COE, KNUST, Kumasi
Professor P.M. Nude (PhD) Dept. of Earth Sciences Univ. of Ghana, Legon
……….................. Lead Supervisor
................................. Co-Supervisor
.........……………. Date
…………….... Date Certified by
……………………. Head of Department ……………………. Signature ……………………. Date
ii
ABSTRACT The AngloGold-Ashanti Obuasi mine in Ghana was officially opened in 1895. For many decades, gold mining activities of the company have resulted in arsenic (As) contamination in mine drainage. At Obuasi, Acid-Base Accounting (ABA) is used to predict mine drainage quality, while Toxicity Characterisation Leaching Procedure (TCLP) and drainage monitoring are used to characterise As leaching in tailings and verify drainage quality, respectively. This research investigated the leaching potential and the remediation of As in mine drainage. In this research, ABA and TCLP tests, leachate, textural and mineralogical analyses of tailings, and porosity and bulk density of subsurface soils were evaluated. From the ABA, mean sulphur and carbonate contents, and, net neutralisation potential and acidity ratio (AR) are 1.46%, 4.34 kgCaCO3/ton, and, -41.29 kgCaCO3/ton and 0.095, respectively. Characterised leachable As in tailings is 2.45 mg/l, while bulk density and porosity of undisturbed and disturbed soils are 2.029 g/cc and 0.411, and, 1.975 g/cc and 0.599, respectively. A 4-data point ABA model with 20% increased amendment quality is proposed to replace the old 3-data point model. TCLP and borehole media are analogous over pH range of 5.7-6.3. From tailings mineralogical data analysis, goethite and alunite occur as secondary minerals. From integrated ABA, TCLP and borehole data, simulated stable As value and, AR and pH ranges are 2.0 mg/l, 0.097-0.121 and 5.4-5.9, respectively, while, pH ranges for As adsorption and secondary mineral formation are 4.3-7.7 and 2.3-4.4, respectively. The predicted pH values are related to the monitored, by a factor of 1.154. In closed systems, the equilibrium concentration of 0.5 mg/l is required for natural attenuation to occur in the borehole. As degrades from 2.50 mg/l to 0.01 mg/l in 4 years in boreholes and over 12 km along stream profile; this information was used to establish As risk assessment model based on documented global impact data. Using porosity, bulk density and degradation constants, As retardation factors of 1.96, 1.86 and 1.07, and corresponding solute velocities of 1.53x10-7, 1.61x10-7 ms-1 and 9.25x10-1 ms-1 were estimated in undisturbed soil, and disturbed soil and along stream profile, respectively. In conclusion, the new ABA Model, As and pH values, risk assessment model, retardation factors and solute velocities can be used to remediate As in mine drainage. iii
DEDICATION I dedicate this work to my wife Eva, sons Schwarzer Nutifafa and Gordon Mawutor and daughter Salem Mawuena for their support, sacrifices and understanding.
iv
ACKNOWLEDGEMENT First and foremost, my thanks go to God Almighty for giving me the strength and will-power to undertake this PhD programme. I am sincerely indebted to my Supervisors, namely Professor S.K.Y. Gawu of the Geological Engineering Department, Kwame Nkrumah University of Science and Technology (KNUST) in Kumasi, and Professor P.M. Nude of Department of Earth Science, University of Ghana, Legon, Accra, for their guidance. I wish to thank all Senior Members of the Geological Engineering Department of KNUST, particularly Dr E.K. Appiah-Adjei and Dr Bukari Ali, and also Professor D.K. Asiedu of the Department of the Earth Science, University of Ghana for their useful criticisms and assistance. My thanks also go to some Senior Management members, Staffs and Technicians of the Environmental and Metallurgical Laboratories of AngloGold Ashanti Obuasi mine, Staffs and Technicians of the Materials and Civil Engineering Departments of KNUST, for assisting in the acquisition of data for this research. I am very grateful to Mr Felix Aidoo of Ghana Atomic Energy Commission (GAEC), Accra, Mr Blestmond Afrifa, a PhD student of the Geological Engineering Department and Miss Lilly Lisa Yevugah, a staff of the Geomatic Engineering Department, both at KNUST, Kumasi, for their roles in the preparation of maps required for this research work. Finally, to all and sundry, who in diverse ways contributed to this work; I say thank you, and may the Good Lord bless you.
v
TABLE OF CONTENTS DECLARATION ...................................................................................................ii ABSTRACT.......................................................................................................... iii DEDICATION ......................................................................................................iv ACKNOWLEDGEMENT .................................................................................... v TABLE OF CONTENTS .....................................................................................vi LIST OF TABLES ................................................................................................. x LIST OF FIGURES ..............................................................................................xi CHAPTER 1 ........................................................................................................... 1 INTRODUCTION ................................................................................................. 1 1.1 Background to the Study.......................................................................... 1 1.2
Problem Statement ................................................................................... 2
1.3
Aim and Objectives of the Research........................................................ 3
1.3.1
Specific Objectives .............................................................................. 3
1.3.2
Research Hypothesis ............................................................................ 4
1.4
Justification of the Research .................................................................... 4
1.5
Approach and Methods Employed .......................................................... 5
1.5.1
Preliminary Work ................................................................................ 5
1.5.2
Solid-phase Sampling and Analyses .................................................... 5
1.5.3
Aqueous-phase Sampling and Analyses .............................................. 5
1.5.4
Drainage Quality Simulation ............................................................... 5
1.6
Research Scope and Limitations .............................................................. 6
1.7
Thesis Structure and Outline.................................................................... 7
CHAPTER 2 ........................................................................................................... 8 LITERATURE REVIEW ..................................................................................... 8 2.1 Drainage Quality Evaluations .................................................................. 8 2.1.1
Acid-Base Accounting ......................................................................... 8
2.1.2
Acid-Base Accounting Data Interpretations ...................................... 10
2.1.3
Drainage Quality and ABA Model Modification .............................. 10
vi
2.1.4
Toxicity Characterisation Leaching Procedure.................................. 14
2.1.5
Mine Spoils and Drainage Contamination ......................................... 16
2.2
Process Models for Arsenic Mobilisation in Mine Drainage ................ 19
2.2.1
Release of Arsenic from the Decomposition of Iron-oxyhydroxide.. 20
2.2.2
Release of Arsenic from Oxidation of As-bearing Sulphides ........... 21
2.2.3
Displacement of Sorbed Arsenic ....................................................... 22
2.3
Contamination and Exposure Pathway .................................................. 22
2.4
Mine-waste Geochemistry and Natural Attenuation ............................. 24
2.5
Geochemical Process Modelling in Mine Drainage .............................. 28
2.6
Contaminant Degradation Capacity and Evaluations ............................ 29
2.6.1
Equilibrium Sorption Isotherms......................................................... 29
2.6.2
Degradation Rate and Model Estimations ......................................... 31
2.6.3
Contaminant Plume Stability Analysis .............................................. 32
2.7
Retardation Factor and Solute Velocity ................................................. 34
2.8
Arsenic Pollution Risk Assessment ....................................................... 36
CHAPTER 3 ......................................................................................................... 38 DESCRIPTION OF THE STUDY AREA ......................................................... 38 3.1 Research Location.................................................................................. 38 3.2
Climatic Conditions and Topography .................................................... 40
3.3
Geological Setting and Ore Types ......................................................... 40
3.4
Mineralogy and Geochemical Signatures .............................................. 44
3.5
Metallurgic History and Mine Spoils..................................................... 45
3.6
Hydrology and Hydrogeology ............................................................... 46
3.7
Soil Profile and Soil Geochemistry ....................................................... 46
CHAPTER 4 ......................................................................................................... 48 METHODOLOGY .............................................................................................. 48 4.1 Drainage Quality Evaluation ................................................................. 48 4.1.1
Acid-Base Accounting ....................................................................... 48
4.1.1.1
Resource Core Sampling and Sample Preparation .................... 48
4.1.1.2
Sulphur Content Determination ................................................. 49
4.1.1.3
Carbonate Content Determination ............................................. 50
4.1.1.4
Data Interpretations and Model Validation ............................... 51 vii
4.1.2
Toxicity Characterisation Leaching Procedure.................................. 51
4.1.3
Sample Site Selection for Monitoring ............................................... 53
4.1.4
Water and Sediment Sampling .......................................................... 55
4.1.5
Review of Some Analytical Instruments and Methods ..................... 56
4.1.6
Evaluation of Physico-chemical Parameters...................................... 57
4.1.6.1
Trace Metal Contamination in Streams ..................................... 57
4.1.6.2
Seasonal and Profile Sampling of Stream.................................. 58
4.1.6.3
Multi-water Media Monitoring and Characterisation ................ 58
4.2
Leaching of Test Materials .................................................................... 60
4.2.1
Soil Sample Analyses from Monitoring Borehole Sites .................... 60
4.2.2
Leaching Tests for Mass-time Monitoring in Tailings ...................... 61
4.3
Chemical and Geotechnical Evaluations ............................................... 61
4.3.1
Chemical Composition and Particle Size Analysis of Tailings ......... 61
4.3.2
Geotechnical Evaluations in Subsurface Material ............................. 63
4.4
Quality Control for Monitoring Data ..................................................... 63
4.5
Arsenic Impact Rating and Pollution Risk Assessment......................... 64
4.6
Conclusion ............................................................................................. 65
CHAPTER 5 ......................................................................................................... 66 RESULTS AND DISCUSSION .......................................................................... 66 5.1 Drainage Quality Evaluation ................................................................. 66 5.1.1
Acid-Base Accounting ....................................................................... 66
5.1.1.1
Acid-Base Accounting Data Presentation and Manipulations... 66
5.1.1.2
Modified ABA Model Validation .............................................. 72
5.1.1.3
Acid-Base Accounting Amendment Capacity ........................... 74
5.1.2
Toxicity Characterisation Leaching Procedure test method .............. 76
5.1.3
Trace Metal Contamination in Streams ............................................. 78
5.1.4
Multi-water Media Monitoring .......................................................... 79
5.1.4.1
Active Tailings Dam Monitoring results ................................... 79
5.1.4.2
Decommissioned Tailings Dam Monitoring Results ................. 79
5.1.5
Test of Hypothesis to Compare Leaching and Field Data ................. 83
5.1.5.1
Comparing Means of As in TCLP (Ast ) and in Stream (Ass) ... 84
5.1.5.2
Comparing Means of Ast and As in Borehole (Asg) .................. 84
viii
5.1.5.3
Comparing Means of Ast and As in Borehole (Asc) .................. 85
5.1.5.4
Deductions from the Test of Hypotheses ................................... 86
5.1.6
Tailing and Contaminated Subsurface Soil Characterisation ............ 87
5.1.6.1
Particle Size, Chemical and Mineral Compositions in Tailings 87
5.1.6.2
Soil Sample Analysis from Contaminated Sites ........................ 91
5.1.7
Process Models for As Mobility in Mine Drainage ........................... 91
5.1.7.1
Reductive Dissolution of Iron-oxyhydroxide ............................ 91
5.1.7.2
Oxidative Dissolution of As-bearing Sulphide .......................... 96
5.2
Drainage Quality Simulations ................................................................ 99
5.2.1
Modelling and Simulation of Remediation Factors ........................... 99
5.2.1.1
Conditions and Simulation Data Layout .................................... 99
5.2.1.2
Arsenic Leaching Risk Characterisation.................................. 100
5.2.1.3
Buffer pH Range for Arsenic Stability and MNA ................... 101
5.2.1.4
Adsorption Capacity and Secondary Mineral Formation ........ 102
5.2.1.5
Predicted Against Monitored Remediation Factors ................. 105
5.2.2
Arsenic Degradation Characteristics and Risk Evaluation .............. 106
5.2.2.1
Arsenic Mass-Time Analyses and Isotherms........................... 106
5.2.2.2
Equilibrium Between Borehole and Leaching media .............. 110
5.2.2.3
Retardation Factor and Solute Velocity in the Subsurface ...... 111
5.2.2.4
Arsenic Mass-Distance Analysis and Freundlich Isotherm ..... 112
5.2.2.5
Retardation Factor and Solute Velocity Along Streambed ...... 113
5.2.2.6
Arsenic Impact Intensity Model Evaluation ............................ 113
CHAPTER 6 ....................................................................................................... 115 CONCLUSION AND RECOMMENDATIONS ............................................. 115 6.1 Conclusion ........................................................................................... 115 6.2
Recommendations ................................................................................ 116
References ........................................................................................................ 117 Key publications from this work ..................................................................... 131 Appendices....................................................................................................... 132
ix
LIST OF TABLES Table 2.1 Arsenic speciation results in stream ...................................................... 18 Table 2.2 Linear regression analysis results .......................................................... 33 Table 2.3 Decision-making criteria ....................................................................... 34 Table 2.4 Bulk density and porosity for some media material .............................. 35 Table 4.1 Health impact rating and classification.................................................. 65 Table 5.1 Data layout for original and estimated models ...................................... 70 Table 5.2 Test of robustness of the 3, 4, 5 and 6 data set models.......................... 72 Table 5.3 Model Validation by analysis of sequence in parameters ..................... 73 Table 5.4 Summary descriptive statistic of TCLP data ......................................... 76 Table 5.5 Means of trace metal concentrations in streams .................................... 78 Table 5.6 Summary statistics of parameter values in seepage from ATD sites..... 79 Table 5.7 Arsenic (mg/l) and pH (values) in water media..................................... 79 Table 5.8 Two sample t-test with equal variances for Ast and Ass ........................ 84 Table 5.9 Two sample t-test with equal variances for Ast and Asg ....................... 85 Table 5.10 Two sample t-test with equal variances for Ast and Asc...................... 85 Table 5.11 Major and trace element composition in tailings using XRF .............. 88 Table 5.12 Identified mineral form patterns list from tailings ............................... 90 Table 5.13 Leaching results from the analysis of contaminated sites ................... 91 Table 5.14 Results and summary statistic in seepage from DTD sites .................. 92 Table 5.15 Correlation among the parameters determined from DTD sites .......... 92 Table 5.16 Means of parameter values from water monitoring boreholes ............ 96 Table 5.17 Integrated ABA, TCLP and borehole data (Foli et al., 2015) ............. 99 Table 5.18 Gradients within which adsorption is feasible (Foli et al., 2015) ...... 104 Table 5.19 The pH ranges for secondary mineral buffer ..................................... 105 Table 5.20 Arsenic degradation in monitoring boreholes.................................... 106 Table 5.21 Statistical analysis ofAs degradation in boreholes ............................ 107 Table 5.22 Arsenic degradation in tailings leaching............................................ 107 Table 5.23 Time estimates from borehole and leaching (Foli et al., 2013) ......... 110 Table 5.24 Test of hypothesis of equality of t1 and t2 values (Foli et al., 2013) . 110 Table 5.25 Stream profile study results for As degradation ................................ 112 Table 5.26 Variation of As compliance values in borehole (field) and stream ... 113
x
LIST OF FIGURES Figure 1.1 Research approach and general methods................................................ 6 Figure 2.1 Conceptualised site to explain the release of As from sediment into drainage (after Appelo & Postma, 2005) ............................................ 20 Figure 2.2 Concentration of benzene versus time (a) and distance (b).................. 31 Figure 3.1 Sketch Map of Obuasi and environ ...................................................... 39 Figure 3.2 Geological map of south-western Ghana (after Pigois et al., 2003) .... 42 Figure 3.3 A sketch of the Geological map of Obuasi area (A-B represents the major ore axis along the Obuasi shear system) .................................. 43 Figure 3.4 Schematic soil profile of the Obuasi area (Foli 2004; Antwi-Adjei et al., 2009) ............................................................................................. 47 Figure 4.1 Sketch map of Obuasi showing sampling locations ............................. 54 Figure 5.1 Control chart for Total Sulphur (TS) % ............................................... 66 Figure 5.2 Control chart for Neutralisation Potential (NP) ................................... 67 Figure 5.3 NP against maximum potential acidity (MPA) .................................... 68 Figure 5.4 Model plots (Solid line represents the reference; broken line represents the estimated model in this work)....................................................... 71 Figure 5.5 NP versus MPA and AR zones (after Downing, 2010) [reference values in parentheses] .................................................................................... 75 Figure 5.6 Control chart for pooled arsenic (Ast) in TCLP data ........................... 77 Figure 5.7 Control chart for acidity (pHt) in TCLP data ....................................... 77 Figure 5.8 Control charts for arsenic (Ass) in stream water .................................. 80 Figure 5.9 Control charts for acidity (pHs) in stream water .................................. 80 Figure 5.10 Control charts for arsenic (Asg) in monitoring borehole .................... 81 Figure 5.11 Control charts for acidity (pHg) in monitoring borehole .................... 81 Figure 5.12 Control charts for arsenic (Asc) in community borehole.................... 82 Figure 5.13 Control charts for acidity (pHc) in community borehole.................... 82 Figure 5.14 The pH range of least As mobilisation in TCLP(t) and borehole(g) .. 86 Figure 5.15 Particle size distribution curves for tailings ....................................... 87 Figure 5.16 Molar plots for Fe2O3 and Al2O3 in tailings (e.g. Bladh, 1982) ......... 89 Figure 5.17 XRD plots showing mineral forms in (a) ATD and (b) DTD ............ 90 Figure 5.18 Fe, As, pH and SO42- against HCO3- during the reductive dissolution of iron-oxyhydroxide at DTD sites ..................................................... 93 xi
Figure 5.19 As/Fe plots for the relative release of the metals at DTD sites .......... 94 Figure 5.20 As and Fe against SO42- showing drainage mixing lines during the reductive dissolution of iron-oxyhydroxide at the DTD sites ............ 95 Figure 5.21 Fe, As, SO42- against time during oxidative dissolution of As-bearing sulphide at DTD sites ......................................................................... 97 Figure 5.22 As and Fe against SO42- showing drainage mixing lines during the oxidative dissolution of As-bearing sulphide at DTD sites ................ 98 Figure 5.23 Projected AR zone for As attenuation in borehole (g) and stability in TCLP (t) data .................................................................................... 100 Figure 5.24 Projected pH zone for As attenuation range for As stability in borehole (g) TCLP (t) data ............................................................... 101 Figure 5.25 Projected pH zones for As sorption capacity and secondary mineral formation in boreholes (g; c) and TCLP (t) data .............................. 103 Figure 5.26 Monitored remediation pH against the predicted ............................. 105 Figure 5.27 As mass-time in field and leaching data ........................................... 108 Figure 5.28 Freundlich (a & b) and Langmuir (c & d) isotherms for field monitoring (g) and leaching data, with their respective R2 values ... 109 Figure 5.29 Equilibrium between borehole and leaching data ............................ 111 Figure 5.30(a) As Mass-distance plot and (b) the Freundlich isotherm .............. 112 Figure 5.31 As Impact Intensity Model [time (solid line); distance (broken line)]: (a) Non-adjusted model data; (b) Adjusted model data .................... 114
xii
CHAPTER 1 INTRODUCTION 1.1
Background to the Study Mining industry constitutes a major source of inorganic contamination of
the environments (Wuana & Okieimen, 2011). The type of contaminants present in the environment is determined by the composition of the ores at sites (Eppinger et al., 1999). The AngloGold Ashanti Obuasi gold mine in Ghana is a world class company located in the Ashanti gold belt within the Birimian terrain. The mine was officially opened for production in 1895 and has operated on a total concession area of about 633 km2. Mining peak was attained in the early 1990s, at a maximum ore treatment capacity of about 5.64 million tonnes per annum. Gold recovery from primary ores was about 75%, while 25% of the gold was trapped in the tailings (Anon, 2006). This level of reconciliation necessitated reclamation of the tailings to recover the residual gold in the late 1990s. Ore mining, metallurgy, tailings deposition and reclamation have resulted in As and pH impacts on soils and water bodies (Smedley, 1996; Akabzaa et al., 2007; Foli & Nude, 2012). Research work at Obuasi shows that As concentrations range from 0.24 mg/kg to 7,592 mg/kg and 0.01 mg/l to 6.32 mg/l in stream sediment and water samples, respectively (Akabzaa et al., 2007). Categories of people at risk to As contamination at Obuasi are i) mine workers who are in frequent contact with the arsenopyrite-rich ores and mine spoils, ii) persons whose water supply contains high levels of As and, iii) unborn babies whose potential mothers are exposed to As contamination (e.g. ATSDR, 2000). 1
1.2
Problem Statement At the Obuasi mine, acid-base accounting (ABA) is used to predict the net
neutralisation potential of ores (Sobek et al., 1978). Toxicity characterization leaching procedure (TCLP) is also used to evaluate As leaching in tailings (USEPA, 1989). Both tests are short-term methods that have failed to capture adequately, the long term As and pH impacts at the mine (Foli et al., 2012; 2015). ABA test is based on pyrite and calcite as the only acid generation and neutralisation (NNP) agents, despite the occurrence of other minerals in the ores (Oberthur et al., 1994). The reactivity of all the other mineral components can be established using the kinetic test (Lappako, 1990b), which is costly and takes a longer time, hence the NNP evaluation is based on the ABA test only. ABA data interpretation is based on a 3-data point model, that is at times defined by acidity ratio (AR) range of 3.0, 1.0 and 0.33, where, the extreme values represent alkaline and acidic conditions, respectively (Ferguson & Morin, 1991). These authors, however, noted that AR 5.7 in 1991 (USEPA, 1992c). Another USEPA (1994) report on the Elko County NV indicated that seepage discharges from a waste rock dump sampled in 1990 had pH values of about 2.4-3.2 at the discharge point and about 6.5-8.6 at 1220 m downstream. Arsenic levels at the discharge point stated above were 46 ppm initially but reduced to 1.5 ppm two months later, while levels of 0.023 ppm and 0.005 ppm, respectively, were recorded at the downstream sampling location (USEPA, 1994). Initial NNP values determined for the waste rock dump of the Elko County NV was -10.6 kgCaCO3/ton, while, three months later, the NNP results from the same location were 5.35 kgCaCO3/ton, and reduced acidity in drainage (USEPA, 1994). The above observation suggests that the waste initially generated enough buffering capacity to neutralise any acid discharges, while distance and time factors also affected contaminant trends. 11
The kinetic test presents the compressed patterns of the natural oxidation rates as compared to field observations, which manifest as changes in pH, SO42and Fe, which form the basis for assessments (BC AMD Task Force, 1989). The pH changes are comparable to ABA model values defined by Downing (2010), such that, AR values extending from >3.0 to1.0 are equivalent to pH >5 and define alkaline trends. The AR range of 1.0-0.33 is also equivalent to pH of 5-3, depicting potential acidity, while, AR range 95% (Newell et al., 2007). The evaluations of COV and CL of data in this research, therefore, forms the basis for metal trend characterisation in drainage. As a guide, a decision-making criterion, based on the trend analysis reviewed from Table 2.2 (e.g. Newell et al., 2007) is presented in Table 2.3.
33
Table 2.3 Decision-making criteria Trend Characteristics (𝑙𝑛 Slope) Confidence level Positive Negative 1=>No trend Trend probably 90%-95% Trend probably decreasing increasing >95% Increasing trend Decreasing trend According to Newell et al. (2007), a low confidence level corresponds to "Stable" or "No Trend", while, higher confidence level (CL) [>95%] indicates a strong likelihood of a trend (Table 2.3); the CL value >95% approximate to mean(μ) ± 3 standard deviation (μ±3σ), which statistically implies that 99.74% of the data lies under the normal distribution curve (Newell et al., 2007). Monitored As degradation data were compared using the t-test statistic performed at 95% confidence interval.
2.7
Retardation Factor and Solute Velocity Information from degradation model equations can be used to determine
the retardation factors and velocity of contaminants in the subsurface (Lovanh et al., 2000). Retardation factor, (R f ), determines the chemical-specific, dynamic process of adsorption to, and desorption from aquifer materials (Asante-Duah, 1996). The R f can be calculated as a function of the chemical‟s soil-water partition coefficient, „K d ‟, the bulk density β and porosity θ of the transport medium (Asante-Duah, 1996; Lovanh et al., 2000), according to the expression; 𝑅𝑓 =1+
βK d θ
………..…………….…....…….…………………………….… (2.8);
where (K d ) is partition coefficient, (β) is bulk density and (θ) is total porosity. From the slope of the Freundlich isotherm plotted from monitored data, the retardation„Rf‟ is defined by:
34
𝑅𝑓 =1+
βAm C m −1 θ
…….………..……………….….……….….…..………..….(2.9);
where A, the intercept on the q-axis, m; the gradient of the curve, C; concentration at equilibrium. The Langmuir isotherm is also given by: β
𝑅𝑓 =1+θ
μλ (1+λC)2
……………………….……..…….……….....…………... (2.10);
where μ represents the amount of solute sorbed by the solid (Lovanh et al., 2000), in mg/kg; λ is adsorption constant. The Rf and solute velocity are related by: Rf =
groundwater velocity solute ve locity
(Nyer, 1993)........................…...…………….… (2.11).
At equilibrium; contaminant fraction (F) can be estimated as: Fdissolved =
1 Rf
1
or, Fsorbed = 1 − R ……….……………................………...(2.12). f
When Rf =1 no retardation occurs, and when Rf >1 the contaminant velocity is less than the seepage velocity (Sharma & Reddy, 2004; Martinez et al., 2006); a value of 2 means the compound is one-half of the groundwater flow rate. Variable material with relative bulk density and porosity as presented in Table 2.4.
Table 2.4 Bulk density and porosity for some media material Aquifer Materials Bulk Density (β) gcm3 Total porosity (𝜃) Iowa default values* 1.86 0.3 Clay 1 to 2.4 0.34 to 0.60 Fine sand 1.37 to 1.81 0.26 to 0.53 Medium sand 1.37 to 1.81 ---Coarse sand 1.37 to 1.81 0.31 to 0.46 Fine gravel 1.36 to 2.19 0.25 to 0.38 Medium gravel 1.36 to 2.19 ---Coarse gravel 1.36 to 2.19 0.24 to 0.36 Domenico & Schwartz (1990)
In the Obuasi mine environment, sediment and water samples were taken on different occasions in 1992 and 1993 by Carboo & Serfo-Armah (1997) for As degradation study. The authors also performed an experiment on sediments using 35
both distilled water and simulated river water as leachants; the composition of the simulated river water was made up of 3.0 ppm Al, 1.0 ppm Mn and 1.5 ppm Fe. These authors showed that, As values reduced away from the discharge point.
2.8
Arsenic Pollution Risk Assessment Likelihood and consequence of an event form the basis for risk assessment
of an environmental aspect (BCI Pty Ltd., 2007). Duoben (1998) stated that the risk assessment process may be one of data analysis or modelling or a combination of the two. It utilises scientific knowledge and data to establish casespecific responses to site management problems (Asante-Duah, 1996). Exposures to As in some endemic areas of the world have been reported (Zaldivar & Ghai, 1980; Murphy et al., 1981; ATSDR, 2000; WHO, 2003). Specific observations published by WHO (2001) and FAO/WHO (2011a & b) indicate that exposure to As contamination in water ingested for long periods has the potential of causing cancer of the internal organs and skin changes. Feinglass (1973) and WHO (2003) reported on early clinical symptoms in humans due to acute As toxicities from ingestion of water containing 1.2-21.0 mg/l. Pigment changes occurred in populations consuming water containing As concentrations ≥0.40 mg/l (ATSDR, 2000). For similar concentration range stated above, dermal lesions were observed after an exposure period of about 5 years (Tseng, 1977; Guo & Valberg, 1997). In a study conducted in Taiwan and China, As content in well water ranging from >0.60 mg/l to 75%
5m
Highly weathered rock 35-75%
Moderately weathered rock 10-35%
BC
Undecomposed
C
rock
Slightly weathered up 10% Slightly weathered
BEDROCK
Oxidised rock Fragments in fine matrix of clay, quartz and Ferro-oxides
10m
up to 10% Fresh rock, < 10% weathered
Figure 3.4 Schematic soil profile of the Obuasi area (Foli 2004; Antwi-Adjei et al., 2009) Bowell (1994) and Smedley (1996), noted that, in the saprolite, secondary As- and Fe- bearing minerals such as scorodite (FeAsO4.2H2O), haematite, arsenolite, iron oxides and arsenates occur. According to Smedley et al. (1996), very low As concentrations of about 0.2-0.3 mg/kg occur in the (A) horizon of uncontaminated soil profiles and decrease to lower values of about 0.01 mg/kg in the argillaceous (B) horizon. Also, in areas of intense mining impacts, As concentration in soils are of much higher values, ranging between 1.0 mg/kg and 1530 mg/kg (Smedley et al., 1996).
47
CHAPTER 4 METHODOLOGY 4.1
Drainage Quality Evaluation Both primary and secondary analytical data were used for the evaluation
and interpretation to achieve the objectives of the research. Secondary data such as ABA test, TCLP and some field monitoring data were sourced to adequately capture various trends and relationships to fully cover issues of As degradation in the mine drainage. All other analyses, including drainage monitoring at active tailings dam (ATD), were done during the course of the research as primary data.
4.1.1
Acid-Base Accounting Acid-base accounting (ABA) method involved resource core sampling,
sample preparation, analyses, and interpretations. The method provided a quick and cheap geochemical method for the evaluation for pre-mining or project planning drainage quality evaluation.
4.1.1.1 Resource Core Sampling and Sample Preparation Underground resource diamond drill cores were sampled from the main Ashanti shear zone. The boreholes were drilled over the period from 2002 to 2007 by the diamond drilling section of the mine. The core size used for the drilling was of diameter 47.6 mm and sampled core lengths were about 0.5 m each. Sampling sites were spread along an 8 km (AB) strike length (Figure 3.3) and within a vertical column of about 1.6 km (Foli et al., 2011). A total of one hundred and ninety-two (192) samples (Foli et al., 2015) were analysed to generate the ABA data. The samples were pulverised to the grain 48
size of 37 microns (Foli et al., 2011; Foli et al., 2015) to attain high surface area pulverised samples. This was to ensure maximum exposure of mineral components in the core mass (BC AMD Task Force, 1989). The samples were analysed at the Sansu Treatment Plant Laboratory at Obuasi from 2008 to 2009. For the ABA analysis, about 2 g of pulverised and homogenised sample portions were fed into a high-temperature furnace, Leco S/CO3 analyser for total sulphur (S) and carbonate (CO32-) content determination (LECO Corporation, 2007). As a confirmation, about 0.2 g of the sample portions were digested with 2.0 ml of 6 M HCl for CO3 determination (e.g. Pile et al., 1998).
4.1.1.2 Sulphur Content Determination Liberated SO2 from the decomposed samples are described by the stoichiometric reactions of sulphide minerals such as pyrite and arsenopyrite, respectively, in the ores as published in Foli et al. (2011) and presented as: 4FeS+7O2=>2Fe2O3+4SO2................................................................................(4.1), 2FeAsS+6O2 (1000oC) =>Fe2O3+As2O5+ 2SO2 ...............................................(4.2) The SO2 was bubbled through hydrogen peroxide solution to form a sulphuric acid (H2SO4), in a reaction time of 10 minutes. The H2SO4 was then titrated against a 0.05N Borax solution, as described by the equation: H2SO4 + Na2B4O7 ===> Na2SO4 + H2B4O7......................................................(4.3). From the above process, the percentage sulphur content was calculated using the relationship: (F*V*100)†M. In the expression, „V‟ represents volume, „M‟ is the mass of the sample and F is the Sodium Tetraborate factor based on Sulphur, as published in Foli et al.(2011). The TS% was used to calculate the maximum potential acidity (MPA) as done by Skousen et al. (2002).
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4.1.1.3 Carbonate Content Determination The carbon dioxide (CO2) liberated from the decomposition of the samples were used for carbonate determination using hydrochloric acid of volume (VHCl) of 2.0 mL of 6 M HCl (Anon, 1994), which is described by the equation, as presented by Zumdahl & DeCoaste (2010). 2HCl(l) + XCO3(s) ==> X2+(aq) + 2Cl-(aq) + H2O(l) + CO2(g).......................(4.4). The CO2 evolved was absorbed in 2M soda lime (NaOH) solution as outlined in Foli et al. (2011) and described by the equation (Zumdahl & DeCoaste (2010); 2NaOH(aq) + CO2(g) ==> Na2CO3(s) + H2O(l)...............................................(4.5). The change in gas volume (∆V) was measured. The volume change was related to the mass of CO2 evolved using experimental measurements and system parameters (Pile et al., 1998). Correction to the gas volume was achieved by using a substance of volume, „Vc‟ that can dissolve but without discharge of gas (Pile et al.; 1998). The correction to the gas volume may arise as the result of added vapour due to evaporation of the HCl solution or sample volume loss due to dissolution (Pile et al., 1998). The mass of CO2 „MCO2' was estimated from: MCO2 = ρCO2 (∆V-VHCl-Vc)......................................................................................... (4.6), where all volumes were measured in ml, masses in grammes, and „ρ‟ representing
density. The percentage CO2 was found by dividing the mass of CO2 by the mass of the reacting sample (Pile et al., 1998). As stated in Foli et al. (2011), an increase in weight of the absorbent was evaluated and used to calculate the amount of CO2 in the samples (e.g. Vogel, 1989). As a check for accuracy of the volume of CO2 absorbed, the NaOH was used in excess; the resultant NaCO3 solution generated, was filtered and titrated with 0.5M HCl to estimate the amount of CO2 absorbed, and used for NP estimation (Foli et al., 2011). 50
4.1.1.4 Data Interpretations and Model Validation The MPA and NP were used to estimate the NNP and the acidity ratio (AR) (Skousen et al., 2002). The AR ratio of the three (3) data point model was extended from 0.33 to 0.1 according to Ferguson & Morin (1991). In addition to the above, two more values were sequentially added to extend the data points to six (6) to evaluate the acidification. The new data sets were used to construct binary plots for interpretation. The original model assumes that only pyrite and calcite occur as the source of acid generation and neutralisation agents present in the ores (Morin, 1990; Ferguson & Morin, 1991). This is, however, not so in nature, since other sulphide minerals namely, arsenopyrite, chalcopyrite, sphalerite, galena, pyrrhotite among others, also occur in association with the pyrite (Oberthur et al., 1994). The models were verified using statistical evaluation of variability in the coefficient of determination (R2) values (e.g. Scholtus & Bakker, 2013), visual observation of the model structure, regression analysis and sequential assessment of data trends. 4.1.2
Toxicity Characterisation Leaching Procedure The toxicity characterisation leaching procedure (TCLP) test method was
used for the evaluation of As leaching potential in tailings (TCLP Method 1311, 1992; Bricka et al., 1992). The TCLP test is known to generate field leachate with constituent concentrations that would be reduced through natural attenuation processes under simulated landfill conditions (Kosson et al., 2002). The material sampled were taken from milled tailing products generated from 2008 to 2009 for metallurgical test work on the proven ore reserve cores. About 0.5 kg portions of fresh tailings were sampled from tailings discharge points with a well-cleaned hand trowel. Eighty-four (84) pairs of 51
samples each, were taken on a weekly basis (Foli et al., 2015). The samples were oven-dried at 90°C overnight as specified by laboratory procedures (Anon, 1994). The dried samples were packed into plastic bags and labelled. The analysis of one set of samples was done at the Obuasi mine Environmental Services and Treatment Plant Laboratories. To characterise the leaching medium, about 5 g of the dried sample was placed in a 500 ml beaker, and about 96.5 ml of distilled water was added and the mixture characterised for the appropriate extraction fluid preparation (Anon, 1994). During extraction, about 50 g of a fresh representative sample of the dried material to be tested was fed into a clean, dry 2-litre flask. About one litre of the appropriate extraction fluid was then added to the 2litre flask containing the representative sample. A stopper was used to cock-up the flask and then clamped firmly on a lateral agitator and agitated vigorously for 1820 hours to stimulate leaching of the arsenic (As). For liquid/solid separation, the content of the 2-litre flask was allowed to stand until the solid phase settles. A laboratory pressure filtration apparatus with a 2 µm glass fibre filter was washed with 500 ml of 1.0 M HNO3 and then used to filter the contents to obtain the desired filtrate (Anon, 1994). The pH of the TCLP extract was then measured and where the pH ≥2.0, concentrated HNO3 was slowly added until a pH ≤2.0 was obtained in the extract, and the pH recorded. For calibration, the pH meter was turned on and the tip of the electrode placed in a standard buffer solution of pH 7.0 for several minutes to equilibrate at room temperature. The „„Cal 2‟‟ knob was set to 100, and the „„Cal 1‟‟ knob adjusted to read pH 7.0. The electrode was removed and thoroughly cleaned with a kimwipe, and then calibrated to pH 4 using the „„Cal 2'' knob. The electrode was
52
again removed from the solution, rinsed with distilled water, and the tip blotted and used for pH determination (Foli et al., 2012; 2015). The TCLP leachates were analysed for As concentrations and pH values. One set of samples was analysed using the Varian 55B atomic absorption spectrometer (AAS) device; the second set was analysed using the inductively coupled plasma optical emission spectrometry (ICP-OES) (e.g. USEPA, 1989). A sample blank and a matrix spike per 20 extractions ensured quality. All analyses were done at the AngloGold Ashanti Environmental Laboratory. The paired As values were validated by a t-test, performed using the two-sided 80% Upper Confidence Level (UCL) for equality of means of As1 and As2. Furthermore, the UCL of pooled As1 and As2 designated as Ast result was evaluated and compared with the standard of 5 mg/l for characterised arsenical waste safe for disposal to the repository (USEPA, 1989). 4.1.3
Sample Site Selection for Monitoring
The sampling map used for the research is presented in Figure 4.1.
53
Figure 4.1 Sketch map of Obuasi showing sampling locations
54
Sampling site selection was influenced by the locations of mine spoil that were identified as posing the potential for impact on water bodies. The „P‟ series of sample sites represent stream water and sediment sampling points. These samples were collected for the evaluation of trace metals on a seasonal basis and also for stream profile study. The „A‟ series also represent active tailing dam (ATD) seepage sites. The ATD facility constitutes an open system where fresh tailings are discharged. The decommissioned tailing dam (DTD) sites represent stream sources (s), tailings dam monitoring borehole sources (g) and community borehole sources (c). The DTD site is a closed system, where tailings discharges are curtailed. Sampling point coordinates are presented in Appendix 4.1. 4.1.4
Water and Sediment Sampling All water media sampling and analyses are discussed in this section,
according to procedures described in (Foli & Nude, 2012). About 500 ml-sized water bottles were washed with distilled and deionized water and then dried in the sunshine (Anon, 1994). Streams samples were taken at about 10 m downstream of bifurcations or at locations of reduced flow rates to avoid tracking. At each site, bottles were flushed thoroughly with sample water before samples were taken. Stream sampling was completed without rainfall interference; according to Eppinger et al. (1999) and Webster et al. (1994), rainfall interference with the sampling activity may influence analytical results through dilution. At the laboratory, samples were filtered with cellulose nitrate membrane filters of pore size 0.45 μm for pH and anion determination (Barcelona et al., 1994). For metal
55
determination, portions of the filtrates were acidified using concentrated HNO3 at pH 2 to prevent precipitation (APHA, 1999). For sediment/soil sampling, a hand trowel was used to scoop samples from sampling sites and mixed thoroughly to avoid bias. About 500 g weights of samples were delivered at each time into clean zip-lock plastic bags and labelled with chosen site identification codes. Duplicate samples and blanks were taken for quality control (e.g. Handy et al., 1985). Split portions of samples were dried in an oven at a temperature of 90o C for 24 hours to obtain dry weights of samples. Pulverised aliquots of the dried sub-samples were digested in aqua regia, which is composed of a mixture of 11.5 N HCl and 15.5 N HNO3 at 90-100o C for one hour and allowed to stand for 30 minutes. The solutions prepared by the above procedure were decanted, filtered and used for trace metal analysis (Garcia & Baez, 2012). About 100 ml of distilled water was added to about 200 g each of the other split samples for pH and anions determination (e.g. Foli & Nude, 2012).
4.1.5
Review of Some Analytical Instruments and Methods Instruments used for water and sediment sample analysis were; the
Corning pH/C 107 meter, AAS, ICP-OES, Ion Chromatography (IC) (Hou & Jones, 2000) and a titration set-up (Anon, 1994). The Corning pH/C 107 meter is a hand-held, dry-cell powered Hanna Digital Electrode that was used to determine pH values, both on and off the field (e.g. Foli & Nude, 2012). The Varian 55B AAS device made of Hollow Cathode Electrodes made of the test elements were used to analyse for As, Cu, Fe, Zn and Pb at a detection limit of 0.01 mg/l in water and sediment samples (e.g. Garcia & Baez, 2012).
56
The ICP/OES, as described by Hou & Jones (2000) was by hydride generation method, where prepared samples were injected into radio-frequencyinduced argon plasma using a nebuliser. The injected sample mist was energised to generate photons, which was processed and converted to an electrical signal by a photodetector (Hou & Jones, 2000), and the signals stored by a personal computer. Detection limit for As on the device is 0.0002 mg/l. Anions were analysed using ion chromatography (IC) with a detection limit of 0.01 mg/l (Anon, 1994). The device was set to the conductivity mode and calibrated using solutions and de-ionised water blank. The samples were injected and values determined from chromatograms using the calibration data. After each high peak chromatogram, a blank was injected into the system and flushed. Alkalinity was determined by titration using dilute hydrochloric acid (HCl) (e.g. APHA, 1999); prepared to desk reagent standards and then standardised against 40 ml 0.05 N Na2CO3, using pH meter as an indicator. The solution was boiled for 3 to 5 minutes, cooled to room temperature and then used for the titration; normality was calculated and then adjusted to 0.10 N solution (0.10 =5.0 mg CaCO3) as described in APHA (1999).
4.1.6
Evaluation of Physico-chemical Parameters
4.1.6.1 Trace Metal Contamination in Streams A total of twenty-one (21) samples each of both water and sediment were taken from stream profile sampling points labelled as P1 to P21. This data was used to investigate the geochemical distribution of trace metals along streams with respect to the mineralogical and geochemical signature of the ores, outlined in section 3.4. Parameters analysed were As, Fe, Cu, Pb and Zn. Data were
57
compared with the primary maximum contamination limit (1o MCL) guideline value for water and the recommended soil quality data.
4.1.6.2 Seasonal and Profile Sampling of Stream Stream profile sampling was designed to study As behaviour in drainage. This was for the purpose of capturing short-term and medium-term geochemical activities such as mobilisation of As in the mine drainage (e.g. Appelo & Postma, 2005). The short-term refers to laboratory time frames to about one year, while the medium-term also refers to extended time frames of at least 3 years. In this respect, results from twelve (12) water samples each, taken along the Kwabrafo stream profile in the wet and dry seasons of the first year of the tailings dam decommissioning programme were analysed. The sites were listed as P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P22 and P23, and the parameters analysed are Fe, As, pH, SO42- and HCO3-. The results were used for various geochemical model evaluations. Analysis for As at sampling points; P5, P6, P7, P8, P22, P23 and additional sampling points, P24 and P26 were repeated for three more times and on a quarterly basis for As mass-distance analysis.
4.1.6.3 Multi-water Media Monitoring and Characterisation The section covered monitoring of water samples taken from tailings dam sites. One hundred and two (102) seepage samples were taken from 6 active tailing dam (ATD) sampling points. Sampling was done within 17 sampling events that were spread over 13 months. The sample sites are designated A1, A2, A3, A4, A5 and A6. Parameters determined were As, pH, SO42-, Fe, and HCO3-. The purpose was to evaluate geochemical characteristics relating to As mobilisation from the facility. Also, three different sources related to the
58
decommissioned tailings dam (DTD) sites, were sampled for drainage characterisation. The sampling sources were selected based on proximity to locations of mine spoils perceived to influence or pose a potential threat of impact on water bodies. This was done to investigate possible geochemical trends of As and pH in the media. The water sources sampled include surface stream (s) which drain through the network of DTD facilities that have channel height of about 3.2 m. Other sources are tailings dam monitoring boreholes (g) and representing a highly aerated or unsaturated groundwater zone of about 30-40 m deep, and finally, community boreholes (c) and representing a limited aerated or saturated groundwater zone of about 60-80 m deep. The „s' and „g' sources are in close proximity to, and under the direct influence of tailings dam facilities. The „c‟ sources are located at about 400-800 m away from/and indirectly under the influence of the tailings dam facilities. The water sources consist of six (6) sampling points each (Figure 4.1). Eighty-four (84) samples from each of the three sources were taken once a month for fourteen (14) times (Foli et al., 2015). This was done during the closing stages of tailings deposition, where degradation trends were not anticipated to be profound because of the continuous deposition of tailings (N‟dur & Norman. 2005). Sampling was completed without rainfall interference, which may influence results through dilution (Eppinger et al., 1999; Webster et al., 1994). The samples were analysed for pH and As values, and the coefficient of variation (COV) estimated to determine the degree of data scatter about the mean. Tests for equality of means of As concentration in TCLP extract, with As concentration in the multi-water media at 95% level of confidence was done, 59
according to conditions outlined in Newell et al. (2007), for linear regression analysis decision making (Table 2.2). Finally, a total of 108 monitored water samples were taken from the six (6) sampling sites in the „g' medium for the purpose of mass-time analysis. The section commenced after a massive clearance of tailings that followed the monitoring data collated from the multiple water sources. Samples were taken in multiples of six (6) for eighteen (18) events that were spread over 24 months; there were therefore 6 missing data, short of achieving a monthly sampling regime, due to seasonal factors. Analytical results were consolidated per event and recorded as one value each time (e.g. Newell et al., 2007). Sampling protocols conformed to requirements defined by Newell et al. (2007) and Barcelona et al. (1994), in order to obtain accurate results. Samples were filtered using the cellulose filter size of 0.45 μm (Foli & Nude, 2012). Regression analysis was used to evaluate data quality and minimise the effect of tertiary factors (O‟Brien et al., 1991). Parameters analysed were As, pH, SO42-, Fe and HCO3- and interpreted using models described by Appelo & Posma (2005) and As mass-time analysis.
4.2
Leaching of Test Materials Leaching rate monitoring of tailings/soil substrate material was undertaken
for the purpose of studying some geochemical transformations, as well as comparing data generated from field monitoring regimes.
4.2.1
Soil Sample Analyses from Monitoring Borehole Sites Six (6) pairs of soil samples were taken from the tailings dam monitoring
sites and labelled „a' and „b'. A Dutch auger sampler was used to extract the 60
samples at about 30cm depths with a sterilised plastic hand trowel. About 200 g of each sample were packaged in sealed plastic bags. Portions of the combined mass of the material were digested to generate supernatant solutions and analysed for As and Fe with AAS device (Garcia & Baez, 2012), while pH was measured using a pH meter (Foli & Nude, 2012). Data from the above procedure was used for secondary mineral evaluation within the soil substrate. 4.2.2
Leaching Tests for Mass-time Monitoring in Tailings Tailings material taken from DTD site was used to study As leaching rate
using a dynamic leaching method in which leachants were replaced with 100 ml volumes after each measurement. A design described by Carboo & Serfo-Armah (1997), where distilled water acidified to pH value of 6, containing 3.0 ppm Al, l.0 ppm Mn, and 1.5 ppm Fe was used at the Environmental Laboratory in Obuasi. On each occasion, the mixture was well stirred for about 30 minutes, after which filtrates were extracted and analysed using the AAS device (e.g Garcia & Baez, 2012). A total of 14 sample extracts were analysed at time intervals of two to three weeks and the data used for mass-time analysis.
4.3
Chemical and Geotechnical Evaluations
4.3.1
Chemical Composition and Particle Size Analysis of Tailings Tailings samples were taken for chemical evaluation and particle size
determination in January 2014. Chemical analysis was done using the X-ray fluorescence (XRF), as described by Wirth & Barth (2016) at Ghana Geological Survey Geochemical Laboratory in Accra. About 4.0 g of samples were weighed and blended for 3 minutes and pressed into pellets for analyses (Foli et al., 2015). The major and trace elements determined were SiO2, Al2O3, MgO, MnO, Fe2O3, 61
K2O, Na2O, CaO, SO3, TiO2, P2O5, and Cr, Mn, Fe Ni, Cu, Zn, As, Pb, respectively, and used to assess enrichment and also secondary mineral presence. Pressed pellets and fused discs can be used for XRF analysis, where common errors may occur during sample preparation, calibration curve settings and equipment configuration. In pressed pellets, platy minerals such as micas, amphiboles, and pyroxenes cover more areas relative to feldspar and quartz grains and cause heterogeneous surfaces, leading to matrix effects of auto-absorption of X-rays by irradiated samples. The fused discs provide homogenous samples, but have the disadvantage of diluting the sample; it is, however, preferred because of easy treatment, good preservation and elimination of matrix effect; the method is best used for major elements, while pressed disc is best used for trace elements (Anon, 2016). Where, the test material has undergone extensive chemical weathering, such as in this research, platy minerals would have been completely decomposed, thereby minimising the disparity in both methods. Grain size analysis was done at the Civil Engineering Laboratory of KNUST, using the standard procedures outlined in BS 1377 (1990). The mass percentage of particles sizes was plotted on a semi-logarithmic scale for interpretation. This was done to establish the influence of particle size characteristics on mineral enrichment in tailings. To determine the mineral forms, an X-ray diffraction (XRD) analysis was run at the Materials Engineering Laboratory of KNUST, using the Siemens D-5000 Diffractometer type, with generator setting at 10-70 degrees, and at 40 kV and 40 mA.
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4.3.2
Geotechnical Evaluations in Subsurface Material The subsurface material was sampled in March 2015 for porosity and bulk
density determination. During the sampling, a cylindrical pipe with a diameter of about 10 cm was driven into the soil profile to trap the soil material. The pipe, with the trapped soil, was extracted as disturbed samples at depths of about 3.33.4 m; this column is similar to the stream sediment column which was at depths of about 3.2 m (Nude et al., 2016), and also the mottled zone. A percussion drill rig was used to extract undisturbed soil samples from bores of depth between 3.4 to 15.5 m; this column also corresponds to borehole environment and also the pallid zone. Bulk density and porosity of the extracted material were determined using procedures described in the BS 1377 (1990).
4.4
Quality Control for Monitoring Data Replicate standards and field split duplicates were taken during field
monitoring and used for quality control purposes (Foli et al., 2012; 2015). Following standard specifications in the TCLP Manual 1311 (1992) and USEPA (1989), two sets of 84 tailing samples were taken Foli et al. (2012; 2015), to satisfy umpire assay procedures; one set of samples were analysed using the ICPOES device, while, the other set was analysed using the AAS (Foli et al., 2015). Results (As1 and As2) were compared using the t-test statistic. The mean values of 2.35 mg/l and 2.15 mg/l and the coefficient of variance (COV) values of 0.61 and 0.62 of the data are found to be statistically consistent. As done by Bempah et al. (2013), the certified reference material; the National Institute of Standard and Technology (NIST) CRM 1643d containing trace elements in water and total As at a certified concentration of 56.02±0.73 mg/l was used. Values
63
obtained were 54.67±0.89 mg/l with an accuracy of 96.41% from certified levels (p >0.05). Arsenic concentration from all aqueous sources was spiked at 10 mg/l fortification levels. Spiking results produced recovered concentrations of 9.19 mg/l, 9.37 mg/l, 9.48 mg/l and 9.22 mg/l respectively, which are all within acceptable limits of ±10% margin of error. To validate the quality of pH data, randomly determined pH values for distilled water were found to average 6.8, which is within the acceptable range of 6-7. Analytical standards used to validate results of the XRF analysis were S-Adenosyl Methionine (SAM) 5 and 6, within the detection range of 15 nM-480 nM. Large data sets were validated using control charts performed at the set rule of μ±3σ.
4.5
Arsenic Impact Rating and Pollution Risk Assessment The threshold for lethal effect of As contamination is assumed to be 0.50
mg/l (WHO, 2001; FAO/WHO, 2011a & b) which is the same as quoted for livestock consumption by Wilson & Salomon (2002). The value of 0.01 mg/l, which corresponds to World Health Organisation (WHO) standard for drinking water was used as the lower reference (WHO, 2010). Other guideline values are 0.1 mg/l of Ghana Environmental Protection Agency (EPA) guideline value for effluent discharges (EPA Ghana, 1994) and the primary maximum contamination level (1o MCL) of 0.05 mg/l for drinking water. The above set of compliance values were used to set limits such as: >0.5 mg/l, 0.5-0.1 mg/l, 01-0.05 mg/l, 0.05-0.01 mg/l and 0.50 Very Extreme dermal conditions and Certain infections/Life-threatening effects. 0.5-0.10 Cancers and skin Certain lesions/Significant health effects. 0.10-0.05
0.05-0.01 │t│) = 0.0165 Pr (T < t) = 0.9917 Variable
Observation
Mean
Standard Standard Error Deviation 0.151 1.382 0.169 1.155 0.115 1.488 0.166
From Table 5.8, the test statistic value of the mean Ast against Ass was 0.992 while the table value was -2.422; the table value is less than the significance value hence, the null hypothesis that the mean of Ast and Ass are equal is rejected (e.g. Beaulieu-Prevost, 2006; Foli et al., 2015). 5.1.5.2 Comparing Means of Ast and As in Borehole (Asg) The comparison of the means of Ast and Asg is presented in Table 5.9.
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Table 5.9 Two sample t-test with equal variances for Ast and Asg Standard Standard 95% Confidence Variable Observation Mean Error Deviation Interval Ast mg/l 84 2.251 0.151 1.382 1.95 2.551 Asg mg/l 84 0.372 0.069 0.634 0.23 0.509 combined 168 1. 311 0.110 1.427 1.09 1.529 diff 1.880 0.166 1.55 2.207 diff = mean (Ast) – mean (Asg) t = 11.3333 Ho: diff = 0 degrees of freedom = 166 Ha: diff < 0 Ha: diff! = 0 Ha: diff < 0 Pr (T < t) = 1.00 Pr (│T│ > │t│) = 0.00 Pr (T < t) = 0.0000 From Table 5.9, the test statistic value of the mean of Ast against the mean of Asg was 0.000, and the table value was 11.333. Since the table value is greater than the significance value, the null hypothesis of the equality of means of Ast and Asg is not rejected (e.g. Beaulieu-Prevost, 2006; Foli et al., 2015).
5.1.5.3 Comparing Means of Ast and As in Borehole (Asc) The comparison of the means of Ast and Asc is presented in Table 5.10.
Table 5.10 Two sample t-test with equal variances for Ast and Asc Standard Standard 95% Confidence Variable Observation Mean Error Deviation Interval Ast mg/l 84 2.251 0.151 1.382 1.951 2.551 Asc mg/l 84 0.010 0.000 0.000 0.010 0.010 combined 168 1.131 0.115 1.487 0.904 1.357 diff 2.241 0.151 1.944 2.539 diff = mean (Ast) – mean (Asc) t = 14.8689 Ho: diff = 0 degrees of freedom = 166 Ha: diff < 0 Ha: diff! = 0 Ha: diff < 0 Pr (T < t) = 1.00 Pr (│T│ > │t│) = 0.00 Pr (T < t) = 0.0000 From Table 5.10, the test statistic value of the mean of Ast against the mean of Asc was 0.000, while the table value was 14.869. Since the table value is greater than the significance value (Foli et al., 2015) the null hypothesis of equality of means of Ast and Asc is not rejected (e.g. Beaulieu-Prevost, 2006; Foli et al., 2015). From Tables 5.9 and 5.10, As in the „t', „g' and „c' media are
85
geochemically related; the field and leaching data can, therefore, be used to study possible geochemical process evaluations such as natural attenuation (Foli et al., 2015). Since As was not mobilised in „c', simulations involving As was limited to the „t‟ and „g' media. The „c‟ medium was, however, used for pH simulations.
5.1.5.4 Deductions from the Test of Hypotheses Composite plot of the TCLP (t) and the active borehole (g) media to determine the existence of any form of relationship is presented in Figure 5.14.
6.3
5.7
9
3
8 2.5 7 2
5 1.5
4 3
Asg
Ast
6
1
2 0.5 1 0
0 4
4.5
5
5.5
6 Ast
6.5
7
7.5
8
Asg
Figure 5.14 The pH range of least As mobilisation in TCLP(t) and borehole(g) In Figure 5.14, the projected pH buffer range of 5.7-6.3 defines the lower turning point of the profile that describes the behaviour of As in „t‟. The range is intrinsic to both the TCLP and borehole data, where As mobilisation is least. The
86
pH range be can be used as the range for the simulation of As remediation factors in the laboratory, for the designing management plans for As remediation.
5.1.6
Tailing and Contaminated Subsurface Soil Characterisation
5.1.6.1 Particle Size, Chemical and Mineral Compositions in Tailings Particle size analysis of material taken from the active tailings dam (ATD) and the decommissioned tailings dam (DTD) sites are presented in Figure 5.15, while, detailed data are also presented in Appendix 5.8.
100 90 80
60 50 40
% Passing
70
30 20 10 0 0.001
0.01
0.1 Sieve size (mm)
ATD % Passing
1
10
DTD % Passing
Figure 5.15 Particle size distribution curves for tailings From Figure 5.15, the particle sizes of the ATD sample cover a range from 0.0045 to 2.0 mm and contained 29% silt and 71% sand fractions, while, the particle sizes for the DTD sample also ranged from 0.0015 to 2.0 mm and 87
contained 2% clay, 44% silt and 54% sand fractions (Foli et al., 2015). The chemical composition of the tailings, indicating the enrichment values of some elements and the molar values of Fe2O3 and Al2O3 are presented in Table 5.11.
Table 5.11 Major and trace element composition in tailings using XRF ATD value DTD value Average Crustal Enrichment Element (mg/kg) (mg/kg) values (mg/kg) ATD DTD Al 71690 68370 82300 0.87 0.83 Fe 30220 41300 56300 0.54 0.73 Cu 16 45.3 55 0.29 0.82 Zn 45.4 87.9 70 0.65 1.26 As 681.2 1491 1.8 378.44 828.33 Pb 1.1 7.1 12.5 0.09 0.57 Ca 15570 16610 Mg 17990 24910 S 3572 3331 Major Molar value ATD DTD Molecular Mass Oxide ATD ATD Fe2O3 43220 59060 156.69 0.28 0.38 MgO 29830 41300 SiO2 594600 695100 CaO 21790 23240 SO3 8920 8318 K2O 21900 19860 Al2O3 135400 129200 101.96 1.33 1.27 Molar value = weight in grams/molecular mass Na2O 17700 14500 From Table 5.11, the trace metals, alkali earth metals (Ca and Mg), and Fe2O3, MgO, SiO2, CaO and TiO2 increased in the DTD, while alkali metals (Na and K), SO3, K2O, Al2O3 and Na2O, and S reduced as compared with the ATD. The depletion of S and SO3 indicate oxidation to generate acidic waters, which probably reacted with minerals present in the environment to form secondary minerals (e.g. Bladh, 1982). The enriched elements are generally bulkier and heavier than those that reduced with age hence the higher potential of settling into sediments, while the lighter elements are washed away.
88
For example, George & Ramollo (2014) noted that heavy metals and metalloids such as As have relatively high density compared to water, while divalent elements such as Ca and Mg have heavier atomic masses than the monovalent elements such as Na and K. The presence of CaO and Ca and Mg as cations, confirms that both carbonate and exchanger ion buffering will occur in drainage. Molar values for Fe2O3 and Al2O3 were plotted to determine secondary mineral types that are precipitated in the tailings as presented in Figure 5.16.
2 1.8 Goethite to Jarosite
1.6 Fe2O3 molar
1.4 Jarosite Alunite Goethite
1.2 1 0.8
Goethite + Alunite
0.6 DTD
0.4 0.2
ATD
0 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Al2O3 molar
Figure 5.16 Molar plots for Fe2O3 and Al2O3 in tailings (e.g. Bladh, 1982) From Figure 5.16, the molar ratios of Fe2O3 and Al2O3 in the tailings plotted below the 1:0.5 line, implying that the secondary minerals formed in the tailings are goethite and alunite. The above interpretations are also confirmed by
89
XRD data presented in Table 5.12 and Figure 5.17, while the detailed information is also presented in Appendix 5.9.
Table 5.12 Identified mineral form patterns list from tailings ID Compound Chemical Formula ATD Score A Quartz SiO2 76 B Fe-oxy-OH FeO (OH)2 22 C Goethite Fe2O3H2O 28 D Alunite KAl3(SO4)2(OH)6 2
DTD Score 82 0 14 2
750 650 A B C D
Intensity
550 450
Quartz Fe-oxy-OH Goethite Alunite
350 250 150 50 -50 10
15
20
25
30
35
40
45
50
55
50
55
Angle (2 theta) [ATD]
550
Intensity
450
A C D
350
Quartz Goethite Alunite
250 150 50 -50 10
15
20
25
30 35 40 Angle (2 theta) [DTD]
45
Figure 5.17 XRD plots showing mineral forms in (a) ATD and (b) DTD
90
From Table 5.12 and Figure 5.17, iron oxyhydroxide and goethite are present in the ATD by scores of 22 and 28, as against 0 and 14 in the DTD, respectively, and therefore confirm the formation of goethite and alunite. The disparities of the secondary mineral contents in the two samples are due to the long time frame and the activities on-going at both the ATD and DTD sites.
5.1.6.2 Soil Sample Analysis from Contaminated Sites Leaching results of contaminated soil samples taken from the active borehole (g) sites and the computed As/Fe ratios are presented in Table 5.13.
Table 5.13 Leaching results from the analysis of contaminated sites Site pH (values) As (mg/kg) Fe (mg/kg) As/Fe g1 7.2 2111 35713.5 0.059 g2 7.5 1799 30367 0.059 g3 6.9 2297 32198.5 0.071 g4 6.4 1500 29330 0.051 g5 6.4 680 30665 0.022 g6 7.0 1188 30867 0.038 From Table 5.13, the As/Fe ratio ranged from 0.022 to 0.071 and lie within the range of 0.02-0.1, while the pH range of 6.4-7.2 is greater than 5; this information confirms the criteria outlined by Casiot et al. (2005) for the presence of secondary minerals. The influence of pH and secondary mineral conditions on As mobility in mine geochemical systems was also investigated using process model applications as additional characterisation. 5.1.7
Process Models for As Mobility in Mine Drainage
5.1.7.1 Reductive Dissolution of Iron-oxyhydroxide Reductive dissolution of iron-oxyhydroxide was used to study the release of As into water, based on results from seasonal drainage sampling of the stream (s) and correlations among the parameters are presented in Tables 5.14 and 5.15. 91
Table 5.14 Results and summary statistic in seepage from DTD sites Sampling point (P) HCO3As pH SO421 40.0 1.46 7.2 834.70 2 40.0 2.00 7.0 563.15 3 44.0 4.50 8.9 586.50 4 20.0 0.75 6.8 365.00 5 29.0 1.10 7.2 642.71 6 NA 0.01 NA 945.50 7 11.0 0.01 6.2 4.43 8 30.0 1.17 7.0 747.50 9 27.0 0.91 7.2 623.84 10 20.0 0.11 7.0 490.05 22 25.5 0.17 6.3 320.33 23 12.0 0.07 6.3 3.19 27.1 1.02 7.0 510.58 Mean (x) Standard deviation (σ) 13.34 1.22 2.23 282.9 Coefficient of variation (COV) 0.49 1.19 0.32 0.55
Fe 0.55 1.50 1.08 0.09 0.33 0.17 0.06 0.44 1.08 0.34 0.19 0.10 0.49 0.45 0.92
pH in ‗values‘ and all other parameters are in ‗mg/l‘
Table 5.15 Correlation among the parameters determined from DTD sites Parameter As pH SO42Fe HCO3 0.83 0.75 0.80 0.76 As 0.93 0.31 0.70 pH 0.59 0.60 2SO4 0.36 From Table 5.14, the pH ranges from 6.2 to 8.9, representing a change of 2.7, is favourable to support As mobilisation in drainage by desorption or dilution (Appelo & Posma, 2005). The dissolution of hydrated iron oxide, therefore, constitute a major source of As release in drainage. The high (>1) COV value of 1.19 for As (Table 5.14) suggests that As may establish a concentration trend until an equilibrium is attained. The concentration trend may be as the result of initial rapid dissolution of sulphides in tailings (e.g. Smedley and Kinniburg, 2001), at the early stage of the tailing dam decommissioning process to produce reducing pH. This condition may favour the formation of secondary minerals such as schwertmannite (e.g.
92
Schwertmann & Carlson, 2005; Burton et al., 2009). From Table 5.15, HCO3- has strong relationships with the other parameters, similarly as As with pH and Fe. Generally, the positive correlations suggest the parameters were derived from similar sources (e.g. Appelo & Postma, 2005). For example, the closeness of correlation values of 0.31 and 0.36 between As, Fe and SO42- suggests arsenopyrite (FeAsS) as a typical source. The HCO3- was also plotted against the other parameters as presented in Figure 5.18.
1.6 1.4 1
As (mg/l)
Fe (mg/l)
1.2 0.8 0.6 0.4 0.2 0 10
20 30 40 HCO3- (mg/l)
50
0
9.5 9 8.5 8 7.5 7 6.5 6 5.5 5
SO42- (mg/l)
pH (value)
0
0
10
20 30 40 HCO3- (mg/l)
5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 10
50
900 800 700 600 500 400 300 200 100 0 0
50
20 30 40 HCO3- (mg/l)
10
20
30
40
50
-
HCO3 (mg/l)
Figure 5.18 Fe, As, pH and SO42- against HCO3- during the reductive dissolution of iron-oxyhydroxide at DTD sites In Figure 5.18, pH, As, Fe and SO42- increased in drainage with increased HCO3-; this condition raised the pH of the drainage towards circum-neutral values 93
of between 6.7 to 7.2 (Table 5.7). The geochemical behaviour of As described above is eminent in the research area, as the result of the high sulphide-bearing tailings that are generated from the ores treated. For example, Foli & Nude (2012) observed that, As concentration increased from 0.72 mg/l in the wet season to 1.8 mg/l in the dry season, for seasonal average temperatures of 29oC and 33oC, respectively. The concentration range represents an increment of 250%. The corresponding pH values also ranged from 6.9 in the wet season to 7.4 in the dry season, and thus confirm values presented in Table 5.14. The values of Fe and As presented in Table 5.14 were further compared to observe any stoichiometric relationship between the parameters. Both the Fe and As parameters were also plotted against HCO3- on the 𝑥-axis to investigate the
0.0
0.5 1.0 Fe (mg/l)
4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
1.5
1.0
0.5
0.0 0
1.5
Fe (mg/L)
4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
As (mg/l)
As (mg/l)
relative rates of release of the ions in the drainage, as presented in Figure 5.19.
50 HCO3As (mg/l) Fe (mg/l)
Figure 5.19 As/Fe plots for the relative release of the metals at DTD sites In Figure 5.19(a), 75% data points are below the diagonal line, which implies that there is an unequal release of As and Fe into mine drainage (Pederson et al., 2006). This is confirmed in Figure 5.19(b) where, As data points plotting
94
closer to the 𝑥-axis than Fe, suggest a delay in the release of As, as observed by Kinniburgh et al. (2003) in reactions involving iron-oxyhydroxides. The SO42- was used as a conservative tracer to normalise As and Fe concentrations with respect to water mixing with mine drainage (Foli et al., 2015). The mixing lines were constructed by linking the highest and the least values of SO42- in the plots. Although SO42- is not suitable as a conservative tracer in mine drainage, concentrations are sufficiently high compared to Fe and As, and therefore was used as the best option (Foli et al., 2015). According to Svensson et al. (2012), Cl- is a better conservative tracer, however, due to the relatively low values determined in mine drainage at Obuasi (Foli & Nude, 2012), the SO42- becomes a better conservative tracer. Berger et al. (2000), indicated that low concentrations of the tracer will lead to high analytical
1.6
5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0
1.4 1.2 Fe (mg/l)
As (mg/l)
error. Binary plots of As and Fe against SO42- are shown in Figure 5.20.
1 0.8 0.6 0.4 0.2
0
0
200 400 600 800 1000
0
SO42- (mg/l)
200
400 600 800 1000 SO42- (mg/l)
Figure 5.20 As and Fe against SO42- showing drainage mixing lines during the reductive dissolution of iron-oxyhydroxide at the DTD sites In Figure 5.20, almost all data plotted for both species lie above the mixing line, indicating that the ions were released by desorption from sediments
95
(e.g. Foli et al., 2015). This may be due to high water table fluctuations (e.g. Foli & Nude, 2012) and the high rainfall patterns associated with the area (Dickson & Benneh, 1995), which may have resulted in excessive water contents diluting the background water (e.g. Berger et al., 2000; Svensson et al., 2012). 5.1.7.2 Oxidative Dissolution of As-bearing Sulphide Oxidative dissolution of As bearing sulphide was used to investigate the mechanism for As release into mine drainage. Monitoring results from monitoring borehole (g) are presented in Appendix 5.10, while the means of parameter concentrations are presented in Table 5.16.
Table 5.16 Means of parameter values from water monitoring boreholes Time (months) pH Value As mg/l SO42- mg/l Fe mg/l SO42-/Fe2+ 1 2 3 4 6 7 9 11 12 14 15 17 18 19 20 21 23 24 Mean Minimum Maximum
6.8 6.7 6.8 6.4 6.3 6.4 6.5 6.5 6.5 6.6 6.6 6.4 6.6 6.5 6.2 6.2 5.7 5.9 6.4 5.7 6.8
2.52 2.00 0.93 1.22 1.64 1.00 0.97 0.73 0.71 0.54 0.52 0.31 0.22 0.40 0.23 0.11 0.14 0.11 0.79 0.11 2.52
1235.20 1553.58 1140.95 1121.38 1363.30 1150.00 1170.00 1339.74 1189.65 1233.28 1333.13 1506.88 1469.26 1632.69 1740.00 1360.00 1560.00 1620.00 1373.28 1121.38 1740.00
0.09 0.01 0.01 0.02 0.10 0.10 0.02 0.02 0.12 0.10 0.03 0.11 0.06 0.17 0.10 0.23 0.18 0.19 0.09 0.01 0.23
13724 155358 114095 56069 13633 11500 58500 66987 9914 12333 44438 13699 24488 9604 17400 5913 8667 8526 35825 5913 155358
The binary plots of parameter concentrations determined in the borehole monitoring are presented in Figure 5.21.
96
0.25
3
0.2
2.5 As (mg/l)
Fe (mg/l)
2 0.15 0.1
1
0.05
0.5
0
0 0
5
10 15 20 Time (mth)
25
0
30
1800
7
1700
6.8
1600
6.6
1500 1400 1300 1100
5.8
1000
5.6 10
15
20
15
20
25
30
25
30
6.2 6
5
10
6.4
1200
0
5
Time (mth)
pH (value)
SO42- (mg/l)
1.5
25
30
0
Time (mth)
5
10 15 20 Time (mth)
Figure 5.21 Fe, As, SO42- against time during oxidative dissolution of Asbearing sulphide at DTD sites In Figure 5.21 (a and c), Fe and SO42- increased in drainage, while As and pH values decreased [Figure 5.21 (b and d)]. The increase in SO42- and Fe values suggest the oxidation of Fe2+ to Fe3+ to form HFO precipitates, which probably triggered the reduction of As in drainage. The oxidation process resulted in the increased acidity and the increased SO42- to support the As degradation. In Figure 5.21(d), the rise in pH from the 6th to 16th month probably represents the period of rapid dissolution of carbonates (e.g. Lapakko, 1990b). The above pH range of values falls within the pH of 4.0-7.0 published by Wang & Mulligan (2006) for As stability in drainage generated from high 97
sulphide rich waste. Also, from Table 5.16, SO42-/Fe2+ ratios are several folds (mean 35,827 mg/kg) higher than the required ratio to favour oxidation and dissolution processes (e.g Foli et al., 2015); this confirms the adsorption of As onto the HFOs. Similar as done in the previous segment, SO42- was used as a conservative tracer to interpret the characteristics of metals in mine drainage (Berger et al., 2000; Svensson et al., 2012) as illustrated in Figure 5.22.
3
0.25
2.5
0.2 0.15
Fe (mg/l)
As (mg/l)
2 1.5 1
0.1
0.5
0.05
0
0
1000
1400
1000
1800
2-
SO4 (mg/l)
1400 SO42- (mg/l)
1800
Figure 5.22 As and Fe against SO42- showing drainage mixing lines during the oxidative dissolution of As-bearing sulphide at DTD sites In Figure 5.22(a), a higher percentage (83%) of points are below the mixing line; this implies that adsorption or precipitation of As ions were occurring in the mine drainage (Foli et al., 2015). Also, in Figure 5.22(b), although 56% of Fe released, about 44% falling below the mixing line, indicate a fairly presence of HFOs in the sediment, which then constitutes a good site for adsorption of the released As. Also, from Table 5.16, the mixing ratio of SO42-, estimated as the ratio of the highest and the lowest concentration in drainage is about 1.6:1; this low ratio, compared with the standard of 200:1 (Berger et al., 2000) confirms that As was indeed removed from drainage probably by adsorption. 98
5.2
Drainage Quality Simulations
5.2.1
Modelling and Simulation of Remediation Factors The ABA, TCLP and monitoring data were integrated for the simulation of
relevant As remediation factors, which are parameters that can be assigned values for use as objectives and targets in environmental management plans.
5.2.1.1 Conditions and Simulation Data Layout Some major assumptions made for modelling and simulation are: 1) The limits of the AR range of 0.17-0.05 is empirical and corresponds to the pH limits and [As]max. (100th percentile). 2) The AR of 0.10 corresponds to the neutral value, and equivalent to pH value of 6.0 inferred from Figure 5.14, for minimum As mobility. 3) Both experimental and monitoring data would demonstrate statistical similarities in expressing the behaviour of the remediation factors in water. The best-matched percentile values of the monitored data sets (Appendices 5.2, 5.6 and 5.7) were estimated and listed as shown in Table 5.17.
Table 5.17 Integrated ABA, TCLP and borehole data (Foli et al., 2015) Determinants (A)= As leaching (B)= pH (C)= 2o min. evaluation AR Percentile Ast mg/l Asg mg/l pHt pHg pHt pHg pHc 0.17 100 5.73 2.52 6.9 8.0 0.16 96 4.75 2.00 6.5 7.6 0.15 92 4.44 1.23 6.4 7.4 0.12 65 2.50 0.23 5.7 6.8 0.10 25 1.27 0.01 5.4 6.4 5.4 6.4 5.4 0.08 65 2.50 0.23 5.3 6.2 5.7 6.8 5.9 0.07 92 4.44 1.23 5.0 6.0 6.4 7.4 6.3 0.06 96 4.75 2.00 4.7 5.8 6.5 7.6 6.5 0.05 100 5.73 2.52 4.3 5.4 6.9 8.0 7.0 Data from Table 5.17 were plotted to derive model equations to simulate the remediation factors. 99
5.2.1.2 Arsenic Leaching Risk Characterisation From Table 5.17(A), As concentrations in the borehole (Asg) and the TCLP (Ast) were plotted against the AR on the x-axis as presented in Figure 5.23.
3
0.05
0.097
0.17
0.121
6
TCLP data (t)
1.5
4
3
2.0 mg/l
1
0.5 -B)………..……...…….……........................(5.21), where „K‟ is the gradient, R2 values transformed to the arbitrary constant B. The transformation of the R2 values to negative values (Bi) is an output characteristic of the Excel software (Appendix 5.11). Equations 5.18, 5.19 and 5.20 can alternatively be used to calculate the projected values indicated in Figure 5.25. Using the mean of the gradient (K) in the model equations as 90.3, the models were validated (Foli et al., 2015), as presented in Table 5.18.
Table 5.18 Gradients within which adsorption is feasible (Foli et al., 2015) Medium Slope (K) │Ki-K mean│ % Error in K „g‟ 100 9.7 10 „t‟ 85 5.3 6 „c‟ 86 4.3 5 From Table 5.18, the percentage error estimates are 10% and below. This indicating that model equations 5.18 to 5.20 are consistent. The combined range of 4.3-7.7 and 5.0-9.0 are consistent with the range of pH=4.0-10.0 established from monitoring by Wang and Mulligan (2006). Finally, the AR range of 0.0500.10 is also consistent with the reference model range of 0.10-1.0 (Figure 5.5). According to Blowes (1997), the pH range from 2.5 to 4.5 would result in the formation of secondary minerals such as goethite and gibbsite in decomposing tailings. In this research, the lower limit (pHl) of this pH range in mine drainage is estimated as; ln│B│ while the upper limit (pHu) is also estimated as ln K. This observation is stated based on equation 5.21 as (e.g. Foli et al., 2015): pH={ln│B│- lnK} ………...........…..............................................................(5.22). Substituting the values of B and K from equations 5.18 to 5.20 into equation 22, the ranges for pHt, pHg and pHc are estimated as 2.3-4.4, 2.5-4.6 and 2.3-4.5, respectively (Foli et al., 2015). The zones are validated using the 104
literature value as standard, with the mean difference shown to be less than 10% margin of error, as presented in Table (5.19).
Table 5.19 The pH ranges for secondary mineral buffer Media
pHl
pHu
pH= pHu-pHl
∆pHl
∆pHu
Mean
[Mean
∆׀pH׀
∆׀pH׀/pH]%
„t‟ „g‟ „c‟ Standard
2.26 2.48 2.31 2.50
4.44 4.60 4.45 4.50
2.18 2.12 2.14 2.00
-0.24 -0.02 -0.19 0
-0.06 0.1 -0.05 0
0.15 0.04 0.12 0
6.9 1.9 5.6 0.0
5.2.1.5 Predicted Against Monitored Remediation Factors The predicted and monitored pH values determined from Figures 5.24 and 5.25 were resolved into Cartesian coordinates (x, y) system; with the predicted as the „x' and the monitored as the „y' (Appendix 5.12) as presented in Figure 5.26.
12
Monitored(M) pH
10
8
6
4
2
0 0
2
4
6
8
Predicted(P) pH Figure 5.26 Monitored remediation pH against the predicted 105
10
In Figure 5.26, the plot line was set to the origin because of the intrinsicness of the TCLP and borehole environment (Table 5.9 and Figure 5.14) The model equation and the R2 value for the predicted (P) and monitored (M) remediation factors are related by: 𝑀 = 1.154𝑃….….......……R² = 0.987…………...........……...………...…. (5.23). The constant 1.154 represents the magnitude of the environmental conditions responsible for the geochemical transformations at the contaminated sites.
5.2.2
Arsenic Degradation Characteristics and Risk Evaluation
5.2.2.1 Arsenic Mass-Time Analyses and Isotherms The mass-time analysis was performed on data generated from the boreholes and leachants generated from tailings to evaluate As desorption. As results from 18 water sampling events from borehole sources, spread over 24 months are presented in Table 5.20.
Table 5.20 Arsenic degradation in monitoring boreholes Months 1 2 3 4 5 6 As mg/l 2.52 2.00 0.93 1.22 1.64 Months 9 10 11 12 13 14 As mg/l 0.97 0.73 0.71 0.54 Months 17 18 19 20 21 22 As mg/l 0.31 0.22 0.40 0.23 0.11 -
7 1.00 15 0.52 23 0.14
8 16 24 0.11
A linear regression analysis performed on the above data indicated a regression coefficient (R2) between concentration and time is 0.7967; the data, therefore, demonstrated about 80% significance at 95% confidence level, with the spread of the data lying between 0.45 and 1.135. A t-test statistic showed that the analysis is significant at a p-value of 0.000 (Foli et al., 2013). The analyses described above are presented in Table 5.21.
106
Table 5.21 Statistical analysis ofAs degradation in boreholes Number of obs = 18 Source SS df MS F(1, 16) = 62.70 Model 6.35276 1 6.353 Prob > F = 0.0000 Residual 1.62108 16 0.101 R-squared = 0.7967 Adj R-squared = 0.7840 Total 7.97384 17 0.459 Root MSE = 0.3183 Standard [95% Confidence As Coefficient t P>│t│ Error Interval] Months -0.08116 0.01025 -7.92 0.000 -0.10289 -0.05943 _cons 1.81346 0.14896 12.17 0.000 1.49767 2.12924 From Table 5.21, the high R2 value suggests that As degradation trend can be established from the borehole data. In Table 5.20, the maximum As value of 2.52 mg/l in borehole medium is within the range of TCLP extract UCL value of 2.45 mg/l, which confirms the intrinsicness between the TCLP and borehole data, and therefore, emphasises the need for comparisons between borehole and leaching data. The tailings leaching data, which were generated during 14 sampling events, spread over 30 weeks is presented in Table 5.22.
Table 5.22 Arsenic degradation in tailings leaching Weeks 1 2 3 4 5 6 As (mg/l) 970 130 350 Weeks 11 12 13 14 15 16 As (mg/l) 360 150 120 Weeks 21 22 23 24 25 26 As (mg/l) 60 20 -
7 17 27 20
8 200 18 80 28 -
9 19 29 -
10 270 20 80 30 5
From Table 5.20 and Table 5.22, As degradation curves for both monitoring borehole (g) and the leaching data were plotted using the log concentration of As against time from both media. The composite plots for the two scenarios are presented in Figure 5.27 for interpretations.
107
log (As mg/l)
4 3.6 3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0 -0.4 0 -0.8 -1.2 -1.6 -2
5
10
15
20
25
30
Time (units) Linear (Field)
Linear (Leaching)
Figure 5.27 As mass-time in field and leaching data In Figure 5.27, equations for both the borehole and leaching data, and their associated R2 values are presented respectively as follows (Foli et al., 2013): log As = 0.401 − 0.053t … R2 = 0.913 ...................................................(5.24); log As = 2.891 − 0.057t … (R2 = 0.848)….................................................(5.25). The general form of the above equations is presented as follows: log[Asn ] = log[𝐴𝑠𝑜 ] − mt n .…......................................…..…....…...............(5.26); where 'm' is the slope and „n' a variable. Equations 5.24 and 5.25 represent adsorption and desorption, respectively. The Freundlich and Langmuir isotherms were applied to the monitoring data (Appendix 5.13) to determine degradation properties as presented in Figure 5.28.
108
0.6
3.5
0.4
3.0 log q (mg/l) lab
log q (mg/l) borehole
0.2 0.0
-1.5 -1.0 -0.5 -0.2 0.0
0.5
1.0
1.5
-0.4 -0.6 -0.8
2.5 2.0 1.5 1.0 R² = 0.862
0.5
R² = 0.911
-1.0
0.0
-1.2
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
log C (mg/l) [Field]
log C (mg/l) [Leaching]
1.4
2.5 2.0
1.0
Kd (q/C) lab
Kd (q/C) field
1.2 R² = 0.479
0.8 0.6 0.4
R² = 0.339
1.5 1.0 0.5
0.2
0.0
0.0 0.0
1.0 2.0 q (mg/l) [Field]
0
3.0
250
500
750
1000
q (mg/l) [Leaching]
Figure 5.28 Freundlich (a & b) and Langmuir (c & d) isotherms for field monitoring (g) and leaching data, with their respective R2 values In Figure 5.28, the R2 value of the Langmuir isotherms is lower than those of the Freundlich isotherms. Due to the weaker predictive model determination (R2) value of the Langmuir isotherm and also for better interpretation purposes, as noted by Bethke & Brady (2002), the Freundlich isotherms were used for further evaluations. The Freundlich model equations for both media are presented as: log 𝑞 = 0.615 log 𝐶 − 0.415 … … R2 = 0.912 ……(field)........................(5.27); log 𝑞 = 0.660 log 𝐶 + 0.466 … . . . R2 = 0.862 ……(leaching)..................(5.28). The gradients (m) of 0.615 and 0.660 above are closer to 1 than 0 and suggest that adsorption is a major process responsible for the degradation (Foli et al., 2013). 109
5.2.2.2 Equilibrium Between Borehole and Leaching media From Table 5.20, the initial concentration of 2.52 mg/l of the borehole monitoring data determined as 2.52 mg/l and some selected standard compliance values, such as 2.0 mg/l, 1.0 mg/l, 0.5 mg/l, 0.1 mg/l and 0.01 mg/l relevant for environmental objective and target setting (Foli et al., 2013), were used to estimate corresponding times (t1; t2) for both trends as shown in Table 5.23.
Table 5.23 Time estimates from borehole and leaching (Foli et al., 2013) C (mg/l) t1= (0.401-logAsn)/0.053 C (mg/l) t2=(logAsn+2.891)/0.056 2.52 0 0.01 -16 2.00 2 0.05 -28 1.00 8 0.10 -34 0.50 13 0.50 -46 0.10 26 1.00 -52 0.05 32 2.00 -57 0.01 45 2.52 -59 From Table 5.23, the concentration values were inverted to differentiate between adsorption (t1) and desorption (t2). A statistic test comparing t1 and t2 for equality of the data was then performed as presented in Table 5.24.
Table 5.24 Test of hypothesis of equality of t1 and t2 values (Foli et al., 2013) C mg/l
t1
t2
Mean (m)
∆t1= (t1-tm)
∆t2= (t2-tm)
2.52 2.00 1.00 0.50 0.10 0.05 0.01
0 2 8 13 26 32 45
59 57 52 16 28 34 46
29.5 29.5 30.0 14.6 27.0 33.0 45.5
-29.5 -27.5 -22.0 -1.6 -1.0 -1.0 -0.5
29.5 27.5 22 1.4 1.0 1.0 0.5
│∆t1│/t1 │∆t2│/t2
NA 13.8 2.8 0.1 0.0 0.0 0.0
0.5 0.5 0.4 0.1 0.0 0.0 0.0
From Table 5.24, it is clear that t1 and t2 are equal from the concentration of 0.5 mg/l and below. This is based on the low deviations in t1 and t2 which are equal to or less than 0.1; the compliance values correspond to 1, 2, 3 and 4 years for a closed system. and illustrated in Figure 5.29. 110
0.5
0.4
0.3
As mg/l
0.2
0.1
0 0
R² = 1 (borehole trend)
24 R² = 1 (leaching trend)
Time in months
12
36 R² = 0.999 (merged trend)
Log. (field)
Log. (Leaching)
48 Log. (Merged)
Figure 5.29 Equilibrium between borehole and leaching data In Figure 5.29, the merged equation is expressed in years is as: t n =0.75-0.66 ln[Asn ]………............…….…………………...……...............(5.29); where „t' is time, 𝐴𝑠𝑛 is As concentration „n' a variable. 5.2.2.3 Retardation Factor and Solute Velocity in the Subsurface Mean bulk density (β) and porosity (θ) values in the borehole and pit zones are 2.029 g/cc and 0.411; and 1.975 g/cc and 0.599, respectively (Appendix 5.14). Substituting „β‟, „θ‟, intercepts (A) and „m‟ of equations 5.27 and 5.28, equilibrium concentration (Ce) of 0.5 mg/l into equation 2.8, the retardation factors for the borehole and leached material are 1.96 and 1.86. Using mean groundwater velocity of 3x10−7 ms-1 the solute velocities (equation 2.11) are 1.53 x10−7 ms-1 and 1.61 x10−7 ms-1. The estimated of retardation factor and/or solute velocity in the various media can be used for effective As remediation planning. 111
5.2.2.4 Arsenic Mass-Distance Analysis and Freundlich Isotherm The detailed Arsenic (As) mass-distance monitoring data and summary statistics (Nude et al., 2016), are presented in Table 5.25.
Table 5.25 Stream profile study results for As degradation Sampling point/km Distances (d) in km As1 (mg/l) As2 (mg/l) As3 (mg/l) As4 (mg/l) Mean As(mg/l) [C] MeanAs(mg/l) [q] Log C Log q
P1 0 2.53 2.47 2.45 2.51 2.49 2.49 0.40 0.40
P2 1.13 1.83 0.84 0.81 0.80 1.07 1.42 0.03 0.15
P3 2.42 0.42 0.41 0.41 0.42 0.42 0.65 -0.38 -0.19
P4 3.49 0.23 0.22 0.18 0.20 0.21 0.21 -0.68 -0.68
P5 4.49 0.19 0.20 0.18 0.17 0.19 0.02 -0.72 -1.70
P6 5.23 0.16 0.15 0.12 0.13 0.14 0.05 -0.85 -1.30
P7 6.97 0.13 0.11 0.09 0.11 0.11 0.03 -0.96 -1.52
P8 9.00 0.08 0.10 0.10 0.09 0.09 0.02 -1.05 -1.70
From Table 5.25, „q‟ is the concentration of adsorbate on the solid at equilibrium (EPA, 2007), and C is the concentration in solution. Plots for As
3
0.6
2.5
0.3
2
0.0 log q
As (mg/l)
degradation and the Freundlich isotherm are presented in Figure 5.30.
1.5
-2.0
-1.0
-0.3
1
0.0
1.0
-0.6
0.5
-0.9
0 0
2
-1.2 log C
4 6 8 10 12 14 Distance (km)
Figure 5.30(a) As Mass-distance plot and (b) the Freundlich isotherm From Figure 5.30(a) As mass-distance degradation model, with the 𝑦intercept set to the initial As concentration of 2.49 mg/l is described by: 𝐶 = 2.49𝑒 −0.46𝑑 ................ (R2= 0.745) or, 𝑑𝑛 =2.174 𝑙𝑛
2.49 𝐶𝑛
.................... (5.30),
where C is the initial concentration in mg/l and „d' is the distance in kilometres. 112
The plot intercept was set to 2.49 mg/l, in order to avoid underestimation of the initial concentration in the model. From equation 5.30, the remediation distances of 3, 7, 9 and 12 km were estimated by substituting the compliance values of 0.5, 0.1, 0.05 and 0.01 mg/l. In Figure 5.30(b) the Freundlich isotherm is given by: log q = 0.542 log C − 0.079 ....…..... (R² = 0.850)…….…........................ (5.31). The gradient of 0.542 is >0.5, indicating that degradation of As is by adsorption.
5.2.2.5 Retardation Factor and Solute Velocity Along Streambed The pit or disturbed material is comparable to the streambed (Nude et al., 2016), hence bulk density (β) and porosity (θ) in pit material were used for the streambed. Using the y-axis intercept (A) and gradient (m) from equation 5.30; and equilibrium concentration for attenuation (Ce), the retardation factor (Rf) and the solute velocity (sV) are estimated from equations 2.8 and 2.10 as 1.074 and 9.25x10−1 ms-1, respectively (Nude et al., 2016).
5.2.2.6 Arsenic Impact Intensity Model Evaluation Substituting the mean value of 2.50 mg/l for both borehole and streambed in equations 5.24 and 5.30, the relative percentages of As intensities between consecutive compliance values, as defined for this research are estimated over time (t) and distance (d) intervals (Nude et al., 2016) are presented in Table 5.26.
Table 5.26 Variation of As compliance values in borehole (field) and stream Asn t d Mass in water As (mg/l) Time Distance % (mg/l) yrs km Asn /As1% Range Range Range Range 2.50 0 0 100 0.50 1 3 20 >0.50 0-1 0-3 100-20 0.10 2 7 4 0.50-0.10 1-2 3-7 20-4 0.05 3 8 2 0.10-0.05 2-3 7-8 4-2 0.01 4 12 0.4 0.05-0.01 3-4 8-12 2-0.4 4 >12
