Appendix H - ABRIDGED Baseline Geochemical Characterization

Technical Report
2023 Prefeasibility Study
Baseline Geochemical Characterization
Kings Mountain Mining Project Rev06
Effective Date: April 17, 2024 ABRIDGED STUDY that includes the
Report Date: April 17, 2024
Report Prepared for report text and Appendix A. The other
deleted appendices may be made
available upon request.
AALBEMARLE"
Albemarle Corporation
4250 Congress Street
Charlotte, NC 28209
Report Prepared by
srk consulting
SRK Consulting (U.S.), Inc.
999 17t" Street, Suite 400
Denver, CO 80202
SRK Project Number: USPR000576
Albemarle Document Number: KM60-EN-RP-9055
Signed by Qualified Persons:
Amy Prestia, Principal Consultant (Geochemistry)
Reviewed by:
Rob Bowell, Corporate Consultant (Geochemistry and Geometallurgy)

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2023 PFS Report—Kings Mountain Mining Project Page ii
Table of Contents
1 Introduction.................................................................................................................. 1
1.1 Study Objectives.................................................................................................................................1
2 Environmental Setting................................................................................................. 3
2.1 Project Location ..................................................................................................................................3
2.2 Mining History and Site Conditions.....................................................................................................4
2.3 Mine Plan ............................................................................................................................................4
2.4 Project Climate....................................................................................................................................5
2.5 Geology and Mineralization ................................................................................................................5
2.6 Kings Mountain Material Types...........................................................................................................9
2.7 Conceptual Model and Program Design.............................................................................................9
3 Sample Collection...................................................................................................... 11
3.1 Future Waste Rock and Ore.............................................................................................................11
3.2 Future Tailings ..................................................................................................................................13
3.3 Cover Material...................................................................................................................................17
3.4 Legacy Mining Waste........................................................................................................................17
3.5 Kings Creek Stream Sediment..........................................................................................................17
4 Laboratory Methods................................................................................................... 22
4.1 General .............................................................................................................................................22
4.1.1 Future Waste Rock and Ore..................................................................................................23
4.1.2 Future Tailings Material.........................................................................................................24
4.1.3 Legacy Mining Waste, Cover Material, and Kings Creek Stream Sediment.........................26
4.2 Stage 1 Static Test Methods.............................................................................................................26
4.2.1 Paste pH................................................................................................................................26
4.2.2 ABA.......................................................................................................................................26
4.2.3 Methodology for Calculating Surrogate AP and NP..............................................................28
4.2.4 Total Carbon, Total Organic Carbon, and Total Inorganic Carbon .......................................28
4.2.5 NAG Testing..........................................................................................................................28
4.2.6 Multi-Element Analysis..........................................................................................................29
4.3 Mineralogical Analyses .....................................................................................................................30
4.4 Short-Term Leach Test Methods ......................................................................................................30
4.4.1 Modified SPLP.......................................................................................................................30
4.4.2 MWMP...................................................................................................................................30
4.4.3 LEAF Testing.........................................................................................................................31
4.4.4 TCLP .....................................................................................................................................32
4.4.5 Short-Term Leach Test Sample Selection ............................................................................32
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4.5 Kinetic Test Methods.........................................................................................................................34
4.5.1 Humidity Cell Tests ...............................................................................................................34
4.5.2 HCT Sample Selection..........................................................................................................34
5 Results and Discussion............................................................................................. 38
5.1 Future Waste Rock and Ore .............................................................................................................38
5.1.1 Mineralogy.............................................................................................................................38
5.1.2 Paste pH................................................................................................................................42
5.1.3 ABA.......................................................................................................................................45
5.1.4 NAG.......................................................................................................................................52
5.1.5 Mineralogical Controls on Acid Generation Potential............................................................54
5.1.6 Summary of Acid Generation Potential Based on ABA and NAG Test Results ...................58
5.1.7 Multi-Element Analysis..........................................................................................................58
5.1.8 Short-Term Leach Tests........................................................................................................60
5.1.9 HCT.......................................................................................................................................72
5.1.10 Comparison of Static and Kinetic Test Results.....................................................................95
5.2 Future Tailings Material ....................................................................................................................98
5.2.1 Mineralogy (SRK Program)...................................................................................................98
5.2.2 ABA.....................................................................................................................................100
5.2.3 NAG.....................................................................................................................................105
5.2.4 Multi-Element Analysis........................................................................................................108
5.2.5 Short-Term Leach Tests......................................................................................................110
5.2.6 Tailings Filtrate Analysis......................................................................................................120
5.2.7 HCT.....................................................................................................................................122
5.3 Cover Material.................................................................................................................................139
5.3.1 SWCA Soil Baseline Study..................................................................................................139
5.3.2 ABA.....................................................................................................................................141
5.3.3 NAG.....................................................................................................................................144
5.3.4 Multi-Element Analysis........................................................................................................146
5.3.5 Short-Term Leach Tests......................................................................................................149
5.4 Legacy Mining Waste......................................................................................................................153
5.4.1 ABA.....................................................................................................................................153
5.4.2 NAG.....................................................................................................................................157
5.4.3 Multi-Element Analysis........................................................................................................160
5.4.4 Lysimeter Tests...................................................................................................................161
5.4.5 Short-Term Leach Tests......................................................................................................162
6 Kings Creek Stream Sediment................................................................................ 172
6.1 ABA 172
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6.1.1 NAG Testing........................................................................................................................173
6.1.2 Multi-Element Analysis........................................................................................................174
6.1.3 Short-Term Leach Tests......................................................................................................175
7 Waste Rock Management........................................................................................ 179
7.1 Site-Specific PAG Estimation..........................................................................................................181
7.1.1 General Approach ...............................................................................................................182
7.1.2 Calculations of AP and NP..................................................................................................182
7.1.3 Case 1 —Traditional Sobek.................................................................................................183
7.1.4 Case 2 —Adjustment of AP to Account for Reduced Reactivity of Sulfides........................184
7.1.5 Case 3—Adjustment of NP to Account for Acid Neutralizing Calcium Silicates.................186
7.1.6 Case 4—Adjustment of Both NP and AP Consistent with Case 2 and 3 ...........................187
7.1.7 Case 5 (Average Case 1 through 4) ...................................................................................188
7.1.8 PAG Estimates....................................................................................................................190
7.1.9 Limitations and Uncertainties on PAG Estimation ..............................................................191
8 Conclusions ............................................................................................................. 192
8.1 Future Waste Rock and Ore ...........................................................................................................192
8.2 Future Tailings Material ..................................................................................................................193
8.3 Cover Material.................................................................................................................................194
8.4 Legacy Mining Waste......................................................................................................................195
8.4.1 Legacy Waste Rock ............................................................................................................195
8.4.2 Legacy Tailings ...................................................................................................................195
8.5 Kings Creek Stream Sediment........................................................................................................196
9 References................................................................................................................ 197
Disclaimer...................................................................................................................... 200
Copyright ....................................................................................................................... 200
List of Tables
Table 2-1: Kings Mountain Geochemical Program Material Types....................................................................9
Table 2-2: Materials Requiring Characterization ..............................................................................................10
Table 3-1: Kings Mountain Sample Matrix........................................................................................................12
Table 3-2 Solid Tailings Samples Included in Characterization Program ........................................................14
Table 4-1: Waste Rock and Ore Sample Frequency and Testing Matrix.........................................................24
Table 4-2: Tailings Sampling and Testing Matrix .............................................................................................25
Table 4-3: Sampling and Testing Matrix for Legacy Mining Waste, Cover Material, and Kings Creek Stream
Sediment..............................................................................................................................................26
Table 4-4: Static Data Non-PAG vs. PAG Criteria Summary...........................................................................27
Table 4-5: Acid Generation Criteria for NAG Results.......................................................................................29
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Table 4-6: Interpretation of GAI Values............................................................................................................30
Table 4-7: Short-Term Leach Test Matrix (Future Waste Rock Composites)..................................................33
Table 4-8: Summary of Future Waste Rock and Ore HCT Samples................................................................37
Table 5-1: Historical Summary of Mineralogy of the Kings Mountain Pegmatites and Contact Wallrock Zones
.............................................................................................................................................................39
Table 5-2: Summary of ABA and NAG Data— Future Waste Rock and Ore...................................................44
Table 5-3: Summary of XRD, ABA and NAG Data for Subset of Waste Rock Samples .................................56
Table 5-4: Summary of Multi-Element Assay Data— Future Waste Rock and Ore .........................................59
Table 5-5: Summary of Future Waste Rock and Ore HCT Static Data and Status..........................................74
Table 5-6: HCT Results within 1 Ox of Reported Blank Concentrations............................................................95
Table 5-7: Mineralogy Results Summary— Future Tailings and OSR .............................................................99
Table 5-8: Summary of ABA Results— Future Tailings and OSR..................................................................101
Table 5-9: Summary of NAG Results - Future Tailings and OSR..................................................................106
Table 5-10: Summary of Multi-Element Assay Data - Future Tailings and OSR ...........................................109
Table 5-11: Short Term Leach Test Matrix— Future Tailings and Ore Sorting Rejects.................................110
Table 5-12: Radiochemistry on Future Tailings and OSR SPLP and MWMP Leachates..............................114
Table 5-13: TCLP Results for Comp 1 Float Tailings and Comp 2 Float Tailings..........................................115
Table 5-14: Geochemistry of Filtrate Solutions from Metallurgical test work.................................................121
Table 5-15: Summary of Future Ore Sorting Rejects and Tailings HCT Static Data and Status...................123
Table 5-16: Summary of Multi-Element Assay Data—Cover Material Sonic Samples..................................147
Table 5-17: Summary of Multi-Element Assay Data—Cover Material from Exploration Database...............148
Table 5-18: Summary of ABA and NAG Results— Legacy Mining Waste.....................................................153
Table 5-19: Summary of Multi-Element Assay Data— Legacy Mining Waste................................................161
Table 5-20: Lysimeter Test Results (Legacy Tailings) ...................................................................................162
Table 5-21: TCLP Results for Legacy Tailings Samples................................................................................171
Table 6-1: Summary of ABA and NAG Test Results - Kings Creek Stream Sediment..................................172
Table 6-2: Summary of Multi-Element Assay Data—Stream Sediment ........................................................175
Table 7-1: Range of Uncertainty in AP and NP Estimation............................................................................182
Table 7-2: Average Sulfide Content from XRD ..............................................................................................185
Table 7-3: AP and NP Calculations for Incorporation into Block Model.........................................................189
Table 7-4: PAG Estimates—Schafer and SRK Approach..............................................................................190
List of Figures
Figure2-1: Location Map....................................................................................................................................3
Figure 2-2: Area of Flocculated Iron Solids Associated with Underdrain Discharge in the Diversion Creek.....4
Figure 2-3: Generalized Stratigraphic Column...................................................................................................6
Figure 2-4: Geologic Model Plan View...............................................................................................................7
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Figure 2-5: Geologic Model Cross-Sectional View.............................................................................................8
Figure 3-1: Geochemical Characterization Samples in Context of March 2023 Shell .....................................12
Figure 3-2: Generalized Flow Diagram for the 2018 Met Program ..................................................................15
Figure 3-3: Generalized Flow Diagram for the 2022 Met Programs ................................................................16
Figure 3-4: Alluvium Sample Locations............................................................................................................18
Figure 3-5: Saprolite Sample Locations ...........................................................................................................19
Figure 3-6: Legacy Waste Rock Sample Locations .........................................................................................20
Figure 3-7: Legacy Tailings Sample Locations ................................................................................................21
Figure 4-1: Characteristics of Sub-Samples Utilized to Prepare Four Composites of the Amphibolite Gneiss-
SchistMaterial .....................................................................................................................................33
Figure 4-2: Waste Rock and Ore Neutralizing Potential Ratio vs. Net Neutralizing Potential..........................35
Figure 4-3: Waste Rock and Ore Acid Generating Potential vs. Neutralizing Potential...................................36
Figure 4-4: Waste Rock and Ore NAG pH vs. Net Acid Generation ................................................................36
Figure 5-1: Summary of XRD Analysis for Waste Rock and Ore Samples......................................................41
Figure 5-2: Paste pH Plotted as a Function of Total Sulfur Content—Waste Rock and Ore...........................43
Figure 5-3: Sulfide Sulfur Plotted as a Function of Total Sulfur— Future Waste Rock and Ore......................46
Figure 5-4: NP (TIC) Plotted as a Function of Measured NP (Sobek)— Future Waste Rock and Ore............47
Figure 5-5: NP versus AP— Future Waste Rock and Ore................................................................................49
Figure 5-6: NPR versus Paste pH — Future Waste Rock and Ore...................................................................50
Figure 5-7: NPR versus Sulfide Sulfur— Future Waste Rock and Ore ............................................................51
Figure 5-8: NNP versus NPR— Future Waste Rock and Ore ..........................................................................51
Figure 5-9: Total NAG versus NAG pH Plot— Future Waste Rock and Ore....................................................53
Figure 5-10: NPR versus NAG pH — Future Waste Rock and Ore...................................................................54
Figure 5-11: Future Waste Rock and Ore SPLP Constituent Release as a Function of pH —Sulfate, Aluminum,
Manganese, and Zinc..........................................................................................................................61
Figure 5-12: Future Waste Rock and Ore SPLP Constituent Release as a Function of pH —Arsenic,Antimony,
Selenium, and Uranium.......................................................................................................................62
Figure 5-13: Future Waste Rock and Ore SPLP Constituent Release as a Function of pH—Fluoride and Lithium
.............................................................................................................................................................63
Figure 5-14: LEAF 1313 Test Results (Future Waste Rock and Ore)—Aluminum, Arsenic, Cobalt, and Lithium
.............................................................................................................................................................66
Figure 5-15: EPA 1316 Test Results (Future Waste Rock and Ore) —Aluminum, Arsenic, Cobalt, and Lithium
.............................................................................................................................................................67
Figure 5-16: Evolution of pH as a function of L/S (EPA 1314).........................................................................69
Figure 5-17: Filtered concentration of Calcium, Sulfate, Arsenic and Selenium (EPA 1314) ..........................70
Figure 5-18: Cumulative release curves for Aluminum, Arsenic, Selenium and Lithium (EPA 1314)..............71
Figure 5-19: Future Waste Rock and Ore HCT Effluent pH.............................................................................75
Figure 5-20: Future Waste Rock and Ore HCT Neutralizing Potential Remaining ..........................................75
Figure 5-21: Future Waste Rock and Ore HCT Ficklin Plot .............................................................................76
Figure 5-22: Future Waste Rock and Ore HCT Effluent Electrical Conductivity..............................................76
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Figure 5-23: Future Waste Rock and Ore HCT Effluent Alkalinity ...................................................................77
Figure 5-24: Future Waste Rock and Ore HCT Effluent Sulfate ......................................................................77
Figure 5-25: Future Waste Rock and Ore HCT Effluent Iron ...........................................................................78
Figure 5-26: Future Waste Rock and Ore HCT Effluent Aluminum .................................................................78
Figure 5-27: Future Waste Rock and Ore HCT Effluent Arsenic......................................................................79
Figure 5-28: Future Waste Rock and Ore HCT Effluent Antimony ..................................................................79
Figure 5-29: Future Waste Rock and Ore HCT Effluent Beryllium...................................................................80
Figure 5-30: Future Waste Rock and Ore HCT Effluent Cadmium ..................................................................80
Figure 5-31: Future Waste Rock and Ore HCT Effluent Cobalt.......................................................................81
Figure 5-32: Future Waste Rock and Ore HCT Effluent Copper......................................................................81
Figure 5-33: Future Waste Rock and Ore HCT Effluent Gross Alpha..............................................................82
Figure 5-34: Future Waste Rock and Ore HCT Effluent Lead..........................................................................82
Figure 5-35: Future Waste Rock and Ore HCT Effluent Manganese...............................................................83
Figure 5-36: Future Waste Rock and Ore HCT Effluent Mercury.....................................................................83
Figure 5-37: Future Waste Rock and Ore HCT Effluent Nickel........................................................................84
Figure 5-38: Future Waste Rock and Ore HCT Effluent Selenium ..................................................................84
Figure 5-39: Future Waste Rock and Ore HCT Effluent Uranium....................................................................85
Figure 5-40: Future Waste Rock and Ore HCT Effluent Zinc...........................................................................85
Figure 5-41: Future Waste Rock and Ore HCT Effluent Carbonate Molar Ratio (defined as the ratio
(Ca+Mg)/SO4 in molar ratios).............................................................................................................86
Figure 5-42: Future Waste Rock and Ore HCT Constituent Release as a Function of pH — Sulfate, Aluminum,
Manganese, and Zinc..........................................................................................................................87
Figure 5-43: Future Waste Rock and Ore HCT Constituent Release as a Function of pH —Arsenic, Antimony,
Selenium, and Uranium.......................................................................................................................88
Figure 5-44: Future Waste Rock and Ore HCT Constituent Release as a Function of pH—Fluoride and Lithium
.............................................................................................................................................................89
Figure 5-45: NNP versus Current HCT pH — Future Waste Rock and Ore......................................................96
Figure 5-46: NPR versus Current HCT pH — Future Waste Rock and Ore......................................................97
Figure 5-47: NAG pH versus Current HCT pH — Future Waste Rock and Ore................................................97
Figure 5-48: Sulfide Sulfur Plotted as a Function of Total Sulfur— Future Tailings and OSR .......................102
Figure 5-49: Total Sulfur Plotted against NPR— Future Tailings and OSR ...................................................102
Figure 5-50: Sulfide-Sulfur versus Paste pH, Future Tailings and OSR........................................................103
Figure 5-51: NP versus AP, Future Tailings Samples— Future Tailings and OSR........................................103
Figure 5-52: NNP versus NPR, Future Tailings Samples— Future Tailings and OSR...................................104
Figure 5-53: Sulfide Sulfur versus NAG Final pH — Future Tailings and OSR...............................................107
Figure 5-54: NPR versus NAG pH, Future Tailings Samples— Future Tailings and OSR.............................107
Figure 5-55: Net Acid Generation Value versus NAG Final pH — Future Tailings and OSR..........................108
Figure 5-56: Future Tailings and OSR MWMP/SPLP Constituent Release as a Function of pH — Sulfate,
Aluminum, Manganese, and Zinc......................................................................................................111
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Figure 5-57: Future Tailings and OSR MWMP/SPLP Constituent Release as a Function of pH — Arsenic,
Antimony, Selenium, and Uranium....................................................................................................112
Figure 5-58: Future Tailings and OSR MWMP/SPLP Constituent Release as a Function of pH — Fluoride and
Lithium ...............................................................................................................................................113
Figure 5-59: Example LEAF 1313 Test Results(Future Tailings and Ore Sorting Rejects)—Aluminum,Arsenic,
Cobalt, and Lithium............................................................................................................................116
Figure 5-60: Example EPA 1316 Test Results (Future Tailings and Ore Sorting Rejects)—Aluminum, Arsenic,
Cobalt, and Lithium............................................................................................................................117
Figure 5-61: Example EPA 1314 Test Results (Future Tailings) — Aluminum, Arsenic, Selenium and Lithium
...........................................................................................................................................................118
Figure 5-62: pH Variation in EPA1316 Method as a Function of L/S Ratio — Future Tailings and OSR........120
Figure 5-63: Future Ore Sorting Rejects and Tailings HCT Effluent pH ........................................................124
Figure 5-64: Future Ore Sorting Rejects and Tailings HCT Neutralizing Potential Remaining......................124
Figure 5-65: Future Ore Sorting Rejects and Tailings HCT Ficklin Plot.........................................................125
Figure 5-66: Future Ore Sorting Rejects and Tailings HCT Effluent Electrical Conductivity..........................125
Figure 5-67: Future Ore Sorting Rejects and Tailings HCT Effluent Alkalinity...............................................126
Figure 5-68: Future Ore Sorting Rejects and Tailings HCT Effluent Sulfate..................................................126
Figure 5-69: Future Ore Sorting Rejects and Tailings HCT Effluent Iron.......................................................127
Figure 5-70: Future Ore Sorting Rejects and Tailings HCT Effluent Aluminum.............................................127
Figure 5-71: Future Ore Sorting Rejects and Tailings HCT Effluent Arsenic.................................................128
Figure 5-72: Future Ore Sorting Rejects and Tailings HCT Effluent Antimony..............................................128
Figure 5-73: Future Ore Sorting Rejects and Tailings HCT Effluent Cadmium..............................................129
Figure 5-74: Future Ore Sorting Rejects and Tailings HCT Effluent Copper.................................................129
Figure 5-75: Future Ore Sorting Rejects and Tailings HCT Effluent Gross Alpha.........................................130
Figure 5-76: Future Ore Sorting Rejects and Tailings HCT Effluent Lead.....................................................130
Figure 5-77: Future Ore Sorting Rejects and Tailings HCT Effluent Manganese..........................................131
Figure 5-78: Future Ore Sorting Rejects and Tailings HCT Effluent Mercury................................................131
Figure 5-79: Future Ore Sorting Rejects and Tailings HCT Effluent Nickel ...................................................132
Figure 5-80: Future Ore Sorting Rejects and Tailings HCT Effluent Selenium..............................................132
Figure 5-81: Future Ore Sorting Rejects and Tailings HCT Effluent Zinc......................................................133
Figure 5-82: Future Ore Sorting Rejects and Tailings HCT Effluent Carbonate Molar Ratio.........................134
Figure 5-83: Future Ore Sorting Rejects and Tailings HCT Constituent Release as a Function of pH —Sulfate,
Aluminum, Manganese, and Zinc......................................................................................................135
Figure 5-84: Future Ore Sorting Rejects and Tailings Constituent Release as a Function of pH — Arsenic,
Antimony, Selenium, and Uranium....................................................................................................136
Figure 5-85: Future Ore Sorting Rejects and Tailings Constituent Release as a Function of pH — Fluoride and
Lithium ...............................................................................................................................................137
Figure 5-86: Soil pH Distribution by Horizon ..................................................................................................140
Figure 5-87: Paste pH Plotted as a Function of Total Sulfur Content—Cover Material.................................141
Figure 5-88: NP Plotted as a Function of AP—Cover Material......................................................................142
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Figure 5-89: Surrogate NP Plotted as a Function of Surrogate AP (Exploration Database Samples) — Cover
Material..............................................................................................................................................142
Figure 5-90: NNP Plotted as a Function of NPR—Cover Material ................................................................143
Figure 5-91: Surrogate NNP Plotted as a Function of Surrogate NPR(Exploration Database Samples)—Cover
Material..............................................................................................................................................143
Figure 5-92: NAG as a Function of NAG pH —Cover Material.......................................................................145
Figure 5-93: NAG pH versus Total Sulfur—Cover Material...........................................................................145
Figure 5-94: Alluvium and Saprolite SPLP Constituent Release as a Function of pH — Sulfate, Aluminum,
Manganese, and Zinc........................................................................................................................150
Figure 5-95: Alluvium and Saprolite SPLP Constituent Release as a Function of pH — Arsenic, Antimony,
Selenium, and Uranium.....................................................................................................................151
Figure 5-96: Alluvium and Saprolite SPLP Constituent Release as a Function of pH — Fluoride and Lithium
...........................................................................................................................................................152
Figure 5-97: Paste pH Plotted as a Function of Total Sulfur Content—Legacy Waste Rock........................153
Figure 5-98: NP Plotted as a Function of Acid Generating Potential — Legacy Waste Rock.........................154
Figure 5-99: NNP Plotted as a Function of NPR— Legacy Waste Rock........................................................154
Figure 5-100: Paste pH Plotted as a Function of Total Sulfur Content— Legacy Tailings.............................156
Figure 5-101: NP Plotted as a Function of Acid Generating Potential —Legacy Tailings..............................156
Figure 5-102: NNP Plotted as a Function of NPR— Legacy Tailings.............................................................157
Figure 5-103: NAG as a Function of NAG pH—Legacy Waste Rock.............................................................158
Figure 5-104: NAG as a Function of Sulfide Sulfur— Legacy Waste Rock....................................................158
Figure 5-105: NAG as a Function of NAG pH — Legacy Tailings...................................................................159
Figure 5-106: NAG as a Function of Sulfide Sulfur— Legacy Tailings...........................................................160
Figure 5-107: Future and Legacy Waste Rock and SPLP Constituent Release as a Function of pH —Sulfate,
Aluminum, Manganese, and Zinc......................................................................................................164
Figure 5-108:Future and Legacy Waste Rock and SPLP Constituent Release as a Function of pH —Arsenic,
Antimony, Selenium, and Uranium....................................................................................................165
Figure 5-109: Future and Legacy Waste Rock SPLP Constituent Release as a Function of pH — Fluoride and
Lithium ...............................................................................................................................................166
Figure 5-110: Future and Legacy Tailings and SPLP Constituent Release as a Function of pH — Sulfate,
Aluminum, Manganese, and Zinc......................................................................................................167
Figure 5-111: Future and Legacy Tailings and SPLP Constituent Release as a Function of pH — Arsenic,
Antimony, Selenium, and Uranium....................................................................................................168
Figure 5-112: Future and Legacy Tailings SPLP Constituent Release as a Function of pH — Fluoride, Lithium
andMercury.......................................................................................................................................169
Figure 6-1: AP versus NP— Kings Creek Stream Sediment..........................................................................172
Figure 6-2: Sulfide Sulfur versus NPR— Kings Creek Stream Sediment.......................................................173
Figure 6-3: NAG pH versus NAG — Kings Creek Stream Sediment...............................................................174
Figure 6-4: Kings Creek Stream Sediment SPLP Constituent Release as a Function of pH—Sulfate,Aluminum,
Manganese, and Zinc........................................................................................................................176
Figure 6-5: Kings Creek Stream Sediment SPLP Constituent Release as a Function of pH—Arsenic,Antimony,
Selenium, and Uranium.....................................................................................................................177
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Figure 6-6: Kings Creek Stream Sediment SPLP Constituent Release as a Function of pH —Fluoride, Lithium,
Lead, and Copper..............................................................................................................................178
Figure 7-1: General Waste Rock Management Approach for Kings Mountain ..............................................180
Figure 7-2: Average Amphibole Gneiss-Schist Results Demonstrating Range of Uncertainty in AP and NP
Estimation..........................................................................................................................................183
Figure 7-3: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 1.....................184
Figure 7-4: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 2.....................185
Figure 7-5: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 3.....................187
Figure 7-6: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 4.....................188
Figure 7-7: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 5.....................189
Appendices
Appendix A: Sample Locations and Distribution
Appendix B: Composite and HCT Sample Selection Graphs
Appendix C: Tabulated Mineralogy Data and Petrolab Mineralogical Reports
Appendix Cl: Tabulated Mineralogy Data
Appendix C2: Petrolab Mineralogical Data
Appendix C3: SGS Laboratory Reports
Appendix D: Tabulated Stage 1 Static Test Data
Appendix D1: Tabulated ABA Data
Appendix D2: Tabulated Multi-Element Data
Appendix E: Tabulated Short Term Leach Data
Appendix E1: Tabulated SPLP Data
Appendix E2: Tabulated LEAF EPA Method 1313 Data
Appendix E3: Tabulated LEAF EPA Method 1314 Data
Appendix E4: Tabulated LEAF EPA Method 1316 Data
Appendix F: Stage 1 Static Test Laboratory Reports
Appendix F1: ABA Laboratory Reports
Appendix F2: Multi-Element Reports
Appendix G: Short Term Leach Tests Laboratory Reports
Appendix H: Humidity Cell Test Laboratory Reports
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Executive Summary
SRK Consulting (U.S.), Inc. (SRK) was engaged by Albemarle Corporation (Albemarle) to conduct a
comprehensive baseline geochemical characterization of the various geological materials that are
anticipated to be encountered at the Kings Mountain Mining Project (KMMP or Project) as part of the
mine redevelopment.The purpose of the characterization program was to define the potential for future
mining wastes, including tailings, waste rock, ore, and pit wall rock, to generate acid or leach
deleterious constituents to support the mine planning and impact assessment for the Project.
The geochemical characterization program is ongoing, and results available to date, including data
collected from the ongoing static and kinetic test work programs, has been compiled, analyzed, and
summarized in this report. This report has been prepared for the North Carolina Department of
Environmental Quality (DEQ) Division of Energy, Minerals and Land Resources (DEMLR) to provide
a description of the characterization program approach, methods and results. The quality of water that
comes into contact with mine waste is described in a related report by SRK(2023).
To accomplish the objectives of the study, representative samples of the various materials have been
collected and characterized following guidelines consistent with the Global Acid Rock Drainage
(GARD) Guide (International Network for Acid Prevention (INAP), 2014), the Mine Environment
Neutral Drainage (MEND) Program Prediction Manual for Drainage Chemistry from Sulfidic Geologic
Materials(MEND,2009), and the U.S. Department of the Interior—Bureau of Land Management(BLM)
Instruction Memorandum NV-2010-014, Nevada Bureau of Land Management Rock Characterization
Resources and Water Analysis Guidance for Mining Activities (BLM, 2013). These are internationally
recognized guidance documents for the geochemical characterization of geological materials and are
derived from best practices of the mining industry, government, academia, and community groups
conducting mine site drainage chemistry assessment and predictions.
Recognizing that the work performed as part of the geochemical characterization study will be needed
to support permitting efforts for the Project, SRK developed an approach for a purpose-built
characterization program that focuses on the following materials and aspects:
• Assessment of waste rock geochemistry to provide a prediction of the potential geochemical
reactivity and chemical stability of future waste rock and construction material for the Project,
and to support predictions of contact water (run-off and seepage) chemistry associated with
the future rock storage facilities (RSFs).
• Evaluation of tailings material geochemistry to support predictions of contact water chemistry
that may change over time and would influence the design, operation, and closure of the
tailings storage facility(TSF).
• Determination of final pit wall geochemistry to define the control that the pit wall rocks would
have on the chemistry of waters removed from the pit during operations and pit lake that will
form after closure.
• Geochemical characterization of legacy wastes in the Project area to determine the
weathering and acid rock drainage (ARD) and metal leaching (ML) characteristics of legacy
rock dumps and tailings that have been exposed to oxidation processes at the surface.
• Geochemical characterization of potential future cover materials (alluvium and saprolite).
• Geochemical characterization of stream sediment in Kings Creek.
The results of the geochemical characterization test work provide a basis for the assessment for
ARDML to support predictions of future contact water quality(i.e., runoff and seepage)associated with
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the future mine facilities. In turn,these results inform decisions on engineering designs, mine planning,
and waste rock and tailings management.
A conceptual geochemical model was developed based on the deposit geology combined with the
proposed mining and processing methods. This model provides the basis for the scope and
methodology of the geochemistry baseline study and defines the approach for sample selection,
laboratory procedures, and key criteria for decision-making throughout the process.
The geochemical test methods selected for this Project include both static and kinetic testing that are
designed to address the bulk geochemical characteristics of the samples and to assess the potential
of the materials to generate acid and/or release metals into solution.
Static testing methods are used to characterize acid generation and metal leaching characteristics of
material at the time of testing and do not account for temporal changes that may occur in the material
as chemical weathering proceeds. Static tests indicate the amount of acid generating and acid
neutralizing components that are present in the material and provide information on the bulk
geochemical properties (e.g., metal composition, mineralogical composition, and leaching potential).
While the results can be used to calculate the overall acid generation or neutralizing potential of the
sample, they do not provide information on the rate at which acid generating and acid neutralizing
reactions will take place.
Kinetic testing methods are used to evaluate the rate of sulfide oxidation and metal release over time
by weathering of mine waste in a column using alternating wet and dry cycles of air and periodic
flushing with water. The objective of kinetic testing is to determine reaction rates under specific
geochemical conditions (e.g., abundant supply of water and oxygen) and calculate depletion times for
acid generating, acid neutralizing, and metal leaching minerals. Samples were selected for kinetic
tests based on the results of static test work. Static and kinetic testing methodologies used for
geochemical characterization of the Kings Mountain materials include the following:
• Static tests:
o Paste pH—pH measurement completed on a saturated paste comprising two parts solids
to one-part deionized water (United State Environmental Protection Agency (US EPA)
600/2-78-054)
o Acid base accounting (ABA) using the modified Sobek method (Sobek et al., 1978)—test
work involving the determination of the neutralization potential and sulfur content (with
sulfur speciation by hydrochloric acid or sodium carbonate (Na2CO3)
o Net acid generating (NAG)testing that reports the final NAG pH and final NAG value after
a two-stage hydrogen peroxide (H2O2) digest (Miller et al., 1997)
o Total carbon and carbon speciation (organic and inorganic carbon)
o Multi-element analysis using four-acid digestion followed by analysis of the digest by a
combination of inductively coupled plasma (ICP) mass spectroscopy (MS) or ICP atomic
emission spectroscopy (AES) (ALS Method ME-MS61 m)
o Mineralogical analysis, including optical microscopy, scanning electron microscopy
(SEM), and x-ray diffraction (XRD)at Petrolab
o Quantitative XRD analysis by Rietveld Refinement at SGS
o Modified synthetic precipitation leaching procedure (SPLP)testing (US EPA, 1994)
o Meteoric water mobility procedure (MWMP)testing (ASTM E2242-02)
o Leaching environmental assessment framework (LEAF) testing using the US EPA 1313,
1314, and 1316 methodologies (US EPA, 2012a, 2012b, and 2017a)
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• Kinetic tests:
0 Humidity cell test(HCT) procedure (ASTM D5744-96)
Future Waste Rock and Ore
The characterization program involved the collection and analysis of 571 samples representative of
waste rock and ore for static testing, with 22 representative samples submitted for kinetic testing.
Considering several factors, including sample neutralization potential ratios (NPR), net neutralization
potential (NNP), and NAG pH, the static test program indicated that the ore is non-PAG, while the
waste rock is variable and ranges from non-PAG to PAG.
To date, the HCT program supports the predictions of acid generation from the ABA tests. For a sub-
set of samples, the HCT program suggests that the NAG test may overpredict acid generation
potential. For these samples, acidic conditions have not developed despite having a NAG pH <4.5.
The geochemical characterization program shows that some of the waste rock associated with the
Project has a potential for acid generation; however, there are currently no signs of acid generation at
the site associated with existing facilities. This disparity between the laboratory and field conditions
may relate to:
• The presence of pyrrhotite in the Kings Mountain mineralization and the potential for pyrrhotite
to oxidize along a non-acid generating reaction pathway (i.e., to form elemental sulfur, which
does not result in acidity).
• The slow or partial oxidation of sulfide minerals, particularly pyrrhotite under field conditions
compared to laboratory tests.
• Armoring of pyrrhotite during oxidation that reduces or prevents further oxidation of sulfide
surfaces.
• Potential contribution of silicate minerals to neutralizing capacity that may not be reflected in
the short duration of the ABA test.
Bulk chemical assays indicated that several elements are enriched with respect to crustal averages in
the future waste rock and ore materials, including arsenic, beryllium, cesium, lithium, rubidium, sulfur,
selenium, tin, thallium, uranium, and tungsten. Of these, SPLP testing indicated that arsenic, lithium,
selenium, sulfur, and uranium would be leachable at detectable, albeit low, filtered concentrations.
LEAF testing has extended the leach datasets to a wider range of conditions (pH and US contact
ratios). Interpretation of the LEAF testing indicates that the leach behavior of many trace elements is
controlled by pH-dependent solubility and/or sorption processes. There is evidence, however, that for
some elements (notably lithium), a key control on leaching may be the presence of small, finite
quantities of a soluble mineral. In such cases, filtered concentrations are strongly influenced by the
liquid:solid contact ratio.
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Future Tailings Material
The ABA results indicate that, overall, the sulfide sulfur content was low or below detection for the
flotation tailings and DMS rejects. Neutralizing potential (NP) is also relatively low in the flotation
tailings and DMS rejects. In comparison,the OSR and MSR samples have higher sulfide sulfur content
in comparison to other waste streams and, as such, higher potential for acid generation, with some
samples indicating a potential for net acid generation despite the generally low sulfide contents (<1%).
The ABA and NAG results demonstrate that the ore sorting and magnetic separation process results
in the removal of sulfide mineral phases and concentration within the OSR and MSR materials. The
DMS rejects and flotation tailings are depleted with respect to sulfide minerals, demonstrating the
effectiveness of sulfide removal in the ore sorting process. Even flotation tailings that did not undergo
ore sorting have lower overall sulfide sulfur, indicating the magnetic separation process also removes
sulfides. Despite the presence of sulfides within the OSR and MSR material, these materials still show
an overall low potential for acid generation, with only three of the OSR rejects being classified as PAG
based on the NAG test. Based on the current HCT data, the two OSR HCTs have maintained neutral
conditions throughout the test despite the ABA and NAG results that indicated these samples have a
potential for acid generation. The two HCTs representative of flotation tails have also maintained
neutral conditions, as predicted by the static test data.
Multi-element analysis shows enrichment is associated with the higher sulfide contents in the OSR,
but overall concentrations are still not particularly high. Constituents showing some potential to be
leached from the tailings material under neutral conditions include aluminum, iron, arsenic, antimony,
manganese, and zinc.
TSF porewater samples obtained from lysimeters installed in the legacy TSFs are circum-neutral,
which is consistent with the static and kinetic test program. Metal concentrations are also low in the
porewater,with many parameters below analytical detection limits, including cadmium, beryllium, lead,
chromium, cobalt, boron, selenium, silver, and thorium. The results showed concentrations of a few
parameters that were higher in comparison to the leach test results of future tailings (e.g., copper and
manganese).
Cover Material
Static characterization testing was completed on 10 samples of alluvium/overburden and 14 samples
of saprolite. The results have been supplemented with multi-element data from the Albemarle
exploration database,with total calcium and sulfur values being used as surrogates for estimating acid
neutralizing potential and acid generating potential, respectively. The static testing results suggest:
• Most of the alluvium and saprolite samples are characterized by low sulfur contents
(<0.01 wt%) and are classified as non-PAG based on ABA and NAG test results.
• Paste pH of the 14 saprolite samples is circum-neutral (ranging between pH 5.2 and 7.9),with
three samples that were pH <6. The paste pH results for the alluvium samples are similar to
the saprolite samples.
• The alluvium and saprolite samples are typically characterized by lower paste pH values
compared to the waste rock (core) samples from the geochemical characterization program.
These lower pH values relate to the removal of primary neutralizing minerals by weathering
processes, leaving behind insoluble acidic iron and aluminum oxide minerals.
• Multi-element data from the exploration database classifies the majority(73%)of the alluvium
and saprolite samples as non-PAG based on surrogate NPR. Only 6% of alluvium/saprolite
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samples within the exploration database are classed as PAG, with the remainder showing
uncertain acid generating characteristics based on surrogate NPR.
• Metals that are found elevated above average crustal concentrations in the alluvium and
saprolite include: arsenic, beryllium, bismuth, cadmium, cobalt, lithium, antimony, silver, tin,
and thallium; this was consistent for both the 24 cover material samples (i.e., saprolite and
alluvium) and the exploration database samples.
Based on the soil baseline study completed by SWCA, soil properties are fairly consistent across the
Project area, where minerals and nutrients have been leached from the AB horizons and have
accumulated in the C horizon. Cover material samples from the sonic drilling program show a similar
range of pH as the SWCA soil samples. The results of the characterization program indicate that there
are no significant geochemical differences between the alluvium and saprolite material, and they can
be used interchangeably for reclamation purposes.
Legacy Mining Waste
The characterization program included 23 samples of legacy tailings and 33 samples of legacy waste
rock that have been exposed to oxidation processes at the surface for over 35 years.
Static tests completed on the legacy waste rock samples, including ABA, NAG, and multi-element
analysis, indicate:
• The samples are characterized by low sulfide sulfur contents (<0.07 wt%) and exhibit either
non-acid generating or uncertain acid generating behavior based on ABA testing. None of the
samples are classed as PAG materials.
• The total sulfur and sulfide sulfur content of the legacy waste rock samples is toward the lower
range of those observed in the future waste rock materials, and the samples show an overall
lower potential to generate acid based on ABA and NAG testing; this is consistent with the
neutral conditions observed on-site.
• The paste pH of the legacy samples is typically lower(i.e., more acidic)compared to the future
waste rock and ore samples, most likely as a result of exposure of the legacy waste to
oxidation processes at surface for over 35 years. Despite this, acidic conditions (pH <5) have
only developed in one sample, and in general the paste pH values for the legacy mine waste
samples are circum-neutral to moderately alkaline.
• Metals that are found elevated above average crustal concentrations in the legacy waste rock
include arsenic, antimony, beryllium, cesium, chromium, cobalt, lead, lithium, magnesium,
manganese, nickel, rubidium, selenium, sulfur, thallium, tin, tungsten, uranium, and zinc; this
is generally consistent with future waste rock and ore samples.
• SPLP results for legacy waste rock indicate the leachate pH was variable, ranging from acidic
to alkaline(pH 4.3 to 9.8). For the majority of the legacy waste rock samples, element release
was low or at the limit of detection under neutral conditions. SPLP concentrations for the
legacy waste rock samples were comparable to the future waste rock samples.
The legacy tailings characterization test work included ABA, NAG, and multi-element analysis. The
results can be summarized as follows:
• Sulfide sulfur is below analytical detection limits in the legacy tailings samples, and all samples
are classed as non-acid generating based on ABA and NAG testing.
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• Paste pH values are circum-neutral to alkaline(pH 6.0 to 9.8), indicating minimal development
of acidic salts on the tailings surface despite exposure to surface oxidation processes for
several decades.
• Metals that are found elevated above average crustal concentrations in the legacy waste rock
include arsenic, antimony, beryllium, cadmium, cesium, cobalt, lithium, rubidium, selenium,
sulfur, thallium, tin, tungsten, and uranium; this is generally consistent with future tailings
samples.
• For the legacy tailings samples, the leachate pH was circum-neutral and less variable in
comparison to the legacy waste rock samples (pH 5.9 to 8.1). Constituent release was
generally low or at the limit of detection. There was no significant difference in SPLP
concentrations for the legacy tailings samples compared to future tailings samples.
Kings Creek Stream Sediments
Three samples of stream sediment were collected from Kings Creek for geochemical characterization
testing, including ABA, NAG, and SPLP. The testing results indicate:
• The stream sediments are characterized by variable sulfide contents, ranging from below
analytical detection limits (<0.01%) in downstream Kings Creek to a maximum of 0.81% in
upstream Kings Creek.
• Based on ABA and NAG test results, the stream sediments are either classed as non-acid
generating or show a low potential for acid generation.
• Metals that are found elevated above average crustal concentrations in the stream sediment
samples include antimony, arsenic, beryllium, cesium, lithium, molybdenum, selenium, sulfur,
thallium, tin, tungsten, and uranium.
• SPLP testing indicated that arsenic, antimony, fluoride, iron, lithium, manganese, and sulfate
are leachable at detectable, albeit low, filtered concentrations from the stream sediments.
Waste Rock Management
The characterization test work indicates a portion of the waste rock associated with the Project has a
potential to generate acid and leach metals and active segregation and management of PAG waste
rock is needed to avoid and/or reduce the potential impacts to groundwater and surface water qualities
from mining facility drainage or runoff. The results from the geochemical characterization program
have been used to develop a site-specific approach to classifying waste rock as non-PAG or PAG
during mining.An estimate of the tonnage of PAG material was developed from the block model based
on this approach and was used to support mine planning and design as well as the water quality
predictions presented in SRK(2023).
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Glossary of Terms
Abbreviation Unit or Term
percent
< less than
> greater than
° degree
°C degrees Celsius
T degrees Fahrenheit
m micron
pmhos/cm micromhos per centimeter
ABA acid base accounting
AES atomic emissionspectroscopy
Ag silver
Al aluminum
Albemarle Albemarle Corporation
albite Na AlSi3O6
alkali beryl Be3Al2 Si6Ols
anatase TiO2
ankerite Ca Fee+,M CO3 2
AP acid potential
APsulfide acid potential based on sulfide
APtotals acid generation potential based on total sulfur
ARID acid rock drainage
As arsenic
Ba barium
Be beryllium
BFZ Brevard Vault Zone
Bi bismuth
BLM Bureau of Land Management
Ca calcium
CaCO3 calcium carbonate
calcite CaCO3
cassiterite SnO2
Cd cadmium
Ce cerium
Cfa humid subtropical climate
chalcop rite CuFeS2
clinochlore M 5AI AISi3Oto OH B
Co cobalt
cookeite LiAla Si3Al Oto OH a
COPC constituent of potential concern
CPS Central Piedmont Sutre
Cr chromium
Cs cesium
Cu copper
DEMLR Division of Energy, Minerals and Land Resources
DEQ North Carolina Department of Environmental Quality
DMS dense media separation
dolomite CaM CO3 2
earlshannonite Mn2+Fe3+2 POa 2 OH 2.4H2O
e /t equivalent per ton
EU European Union
eucryptite LiAlSiO4
Fe iron
Fe1-xS pyrrhotite
ferrisicklerite Li,-x Fe3+xFe211_x POa
fluora atite Cas POa 3F
ft foot
ram
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Abbreviation Unit or Term
Ga gallium
GAI geochemical abundance index
GARD Global Acid Rock Drainage
Ge germanium
H2O2 hydrogen eroxide
H2SO4 sulfuric acid
HCI hydrochloric acid
HCT humidity cell test
Hg mercury
holm uistite Lie M 3AI2 SiaO22 OH 2
ICP inductively coupled plasma
ilmenite Fee+TiO3
In indium
INAP International Network for Acid Prevention
K potassium
kg kilogram
k /t kilograms per ton
Project Kings Mountain Project
km kilometer
KMSZ Kings Mountain Shear Zone
L/S liquid-to-solid
La lanthanum
LEAF leaching environmental assessment framework
Li lithium
marcasite FeS2
MEND Mine Environment Neutral Drainage
Mg magnesium
m /L milligrams per liter
microcline K AlSi3Oa
ML metal leaching
mL milliliter
Mn manganese
Mo molybdenum
monazite Ce,La,Nd,Th POa
montebrasite LiAI POa OH
MS mass spectroscopy
MSR magnetic separation reject
muscovite Kale AISi3O10 OH 2
MWMP meteoric water mobility procedure
Na sodium
NAG net acid generating
NaOH sodium hydroxide
Nb niobium
Ni nickel
NNP net neutralization potential
NP neutralizing potential
NPR neutralization potential ratio
NRCS Natural Resources Conservation Service
OSR ore sorting reject
P phosphorus
PAG potentially acid generating
Pb lead
PbS galena
Ci/L picocuries per liter
Po P rrhotite
polylithionite KIi2Al SiaO10 F,OH 2
pm parts per million
QEMSCAN quantitative evaluation of minerals by scanning electron microscopy
Rb rubidium
Re rhenium
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Abbreviation Unit or Term
RoM run-of-mine
RSF Rock Storage Facility
S sulfur
S.U. standard unit
Sb antimony
Sc scandium
Se selenium
SEM scanning electron microscopy
silicates quartz SiO2
SMU soil mapping unit
Sn tin
s halerite ZnS
SPLP synthetic precipitation leaching procedure
s odumene LiAISi2O6
Sr strontium
SRK SRK Consulting(U.S.), Inc.
strunzite Mn2+Fe3+2 POa 2 OH 2.6H2O
Ta tantalum
TCLP Toxicity Characteristic Leaching Procedure
Te tellurium
Th thorium
Ti titanium
TIC total inorganic carbon
TIMA Tescan integrated mineral analyzer
TI thallium
TOC total organic carbon
TSF tailings storage facility
U uranium
UC uncertain
uraninite UO2
US EPA United States Environmental Protection Agency
v vanadium
w tungsten
wt% percent by weight
xenotime YP04
XRD x-ray diffraction
XRT x-ray transmission
Y yttrium
zircon Zr SiOa
Zn zinc
Zr zirconium
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1 Introduction
SRK Consulting(U.S.), Inc. (SRK)prepared this report on behalf of Albemarle Corporation(Albemarle)
to provide a description of the approach and test methods being used for the geochemical
characterization of existing (legacy) mine wastes, stream sediments, potential future cover material,
and future waste rock, ore, and tailings that will be extracted and processed at the Kings Mountain
Mining Project (KMMP or Project) located in North Carolina. The primary purpose of the study is to
develop baseline geochemical characterization data to support the planning and impact assessment
for the Project. The quality of water that comes into contact with mine waste is described in a related
report by SRK (2023). This report has been prepared for the North Carolina Department of
Environmental Quality (DEQ) Division of Energy, Minerals and Land Resources (DEMLR) to support
permitting efforts. The geochemical characterization program is ongoing, and results available to date,
including data collected from the ongoing static and kinetic test work program, are provided in this
report.
1.1 Study Objectives
The purpose of this investigation is to provide an understanding of the geochemical characteristics of
geological materials specific to the Project and to define the potential for future mining wastes,
including tailings,waste rock, ore, and pit wall rock, to generate acid or leach deleterious constituents.
To accomplish the objectives of the study, representative samples have been collected and
characterized following guidelines consistent with the Global Acid Rock Drainage (GARD) Guide
(International Network for Acid Prevention (INAP), 2014), the Mine Environment Neutral Drainage
(MEND) Program Prediction Manual for Drainage Chemistry from Sulfidic Geologic Materials (MEND,
2009), and the U.S. Bureau of Land Management (BLM) Instruction Memorandum NV-2010-014,
Nevada Bureau of Land Management Rock Characterization Resources and Water Analysis Guidance
for Mining Activities (BLM, 2013). These are internationally recognized guidance documents for the
geochemical characterization of geological materials and are derived from best practices of the mining
industry, government, academia, and community groups conducting mine site drainage chemistry
assessment and predictions.
Recognizing that the work performed as part of the geochemical characterization study will be
integrated into the subsequent permitting documents prepared for the site, SRK developed an
approach for a purpose-built characterization program that focuses on the following aspects:
• Assessment of waste rock geochemistry to provide a prediction of the potential geochemical
reactivity and chemical stability of future waste rock and construction material for the Project,
and to support predictions of contact water (run-off and seepage) chemistry associated with
the future rock storage facilities (RSFs) (SRK, 2023).
• Evaluation of tailings material geochemistry to support predictions of contact water chemistry
that may change over time and would influence the design, operation, and closure of the
tailings storage facility (TSF) (SRK, 2023).
• Determination of final pit wall geochemistry to define the control that the pit wall rocks would
have on the chemistry of waters removed from the pit during operations and pit lake that will
form after closure.
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• Geochemical characterization of legacy wastes in the Project area to determine the
weathering and acid rock drainage (ARID) and metal leaching (ML) characteristics of legacy
rock dumps and tailings that have been exposed to oxidation processes at the surface.
• Geochemical characterization of potential future cover materials (alluvium and saprolite).
• Geochemical characterization of stream sediment in Kings Creek.
The results of the geochemical characterization test work provide a basis for the assessment for
ARDML to support predictions of future contact water quality(i.e., runoff and seepage)associated with
the future mine facilities. In turn,these results inform decisions on engineering designs, mine planning,
and waste rock and tailings management.
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2 Environmental Setting
2.1 Project Location
The Kings Mountain Mining Project is located in southwestern North Carolina, USA, adjacent to the
city of Kings Mountain on the Interstate Highway 85(1-85)transit corridor, approximately 33 miles west
of the city of Charlotte(Figure 2-1).The property is located at approximately 35 degrees(°), 13 minutes
north latitude and 81°, 21 minutes west longitude.
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2.2 Mining History and Site Conditions
The site contains several historic waste rock storage facilities, a TSF, and river diversion, none of
which show signs of active ARD or metal precipitation except for localized iron (Fe) precipitates in a
few locations. Although iron precipitates may be representative of ARD, low pH conditions were not
observed, and the surrounding vegetation was healthy, suggesting that iron staining may be related to
discharge of reducing groundwater or corroded drainage pipes formerly used to discharge water
around the pit. Most former mining facilities in the Project area are heavily vegetated, and none of the
vegetation shows signs of metal or acid stress, although there is development of flocculated iron
hydroxides or ochre in drainages (Figure 2-2). Samples of legacy waste rock and tailings were
collected as part of the geochemical characterization program described herein (Section 5.4).
ro rY
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Figure 2-2: Area of Flocculated Iron Solids Associated with Underdrain Discharge in the
Diversion Creek
2.3 Mine Plan
The Project will be developed as an expansion of a previously mined open pit. The Albemarle-owned
site has an existing lithium (Li) conversion plant and research/administrative facility. KMMP includes
redevelopment of the open pit, spodumene concentrator, and associated infrastructure and
construction of RSFs/TSFs.
Ore and waste rock will be mined using conventional open pit mining techniques, including drill and
blast, loading, and truck haulage. Mining will take place below the regional water table, and the pit will
slowly refill with water upon cessation of mining.Waste rock will be generated from mining and placed
in rock storage facilities. The operation will employ filtered tailings technologies to reduce the amount
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2023 PFS Report—Kings Mountain Mining Project Page 5
of water required in the mining process, improve the stability of the dry-stacked TSF, and minimize the
overall footprint of the operation.
2.4 Project Climate
South-Central North Carolina is situated in a humid subtropical climate (Cfa) per the Koppen climate
classification system. This climate is generally characterized by hot humid summers and cold winters.
Average winter temperatures vary between 30 degrees Fahrenheit (°F) and 50°F. Average summer
temperatures vary between 70°F and 90°F.
Average monthly precipitation varies between 3 and 5 inches. Average annual precipitation is
42 inches, with an even distribution of rainfall throughout the year, and an average annual snowfall of
4 inches. South-Central North Carolina is prone to thunderstorms during the summer and ice storms
during the winter.
2.5 Geology and Mineralization
As described by SRK (2021), North Carolina's tin (Sn)-spodumene belt lies within the Inner Piedmont
terrane, an orogenic core formed during the Devonian-Mississippian, Acadian-Neo Acadian orogeny
of the southern Appalachians.The Inner Piedmont stretches for some 700 kilometers(km)strike length
from Winston-Salem in North Carolina to the Coastal Plain in Alabama, bound between the Brevard
Fault Zone (BFZ) to the northwest and the Central Piedmont Suture (CPS) to the southeast
(Merschat et al., 2012). Rocks of the Eastern Inner Piedmont, the Cat Square terrane, are
unconformably abutted against the exotic peri-Gondwanan Carolina super terrane along the CPS;
spodumene pegmatites, hosted by rocks of the Cat Square terrane, occur along a reactivation of this
major tectonic boundary, the Kings Mountain shear zone (KMSZ).
In the Kings Mountain area, the Cat Square terrane is composed of amphibolite, mica schist, and mica
gneiss; these are interpreted to represent the deposition of pelitic sediments, with subsequent mafic
and ultramafic intrusions, in the remnant Iapetus Ocean basin that existed between Laurentia and the
Carolina super terrane. Mississippian aged Cherryville Granite, granite pegmatite, and spodumene
pegmatites intrude the mica schist and amphibolite units of the Cat Square terrane along the KMSZ.
Spodumene pegmatites, averaging up to 20 percent (%) spodumene, vary in thickness and overall
extent; hundreds of these spodumene pegmatite dikes occur in the Kings Mountain area, with most
less than (<) 10 feet (ft) thick, while the largest spodumene pegmatite dikes are approximately 400 ft
thick and 3,300 ft long.
The Kings Mountain deposit is a lithium-bearing, rare-metal pegmatite intrusion that has penetrated
along the KMSZ. The pegmatite field at Kings Mountain is approximately 1,500 ft wide at its widest
point in the historic pit area and narrows to approximately 400 to 500 ft in width at its narrowest point
south of the historic pit. The lithium mineralization at the Kings Mountain deposit occurs within albite-
spodumene pegmatites and to a lesser extent within metamorphosed altered wall-rock adjacent to
mineralized pegmatites. Figure 2-3 provides a generalize stratigraphic column.
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2023 PFS Report—Kings Mountain Mining Project Page 6
480
No
L 4) O upper mica schist �■
(upper mica sch)
CL
54D &n E
C7 CDamphibole O
t>a
3G0 = gneiss-schist{amp
gn-sch};Sheaf sehisl
-(3 {shear schist)
580
OL
_ L QY r
J M4
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mica schist(mrca scR) .
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C
B�6 CL silica mina schist(silica QD
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++ schist-marble
transition zone M
Goo Z E C (sCh-mbl} j 4; �
C 2 ME
— — marble(mbi)
68Q D C C a
M
700 phy hte(phyllitel tfi
i3 C_ u
720 1im
Figure 2-3: Generalized Stratigraphic Column
A detailed geological model has been developed by Albemarle and various consultants as part of the
exploration program and to provide information for mine planning purposes. Figure 2-4 and Figure 2-5
provide a plan view and cross-sectional view of the geologic model from SRK (2021). Refinement of
this geological model is ongoing, and the geologic interpretations are subject to change.
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Full
—Rock Name 13
w u� m Merged-2018
o �n o xe
.Amphibole Gneiss-Schist
m m m Chlorite Schist
■Marble
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i
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f ■Spud Peg
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+543000 N
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e
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1
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Figure 2-4: Geologic Model Plan View
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2023 PFS Report—Kings Mountain Mining Project Page 8
Full Rack Mme E3
Merged_201B
■Amphibole Gneiss-Schist
■Amphilbolite
■Aplite
■Chlorite Schist
DL.base
0
Granite
■Mafic
■Marble
Mica Schist
■Muse Peg
■NR
■Overburden
PPeg
F
*600 phyllite
Quartzolite
■Schist-Marble
Shear Schist
■Silica Mica Schist
■spod Peg
Upper Mica Schist
■Water
*300
+-0 +0
Plunge 00
Azimuth 015 1111111110
0 100 200 300
AL
Figure 2-5: Geologic Model Cross-Sectional View
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2.6 Kings Mountain Material Types
A total of 12 material types have been identified based on lithology and grade (i.e., ore versus waste)
for the purposes of the Kings Mountain geochemical characterization program. Table 2-1 summarizes
the Kings Mountain material types along with the estimated proportions to be mined based on the
current mine plan and geologic model. The material type proportions in Table 2-1 may change as the
geologic interpretation of the deposit is refined, the geologic model is updated, and the mine plan
evolves.
Table 2-1: Kings Mountain Geochemical Program Material Types
Category Main Material Type Estimated Percentage
of Total to be Mined
Alluvium 4%
Amphibolite Gneiss-Schist 45%
Biotite Gneiss 2%
Mica Schist 10%
Pyrrhotite (Po)Mica Schist 3%
Waste Upper Mica Schist 1%
Rock Shear Schist 4%
Silica Mica Schist 4%
Diabase <1%
Granite <1%
Schist-Marble <1%
Chlorite Schist <1%
Ore Pegmatite 1%
Spodumene Pegmatite 25%
Source: PAG-NPAG table for distribution.xslx,received from Albemarle on March 22,2023
'Proportions are an estimate and subject to change based on ongoing modeling and mine planning.
2.7 Conceptual Model and Program Design
A conceptual geochemical model has been developed based on the deposit geology, hydrology and
geochemistry combined with the proposed mining and processing methods. This conceptual model
provides the basis for the scope and methodology of the geochemistry baseline study and defines the
approach for sample selection, laboratory procedures, and key criteria for decision-making throughout
the process.
KMMP will be developed as an expansion of a previously mined open pit. The Albemarle-owned site
has an existing lithium conversion plant and research/administrative facility. The Project includes
redevelopment of the open pit, spodumene concentrator, and associated infrastructure and
construction of RSFs/TSFs.
Ore and waste rock will be mined using conventional open pit mining techniques, including drill and
blast, loading, and truck haulage. Mining will take place below the regional water table, and the pit will
refill slowly upon the cessation of mining. Waste rock will be generated from mining and placed in
waste rock storage facilities. Potentially acid generating (PAG) waste rock mined from the pit will be
managed to mitigate risks to the environment. The operation will employ filter-pressed tailings
technologies to reduce the amount of water required in the mining process, improve the stability of the
TSF, and minimize the overall footprint of the operation.
The design of the geochemical characterization program has been developed based on the geology
of the site and the mine plan information. Table 2-2 provides a list of the mine materials that will require
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geochemical characterization and the types of geochemical data required. Because Albemarle is
currently evaluating variations within the mineral processing flowsheet, including ore sorting, magnetic
separations, and dense media separation (DMS) rejects, the specific types of waste produced may
change. Table 2-2 summarizes the potential waste streams that will be generated from the lithium
extraction process; however,the waste streams and management of these waste streams may change
as the ongoing metallurgical testing program and the flowsheet are finalized.
Table 2-2: Materials Requiring Characterization
Mine Material Available
Component/ Location Duration Composition for Characterization
Facility
Non-PAG and PAG waste rock Core'
Sonic core
Waste rock storage facilities Mine area Permanent Ore sorting rejects(OSR) Met program3
DMS reject material Met program3
Magnetic separation rejects(MSR) Met program3
Ore stockpiles Plant site Temporary Ore grade material Core'
Pit walls Open pit Permanent Waste rock and low-grade ore Core'
Pit backfill Open pit Permanent Non-PAG and PAG waste rock Core' 2
Sonic core
Dry stack TSF Mine area Permanent Filter-pressed flotation tails Met program3
Sonic core
Reclamation Various Permanent Soil for reclamation (pre-strip) Sonic core'
covers
Borrow areas Various Permanent Non-PAG waste rock for Core'
construction material
Legacy mining Various Not Legacy waste rock and tailings Sonic core2
waste applicable from historical operations
Kings Creek Kings Creek Not Stream sediments Grab samples
stream sediment applicable
Note:The ore processing flowsheet is still under development, and the information in this table may change as the details are
finalized
'Core from exploration drillholes throughout the deposit(unweathered material)
2Sonic core from legacy facilities containing weathered material
'Residues from metallurgical test work programs
'Sonic core from holes drilled into alluvium within mine facility footprints
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3 Sample Collection
The following sections provide a description of the sample collection for the various material types.
3.1 Future Waste Rock and Ore
Sample selection is a fundamental step in any geochemical characterization program. The number of
samples selected for testing is typically based on the number of discrete material types identified for a
deposit as well as the relative percentage of each material type predicted to be mined according to the
geologic model. For the Kings Mountain waste rock and ore geochemical characterization program,
material types have been delineated based on lithology and grade(i.e.,ore versus waste)as described
above.
In 2022, SRK selected a total of 474 samples of exploration drill core for geochemical characterization
test work. In 2023, an additional 97 samples were collected from core holes drilled in 2023 to fill spatial
and lithologic gaps in the dataset. The resulting dataset consists of a total of 571 samples. Based on
assessment of the geological database and examination of the core, SRK believes the selected
samples are representative of future waste rock and ore that is planned to be mined at the site.
Figure 3-1 shows the spatial distribution of the samples selected for test work. Smaller disks represent
samples collected in 2022 and larger disk represent samples collected in 2023. Appendix A provides
additional images showing the sample distribution relative to the March 2023 pit shell. At the time of
sample selection in 2022, the pit shell was larger than the current pit shell for the Project; therefore,
some samples fall outside the pit shell. However, based on the uniform geology both laterally and
vertically, the samples located outside the pit shell are considered representative of material that will
be encountered within the smaller pit shell.
The assessment of the proportions of the different Iithologic units present was based on the exploration
database. The cumulative length of each Iithology intersected in the drillholes was used to estimate
the relative abundance of each Iithology present within the exploration database. The resulting
abundance was used to define the sample distribution, with the number of samples selected per
Iithology weighted with more samples selected from the more abundant Iithological units present within
the exploration database (i.e., a greater number of samples were selected from the more-dominant
rock types).
The number of samples for the Project was based on the distribution and occurrence of different
lithologies and alteration types, presence of oxide or sulfide facies, and known geochemical and/or
mineralogical variability and accounted for lateral and vertical variability by selecting sufficient samples
to cover the distribution of each Iithology.
Table 3-1 provides a summary of the number of sample intervals selected for each Iithology. Table 3-1
also provides the estimated percentage of waste rock that will be mined from the planned pit based
on the block model.
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Table 3-1: Kings Mountain Sample Matrix
Estimated No.
Category Main Material Type Percentage of Samples
Total to be Mined
Alluvium 4% 5
Amphibolite Gneiss-Schist 45% 132
Biotite Gneiss 2% 31
Mica Schist 10% 63
Po Mica Schist 3% 45
Waste Upper Mica Schist 1% 19
Rock Shear Schist 4% 49
Silica Mica Schist 4% 30
Diabase <1% 2
Granite <1% 2
Schist-Marble <1% 7
Chlorite Schist <1% 19
Ore Pegmatite 1% 110
Spodumene Pegmatite 25% 57
Total 100% 571
Source: PAG-NPAG table for distribution.xslx,received from Albemarle on March 22,2023.
'Proportions are an estimate based on the current block model and are subject to change based on ongoing modeling and mine
planning.
The material type proportions in Table 3-1 may change as the geologic interpretation of the deposit is
refined,the geologic model is updated, and the mine plan changes. However, the number and location
of samples is considered representative and adequate to characterize the material associated with the
Project and predict water quality for the future mine facilities.
RockType-Grouped
�- - ■Amphihole Gneiss-Schist
4` ■Amphiholite
'. ■Chlorite SchtM
.545000 ■Nahase
.Granite
Mlca Schist
1544500 .Musc Peg
■NR
"'.� ■overhurden
+544000 I ■Peg
■Po Mica Schist
•5 ■Schin-Mvhle
+543500 ■Shear Schist
■Silica Mica Schist
■SW Peg
+543000
Upper Mica Schist
� � i
+542500
+542000 •
i •
+541500 •�t�
+54J" `
PN
List(H) Azimuth
00
Looking
945C6+1 5000 +1295500 +1296000 +1296500 +1297000 +1297500 +1298000 gd tlown
Note:Color of disc relates to sample lithology.
Figure 3-1: Geochemical Characterization Samples in Context of March 2023 Shell
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3.2 Future Tailings
Samples representative of future tailings material have been sourced from various metallurgical test
programs for geochemical characterization testing; this includes 6 samples from the 2018 pilot plant
and 25 samples from the 2022 North Met program, as summarized in Table 3-2.
Figure 3-2 provides a flowsheet that shows the points in the process at which the 2018 Met program
samples were collected. A separate flowsheet is provided in Figure 3-3 for the 2022 North Met
program. These flowsheets have been provided to show the source of the samples included in the
characterization program that were obtained from the metallurgical test programs and are not intended
to represent the final proposed process for the Project.
Although subject to change, the current plan is for ore from the pit to be crushed and ground and sent
through a heavy liquid separation process that produces the DMS rejects. The DMS media proposed
is ferrosilicon. Remaining ore will be ground to -300 pm for secondary processing. Several waste
streams will be generated during the flotation process that will be combined and co-disposed in the
TSF, as shown on Figure 3-2 and Figure 3-3. The DMS rejects are proposed to be co-mingled with
the waste rock and deposited in the RSF-A, and the flotation tailings will be filtered and dry stacked in
the TSF. Process water will be recycled, to the extent possible.
XRT-based ore sorting will likely be the chosen preconcentration method included in the process.
Materials generated from this process will be co-disposed with waste rock in RSF-X. Samples of this
material are available from the 2022 North Met program and have been included in the characterization
program. Albemarle is also currently evaluating magnetic separation at various points in the process
that may be included in the final process flowsheet.
As part of the 2022 North Met program, testing has been conducted on two master composites with
preconcentration. The ore feed for Composite 1 was pegmatite hosted in amphibolite gneiss, and the
ore feed for Composite 2 was pegmatite hosted in mica schist. Four variability composites were also
included in this test program to capture the range of ore properties. In addition, process solutions from
the metallurgical test work (i.e., filtrate) were submitted for analysis. The filtrate samples were coarse
filtered using a 25 µm filter and represent the total metals fraction, not the filtered metals fraction (i.e.,
< 0.45 µm).
Additional samples from the 2022 North Met program became available in 2023, including two filtered
flotation tailings with preconcentration as well as an additional sample representative of the magnetic
separation rejects.The two filtered flotation tailings were generated by combining the spodumene tails
(80%) and mica tails (20%) to produce samples that represent the combined tailings waste stream
based on the plant material balance. These samples were generated for Comp 1 and Comp 2 from
the latest flowsheet (including ore sorting) and are the most representative samples of future tailings
that will be placed in the TSF.
Various waste streams from the 2018 Pilot Plant study were also included in the characterization
program, including rejects from the magnetic separation. However, the flowsheet for the 2018 Pilot
Plant was slightly different from the 2022 North Met program and ore sorting was done using magnetic
separation, rather than XRT, as shown in Figure 3-2.
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Table 3-2 Solid Tailings Samples Included in Characterization Program
Location on Metallurgical Category Laboratory Sample Name
Flowsheet Program ID
2 on Figure 3-2 2018 Pilot Plant DMS-no sorting L47924-03 DMZ 1ST PASS FLOATS
4,6,7,8 on Figure 3-2 2018 Pilot Plant Flotation tails-no sorting L76180-03 COMBINED TAILINGS (HATCH-NO FLOC)
8 on Figure 3-2 2018 Pilot Plant Flotation tails-no sorting L47924-01 BATCH FLOT RO SCAV TAILS
8 on Figure 3-2 2018 Pilot Plant Flotation tails-no sorting L47924-02 LCT FLOAT TAILS
1 on Figure 3-2 2018 Pilot Plant Mag separation rejects L76180-01 M+6 MAGS
1 on Figure 3-2 2018 Pilot Plant Mag separation rejects L76180-02 -6M/+32M MAGS
2 on Figure 3-3 2022 North Met DMS- no sorting L75603-01 COMP 1 DMS TAILINGS NO SORTING 2 OF 2
2 on Figure 3-3 2022 North Met DMS- no sorting L75603-02 COMP 2 DMS TAILINGS NO SORTING 2 OF 2
2 on Figure 3-3 2022 North Met DMS - sorting L75603-03 SORTED COMP 1 DMS TAILINGS 2 OF 2
2 on Figure 3-3 2022 North Met DMS -sorting L75603-04 SORTED COMP 2 DMS TAILINGS 2 OF 2
2 on Figure 3-3 2022 North Met DMS -sorting L75603-05 SORTED VAR 1 DMS TAILINGS 2 OF 2
2 on Figure 3-3 2022 North Met DMS -sorting L75603-06 SORTED VAR 2 DMS TAILINGS 2 OF 2
2 on Figure 3-3 2022 North Met DMS -sorting L75603-07 SORTED VAR 3 DMS TAILINGS 2 OF 2
2 on Figure 3-3 2022 North Met DMS- sorting L75603-08 SORTED VAR 4 DMS TAILINGS 2 OF 2
5 on Figure 3-3 2022 North Met Flotation tails - no sorting L75603-15 COMP 1 FLOAT TAILINGS
5 on Figure 3-3 2022 North Met Flotation tails - no sorting L75603-16 COMP 2 FLOAT TAILINGS
5 on Figure 3-3 2022 North Met Flotation tails - sorting L77114-01 Sorted Var 1 Ore Sorting Float Tails
5 on Figure 3-3 2022 North Met Flotation tails -sorting L77114-02 Sorted Var 2 Ore Sorting Float Tails
5 on Figure 3-3 2022 North Met Flotation tails -sorting L77114-03 Sorted Var 3 Ore Sorting Float Tails
5 on Figure 3-3 2022 North Met Flotation tails -sorting L77114-04 Sorted Var 4 Ore Sorting Float Tails
4 on Figure 3-3 2022 North Met Flotation tails -sorting L78487-01 LCT-1 COMBINED TAILINGS Master composite 1
4 on Figure 3-3 2022 North Met Flotation tails -sorting L78487-02 LCT-2 COMBINED TAILINGS Master composite 2
1 on Figure 3-3 2022 North Met Ore sorting rejects L75603-09 SORTED COMP 1 ORE SORTING WASTE
1 on Figure 3-3 2022 North Met Ore sorting rejects L75603-10 SORTED COMP 2 ORE SORTING WASTE
1 on Figure 3-3 2022 North Met Ore sorting rejects L75603-11 SORTED VAR 1 ORE SORTING WASTE
1 on Figure 3-3 2022 North Met Ore sorting rejects L75603-12 SORTED VAR 2 ORE SORTING WASTE
1 on Figure 3-3 2022 North Met Ore sorting rejects L75603-13 SORTED VAR 3 ORE SORTING WASTE
1 on Figure 3-3 2022 North Met Ore sorting rejects L75603-14 SORTED VAR 4 ORE SORTING WASTE
3 on Figure 3-3 2022 North Met Mag separation rejects L77419-41 MAG SEP PRODUCTS
4,5 on Figure 3-3 2022 North Met Flotation tails -sorting L80362 Comp 1
4,5 on Figure 3-3 2022 North Met Flotation tails -sorting L80363 Comp 2
'The 2018 Pilot Plant used dry magnetic separation for preconcentration, and the 2022 North Met program used x-ray transmission(XRT)-based ore sorting for preconcentration.
XRT-based ore sorting is expected to be the preconcentration step used going forward.Categories that are listed as ore sorting have undergone the XRT preconcentration step.
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85Opm
F"" Screen
f4}
Dry Magnetic
Separation
norm
❑MS 2nd Pass
�- - - - - � SG2.90 i3�
❑hIS Pass JIB 2
SG 2.70
7
d
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❑sliming 1 Flotation
Mica
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_ ' SPWd 1"CI
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5
6 Spd 2""CI
Flotation
Product Legend High Density
7 Magnefic Concentrate 1 Suuhhing
2 DMS Tailings Spd 3�CI
Flotation
3 DMS Concentrate
d Slimes 1
5 Magnefic Concentrate 2
6 Mica Concentrate
7 Slimes A
d Spodurnene Flotation Tails
9 j Final Spoduntene Cane
Figure 3-2: Generalized Flow Diagram for the 2018 Met Program
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Ore from primary Crushing and -sorCoarse rejects O
crusher(-1000mm) Screening 7 1—
- NoL DMS Tails
1/4" Coarse magnetic
concentrate
V-6.3mm/+0.85mm �- �
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Middlings
-0.85mm
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Fine magnetic
concentratelump=
Concentrate Spodumend111k� Desliming and MICA
thickener LW— flotation A conditioning Micaflotation Concentrate Mica tails filter
Concentrate filter poclumene tails A L'20 concentrate(55:45) Mica Tails O
thickener
B Potential by-products
C To PAG storage
poclumene tails
Fine Li20 filter
Concentrate D To TSF
E To Non-PAG RSF
Spodumene Tails
Figure 3-3: Generalized Flow Diagram for the 2022 Met Programs
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3.3 Cover Material
Samples representative of potential future reclamation cover material were collected for geochemical
characterization testing. Sample locations of the alluvium samples are shown in Figure 3-4, and the
saprolite sample locations are shown in Figure 3-5.
The purpose of this testing was to understand the geochemical characteristics of potential cover
material (primarily alluvium and overburden)that will be used to reclaim the future mine facilities. The
samples were derived from various sources:
• The waste rock and ore characterization program described in Section 3.1 included the
collection and analysis of five samples of overburden material(i.e., alluvium and saprolite)that
were collected from exploration drill core.
• An additional 24 samples representative of potential future cover material were collected from
the 2022 sonic drilling program for geochemical characterization testing; this included
10 samples of alluvium/overburden and 14 samples of saprolite. The alluvium/overburden
material corresponds to the AB horizon and the saprolite material corresponds to the C horizon
described by SWCA(2023).
• The Albemarle exploration database also contains multi-element data for 91 alluvium samples
and 134 saprolite samples collected from the sonic drilling program. These data have been
used to supplement the results of the cover material geochemical characterization testing
presented herein. Specifically, the calcium (Ca) and sulfur (S) data from the exploration
database were used as surrogates for estimating acid neutralizing potential and acid
generating potential, respectively.
3.4 Legacy Mining Waste
Samples of legacy mine waste (waste rock and tailings) were collected from the existing facilities in
the Project area for geochemical characterization testing. The purpose of this sampling was to
determine the weathering and ARDML characteristics of legacy rock dumps and tailings that have
been exposed to oxidation processes at the surface for over 35 years.
A total of 60 samples were collected between September and December 2022 from the sonic drill
program, including 23 samples of historical tailings and 33 samples of legacy rock dumps. Sample
locations of legacy waste rock are shown in Figure 3-6, and the legacy tailings locations are shown in
Figure 3-7.
3.5 Kings Creek Stream Sediment
Three samples of stream sediment were collected from Kings Creek for geochemical characterization
testing. The purpose of this sampling was to understand sediment characteristics in the creek prior to
the initiation of mining. Samples were collected from the following locations:
• Two upstream samples were collected at the Martin Marietta property (one from the north of
the culvert and one from the source of the culvert).
• One downstream sample was collected from where Kings Creek leaves the Project boundary.
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4 Laboratory Methods
4.1 General
The geochemical test methods selected for this Project include both static and kinetic testing that are
designed to address the bulk geochemical characteristics of the samples and to assess the potential
of the materials to generate acid and/or release metals into solution.
Static testing methods are used to characterize acid generation and metal leaching characteristics of
material at the time of testing and do not account for temporal changes that may occur in the material
as chemical weathering proceeds. Static tests indicate the amount of acid generating and acid
neutralizing components that are present in the material and provide information on the bulk
geochemical properties (e.g., metal composition, mineralogical composition, and leaching potential).
While the results can be used to calculate the overall acid generation or neutralizing potential of the
sample, they do not provide information on the rate at which acid generating and acid neutralizing
reactions will take place.
Kinetic testing methods are used to evaluate the rate of sulfide oxidation and metal release over time
by weathering of mine waste in a column using alternating wet and dry cycles of air and periodic
flushing with water. The objective of kinetic testing is to determine reaction rates under specific
geochemical conditions (e.g., abundant supply of water and oxygen) and calculate depletion times for
acid generating, acid neutralizing, and metal leaching minerals. Samples are usually selected for
kinetic tests based on the results of static test work. Static and kinetic testing methodologies used for
geochemical characterization of the Kings Mountain materials include the following:
• Static tests:
o Paste pH —pH measurement completed on a saturated paste comprising two parts solids
to one-part deionized water (United State Environmental Protection Agency (US EPA)
600/2-78-054).
o Acid base accounting (ABA) using the modified Sobek method (Sobek et al., 1978)—test
work involving the determination of the neutralization potential and sulfur content with
sulfur speciation by hydrochloric acid (HCI)or sodium carbonate (Na2CO3).
o Net acid generating (NAG)testing that reports the final NAG pH and final NAG value after
a two-stage hydrogen peroxide (H2O2)digest(Miller et al., 1997).
o Total carbon and carbon speciation (organic and inorganic carbon).
o Multi-element analysis using four-acid digestion followed by analysis of the digest by a
combination of inductively coupled plasma (ICP) mass spectroscopy (MS) or ICP atomic
emission spectroscopy (AES) (ALS Method ME-MS61m).
o Mineralogical analysis, including optical microscopy, scanning electron microscopy
(SEM), and x-ray diffraction (XRD) at Petrolab.
o Quantitative XRD analysis by Rietveld Refinement at SGS Modified synthetic precipitation
leaching procedure (SPLP)testing (US EPA, 1994).
o Meteoric water mobility procedure (MWMP)testing (ASTM E2242-02).
o Leaching environmental assessment framework (LEAF) testing using the US EPA 1313,
1314, and 1316 methodologies (US EPA, 2012a, 2012b, and 2017a).
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o Toxicity Characteristic Leaching Procedure (TCLP) using inductively coupled plasma —
optical emission spectrometry(ICP-OES)for all constituents except for Mercury analyzed
by EPA Method 7470A, Mercury in Liquid Waste.
• Kinetic tests:
o Humidity cell test(HCT) procedure (ASTM D5744-96) and analysis of leach extract
Composites of the core material were generated for the SPLP and LEAF testing program. In addition,
one of the humidity cell samples (HCT-19) is a composite of two pegmatite samples. Appendix B
provides graphs that illustrate the selection of subsamples for the composites as well as the HCTs.
Appendix C provides the tabulate mineralogy data and the Petrolab mineralogy reports. As part of the
metallurgical program, mineralogical analysis by quantitative evaluation of minerals by scanning
electron microscopy (QEMSCAN) and Tescan integrated mineral analyzer (TIMA) has also been
undertaken, and applicable results have been summarized below.
Appendix D provides the tabulated data from the Stage 1 static test program (ABA, NAG, and multi-
element analysis), and Appendix E contains the tabulated short-term leach test data, including the
SPLP data and LEAF testing programs. Laboratory reports for the Stage 1 static test program, short-
term leach test and humidity cell test programs are provided in Appendix F, G, and H, respectively.
4.1.1 Future Waste Rock and Ore
Table 4-1 presents a summary of the main material types sampled and includes the sample and test
numbers for each material type.
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Table 4-1: Waste Rock and Ore Sample Frequency and Testing Matrix
HCT/ LEAF
Estimated ABA/ Mineralogy (EPA
Percentage NAG/ (Optical, 1313 LEAF
Main Material of Total to Multi- SEM, Modified and (EPA
Category Type be Mined' Element2 XRD)3 SPLP° XRD 1316)5 1314)6
Alluvium 4% 5 1 0 0 0 0
Amphibolite
Gneiss-Schist 45% 132 5 4 18 4 2
Biotite Gneiss 2% 31 2 3 8 3 1
Mica Schist 10% 63 3 3 9 1 2
Po Mica Schist 3% 45 2 2 11 2 2
Upper Mica
Schist 1% 19 2 1 3 1 1
Shear Schist 4% 49 3 4 11 2 2
Silica Mica
Schist 4% 30 1 0 0 0 0
Diabase <1% 2 0 0 0 0 0
Granite <1% 2 0 0 0 0 0
Waste Schist-Marble <1% 7 0 0 0 0 0
Rock Chlorite Schist <1% 19 0 0 0 0 0
Pegmatite 1% 110 2 3 9 2 1
Spodumene
Ore Pegmatite 25% 57 1 2 5 2 1
Total 571 22 22 75 15 14
Source: PAG-NPAG table in file distribution.xslx, received from Albemarle on March 22,2023
'Proportions are an estimate based on the current block model and are subject to change based on ongoing modeling and mine
planning.
2ABA, NAG,and multi-element analysis conducted on discrete core samples.
3HCT completed on discrete core samples.
°Synthetic precipitation leaching procedure(EPA, 1312)completed on composites.
'Leaching environmental assessment framework EPA method 1313 (pH batch test)and 1316 (L:S batch test)completed on
composites.
61-eaching environmental assessment framework EPA method 1314(upflow percolation test)completed on composites.
4.1.2 Future Tailings Material
Table 4-2 presents a summary of the tailings samples and testing conducted for each sample. Tailings
materials associated with the Project include:
• DMS rejects (both preconcentration methods)
• Flotation tailings (both preconcentration methods)
• MSR (XRT-based ore-sorting only)
• OSR
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Table 4-2: Tailings Sampling and Testing Matrix
ABA,
Metallurgical Category Laboratory NAG, MWMP SPLPZ LEAF HCT
Program ID Multi-
Element'
2018 Pilot Plant DMS-no sorting L47924-03 X X
2018 Pilot Plant Flotation tails-no sorting L76180-03 X X
2018 Pilot Plant Flotation tails-no sorting L47924-01 X X
2018 Pilot Plant Flotation tails-no sorting L47924-02 X X
2018 Pilot Plant Mag separation rejects L76180-01 X X
2018 Pilot Plant Mag separation rejects L76180-02 X X
2022 North Met DMS-no sorting L75603-01 X X X
2022 North Met DMS-no sorting L75603-02 X X X
2022 North Met Flotation tails-no sorting L75603-15 X X
2022 North Met Flotation tails-no sorting L75603-16 X X
2022 North Met DMS-sorting L75603-03 X X X
2022 North Met DMS-sorting L75603-04 X X X
2022 North Met DMS-sorting L75603-05 X
2022 North Met DMS-sorting L75603-06 X
2022 North Met DMS-sorting L75603-07 X
2022 North Met DMS-sorting L75603-08 X
2022 North Met Flotation tails-sorting L77114-01 X
2022 North Met Flotation tails-sorting L77114-02 X
2022 North Met Flotation tails-sorting L77114-03 X
2022 North Met Flotation tails-sorting L77114-04 X
2022 North Met Flotation tails-sorting L78487-01 X
2022 North Met Flotation tails-sorting L78487-02 X
2022 North Met Ore sorting rejects L75603-09 X X X X
2022 North Met Ore sorting rejects L75603-10 X X X X
2022 North Met Ore sorting rejects L75603-11 X
2022 North Met Ore sorting rejects L75603-12 X
2022 North Met Ore sorting rejects L75603-13 X
2022 North Met Ore sorting rejects L75603-14 X
2022 North Met Mag separation rejects L77419-41 X
2023 Met Flotation tails-sorting L80362 X X X
2023 Met Flotation tails-sorting L80363 X X X
'Four-acid digest used for multi-element analysis
2Synthetic precipitation leaching procedure(EPA, 1312)
31-eaching environmental assessment framework EPA method 1313 and 1316
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4.1.3 Legacy Mining Waste, Cover Material, and Kings Creek Stream Sediment
Table 4-3 presents a summary of the sample and test numbers for the legacy mining waste, cover
material, and Kings Creek stream sediment.
Table 4-3: Sampling and Testing Matrix for Legacy Mining Waste, Cover Material, and Kings
Creek Stream Sediment
Category ABA/NAG/Multi-Element SPLP
Legacy Waste Rock 33 7
Legacy Tailings 23 8
Cover Material-Alluvium 8 3
Cover Material-Saprolite 10 3
Kings Creek Stream Sediment 3 3
4.2 Stage 1 Static Test Methods
The following sections provide a summary of the test methods used for the Kings Mountain
geochemical characterization program.
4.2.1 Paste pH
Paste pH measurements involve combining the sample with deionized water at a 1:2 solid/water ratio,
after which the sample is stirred. The pH of the resultant slurry is measured after a predetermined
time.
4.2.2 ABA
The ABA method used for the Project is the modified Sobek ABA method (Sobek et al., 1978). This
method included laboratory analysis to determine the total sulfur, sulfate sulfur, sulfide sulfur, and
residual sulfur content using LECO analysis. Tests also included an assessment of neutralizing
potential (NP) and empirical calculations based on acid potential (AP) and NP.
The AP values were calculated from sulfide sulfur' concentrations and reported as kilograms (kg)
equivalents per ton (eq/t) of rock for calcium carbonate (CaCO3):
AP (kg CaCO3 eq/t) = Sulfide S (%) x 31.252
The NP values were determined using the modified Sobek protocol that involves reacting the sample
with hydrochloric acid, followed by titration with sodium hydroxide (NaOH)to pH 8.3 to determine the
amount of acidity consumed by neutralizing minerals. Neutralizing potential is calculated as kilograms
per ton (kg/t) as CaCO3.
'The fraction reported by the laboratory as sulfide sulfur is a combination of sulfide sulfur plus residual (or non-
sulfate)sulfur. This fraction may include minerals such as barite (if present)and thus represents a
conservative approach for estimating AP.
2The formula assumes that pyrite is the only sulfide mineral present and that it completely oxidizes to sulfate
during the reaction. This is a conservative assumption for Kings Mountain, as other sulfide minerals are
present and more abundant in the mineralization (e.g., pyrrhotite).Additional discussion on site-specific
estimation of acid generating potential is provided in Section 5.6.
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The balance between the acid generating mineral phases and acid neutralizing mineral phases is
referred to as the net neutralization potential (NNP), which is calculated as follows:
NNP = NP -AP
The NNP allows classification of the samples as potentially acid consuming or acid producing. A
positive value of NNP indicates the sample neutralizes more acid than is produced during oxidation. A
negative NNP value indicates there are more acid producing constituents than acid neutralizing
constituents. Material that would be considered to have a high potential for acid neutralization
produces a net neutralizing potential greater than (>) 20 kg CaCO3 eq/t. Those materials considered
to have a higher potential for acid generation produce an NNP <-20 kg CaCO3 eq/t.
The ratio of the NP to AP is referred to as the neutralization potential ratio (NPR) and is calculated as
follows:
NPR = NP/AP
NPR values <1 indicate a higher potential for acid generation and >3 indicate significant acid
neutralization.
ABA results are typically compared to criteria provided by the BLM (2008) in order to determine the
potential for the waste rock and ore material to generate acid. The Nevada BLM Water Resource Data
and Analysis Guide for Mining Activities (BLM, 2008) establishes the following guidelines for the
evaluation of the ABA test results:
• ANP:AGP (NPR) values greater than 3 and NNP values greater than 20 kg CaCO3 eq/t are
not acid generating and do not require further testing.
• ANP:AGP (NPR) values less than 3 and/or NNP values less than 20 kg CaCO3 eq/t have
uncertain potential for acid generation and require further evaluation using kinetic test
methods.
Screening level criteria for ABA data are summarized in Table 4-4 and were used to evaluate the ABA
data for Kings Mountain. Also included in Table X are the BLM (2008) criteria for comparison. For the
Kings Mountain data evaluation, samples were identified as potentially acid generating (PAG) if the
NNP values are less than -20 kg CaCO3 eq/t and the NPR values are less than 1. Samples are
classified as non-PAG if the NNP values are greater than 20 kg CaCO3 eq/t and the NPR values are
greater than 3. Samples that fall between these criteria exhibit uncertain acid generating
characteristics. The combined results of ABA, NAG, HCT,and mineralogy results were ultimately used
to develop site-specific criteria for acid generation potential as summarized in Section 5.6 and included
in Table 4-4 below.
Table 4-4: Static Data Non-PAG vs. PAG Criteria Summary
BLM (2008) Kings Mountain Data Site Specific Cut-off for
Category Evaluation PAG Estimate
NNP NPR NNP NPR NNP
Non-PAG >20 >3 >20 >3 >-1
Uncertain <20 <3 <20 but>-20 <3 but>1 --
PAG <-20 <1 <-1
Notes:
Net Neutralization Potential(NNP)=Neutralization Potential(NP)versus Acid Potential(AP)in kg CaCO3 eq/t.
Neutralization Potential Ratio(NPR)=NP/AP.
1 Refer to Section 5.6 for discussion of PAG estimation using the site block model.
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4.2.3 Methodology for Calculating Surrogate AP and NP
The Albemarle exploration database has been used to supplement the results of the geochemical
characterization testing presented herein. Specifically, the surrogate acid generating potential and
surrogate neutralizing potential have been calculated from total sulfur and calcium data presented in
the exploration database.
Surrogate AP values were calculated from the total sulfur content (from ICP analysis) as follows:
Surrogate AP = Total sulfur wt% * 31.25
The above equation assumes that all sulfur is present as pyrite and will oxidize to form sulfuric acid.
Surrogate NP values were calculated from the total calcium content (from ICP analysis) as follows:
Surrogate NP = Total calcium wt% * 1000/41
The above equation assumes that all calcium is associated with acid buffering mineral phases.
4.2.4 Total Carbon, Total Organic Carbon, and Total Inorganic Carbon
The samples were submitted for determination of the total carbon content as well as carbon speciation
testing (i.e., total organic carbon (TOC) and total inorganic carbon (TIC)).
The total carbon content is determined by LECO analyzer. The sample is combusted in oxygen in a
LECO furnace at 1,350 degrees Celsius (°C). Carbon present in the sample is evolved as carbon
dioxide and swept to a measurement cell for quantification by infrared detection.
Inorganic carbon (carbonates and bicarbonates) is removed by reaction with dilute HCI. After drying,
the remaining sample is combusted in oxygen in a LECO furnace at 1,350°C. Any organic carbon in
the sample present as organic matter or graphite is evolved as carbon dioxide and swept to a cell for
quantification by infrared detection.
The TIC content is determined by difference:
TIC = Total C -TOC
The carbonate-based NP (NP(TIC)) may be calculated from the inorganic carbon content as follows:
NP(TIC) (kg CaCO3 eq/t) = TIC (%) * 83.33
4.2.5 NAG Testing
NAG testing is being used as a second measure of ARID potential for the Kings Mountain waste rock
and ore samples. The NAG test differs from the ABA test in that it provides a direct empirical estimate
of the overall sample reactivity, including any acid generated by semi-soluble sulfate minerals as well
as PAG sulfide minerals that are oxidized by H2O2 addition.
Single-addition NAG testing was carried out in accordance with the method described by Miller et al.
(1997).The method involves the addition of 250 milliliters(mL)of 15% H2O2 to 2.5 grams(g)of sample
to oxidize reactive sulfides. The net result is that acid production and neutralization can be measured
directly. The peroxide is allowed to react with the sample overnight, and the following day the sample
is gently heated to accelerate the oxidation of any remaining sulfides then vigorously boiled for several
minutes to decompose residual peroxide. When cool, the pH and acidity of the NAG liquor are
measured. The leachate is titrated with NaOH in two stages (to pH 4.5 and 7) to determine the NAG
value, which is calculated as follows:
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NAG = (Vtnit/X) (49 * VNeOH *M)/W
Where:
NAG = net acid generation (kilograms (kg)sulfuric acid (H2SO4) per metric ton)
Vt,,;t= volume of initial hydrogen peroxide solution (mL)
X= volume used to determine NAG by titration (mL)
VNaOH=volume of NaOH used in titration (mL)
M = concentration of NaOH used in titration (moles/liter)
W= weight of sample reacted (g)
Table 4-5 summarizes the criteria used for assessing the acid generation potential based on NAG
results and the NAG method. In general, NAG pH values >4.5 standard units (s.u.) indicate non-PAG
material; NAG pH values <4.5 s.u. indicate PAG material. By convention, any NAG value below 10 kg
H2SO4 eq/t has a limited potential for acid generation, and the results are considered inconclusive
because a blank H2O2 solution can generate a NAG artifact value of up to 10 kg H2SO4 eq/t(Sapsford
et al., 2009).
Table 4-5: Acid Generation Criteria for NAG Results
Acid Generation Capacity Final NAG pH (s.u.) Static NAG (kg H2SO4 eq/t)
PAG Higher capacity <4.5 >10
Lower capacity <4.5 <10, >0.5
Non-PAG >4.5 0.5
4.2.6 Multi-Element Analysis
The elemental composition of the samples has been determined based on a four-acid digest of the
solids followed by analysis of the digest via ICP-MS/ICP-AES. The four-acid digest is able to dissolve
most minerals in the sample matrix and provides near-total quantitative results.
The results of the multi-element analysis are used to calculate the geochemical abundance index(GAI)
(INAP, 2014),which compares the concentration of an element in a given sample to its average crustal
abundance. GAI values are particularly useful in determining the relative enrichment of elements
based on lithology and may be used to identify elements enriched above average crustal
concentrations.
GAI values are calculated as follows:
GAI = I092[C/(1.5*S)]
Where C is the concentration of an element as determined from the multi-element assay, and S is the
average crustal abundance of the element of interest(Mason, 1966).
Materials are then assigned a GAI value between zero and six based on the degree of enrichment, as
shown in Table 4-6. According to the INAP (2014) protocol, a GAI value >3 indicates significant
enrichment.
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Table 4-6: Interpretation of GAI Values
GAI Value Interpretation
0 <3 times average crustal concentrations
1 3 to 6 times average crustal concentrations
2 6 to 12 times average crustal concentrations
3 12 to 24 times average crustal concentrations
4 24 to 48 times average crustal concentrations
5 48 to 96 times average crustal concentrations
6 >96 times average crustal concentrations
4.3 Mineralogical Analyses
The following mineralogical analyses have been completed as part of the Kings Mountain
characterization program:
Detailed mineralogical assessment completed by Petrolab on 22 samples representative of
the main rock types that were submitted for HCT. Testing included optical microscopy, SEM
and qualitative XRD analysis.
• Qualitative XRD analysis was conducted by Petrolab on the 22 waste rock composite samples
submitted for SPLP and LEAF testing.
• 53 samples were selected from the static test dataset for quantitative XRD analysis by Rietveld
Refinement at SGS. Splits of these samples were also submitted to SGS for ABA, NAG, and
multi-element analysis. This testing was done to aid in the interpretation of the ABA and NAG
data for the larger dataset completed at ACZ.
4.4 Short-Term Leach Test Methods
4.4.1 Modified SPLP
The SPLP (EPA Method 1312) is an agitated extraction method that requires particle size reduction to
<9.5 mm. A modified method was used that involved the use of deionized water as the leach solution
at a water to solids ratio of 2:1. The results of the SPLP provide a qualitative evaluation of constituents
that could be mobilized from the materials; however, concentrations are not considered to be
conclusive or to represent actual predictions of water quality.
4.4.2 MWMP
MWMP testing was carried out to give an indication of constituent mobility. The MWMP test was
developed to simulate the leaching of mine waste materials with meteoric water during precipitation
events. The results of the MWMP test can be used to identify the presence of leachable constituents
and readily soluble salts stored in the material as well as provide an indication of their availability for
dissolution and transport in response to a precipitation event.
The MWMP test was conducted according to standard test methods (MWMP-E2242-13, ASTM 2013)
that involve a 24-hour, single pass column leach using a 1:1 distilled water-to-rock ratio. The resulting
leachate was submitted for metals analysis.
Due to differences in the liquid-to-solid (L/S) ratio used in the test compared to typical site conditions,
actual concentrations measured in MWMP leachates only provide a qualitative evaluation of
constituents and are not considered to be conclusive or to represent actual predictions of water quality.
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Furthermore, the MWMP test is best applied to oxidized samples that contain soluble weathering
products available for leaching rather than fresh core material. Therefore, the results provided herein
for some of the core samples may underpredict the potential for metal leaching from the waste rock
and ore material that could occur under site conditions. It should also be noted that the solubility of
trace elements in leachate is commonly pH-dependent, and the MWMP test does not account for
future changes in pH conditions that could occur (e.g., as a result of sulfide mineral oxidation). To
address the uncertainties and limitations of the MWMP test, kinetic testing is required.
4.4.3 LEAF Testing
The LEAF is a leaching evaluation protocol that consists of up to four leaching methods, data
management tools, and scenario assessment approaches designed to work individually or to be
integrated to provide a description of the release of inorganic constituents of potential concern (COPC)
for a wide range of solid materials (US EPA, 2022). The LEAF methods were originally developed in
the European Union (EU) for evaluating coal combustion products and are designed to consider the
effect of key environmental conditions and waste properties on leaching.
Of the four LEAF test methods available, the following three were applied to the Kings Mountain
samples':
• EPA Method 1313: Liquid-Solid Partitioning as a Function of Eluate pH Using a Parallel Batch
Extraction Procedure (US EPA, 2012a).
• EPA Method 1314: Liquid-Solid Partitioning as a Function of Liquid-to-Solid Ratio Using an
Up-Flow Percolation Column Procedure (US EPA, 2017a).
• EPA Method 1316: Liquid-Solid Partitioning as a Function of Liquid-to-Solid Ratio Using a
Parallel Batch Extraction Procedure (US EPA, 2012b).
The EPA 1313 method is designed to evaluate the partitioning of constituents between liquid and solid
phases at or near equilibrium conditions over a wide range of pH values (pH 2 to 12). The method
consists of a series of parallel batch extractions of solid material at various target pH values, achieved
with an aliquot of either dilute acid or base. Testing is undertaken on a pulverized sample at a fixed
L/S ratio of 10 and produces a liquid-solid portioning curve of constituents as a function of pH.
The EPA 1314 method is a column percolation test designed to evaluate constituent release from solid
materials as a function of cumulative leaching across a range of L/S ratios. The method provides an
estimate of porewater concentrations at low L/S ratio and allows determination of the changes in liquid-
solid partitioning as soluble constituents are released during successive leach stages. The method is
applicable to low oxygen conditions where rock is submerged (e.g., pit backfill).
The EPA 1316 method is a series of batch dilution tests intended to provide eluate solutions as a
function of the L/S ratio. This method consists of five parallel batch extractions in reagent water over
a range of L/S ratios (2:1, 5:1, 10:1, 50:1, and 100:1). The method provides liquid-solid partitioning at
the natural pH of a solid material as a function of L/S ratio.
'Method EPA 1315 is used to assess diffuse constituent release from monolithic or compacted material (e.g.,
paste backfill)and is therefore not applicable to the Kings Mountain waste rock and tailings materials.
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4.4.4 TCLP
The Toxicity Characteristic Leaching Procedure (TCLP) is a test method designed to simulate the
leaching process that waste materials undergo when disposed of in a landfill. The procedure is used
to determine if the waste is classified as hazardous according to federal Resource Conservation and
Recovery Act(RCRA)criteria.The TCLP test was conducted according to standard test methods(EPA
Method 1311). A brief summary of the TCLP process is provided below:
a) The sample is crushed or ground to reduce the particle size to <9.5 mm.
b) An extraction fluid is chosen depending on the alkalinity of the waste. Extraction Fluid #1 is
acetic acid buffered to a pH of 4.9 s.u. and is used if the waste is expected to be alkaline.
Extraction Fluid #2 is sodium hydroxide buffered to a pH of 2.9 s.u. and is used if the waste is
expected to be acidic.
c) The extraction fluid is added to the prepared waste at a liquid-to-solid ratio of 20:1.
d) The mixture of waste and extraction fluid is agitated using a rotary agitation device, such as a
tumbler,for 18±2 hours at 30±2 rotations per minute(rpm)to simulate the leaching process.
e) After agitation, the leachate is separated from the solid phase by filtration.
f) The leachate is then analyzed for specific inorganic elements using Inductively Coupled
Plasma — Optical Emission Spectrometry (ICP-OES) for all constituents except for Mercury
analyzed by EPA Method 7470A, Mercury in Liquid Waste.
The concentrations of inorganic elements in the leachate are then compared to regulatory levels.
Results of the TCLP analysis are presented in Section 5.1.8 and Section 5.4.5 for future tailings
samples and legacy samples, respectively.
4.4.5 Short-Term Leach Test Sample Selection
Short-term leach testing was conducted on composite samples of waste rock and ore material. The
composites were generated from the core samples to capture the range of static test results(ABA and
NAG)for each of the main material types. The sub-samples selected for each of the composites were
characterized by similar sulfide sulfur content and demonstrated similar ABA and NAG characteristics.
For example, four composites were generated for the Amphibolite Gneiss-Schist material (as shown
on Figure 4-1), which ranged from PAG to non-PAG in terms of ABA and NAG characteristics.
Figure 4-1 also shows the samples selected for HCT. Appendix B provides graphs showing the sub-
samples included in the composites for the remaining material types. Composites were not generated
for the overburden (alluvium), granite, diabase, chlorite schist and schist-marble material types
because these material types comprise an insignificant portion (<1%) of the waste rock material. A
total of 22 composite samples were prepared, and Table 4-7 presents the short-term leach test matrix.
The following section provides results for the short-term leach tests(SPLP, EPA 1313,and EPA 1316).
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NP vs.AP
1000
Non-PAG
100 ♦ ♦ .'
s 0 .-Uncn
p 000 ♦ Amphibole Gneiss-Schist
g QO 000�' NPR=1
O 0 0g �'0 ----NPR=2
U 10 $ g Q ® O HCTS
Y ♦ v 0 ♦ 0 O Comp 1
Z 0 Comp 2
0 Comp 3
PAG O Comp 4
1
Selected for EPA 1314
0.1
0.10 1.00 10.00 100.00
AP(kg CaCO3/0
Source:SRK,2022(Kings Mountain Geochemical Characterization Work Plan)
Figure 4-1: Characteristics of Sub-Samples Utilized to Prepare Four Composites of the
Amphibolite Gneiss-Schist Material
Table 4-7: Short-Term Leach Test Matrix (Future Waste Rock Composites)
Composite# Description SPLP EPA 1313 EPA 1314 EPA 1316
COMP 1 Comp 1 Amphibole Gneiss-Schist X X X X
COMP 2 Comp 1 Biotite Gneiss X X X X
COMP 3 Comp 1 Mica Schist X X X X
COMP 4 Comp 1 Pegmatite X
COMP 5 Comp 1 Po Mica Schist X X X X
COMP 6 Comp 1 Shear Schist X X X X
COMP 7 Comp 1 Spod Pegmatite X X X X
COMP 8 Comp 1 Upper Mica Schist X X X X
COMP 9 Comp 2 Amphibole Gneiss-Schist X X X X
COMP 10 Comp 2 Biotite Gneiss X X
COMP 11 Comp 2 Mica Schist X X X X
COMP 12 Comp 2 Pegmatite X X X X
COMP 13 Comp 2 Po Mica Schist X X X X
COMP 14 Comp 2 Shear Schist X X X X
COMP 15 Comp 2 Spod Pegmatite X X
COMP 16 Comp 3 Amphibole Gneiss-Schist X X X
COMP 17 Comp 3 Biotite Gneiss X
COMP 18 Comp 3 Mica Schist X
COMP 19 Comp 3 Pegmatite X X X
COMP 20 Comp 3 Shear Schist X
COMP 21 Comp 4 Amphibole Gneiss-Schist X X X
COMP 22 Comp 4 Shear Schist X
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4.5 Kinetic Test Methods
4.5.1 Humidity Cell Tests
HCTs were completed on a subset of samples submitted for static testing. The test method adopted
was ASTM D5744-13e1 (ASTM, 2013). Under ASTM methodology, the test is carried out on material
sized to pass a 6.3-mm (0.25-inch) Tyler screen. The test follows a 7-day cycle, during which air that
is humidified and at a temperature of 25°C is introduced at the bottom of the column for 3 days of each
cycle followed by 3 days of dry air. On the seventh day, the sample is rinsed with distilled water, and
the extracted solution is collected for analysis. Key parameters, including pH, alkalinity, acidity,
electrical conductivity, calcium, magnesium (Mg), iron, and sulfate, are measured on a weekly basis
to provide intermediate reference points between full analyses that are conducted less frequently.
Analysis of major and trace element chemistry is carried out on a weekly basis for the first 4 weeks of
the test, after which the frequency of analysis is reduced to every fourth week.
Geochemical reactions and reaction rates monitored throughout the test include sulfide oxidation,
depletion of NP4 secondary mineral precipitation, adsorption-desorption reactions, and mineral
dissolution (INAP, 2014).
4.5.2 HCT Sample Selection
Results from the static geochemical characterization tests (ABA, NAG and multi-element) were used
to select a sub-set of 22 samples representing the main waste rock and ore types for kinetic testing
(Table 4-8).The number of kinetic test samples selected for each material type is based on the relative
importance or mass of the Iithological unit with respect to the total mass in the deposit. Samples of
granite and diabase material types were not selected for kinetic testing because these material types
comprise an insignificant portion (<1%)of the waste rock material. This is also the case for the chlorite
schist and schist-marble lithologies.
Unlike the samples selected for SPLP and LEAF testing, the samples selected for the HCTs consist
of a discrete sample selected from the static test program (i.e., are not composites of multiple
samples). The exception to this is the HCT sample to represent pegmatite, for which limited material
was available. For this HCT, a composite using two samples was generated to represent this material
type.
The HCT sample set is considered lithologically and geochemically representative of the deposit.
Sulfide sulfur, NPR, NNP, NAG and NAG pH were the key parameters used for selection of the HCT
samples. In addition, total metal content was considered in the sample selection process to the extent
possible. The geochemical properties of the HCT samples based on the ABA and NAG test results
are illustrated in the scatter plots presented in Figure 4-2 through Figure 4-4. These graphs show the
distribution of the samples selected for kinetic testing in relation to the entire dataset and demonstrate
that the samples selected for kinetic testing are representative of the range of ABA and NAG results.
Appendix B provides graphs by material type that show the selected HCTs. The selected HCTs have
also been compared to the range of total metal content observed for each material type based on the
4Calculated from sulfate and alkalinity release during the HCT.
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multi-element analysis. Graphs of total metals (antimony, arsenic, cadmium, copper, iron, lithium and
sulfur) versus sulfide sulfur content are provided in Appendix A and show the distribution of the HCT
samples in relation to the entire dataset. In addition, tables summarizing the statistics of these key
parameters for each material type are provided in Appendix A along with the corresponding total metal
content for the HCT samples. These tables show that the selected HCTs are generally representative
of the range of the key parameters for the main material types (>10% of the total mined material)with
a minimum of one HCT sample per material type that represents the median, 75th percentile or higher
for each key parameter. The exception to this is the Spodumene Pegmatite for which only one HCT
was selected and this sample shows lower total metal content compared to the range seen for the
Spodumene Pegmatite material in the database.
The selected HCTs have also been compared to the range of total metal content observed for each
material type based on the multi-element analysis. Graphs of total metals (antimony, arsenic,
cadmium, copper, iron, lithium and sulfur)versus sulfide sulfur content are provided in Appendix A and
show the distribution of the HCT samples in relation to the entire dataset. In addition, tables
summarizing the statistics of these key parameters for each material type are provided in Attachment
1 along with the corresponding total metal content for the HCT samples. These tables show that the
selected HCTs are generally representative of the range of the key parameters for the main material
types (>10% of the total mined material) with a minimum of one HCT sample per material type that
represents the median, 75th percentile or higher for each key parameter. The exception to this is the
Spodumene Pegmatite for which only one HCT was selected and this sample shows lower total metal
content compared to the range seen for the Spodumene Pegmatite material in the database.
0
❑
A A 0
O
0
0 4 0
0 O ,OO 0
O
O O 09 X o
<D O <�o•�AO 0
^10 co
A ■
O A�
0
A 0
Figure 4-2: Waste Rock and Ore Neutralizing Potential Ratio vs. Net Neutralizing Potential
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100 ° EJ A A o
Non-PAG El
X O O 00 p o J� o Amphibole Gneiss-Schist
• ��� p O o Biotite Gneiss
• 0 �O 0 O Ooe*��m g 0 0 Mica Schist
•� NO 0 O ,� ♦ Upper Mica Schist
A C£ 0O 10 0 Po Mica Schist
: � �O c�SL. ��.� 0, ��� 4�
Y • VS
C: d ^�G �g�ypppQ ♦ Shear Schist
O ■ • •� (�� A KYO • Silica Mica Schist
• ■ • • A m
® • �1• • Q A • Pegmatite
Y
a ■ ■. • • • Spod Pegmatite
z �
■ Granite
� PAG
1 •v ■ x Diabase
O HCTs
❑ Schist-Marble
♦ Chlorite Schist
—NPR=1
© — — NPR=3
0.2
0.1 1 10 100
AP(kg CaCO3/0
Figure 4-3: Waste Rock and Ore Acid Generating Potential vs. Neutralizing Potential
110.1
i
IQ
PAG
100.1 A I a ■Alluvium
90.1 I z ♦Amphibole Gneiss-Schist
A I ♦Biotite Gneiss
80.1 I o Mica Schist
I
♦Upper Mica Schist
70.1
o I A Po Mica Schist
= 60.1 A I non-PAG ♦Shear Schist
•Silica Mica Schist
z50.1 •Pegmatite
•Spod Pegmatite
40.1
■Granite
I
30.1
x Diabase
I
O HCTs
20.1 ( I • ❑Schist-Marble
I �
•Chlorite Schist
10.1
0.1 4 0
0 2 4 6 8 10 12 14
NAG pH(s.u.)
Figure 4-4: Waste Rock and Ore NAG pH vs. Net Acid Generation
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Table 4-8: Summary of Future Waste Rock and Ore HCT Samples
Paste Sulfide Sulfate Sulfur AP (kg NP (kg TIC NNP (kg NPR' NAG (kg NAG
Main Material Type HCT ID Hole ID From To Rationale pH ° ° Total CaCO3 CaCO3 ° CaCO3 H2SO4 pH
(s.u.) (�O) (�0) (%) eq/t) eq/t) (�O) eq/t) Ratio eq/t) (s.u.)
Alluvium HCT-01 DDKM17-121 0 16.4 5.5 <0.005 0.03 0.03 <0.15 <0.50 <0.05 0.35 3.33 2 5
Upper Mica Schist HCT-02 DDKM17-076 194.4 199 High AP 8.0 0.67 0.23 0.9 20.9 8 <0.05 -12.9 0.38 23 2.8
HCT-03 DDKM17-080 842.5 846.94 Low AP 4.0 0.03 0.02 0.05 0.9 6 <0.05 5.1 6.67 4 5.8
HCT-04 DDKM17-042 600.24 604 High AP 9.8 0.55 1 0.32 0.87 17.2 1 6 <0.05 -11.2 1 0.35 20 2.6
Amphibole HCT-05 DDKM17-017 533.97 538.06 Low AP 9.6 0.1 0.02 0.12 3.1 10 <0.05 6.9 3.23 <0.5 6.6
Gneiss-Schist HCT-06 DDKM17-054 1062 1066.78 Low Sulfide 9.4 0.02 0.02 0.04 0.6 14 <0.05 13.4 23.3 <0.5 7
HCT-07 DDKM17-083 797 802.51 Uncertain AP 9.7 0.2 0.25 0.45 6.2 10 <0.05 3.8 1.61 5 3.5
HCT-08 DDKM18-216 244.06 249.54 High NP 10.0 0.1 0.04 0.14 3.1 43 0.4 39.9 13.9 <0.5 11.2
Biotite Gneiss HCT-09 DDKM17-052 1223.5 1226.21 High AP 8.0 1.07 0.62 1.69 33.4 6 <0.05 -27.4 0.18 35 2.5
HCT-10 DDKM18-293 561.02 567.09 Uncertain AP 8.8 0.28 0.04 0.32 8.8 13 <0.05 4.20 1.48 3 3.9
Po Mica Schist HCT-11 DDKM18-340 476.74 479.92 High AP 8.7 0.3 1.99 2.29 9.40 6 <0.05 -3.40 0.64 47 2.2
HCT-12 DDKM17-055 865.23 868 High AP 8.1 1.08 0.47 1.55 33.8 5 <0.05 -28.8 0.15 3 2.5
HCT-13 DDKM18-366 187.3 191.93 High AP 6.3 1.37 0.3 1.67 42.8 4 <0.05 -38.8 0.09 35 2.4
Mica Schist HCT-14 DDKM17-019 1289.22 1292 Low AP 9.4 0.04 0.1 0.14 1.2 9 <0.05 7.8 7.50 <0.5 7.2
HCT-15 DDKM17-110 511.5 515.5 Uncertain AP 9.5 0.1 0.17 0.27 3.1 5 <0.05 1.9 1.61 4 3.4
HCT-16 DDKM17-021 1718 1722 Uncertain AP 9.2 0.59 0.4 0.99 18.4 30 0.2 11.6 1.63 3 3.9
Shear Schist HCT-17 DDKM17-096 568 572:12 High AP 9.0 1.13 0.55 1.68 35.3 20 0.2 -15.3 0.57 21 2.7
HCT-18 DDKM18-206 845.8 850.75 Low AP 8.7 0.26 0.15 0.41 8.1 36 0.3 27.9 4.44 <0.5 10.4
HCT-19 DDKM18-329 1064.63 1 1067.52 High AP(Composite) 6.5 0.07 0.155 0.225 2.2 2.5 <0.05 0.3 1.12 4.5 3.45
Pegmatite DDKM17-055 36.09 44.3
HCT-20 DDKM17-077 43 46 Low AP,some NAG 9.1 <0.005 <0.005 <0.005 <0.15 18 <0.05 2.85 20 20 5.5
Spod Pegmatite HCT-21 DDKM17-037 1381.27 1384.71 Low AP, some NAG 9.8 <0.005 <0.005 <0.005 <0.15 3 <0.05 2.85 20 10 6
Silica Mica Schist HCT-26 DDKM18-317 189 194 Average AP 10 <0.05 <0.03 <0.08 <1.60 18 <0.05 16.4 11.3 0.5 7.4
Source:htti)s:Hsrk.sharepoint.com/sites/NAUSPR000576/Internal/0400 Geochemical/Lab%20Data/HCT/KM Database HCT Rev04.xlsx
'NPR<1 =PAG;NPR between 1 and 3=Uncertain;NPR>3=non-PAG
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5 Results and Discussion
5.1 Future Waste Rock and Ore
5.1.1 Mineralogy
Several campaigns of mineralogy work have been undertaken on the Kings Mountain material. There
is historical work and the initial work by SRK. In addition, mineralogical studies on ore material were
also undertaken by SGS as part of the metallurgical study; these are presented in the following
sections. Appendix C provides the Petrolab mineralogy reports along with the tabulated mineralogy
data.
Historical Mineralogy
Mineralogical studies on Kings Mountain lithium mine started with King (1931)and extensive mapping
of the pegmatites in the Gaffney-Kings Mountain range of South Carolina. A comprehensive
description of the mineralogy of the original Foote Mine has been published by Wilson and MacKenzie
(1980) and Hanahan (1985). Table 5-1 summarizes the mineralogy from this program.
As can be seen from Table 5-1 , although the mineralogy is complex it is actually dominated by fewer
than 10 minerals(i.e., major mineral phases present at >10% abundance); these tend to be feldspars,
quartz, micas, clays, and minerals associated with contact skarn (presumably included the marble
quarry contact and include Actinolite-Tremolite,Talc,Grossular,and Diopside).The majority of COPCs
trace elements (e.g., lead (Pb), zinc (Zn), cadmium (Cd), uranium (U), etc.) are present in minerals of
trace amounts (<2% abundance), but it is important to note that they do occur (e.g., lead in galena
(PbS) and uranium in several trace to ultra-trace phases (<0.2% abundance)). Acid generating
potential occurs in sulfide minerals,of which pyrrhotite(Fei-xS)is the most abundant,followed by pyrite
(FeS2), marcasite (FeS2)and chalcopyrite (CuFeS2).
Carbonate buffering is well represented by the skarn and veinlet material comprising calcite (CaCO3),
dolomite (CaMg(CO3)2), and ankerite (Ca(Fe21 Mg)(CO3)2)that also occur in some of the schists.
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Table 5-1: Historical Summary of Mineralogy of the Kings Mountain Pegmatites and Contact Wallrock Zones
Abundance Relative Minerals
Range
Major >10% Actinolite, Albite,Almandine, Biotite, Calcite, Chlorite, Diopside, Dolomite, Grossular, Microcline, Muscovite , Quartz, Spodumene
<2%to Byssolite,Analcime, Ankerite,Apatite Bertandite, Beryl, Chalcopyrite, Dravite, Fluorapatite, Fluorite, Goethite, Graphite, Gypsum,
Minor 10% Holmquisite, Kaolinite, Lepidolite, Magnesite, Magnetite, Montmorillonite, Natrolite, Opal Petalite, Pyrrhotite, Schrol, Serpentine,
Siderite, Staurolite, Tourmaline,Tremolite,Zinnwaldite
Amblygonite-Montebrasite,Arsenopyrite,Axinite, Biktaite, Birnessite, Cassiterite, Clinochlore, Clinozoisite, Cookeite, Eucryptite„
Trace <2% Galena, Hydroxyanthophyllite„ Laumonite„ Leucophosphite, Lithophorite, Malachite, Marcasite, Milarite, Mitridatite, Monazite ,
Phenakite, Phlogopite, Prehenite, Pyrite, Rhodochrosite, Rutile, Scholzite, Spessartine, Sphalerite, Strengite, Strunzite,Talc,
Titanite,Torbernite, Tri h lite, , Variscite, Vivianite,Vermiculite, Whiteite, Zircon
Altaite,Autunite,Azurite, Baryte, Bassetite, Bavenite, Beraunite, Bermanite, Bismuttite, Bityite, Bornite, Boulangerite,
Brannockite, Cacoxenite, Calcioancylite (Ce), Calcioferrite, Chalcanthite, Childenite, Chrysocolla, Collinsite, Columbite, Cosalite,
Cryplomelane, Cyrilovite, Diadochite, Dufrenite, Dumortierite, Eakerite, Earlshannonite, Eosphorite, Epidote, Fairfieldite,
Fanfanite, Ferraioloite, Ferroberaunite, Fersmite, Footmineite, Frondelite, Gold, Halloysite, Helvine, Herdenite, Heterosite,
Ultratrace/ <0 Hureaulite, Hydromagnesite, Hydroxyherdenite,Jahnsite, Jasonsmithite, Kastningite, Kayrobertsonite, Kingsmounite, Kobellite,
Rare .2% Laueite, Lithomarstu rite, Lithophosphate, Manganogordanite, Matulaite, Messelite, Meta-autunite, Metaswitzerite, , Montebrasite,
Nagyagite, Neotocrite, Nizamoffite Nordgauite, Otavite, Parascholzite, Paravauxite, Parsettensite, Phosphyllite, Phosphosiderite,
Pseudolaueite, Purpurite, Pyargyrite, Reddingite, Rittmannite, Robertsite, Rockbridgeite, Rosherite, Roscoelite, Santabarbaraite,
Schoonerite, Silver(Ag), Steinmetzite, Stewardtite, Sulfur, Swinefordite, Switzerite, Tetrahedrite, Tetrawickmanite, Tinticite,
Uraloite, Urannite, Uranophane, Whiteite, Whitmoreite, Wickmanite, Xanthoxenite,Zabu elite,
Source: Hanahan, 1985
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Exploration Mineralogy Study
Several reports on mineralogy have been completed by Dahrouge Geological Consulting. These
studies only focused on the mineralogy of the pegmatites. All samples contain variable proportions of
the main silicates quartz (SiO2), albite (Na(AlSi3O8)), and microcline (K(AlSi3O8)). Minor phases vary
from sample to sample and include tourmaline, muscovite (Ka12(AISi3O1o)(OH)2), iron-magnesium
amphiboles, clinochlore (Mg5Al(AISi3O10)(OH)8), fluorapatite (Ca,5(PO4)3F), iron-manganese (Mn)
phosphates, (iron-manganese-calcium-) carbonates, and iron-manganese-oxides. Observed
accessory phases are the iron-sulfides pyrrhotite and pyrite, zircon (Zr(SiO4)), alkali beryl
(Be3Al2(Si6O18)), monazite ((Ce,La,Nd,Th)PO4), xenotime (YPO4), uraninite (UO2), (niobium-)rutile/
anatase (TiO2), ilmenite (Fe2+TiO3), cassiterite (SnO2), sphalerite (ZnS), columbite-tantalite, and trace
amounts of chalcopyrite in addition to tellurides of bismuth (Bi) and lead.
The lithium-bearing minerals include the silicates spodumene (LiAISi2O6) and holmquistite
((Li2)(Mg3Al2)(Si8O22)(OH)2), and a range of aluminum (AI) and iron-manganese phosphates which
have been tentatively identified based on semi-quantitative SEM data; these include montebrasite
(LiAI(PO4)(OH)), earlshannonite (Mn2+Fe3+2(PO4)2(OH)2.4H2O), strunzite (Mn2+Fe3+2(PO4)2(OH)2•
6H2O), and ferrisicklerite(Li1_x(Fe3+xFe2+'-x)PO4)in the phosphates group and eucryptite(LiAlSiO4)and
cookeite (LiAl4(Si3Al)O1o(OH)8) in the silicate group. In addition, lithium was identified with micas, such
as lithium-muscovite and polylithionite (KIi2Al(Si4O1o)(F,OH)2).
SRK Program Mineralogy
Detailed mineralogical assessment has been completed by Petrolab on the 22 samples submitted for
HCT. Testing included optical microscopy, SEM, and qualitative XRD analysis. Qualitative XRD
analysis was also conducted by Petrolab on the 22 waste rock composite samples that were submitted
for SPLP and LEAF testing. The full Petrolab reports are provided in Appendix C.
A subset of 53 samples (out of 571)were selected from the static test dataset for quantitative XRD by
Rietveld Refinement at SGS (these samples are referred to herein as waste rock). Splits of these
samples were also submitted to SGS for ABA, NAG, and multi-element analysis to aid in the
interpretation of the ABA and NAG data for the larger dataset completed at ACZ.A discussion of these
data is provided in Section 5.1.5. The SGS laboratory reports are provided in Appendix C3. Sample
selection was aimed at targeting samples with higher sulfide sulfur(high AP)and samples with higher
NP. Samples with high NAG pH and samples with static test data that were not internally consistent
(i.e., samples with high AP/low NP and neutral NAG pH)were also targeted.
Figure 5-1 summarizes the results of the XRD analysis for the HCT, SPLP, LEAF and the subset of
waste rock samples; the full mineralogy reports are provided in Appendix C.
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100%
90%
80% ■Accessory phases
70%
■Phosphates
—
■Aluminum hydroxides
50% ■Fe-Ti Oxides
o ■Iron oxides and oxyhydroxides
50%
n ■Phyllosilicates
a
40% — ■Bulk silicates
■Lithium-bearing minerals
30%
■Carbonates
20% ■Sulfides
10%
ass
�1§ cr`caycF x�y <<' 4V ¢a�116,
FQ
P
Figure 5-1: Summary of XRD Analysis for Waste Rock and Ore Samples
From the mineralogical analysis completed on the HCT, SPLP, LEAF and the subset of waste rock
samples, the main minerals comprise the bulk silicates quartz, albite, actinolite, and muscovite.
Overall, silicate mineral assemblages accounted for between 62% by weight (wt%) and 99 wt%. The
lithium-bearing mineral polylithionite was also recorded in relatively high concentrations of up to
21.3 wt%.Although not observed in the waste rock samples assessed, spodumene is the main lithium-
bearing mineral at Kings Mountain. Lithium minerals identified in most of the subset of waste rock
samples included holmquistite at concentrations up to 24.7 wt% and spodumene was identified in two
spodumene pegmatite samples at concentrations between 2.3 wt% and 13.8 wt%.
The main sulfur-bearing minerals identified in the samples were the sulfides; pyrrhotite, pyrite,
sphalerite,and chalcopyrite. Lesser quantities of some other sulfides were identified by SEM; however,
these were present at concentrations that were below the limit of detection by XRD. Generally, sulfide
minerals were most abundant in the pyrrhotite mica schist, mica schist, and upper mica schist
lithologies. Pyrrhotite was detected in the majority of HCT, SPLP and LEAF samples, and greatest
concentrations were seen for the pyrrhotite mica schist (5.1 wt%). Concentrations of pyrite were
elevated in the same samples as for pyrrhotite, although the maximum concentration was lower at 0.9
wt%. Further assessment to determine the average elemental composition of pyrrhotite indicated that
it comprised 36.5 wt% sulfur and 63.5 wt% iron. Pyrrhotite was the only sulfide mineral identified in the
subset of waste rock samples analyzed by SGS. The highest concentration (8.1 wt%)was present in
a shear schist sample.
Sulfate minerals were not identified in any of the samples, despite ABA testwork from ACZ indicating
that sulfate was present in the samples at concentrations up to 3% or in efflorescent sulfate salts that
are extremely difficult to determine by XRD due to poor structure. Alternatively, it could be that
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pyrrhotite is being partially digested in the ABA sulfate sulfur test(HCI digest)and incorrectly reported
as sulfate.
The carbonate minerals calcite, dolomite, magnesite, and siderite were also identified in the HCT,
SPLP and LEAF samples(note that only calcite and siderite were identified in the subset of waste rock
samples). Calcite was consistently present at between 4.1 wt%and 5.6 wt% in samples of shear schist
(HCT-16 to HCT-18), and up to 5.5 wt% was recorded in one sample of upper mica schist (HCT-02),
with the other sample of upper mica schist recording 0.1 wt% calcite.
Similar concentrations of calcite were measured in the SPLP and LEAF samples. The highest
concentration was measured in the shear schist — between 8.2 wt% (Composite 20) and 4.9 wt%
(Composite 22). Two of the amphibole gneiss schist samples also contained high calcite
concentrations; 6.1 wt% (Composite 4) and 3.5 wt% (Composite 1).
Higher calcite concentrations were measured in the subset of waste rock samples submitted to SGS
— the highest being 11.5 wt% (amphibole gneiss-schist), followed by 10.9 wt% (pegmatite) and 5.7
wt% (amphibole gneiss-schist).
Dolomite was less prevalent than calcite and was identified in around 60% of HCT, SPLP and LEAF
samples; concentrations were between 0.1 wt% and 1.1 wt%. The iron carbonate siderite was
generally ubiquitous in these samples and ranged from 0.2 wt% and 2.1 wt%. Siderite was less
prevalent in the SGS dataset, being present at concentrations between 0.3 wt% and 1.1 wt% in just
over 10% of the samples tested. These minerals tend to occur as skarn veinlets that cut across the
rock fabric. The minerals are highly variable, and this is reflected in the large range of concentrations
observed for TIC.
Other minerals present include metal oxides, hydroxides, and oxyhydroxides (present at relative
concentrations of between 0.2 wt% and 13.4 wt%) and phosphates (up to 3.3 wt%).
5.1.2 Paste pH
Figure 5-2 shows paste pH results against total sulfur concentration (with the samples differentiated
by lithology); Table 5-2 summarizes these results. This test gives an indication of the availability of
readily soluble sulfate salts.An acidic pH (pH <5)may indicate the presence of acidic reaction products
generated by sulfide oxidation. An alkaline pH (pH >7) suggests the presence of reactive neutralizing
minerals or, if categorized as potentially acid generating, that the sample has not yet oxidized
sufficiently to become acidic. Figure 5-2 shows that the majority of paste pH results are circum-neutral
to alkaline, although eight samples are recorded with acidic pH values (overall, the samples ranged
between pH 3.4 and 10.7). The samples yielding acidic pH values were the upper mica schist (five
samples) and pegmatite (three samples) lithologies. There is no obvious trend with respect to paste
pH and total sulfur content for samples from any of the lithologies tested.
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11
• O O
O
10 .., �,00 •0
O
9 T=. < v�0 O O, p pp� ElAlluvium EY
& 0` p o �� 0 0 o Amphibole Gneiss-Schist
p :& �Q A
g • O CF�p y� yp �A p o Biotite Gneiss
• O A A 0 Mica Schist
2 o Upper Mica Schist
A Po Mica Schist
6
= o Shear Schist
Q
B. 5 •Silica Mica Schist
a • •Pegmatite
4 •Spod Pegmatite
■Granite
3 —
x Diabase
2 ❑Schist-Marble
♦Chlorite Schist
1
0
0 0.5 1 1.5 2 2.5 3 3.5 4
Tota 15
N
Figure 5-2: Paste pH Plotted as a Function of Total Sulfur Content—Waste Rock and Ore
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Table 5-2: Summary of ABA and NAG Data- Future Waste Rock and Ore
Parameter, Units, and Limit of Detection'
Paste Total NAG AP NPR
Rock Type Statistic2 PH Sulfur Sulfide Sulfate TC TIC TOC pH2 NAG NP AP total s sulfide NNP Ratio
s.u.
(s.u.) wt% kg CaCO3 eq/t
0.1 0.01 0.01 0.01 0.1 0.1 0.1 0.1 1 0.3 - - -No. Samples 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Alluvium Minimum 5.5 1 0.01 0.01 0.01 0.1 0.1 0.1 1 5 1 0.5 0.16 0.15 0.35 1.7
Average 6.8 0.084 0.068 0.022 0.19 0.12 0.2 7.1 0.94 9.3 2.63 2.12 7.2 -
Maximum 9.6 0.33 0.29 0.04 0.4 0.4 0.5 11 2.1 41 10 9.1 32 13
No. Samples 19 19 19 19 19 19 19 19 19 19 19 19 19 19
Upper Mica Minimum 3.4 0.01 0.01 0.01 0.1 0.1 0.1 2.3 1.0 0.3 0.31 0.31 -46 -
Schist Average 6.8 0.96 0.61 0.31 0.12 0.1 0.12 3.5 22 5 30 19 -14 1.6
Maximum 9.7 2.52 1.68 0.71 0.3 0.1 0.2 6.6 64 8 79 53 5.1 16
No. Samples 130 132 132 132 132 132 132 132 132 132 132 132 132 132
Amphibole Minimum 7.1 0.01 0.01 0.01 0.1 0.1 0.1 2.6 1 6 0.31 0.3 -11 0.3
Gneiss Schist Average 9.6 0.28 0.15 0.12 0.25 0.23 0.12 7.3 2.9 24 8.65 4.8 19 16
Maximum 10.7 1.37 0.72 0.94 1.8 1.3 1.2 11.4 26 118 43 23 118 381
No. Samples 30 31 31 31 31 31 31 31 31 31 31 31 31 31
Biotite Minimum 7.7 0.01 0.03 0.02 0.1 0.1 0.1 2.5 1 6 1.56 0.9 -35 0.1
Gneiss Average 9 0.74 0.47 0.27 0.1 0.07 0.09 4.2 12 12 23 15 -2.5 -
Maximum 9.8 1.69 1.32 0.96 0.4 0.4 0.3 11 36 44 53 41 35 16
No. Samples 62 63 63 63 63 63 63 63 63 63 63 63 63 63
Mica Schist Minimum 6.3 0.01 0.01 0.01 0.1 0.1 0.1 2.3 1 4 0.31 0.31 -44 0.08
Average 9 0.45 0.31 0.13 0.11 0.1 0.11 4.3 8.5 7.5 14 9.5 -2 5
Maximum 10 2.13 1.52 0.61 0.3 0.1 0.3 8.6 53 18 67 48 18 58
No. Samples 45 45 45 45 45 45 45 45 45 45 45 45 45 45
Po Mica Minimum 7.1 0.39 0.04 0.04 0.1 0.1 0.1 2.1 3.1 0.3 12 1.2 -74 -
Schist Average 8.3 1.84 0.67 1.13 0.29 0.11 0.26 2.6 38 8 57 21 -13 0.9
Maximum 9.4 4.8 2.54 4.5 2.1 0.5 1.6 3.9 100 21 152 79 11 10
No. Samples 49 49 49 49 49 49 49 49 49 49 49 49 49 49
Shear Minimum 6.2 0.01 0.01 0.01 0.1 0.1 0.1 2.4 1 5 0.31 0.31 -48 0.2
Schist Average 9.0 0.87 0.39 0.48 0.23 0.20 0.08 5.0 11 24 27 12 12 -
Maximum 9.9 3.56 1.82 3.08 1.7 1.2 1.4 11 52 103 111 57 99 153
No. Samples 110 110 110 110 110 110 110 110 110 110 110 110 110 110
Pegmatite Minimum 4.2 0.01 0.01 0.01 0.1 0.1 0.1 3.2 1 0.15 0.31 0.31 -0.16 0.4
Average 9.3 0.04 0.02 0.03 0.12 0.12 0.1 6.1 6.6 7.4 1.35 0.67 6.7 18
Maximum 10.5 0.34 0.18 0.22 1.1 1.1 0.2 11 25 101 11 5.6 101 325
No. Samples 57 57 57 57 57 57 57 57 57 57 57 57 57 57
Spodumene Minimum 8.6 0.01 0.01 0.01 0.1 0.1 0.1 2.3 0.5 2 0.16 0.15 -4.9 0.6
Pegmatite Average 9.9 0.05 0.01 0.04 0.06 0.06 0.06 6.2 6.9 5.5 1.63 0.4 5.1 16
Maximum 10.7 2.16 0.35 1.81 0.3 0.3 0.2 10 47 34 68 11 34 109
No. Samples 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Diabase Minimum 8.5 0.02 0.01 0.01 0.1 0.1 0.1 7 1 22 0.63 0.15 20 12
Average 9.15 0.045 0.03 0.015 0.125 0.125 0.05 8.8 0.5 33 1.41 1.0 31 -
Maximum 9.8 0.07 0.06 0.02 0.2 0.2 0.05 11 0.5 43 2.19 1.9 43 287
No. Samples 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Granite Minimum 8.9 0.04 0.02 0.02 0.1 0.1 0.1 7.1 1 4 1.25 0.6 3.4 3.2
Average 9.1 0.19 0.11 0.08 0.075 0.075 0.05 7.3 0.5 12 5.94 3.4 8.6 -
Maximum 9.3 0.34 0.2 0.14 0.1 0.1 0.05 7.5 0.5 20 11 6.2 14 6.7
No. Samples 30 30 30 30 30 30 30 30 30 30 30 30 30 30
Silica Mica Minimum 9.4 0.03 0.02 0.02 0.1 0.1 0.1 4.7 1 8 0.9 0.6 6.4 2.5
Schist Average 9.9 0.1 0.058 0.05 0.1 0.1 0.1 7.2 1.1 1 2.98 1.8 12 10
Maximum 10.4 0.44 0.24 0.15 0.3 0.3 0.1 11 3.1 43 14 7.5 42 48
No. Samples 7 7 7 7 7 7 7 7 7 7 7 7 7 7
Schist- Minimum 6.7 0.02 0.02 0.01 0.1 0.1 0.1 4.9 1 10 0.63 0.63 4.69 1.9
Marble Average 8.9 0.2 0.14 0.05 2.9 2.8 0.2 9.8 1.14 230 6.3 4.3 226 214
Maximum 9.6 0.59 0.39 0.09 8.1 8.1 0.6 11.6 2 641 18 12 639 1010
No. Samples 19 19 19 19 19 19 19 19 19 19 19 19 19 19
Chlorite Minimum 8.8 0.01 0.01 0.01 0.1 0.1 0.1 6.9 1 11 0.31 0.31 10.69 10.26
Schist Average 9.7 0.02 0.013 0.016 0.1 0.1 0.1 8.07 1.1 17 0.7 0.4 16.7 52
Maximum 10.2 0.21 0.05 0.07 0.2 0.2 0.1 10.3 2 33 6.6 1.56 32.7 106
No. Samples 567 571 571 571 571 571 571 571 571 571 571 571 571 571
All Samples Minimum 3.4 0.01 0.01 0.01 0.1 0.1 0.1 2.1 1 0.3 0.31 0.31 -74.4 -
Average 9.2 0.43 0.21 0.21 0.21 0.18 0.13 5.8 8.9 16 13 6.6 9.5 15
Maximum 10.7 4.88 2.54 4.5 8.1 8.1 11.6 11.6 100 641 152 79 639 1009
APSuiM&:Acid potential based on sulfide
APtotals:Acid generation potential based on total sulfur
TC:Total carbon
'Values that were less than the limit of detection are shown to be at the limit of detection in the table.
2Average values represent the arithmetic mean.
AAverage paste pH values are based on the mathematical average of pH values reported by the laboratory.
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5.1.3 ABA
The AP can be calculated based on the total sulfur or sulfide sulfur content and assumes that all the
sulfur is present as pyrite and will oxidize to form sulfuric acid. However, sulfur may exist in other non-
acid generating forms (e.g., sulfate minerals such as gypsum and barite), in which case the AP will
overestimate the amount of acid that is generated.
An alternative and more widely applied method for the calculation of AP is based on the sulfide sulfur
content of the sample. This method has been applied herein as an initial, screening-level method of
assessing the acid generating potential of the waste rock and ore samples based on ABA results.
However, site-specific estimates of acid generation and PAG waste rock quantities have ultimately
been developed based on the combined results of the ABA, NAG, HCT, and mineralogy testing and
are discussed in detail in Section 5.6.
Based on the analytical method used, the sulfide sulfur content is assumed to be the difference
between total sulfur and acid-soluble sulfate sulfur. Not all sulfate minerals are readily soluble under
the conditions of the HCI extraction in Sobek test. The mineralogical assessment did not identify non-
extractable sulfate minerals (e.g., barite). Therefore, an overestimate of sulfide sulfur content using
this method is not expected. However, leaching with HCI may lead to volatilization of sulfides such as
pyrrhotite,where sulfur is lost as hydrogen sulfide gas(Jennings and Dollhopf, 1995; Price, 2009)and
could result in the overestimation of sulfate sulfur content and underestimation of sulfide sulfur.
However, the analytical method used (i.e., modified Sobek method (Sobek et al., 1978)) is the least
aggressive of the HCI methods in removing sulfide sulfur, as it does not include heating. Table 5-2
shows that the total sulfur content of the core samples ranged between less than the limit of detection
(0.01%) and 4.1%. Concentrations were highest in a pyrrhotite mica schist sample (4.1%)followed by
a shear schist sample (3.6%). The highest average total sulfur contents of 1.8% (pyrrhotite mica
schist), followed by 0.95% (upper mica schist), and 0.87% (shear schist). The upper mica schist
samples show a greater range of total sulfur values than the shear schist samples. A high total sulfur
content was measured in a single spodumene pegmatite sample (2.2%); however, this appears to be
anomalous; the next highest value is 0.23%, with most spodumene pegmatite samples containing
<0.01% (45 out of 57 samples).
The total sulfur content was low(<0.1%)for 43% of the waste rock and ore samples tested (or 249 out
of the 571 samples).The total sulfur content was below the limit of detection for most of the spodumene
pegmatite samples (45 out of 57) and over half of the pegmatite samples (64 out of 109). Figure 5-3
shows the total sulfur content (by LECO) plotted as a function of sulfide sulfur content. The black line
in the plot is a line of equivalence, where sulfide sulfur is equal to total sulfur. The plot shows that
nearly all samples plot below the line of equivalence, indicating that both sulfide and sulfate species
were measured in the material. Samples with an apparent sulfide sulfur content greater than total sulfur
content (circled on Figure 5-3) are a result of higher analytical errors associated with sulfur values
close to the analytical reporting limit.
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10
■ Alluvium
A
♦ Amphibole Gneiss-Schist
1 ♦ Biotite Gneiss
C
A Mica Schist
� 9AA ♦ Upper Mica Schist
A Po Mica Schist
wo �ti O Ap ♦ Shear Schist
0.1 oeS`o��\\� -' <a O A • Silica Mica Schist
OO O • Pegmatite
'EA • Spod Pegmatite
® ,® *M 00 IN Granite
x Diabase
0.01 �♦ ❑ Schist-Marble
♦ Chlorite Schist
---•Equivalence
0.001
0.001 0.01 0.1 1 10
Total S
N
Figure 5-3: Sulfide Sulfur Plotted as a Function of Total Sulfur—Future Waste Rock and Ore
For all rock types, sulfide sulfur was the dominant sulfur species in most of the samples tested.
However, Figure 5-3 shows that there is considerable scatter in some of the data, particularly for the
pyrrhotite mica schist where the sulfate sulfur comprises more than 75% of the total sulfur for 16 out
of 45 samples; this includes a number of samples with very high total sulfur, such as L72143-28
(4.08%)and L81729-03(4.88%).These high sulfate concentrations are not reflected in the mineralogy
results that indicate there is a general absence of sulfate minerals. The high sulfate sulfur
concentrations are likely due to the volatilization of pyrrhotite in the HCI digest resulting in it being
incorrectly accounted as sulfate in the modified Sobek method.
The calculation of AP assumes that pyrite is the only sulfide mineral present and that it completely
oxidizes to sulfate during the reaction; this is a conservative assumption for Kings Mountain, as other
sulfide minerals (in particular pyrrhotite) are present and more abundant in the waste rock. The ABA
results indicate that total sulfur may be a better indicator of AP (albeit more conservative) than the
more widely applied sulfide sulfur method; this is discussed further in the site-specific estimates of
PAG in Section 5.6.
Acid Neutralization Potential
Acid generated from sulfide oxidation may be neutralized by carbonate minerals (i.e., calcite and
dolomite), which react quickly to neutralize acidity and buffer conditions at near neutral pH. Other
minerals may also neutralize acidity(e.g., some silicates); however,they tend to react slowly at neutral
pH conditions.
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Table 5-2 shows that the measured Sobek NP of the samples ranged from <0.3 to 641 kg CaCO3 eq/t.
The Sobek NP of most samples was low, with measured NP values being <20 kg CaCO3 eq/t for
465 samples. The schist—marble has the highest NP (641 kg CaCO3 eq/t)followed by the amphibolite
gneiss schist (118 kg CaCO3 eq/t) followed by the shear schist (103 kg CaCO3 eq/t) and pegmatite
(101 kg CaCO3 eq/t). However, only 24 of 571 samples have modest to high NP values (i.e., >50 kg
CaCO3 eq/t).
The TIC content may be used to infer the speciation of carbonate minerals, source of neutralization,
and potential neutralization efficiency. Where there is good agreement between the NP (TIC) and the
NP (Sobek), it can be inferred that most of the NP (TIC) is present in the form of calcium carbonate
minerals. NP (TIC) values were calculated for all samples and ranged from less than the limit of
detection (8.3 kg CaCO3 eq/t)to 675 kg CaCO3 eq/t.
Figure 5-4 shows a plot of the NP (TIC) as a function of NP (Sobek). The black dotted line shows
where the NP (TIC) and NP (Sobek) are equivalent (data points that plot close to the line show good
agreement between the two values). For samples above the line,the NP(TIC)exceeds the NP(Sobek)
and suggests that the sample may contain carbonate minerals that are not reactive or are present in
forms that do not yield an equivalent neutralization potential (e.g., siderite and FeCO3). For samples
that fall below the line, silicates or magnesium carbonate may be present that contribute to the
neutralization potential.
0
0
O • o
O
<E1 ❑
0 '� O O K
>
.OI,• O• ❑
0
4 of X IP '>01 X
{=O •
Figure 5-4: NP (TIC) Plotted as a Function of Measured NP (Sobek) —Future Waste Rock and
Ore
There was good agreement between the NP (TIC) and the NP (Sobek)for 41 out of 114 samples that
plotted close to the line of equivalence. However, there was poor agreement for most samples (the
results plotting below the line of equivalence), suggesting the NP (TIC)only comprises a portion of the
total NP (Sobek). These results suggest that the majority of the NP (Sobek) is associated with silicate
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minerals. A few samples plot above the line suggesting the presence of siderite. This is supported by
XRD analysis (Appendix C), which showed siderite is present at up to 2.1% by volume in the Kings
Mountain waste rock and ore samples.
Owing to the relatively slow neutralization of acidity by silicate minerals, the short-duration HCI
dissolution evaluated in the Sobek test may underestimate the true NP from silicate minerals.
Lawrence and Wang (1996) demonstrated that longer acid contact times resulted in higher NP
measurements. Candidate calcium- and magnesium-containing silicate minerals that may be
important neutralizers include anorthite, actinolite, enstatite, chlorite, prehnite, pyrope, grossular,
epidote, clinozoisite, and staurolite, all of which were noted by XRD analysis in the Kings Mountain
waste rock and ore samples (Appendix C).
NNP
The difference between the AP and NP is referred to as the NNP, which is equal to the difference
between NP and AP (i.e., NP—AP). For the calculation, AP is calculated from sulfide sulfur. The NNP
allows classification of the samples as potentially acid consuming or acid producing. A positive NNP
indicates the sample neutralizes more acid than is produced during oxidation. A negative NNP value
indicates there are more acid producing constituents than acid neutralizing constituents. Based on
BLM guidance (BLM, 2008), materials that would be considered to have a high potential for acid
neutralization produce an NNP >20 kg CaCO3 eq/t. Those materials considered to have a higher
potential for acid generation produce an NNP <-20 kg CaCO3 eq/t. In between these end members,
the results are classed as uncertain, requiring other methods to confirm acid generation
characteristics. These NNP criteria have been applied herein as a screening-level method for
assessing the acid generating potential of the Kings Mountain waste rock and ore samples based on
ABA results. However,the combined results of ABA, NAG, HCT, and mineralogy have ultimately been
used to develop site-specific criteria for acid generation potential (Section 5.6).
Table 5-2 summarizes NNP values. While most samples (445 out of 571 samples, or 78%) have NNP
values that were positive, NNP values of 20 kg CaCO3 eq/t or more (suggesting high potential for acid
neutralization) were only calculated for 78 samples (14%). Most of the samples with NNP values
>20 kg CaCO3 eq/t were from the schist—marble (6 out of 7 samples, or 86%) rock type, followed by
the amphibolite gneiss (45 out of 132 samples, or 34%) rock type, and the shear schist (11 out of
49 samples, or 22%) rock types.
The majority of samples are classified as having an uncertain classification with respect to acid
generation, with NNP values of between -20 and 20 kg CaCO3 eq/t (irrespective of Iithology).
Calculated NNP values were <-20 kgCaCO3 eq/t for 30 samples(or 5%), suggesting a higher potential
for acid generation for a small number of samples. Most of these samples were from the upper mica
schist (7 out of 19 samples, or 37%) rock types, followed by the pyrrhotite mica schist (13 out of 45
samples, or 28%) rock types.
Calculated NNP values for almost all spodumene pegmatite samples (56 out of 57 samples, or 98%)
and most pegmatite samples (106 out of 110, or 96%)were between -20 and 20 kg CaCO3 eq/t, with
the remaining spodumene pegmatite samples and pegmatite samples having NNP values of <20 kg
CaCO3 eq/t.
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NPR
ABA data are also assessed using the ratio of NP to AP termed the NPR, which is calculated by
dividing the NP by the AP (Price, 2009). NPR values <1 indicate a higher potential for acid generation
and >3 indicate significant acid neutralization.
Figure 5-5 provides a plot showing the NP plotted as a function of AP. The plot includes the divisions
between the PAG and uncertain classification (black line, where NPR equals 1)and the uncertain and
non-PAG classification (dotted line, where NPR equals 3). The black line also indicates where NNP is
equal to 0. The plot shows the NPR values for about 60% of the samples (343 out of 571) are 3 or
more, indicating that they would be classified as non-PAG. The remaining 40% of the samples are
either uncertain or classified as PAG. However, looking at waste rock only, 31% (or 126 out of 404)
samples were PAG with an NPR<1. For the spodumene pegmatite 56 out of 57 samples (98%)were
non-PAG and for the pegmatite 95 out of 110 samples (86%) were non-PAG with NPR values 3 or
greater. Twenty-nine (29) out of thirty (30) samples of silica mica schist and all samples of diabase
and granite were also non-PAG with NPR values greater than 3.
100 ° A A
0
Non-PAG O O❑ 0 ❑ ° '�oet�a`c ■ Alluvium
0 00 O O J
• ♦0 0,� 0 0 ♦ Amphibole Gneiss-Schist
O 00p00 o❑8� 0 .0�0 8 ♦ Biotite Gneiss
O 0 8 .O 1 we n O Mica Schist
Z •�^�•SpO AO u ♦ Upper Mica Schist
10 O O ❑O ' A A Po Mica Schist
0 C d 7 O��CAppOL� A
' O C:�" A 0 AO n ♦ Shear Schist
0 • �' A A A A
• * A m • Silica Mica Schist
v
Y • ♦ ♦ A A • Pegmatite
z ® Q • Spod Pegmatite
PAG ■ Granite
1 �� ❑ x Diabase
i
❑ Schist-Marble
' ♦ Chlorite Schist
' —NPR=1
— — NPR=3
0.1
0.1 1 10 100
AP(kg CaCO3/t)
Figure 5-5: NP versus AP— Future Waste Rock and Ore
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Most of the 126 samples with NPR <1 (i.e., a higher potential for acid generation) consist of pyrrhotite
mica schist and upper mica schist samples. The majority of the upper mica schist samples (16 out of
19, 84%) had NPR values <1, and 36 out of 45 of the pyrrhotite mica schist samples (or 80%) had
NPR values <1.
NPR and paste pH show a very weak positive relationship whereby some of the higher NPR values
are associated with higher paste pH values (Figure 5-6).This indicates that the reactive salts are more
likely to be acid neutralizing, such as carbonates. In contrast, NPR and sulfide sulfur, shown on
Figure 5-7, demonstrate an inverse relationship, with higher sulfide sulfur content associated with
lower NPR. This illustrates the large control that sulfide sulfur content has on the overall classification
of samples with respect to their acid generation potential. Figure 5-8 shows a plot of NPR and NNP,
which combined show that many samples are classified as having an uncertain acid generating
potential with a lesser amount of waste rock classified as PAG.
10000
1000 ❑ ❑Alluvium
4-1 o Amphibole Gneiss-Schist
100 n
� O Bioti
• O
O
10 G
t ❑ rYKC •
1 4�1 b ;-
0.1 Ao
x
0.01
°
0.001
0 2 4 6 8 10 12
Paste pH(s.u.)
Figure 5-6: NPR versus Paste pH —Future Waste Rock and Ore
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� o
I o
4
� O
eOd ^
VD. o ❑
°
El
A
A
A
❑
0
Figure 5-7: NPR versus Sulfide Sulfur— Future Waste Rock and Ore
0
• ❑
♦ o
O p
Q
O O
O[ O
Gb O
6J�o0�0 O °
A
0
A
Figure 5-8: NNP versus NPR—Future Waste Rock and Ore
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5.1.4 NAG
Static NAG testing was used as an additional measure of ARD potential for the Kings Mountain waste
rock and ore samples. The NAG test differs from the ABA test in that it provides a direct empirical
estimate of the overall sample reactivity, including any acid generated by semi-soluble acid sulfate
minerals as well as potentially acid-generating sulfide minerals. A challenge with the application of the
NAG test to the Kings Mountain rock types is the reactivity of pyrrhotite with H2O2; this presents two
potential challenges with interpretation of NAG data:
• Sulfur may be volatilized during the NAG test, which would underestimate the acid generating
risk.
• Conversely, the high reactivity of pyrrhotite under laboratory conditions with H2O2 in the NAG
test may overestimate the acid generating ability in the laboratory, as under field conditions
the pyrrhotite may only partially react due to the formation of elemental sulfur and iron
hydroxide that would lead to armoring of the pyrrhotite surface (thus reducing reactivity).
The results of the NAG testing demonstrate that 64% of waste rock and ore samples (i.e., 364 out of
571 samples) have NAG pH of equal to or>4.5 and are considered non-acid generating (Figure 5-9).
The remaining samples were classified as PAG with NAG pH values of <4.5, with this classification
mostly associated with the biotite gneiss(77% PAG), shear schist(67% PAG), upper mica schist(79%
PAG), mica schist (68% PAG), and pyrrhotite mica schist (100% PAG). As shown on Figure 5-9, the
pyrrhotite mica schist, upper mica schist, and shear schist samples show a higher capacity of acid
generation, although the amphibole-gneiss schist, biotite gneiss, and mica schist also have some
samples that fall within the high-capacity classification. The other lithologies also have some samples
that are classified as PAG, except for the alluvium, which was all non-PAG. The pegmatite and
spodumene pegmatite samples were generally non-PAG,with NAG pH >4.5; however, a subset of the
pegmatite and spodumene pegmatite samples have measurable NAG despite neutral NAG pH values
showing consumption of peroxide in the test by reduced minerals that do not release hydrogen ions
(possibly minerals like tantalite, columbite, or iron-bearing phosphates).
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100 -
PAG-High Ca city
I �
° ■Alluvium
z ♦Amphi bole Gneiss-Schist
I ♦Biotite Gneiss
` O I 0 Mica Schist
NAG=10 kg H2SO4/t, I ♦Upper Mica Schist
0 Po Mica Schist
non-PAG
Y I • •• ♦Shear Schist
(D I� •Silica Mica Schist
a
0 Pegmatite
PAG-Low Capacity I •Spod Pegmatite
I ■Granite
x Diabase
I
I ❑Schist-Marble
♦Chlorite Schist
I
I
0.1
0 2 4 6 8 10 12 14
NAG pH(s.u.)
Figure 5-9: Total NAG versus NAG pH Plot—Future Waste Rock and Ore
Figure 5-10 shows NAG pH plotted against NPR,with lower NAG pH values generally associated with
low NPR values for the majority of results.There is a less positive relationship, particularly for samples
with higher NPR where the data are more scattered and in particular for the amphibole gneiss-schist.
Overall, NAG testing suggests that 36% of waste rock and ore samples were PAG, which is similar to
the number of samples classified as having either uncertain or PAG characteristics by NPR (43%).
Therefore, there is a reasonable amount of agreement between the two methods, although both
methods may be affected by the presence of pyrrhotite and the potential volatilization of sulfur, as
described above.
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I
I
I
0
I ao0, .;
J ' ' 'V • s •
0
41
-er O * Z .
A�'T❑'�� oo o 10♦ •
0 x
I ❑
I
I •
I
I
I
Figure 5-10: NPR versus NAG pH — Future Waste Rock and Ore
5.1.5 Mineralogical Controls on Acid Generation Potential
Detailed mineralogical analysis was conducted for a sub-set of the static test samples to provide a
better understanding of the mineralogic controls on acid generation for the Kings Mountain waste rock
and ore. The results of quantitative mineralogical (XRD) testwork on the subset of 53 waste rock
samples is provided in (Appendix C)and a summary of the acid generating (i.e., pyrrhotite and pyrite)
and acid neutralizing minerals(i.e., calcite)and associated ABA and NAG data from SGS are provided
in Table 5-3. In addition, mineralogical analysis completed by Petrolab on the samples selected for
HCT, SPLP and LEAF presented in Section 5.1.1 provides information on the mineralogical controls
on acid generation in addition to metal leaching.
Pyrrhotite was the only sulfide mineral identified by Rietveld XRD and was present at concentrations
between 0.3 wt% and 8.1 wt% in 35 of the 53 samples tested by SGS. Pyrrhotite was also identified
by XRD in the HCT, SPLP and LEAF samples submitted to Petrolab but at lower concentrations
ranging from below laboratory detection to 5.1 wt%. In addition, pyrite, chalcopyrite and sphalerite
were identified in the HCT, SPLP and LEAF samples but not in the SGS samples.
It may be possible that other sulfide minerals are present in the samples submitted to SGS, but at
concentrations that are too low for identification by Rietveld XRD. Total sulfur concentrations
calculated from the pyrrhotite content ranged between 0.1% and 3.2% and, for 23 of the 35 samples
where pyrrhotite was identified, calculated concentrations were greater than those measured by
LECO. In some cases, this may be associated with normalization of the XRD data (where the amount
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of amorphous/non identified phases was not determined); however, for around 30% of the samples
the differences were significant(the relative percentage difference (RPD)was 50% or more)and may
be associated with uncertainty in estimated concentrations determined by Rietveld XRD.
Measured sulfide sulfur concentrations were also generally lower than values calculated from the
pyrrhotite concentration. The differences were significant for some samples, with the RPD being
greater than 50% for more than half of the samples submitted to SGS. For example, calculated
pyrrhotite sulfur concentrations ranged between 0.6% and 2.1%, but measured sulfide sulfur
concentrations were below the limit of detection (<0.04%)for five of the samples.
Pyrrhotite was present in most of the material types, but was not identified in any of the pegmatite,
spodumene pegmatite or upper mica schist samples. The pegmatite, spodumene pegmatite and
upper mica schist material types generally contained low to negligible total sulfur and are expected to
be non-PAG with the possible exception of one spodumene pegmatite sample (DDKM17-101_218-
221)that contained 0.2% sulfide sulfur.
Based on the mineralogy results, pyrrhotite is the most common mineral and is likely the main source
of acid generation in the waste rock samples. This is supported by the NAG results that show only
samples containing pyrrhotite produced acid. For the HCT, SPLP and LEAF samples submitted to
Petrolab, other sulfides were identified that could contribute to acid generation (e.g., pyrite and
chalcopyrite). In general, the mineralogy data shows that pyrrhotite is widespread in the waste rock
material types; however, not all samples with pyrrhotite generate acid. For samples containing
pyrrhotite, the potential for acid generation to occur relies mainly on the presence and availability of
neutralizing minerals.
Calcite appears to be the main mineral controlling neutralization. For example, in sample DDKM18-
227_369.09-371.98 (amphibole schist), most NP was present as calcite (4.8%; NP of 34 kg CaCO3/t).
In this sample, there was sufficient NP available to neutralize the acidity generated from the sulfide
sulfur present (resulting in an alkaline NAG pH value of 10). The results of testwork infer that where
calcite is present, most of it is in a form that it is readily available/reactive.
For a sub-set of the acid generating samples, NP is present in the form of calcium bearing silicate
minerals (e.g., actinolite)which do not appear to be readily available and sufficiently reactive/effective
to neutralize the acidity generated. For example,the NP of sample DKM17-131_454.34-455.65(biotite
gneiss) was 46 kg CaCO3/t from the ABA test and it contained negligible carbonate (<0.01%). While
the NNP was positive (7.3 kg CaCO3/t), the NAG pH was 2.6 and sample generated 22 kg CaCO3/t of
acidity. In the case where calcite is absent and sulfide present, samples generated acid in the NAG
test indicating that the calcium bearing silicate minerals present in the samples are not sufficiently
reactive in terms of acid buffering and/or reaction rates to neutralize all the acidity generated. However,
the NAG test only provides an estimate of total acid generation potential and doesn't take into account
the kinetic factors that would allow silicates to neutralize acid if the acid from sulfide oxidation is slow
enough.
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Table 5-3: Summary of XRD, ABA and NAG Data for Subset of Waste Rock Samples
Pyrrhotite Calcite Siderite Carbonate Actinolite CO3 NAG NAG
Fe(1-x)S Total S1 LECO SZ Sulfide2 AP3 CaCO3 FeCO3 Total C Silicate TIC NP4 NP Net NP pH to pH 7
Sample ID Material Type Comments
% % % % kgCaCO3/t % % % % % kgCaCO3/t pH kgCaCO3/t
units
DDKM17-025_544.15-547.9 Amphibole Gneiss-Schist 4.9 1.9 0.7 .6 18 2.6 - 0.3 47.2 0.4 32 39 21 10.4 - Sufficient carbonate NP to neutralize acidity
DDKM17-054_160.32-162 Amphibole Gneiss-Schist 4.1 1.6 1.0 0.9 28 - - - 30.8 0.1 5 16 -12 2.8 14 Good agreement between NAG and NNP
DDKM17-059_144.36-148.52 Amphibole Gneiss-Schist - - 0.1 0.09 2.8 - - - 57.3 0.03 3 16 13 7.2 - AGP expected to be negligible-very low S
DDKM17-064 127.82-130.85 Amphibole Gneiss-Schist - - 0.03 <0.04 <1.25 4.6 - 0.5 31.9 0.6 47 59 58 11.0 AGP expected to be negligible/high calcite sample
DDKM17-080_473-478.09 Amphibole Gneiss-Schist - - 0.01 0.04 <1.25 - - - 38.5 0.1 6 49 48 7.8 AGP expected to be negligible-very low S
DDKM 17-09271.92-75.15 Amphibole Gneiss-Schist 4.0 1.6 0. 0.6 18 1.9 0.3 0.3 39.9 0.2 14 36 18 7.9 Sufficient NP to neutralize acid generated(if any)
DDKM17-115_673-677.4 Amphibole Gneiss-Schist 3.7 1.5 0.9 0.8 24 0.9 - 0.1 43.8 0.1 7 43 19 3.0 12 Calcite reacting, but insufficient present for acid
produced
DDKM17-128 441.85-445.58 Amphibole Gneiss-Schist 3.5 1.4 0.9 0.9 28 - - - 58.0 <0.01 <0.1 16 -12 2.7 18 Good agreement between NAG and NNP
DDKM18-222 127.95-131.23 Amphibole Gneiss-Schist - - 0.02 <0.04 <1.25 11.5 - 1.4 46.9 1.2 104 4 2.6 11.3 - AGP expected to be negligible/high calcite sample
DDKM18-227 369.09-371.98 Amphibole Gneiss-Schist 3.1 1.2 1.0 0.9 29 4.8 - 0.6 26.4 0.5 40 34 4.7 10.0 Sufficient carbonate NP to neutralize acidity
DDKM18-228_396.98-401.9 Amphibole Gneiss-Schist 1.1 0.4 0.1 0.1 1.6 5.8 - 0.7 17.1 0.6 46 96 94 11.1 Sufficient carbonate NP to neutralize acid
generated if an
DDKM18-289_238.06-241.5 Amphibole Gneiss-Schist 1.3 0.5 0.1 0.1 1.9 1.6 0.4 0.2 30.8 0.1 9 44 42 9.5 Sufficient carbonate NP to neutralize acid
generated if an
DDKM18-336 456.23-459.32 Amphibole Gneiss-Schist 2.4 1 0.6 0.4 13 2.0 - 0.2 34.2 0.4 29 24 12 10.1 Sufficient carbonate NP to neutralize acidity
DDKM18-336_998.03-1000.66 Amphibole Gneiss-Schist 1.6 0.6 0.02 <0.04 <1.25 2.7 0.5 0.4 21.9 0.4 36 48 46 9.85 Sufficient carbonate NP to neutralize acid
generated if an
DDKM17-074 610.53-614 Biotite Gneiss 2.8 1.1 0.4 <0.04 <1.25 - 0.4 0.05 52.61 0.1 5 9 7.6 3.6 6 Unreactive NP
DDKM17-111 1118.77-1121 Biotite Gneiss - - 0.3 0.28 8.8 - - 23.8 0.02 2 8 -0.8 5.4 3 Sufficient NP to neutralize acid generated(if any)
DDKM17-131 454.34-455.65 Biotite Gneiss 3.9 1.6 1.2 1.2 39 - - - 2.7 <0.01 <0.1 46 7.3 2.6 22 Unreactive NP
DDKM18-295 862.86-865.19 Biotite Gneiss 6.2 2.5 1.5 1.5 47 0.4 - 0.04 19.27 0.2 14 30 -17 1 2.8 22 Good agreement between NAG and NNP
DDKM18-311 838.25-839.9 Biotite Gneiss 4.7 1.9 1.4 1.4 42 - - - - 0.1 6 11 -31 2.5 25 Good agreement between NAG and NNP
DDKM17-067 554-556.85 Mica Schist 1.5 0.6 0.2 <0.04 <1.25 - 6.6 0.04 3 19 18 5.0 1 Sufficient NP to neutralize acid generated(if any)
DDKM17-070 389.14-393.49 Mica Schist 0.7 0.3 0.4 0.3 10 - - 0.1 6 39 29 3.1 7 Unreactive NP
DDKM18-192 197.34-200.13 Mica Schist 1.6 0.6 1.1 1.1 35 - <0.01 <0.1 18 -17 2.5 33 Silicate NP(all sulfide reacting,but NP
unreactive
DDKM18-194 564.3-568.9 Mica Schist 0.3 0.1 0.4 0.4 12 <0.01 <0.1 13 1.6 3.1 15 Silicate NP(all sulfide reacting,but NP
Aft unreactive
DDKM18-308 562.17-564.3 Mica Schist - - 0.04 <0.04 <1.25 0.1 5 25 23 7.4 - AGP expected to be negligible-very low S
DDKM18-366 193.57-196.85 Mica Schist 3.1 1.2 1.9 1.8 55 - - 0.1 8 40 -14 2.5 35 Silicate NP(all sulfide reacting,but NP
unreactive
DDKM17-009_124.68-126 Pegmatite - - 0.02 <0.04 <1.25 2.2 0.3 - 0.3 26 29 28 11.1 - AGP expected to be negligible/high calcite sample
DDKM17-047 848.63-850.6 Pegmatite 0.01 <0.04 <1.25 11.0 - 1.3 1.6 1.4 115 15 13 11.6 - AGP expected to be negligible/high calcite sample
DDKM17-064 506.87-510.32 Pegmatite 0.1 <0.04 <1.25 1.1 0.1 1.5 <0.01 <0.1 7 5.6 6.5 1 AGP expected to be negligible-very low S
DDKM17-080 865.94-868.52 Pegmatite - 0.03 <0.04 <1.25 0.8 - 0.1 1.3 0.03 2 44 42 7.4 - AGP expected to be negligible-very low S
DDKM17-103 331.36-333.89 Pegmatite - 0.1 0.08 2.5 - - - 0.9 0.01 1 23 21 7.3 - AGP expected to be negligible-very low S
DDKM18-298 454.13-456.17 Pegmatite - - 0.1 0.04 1.25 - - 0.1 5 9 7.9 5.2 1 AGP expected to be negligible-very low S
DDKM17-010 525.5-528 Po Mica Schist 4.9 1.9 1.9 IMIU 17 - 1.6 0.4 32 11 -5.5 2.5 37 Unreactive NP
DDKM17-047 968.25-971.13 Po Mica Schist 4.5 1.8 1.4 1.3 40 - - <0.01 <0.1 26 -14 2.6 33 Silicate NP(all sulfide reacting,but NP
unreactive
DDKM17-048 134.51-136.75 Po Mica Schist 4.8 1.9 1.9 1.5 48 - 0.04 4 13 -35 2.4 38 Good agreement between NAG and NNP
DDKM17-072 1263.5-1266 Po Mica Schist 3.7 1.5 2.0 0.3 10 - 0.1 5 23 13 2.4 38 Unreactive NP
DDKM17-104 946-950.5 Po Mica Schist 5.0 2 1.9 1.9 59 - 0.01 1 16 -42 2.4 40 Good agreement between NAG and NNP
DDKM18-210_491.96-495.47 Po Mica Schist 4.4 1.7 1.6 1.7 53 - <0.01 <0.1 6 -47 2.5 31 Incomplete sulfide oxidation
DDKM18-229 282.15-286.02 Po Mica Schist 3.3 1.3 1.8 1.8 57 - - - - 0.04 3 43 -13 2.5 36 Silicate NP unreactive/incomplete sulfide
oxidation?
DDKM 18-250189.9-193.34 Po Mica Schist 4.5 1.8 1.7 1.7 54 0.1 - 0.01 7.14 <0.01 <0.1 33 -21 2.7 26 Good agreement between NAG and NNP
DDKM18-340 259.06-262.4 Po Mica Schist 3.0 1.2 1.2 1.1 36 0.3 - 0.03 - 0.1 11 35 -0.2 2.6 23 Silicate NP/calcite neutralizing
DDKM17-009 969.89-973.44 Shear Schist 3.0 1.2 0.6 <1.25 0.6 - 0.1 20.8 0.1 8 32 31 6.0 1 Good agreement between NAG and NNP
DDKM17-021 1716-1718 Shear Schist 5.2 2.1 1.1 <0.04 <1.25 4.1 - 0.5 3.1 0.6 47 87 85 10.7 _ Sufficient carbonate NP to neutralize acid
generated if an
DDKM17-034 971.5-974.45 Shear Schist 3.1 1.2 2.0 1.7 53 4.9 0.4 0.6 10.2 0.6 52 98 46 9.8 - Sufficient carbonate NP to neutralize acid
generated
DDKM17-045 947-951.44 Shear Schist 8.1 3.2 1.9 1.9 60 - - - 7.1 0.2 19 22 -38 2.6 37 Good agreement between NAG and NNP
DDKM17-055 60.52-65.12 Shear Schist 2.9 1.2 1.3 1.2 37 - - 25.9 0.0 2 12 -24 2.5 30 Good agreement between NAG and NNP
DDKM17-080 61.1-63.92 Shear Schist 2.4 1 1.2 0.2 5.9 2.3 0.3 37.1 0.3 26 21 15 3.5 5 Calcite reacting, but insufficient present for acid
- produced
DDKM18-311 645.67-652.43 Shear Schist 3.3 1.3 1.7 1.7 52 0.2 0.02 17.98 0.1 10 33 -19 2.6 22 Good agreement between NAG and NNP
DDKM17-014 1722-1724.88 Spod Pegmatite - - <0.005 <0.04 <1.25 - - - <0.01 <0.1 4 2.9 6.7 1 AGP expected to be negligible-very low S
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Pyrrhotite Calcite Siderite Carbonate Actinolite CO3 NAG NAG
Fe(1-x)S Total St LECO SZ Sulfide2 AP3 CaCO3 FeCO3 Total C Silicate TIC NP4 NP Net NP pH to pH 7
Sample ID Material Type Comments
% % % % kgCaCO3/t % % % % % kgCaCO3/t pH kgCaCO3/t
units
DDKM17-101_218-221 Spod Pegmatite - - 0.3 0.2 5.9 1.0 0.1 1.2 0.03 2 25 19 4 2 Unreactive NP
DDKM18-224 749.02-752.3 Spod Pegmatite - - 0.01 <0.04 <1.25 - - - <0.01 <0.1 1 0.05 6.8 1 AGP expected to be negligible-very low S
DDKM18-333_1130.18- Spod Pegmatite - - 0.02 <0.04 <1.25 - 0.1 5 14 12 6.5 1 AGP expected to be negligible-very low S
1133.73
DDKM17-083 70.12-74.96 Upper Mica Schist - - <0.005 <0.04 <1.25 - <0.01 <0.1 54 52 6.9 1 AGP expected to be negligible-very low S
DDKM17-090 19.2-22.52 Upper Mica Schist - - <0.005 <0.04 <1.25 - <0.01 <0.1 50 49 5.8 5 AGP expected to be negligible-very low S
Notes:S-sulfur;C-carbon;TIC-total inorganic carbon;CO3 NP-carbonate neutralizing potential;NP-neutralizing potential;NNP-net neutralizing potential; NAG-net acid generation;AGP-acid generation potential.
t Cells shaded orange-total S calculated from the pyrrhotite content(where detected)was greater than that measured by LECO.
2 Cells shaded gray-relative percent difference with total S calculated from pyrrhotite content(where detected)was greater than 50%.
3 AP calculated from sulfide sulfur content.
° CO3-NP calculated from TIC.
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5.1.6 Summary of Acid Generation Potential Based on ABA and NAG Test Results
Despite the general agreement between the ABA and NAG results, there are still some discrepancies
between the test results with the NAG test showing an overall higher potential for acid generation in
comparison to the ABA results. Some of the site-specific mineralogical aspects that complicate the
interpretation of the static test results include:
• The widespread presence of pyrrhotite in the Kings Mountain mineralization and the potential
for pyrrhotite to oxidize along a non-acid generating reaction pathway (i.e., to form elemental
sulfur, which does not result in acidity (Schumann et al., 2015)).
• The potential for reduced minerals in the pegmatite that do not generate acid to consume
peroxide in the NAG test leading to overestimation of the NAG potential.
• The slow or partial oxidation of sulfide minerals, particularly pyrrhotite, under field conditions
compared to laboratory tests.
• The potential for volatilization of sulfur in the ABA and NAG test, resulting in an underestimate
of acid generating potential.
Potential contribution of silicate minerals to neutralizing capacity that may not be reflected in the short
duration of the ABA test. One of the main objectives of the HCT program described in Section 5.1.8
was to address the uncertainties in the static test predictions. The combined results of the ABA, NAG,
HCT, and mineralogy testing were used to develop site-specific criteria for developing estimates of
acid generating potential; this is discussed in detail in Section 5.6.
Predictions of future behavior are also supported by empirical data from legacy rock dumps and ore
materials on site. Current site conditions indicate that acid generation has not occurred despite long-
term exposure of waste rock and ore in legacy rock storage facilities and pit wall exposures. This is
demonstrated by the alkaline pit lake conditions and seep chemistry, as well as neutral to alkaline
paste pH values obtained from the legacy waste rock samples (discussed in more detail in
Section 5.4). Current site conditions are not consistent with the ABA and NAG test results that indicate
a portion of the future waste rock has a potential to generate acid. This discrepancy suggests that the
ABA and NAG tests may overestimate the overall potential for acid generation for the Kings Mountain
waste rock.
5.1.7 Multi-Element Analysis
Multi-element analysis was carried out to determine the geochemical composition of the samples and
to identify any constituents that are elevated above the average crustal abundance. Data for key
parameters relating to ARDML are compared to average crustal concentrations using the GAI, which
is described in Section 4.2.6. Tabulated multi-element data and laboratory reports are provided in
Appendix D and F, respectively. When compared to average crustal abundance (Mason, 1966), multi-
element data can provide an indication of element enrichment that may have environmental
importance and can identify parameters that might be of concern for the Project. Table 5-4 presents
overall summary results of the multi-element assay. The results show that lithium (Li), selenium (Se),
tin (Sn), and tungsten (W) are elevated relative to average crustal abundance across all lithologies.
Arsenic is also generally elevated in all but the upper mica schist and granite samples, and less
commonly elevated values are also noted for cadmium, uranium , zinc, copper, molybdenum, lead,
thallium, cobalt, chromium, antimony, and manganese.
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Table 5-4: Summary of Multi-Element Assay Data-Future Waste Rock and Ore
Rock Type TStatistic' Al As Ca Cd Co Cr Cu Fe K Li Mg Mn Mo Ni Pb Sb Se Sn TI U W Zn
Yp % ppm % ppm ppm ppm ppm % % ppm % ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm
Average crustal abundance(Mason, 1966)>> 8.13 1.8 3.63 0.2 25 100 55 5 2.59 20 2.09 950 1.5 75 13 0.2 0.01 2 0.5 1.8 1.5 165
No.Samples 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Min 8.1 1.2 0.02 0.02 26 63 35 5.7 0.08 110 0.11 530 0.24 35 1.3 0.06 1 2.2 0.83 0.5 1 61
Alluvium Average 10 7.6 3 0.2 40 100 100 8.5 0.58 970 1.5 1000 1.3 46 11 0.22 1.2 16 1.5 2.4 17 120
Max 14 12 8.5 0.47 60 130 200 13 1.8 1800 3.4 1600 2.5 73 25 0.46 2 35 2.5 6.5 30 150
No.Samples 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
Upper Mica Schist Min 8.1 0.1 0.01 0.01 1 23 56 15 3.5 0.44 129 0.56 350 0.15 16 5.4 0.025 0.5 4 0.2 0.8 34 52
Average 9.2 4.8 2.1 0.3 40 100 68 5.8 2.8 843 1.6 720 4.9 61 20 0.035 2 20 1.8 4.2 77 118
Max 10 59 8.4 1.1 56 201 136 8.9 5.2 2040 4.1 1550 1 11 123 45 0.09 1 5 66 3.4 8.8 151 181
No.Samples 132 132 1 132 132 132 1 132 1 132 132 1 132 132 132 132 132 132 132 132 1 132 132 1 132 132 132 1 132
Amphibole Gneiss-Schist Min 7 0.1 4.4 0.01 28 11 14 3.9 0.07 200 2 690 0.13 19 0.9 0.025 0.5 1.1 0.05 0.1 6.4 44
Average 8.3 2.2 8.1 0.15 46 76 69 7.5 0.42 1000 3.5 1300 0.92 46 3.7 0.11 1 28 1.2 0.78 39 97
Max 12 16 12 2.5 68 160 330 13 1.2 4200 4.4 2000 5.8 99 14 0.57 3 300 10.95 4.9 800 590
No.Samples 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
Min 7 0.3 1.2 0.02 27 40 32 5.9 0.12 470 1.5 480 0.23 25 2.9 0.025 0.5 1.3 0.15 0.1 22 86
Biotite Gneiss Average 8.3 14 4.7 0.21 41 92 77 7.4 1.2 1200 2.9 1 1200 2.5 54 11 0.072 0.98 22 1.9 1 1.5 48 120
Max 10 220 8.1 0.75 1 58 200 170 10 2.7 2200 4.5 2600 1 23 120 29 0.24 1 81 6.7 4.6 283 270
No.Samples 63 63 63 63 63 63 63 63 1 63 63 63 63 63 63 63 1 63 63 63 63 63 63 63
Mica Schist Min 7.6 0.2 0.18 0.01 22 31 23 2.5 1.4 387 0.51 370 0.05 22 13 0.025 0.5 3.1 0.7 0.9 18 35
Average 9.2 12 0.85 0.17 33 90 60 5.9 3 1140 1.4 1900 0.82 59 26 0.065 1.1 27 2.3 3 52 90
Max 11 150 4.4 5.2 51 322 109 8 3.9 2500 4.7 4300 3.5 154 37 0.17 1 3 130 11 16 141 189
No.Samples 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 1 45 45 45 1 45
Po Mica Schist Min 7.6 0.3 0.28 0.01 19 61 41 4.8 0.63 289 1.1 410 0.68 43 7.6 0.03 1 3.6 1.19 1.2 22 43
Average 9 9.1 1.7 0.4 34 86 79 6.7 2.5 1320 1.7 710 3.8 69 26 0.06 1.4 36 3.8 4.2 45 138
Max 10 103 6.8 1.9 48 124 160 10 3.8 2400 3.9 1535 12 135 58 0.17 5 100 14.6 10 183 288
No.Samples 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49
Min 5.4 0.1 1 0.01 22 21 23 3.1 0.09 570 1.1 560 0.24 16 3.3 0.025 0.5 4 0.17 0.2 7 87
Shear Schist Average 7.7 8.2 4.5 1.6 42 70 210 7.4 0.86 2200 4.8 1500 1.8 50 10 0.15 1.2 55 3.5 1.8 26 350
Max 9.7 170 14 66 1 76 220 1 3000 10 4.8 6500 7.8 2700 12 240 1 28 2.1 1 5 130 1 22.7 23 94 6400
No.Samples 110 110 110 110 110 110 110 110 1 110 110 110 110 110 110 110 110 110 110 110 110 109 110
Pegmatite Min 4.9 0.1 0.03 0.01 7.9 1 0.3 0.06 0.03 21 0.005 38 0.025 0.1 1.7 0.025 0.5 0.5 0.03 0.5 37 1
Average 7.3 6.4 0.72 0.29 18 3.8 17 0.55 1.8 300 0.14 730 0.15 2.4 19 0.06 0.79 35 2.15 14 121 70
Max 9.7 390 14 3.3 33 45 880 3.4 5.1 2200 1.5 4500 3.1 30 280 1.2 1 380 12.9 49 239 1400
No.Samples 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
Spod Pegmatite Min 5.6 0.1 0.04 0.01 7.9 1 0.2 0.06 0.1 46 0.005 57 0.025 0.1 5.4 0.025 0.5 5.5 0.12 2.4 42 7
Average 6.8 2.3 0.43 0.13 18 4.4 4.1 0.32 1.7 2800 0.052 410 0.16 2.4 14 0.061 0.84 41 5.34 11 134 80
Max 9.3 48 1 4 1.9 1 30 91 1 54 5.9 1 4.5 10000 1.3 1700 1 3.2 61 1 34 0.3 1 1 120 1 52 32 240 440
No.Samples 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Min 4.6 1.6 2.2 0.08 29 220 9.7 3.4 1.6 1600 3.9 490 0.11 290 16 0.15 0.5 7 0.64 6.1 14 81
Diabase Average 4.9 86 3 0.09 32 250 19 3.6 3.9 1600 4.6 860 0.12 340 27 0.16 0.75 8 0.92 7.8 25 120
Max 5.3 170 3.9 0.1 35 270 28 3.7 6.2 1600 5.3 1200 0.13 400 38 0.17 1 8.9 1.2 9.5 35 160
No.Samples 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Granite Min 6.6 0.7 0.4 0.1 1 17 2 10 0.45 0.23 150 0.04 610 0.07 0.5 8.6 0.025 0.5 1.3 0.27 1.8 70 83
Average 7.4 2.3 1.6 0.47 22 25 52 2.2 1.4 340 0.83 1300 0.5 12 12 0.038 0.75 7.7 1.16 11 93 91
Max 8.2 3.8 2.8 0.83 27 48 93 4 2.7 530 1.6 1900 0.92 24 16 0.05 1 14 2.04 20 116 99
No.Samples 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
Min 7.3 0.7 1.3 0.02 16 26 20 3 1.1 50 0.8 770 0.12 15 7.7 0.05 1 2.1 0.37 0.8 15 72
Silica Mica Schist Average 7.9 88 1.98 0.058 25 73 38 4.5 2.4 186 1.6 905 0.71 45 16 0.14 1.3 7.7 0.93 2.2 61 89
Max 8.7 1690 4.1 0.08 45 410 150 6.8 6.8 935 5.1 1600 3.1 280 18 0.65 3 100 8.68 3.2 134 100
No.Samples 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
Schist-Marble Min 2.6 3.7 1 0.01 9.5 26 8.7 2 0.89 1 34.3 0.85 315 0.24 13.4 2.9 0.09 1 1 0.19 0.9 10.8 18
Average 5.1 23 10 0.1 19 50.4 24.7 3.5 1.97 77.1 1.5 458.6 0.69 28.5 11.6 0.14 1.49 2.46 0.48 1.8 36.6 61
Max 7.4 76 26.4 0.3 29 79 43.3 5.2 3.2 166.5 2.7 743 2.13 47.2 32.9 0.25 2 7.5 1.16 2.5 90.6 123
No.Samples 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
Chlorite Schist Min 5 30 1 3.7 0.01 1 56.9 738 1 4.6 6.71 0.07 134 8.12 1175 0.05 619 0.9 0.08 1 2.4 0.07 0.1 0.5 65
Average 5.5 146 5.7 0.1 71.2 938.5 64 7.1 0.43 337.2 10.95 1261.3 0.24 815 1.91 0.2 1.4 19.5 0.36 0.2 10.4 73.1
Max 6.8 394 6.5 0.2 92.2 1225 113 8.54 1.55 589 12.05 1535 2.05 1090 4.5 0.55 3 64.7 1.09 0.7 115 87
No.Samples 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571 571
All Samples Min 2.61 0.1 0.01 0.01 7.9 1 0.2 0.06 0.03 21 0.005 38 0.025 0.1 0.9 0.025 0.5 0.5 0.03 0.1 1 1
Average 8 7.8 3.3 0.35 34 54 63 4.9 1.5 1085 2.2 1100 1.1 64 14 0.085 1.1 31 2.3 5.2 67 114
Max 14 1690 26 66 92 1225 3000 13 6.2 10000 12 4500 23 1090 280 1 5 380 52 1 49 1 800 1 6400
Notes
Average values represent the arithmetic mean
Indicates<3 times average crustal concentrations
Indicates between 3 and 6 times average crustal concentrations
Indicates between 6 and 12 times average crustal concentrations
Indicates>12 times average crustal concentrations
ppm: Parts per million
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5.1.8 Short-Term Leach Tests
SPLP and MWMP
A modified SPLP method was used for the waste rock and ore samples; the extraction solution
consisted of deionized water, and the L/S ratio is 2:1.The results of the SPLP test provide a screening
assessment of constituent leaching behavior and allow identification of constituents that may be readily
leached from the Kings Mountain waste rock and ore materials.This is a laboratory test using a defined
solid-to-water ratio much higher than would be observed in nature. As such, the concentrations are
not considered to represent actual predictions of water quality.
Appendix E and Appendix G provide the tabulated SPLP data and laboratory reports, respectively.
Figure 5-11 and Figure 5-13 provide graphs showing the release of key constituents as a function of
pH. In 20 of the 22 SPLP tests, the leachate pH was mildly alkaline to alkaline (pH 8.4 to 10.1). The
remaining two tests gave very mildly acidic to neutral pH leachate (pH 6 — COMP 8 (Comp 1 Upper
Mica Schist) and pH 6.6 — COMP 12 (Comp 2 Pegmatite)). The mildly acidic leachate is often
associated with higher solute concentrations (for example,this sample gave the highest filtered sulfate
(314 milligrams per liter(mg/L)), calcium (34.1 mg/L), and magnesium (18.4 mg/L) concentrations and
was associated with higher concentrations for a range of parameters(e.g., barium (Ba), strontium (Sr),
selenium, and many transition metals)). In the alkaline leachates, filtered concentrations of many trace
parameters were at or close to detection limits.
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1000 10
•
•
•Amphibolite Gneiss-Schist •c• •Amphibolite Gneiss-Schist
100 1 A b ��
♦Biotite Gneiss E O Biotite Gneiss
Mica Schist E A A Mica Schist
A
•Pegmatite £ A •Pegmatite
� •
J • • A Po Mica Schist Q A Po Mica Schist
a 10 a 0.1
A Shear Schist Ja • A Shear Schist
V)
•Spodumene Pegmatite •Spodumene Pegmatite
• ♦Upper Mica Schist ♦Upper Mica Schist
1 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
10 1 T
J •Amphibolite Gneiss-Schist J *Amphibolite Gneiss-Schist
to
1 ♦Biotite Gneiss E A ♦Biotite Gneiss
A Mica Schist A Mica Schist
jV 0.1
•Pegmatite J •Pegmatite
A Po Mica Schist A Po Mica Schist
� 0.1
a A Shear Schist A Shear Schist
a
•Spodumene Pegmatite •Spodumene Pegmatite
• ♦Upper Mica Schist • ♦Upper Mica Schist
0.01 A!' <8 ru 0.01 �--I
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-11: Future Waste Rock and Ore SPLP Constituent Release as a Function of pH —Sulfate, Aluminum, Manganese, and Zinc
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1 1
• O
0.1 � 0.1
J •AmphiboliteGneiss-Schist J •AmphiboliteGneiss-Schist
E ♦Biotite Gneiss E ♦Biotite Gneiss
O I
'_' • A Mica Schist c A Mica Schist
v
0.01 0 0.01
• •Pegmatite E •Pegmatite
Q -1
J A Po Mica Schist Q •A A Po Mica Schist
CL
a
•
A Shear Schist a A • A Shear Schist
0.001 A '^ 0.001 A
. • q� A •Spodumene Pegmatite • 0 •Spodumene Pegmatite
♦Upper Mica Schist • ♦Upper Mica Schist
0.0001 0.0001 �1
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
1 1
0.1 0.1
J •AmphiboliteGneiss-Schist J •AmphiboliteGneiss-Schist
E ♦Biotite Gneiss ♦Biotite Gneiss
_. •
E A Mica Schist E • A Mica Schist
0.01 2 0.01
y •Pegmatite • •Pegmatite
L
A Po Mica Schist
a • A Po Mica Schist
J •
LA 0.001 0.001
A Shear Schist N A A Shear Schist
A
g •Spodumene Pegmatite •Spodumene Pegmatite
• A�
♦Upper Mica Schist A ♦Upper Mica Schist
O
0.0001 • 0.0001 A Z A. . . .- .
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
Figure 5-12: Future Waste Rock and Ore SPLP Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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10 100
10
J •AmphiboliteGneiss-Schist J L.\� •AmphiboliteGneiss-Schist
£ A ♦Biotite Gneiss E 1 0 ♦Biotite Gneiss
d I
•� Mica Schist • • A Mica Schist
•L 1 DA �
3 A •Pegmatite J •Pegmatite
J0 Po Mica Schist J 0'1 A Po Mica Schist
a
vai • A Shear Schist `� A Shear Schist
•
•Spodumene Pegmatite 0.01 • •Spodumene Pegmatite
♦Upper Mica Schist ♦Upper Mica Schist
0.1 0.001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-13: Future Waste Rock and Ore SPLP Constituent Release as a Function of pH —Fluoride and Lithium
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Of the elements identified as enriched in the solids (Section 5.1.6), arsenic, lithium, selenium, sulfate,
and uranium were readily detectable in the SPLP leachates; this demonstrates that these constituents
may be leached from the Kings Mountain waste rock and ore materials, albeit at generally low
concentrations. The main exception was arsenic, with two tests generating elevated arsenic
concentrations of 0.267 mg/L (COMP 4 (Comp 1 Pegmatite)) and 0.217 mg/L (COMP 17 (Comp 3
Biotite Gneiss)). SPLP arsenic release was inversely correlated with pH, with higher arsenic release
observed under more alkaline pH conditions (Figure 5-14). Similar behavior was observed for
antimony, fluoride, lithium, and aluminum.
The following elements were consistently below laboratory detection limits in the SPLP leachates,
demonstrating they show a low potential to be released from the Kings Mountain waste rock and ore
materials: beryllium, bismuth, chromium, copper, molybdenum, scandium, silver, thorium, tin, and
titanium.
21 of the 22 SPLP leachates were submitted for radiochemistry analysis, including gross alpha and
beta, radium-226, and alpha emitting radium isotopes. One sample was excluded from radiochemical
analysis due to limited sample material. Based on these results, there was no detectable radioactivity
in the waste rock and ore SPLP leachates.
LEAF Tests
The LEAF tests are a set of non-regulatory tests that provide more flexibility by evaluating leaching
under a wider range of environmental conditions as a series of short-term static leach tests. LEAF
tests are being undertaken to provide additional information on the leachability of the waste rock and
ore material and provide the most comprehensive characterization of this material as possible. Two
main aspects are being assessed:
• Leachability as a function of pH (Method 1313) — Method 1313 generates data as a function
of pH. For many elements, leachability is controlled by the solubility of host mineral phases or
the nature of interactions with mineral surfaces (i.e., surface adsorption/desorption). Solubility
and sorption are both strongly influenced by solution pH, and collecting data as a function of
pH gives valuable insight to the potential mass of leachable constituent, likely concentrations
at different solution pH levels, and predicted leaching behavior.
• Leachability as a function of contact ratio(EPA 1314 and 1316)—EPA 1314 and 1316 are two
different methodologies that provide insight to leaching as a function of US contact ratio.
Different constituents behave differently in leaching tests. Highly soluble constituents, like
chloride, sodium (Na), or nitrate, tend to partition into the liquid phase, so concentrations in
the initial solutions (e.g., at a low US ratio)are quite high but then drop quickly beyond an US
ratio of 1. For less-soluble constituents, the majority of constituent mass remains in the solid
phase, and concentration is more affected by pH while showing less dependence on US ratio.
Results for the EPA 1313, EPA 1314, and EPA 1316 tests are summarized below, and tabulated
results are provided in Appendix E. The LEAF laboratory reports are provided in Appendix G.
Figure 5-14 provides examples of results from LEAF Method 1313 (for aluminum, arsenic, cobalt, and
lithium). A number of trends as a function of pH are observed:
• Aluminum — Filtered concentrations are greatest at acidic pH, decrease to a minimum in the
near-neutral pH range, and then increase again at alkaline pH (amphoteric behavior).
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• Arsenic—Arsenic also shows a certain amount of amphoteric behavior; however, the highest
concentrations are typically observed at alkaline pH. The highest leaching at alkaline pH (8 or
higher) is observed for samples COMP2 (Comp 1 Biotite Gneiss), COMP3 (Comp 1 Mica
Schist), and COMP19 (Comp 3 Pegmatite).
• Cobalt — Filtered concentrations are greatest at acidic pH and decrease as pH increases.
Filtered concentrations over the neutral to alkaline range are low (close to or below analytical
detection limits).
• Lithium — For many of the samples, filtered concentrations are relatively constant but trend
upwards slightly at the most acidic pH values evaluated. An exception is COMP? (Comp 1
Spodumene Pegmatite), which shows higher lithium leaching than the other samples studied
and shows strong pH dependence.
• Many of these trends can be readily explained on the basis of mineral solubility,which is often
greatest at acidic pH.Another important influence is likely to be sorption onto mineral surfaces.
Cationic solution species sorb most strongly at alkaline pH while anionic species sorb more
strongly at acid pH. Metals (e.g., cobalt) often form cationic species (uncomplexed metal ions
(CO2+))or possibly hydrolysis products (e.g., Co(OH)+,AI(OH)a'). High sorption at alkaline pH
would result in lower filtered concentrations.On the other hand,arsenic often forms oxyanionic
species (e.g., As042-), potentially explaining the higher filtered concentrations of arsenic at
alkaline pH, when anionic species sorb more weakly.
Figure 5-16 presents results from the EPA 1316 method for the same four elements (aluminum,
arsenic, cobalt, and lithium). For most of the samples,the aluminum and arsenic filtered concentrations
are relatively constant irrespective of the L/S contact ratio, which may suggest evidence of a solubility
or sorption control (i.e., filtered concentrations are constant (and typically low) irrespective of the
volume of water added in the test). There are some exceptions, including aluminum leaching from
COMP7 (Comp 1 Spodumene Pegmatite) and arsenic leaching from COMP19 (Comp 3 Pegmatite),
which show trends similar to lithium, discussed below.
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Al As
250 Amphibole Gneiss Schist(COMP1) 0.16 Amphibole Gneiss Schist
(COMP1)
Biotite Gneiss(CO MP2) 0.14 f Biotite Gneiss(COMP2)
200 Mica Schist(COMP3) --*--Mica Schist(COMP3)
--*--Po Mica Schist(COMPS) 0.12 --*--Po Mica Schist(COMPS)
--*--Shear Schist(COMP6) Shear Schist(COMP6)
J
0 1
E 150 — �Spod Pegmatite(COMP7) E —0—Spod Pegmatite(COMP7)
c I c
- Upper Mica Schist(COMP8) - 0.08 Upper Mica Schist(COMP8)
c c Amphibole Gneiss Schist
100 _ Amphibole Gneiss Schist(COMP9) V
(COMP9)
0 0 0.06 Mica Schist(COMP11)
Mica Schist(COMP11) U
—0—Pegmatite(COMP12)
--*.--Pegmatite(COMP12) 0.04
50 — — --*--Po Mica Schist(COMP13) --O—Po Mica Schist(COMP13)
\ Shear Schist(COMP14) 0.02 Shear Schist(COMP14)
Amphibole Gneiss Schist --*--Amphibole Gneiss Schist
0 0 (COMP16)
(COMP16) Pegmatite(COMP19)
0 2 4 6 8 10 12 14—*--Pegmatite(COM P19) 0 2 4 6 8 10 12 14
pH pH tAmphibole Gneiss Schist
Amphibole Gneiss Schist
(COMP21)
(COMP21)
Co Li
1.8 25 —0—Amphibole Gneiss Schist(COMP1)
Amphibole Gneiss Schist(COMP1)
1.6 Biotite Gneiss(COMP2) Biotite Gneiss(COMP2)
1.4 —0—Mica Schist(COMP3) 20 --*--Mica Schist(COMP3)
Po Mica Schist(COMPS) Po Mica Schist(COMPS)
� 1.2 --*—Shear Schist(COMP6) —0—Shear Schist(COMP6)
E L— Spod Pegmatite(COMP7) E 15 fSpod Pegmatite(COMP7)
C 1 c:
f Upper Mica Schist(COMP8) m Upper Mica Schist(COMP8)
0.8 —0—Amphibole Gneiss Schist(COMP9) c
u °Q' 10 tAmphibole Gneiss Schist(COMP9)
C f Mica Schist(COMP11) 0
U 0.6 v —0—Mica Schist(COMP11)
— Pegmatite(COMP12)
—0—Pegmatite(COMP12)
0.4 —*--Po Mica Schist(COMP13)
5 t Po Mica Schist(COMP13)
Shear Schist(COMP14)
0.2 --*--Shear Schist(COMP14)
Amphibole Gneiss Schist(COMP16)
romar 0 ♦Amphibole Gneiss Schist
Pegmatite(COMP19) 0
(COMP16)
0 2 4 6 8 10 12 1+Amphibole Gneiss Schist(COMP21) 0 2 4 6 8 10 12 1#*--Pegmatite(COMP19)
PI pH
Figure 5-14: LEAF 1313 Test Results (Future Waste Rock and Ore) —Aluminum, Arsenic, Cobalt, and Lithium
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Al Amphibole Gneiss Schist(COMP1) AS
Amphibole Gneiss Schist
(COMP1)
16
—0— 0.7
Biotite Gneiss(COMP2) �Biotite Gneiss(COMP2)
Mica Schist(COMP3)
f Mica Schist(COMP3)
14 —0—Po Mica Schist(COMP5) 0.6 --*--Po Mica Schist(COMP5)
12 Shear Schist(COMP6) Shear Schist(COMP6)
t J 0.5 �Spod Pegmatite(COMP7) Spod Pegmatite(COMP7)
J
b\0 10 w
E —0--Upper Mica Schist(COMP8) E 0.4
-0—Upper Mica Schist(COMP8)
c c
Amphibole Gneiss Schist(COMP9) °
8 � tAmphibole Gneiss Schist
tMica Schist(COMP11) v 0.3 (COMPS)
6 Mica Schist(COMP11)
u Pegmatite(COMP12) v
Pegmatite(COMP12)
0.2
4 Po Mica Schist(COMP13)
�Po Mica Schist(COMP13)
Shear Schist(COMP14)2 0.1 � --*--Shear Schist(COMP14)
—
I--o—Amphibole Gneiss Schist
(COMP16) Amphibole Gneiss Schist
0 0 (COMP16)
Pegmatite(COMP19) Pegmatite(COMP19)
0 2 4 6 8 10 12 0 2 4 6 8 10 12
Liquid-to-solid contact ratio L k Amphibole Gneiss Schist Liquid-to-solid contact ratio L k
q ( / g) (COMP21) q ( / g) Amphibole Gneiss Schist
(COMP21)
Amphibole Gneiss Schist ♦Amphibole Gneiss Schist
CO (COMP1) Li (COMP1)
2 tBiotite Gneiss(COMP2) 35 �Biotite Gneiss(COMP2)
Mica Schist(CO MP3) Mica Schist(COMP3)
1.8
�Po Mica Schist(CO MP5) 30 —0—Po Mica Schist(COMP5)
1.6
Shear Schist(COMP6) Shear Schist(COMP6)
\ 1.4 --*--Spod Pegmatite(COMP7) J 25 --O--Spod Pegmatite(COMP7)
c1.2 Upper Mica Schist(COMP8) E 20
o � A --*--Upper Mica Schist(COMP8)
'L 1 --*--Amphibole Gneiss Schist o
+, Amphibole Gneiss Schist
(COMPS)
QUI Mica Schist COMP11 c (COMPS)
0.8 (COMP11) u 15 Mica Schist(COMP11)
v 0 6 Pegmatite(COMP12) Pegmatite(COMP12)
tPo Mica Schist(COMP13) 10
0.4 --*--Po Mica Schist(COMP13)
Shear Schist(COMP14)
0.2 5 — Shear Schist(COMP14)
Amphibole Gneiss Schist
0 (COMP16) Amphibole Gneiss Schist
0 4 6 8 11 12 --*--Pegmatite(COMP19) 0 I (COMP16)
Liquid-to-solid contact ratio(L/kg) Amphibole Gneiss Schist
0 2 4 6 8 10 12 --*--Pegmatite(COMP19)
(COMP21) Liquid-to-solid contact ratio(L/kg) +Amphibole Gneiss Schist
(COMP21)
Figure 5-15: EPA 1316 Test Results (Future Waste Rock and Ore) —Aluminum, Arsenic, Cobalt, and Lithium
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Lithium shows strong dependence on L/S ratio, with observed trends towards lower filtered
concentrations as the L/S ratio increases. This trend is common for elements hosted in small, finite
quantities of a soluble mineral. In such cases, the host mineral may dissolve entirely in the contact
solution, and filtered concentrations are therefore strongly influenced by the volume of water added.
This outcome for lithium could be broadly consistent with the apparent lack of pH dependence shown
using the LEAF 1313 method (i.e., a key lithium-bearing source mineral is present in low, finite
quantities that dissolves entirely without reaching an equilibrium solubility limit in the contact water).
For most samples, filtered cobalt concentrations were at analytical limits of detection at all contact
ratios. There are exceptions; in particular, cobalt leaching from sample COMP8 (Comp 1 Upper Mica
Schist) is detectable at all contact ratios, and leaching from COMP7 (Comp 1 Spodumene Pegmatite)
is detectable at the lowest contact ratios.
Figure 5-16 shows the evolution of leachate pH as a function of L/S using the EPA 1314 method. The
pH of the leachates range between pH 5.5 (Comp 8) and pH 9.9 (Comp 21). In most cases, the pH is
greatest at the start of the test(0.2 L/S)and decreases to values that are lower, but relatively constant,
as the cumulative L/S increases. For some of the samples, the pH decreases by between 2 and 3 pH
units; being greatest for samples where the pH at the start of the test is alkaline (pH 9.2 to 9.9).
Figure 5-17 shows concentrations of calcium, sulfate, arsenic, and selenium as a function of L/S
contact ratio. The plots show that concentrations were greatest at the start of the test (when the L/S
was low) and decreased with increasing cumulative L/S. Similar leaching trends were observed for
other species, including major cations and anions and other trace elements (e.g., aluminum, lithium,
strontium, and uranium) that were readily detectable in the leachates. This trend is typical of flushing
of readily soluble salts from the samples.
Most trace elements leached at concentrations that were very low. This includes boron, barium,
cadmium, copper, manganese, nickel, antimony, titanium, and vanadium that were detectable at low
L/S (i.e., 0.2 and 0.5) before decreasing to concentrations below the limit of detection as the L/S
increased. Concentrations of beryllium, bismuth, chromium, cobalt, iron, mercury, molybdenum,
scandium, thallium, thorium, tin, and zinc were less than the limit of detection from the start of the test.
Figure 5-18 shows plots of the cumulative mass leached as a function of L/S(using a logarithmic scale)
for aluminum, arsenic, selenium, and lithium(similar to the four elements assessed for LEAF 1313 and
EPA 1316, except for cobalt which was below the limit of detection). These plots can be used to
determine if the data are consistent with a solubility control, where a slope of unity is indicative of a
solubility control (the dotted lines in the plot indicate an approximate range of unity associated with the
data).
The results support the findings of the EPA 1316 testwork. In most cases, aluminum, arsenic, and
selenium show some evidence of solubility control under the conditions of the EPA 1314 test with
cumulative mass leached plotting along trends consistent with the line of unity. There were some
exceptions: for example aluminum (Comp 8), arsenic(Comp 7, Comp 9 and Comp 15), and selenium
(Comp 1).These samples exhibit trends similar to those observed for lithium for the majority of samples
(except Comp 7) where the data show slopes shallower than the line of unity. This is indicative of
`wash out' behavior associated with flushing and depletion of a small quantity of soluble salt.
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pH
12
Amphibole Gneiss Schist(COMP1)
fBiotite Gneiss(COMP2)
10 --*--Mica Schist(COMP3)
—0—Po Mica Schist(COMP5)
8 Shear Schist(COMP6)
= fSpod Pegmatite(COMP7)
a
Upper Mica Schist(COMP8)
6
� ♦Amphibole Gneiss Schist(COMPS)
m
J
fBiotite Gneiss(COMP10)
4 f Mica Schist(COMP11)
f Pegmatite(COMP 12)
2 f Po Mica Schist(COMP13)
Shear Schist(COMP14)
0
fSpod Pegmatite(COMP15)
-
D 2 4 6 8 10 12
Liquid-to-solid contact ratio(L/kg)
Figure 5-16: Evolution of pH as a function of US (EPA 1314)
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Ca SO4
35 200 —0--Biotite Gneiss(COMP2)
+Amphibole Gneiss Schist(COM Pl)
30
—4—eiotite Gneiss(COMP2) 180 —4--Mica Schist(COMP3)
—
f Mica Schist(COMP3) 160 Po Mica Schist(COMP5)
25
—Po Mica Schist(COMPS) t Shear Schist(COMP6)
140
Shear Schist(COMP6) — —4—Spod Pegmatite(COMP7)
£20 —4�—Spcd Pegmatite(COMP7) E 120 tU pper Mica Schist(COMP8)
Upper Mica Schist(COMP8) `0 100 +Amphibole Gneiss Schist(COMP3)
w15 —4—Amphibole Gneiss Schist(COMP9) Amphibole Gneiss Schist(COMP9)
—0—Biotite Gneiss(COMP10) c 80 t Biotite Gneiss(COMP30)
u 10 —4—Mica Schlst(COMP11) u60
Mica Schist(COMP31)
—4—Pegmatite(COMP32) 40 Pegmatite(COMP32)
5 —0—Po Mica Schist(COMP13) —0--Po Mica Schist(COMP13)
Shear Schist(COMP14) 20 +Shear Schlst(COMP14)
fSpod Pegmatite(COMP15)
0 0 0 8 ISpod Pegmatite(COMP15)
0 2 4 6 8 10 12 0 2 4 6 8 10 12
Liquid-to-solid contact ratio(L/kg) Liquid-to-solid contact ratio(L/kg)
As Se
0.06 0.007
Amphibole Gneiss Schist(COMPS) I ♦Amphibole Gneiss Schist(COMPl)
f Biotite Gneiss(COMP2) f Biotite Gneiss(COMP2)
0.006
0.05 Mica Schist(COM P3) f Mica Schist(COM P3)
f Po Mica Schist(COMPS) 0.005 f Po Mica Schist(COMP5)
—
?0.04 }Shear Schist(COM P6) —&--Shear Schist(COM P6)
e� eo
E --*—Spod Pegmatite(COMP7) £0.004 --@—Spod Pegmatite(COMP7)
0.03 --*--Upper Mica Schist(COMP8) 4 Upper Mica Schist(COMP8)
1.
Amphibole Gneiss Schist(COMP9) f Amphibole Gneiss Schist(COM P9)
w a 0.003
f Biotite Gneiss(COMP10)o " —0—Biotite Gneiss(COMP10)`o
u 0.02 t Mica Schist(COM P31) u 0.002 t M ica Schist(COM P31)
Pegmatite(COM P32) f Pegmatite(COMP12)
0.01 —4�—Po Mica Schist(COMP13) 0.001 —0—Po Mica Schist(COMP13)
Shear Schist(COM P14) f Shear Schist(COM P34)
0Sam _ "pod Pegmatite(COMP35) 0 —&--Spod Pegmatite(COMP35)
0 2 4 6 8 10 12 0 2 4 6 8 10 12
Liquid-to-solid contact ratio(L/kg) Liquid-to-solid contact ratio(L/kg)
Figure 5-17: Filtered concentration of Calcium, Sulfate, Arsenic and Selenium (EPA 1314)
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Al As
1000 30 t Amphibole Gneiss Schist(COMP1)
t Amphibole Gneiss Schist(COMPl)
—4�--Biotite Gneiss(COMP2) f Biotite Gneiss(COMP2)
100 —.0—Mica Schist(COMP3) 1 t Mica Schist(COMP3)
--40.—Po Mica Schist(COMP5) --4--Po Mica Schist(COMPS)
t Shear Schist(COMP6) 01 �-Shear Schist(COMP6)
30 �Spod Pegmatite(COMP7) E f Spod Pegmatite(COMP7)
f Upper Mica Schist(COMPS) Upper Mica Schist(COMPS)
w 0.01
Amphibole Gneiss Schist(COMP9) Amphibole Gneiss Schist(COMPS)
v
1 ,.• Biotite Gneiss(COMP30) t Biotite Gneiss(COMP30)
t Mica Schlst(COMP11) 0.001 tMica Schist(COMP13)
�Pegmatite(COMP32) t Pegmatite(COMP32)
0.1 �Po Mica Schist(COMP33) f Po Mica Schist(COMP33)
0.0001
f Shear Schist(COMP34) t Shear Schist(COMP34)
Spod Pegmatite(COMP15) t Spod Pegmatite(COMP15)
0.01 ......Unity 0.00001 ......Unity
0.1 1 10 0.1 1 10
Liquid-to-solid contact ratio(L/kg) Liquid-to-solid contact ratio(L/kg)
Se Li
10000
0.08 —4�--Amphibole Gneiss Schist(COMPS) f Amphibole Gneiss Schist(COM P1)
f Biotite Gneiss(COMP2) Biotite Gneiss(COMP2)
•' Mica Schist(COMP3) 1000 ----*—Mica Schist(COMPS)
Po Mica Schist(COMPS) f Po Mica Schist(COMPS)
Shear Schist(COMP6) 100 f Shear Schist(COM P6)
0.008
E f Spod Pegmatite(COMP7) f Spod Pegmatite(COMP7)
-o Upper Mica Schist(COMPS) Upper Mica Schist(COMP8)
t Amphibole Gneiss Schist(COMP9) y 10 t Amphibole Gneiss Schist(COMP9)
—0--Biotite Gneiss(COMP30) t Biotite Gneiss(COMP10)
0.0008 —0—Mica Schlst(COMP11) 1 Mica Schist(COMP11)
Pegmatite(COMP12) Pegmatite(COMP32)0.1
Po Mica Schist(COMP33) t Po Mica Schist(COMP13)
�Shear Schist(COMP34) f Shear Schist(COM P14)
�Spod Pegmatite(COMP15) f Spod Pegmatite(COMP15)
0.00008 •• —••.•••Unity 0.01 •••••'. ••••••Unity
0.1 1 10 0.1 1 10
Liquid-to-solid contact ratio(L/kg) Liquid-to-solid contact ratio(L/kg)
Figure 5-18: Cumulative release curves for Aluminum, Arsenic, Selenium and Lithium (EPA 1314)
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5.1.9 HCT
A subset of 22 waste rock and ore samples were selected from the static test database for the standard
HCT procedure (ASTMD-5744-96). Table 5-5 lists the samples along with key static test data. The
objective of the HCT program was to address the uncertainties of the ABA and NAG data and provide
source term leachate chemistry for the main rock units associated with the Project that would be used
as input chemistry for predictive geochemical modeling. The waste rock and ore humidity cells consist
of:
• One Alluvium cell (HCT-01)
• Two Upper Mica Schist cells (HCT-02 and HCT-03)
• Five Amphibole Gneiss-Schist cells (HCT-04 to HCT-08)
• Two Biotite Gneiss cells (HCT-09 and HCT-10)
• Two Po Mica Schist cells (HCT-11 and HCT-12)
• Three Mica Schist cells (HCT-13, HCT-14, and HCT-15)
• Three Shear Schist cells (HCT-16, HCT-17, and HCT-18)
• Three Pegmatite cells (HCT-19, HCT-20, and HCT-21)
• One Silica Mica Schist cells (HCT-26)
The waste rock and ore humidity cells have been operational for between 66 and 74 weeks at the time
of writing.The HCT leachate was collected for weekly analysis of all parameters for Week 0 to Week 4;
physiochemical parameters and major ions continue to be analyzed weekly, whereas trace elements
are analyzed monthly(Week 8, Week 12, etc.). Figure 5-19 to Figure 5-41 provide time-series plots of
elemental release during the HCT, and Figure 5-42 to Figure 5-44 provide scatter plots showing the
relationship between pH and constituent release. The following subsections provide a detailed
description of geochemical behavior for each humidity cell, and Table 5-5 provides a summary.
Appendix G provides the laboratory reports for the HCTs.
General Observations
Figure 5-19 presents the trends of effluent pH for each of the HCTs through Week 74. Effluent pH
ranges between 2.6 to 9.2. Some samples were acidic from test initiation and remained acidic
throughout the duration of the test (HCT-02, HCT-13 and HCT-19). Others started at an alkaline pH,
but declined in pH between Weeks 5 and 20 (HCT-05, HCT-09, HCT-11, and HCT-12), and HCT-01,
HCT-04, and HCT-20 had a moderately acidic pH (around pH 5) by Week 26. All other cells remained
alkaline (pH>7) throughout the testing period. The following cells are reporting effluent pH <5.5 by
Week 51 of testing: HCT-01, HCT-02, HCT-04, HCT-05, HCT-09, HCT-11, HCT-12, HCT-13, HCT-1 5,
HCT-19, and HCT-20, which is consistent with the ABA and NAG data for these cells.
The humidity cells have between 0% (HCT-13) and 95% (HCT-08) of their original neutralizing
potential remaining at Week 74 (Figure 5-20). Leachate sulfate concentrations for the cells ranged
from below analytical detection limits (<1 mg/L) to a maximum of 330 mg/L (Figure 5-24).
Figure 5-41 shows molar ratios of calcium and magnesium to sulfate (Ca+Mg/SO4). Cells that have
reported alkaline to circum-neutral pH values throughout testing typically report Ca+Mg/SO4 ratios
above two; this indicates that the dissolution of neutralizing minerals in the cells is greater than the
dissolution associated with buffering of acidity produced and is consistent.
The Ficklin plot presented on Figure 5-21 shows that leachates from the humidity cells can be
classified as low- to high-metal waters, which range from near-neutral to acidic. The majority of the
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humidity cells are classified near-neutral, low-metal waters based on effluent pH >5.5 and Ficklin
metals concentrations <1 mg/L. Leachates from HCT-02 (upper mica schist), HCT-13 (mica schist),
and HCT-19 (pegmatite) have leachates classified as acid, high-metal water based on pH <5.5 and
Ficklin metal concentrations>1 mg/L. Leachates from these three cells also fall into the acid, low-metal
classification along with leachates from HCT-01 (alluvium), HCT-04 (amphibole gneiss schist), HCT-
09 (biotite gneiss), and HCT-12 (po mica schist).
In general, constituent release from the Kings Mountain waste rock and ore HCTs can be grouped into
three classes:
• Parameters that show an inverse correlation with pH (i.e., elevated release at lower/more-
acidic pH values). These parameters include sulfate, beryllium, cobalt, copper, iron, fluoride,
manganese, nickel, uranium, and zinc. Release of these constituents was typically greatest in
the mica, schist, upper mica-schist, and pegmatite cells.
• Parameters that show a positive correlation with pH (i.e., elevated release at higher/more-
alkaline pH values). This was primarily observed for antimony, which showed the greatest
release in the first flush from the amphibole gneiss-schist cells.
• Parameters that show a parabolic relationship with pH, where elevated release is observed at
both acidic and alkaline pH values, with the lowest release occurring at circum-neutral pH.
This was observed for aluminum,arsenic, lithium, and selenium. Release of these constituents
was typically greatest in the mica schist, upper mica schist, and silicate mica schist cells.
The multi-element assay data presented in Section 5.1.7 showed that lithium, selenium, tin, and
tungsten are elevated relative to average crustal abundance across all of the Kings Mountain waste
rock and ore lithologies. Arsenic is also generally elevated in all but the upper mica schist and granite
samples, and less common exceedances are also noted for cadmium, uranium, zinc, copper,
molybdenum, lead, thallium, cobalt, chromium, antimony, and manganese. Based on the HCT results
available to date, the majority of these parameters have the potential to be mobilized from the waste
rock and ore materials in meteoric waters, with detectable concentrations of these parameters during
the test. The exceptions are tin, molybdenum, and chromium, which were typically below analytical
detection limits in the HCT leachates and show a low potential for mobilization.
'Ficklin metals are the base metals copper,cadmium,cobalt, lead, nickel,and zinc(Ficklin et al., 1992).
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Table 5-5: Summary of Future Waste Rock and Ore HCT Static Data and Status
Paste Sulfide Sulfate Sulfur AP (kg NP (kg TIC NNP (kg NPR' NAG (kg NAG HCT Current NP
Main Material Type HCT ID Hole ID From To Rationale pH ° ° Total CaCO3 CaCO3 ° CaCO3 H2SO4 pH HCT pH Remaining
(s.u.) (/O) (/0) (%) eq/t) eq/t) (/O) eq/t) Ratio eq/t) (s.u.) Status (s.u.
Alluvium HCT-01 DDKM17-121 0 16.4 5.5 <0.005 0.03 0.03 <0.15 <0.50 <0.05 0.35 3.33 2 5 Week 51 4.7 69%
Upper Mica Schist HCT-02 DDKM17-076 194.4 199 High AP 8.0 0.67 0.23 0.9 20.9 8 <0.05 -12.9 0.38 23 2.8 Week 51 2.9 26°%
HCT-03 DDKM17-080 842.5 846.94 Low AP 4.0 0.03 0.02 0.05 0.9 6 <0.05 5.1 6.67 4 5.8 Week 51 5.3 93/o
HCT-04 DDKM17-042 600.24 604 High AP 9.8 0.55 1 0.32 0.87 17.2 1 6 <0.05 -11.2 1 0.35 20 2.6 1 Week 51 3.5 78%
Amphibole HCT-05 DDKM17-017 533.97 538.06 Low AP 9.6 0.1 0.02 0.12 3.1 10 <0.05 6.9 3.23 <0.5 6.6 Week 51 5.6 96%
Gneiss-Schist HCT-06 DDKM17-054 1062 1066.78 Low Sulfide 9.4 0.02 0.02 0.04 0.6 14 <0.05 13.4 23.3 <0.5 7 Week 51 6.2 96%
HCT-07 DDKM17-083 797 802.51 Uncertain AP 9.7 0.2 0.25 0.45 6.2 10 <0.05 3.8 1.61 5 3.5 Week 51 6.3 92%
HCT-08 DDKM18-216 244.06 249.54 High NP 10.0 0.1 0.04 0.14 3.1 43 0.4 39.9 13.9 <0.5 11.2 Week 51 6.6 97%
Biotite Gneiss HCT-09 DDKM17-052 1223.5 1226.21 High AP 8.0 1.07 0.62 1.69 33.4 6 <0.05 -27.4 0.18 35 2.5 Week 51 3.5 47%
HCT-10 DDKM18-293 561.02 567.09 Uncertain AP 8.8 0.28 0.04 0.32 8.8 13 <0.05 4.20 1.48 3 3.9 Week 51 5.9 95%
Po Mica Schist HCT-11 DDKM18-340 476.74 479.92 High AP 8.7 0.3 1.99 2.29 9.40 6 <0.05 -3.40 0.64 47 2.2 Week 51 3.5 37%
HCT-12 DDKM17-055 865.23 868 High AP 8.1 1.08 0.47 1.55 33.8 5 <0.05 -28.8 0.15 3 2.5 Week 51 3.3 33%
HCT-13 DDKM18-366 187.3 191.93 High AP 6.3 1.37 0.3 1.67 42.8 4 <0.05 -38.8 0.09 35 2.4 Week 51 2.7 0%
Mica Schist HCT-14 DDKM17-019 1289.22 1292 Low AP 9.4 0.04 0.1 0.14 1.2 9 <0.05 7.8 7.50 <0.5 7.2 Week 51 6.2 94%
HCT-15 DDKM17-110 511.5 515.5 Uncertain AP 9.5 0.1 0.17 0.27 3.1 5 <0.05 1.9 1.61 4 3.4 Week 51 4.1 81%
HCT-16 DDKM17-021 1718 1722 Uncertain AP 9.2 0.59 0.4 0.99 18.4 30 0.2 11.6 1.63 3 3.9 Week 51 6.5 96%
Shear Schist HCT-17 DDKM17-096 568 572:12 High AP 9.0 1.13 0.55 1.68 35.3 20 0.2 -15.3 0.57 21 2.7 Week 51 6.5 90%
HCT-18 DDKM18-206 845.8 850.75 Low AP 8.7 0.26 0.15 0.41 8.1 36 0.3 27.9 4.44 <0.5 10.4 Week 51 6.7 96%
HCT-19 DDKM18-329 1064.63 1 1067.52 High AP(Composite) 6.5 0.07 0.155 0.225 2.2 2.5 <0.05 0.3 1.12 4.5 3.45 Week 51 3.8 48%
Pegmatite DDKM17-055 36.09 44.3
HCT-20 DDKM17-077 43 46 Low AP,some NAG 9.1 <0.005 <0.005 <0.005 <0.15 18 <0.05 2.85 20 20 5.5 Week 51 4.6 93%
Spod Pegmatite HCT-21 DDKM17-037 1381.27 1384.71 Low AP, some NAG 9.8 <0.005 <0.005 <0.005 <0.15 3 <0.05 2.85 20 10 6 Week 51 6.1 91%
Silica Mica Schist HCT-26 DDKM18-317 189 194 Average AP 10 <0.05 <0.03 <0.08 <1.60 18 <0.05 16.4 11.3 0.5 7.4 Week 43 6.9 97%
Source:htti)s:Hsrk.sharepoint.com/sites/NAUSPR000576/Internal/0400 Geochemical/Lab%20Data/HCT/KM Database HCT Rev04.xlsx
'NPR<1 =PAG(red shading);NPR between 1 and 3=Uncertain(yellow shading); NPR>3=non-PAG(no shading)
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Kings Mountain HCTs:Weekly pH(s.u.)
10 tAlluvium(HCT-01)
O Upper Mica Schist(HCT-02)
9 ° Upper Mica Schist(HCT-03)
Amphibole Gneiss-Schist(HCT-04)
8 '4! — Amphibole Gneiss Schist(HCT-05)
nn^n
tAmphibole Gneiss-Schist(HR-06)
] —HAmphibole Gneiss-Schist(HCT-07)
u .1 y �° ° ❑ tAmphibole Gneiss Schist(HCT-08)
7[ d A 0-A n.0. e Jvnn n_,n9Cn'A�C-
"'v0 ° OE/\ �° ^ °° n �Biotite Gneiss(HCT-09)
7 6
Biotite Gneiss(HCr-10)
2 0 0 oo Y�° �° n ,rt °o
C. 5 O , O p p tPo Mica Schist(HCT-33)
t Po Mica Schist(HCT-12)
Y P lr
0) 00
O �1 `^➢ p0 Mica Schist(HCr-13)
3 4OEle
N 0 �7q O
T; p sg — t Mica Schist(HCr-14)
000 � rnn n n O Mica Schist(HR-15)
3 89P-00800808°"ts"0t cdQ80000 0--- --`--- -----)o o Shear Schist(HCT-36)
0 000000000000000000000000000
° Shear Schist(HCT-17)
2 ❑ Shear Schist(HCT-38)
t Pegmatite(HCT-19)
1 fr Pegmatite(HCT-20)
t Spod Pegmatite(HCT-21)
t Silica Mica Schist(HCr-26)
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-19: Future Waste Rock and Ore HCT Effluent pH
Kings Mountain HCTs:NP Remaining(%)
100%
++
., -_...._.................� ...,,..,.,...,.,,.,,.., +Alluvium(HCT-01)
uuv,,, ' �
nnn nnnnnnnn^^^ --*—Upper Mica Schist(HCT-02)
���0000000 _
00
90� 00o0pp o00000 °°°°°per° -°°�°° —A—Upper Mica Schist(HCT-03)
Op °° OOp00 0 'IA Amphibole Gneiss-Schist(HCT-04)
O
80% ° 0 °° "0 0po00o —A—Amphibole Gneiss Schist(HCT-05)
C ]0� 000 OO8 °090 °°OpG°Op 00p00p0p0p0p0p00 Amphibole Gneiss-Schist(HCT-07)
0
•to OO 000pp °° °0 0p0O tAmphibole Gneiss Schist(HCT-08)
t0 60% o Opp° n p
o t° O° Biotite Gneiss(HCT-09)
C p ti
p —A—Biotite Gneiss(HCT-30)
C O "o o tPo Mica Schist(HCT-11)
to 50% p °oc-
0 O `� —A—Po Mica Schist(HCT-12)
C Op 9p° °0 —O Mica Schist(HCT-13)
o °
2 4U� 00 0n 0 IN pp -° Mica Schist(HCT-14)
N p 0 n° O
•� o pp °o p -0 Mica Schist(HCT-15)
30% 0 0p °°Qp -- -0 Shear Schist(HCT-16)
3 ° p0
Z Op Opp °Ap°p -A Shear Schist(HCT-17)
20% 0 Opp °oop0 —❑ Shear Schist(HCT-18)
00 Opp °o�po tPegmatite(HCT-19)
O
10% 00 OOp ° DoO —A—Pegmatite(HCT-20)
O O ° 00
°
00. IN Opp �Spod Pegmatite(HCT-21)
0� 00 °° tSilica Mica Schist(HCT-26)
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-20: Future Waste Rock and Ore HCT Neutralizing Potential Remaining
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Kings Mountain HCT's:pH v.Ficklin Metals(mg/L)
10000 •Alluvium(HCT-01)
•Upper Mica Schist(HCT-2)
♦Upper Mica Schist(HCT-3)
1000 *Amphibole Gneiss-Schist(HCr-04)
00 ♦Amphibole Gneiss Schist(HCT-05)
N
High acid, Acid. Near neutral, ■Amphibole Gneiss-Schist(HCT-06)
+ Extreme metal Extreme metal Extreme metal ♦Amphibole Gneiss-Schist(HCT-07)
Z 100 ■Amphibole Gneiss Schist(HCT-08)
+ High acid, Acid, Near neutral, •Biotite Gneiss(HCT-09)
a High metal High metal High metal ♦giotite Gneiss(HCT-10)
0 1U •Po Mica Schist(HCT-11)
+ p ♦Po Mica Schist(HCT-12)
l�J o O Mica Schist(HCr-13)
t
3 o Mica Schist(HCT-14)
V o0
�n 1 00 o 0 o Mica Schist(HCT-15)
m _Q
y♦♦♦♦�����G • M oShear Schist(HCT-16)
2 •• 0 O Shear Schist(HCT-17)
A
1, • ♦ •• ❑Shear Schist(HCr-18)
u 0.1 ♦AA• ♦NO•A •Pegmatite(HCr-19)
LL ♦Ww• .0 AAA
F ♦Pegmatite(HCT-20)
= High acid, Acid, Near neutral, •Spod Pegmatite(HCT-21)
Low metal 0.01 Law metal Law metal •Silica Mica Schist(HCT-26)
0 1 2 3 4 5 6 7 8 9 10
pH(s.u.)
Figure 5-21: Future Waste Rock and Ore HCT Ficklin Plot
Kings Mountain HCTs:Weekly Electrical Conductivity(VS/cm)
1400 --*--Alluvium(HCT-01)
O—Upper Mica Schist(HCT-02)
—A—Upper Mica Schist(HCT-03)
1200 00 0 O Amphibole Gneiss-Schist(HCT-04)
0
0 00 00 0 0 -h-Amphibole Gneiss Schist(HCT-05)
0 0 0 00 0 0
0 0 00 00 tAmphibole Gneiss-Schist(HCT-06)
1000 000 Amphibole Gneiss-Schist(HCT-07)
H 0 0 tAmphibole Gneiss Schist(HCT-08)
=L tBiotite Gneiss(HCT-09)
800 O —A--Biotite Gneiss(HCT-10)
7
o t Po Mica Schist(HCT-11)
'O o 00C 0 0 —A—Po Mica Schist(HCT-12)
00
O 0000 0 0 00 �O
V 600 000 9 0 0 0�0 O Mica Schist(HCT-13)
iii u 0 0 0 0 Y oo o Mica Schist(HCT-14)
0 0 0
aL+ 0 000 0 0000 00 O Mica Schist(HCT-15)
V 086 O 0 00L AAAAA
o —o Shear Schist(HCT-16)
W 400 0 O
Y 0 0 000008 TTT�Oo000o 0 0� 0 —A Shear Schist(HCT-17)
dd�"_•�' rOr,
00 000
000 -^0 AAAAAAAAa Shear Schist(HCT-18)
O Ow,.
3 200 ^ �0 mAAAAA r^ 0 -F Pegmatite(HCT-19)
o o e
A DADA..A?�„A.� Pegmatite(HCT-20)
AAA
"pA.AA_nAnA��7 ) A t5pod Pegmatite(HCT-21)
k�v''�P�UT10 AAAAAAAAAAIIAnnnnnnAAw,,uuu,
.. .. --*—Silica Mica Schist(HCT-26)
0
0 4 8 12 16 LU L4 LZS iL ib 4U 44 46 154 156 72 76
Test Week
Figure 5-22: Future Waste Rock and Ore HCT Effluent Electrical Conductivity
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Kings Mountain HCTs:Weekly Electrical Conductivity(VS/cm)
1400
--*--Alluvium(HCT-01)
-e- Upper Mica Schist(HCT-02)
-&--Upper Mica Schist(HCT-03)
1200 0°
o 0 --*--Amphibole Gneiss-Schist(HCT-04)
0
O 00 00 p0 0 0 0 0 -h-Amphibole Gneiss Schist(HCT-05)
0 6 0 o p 00 00 tAmphibole Gneiss-Schist(HCT-06)
1000 000 tAmphibole Gneiss-Schist(HCT-07)
00 tAmphibole Gneiss Schist(HCT-08)
1 °
t Biotite Gneiss(HCT-09)
800 0 -A-Biotite Gneiss(HCT-10)
o tPo Mica Schist(HCT-11)
'O 0 0 pO°R -,t Po Mica Schist(HCT-12)
US 600 0 0°0�9 po 0 0 O 0000 -0 Mica Schist(HCT-13)
u 0 0 ° p 0° 0 Mica Schist(HCT-14)
0 0 0 0
p 0 Mica Schist(HCr-15)
O Qom° O O° O°00 mQQ
Lu 400 0 p A_ o o Shear Schist(HCT-16)
O O
O 0po°oe s �0� o o A Shear Schist(HCT-17)
W
jA 00 0 00o *AA A AA �00°0 -E Shear Schist(HCT-18)
200 A[ OHO o p, ,,°m 000000000co00C�C>C�C)C>C36 a eo� �Pegmatite(HCT-19)
A 0 tPegmatite(HCT-20)
�wwwall
�-no� ,�44DO
yr .,,��f +Spod Pegmatite(HCT-21)
'AAAA°AAAAII`A"^AAAA�`"A^ Silica Mica Schist(HCT-26)
0 ww:;ww�00000�0ooax�oo0�owoo�
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-23: Future Waste Rock and Ore HCT Effluent Alkalinity
Kings Mountain HCTs:Weekly Sulfate(mg/L)
350
Alluvium(HCT-01)
Upper Mica Schist(HCT-02)
300
-k Upper Mica Schist(HCT-03)
tAmphibole Gneiss-Schist(HCT-04)
f Amphibole Gneiss Schist(HCT-05)
f Amphibole Gneiss-Schist(HCT-06)
250
-4-Amphibole Gneiss-Schist(HCT-07)
Injo
tAmphibole Gneiss Schist(HCT-08)
A 200 ♦Biotite Gneiss(HCT-09)
-A-Biotite Gneiss(HCT-10)
%
Y ° 000 o O --40-.-Po Mica Schist(HCT-11)
150 o 0 0 0 0 0 -A-Po Mica Schist(HCT-12)
0 0 p o
O 00 ° 8°0 000 0 00 O Mica Schist(HCT-13)
O
' ° o o � 0 0 °�Qo ° O°Oopp po 0 o Mica Schist(HCT-14)
0 00-
O ° gpOaoo 0 00g8p 000 ° 00 ° -I o Mica Schist(HCT-15)
100 �° p f�m�o o °p o 00 °°p O�f
A o 0o ao 000 9 o Shear Schist(HCT-16)
0 0 0
000000 p A Shear Schist(HCT-17)
Q a I
50 o " A ❑ Shear Schist(HCT-18)
❑ p0 p 0 o tPegmatite(HCT-19)
0 0
ofto
Pegmatite(HCT-20)
0 tSpod Pegmatite(HCT-21)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-24: Future Waste Rock and Ore HCT Effluent Sulfate
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 78
Kings Mountain HI Weekly Iron(mg/L)
80 --*—Alluvium(HCT-OS)
Upper Mica Schist(HCT-02)
70 —A—Upper Mica Schist(HCT-03)
Al
O tAmphibole Gneiss-Schist(HCT-04)
A--Amp
hibole Gneiss Schist(HCT-05)
60 tAmphibole Gneiss-Schist(HCT-06)
Amphibole Gneiss-Schist(HCT-07)
tAmphibole Gneiss Schist(HCT-08)
50 —e—Biotite Gneiss(HCT-09)
BD
E —A—Biotite Gneiss(HCT-10)
O 40 f Po Mica Schist(HCT-11)
A Po Mica Schist(HCT-12)
T
o Mica Schist(HCT-13)
3 30 o A Mica Schist(HCT-14)
O Mica Schist(HCT-15)
O
00 O O
0 0 00 000 <00.
0o0 o Shear Schist(HCT-16)
20 oc o 0 0 0 A Shear Schist(HCT-17)
O o
0 O AAAA OO 00 O Oo ❑ Shear Schist(HCT-18)
o A O O
C o o0 O A AO Pegmatite(HCT-19)
O O OpAAA
10 00o 000
p ,. �0 —A—Pegmatite(HCT-20)
000— _o0 n nno oo000p0p(ONWM0o tSpod Pegmatite(HCT-21)
11—
0
1)— vvv� --*--Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-25: Future Waste Rock and Ore HCT Effluent Iron
Kings Mountain HCTs:Aluminum(mg/L)
20
+Alluvium(HCT-01)
t Upper Mica Schist(HCT-02)
18 —Upper Mica Schist(HCT-03)
Amphibole Gneiss-Schist(HCT-04)
16 —A—Amphibole Gneiss Schist(HCT-05)
tAmphibole Gneiss-Schist(HCT-06)
14 tAmphibole Gneiss-Schist(HCT-07)
tAmphibole Gneiss Schist(HCT-08)
12 f Biotite Gneiss(HCT-09)
to
E —A—Biotite Gneiss(HCT-30)
E 10 t Po Mica Schist(HCT-11)
7
C t Po Mica Schist(HCT-12)
O Mica Schist(HCT-13)
_7 8
Q A Mica Schist(HCT-14)
O Mica Schist(HCT-15)
6 O Shear Schist(HCT-16)
A Shear Schist(HCT-17)
4 ❑ Shear Schist(HCT-18)
t Pegmatite(HCT-19)
2 —4r Pegmatite(HCT-20)
0 r, O �+ t5pod Pegmatite(HCT-21)
0 ♦Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-26: Future Waste Rock and Ore HCT Effluent Aluminum
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 79
Kings Mountain HCTs:Arsenic(mg/L)
1
t Alluvium(HCT-01)
o Upper Mica Schist(HCT-02)
0-Upper Mica Schist(HCT-03)
9—Amphibole Gneiss-Schist(HCT-04)
- Amphibole Gneiss Schist(HCT-05)
0.1 fAmphibole Gneiss-Schist(HCT-06)
t Amphibole Gneiss-Schist(HCT-07)
a--Amphibole Gneiss Schist(HCT-08)
J tBiotite Gneiss(HCT-09)
to —A—Biotite Gneiss(HCT-10)
E 0.01 + t Po Mica Schist(HCT-11)
V
.� -o Po Mica Schist(HCT-12)
N
Qp o Mica Schist(HCT-13)
A Mica Schist(HCT-14)
0 o Mica Schist(HCT-15)
o Shear Schist(HCT-16)
0.001 6 Shear Schist(HCT-17)
❑ Shear Schist(HCT-18)
� p Y
n.
y-' v tPegmatite(HCT-19)
A —A—Pegmatite(HCT-20)
❑ 8
tspod Pegmatite(HCT-21)
0.0001
t Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-27: Future Waste Rock and Ore HCT Effluent Arsenic
Kings Mountain HCTs:Antimony(mg/L)
0.01
—*--Alluvium(HCT-01)
--e Upper Mica Schist(HCT-02)
-a Upper Mica Schist(HCT-03)
Amphibole Gneiss-Schist(HCT-04)
—,Amphibole Gneiss Schist(HCT-05)
fAmphibole Gneiss-Schist(HCT-06)
fAmphibole Gneiss-Schist(HCT-07)
fAmphibole Gneiss Schist(HCT-08)
J
b�q fBiotite Gneiss(HCT-09)
—A—Biotite Gneiss(HCT-10)
C 8 t P
O 0.001 o Mica Schist(HCT-11)
h
E ?t -o Po Mica Schist(HCT-12)
Q I o O Mica Schist(HCT-13)
O
o Mica Schist(HCT-14)
O o Mica Schist(HCT-15)
o Shear Schist(HCT-16)
Shear Schist(HCT-17)
❑ Shear Schist(HCT-18)
o Pegmatite(HCT-19)
——Pegmatite(HCT-20)
t Spod Pegmatite(HCT-21)
0.0001 Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-28: Future Waste Rock and Ore HCT Effluent Antimony
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 80
Kings Mountain HCTs:Beryllium(mg/L)
0.04
tAlluvium(HCT-01)
O Upper Mica Schist(HCT-02)
0.035 0-Upper Mica Schist(HCT-03)
Amphibole Gneiss-Schist(HCT-04)
0.03 --A--Amphibole Gneiss Schist(HCT-05)
f Amphibole Gneiss-Schist(HCT-06)
Amphibole Gneiss-Schist(HCT-07)
0.025 tAmphibole Gneiss Schist(HCT-08)
J
b �Biotite Gneiss(HCT-09)
E 0.02 O —A—Biotite Gneiss(HCr-SO)
? O Po Mica Schist(HCr-11)
i 4 Po Mica Schist(HCT-12)
N
m 0.015 O Mica Schist(HCT-13)
0 Mica Schist(HCT-14)
0.01 A Shear Schist(HCT-17)
❑ Shear Schist(HCr-18)
O Pegmatite(HCT-19)
0.005 000 Pegmatite(HCT-20)
O --o—spod Pegmatite(HCr-21)
0 0 —0--Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-29: Future Waste Rock and Ore HCT Effluent Beryllium
Kings Mountain HCTs:Cadmium(mg/L)
0.1
Alluvium(HCT-01)
Upper Mica Schist(HCT-02)
—A—Upper Mica Schist(HCT-03)
f Amphibole Gneiss-Schist(HCT-04)
—&—Amphibole Gneiss Schist(HCT-05)
0.01 tAmphibole Gneiss-Schist(HCT-06)
Amphibole Gneiss-Schist(HCT-07)
tAmphibole Gneiss Schist(HCT-08)
--o—Biotite Gneiss(HCT-09)
J
\ —A—Biotite Gneiss(HCr-10)
bD
0 Po Mica Schist(HCr-11)
0.001
� --t Po Mica Schist(HCT-12)
E O Mica Schist(HCT-13)
� O
v
A Mica Schist(HCT-14)
O Mica Schist(HCT-15)
O Shear Schist(HCT-16)
0.0001 O 0 Shear Schist(HCT-17)
O
O O O O ❑ Shear Schist(HCT-18)
—Pegmatite(HCT-19)
—rr Pegmatite(HCT-20)
�spod Pegmatite(HCr-21)
--*--Silica Mica Schist(HCT-26)
0.00001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-30: Future Waste Rock and Ore HCT Effluent Cadmium
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_RevO6.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 81
Kings Mountain HCTs:Cobalt(mg/L)
1.2
--*—Alluvium(HCT-01)
--o Upper Mica Schist(HCT-02)
—A Upper Mica Schist(HCT-03)
1 —tAmphibole Gneiss-Schist(HCT-04)
—A—Amphibole Gneiss Schist(HCT-05)
(Amphibole Gneiss-Schist(HCT-06)
tAmphibole Gneiss-Schist(HCT-07)
0.8 tAmphibole Gneiss Schist(HCT-08)
Biotite Gneiss(HCT-09)
—A—Biotite Gneiss(HCT-10)
tPo Mica Schist(HCT-11)
F
0.6 A Po Mica Schist(HCT-12)
o Mica Schist(HCT-13)
O
V A Mica Schist(HCT-14)
0.4 O Mica Schist(HCT-15)
o Shear Schist(HCT-16)
A Shear Schist(HCT-17)
ElShear Schist(HCT-18)
0'2 t Pegmatite(HCT-19)
—A—Pegmatite(HCT-20)
t Spod Pegmatite(HCT-21)
0 tSilica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-31: Future Waste Rock and Ore HCT Effluent Cobalt
Kings Mountain HCTs:Copper(mg/L)
1
t Alluvium(HCT-01)
--Q Upper Mica Schist(HCT-02)
—Ar Upper Mica Schist(HCT-03)
--*--Amphibole Gneiss-Schist(HCT-04)
—A—Amphibole Gneiss Schist(HCT-05)
tAmphibole Gneiss-Schist(HCT-06)
tAmphibole Gneiss-Schist(HCT-07)
0'1 tAmphibole Gneiss Schist(HCT-08)
t Biotite Gneiss(HCT-09)
J
—A—Biotite Gneiss(HCT-30)
n4
--0—Po Mica Schist(HCT-11)
O. A Po Mica Schist(HCT-12)
CL p O Mica Schist(HCT-13)
O
tJ A Mica Schist(HCT-14)
O
0.01 o Mica Schist(HCT-IS)
o Shear Schist(HCT-16)
A Shear Schist(HCT-17)
❑ Shear Schist(HCT-18)
♦—Pegmatite(HCT-19)
—*—Pegmatite(HCT-20)
--*—Spod Pegmatite(HCT-21)
0.001 t Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-32: Future Waste Rock and Ore HCT Effluent Copper
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 82
Kings Mountain HCTs:Gross Alpha(pCi/L)
600
All wizen(H LT-01)
❑ Upper Mira Schist IHCr-02]
500 ❑ Upper Mira SchistlHCr-03]
Amphibole GneissSchist(HCT-04)
/ Amphibole Gneiss Schist(HCT-051
f Amphibole GneissSchist(HCT-06)
400 Amphibole GneissSchist(HCT-07)
■ Amphibole Gneiss Schist(HCT-081
J -0-Biotite Gmim(HCT-091
V 300 Biotite G mi.(HCr-1BI
ra ❑ Po Mira Schist(HCr-11)
j ❑ Po Mira Schist(HCr-12)
a
a zao O Mira Schist l HCr-13]
y ❑ Mira Schist IHCr-14)
o Mira Schist IHCr-15)
o Shear Schist(H Cr-16)
100 -- -- -� ❑ Shear Schist(H Cr-17)
A _ _�— ❑ Shear Schist(H Cr-18)
` 0 ..-. -A_ p C • Pegmatite(HCT-191
- ❑
0 Pegmatite(HCT-201
�-Spod Pegmatite(H CF21)
Silica Mica S&ist(HCr-261
-100
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-33: Future Waste Rock and Ore HCT Effluent Gross Alpha
Kings Mountain HCTs:Lead(mg/L)
0.014 Alluvium(HCT-01)
Upper Mica Schist(HCT-02)
-A-Upper Mica Schist(HCT-03)
0.012 tAmphibole Gneiss-Schist(HCT-04)
— Amphibole Gneiss Schist(HCT-05)
tAmphibole Gneiss-Schist(HCT-06)
0.01 Amphibole Gneiss-Schist(HCT-07)
tAmphibole Gneiss Schist(HCT-08)
--O--Biotite Gneiss(HCT-09)
0.008 -A-Biotite Gneiss(HCT-10)
to t Po Mica Schist(HCT-11)
-�Po Mica Schist(HCT-12)
N 0.006 o Mica Schist(HCT-13)
J 0 Mica Schist(HCT-14)
O Mica Schist(HCT-15)
0.004 -0 Shear Schist(HCT-16)
--A Shear Schist(HCT-17)
-o Shear Schist(HCT-18)
-.*--Pegmatite(HCT-19)
0.002 -A-Pegmatite(HCr-20)
4 -411t Spod Pegmatite(HCT-21)
n --*-Silica Mica Schist(HCT-26)
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-34: Future Waste Rock and Ore HCT Effluent Lead
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Repoli—Kings Mountain Mining Project Page 83
Kings Mountain HCTs:Manganese(mg/L)
100
Alluvium(HCT-01)
t Upper Mica Schist(HCT-02)
— Upper Mica Schist(HCT-03)
Amphibole Gneiss-Schist(HCT-04)
— Amphibole Gneiss Schist(HCT-05)
10 tAmphibole Gneiss-Schist(HCT-06)
tAmphibole Gneiss-Schist(HCT-07)
tAmphibole Gneiss Schist(HCT-08)
to Biotite Gneiss(HCT-09)
E
H DO
—A—Biotite Gneiss(HCT-30)
N 1 t Po Mica Schist(HCT-11)
c
be 00 -k-- Po Mica Schist(HCT-12)
C
O Mica Schist(HCT-13)
o Mica Schist(HCT-14)
o Mica Schist(1-11-15)
O Shear Schist(HCT-16)
0.1 o Shear Schist(HCT-17)
-❑ Shear Schist d8)
--*--Pegmatite❑
(HCT-19)CT-19)
o A 0
--*—Pegmatite(HCT-20)
O B 8 O g O tspod Pegmatite(HCT-21)
0.01 Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-35: Future Waste Rock and Ore HCT Effluent Manganese
Kings Mountain HCTS:Mercury(ng/L)
S
+Alluvium(HCT-Ol)
Upper Mica Schist(HCT-02)
— Upper Mica Schist(HCT-03)
2.5 40--Amphibole Gneiss-Schist(HCT-04)
—r Amphibole Gneiss Schist(HCT-05)
f Amphibole Gneiss-Schist(HCT-06)
tAmphibole Gneiss-Schist(HCT-07)
2 0--Amphibole Gneiss Schist(HCT-08)
Biotite Gneiss(HCT-09)
J
—�Biotite Gneiss(HCT-SO)
bA
--*--Po Mica Schist(HCT-11)
>. 1.5
—*--Po Mica Schist(HCT-12)
V �
N
O Mica Schist(HCT-13)
4
Mica Schist(HCT-14)
1 o Mica Schist(HCT-15)
�{ O Shear Schist(HCT-16)
/ ❑ A Shear Schlst(HCT-17)
❑ Shear Schist(HCT-18)
0.5 0 ♦Pegmatite(HCT-19)
` 0
—�Pegmatite(HCT-20)
�Spod Pegmatite(HCT-21)
0 ♦Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-36: Future Waste Rock and Ore HCT Effluent Mercury
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 84
Kings Mountain HCTs:Nickel(mg/L)
1
Alluvium(HCT-01)
Upper Mica Schist(HCT-02)
t Upper Mica Schist(HCT-03)
--*--Amphibole Gneiss-Schist(HCT-04)
—*—Amphibole Gneiss Schist(HCT-05)
t Amphibole Gneiss-Schist(HCT-06)
t Amphibole Gneiss-Schist(HCT-07)
0.1
t Amphibole Gneiss Schist(HCT-OS)
t Biotite Gneiss(HCT-09)
—A—Biotite Gneiss(HCT-30)
E t Po Mica Schist(HCT-11)
—y --A�Po Mica Schist(HCT-12)
s
Z o Mica Schist(HCT-13)
0 Mica Schist(HCT-14)
0.01 O Mica Schist(HCT-15)
o Shear Schist(HCT-16)
A Shear Schist(HCT-17)
❑ Shear Schist(HCT-18)
--w Pegmatite(HCT-19)
A�Pegmatite(HCT-20)
f Spod Pegmatite(HCT-21)
t Silica Mica Schist(HCT-26)
0.001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-37: Future Waste Rock and Ore HCT Effluent Nickel
Kings Mountain HCTs:Selenium(mg/L)
0.0025
+Alluvium(HCT-Ol)
Upper Mica Schist(HCT-02)
— Upper Mica Schist(HCT-03)
t Amphibole Gneiss-Schist(HCT-04)
�•002 } —r Amphibole Gneiss Schist(HCT-05)
+Amphibole Gneiss-Schist(HCT-06)
tAmphibole Gneiss-Schist(HCT-07)
f Amphibole Gneiss Schist(HCT-08)
0.0015 Biotite Gneiss(HCT-09)
to
£ —�Biotite Gneiss(HCT-SO)
--0—P0 Mica Schist(HCT-11)
—&—Po Mica Schist(HCT-12)
v o Mica Schist(HCT-13)
N 0.001
0 Mica Schist(HCT-14)
O O Mica Schist(HCT-15)
O
00 Shear Schist(HCT-16)
�° O ° —A Shear Schist(HCT-17)
Ar O
0.0005 / 'o` O ° °
O v"� ❑ Shear Schist(HCT-18)
O t Pegmatite(HCT-19)
❑ g ❑ �� —A—Pegmatite(HCT-20)
--*—Spod Pegmatite(HCT-21)
0 tSilica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-38: Future Waste Rock and Ore HCT Effluent Selenium
AP/RB KingsMountain_BaselineGeochemChar_Report_USPR000576_Rev06.docx April 2024

SRK Consulting(U.S.), Inc.
2023 PFS Report—Kings Mountain Mining Project Page 85
Kings Mountain HCTs:Uranium(mg/L)
1
+Alluvium(HCT-Ol)
t Upper Mica Schist(HCT-02)
—�Upper Mica Schist(HCT-03)
t Amphibole Gneiss-Schist(HCT-04)
—A mphibole Gneiss Schist(HCT-05)
0,1 +Amphibole Gneiss-Schist(HCT-06)
t Amphibole Gneiss-Schist(HCT-07)
t Amphibole Gneiss Schist(HCT-08)
Biotite Gneiss(HCT-09)
J
—A--Biotite Gneiss(HCT-30)
ba
—0--Po Mica Schist(HCT-11)
0.01 —k Po Mica Schist(HCT-12)
O
N O Mica Schist(HCT-13)
7 A Mica Schist(HCT-14)
O Mica Schist:HCT
o Shear Schist(HCT-16)
0.001 A Shear Schist(HCT-17)
❑ Shear Schist(HCT-18)
t Pegmatite(HCT-19)
0 O p tPegmatite(HCT-20)
O A A $ tspod Pegmatite(HCT-21)
❑ O O O g A �. v tSilica Mica Schist(HCT-26)
0.0001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-39: Future Waste Rock and Ore HCT Effluent Uranium
Kings Mountain HCTs:Zinc(mg/L)
10
t Alluvium(HCT-01)
Upper Mica Schist(HCT-02)
—a—Upper Mica Schist(HCT-03)
t Amphibole Gneiss-Schist(HCT-04)
— Amphibole Gneiss Schist(HCT-05)
f Amphibole Gneiss-Schist(HCT-06)
t Amphibole Gneiss-Schist(HCT-07)
1 tAmphibole Gneiss Schist(HCT-08)
t Biotite Gneiss(HCT-09)
J
—A—Biotite Gneiss(HCT-10)
4--Poo Mica Schist(HCT-11)
0o
C —�Po Mica Schist(HCT-12)
N
-o Mica Schist(HCT-13)
O G Mica Schist(HCT-14)
0.1 8
O Mica Schist(HCT-15)
O Shear Schist(HCT-16)
-A Shear Schist(HCT-17)
--M Shear Schist(HCT-18)
p t Pegmatite(HCT-19)
—A—Pegmatite(HCT-20)
--*--Spod Pegmatite(HCT-21)
0.01 tSilica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76
Test Week
Figure 5-40: Future Waste Rock and Ore HCT Effluent Zinc
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Kings Mountain HCT's:Test Week v.Carbonate Molar Ratio
100 Alluvium(HCT-01)
t Upper Mica Schist(HCT-02)
—A—Upper Mica Schist(HCT-03)
tAmphibole Gneiss-Schist(HCT-04)
—&—Amphibole Gneiss Schist(HCT-05)
K10 ■,A f 44.T°rp`�,A,E' 0 AAA° ° "A ° °° °° (Amphibole Gneiss-Schist(HCT-06)
°OO Is A �• I ° °44° ° o ° ° tAmphibole Gneiss-Schist(HCT-07)
C A-1�7!
8�°0 ° O0 hr -1-�b❑❑eeN❑❑❑❑�� ��.potAmphibole Gneiss Schist(HCT-08)
° ° -dQGgQQQ ♦ ❑❑ °� ❑❑❑ ❑off ❑ tBiotite Gneiss(HCT-09)
o oo O 0 + —�Biotite Gneiss(HCT-30)
O °°n` ^"°� — per �0 ❑ 0 b +
O OO..Q1 '� Po Mica Schist(HCT-11)
M n .1y ,ouy�t4� ' ?6 O —*—Po Mica Schist(HCT-12)
' 0 00
0000 - "' O Mica Schist(HCT-13)
Ov..��- ��0 O 0 0�
0 -o ° ° Mica Schist(HCT-14)
° °"°00000 ° pOpO8888$ geeeegQ%?. 9Bo 00 0°
0 O o 00 g 8o0go000°0 e 0000 OOO° O°o o Mica Schist(HCT-15)
000000oa� o�ogog088o�Q�8ge8ee0°�geg°go°88008goeo06-g2 6 A9 04 AAAA
0 ° o 0 Shear Schist(HCT-16)
00000000p 00000 O8008o0pO00 000000000p0 pOO p0000
0.1 ° Shear Schist(HCT-17)
❑ Shear Schist(HCT-18)
t Pegmatite(HCT-19)
0 —A—Pegmatite(HCT-20)
t5pod Pegmatite(HCT-21)
0.01 Silica Mica Schist(HCT-26)
0 4 8 12 16 20 24 28 32TAtW%k44 48 52 56 60 64 68 72 76
Figure 5-41: Future Waste Rock and Ore HCT Effluent Carbonate Molar Ratio (defined as the
ratio (Ca+Mg)/SO4 in molar ratios)
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Kings Mountain HCTs:HCT pH v.Weekly Sulfate(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Aluminum(mg/L)
350 100
•Alluvium(HCT-01) •Alluvium(HCT-01)
0 Upper Mica Schist(HCT-02) 0 Upper Mica Schist(HCT-02)
A Upper Mica Schist(HCT-03) A Upper Mica Schist(HCT-03)
300 a Amphibole Gneiss-Schist(HCT-04) •Amphibole Gneiss-Schist(HCT-04)
A Amphibole Gneiss Schist(HCT-05) 01 00 ♦Amphibole Gneiss Schist(HCT-05)
o ❑Amphibole Gneiss-Schist(HCT-06) 10 00po ■Amphibole Gneiss-Schist(HCT-06)
250 00 o Amphibole Gneiss-Schist(HCT-07) 9Og 0 ♦Amphibole Gneiss-Schist(HCT-07)
8 J Qu
0 0 +Amphibole Gneiss Schist(HCT-08) O a oo ObD
■Amphibole Gneiss Schist(HCT-08)
00 00 •Biotite Gneiss(HCT-09) £ A O •Biotite Gneiss(HCT-09)
200 0 0 ♦Biotite Gneiss(HCT-10) E 814�800 ♦Biotite Gneiss(HCT-10)
N 0 7 0
JT og • •Po Mica Schist(HCT-11) goo • • • •Po Mica Schist(HCT-11)
to 00� ♦Po Mica Schist(HCT-12) j 1 0$•*$ 0 h•o0 • A ❑ ♦PO Mica Schist(HCT-12)
Y 150 0 0 �� • 0 Mica Schist(HCT-13) > •,~ 008 +DA o Mica Schist(HCT-13)
y 00 0 He 00 A Mica Schist(HCT-14) � • O o O 0 A Mica Schist(HCT-14)
• o s •
0 •• , 0 O O O Q O Mica Schist(HCT-15) j • O o ♦ ■ ♦ ♦ O Mica Schist(HCT-15)
I�0 • ♦ o 0 Shear Schist(HCT-16) ' g y 1 o Shear Schist(HCT-16)
100 ♦ ,� •s � . Qo ♦ ♦
S• •� Q A• A A Shear Schist(HCT-17) 0.1 0 A Shear Schist(HCT-17)
_ • *A
o 0 o A� .•• •
o �♦ • • • ♦ so ❑Shear Schist(HCT-18) O A o= ♦ ❑Shear Schist(HCT-18)
• ♦ ♦ Q /NNYN�/�Am/1
• • •Pegmatite(HCi-19) •Pegmatite(HCT-19)
50 • o •• �a ♦ • s• o 0
If";
� • Qgo • ~•�•R ❑ O ♦Pegmatite(HCT-20) ♦Pegmatite(HCT-20)
'�eAg'g'ovoe ®®oo♦® r'W■�� o ❑ •Spod Pegmatite(HCT-21) •Spod Pegmatite(HCT-21)
� e
VV
0 n ❑ AA oA � ■ ♦ •Silica Mica Schist(HCT-26) 0.01 •Silica Mica Schist(HCi-26)
2 3 4 5 6 7 8 9 10 2 4 6 8 10
HCT pH(s.u) HCT pH(s.u)
Kings Mountain HCTs:HCT pH v.Weekly Manganese(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Zinc(mg/L)
100 10
•Alluvium(HCT-OS) •Alluvium(HCT-OS)
O Upper Mica Schist(HCT-02) O Upper Mica Schist(HCT-02)
A Upper Mica Schist(HCT-03) A Upper Mica Schist(HCT-03)
•Amphibole Gneiss-Schist(HCT-04) O Amphibole Gneiss-Schist(HCT-04)
• ♦Amphibole Gneiss Schist(HCT-05) A Amphibole Gneiss Schist(HCT-05)
•
10 ■Amphibole Gneiss-Schist(HCT-06) ❑Amphibole Gneiss-Schist(HCT-06)
• ♦Amphibole Gneiss-Schist(HCT-07) 00 O Amphibole Gneiss-Schist(HCT-07)
ba O
■Amphibole Gneiss Schist(HCT-08) 1 O +Amphibole Gneiss Schist(HCT-08)
O
N • •Biotite Gneiss(HCT-09) 0 0go O O •Biotite Gneiss(HCT-09)
C j A ♦Biotite Gneiss(HCT-10) r- 0 go 00 0 ♦Biotite Gneiss(HCT-10)
b' 000 •� 000 0 •Po Mica Schist(HCr-11) T 0 O 0 O oa0 ♦ O •Po Mica Schist(HCT-11)
1 8 • O 0 A QA A Po Mica Schist(HCT-12) 0 O o o ♦Po Mica Schist(HCT-12)
_T e O • e A O Q O Q O Mica Schist(HCT-13) 3 8 ep O O O O Mica Schist(HCT-13)
Y }bi Q O O gp
v 0 eQ O 0 A Mica Schist(HCT-14) O 8" O• A Mica Schist(HCT-14)
8 Q o O aa
3 800k�AO�OAO 0 O Q • O Mica Schist(HCT-15) 0.1 0 groo9oQ O ♦ O Mica Schist(HCT-15)
88
00 O o O Shear Schist(HCT-16) 6 On
O A O Shear Schist(HCT-16)
O� O O •
0.1 • p00 S � ♦ — AShear Schist(HCT-17) o O � Q AShear Schist(HCT-17)
• • •♦ • 0 8 0
0:®O• • ♦ • O • ❑Shear Schist(HCT-18) 000 O 0 A BA 8 ❑Shear Schist(HCT-18)
000 ♦ • • 0• A A O Pegmatite(HCT-19) AAA •Pegmatite(HCT-19)
90 o 0 o •
O �p ❑ A Pegmatite(HCT-20) ♦Pegmatite(HCT-20)
♦ O y❑� ••NV•OOA♦1♦•0.O■�■� ♦• OAA O QUA
N ♦ A♦ 00 ❑0 8� •Spod Pegmatite(HCT-21) •Spod Pegmatite(HCT-21)
•� 000 O ❑❑❑
0.01 ♦g c S0 0A *Silica Mica Schist(HCT-26) 0.01 •Silica Mica Schist(HCT-26)
2 4 6 8 10 2 4 6 8 10
HCT pH(s.u) HCT pH(s.u)
Figure 5-42: Future Waste Rock and Ore HCT Constituent Release as a Function of pH —Sulfate, Aluminum, Manganese, and Zinc
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Kings Mountain HCTs:HCT pH v.Weekly Arsenic(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Antimony(mg/L)
1 0.009
•Alluvium(HCT-01) •Alluvium(HCT-01)
•
O Upper Mica Schist(HCT-02) O Upper Mica Schist(HCT-02)
A Upper Mica Schist(HCT-03) 0.008 O O+❑ A A Upper Mica Schist(HCT-03)
••
� •Amphibole Gneiss-Schist(HCT-04) O Amphibole Gneiss-Schist(HCT-04)
• ♦Amphibole Gneiss Schist(HCT-05) 0.007 A Amphibole Gneiss Schist(HCT-05)
0.1 • ■Amphibole Gneiss-Schist(HCT-06) ❑Amphibole Gneiss-Schist(HCT-06)
•
•• ♦Amphibole Gneiss-Schist(HCT-07) O Amphibole Gneiss-Schist(HCT-07)
■Amphibole Gneiss Schist(HCT-08) 0.006 +Amphibole Gneiss Schist(HCT-08)
•Biotite Gneiss(HCT-09) E A •Biotite Gneiss(HCr-09)
,C • ♦Biotite Gneiss(HCT-10) O 0.005 ♦Biotite Gneiss(HCT-10)
u�r O
Q •Po Mica Schist(HCT-11) E •Po Mica Schist(HCT-31)
T 0.01 + +
NO QA 0 0g00 ■++A +,� APO Mica Schist(HCT-12) Q ♦PO Mica Schist(HCT-12)
3 A ■t A A O Mica Schist(HCT-13) 2. 0.004 O O Mica Schist(HCT-13)
�e O AA O O ♦• 0 ° A Mica Schist(HCT-14) y • A Mica Schist(HCT-14)
♦ A •
O ■ ■ 0)
08 00 O O Q goAA 600 0 ° ■ ♦ O Mica Schist Hcr-ls 0.003 •
,,Q ( ) �j O Mica Schist(HCT-15)
�jV�O� A� 0 00 O A aAp■p� ■ O O Shear Schist(HCT-16) % e O Shear Schist(HCT-16)
0.001 gS Aa O O =j AAB BOB +06+0 A Shear Schist(HCT-17) A A Shear Schist(HCT-17)
• • • ♦ ♦6 A • W' A 0 ♦ ❑Shear Schist(HCT-18) 0.002 O 0 OA # ❑Shear Schist(HCT-18)
• AA ».♦ :• Y8 s♦ O O Pegmatite(HCT-19) A •
♦ • a •Pegmatite(HR-19)
• . . EE ♦ A
0 A 0 ♦Pegmatite(HCT-20) 0.001 A ♦AO ❑ ❑ ♦Pegmatite(HCr-20)
• ••••W!1 LA■L11■!O�/OTS>�00 ❑ • A O° •Spod Pegmatite(HCT-21) A • • 0AA 0 O° •Spod Pegmatite(HCT-21)
•
0.0001 •Silica Mica Schist(HCr-26) O •Silica Mica Schist(HCr-26)
2 4 6 8 10 2 4 6 8 10
HCT pH(s.u) HCT pH(s.u)
Kings Mountain HCTs:HCT pH v.Weekly Selenium(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Uranium(mg/L)
0.01 1
•Alluvium(HCT-OS) •Alluvium(HCT-OS)
O Upper Mica Schist(HCT-02) O Upper Mica Schist(HCT-02)
A Upper Mica Schist(HCT-03) ♦ A Upper Mica Schist(HCr-03)
•Amphibole Gneiss-Schist(HCT-04) • O Amphibole Gneiss-Schist(HCT-04)
♦Amphibole Gneiss Schist(HCT-OS) ♦ A Amphibole Gneiss Schist(HCT-05)
■Amphibole Gneiss-Schist(HCT-06) 0.1 ♦� ❑Amphibole Gneiss-Schist(HCT-06)
t7\0 O •
♦Amphibole Gneiss-Schist(HCT-07) 0 ♦ O Amphibole Gneiss-Schist(HCT-07)
O O+❑ A ■Amphibole Gneiss Schist(HCT-08) O ♦ ♦ +Amphibole Gneiss Schist(HCT-08)
3 O 00 •Biotite Gneiss(HCT-09) E bD O 00 •0O _ ♦ •Biotite Gneiss(HCT-09)
�
W 00 00 0 00 ♦Biotite Gneiss(HCT-10) j 0 9 • • •~ ♦� ♦Biotite Gneiss(HCT-10)
O p
H 06 O O • •Po Mica Schist(HCr-11) C 0 0 0 ♦♦ •Po Mica Schist(HCT-I1)
0.001 A4 WOO • A °• 0.01 QQ •O
Y �• p •O O ♦Po Mica Schist(HCT-12) 8 8 0 0A ♦Po Mica Schist(HCT-12)
0) 9 O •O 0 Q O OA 0 0 •• 0A O Mica Schist(HCT-13) 8 O•06 8 0 O O Mica Schist(HCT-13)
3 0 ep AD 0• �•8• • • A Mica Schist(HCT-14) y ; 0 A Mica Schist(HCT-14)
® 00 . A O O O A A
80 0 A04 • O ■ A • A O Mica Schist(HCT-15) A O • ♦■ ♦ O Mica Schist(HCT-15)
8 A ■ 0 •
O , 8 O O A ♦� OShear Schist(HCT-16) OA 00 A DA A � OShear schist(HCT-16)
00 8 AM A • O 0 A�� • ♦ A Shear Schist(HCT-17) 0.001 A 0 AA 9 AA AShear Schist(HCT-17)
O A • , i� A ♦ O ❑Shear Schist(HCT-18) g0 Ae"NAA • ❑Shear Schist(HCT-18)
O O 0 0 0•°AA 00 • OA ♦A
♦ S • ♦ 0 4YA�n BA *0 ■ O Pegmatite(HCT-19) ♦ AO O O AOAA �AA♦ �• ■■ •Pegmatite(HCT-19)
Q W ♦ AA❑—A �♦ A Pegmatite(HCT-20) $O O O •A • A ♦Pegmatite(HCT-20)
a O A ♦ 00 O S ii
• ♦ °� p •Spod Pegmatite(HCT-21) • ♦ 9A AQ�A 0Q5-9 0 ° • •Spod Pegmatite(HCT-21)
0.0001 • • • • • ♦ 1 A00 AAAA O ■ *Silica Mica Schist(HCr-26) O.000l O 0 it Aa Y�a0• O •Silica Mica Schist(HCr-26)
2 4 6 8 10 2 4 6 8 10
HCT pH(s.u) HCT pH(s.u)
Figure 5-43: Future Waste Rock and Ore HCT Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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Kings Mountain HCTs:HCT pH v.Weekly Fluoride(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Lithium(mg/L)
10 10
•Alluvium(HCT-01) •Alluvium(HCT-01)
O Upper Mica Schist(HCT-02) • • A O O Upper Mica Schist(HCT-02)
A Upper Mica Schist(HCT-03) A• AA , ❑ A A Upper Mica Schist(HCT-03)
O 0 •Amphibole Gneiss-Schist(HCT-04) O E • ♦ ]❑� OAmphibole Gneiss-Schist(HCT-04)
OO ♦Amphibole Gneiss5chist(HCT-05) ry O O O O a ❑ A A O k AAmphibole Gneiss Schist(HCT-05)
0 0 ■Amphibole Gneiss-Schist(HCT-06) 1 IO�O, O � A � ♦ •A ❑Amphibole Gneiss-Schist(HCT-06)
♦Amphibole Gneiss-Schist(HCT-07) Oar;$ O O A aA■ e+ O OAmphibole Gneiss-Schist(HCT-07)
0 ■Amphibole Gneiss Schist(HCT-08) 8 $SZOO O O e : AA ae❑ +Amphibole Gneiss Schist(HCT-08)
d •
0 •Biotite Gneiss(HCT-09) £ AA Q •• A Q 5A •Biotite Gneiss(HCr-09)
000®AO 1gpQeAA AO A A A 90 A Biotite Gneiss(HCT-10)
•00 O O O ♦Biotite Gneiss(HCT-10) •••• • • A •
'O 1 •� 0 0 •Po Mica Schist(HCT-11) .3 .00801 O= A O • •Po Mica Schist(HCT-31)
+� 0.1 R ,� • • A ❑ ❑•
3 O • 00000 A Po Mica Schist(HCT-12) OQ : 000• ♦A • A AA• A A Og A PO Mica Schist(HCT-12)
LL
T 0 A =� O Mica Schist(HCT-13) Y O O AAA. AA n A~Bg • O Mica Schist(HCT-13)
d • A Mica Schist(HCT-14) v A AAA
A!. ��00� A Mica Schist(HCT-14)
3 O O n *Mica
Schist(HCT-15) �• 8 A �Q O Mica Schist(HCT-15)
` O Shear Schist(HCT-16) • • • •• • O $ OShear Schist(HCT-16)
O A ❑ • AShear Schist(HCT-17) U.U1 • N A & A Shear Schist(HCT-17)
A 0 0 0 • A 0 A A�■••■ AAA A AO ■ ■
0 n O A ❑Shear Schist(HCT-18) ❑Shear Schist(HCT-18)
0 A'No
O O Pegmatite(HCT-19) •Pegmatite(HCr-19)
O O
00 O •• A • A O ❑ OD A Pegmatite(HCT-20) A Pegmatite(HCT-20)
00®.. .—0 .. MVV�'1A0A�n ❑ AA A O + A
•Spod Pegmatite(HCT-21) •Spod Pegmatite(HCT-21)
0.1 •Silica Mica Schist(HCT-26) 0.001 •Silica Mica Schist(HCr-26)
2 6 10 2 4 6 8 10
HCT pH(s.u) HCT pH(s.u)
Figure 5-44: Future Waste Rock and Ore HCT Constituent Release as a Function of pH — Fluoride and Lithium
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Alluvium (HCT-01)
One sample of alluvium (HCT-01) was submitted for testing as part of the HCT program. Data are
available through Week 74 at the time of reporting. NP and AP for this sample were reported at
concentrations less than analytical limits of detection, although there was 0.03% sulfate present.
The alluvium cell has exhibited slightly acidic conditions throughout the test, ranging from a maximum
of pH 5.2 at Week 0 down to a minimum of pH 4.4.At Week 74 of testing,the pH is 4.8, and the sample
has approximately 53% of its original NP remaining. These pH values are comparable to the pH of the
deionized water used in the test, as indicated by the blank test results which average pH 4.7 up to
Week 68 (subsection below "Blank Sample (PBS-HCT)"). The low AP and NP reflect natural
weathering of the alluvium and leaching and depletion of acid generating and acid neutralizing mineral
phases over geologic time. A small amount of sulfate is present in the leachate (up to a maximum of
2.5 mg/L), although it has typically been below the limit of detection (<1.0 mg/L) after Week 28.
Concentrations of antimony, arsenic, barium, beryllium, copper, fluoride, manganese, nickel, uranium,
vanadium, and zinc were consistently reported at concentrations less than analytical limits of detection
for the 49 weeks of testing, indicating minimal potential to leach these constituents from the alluvium.
Upper Mica Schist(HCT-02 and HCT-03)
Two samples of upper mica schist waste rock were submitted as part of the HCT program, including
one sample predicted to be PAG (HCT-02) and one sample predicted to be non-PAG (HCT-03) from
the NAG test. Based on the ABA results, the acid generation potential for both samples is uncertain.
Effluent pH for the PAG upper mica schist cell (HCT-02) is acidic and has gradually declined from pH
5.1 at Week 0 to pH 3.0 at Week 25, after which point it has remained relatively stable. The acidic pH
for this sample has been accompanied by an increase in the concentrations of sulfate over time, from
a minimum of 71 mg/L to a maximum of 217 mg/L at Week 39. HCT-02 has approximately 0% of its
original NP remaining at Week 66. Constituent release from HCT-02 is typically higher relative to the
other waste rock and ore HCTs, which is a function of the acidic pH and higher sulfide content of this
sample. In particular, HCT-02 exhibits elevated concentrations of sulfate, aluminum, cadmium,
fluoride, manganese, nickel, selenium, and thallium relative to the other waste rock and ore HCTs.
The pH of the non-PAG Upper Mica Schist cell (HCT-03) has been circum-neutral, ranging from pH
7.0 to pH 5.8, although there has been a slight decline over the course of the test. Concentrations of
sulfate from HCT-03 have been consistently low (<5 mg/L), and the cell has approximately 90% of its
original NP remaining at Week 74.
Amphibole Gneiss-Schist (HCT-04, HCT-05, HCT-06, HCT-07, and HCT-08)
Five samples of Amphibole Gneiss-Schist waste rock were submitted as part of the HCT program,
including one sample with a higher potential for acid generation (HCT-04), two samples predicted to
be non-PAG based on the NAG results (HCT-05, HCT-06), one sample with uncertain acid generating
potential (HCT-07) based on the ABA and NAG data, and one sample (HCT-08) predicted to be non-
PAG based on the ABA and NAG tests.
HCT-04 has shown pH gradually declining from pH 7.3 to 3.4 throughout the 74 weeks of testing.
Effluent sulfate concentrations in HCT-04 were also relatively high compared to other Amphibole
Gneiss-Schist samples and steadily increased between weeks 10 and 70 from 12 to 41 mg/L. The
effluent sulfate concentration at Week 74 was 36 mg/L. This cell has approximately 63% of its original
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NP remaining (Figure 5-20)and 88%of the original sulfide is estimated to remain at this point. Results
for HCT-04 are consistent with the NAG test results that indicate this sample is acid generating.
Iron concentrations in the PAG Amphibole Gneiss-Schist cell (HCT-04) increased steadily between
weeks 8 and 24, ranging from <0.06 mg/L to a maximum of 5.4 mg/L. However, concentrations
declined from Week 24 onwards and have continued to decline during the test. A similar trend was
seen for manganese. Although metal release is generally low or declining under acidic conditions,
sulfate concentrations are increasing for this cell.
HCT-05 has shown a decline in pH throughout the HCT, with effluent pH declining from 9.2 at Week 0
and is stable around 5.5 at Week 74. However, this cell still has 95% of the original NP remaining at
Week 74, and pH is unlikely to decrease further. Under these conditions, metals and sulfate release
are low, with the exception of arsenic which is showing a declining trend and is currently below 0.001
mg/.
HCT-06 has shown a decline in pH throughout the test, with effluent pH declining from 9.0 at Week 0
to 5.7 at Week 73. This cell still has 94% of the original NP remaining at Week 74, and it is unlikely
that this cell will go acid. Under these conditions, metals and sulfate release are low.
HCT-07 has shown a steady decline in pH throughout the HCT, with effluent pH declining from 8.8 at
Week 0 to 5.9 at Week 67. Between weeks 67 and 74, the pH has declined more rapidly to 4.6 at
Week 74even though this cell still has 87% of the original NP remaining at Week 74Metals have been
increasing as pH conditions have transitioned to acidic levels including arsenic, copper, iron, lead,
manganese, nickel, selenium and zinc.
The non-PAG Amphibole Gneiss-Schist sample (HCT-08) has exhibited circum-neutral to moderately
alkaline conditions throughout the HCT,with effluent pH between 6.1 and 9. Metals concentrations are
low under these pH conditions, except for arsenic, which is elevated relative to the other waste rock
cells, although concentrations show a declining trend with 0.009 mg/L reported at Week 74. Sulfate
concentrations show an increase over time, but are generally low (less than 25 mg/Q. This cell has
95%of the original NP remaining at Week 74;this is consistent with the static test predictions and high
neutralizing potential of the sample.
Biotite Gneiss (HCT-09 and HCT-10)
Two samples of Biotite Gneiss waste rock were submitted as part of the HCT program, including one
PAG sample (HCT-09) and one sample with uncertain acid generating potential (HCT-10) based on
the ABA and NAG data (Table 5-5).
HCT-09 has shown a progression from slightly alkaline to acidic conditions throughout the HCT, with
pH decreasing from 8.0 at Week 0 to 3.7 at Week 73 (Figure 5-19). Accompanying the declining pH
has been an increase in the concentration of sulfate, particularly after Week 16,when there is a steady
increase from 35 mg/L to a peak of 98 mg/L at Week 29. The original NP in the sample continues to
be consumed, and only 26% of the original NP is remaining at Week 73. Aluminum, arsenic, and lead
concentrations show an upward trend at Week 61, as shown in Figure 5-26, Figure 5-27, and
Figure 5-34.
Conditions in HCT-10 are broadly circum-neutral, progressing to slightly acidic conditions, ranging
between pH 7.3 and 5.4 at Week 73. Approximately 91% of the original NP remains at Week 73
(Figure 5-20). Sulfate and metal release are also low and relatively stable.
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Po Mica Schist (HCT-11 and HCT-12)
Two samples of Po Mica Schist waste rock were submitted as part of the HCT program. Both cells are
predicted to be acid generating based on the NAG test results (Table 5-5). Based on the ABA results,
HCT-11 has an uncertain potential for acid generation and HCT-12 is classified as PAG, with notably
higher total sulfur content (1.1%)than HCT-11 (0.3%).
HCT-11 has exhibited a declining pH trend throughout the HCT program, ranging from a pH of
approximately 7.0 at Week 0 to approximately 3.4 at 73 weeks. This cell has 6% of the original NP
remaining at Week 73. Effluent pH has been relatively stable from Week 34 onwards. However,
arsenic, cadmium, lead, and uranium are increasing, as shown in Figure 5-27, Figure 5-30,
Figure 5-34, and Figure 5-39, respectively.
HCT-12 has exhibited a declining pH trend throughout the HCT program, ranging from a pH of 7.1 at
Week 0 to a pH of approximately 3.1 at 74 weeks. This cell has 0% of the original NP remaining at
Week 70. Effluent pH has been relatively stable from Week 34 onwards. However, aluminum, arsenic,
cadmium, and uranium are increasing, as shown in Figure 5-26, Figure 5-27, Figure 5-30, and
Figure 5-39, respectively.
Mica Schist(HCT-13, HCT-14, and HCT-15)
Three samples of Mica Schist waste rock were submitted as part of the HCT program, including one
sample classified as PAG (HCT-13), one sample classified as non-PAG (HCT-14), and one sample
with uncertain acid generating potential (HCT-15) based on the ABA and NAG data (Table 5-4).
The PAG Mica Schist cell (HCT-13) has reported acidic leachate conditions from test initiation, with
pH declining from 5.8 to 3.0 between weeks 0 and 26, after which time it has remained stable.
Concentrations of sulfate and iron have been elevated in HCT-13, in comparison to HCT 14 and HCT-
15,with sulfate concentrations showing an increasing trend from Week 23 onwards up to 303 mg/L at
Week 51.The cell has consumed the greatest amount of original NP,with all the original NP consumed
by Week 37; this is consistent with the ABA and NAG test results (Table 5-5).
HCT-14 was predicted to be non-PAG based on the NAG test results and has exhibited slightly alkaline
to circum-neutral conditions, with pH between 8.3 and 5.8. Concentrations of sulfate in leachate from
HCT-14 have been low and stable (typically <5 mg/L). The sample has approximately 91% of its
original NP remaining at Week 73. Barium, beryllium, boron, cadmium, cobalt, chromium, copper, iron,
manganese, mercury, molybdenum, nickel,silver,thallium,vanadium,and zinc have consistently been
reported at concentrations less than analytical limits of detection.
HCT-15 initially exhibited circum-neutral pH conditions; however, a significant decline in pH from
approximately 6.0 to approximately 4.5 was observed from Week 28 onwards; this has been
accompanied by an increase in effluent sulfate, iron, aluminum, manganese, nickel, cadmium, cobalt,
and zinc concentrations. Effluent pH in this cell is currently stable at around pH 4. The sample has
approximately 67% of its original NP remaining at Week 73. Boron, chromium, mercury, molybdenum,
silver, thallium, and vanadium have consistently been reported at concentrations less than analytical
limits of detection. However,concentrations of aluminum,arsenic, copper,and uranium are increasing,
as shown in Figure 5-26, Figure 5-27, Figure 5-32, and Figure 5-39, respectively.
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Shear Schist(HCT-16, HCT-17, and HCT-18)
Three samples of Shear Schist waste rock were submitted as part of the HCT program, including two
samples with uncertain acid generating potential (HCT-16 and HCT-17) based on the ABA test
(Table 5-5), and one sample classified as non-PAG (HCT-18). The NAG results for HCT-17 indicate
this sample is potentially acid generating.
HCT-16 has maintained alkaline to circum-neutral conditions,with pH ranging from 9.0 to 6.0. This cell
has 94% of the original NP remaining at Week 73, and has generated leachates low metal and sulfate
concentrations. Trace element concentrations in the leachates from HCT-16 are generally low. For
example, arsenic, barium, beryllium, cadmium, chromium, cobalt, copper, iron, nickel, and zinc are
consistently at or less than analytical limits of detection.
HCT-17 has maintained alkaline to circum-neutral conditions,with pH ranging from 8.4 to 6.0. This cell
has 84% of the original NP remaining at Week 73 and has generated leachates with low metal and
sulfate concentrations. Trace element concentrations in the leachates from HCT-17 are generally low,
similar to HCT-16. Although concentrations are low, sulfate concentrations show an overall upward
trend.
HCT-18 has maintained alkaline to circum-neutral conditions,with pH ranging from 9.1 to 6.2. This cell
has 94% of the original NP remaining at Week 73, and has generated leachates with stable
concentrations of sulfate after the first flush in the earlier weeks of testing. Trace element
concentrations in the leachates from HCT-18 are generally low, similar to HCT-16.
Pegmatite—Ore (HCT-19, HCT-20, and HCT-21)
Two composite samples of Pegmatite ore and one composite sample of Spodumene Pegmatite ore
were submitted as part of the HCT program. One Pegmatite cell (HCT-19) was selected to represent
higher acid generating potential, and the other Pegmatite cell (HCT-20) shows a lower potential for
acid generation based on the ABA and NAG data (Table 5-4). The Spodumene Pegmatite cell (HCT-
21) shows an uncertain acid generating potential from the ABA and is non-PAG based on the NAG
results.
Acidic conditions have consistently been reported in the Pegmatite cell with higher acid generating
potential based on ABA and NAG results(HCT-19),with pH ranging from 4.5 to 3.7. Despite the acidic
conditions for this cell, sulfate concentrations have been relatively low and stable. The cell has 37% of
its original NP remaining at Week 73. Elevated, but generally declining concentrations of aluminum,
cobalt, iron, manganese, nickel, sulfate, and zinc have been recorded.
The effluent pH for Pegmatite cell HCT-20 has been moderately acidic, ranging between 4.5 and 5.6.
The consumption of NP has been lower, relative to HCT-19, with approximately 90% of the original
NP remaining at Week 73 and no original sulfide remaining after Week 61.
The Spodumene Pegmatite cell (HCT-21) has exhibited circum-neutral pH through the test, with
effluent pH between 5.8 and 7.5. The pH conditions exhibited in HCT-21 are consistent with the NAG
test. HCT-21 has approximately 89% of its original NP remaining at Week 59 of testing.
Silica Mica Schist (HCT-26)
One sample of Silica Mica Schist waste rock was submitted as part of the HCT program. Based on
static testing data, the sample (HCT-26) is classified as non-PAG based on the NAG results and has
an uncertain potential based on the ABA results.
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Leachate from the Silica Mica Schist cell has been circum-neutral, ranging from pH 6.3 to 7.4, and the
cell has 95% of its original NP remaining at Week 66. Arsenic release from the Silica Mica Schist cell
is notably elevated relative to the other waste rock and ore cells, with effluent concentrations ranging
from 0.0061 mg/L to 0.68 mg/L. Barium, beryllium, boron, cadmium, chromium, cobalt, iron, lead,
manganese, molybdenum, nickel, silver, thallium, vanadium, and zinc have consistently been below
analytical limits of detection.
Blank Sample (PBS-HCT)
A blank HCT (PBS-HCT) was initiated along with the other HCTs to determine the potential for the
laboratory extraction procedures used during the HCT program to introduce contaminants that could
compromise the analytical results. The blank sample is generated by running deionized water through
an empty test column.
The pH values for the blank HCT range from 4.4. and 5.0, and are indicative of the pH of the deionized
water(DI) used in the HCT program. For the blank sample, constituent concentrations were below the
laboratory detection limit throughout the test except for:
• Iron: Week 18
• Lead: Week 12
• Mercury: Week 0
• Nickel: Week 20
• Selenium: Week 8
• Thallium: Week 12
Qualification of data from samples associated with contaminated blanks is applied according to the
following guidelines:
• If constituent is found in the blank, but not found in the sample, no qualification applies.
• If the result is greater than the laboratory reporting limit, but less than ten times the blank
concentration, the result is qualified for blank contamination (US EPA, 2016).
• If the concentration in the sample exceeds ten times the amount detected in the blank, the
sample result is reported without qualification.
Sample results qualified for blank contamination based on these criteria are summarized in Table 5-6
below. The analysis of blank contamination was not completed for major elements (e.g., chloride,
calcium) due to the potential for these parameters to be present in the DI water.
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Table 5-6: HCT Results within 10x of Reported Blank Concentrations
Analyte Blank MDL HCT ID HCT Reported Result
Concentration
HCT-09-HCT-WEEK 18 2.55
Iron,filtered(mg/L) 0.26 0.06 HCT-1 1-HCT-WEEK 18 0.56
HCT-19-HCT-WEEK 18 0.43
HCT-12-HCT-WEEK 12 0.00021
HCT-13-HCT-WEEK 12 0.0011
Lead,filtered(mg/L) 0.00013 0.0001 HCT-02-HCT-WEEK 12 0.00039
HCT-19-HCT-WEEK 12 0.00025
HCT-20-HCT-WEEK 12 0.00033
HCT-01-HCT-WEEK 0 0.77
HCT-05-HCT-WEEK 0 1.1
HCT-06-HCT-WEEK 0 2.6
HCT-08-HCT-WEEK 0 0.78
HCT-19-HCT-WEEK 0 0.68
Mercury,total(ng/L) 0.62 0.6 HCT-17-HCT-WEEK 0 0.68
HCT-21-HCT-WEEK 0 1.0
HCT-17-HCT-WEEK 0 0.68
HCT-21-HCT-WEEK 0 1.0
HCT-22-HCT-WEEK 0 3.4
HCT-23-HCT-WEEK 0 1.3
HCT-26-HCT-WEEK 0 0.64
HCT-03-HCT-WEEK 20 0.009
Nickel,filtered(mg/L) 0.0087 0.008 HCT-08-HCT-WEEK 20 0.010
HCT-09-HCT-WEEK 20 0.080
HCT-11-HCT-WEEK 20 0.033
HCT-01-HCT-WEEK 8 0.00026
HCT-02-HCT-WEEK 8 0.0014
HCT-04-HCT-WEEK 8 0.00027
Selenium,filtered(mg/L) 0.00017 0.0001 HCT-09-HCT-WEEK 8 0.00024
HCT-11-HCT-WEEK 8 0.00019
HCT-12-HCT-WEEK 8 0.00077
HCT-13-HCT-WEEK 8 0.00087
HCT-18-HCT-WEEK 8 0.00017
HCT-02-HCT-WEEK 12 0.00032
HCT-03-HCT-WEEK 12 0.00013
HCT-04-HCT-WEEK 12 0.00017
HCT-05-HCT-WEEK 12 0.00012
Thallium,filtered(mg/L) 0.0003 0.0001 HCT-06-HCT-WEEK 12 0.00015
HCT-07-HCT-WEEK 12 0.00014
HCT-08-HCT-WEEK 12 0.00014
HCT-12-HCT-WEEK 12 0.00010
HCT-01-HCT-WEEK 12 0.00017
5.1.10 Comparison of Static and Kinetic Test Results
The predictions of acid generation from the static test results have been compared to the available
HCT data to evaluate if the ABA and NAG test results are reliable indicators of acid generation
potential. Figure 5-42, Figure 5-43, and Figure 5-44 plot the NNP, NPR, and NAG pH values versus
the current HCT pH (Week 74). This comparison shows that there is a relatively good correlation
between the static and kinetic test results,where samples with NNP values >0 kg CaCO3 eq/t are non-
acid generating in the HCT. Only one sample has an NNP value <0 kg CaCO3 eq/t that has remained
neutral to this point in the HCT program (HCT-17, Shear Schist). A similar relationship is seen for the
NPR values, where samples with an NPR>1 are maintaining neutral conditions.
The correlation between NAG pH and the current HCT pH is not as definitive, with some samples
showing a good correlation and others showing a poor correlation,where the NAG pH values are lower
than the current HCT pH suggesting the NAG test may overpredict acid generation potential.
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Due to differences in the test conditions, the results of the NAG test (i.e. NAG pH and acidity
generated) cannot be directly compared to the HCT results. In the NAG test, a pulverized split of the
sample is exposed to an aggressive reagent that is intended to rapidly oxidize sulfide minerals to
produce acidity. Neutralizing minerals in the sample will react at different rates to neutralize the acidity,
with the most important being the carbonates that react quickly and buffer the solution at neutral pH
values. Silicate minerals are generally expected to be of less importance as they react at a significantly
slower rate; however, at Kings Mountain a significant amount of the acid buffering appears to be from
silicate minerals which may not be apparent in the NAG test.
Conditions in the HCT differ in a number of ways to the NAG test including particle size (typically
crushed core) and leaching solution (deionized water). While optimized to facilitate sulfide oxidation,
the kinetic test takes place at a slower rate than the NAG test and over a much longer period of time,
resulting in a range of geochemical processes that could influence the outcomes of the test. These
include different rates of acid generation (e.g. pyrrhotite will generate less acidity in the HCT than
equivalent sulfide concentration of pyrite), different rates of acid neutralization (where silicates may
play a more important role when acid generation rates are slow)and interactions occurring at mineral
surfaces (including production of secondary minerals that may armor the surface of sulfide minerals).
50
•Alluvium(HCT-01)
*Upper Mica Schist(HCT-02)
40 Acid y Non Acid+ ♦Upper Mica Schist(HCT-03)
*Amphibole Gneiss-Schist(HCT-04)
30 ♦Amphibole Gneiss Schist(HCT-05)
I ❑
■Amphibole Gneiss-Schist(HCT-06)
Q
� 20 _ *Amphibole Gneiss-Schist(HCT-07)
U ■Amphibole Gneiss Schist(HCT-08)
U
m
U •Biotite Gneiss(HCT-09)
Y 10 —
♦Biotite Gneiss(HCT-10)
m o
E •Po Mica Schist(HCT-11)
N 0
a ♦Po Mica Schist(HCT-12)
c
0 OMica Schist(HCT-13)
-10 N —
N AMica Schist(HCT-14)
*Mica Schist(HCT-15)
Z -20
� OShear Schist(HCT-16)
Z
OShear Schist(HCT-17)
0
-30 ❑Shear Schist(HCT-18)
ABA overpredicts AP
*Pegmatite(HCT-19)
-40 0 ♦Pegmatite(HCT-20)
•Spod Pegmatite(HCT-21)
-50 J •Silica Mica Schist(HCT-26)
0 1 2 3 4 5 6 7 9 9 10
Current HCT pH(s.u.)
Figure 5-45: NNP versus Current HCT pH — Future Waste Rock and Ore
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100 *Alluvium(HCT-01)
Acid y Non-Acid 0 Upper Mica Schist(HCT-02)
♦Upper Mica Schist(HCT-03)
❑ •Amphibole Gneiss-Schist(HCT-04)
•
+ ♦Amphibole Gneiss Schist(HCT-05)
10 • ■Amphibole Gneiss-Schist(HCT-06)
o ° ♦Amphibole Gneiss-Schist(HCT-07)
O ❑ ■Amphibole Gneiss Schist(HCT-08)
• ° •Biotite Gneiss(HCT-09)
10 ♦Biotite Gneiss(HCT-10)
t O ♦ o
0 1 •Po Mica Schist(HCT-11)
IL
♦Po Mica Schist(HCT-12)
o
N 0Mica Schist(HCT-13)
�` •• AMica Schist(HCT-14)
5
d
Z *Mica Schist(HCT-15)
• ABA overpredicts AP♦ o Shear Schist(HCT-16)
0.1 O OShear Schist(HCT-17)
❑Shear Schist(HCT-18)
*Pegmatite(HCT-19)
♦Pegmatite(HCT-20)
•Spod Pegmatite(HCT-21)
0.01 - •Silica Mica Schist(HCT-26)
0 1 2 3 4 5 6 7 & 9 10
Current HCT pH(s.u.)
Figure 5-46: NPR versus Current HCT pH — Future Waste Rock and Ore
12
•Alluvium(HCT-01)
11 + *Upper Mica Schist(HCT-02)
El ♦Upper Mica Schist(HCT-03)
10 •Amphibole Gneiss-Schist(HCT-04)
♦Amphibole Gneiss Schist(HCT-05)
9 ■Amphibole Gneiss-Schist(HCT-06)
♦Amphibole Gneiss-Schist(HCT-07)
8 ■Amphibole Gneiss Schist(HCT-08)
'? • •Biotite Gneiss(HCT-09)
= 7 ❑
�- ♦Biotite Gneiss(HCT-10)
C7 4
Q •Po Mica Schist(HCT-11)
Z 6
° ♦Po Mica Schist(HCT-12)
5 • OMica Schist(HCT-13)
o Mica Schist(HCT-14)
4 ♦ C *Mica Schist(HCT-15)
* * o Shear Schist(HCT-1 6)•
3 O A Shear Schist(HCT-1 7)
4
O A o • ❑Shear Schist(HCT-1 8)
z
*Pegmatite(HCT-19)
♦Pegmatite(HCT-20)
1
•Spod Pegmatite(HCT-21)
0
*Silica Mica Schist(HCT-26)
0 1 2 3 4 5 6 7 8 9 10 11 12
Current HCT pH(s.u.)
Figure 5-47: NAG pH versus Current HCT pH — Future Waste Rock and Ore
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5.2 Future Tailings Material
5.2.1 Mineralogy (SRK Program)
Table 5-7 shows summary results of the XRD analysis undertaken as part of the SRK geochemical
characterization, and Appendix C provides a full report. Sulfide minerals were more prevalent in the
OSR samples in comparison to the float tailings composites and DMS rejects. In the two OSR samples
(HCT-22 and HCT-23), chalcopyrite was measured at 2.9 wt% and 0.5 wt%, respectively, and
pyrrhotite was 1.1 wt% and 0.6 wt%. For these samples, lesser amounts of pyrite (0.7 wt% and
0.3 wt%) and sphalerite (0.0 wt% and 0.1 wt%) were recorded. In contrast, sulfide minerals were
generally lower and typically 0.1 wt% or less in the flotation tailings (although one sample recorded up
to 0.4 wt% pyrite).
The carbonate minerals calcite, magnesite,dolomite,and siderite were also identified in most samples.
Calcite was present at the highest relative concentration in the OSR samples (HCT-22 and HCT-23),
with 7.5 wt% and 3.4 wt% recorded, respectively. Calcite was also lower in the flotation tailings
(0.7 wt%) and DMS rejects (1.0 wt% and 0.1 wt%). As for the waste rock samples, siderite was
identified in all samples, between 0.6 wt% and 1.9 wt %, with concentrations generally highest in the
OSR samples. Dolomite was less prevalent than calcite and was not identified in the OSR; it was
measured between 0.3 wt% and 0.4 wt% in the flotation tailings and DMS rejects. Magnesite was only
identified in the OSR samples, with 0.5 wt% and 0.3 wt% recorded in HCT-22 and HCT-23,
respectively.
As with the waste rock, the main minerals identified in the samples comprise the bulk silicates quartz,
albite, microcline, actinolite, and muscovite depending on the sample. Overall, silicate mineral
assemblages accounted for between 60 wt% and 90 wt%. The lithium bearing mica mineral
polylithionite was also recorded in relatively high concentrations in the OSR samples, of up to
17.4 wt%, but was less abundant in the flotation tailings and DMS rejects. Other minerals present
include metal oxides, hydroxides and oxyhydroxides (present at relative concentrations of between
0.1 wt% and 3.3 wt%), and phosphates (up to 0.6 wt%).
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Table 5-7: Mineralogy Results Summary- Future Tailings and OSR
Mineral Abundance wt% for Sample ID and Type
Phase Approx.Formula Group HCT-22 HCT-23 HCT-24 HCT-25 L75603-03 L75603-04
OSR Float Tails Comp 1 Float Tails Comp 2 DMS DMS
Pyrite FeS2 0.7 0.3 0.1 0.4 - 0.2
P rrhotite Fe 1_x S x--o-o.17 Sulfides 1.1 0.6 0.1 0.2 0.1 0.1
S halerite Zn,Fe S - 0.1 0.1 - - -
Chalcopyrite CuFeS2 2.9 0.5 0.1 0.1 0.3 -
Calcite CaCO3 7.5 3.4 0.7 0.7 1 0.1
Siderite Fe"CO3 1.9 1.1 0.9 1.2 0.6 0.8
Ma nesite M CO3 Carbonates 0.5 0.3 - - - -
Dolomite CaM CO3 2 - - 0.4 0.4 0.3 0.4
Polylithionite KLi2AISig01o(F,OH)2 Li-bearing 12.9 17.4 0.9 0.7 0.5 0.2
minerals
Quartz Si02 4.3 22.5 27.8 27.8 20.3 23.2
Albite NaAISi3O8 9.6 4.7 34.3 32.1 46.8 40.6
Microcline KAISi3O8 1.8 4.3 11.2 8.9 14.8 14.6
Orthoclase KAISi308 1.4 0.6 2 1.3 1.4 1.8
Anorthite CaAl2Si2O8 6.2 6 2.1 2.7 0.8 2.6
Sillimanite Al2SiO5 1.3 1.5 0.2 0.4 - 0.2
Actinolite Cat M ,Fe"5Si8022 OH 2 Bulk Silicates 10 1.6 0.3 - 0.1 -
Enstatite M 2Si2O8 3.8 1.9 2.2 3.4 2.4 2.2
Au ite Ca,Na M ,Fe,AI,Ti Si,Al2O6 4.1 1.9 1 0.5 0.3 0.8
Kaolinite Al2Si205 OH q 2.8 0.3 1.5 1.6 0.2 0.9
Chlorite lib M ,AI,Fe"12 Si,AI 602o OH 8 4.7 5.9 2 2.1 0.4 0.9
Muscovite KAl2 Si3Al 010 OH,F 2 4.8 15.5 4.9 7.1 0.8 2
Biotite K Fe,M 3 AISI3O1p OH 2 1.3 1.6 0.4 0.2 0.1 -
Prehnite Ca2Al2Si3O10 OH 2 Phyllosilicates 3.3 0.7 0.4 0.5 0.2 0.2
Hematite Fe203 0.6 0.3 0.1 0.1 0.1 -
Magnetite Fe304 Iron Oxides and 0.1 - 0.1 - - 0.1
Goethite FeO(OH) Oxyhdroxides 0.1 - 0.4 0.3 0.2 0.2
Rutile Ti02 - 0.7 0.1 0.2 0.2 0.3
Anatase Ti02 0.9 0.7 - - 0.1 0.1
Ilmenite FeTiO3 Fe-Ti Oxides 0.5 0.4 0.2 0.1 0.4 0.1
Titanite CaTiSiO5 2.3 0.5 0.2 0.5 0.6 1
Aluminum _ _ _ _ _ _
Gibbsite AI(OH)3 Hydro ide
Apatite Cas POq 3 OH,F,CI 0.6 0.2 0.4 0.4 0.2 0.3
Monazite Ce,La,Nd,Th POq Phosphates 0.3 0.2 0.1 0.2 0.4 0.3
Zircon ZrSiO4 0.5 0.4 - 0.1 0.1 0.1
Topaz Al2SiO4 F,OH 2 2 - 1.3 1.3 1.3 1.4
Schorl NaFe`*3A16 BO3 3 Si6O18 OH q - 1.8 2.4 3.3 2.6 2.8
P roe M 3AI2 SIOq 3 Accessory 0.5 0.5 0.1 - - 0.1
Grossular Ca3Al2 SiOq 3 Phases 0.3 0.1 0.1 - - I -
E idote Caz Fe,AI AI2 SiOa Si207 O OH q 0.4 0.3 0.3 0.1 0.2 0.3
Clinozoisite Ca2Al3 SiOa 3 OH 2.6 0.3 0.7 1.3 0.2 0.4
Staurolite Fe",M 2AI9 Si,AI gOzo O,OH q 1.5 0.9 0.2 - 0.2 0.2
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Metallurgical Study
Several studies have been completed on metallurgical samples. Process Mineralogy Consulting
determined the level of iron in the main ore mineralogy, spodumene (Process Mineralogy Consulting,
2018). The following findings were made:
• The applied methodology allows for rapid acquisition of high-resolution mineralogical data
from spodumene pegmatites, including standard-supported, (semi-)quantitative
measurements of iron concentration below 1 wt.% in spodumene.
• The observed bulk modal contents broadly correlate with drill core observation, validating the
approach.
• The iron deportment in the samples appears to be dominated by spodumene and
ferrisicklerite.
• The compositional data of spodumene grains reflects the iron variability previously determined
by electron-microprobe analysis.
The presented dataset significantly expands the amount of information available for the development
of a geological model of the spodumene deposit. In particular, the spatial distribution of zones and
domains that have the potential to yield concentrates with unfavorable iron concentrations due to
mineralogical constraints can be potentially modeled by combining the mineralogical and geochemical
datasets. A more-thorough evaluation of the presented data is required to identify relevant
relationships and determine potential proxies.
Steinert (2018) assessed the application of ore sorting on the pegmatite ore (report dated May 2,
2018). The three test samples were run-of-mine (RoM) material comprised of felsic pegmatite ore and
mafic waste rock being largely amphibole schists. Initial test work indicated that the induction sensor
is best suited to separate mafic waste from felsic ore based on the higher content of conductive
minerals in the mafic rocks. The presented results confirm a successful separation of these two
lithologies. Despite positive statements, the lithium recoveries for the three tested samples range
between 83.2% and 91.5%, indicating a loss of 8.5% to 16.8% during sorting.
The lithium can be either contained in holmquisite, which is abundantly present in some metamorphic
or mafic rock types, or in the pegmatite ore. The sorted fractions included waste leucocratic material
in the waste fraction that resembles pegmatite ore.
5.2.2 ABA
ABA was carried out on a total of 31 samples to assess the balance of acid generating and acid
neutralizing minerals for the tailings materials. Table 5-8 and Figure 5-48 to Figure 5-52 present the
results of the testing. Appendix D and Appendix F provide the tabulated ABA data and laboratory
reports, respectively. The tailings ABA results presented in Figure 5-48 tot Figure 5-52 include a
comparison against the Run of Mine (RoM) ore samples (i.e., pegmatite and spodumene pegmatite)
included as part of the waste rock and ore characterization program described in Section 5.1.
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Table 5-8: Summary of ABA Results-Future Tailings and OSR
Laboratory pH, Sulfur Sulfur Acid Neutralization NNP
Met Program Category ID Sample Name Saturated Total Pyritic Potential Potential NPR (kg CaCO3 eq/t)
Paste (s.u.) (wt%) Sulfide(wt%) (kg CaCO3 eq/t) (kg CaCO3 eq/t)
North Met DMS-no sorting L75603-01 COMP 1 DMS TAILINGS(NO SORTING)(2 OF 2) 9.9 <0.01 <0.01 0.31 5 16.1 4.7
North Met DMS-no sorting L75603-02 COMP 2 DMS TAILINGS(NO SORTING)(2 OF 2) 9.9 <0.01 <0.01 0.31 3 9.7 2.7
2018 Pilot Plant DMS-no sorting L47924-03 DMZ 1 ST PASS FLOATS - <0.01 0.01 0.31 6 19.4 5.7
North Met DMS-sorting L75603-03 SORTED COMP 1 DMS TAILINGS(2 OF 2) 10 <0.01 <0.01 0.31 3 9.7 2.7
North Met DMS-sorting L75603-04 SORTED COMP 2 DMS TAILINGS(2 OF 2) 10 <0.01 <0.01 0.31 4 12.9 3.7
North Met DMS-sorting L75603-05 SORTED VAR 1 DMS TAILINGS(2 OF 2) 10 <0.01 <0.01 0.31 3 9.7 2.7
North Met DMS-sorting L75603-06 SORTED VAR 2 DMS TAILINGS (2 OF 2) 10.2 <0.01 <0.01 0.31 4 12.9 3.7
North Met DMS-sorting L75603-07 SORTED VAR 3 DMS TAILINGS (2 OF 2) 10.2 <0.01 <0.01 0.31 4 12.9 3.7
North Met DMS-sorting L75603-08 SORTED VAR 4 DMS TAILINGS(2 OF 2) 10.1 <0.01 <0.01 0.31 4 12.9 3.7
North Met Flotation tails-no sorting L75603-15 COMP 1 FLOAT TAILINGS 9.8 0.03 0.02 0.94 5 8.3 4.4
North Met Flotation tails-no sorting L75603-16 COMP 2 FLOAT TAILINGS 9.7 <0.01 <0.01 0.31 4 12.9 3.7
2018 Pilot Plant Flotation tails-no sorting L76180-03 COMBINED TAILINGS(HATCH-NO FLOC) 9.8 <0.01 <0.01 0.31 9 29.0 8.7
2018 Pilot Plant Flotation tails-no sorting L47924-01 BATCH FLOT RO SCAV TAILS - <0.01 0.03 0.31 2 6.5 1.1
2018 Pilot Plant Flotation tails-no sorting L47924-02 LCT FLOAT TAILS - <0.01 0.02 0.31 2 6.5 1.4
North Met Flotation tails-sorting L77114-01 Sorted Var 1 Ore Sorting Float Tails 7.9 <0.01 <0.01 0.31 3 9.7 2.7
North Met Flotation tails-sorting L77114-02 Sorted Var 2 Ore Sorting Float Tails 8.2 <0.01 <0.01 0.31 2 6.5 1.7
North Met Flotation tails-sorting L77114-03 Sorted Var 3 Ore Sorting Float Tails 8.2 <0.01 <0.01 0.31 2 6.5 1.7
North Met Flotation tails-sorting L77114-04 Sorted Var 4 Ore Sorting Float Tails 8.5 <0.01 <0.01 0.31 2 6.5 1.7
North Met Flotation tails-sorting L78487-01 LCT-1 COMBINED TAILINGS(Master composite 1) 9.6 <0.01 <0.01 0.31 5 16.1 4.7
North Met Flotation tails-sorting L78487-02 LCT-2 COMBINED TAILINGS(Master composite 2) 9.7 <0.01 <0.01 0.31 5 16.1 4.7
2023 Met Flotation tails-sorting L80362 Comp 1 8.5 <0.01 <0.01 0.31 3 9.7 2.7
2023 Met Flotation tails-sorting L80363 Comp 2 8.5 <0.01 <0.01 0.31 3 9.7 2.7
2018 Pilot Plant Mag separation rejects L76180-01 +6 MAGS 9.5 0.45 0.16 5 25 5.0 20.0
2018 Pilot Plant Mag separation rejects L76180-02 -6M/+32M MAGS 9.6 0.3 0.12 3.8 27 7.1 23.3
North Met Mag separation rejects L77419-41 MAG SEP PRODUCTS 9.6 0.44 0.19 5.9 16 2.7 10.1
North Met Ore sorting rejects L75603-09 SORTED COMP 1 ORE SORTING WASTE 9.4 0.74 0.35 10.9 22 2.0 11.1
North Met Ore sorting rejects L75603-10 SORTED COMP 2 ORE SORTING WASTE 9.4 0.24 0.16 5.0 8 1.6 3.0
North Met Ore sorting rejects L75603-11 SORTED VAR 1 ORE SORTING WASTE 10.1 0.55 0.29 9.1 20 2.2 10.9
North Met Ore sorting rejects L75603-12 SORTED VAR 2 ORE SORTING WASTE 9.7 0.57 0.15 4.7 30 6.4 25.3
North Met Ore sorting rejects L75603-13 SORTED VAR 3 ORE SORTING WASTE 9.4 0.15 0.1 3.1 12 3.9 8.9
North Met Ore sorting rejects L75603-14 SORTED VAR 4 ORE SORTING WASTE 9.3 0.53 0.34 10.6 12 1.1 1.4
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1
,
,
,
•
•
•
0.1 , ■ Flotation tails-no sorting
,' ■ Flotation tails-sorting
3 ■ DIMS-no sorting
3 �
3 ■ ■ DMS-sorting
v ■ ' ■ • Ore sorting rejects
.', • Mag separation rejects
0.01 , ----1:1Line
,
,
0.001
0.001 0.01 0.1 1
Total Sulfur(%)
Figure 5-48: Sulfide Sulfur Plotted as a Function of Total Sulfur— Future Tailings and OSR
1000.0
100.0
t' Non-PAG
p ■Flotation tails-no sorting
r n
■Flotation tails-sorting
m
m
■DMS-no sorting
10.0 ■DIMS-sorting
0 • •Ore sorting rejects
c •
•Mag separation rejects
m •
° Uncertain ROM Ore
Z 1.0
PAG
0.1
0.001 0.01 0.1 1
Sulfide Sulfur(wt%)
Figure 5-49: Total Sulfur Plotted against NPR— Future Tailings and OSR
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Sulfide Sulfur vs.Paste pH
1000
PAG
100 '
1
I
10 '
■ Flotation tails-no sorting
Y ' Non-PAG ■ Flotation tails-sorting
3 �
■ DMS-no sorting
1 '
■ DMS-sorting
v I C • Ore sorting rejects
�° • Mag separation rejects
0.1 1 0�
� ROM Ore
i — — Paste pH=4.5
O
0.01 1 W X
111111110—
uJo 0
1 I
1
0.001
0 2 4 6 8 10 12
Paste PH(s.u.)
Figure 5-50: Sulfide-Sulfur versus Paste pH, Future Tailings and OSR
NP vs.AP
100
J� '
G Non-PAG
Cr 0 - '
o o�
O 10
c� O o o ■ Flotation tails-no sorting
u p '
Y O ' ■ ■ Flotation tails-sorting
O O ♦ ■ DMS-no sorting
c '
O ■ 0 0 0 ■ DMS-sorting
i
ao ° O PAG • Ore sorting rejects
o '
i • Mag separation rejects
.� 1 4-1i ROM Ore
0
w — — NPR=1
z '
_NPR=3
i
i
i
i
i
P
� I
0.1
0.1 1 10 100
Acidification Potential(kg CaCO3 eq/t)
Figure 5-51: NP versus AP, Future Tailings Samples— Future Tailings and OSR
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140
120
a 100
° Non-PAG
so
a ■Flotation tails-no sorting
Z 60 ■Flotation tails-sorting
m
Uncertain ■DMS-no sorting
a 40 ■DIMS-sorting
0
a
c •Ore sorting rejects
20
H
7i To : •Mag separation rejects
7 0 e ngoono00 ROM Ore
2
iv
Z -20
PAG
-40
-60
0.1 1.0 10.0 100.0 1000.0
Neutralization Potential Ratio(NPR)
Figure 5-52: NNP versus NPR, Future Tailings Samples— Future Tailings and OSR
Overall, the total sulfur content was low for all tailings material samples and always <1%. This is
reasonable given the generally low sulfide content in the pegmatite. Total sulfur for the flotation tailings
and DMS rejects were lower than the OSR and MSR samples, with the former typically at the limit of
detection. Figure 5-48 shows the total sulfur content plotted as a function of sulfide sulfur content. The
black line in the plot is a line of equivalence,where sulfide sulfur is equal to total sulfur.The plot shows
that nearly all samples plot below the line of equivalence, indicating that both sulfide and sulfate
species are present in the OSR and MSR material. Sulfide sulfur is the dominant form of sulfur in the
materials and is used to calculate AP.
The flotation tailings and DMS rejects tested contain low concentrations of total sulfur, with all but one
sample recorded at the limit of detection (<0.01%), as shown in Table 5-9. As a result of the low total
sulfur values,the flotation tailings and DMS rejects have low AP values. NP is also relatively low in the
flotation tailings and DMS rejects, between 2 and 9 kg CaCO3eq/t, and, overall, these results generate
NPR values of between 5.3 and 29 (non-PAG) and NNP values of<2 kg CaCO3 eq/t, but not below-
20 kg CaCO3 eq/t, which yields an uncertain acid generating classification (Figure 5-49).
In contrast,the OSR and MSR samples have higher sulfide sulfur content, and some of these samples
are classified as having an uncertain acid generating potential. NP is elevated in the OSR and MSR
in comparison to flotation tailings and DMS rejects, with between 8.0 and 30 kg CaCO3 eq/t recorded
(Figure 5-51).As shown on Figure 5-51, all of the tailings samples have an uncertain potential for acid
generation, with NPR values between 1 and 3 and NNP values between 20 and -20 kg CaCO3 eq/t.
Paste pH for the tailings material range from pH 7.9 to 10.2, and indicate absence of soluble acid
sulfate salts on the mineral surface and little to no short-term potential for acid generation. No
correlation exists for paste pH and sulfide sulfur for any of the samples (Figure 5-50).
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5.2.3 NAG
Table 5-9 presents the results of the NAG testing for the tailings materials.Appendix D and Appendix F
provide the tabulated data and the full laboratory reports, respectively. The results of the NAG testing
demonstrate that all but three samples of OSR material produced NAG pH values of >4.5 and are
classified as non-PAG. For all samples, NAG values were low, and all but three results were <10 kg
H2SO4 eq/t, indicating a limited potential for acid generation overall. All three of the samples with NAG
>10 kg H2SO4 eq/t also had NAG pH >4.5 (i.e., none of the tailings material samples fall within the
high PAG capacity category).
Overall,the NAG results correspond reasonably well with the ABA results,with the majority of samples
containing low total sulfur content yielding circum-neutral to alkaline NAG pH (Figure 5-53). The OSR
and MSR samples show more variability, and some samples have higher total sulfur content in
comparison to the flotation tailings and DMS rejects and variable NAG pH. There is a reasonable
correlation between NPR and NAG pH for all tailings material samples (Figure 5-54).
The ABA and NAG results for the tailings materials demonstrate that the ore sorting and magnetic
separation process results in the removal of sulfide mineral phases and concentration within the OSR
and MSR materials. The DMS rejects and flotation tailings are depleted with respect to sulfide
minerals, demonstrating the effectiveness of sulfide removal in the ore sorting process. Even flotation
tailings, that did not undergo ore sorting, have lower overall sulfide sulfur, indicating the magnetic
separation process also removes sulfides. Despite the presence of sulfides within the OSR and MSR
material, these materials still show an overall low potential for acid generation with only three of the
OSR rejects being classified as potential acid generating. As shown on Figure 5-55, these three
samples show a lower capacity for acid generation with NAG values <10 kg H2SO4 eq/t.
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Table 5-9: Summary of NAG Results - Future Tailings and OSR
Met Program Category Laboratory Sample Name NAG NAG
ID k H2SO4e /t H units
North Met DMS-no sorting L75603-01 COMP 1 DMS TAILINGS (NO SORTING)(2 OF 2) 13 5.6
North Met DMS-no sorting L75603-02 COMP 2 DMS TAILINGS (NO SORTING)(2 OF 2) 8 6.1
2018 Pilot Plant DMS-no sorting L47924-03 DMZ 1 ST PASS FLOATS - -
North Met DMS-sorting L75603-03 SORTED COMP 1 DMS TAILINGS(2 OF 2) 8.0 6.1
North Met DMS-sorting L75603-04 SORTED COMP 2 DMS TAILINGS(2 OF 2) 8.0 6.1
North Met DMS-sorting L75603-05 SORTED VAR 1 DMS TAILINGS (2 OF 2) 9 5.9
North Met DMS-sorting L75603-06 SORTED VAR 2 DMS TAILINGS 2 OF 2 9 6
North Met DMS-sorting L75603-07 SORTED VAR 3 DMS TAILINGS 2 OF 2 8 6.2
North Met DMS-sorting L75603-08 SORTED VAR 4 DMS TAILINGS 2 OF 2 7 6.2
North Met Flotation tails-no sorting L75603-15 COMP 1 FLOAT TAILINGS <1 7.1
North Met Flotation tails-no sorting L75603-16 COMP 2 FLOAT TAILINGS <1 6.8
2018 Pilot Plant Flotation tails-no sorting L76180-03 COMBINED TAILINGS HATCH-NO FLOC <1 8.6
2018 Pilot Plant Flotation tails-no sorting L47924-01 BATCH FLOT RO SCAV TAILS - -
2018 Pilot Plant Flotation tails-no sorting L47924-02 LCT FLOAT TAILS - -
North Met Flotation tails-sorting L77114-01 Sorted Var 1 Ore Sorting Float Tails 8 5.4
North Met Flotation tails-sorting L77114-02 Sorted Var 2 Ore Sorting Float Tails 8 5.5
North Met Flotation tails-sorting L77114-03 Sorted Var 3 Ore Sorting Float Tails 9 5.5
North Met Flotation tails-sorting L77114-04 Sorted Var 4 Ore Sorting Float Tails 7 5.8
North Met Flotation tails-sorting L78487-01 LCT-1 COMBINED TAILINGS Master composite 1 9 6
North Met Flotation tails-sorting L78487-02 LCT-2 COMBINED TAILINGS(Master composite 2) 7 6.3
2023 Met Flotation tails-sorting L80362 Comp 1 12 5.9
2023 Met Flotation tails-sorting L80363 Comp 2 12 6
2018 Pilot Plant Mag separation rejects L76180-01 +6 MAGS <1 9
2018 Pilot Plant Mag separation rejects L76180-02 -6M/+32M MAGS <1 8.8
North Met Mag separation rejects L77419-41 MAG SEP PRODUCTS <1 5.8
North Met Ore sorting rejects L75603-09 SORTED COMP 1 ORE SORTING WASTE 4 3.8
North Met Ore sorting rejects L75603-10 SORTED COMP 2 ORE SORTING WASTE 2 3.7
North Met Ore sorting rejects L75603-11 SORTED VAR 1 ORE SORTING WASTE <1 7.2
North Met Ore sorting rejects L75603-12 SORTED VAR 2 ORE SORTING WASTE <1 8.7
North Met Ore sorting rejects L75603-13 SORTED VAR 3 ORE SORTING WASTE <1 7
North Met Ore sorting rejects L75603-14 SORTED VAR 4 ORE SORTING WASTE 7 3.4
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1000
PAG '
1
100 '
I
1
10 1 ■ Flotation tails-no sorting
1 A Flotation tails-sorting
1 Non-PAG
3 � ❑ DMS-no sorting
3 1 1 + DMS-sorting
v3i 1 • Ore sorting rejects
o O 1 • Mag separation rejects
0.1
° • •• ROM Ore
1 • •
NAG pH=4.5
1
1 ❑
0.01 1 V ` ❑ 0
I
1
0.001 1 —
0 2 4 6 8 10 12
NAG pH(s.u.)
Figure 5-53: Sulfide Sulfur versus NAG Final pH — Future Tailings and OSR
NPR vs. NAG pH
1000
I
PAG , Non-PAG
I
100 ' -
1
o I q °
' a 00
1 � °° ■ ❑ Flotation tails-no sorting
-DOD o °
1 J ■ X Flotation tails-sorting
0
a 10 1 f ■ ] DMS-no sorting
�
0 I ■
�� o � - DMS-sorting
M 1 •
1 e • Ore sorting rejects
1 • ° • Mag separation rejects
z Uncertain °
� ' ROM Ore
1 � '
1 — — NAG pH=4.5
I
I
0.1 1
0 2 4 6 8 10 12
NAG pH(s.u.)
Figure 5-54: NPR versus NAG pH, Future Tailings Samples—Future Tailings and OSR
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100
I
PAG-High Capacity
2 I
0 1
WL 10 _________________1
O �
0 I K ■ Flotation tails-no sorting
)o Non PAG
0 1 MD ■ Flotation tails-sorting
o I J O ❑ DMS-no sorting
`y D ODO
a I + DMS-sorting
0 1 3D OMC M
1 • Ore sorting rejects
a PAG-Low Capacity
v 1 1 • Mag separation rejects
Z 1
ROM Ore
I
1 O O O OID NAG pH=4.5
1 ----NAG=10kg H2SO4/ton
1
1
0.1 1
0 2 4 6 8 10 12
NAG pH(s.u.)
Figure 5-55: Net Acid Generation Value versus NAG Final pH — Future Tailings and OSR
5.2.4 Multi-Element Analysis
Multi-element analysis was carried out for all tailings and OSR samples to determine the geochemical
composition of the samples and to identify any constituents that are elevated above the average crustal
abundance. Data for key parameters relating to ARDML are compared to average crustal
concentrations using the multi-element analysis.When compared to average crustal abundance, multi-
element data can provide an indication of element enrichment that may have environmental
importance and can identify parameters that might be of concern for the Project. Table 5-10 shows
summary results for the multi-element assays and the comparison to average crustal abundances for
key parameters relating to ARDML.
The data show that the OSR are routinely elevated in comparison to average crustal abundance for a
number of elements, including: arsenic, copper, lead, selenium, thallium, tungsten, and zinc. Less
common exceedances are also noted for antimony, cadmium, uranium, manganese, and silver. With
respect to the other tailings samples,and with the exception of the Flotation Tailings(Sorting)samples,
systematic exceedances of average crustal abundances are recorded for lithium, selenium, thallium,
uranium, and tungsten. In contrast, systematic exceedances of the average crustal abundances are
noted for the flotation tailings(sorting)samples, including for arsenic, mercury, molybdenum, and lead.
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Table 5-10: Summary of Multi-Element Assay Data - Future Tailings and OSR
Category Laboratory ID Ag Al As Ca Cd Cr Cu Fe Hg Mg Mn Mo Ni Pb S Sb Se TI U W Zn
Units ppm % ppm % ppm ppm ppm % ppm % ppm ppm ppm ppm % ppm ppm ppm ppm ppm ppm
Average Crustal Abundance(Mason, 1966) 0.07 8.13 1.8 3.63 0.2 100 55 5 0.08 2.9 950 1.5 75 13 0.026 0.2 0.05 0.5 1.8 1.5 70
DMS-no sorting L75603-01 0.01 6.62 5.9 0.19 <0.02 2 2.9 0.12 <0.005 0.01 298 0.22 1 25.8 0.01 0.09 1 7.49 8 201 45
DMS-no sorting L75603-02 0.04 6.57 3.3 0.16 <0.02 2 2 0.12 <0.005 0.01 437 0.15 0.6 28.8 0.01 0.08 1 8.97 6.2 225 56
DMS-no sorting L47924-03 <1 0.256 0.5 0.127 0.06 2 1 0.107 0 20 296 <2 <0.8 4.98 0 <0.2 <0.05 0.34 8.61 0 67
DMS-sorting L75603-03 0.01 6.67 1.1 0.2 0.05 2 1.5 0.11 0.011 0.01 359 0.07 0.5 27.2 0.01 0.05 1 7.88 9.5 233 57
DMS-sorting L75603-04 <0.01 7.03 1.3 0.16 0.04 2 0.9 0.14 <0.005 0.01 490 0.06 1.9 27.7 0.01 0.06 1 8.53 6.1 244 54
DMS-sorting L75603-05 0.02 6.95 1.7 0.23 0.05 2 4.8 0.13 <0.005 0.01 426 0.08 0.7 26 0.01 <0.05 1 7.14 10.6 233 55
DMS-sorting L75603-06 <0.01 6.2 1.9 0.22 0.07 7 1.9 0.14 <0.005 0.03 488 0.05 3.4 22.3 0.01 <0.05 1 6.41 7.8 217 73
DMS-sorting L75603-07 0.01 6.37 2.6 0.21 <0.02 5 0.9 0.13 <0.005 0.03 428 0.06 30.9 26.2 0.01 <0.05 1 8.62 8.2 215 47
DMS-sorting L75603-08 0.03 6.53 1.8 0.23 0.03 2 1.4 0.16 <0.005 0.01 564 0.08 1.9 28.3 0.01 <0.05 1 8.94 6.6 224 62
Flotation tails-no sorting L75603-15 0.07 7.27 4.1 0.4 <0.02 66 25.4 0.93 <0.005 0.28 1040 1.55 18.7 23.2 0.08 0.51 1 5.05 11.8 154.5 124
Flotation tails-no sorting L75603-16 2.04 8.03 11 0.23 <0.02 60 26.2 0.9 <0.005 0.15 1035 1.53 18.9 18.5 0.04 0.41 1 5.71 10.2 121 125
Flotation tails-no sorting L76180-03 0.1 7.57 1 2.3 0.71 <0.02 26 13.6 1 <0.005 0.26 840 0.49 7.5 14.6 0.03 0.07 1 4.77 8.9 123 149
Flotation tails-no sorting L47924-01 <1 0.0917 0.2 0.027 <0.03 <1 1 0.0087 N/A <0.002 17.7 <2 <0.8 2.37 N/A <0.2 <0.05 0.13 0.9 N/A 4
Flotation tails-no sorting L47924-02 <1 0.111 0.1 0.021 <0.03 <1 1 0.0152 N/A 0.003 19.3 <2 <0.8 1.64 N/A <0.2 <0.05 0.16 0.7 N/A 5
Flotation Tails-Sorting L77114-01 0.07 6.87 5.5 0.3 0.16 123 30.5 0.61 0.008 0.03 899 4.99 65.7 26.4 0.04 1.1 1 4.04 17.9 2.2 156
Flotation Tails-Sorting L77114-02 0.1 6.91 2.2 0.28 0.13 137 16.4 0.6 <0.005 0.03 984 5.6 68.6 31.7 0.03 0.63 1 4.52 25.3 1.5 177
Flotation Tails-Sorting L77114-03 0.03 6.78 1.3 0.27 <0.02 123 19 0.62 <0.005 0.02 1180 5.35 62 23.9 0.02 0.24 <1 4.65 32.7 1.6 155
Flotation Tails-Sorting L77114-04 <0.01 6.62 1.3 0.22 <0.02 76 12.8 0.53 <0.005 0.03 1255 2.93 33.6 25 0.02 0.2 1 4.53 20.2 1.7 162
Flotation tails-sorting L78487-01 0.3 6 5.2 0.16 0.04 41 8.4 0.37 0.053 0.02 671 1.27 14.3 29.1 0.02 3.63 2 3.84 6.4 370 113
Flotation tails-sorting L78487-02 0.03 5.91 2 0.12 <0.02 39 8 0.35 0.038 0.02 666 1.52 17 17.7 0.01 0.21 2 4.26 5.7 371 94
Flotation tails-sorting L80362 0.06 6.94 2.1 0.37 0.1 120 14.2 0.61 0.007 0.04 976 4.75 56.3 27.4 0.03 0.46 2 5.02 21.2 2.3 141
Flotation tails-sorting L80363 0.01 6.87 2 0.32 0.1 105 7.5 0.54 0.007 0.03 1030 4.14 51.1 22.9 0.02 0.3 3 5.51 14 2.1 137
Mag separation rejects L76180-01 0.1 8.13 5 6.68 <0.02 74 99.5 7.61 <0.005 3.49 2020 0.94 46.8 8.8 0.5 0.16 1 3.18 2.8 41 167
Mag separation rejects L76180-02 0.12 8.85 7.4 5.93 <0.02 71 68.9 6.85 <0.005 3.02 3390 1.02 37.6 9 0.41 0.22 1 4 21.6 40.7 246
Ore sorting rejects L75603-09 0.21 7.75 1 5.6 4.81 0.43 50 415 7.57 0.021 3.78 2290 1.44 39.6 30.1 0.87 0.68 2 5.36 5.2 61.5 510
Ore sorting rejects L75603-10 0.1 9.36 5.8 0.75 <0.02 84 74.9 5.83 <0.005 1.46 1200 0.99 55.4 35.5 0.38 0.26 1 5.48 3.3 92.8 170
Ore sorting rejects L75603-11 0.19 7.61 4.7 5.38 0.12 59 296 7.59 0.01 3.99 1575 1.93 37.9 13.7 0.55 0.39 2 3.77 5.3 50.2 311
Ore sorting rejects L75603-12 0.17 7.81 13.8 6.22 1.39 72 328 7.98 <0.005 3.62 1725 0.99 55 11.7 0.64 0.28 2 4.52 2.8 43.3 502
Ore sorting rejects L75603-13 0.14 9.3 11 1.45 <0.02 89 87.2 6.32 <0.005 1.76 1555 1.47 61.5 30.5 0.26 0.26 1 3.65 3 69.6 174
Ore sorting rejects L75603-14 0.11 9.15 8 1.96 0.15 114 81.8 6.5 <0.005 2.05 1420 1.96 87.9 28.6 0.69 0.23 1 3.47 3.6 79.4 178
Notes
N/A=not analyzed
Indicates<3 times average crustal concentrations
Indicates between 3 and 6 times average crustal concentrations
Indicates between 6 and 12 times average crustal concentrations
Indicates>12 times average crustal concentrations
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5.2.5 Short-Term Leach Tests
Test Matrix
Table 5-11 presents the short-term leach test matrix.
Table 5-11: Short Term Leach Test Matrix—Future Tailings and Ore Sorting Rejects
Sample SPLP MWMP EPA 1313 EPA 1316 TCLP(EPA 1311)
COMP 1 DMS TAILINGS X X X
COMP 2 DMS TAILINGS X X X
SORTED COMP 1 DMS TA X
SORTED COMP 2 DMS TA X
DMZ 1ST PASS FLOATS X
SORTED COMP 1 ORE SO X X X
SORTED COMP 2 ORE SO X X X
BATCH FLOT RO SCAV TAILS X
LCT FLOAT TAILS X
Comp 1 Float Tails X X X X
Comp 2 FLT Tails X X X X
SPLP and MWMP
Section 6 provides descriptions of the SPLP and MWMP methods. The results of these short-term
tests give insight to constituents that are present in leachable form from the tailings and OSR samples.
Figure 5-56 to Figure 5-58 present scatter plots of constituent release as a function of pH. SPLP and
MWMP results have been plotted together and have been converted to milligrams per kilogram release
to normalize the data and account for the difference in US ratio between the MWMP and SPLP tests.
Tabulated SPLP data are provided in Appendix E and the full laboratory reports are provided in
Appendix G.
Results show a generally low potential for metal mobilization from the tailings and OSR materials in
response to rinsing with distilled water. The results provide a qualitative indication of constituents that
could leach from the tailings and OSR materials, and the concentrations in laboratory tests may not
be representative of concentrations in seepage from the TSF; this is assessed is part of the water
quality prediction modeling presented in SRK(2023).
The SPLP and MWMP leachates for the tailings and OSR materials are circum-neutral to moderately
alkaline (pH 7.5 to 9.9). In general, the two flotation tailings samples (Batch Flot Ro Scav Tails and
LCT Float Tails) show the lowest constituent release during the leach tests. Constituent release is
generally inversely correlated with pH, with the highest constituent release observed under more
alkaline pH conditions.
The SPLP results for the flotation tailings samples from the 2023 Met program that underwent ore
sorting are generally comparable to the MWMP results from flotation tailings from the 2018 met
program with circum-neutral leachate and low overall metal release. Sulfate is slightly higher for the
2023 flotation tailings in comparison to the 2018 flotation tailings.This difference may be related to the
difference in the US ratio between the two different tests, since a higher sulfate concentration would
be expected for material from the 2018 met program because it did not include the preconcentration
(i.e., ore sorting)step.
In terms of the radiochemistry (Figure 5-12), most of the values are low in the SPLP and MWMP
Ieachates, reflecting the low uranium and thorium content of the tailings materials.
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100 7
O
A Comp 1 DMS Tailings(SPLP) 6 ♦Comp 1 DMS Tailings(SPLP)
0 Comp 2 DMS Tailings(SPLP) •Comp 2 DMS Tailings(SPLP)
E O Sorted Comp 1 DMS Tailings(SPLP) E 5 ♦Sorted Comp 1 DMS Tailings(SPLP)
41 ♦ x Sorted Comp 2 DMS Tailings(SPLP) 3 • x Sorted Comp 2 DMS Tailings(SPLP)
C
v3i ♦ DMZ 1ST Pass Floats(MWMP) E 4 +DMZ 1ST Pass Floats(MWMP)
a p
10 o Sorted Comp 1 Ore Sorting Rejects(SPLP) as •Sorted Comp 1 Ore Sorting Rejects(SPLP)
a + ❑Sorted Comp 2 Ore Sorting Rejects(SPLP) N 3 ■Sorted Comp 2 Ore Sorting Rejects(SPLP)
0 Batch Flot Ro Scav Tails(MWMP) a O •Batch Flot Ro Scav Tails(MWMP)
O
x ❑LCT Float Tails(MWMP) 2 ■LCT Float Tails(MWMP)
A COMP 1 Float Tails(SPLP) ❑ ♦COMP 1 Float Tails(SPLP)
• O o COMP 2 FLT Tails(SPLP)
1 O O ♦COMP 2 FLT Tails(SPLP)
1 0
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH (s.u.) SPLP pH(s.u.)
1 1
A Comp 1 DMS Tailings(SPLP) ♦Comp 1 DMS Tailings(SPLP)
0 Comp 2 DMS Tailings(SPLP) 1-. •Comp 2 DMS Tailings(SPLP)
E o Sorted Comp 1 DMS Tailings(SPLP) E ♦Sorted Comp 1 DMS Tailings(SPLP)
y U
QJ c x Sorted Comp 2 DMS Tailings(SPLP) N x Sorted Comp 2 DMS Tailings(SPLP)
t +DMZ 1ST Pass Floats(MWMP) d +DMZ 1ST Pass Floats(MWMP)
0.1 0 Sorted Comp 1 Ore Sorting Rejects(SPLP) 0•1 •Sorted Comp 1 Ore Sorting Rejects(SPLP)
a a
❑Sorted Comp 2 Ore Sorting Rejects(SPLP) ■Sorted Comp 2 Ore Sorting Rejects(SPLP)
0 Batch Flot Ro Scav Tails(MWMP) •Batch Flot Ro Scav Tails(MWMP)
a +
❑LCT Float Tails(MWMP) ■LCT Float Tails(MWMP)
A COMP 1 Float Tails(SPLP) A COMP 1 Float Tails(SPLP)
♦ O o COMP 2 FLT Tails(SPLP) 00 ♦COMP 2 FLT Tails(SPLP)
• ❑
0.01 O ❑ ---1 0.01 + 11
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-56: Future Tailings and OSR MWMP/SPLP Constituent Release as a Function of pH —Sulfate, Aluminum, Manganese, and Zinc
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1 1 =
0 Comp 1 DMS Tailings(SPLP) 0 Comp 1 DMS Tailings(SPLP)
O Comp 2 DMS Tailings(SPLP) O Comp 2 DMS Tailings(SPLP)
0.1
E O Sorted Comp 1 DMS Tailings(SPLP) o Sorted Comp 1 DMS Tailings(SPLP)
0.1
x Sorted Comp 2 DMS Tailings(SPLP) p x Sorted Comp 2 DMS Tailings(SPLP)
a E
Q +DMZ 1ST Pass Floats(MWMP) +. +DMZ 1ST Pass Floats(MWMP)
J O Sorted Comp 1 Ore Sorting Rejects(SPLP) Q 0.01 o Sorted Comp 1 Ore Sorting Rejects(SPLP)
a P g j a
{n J
❑Sorted Comp 2 Ore Sorting Rejects(SPLP) (AO ❑Sorted Comp 2 Ore Sorting Rejects(SPLP)
a �
0.01 + o Batch Flot Ro Scav Tails(MWMP) a p o Batch Flot Ro Scav Tails(MWMP)
❑ ❑LCT Float Tails(MWMP) 3: 0.001 + ❑LCT Float Tails(MWMP)
O
A COMP 1 Float Tails(SPLP) O 0 COMP 1 Float Tails(SPLP)
r 0 O ED AM
L OO OX o COMP 2 FLT Tails(SPLP) o COMP 2 FLT Tails(SPLP)
O ❑
0.001 0.0001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
1 � 1
A Comp 1 DMS Tailings(SPLP) A Comp 1 DMS Tailings(SPLP)
O Comp 2 DMS Tailings(SPLP) O Comp 2 DMS Tailings(SPLP)
E 0.1 tk 0.1
o Sorted Comp 1 DMS Tailings(SPLP) o Sorted Comp 1 DMS Tailings(SPLP)
3 x Sorted Comp 2 DMS Tailings(SPLP) 3 x Sorted Comp 2 DMS Tailings(SPLP)
+DMZ 1ST Pass Floats(MWMP) i +DMZ 1ST Pass Floats(MWMP)
ai D
H
J 0.01 O Sorted Comp 1 Ore Sorting Rejects(SPLP) J 0.01 o Sorted Comp 1 Ore Sorting Rejects(SPLP)
a a
❑Sorted Comp 2 Ore Sorting Rejects(SPLP) ❑Sorted Comp 2 Ore Sorting Rejects(SPLP)
� a
a o Batch Flot Ro Scav Tails(MWMP) o Batch Flot Ro Scav Tails(MWMP)
❑LCT Float Tails(MWMP) 4 ❑LCT Float Tails(MWMP)
0.001 2 0.001 O O
0 COMP 1 Float Tails(SPLP) 0 COMP 1 Float Tails(SPLP)
O o COMP 2 FLT Tails(SPLP) o COMP 2 FLT Tails(SPLP)
0.0001 AO 13> Elm+ 0.0001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-57: Future Tailings and OSR MWMP/SPLP Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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10 10
O
0 Comp 1 DMS Tailings(SPLP) 0 Comp 1 DMS Tailings(SPLP)
o Comp 2 DMS Tailings(SPLP) ❑ o Comp 2 DMS Tailings(SPLP)
E O Sorted Comp 1 DMS Tailings(SPLP) O Sorted Comp 1 DMS Tailings(SPLP)
ai 1 O O
+ x Sorted Comp 2 DMS Tailings(SPLP) 7 x Sorted Comp 2 DMS Tailings(SPLP)
0 El s ❑
2 +DMZ 1ST Pass Floats(MWMP) ❑ DMZ 1ST Pass Floats(MWMP)
U.
a o Sorted Comp 1 Ore Sorting Rejects(SPLP) Ja 1 o Sorted Comp 1 Ore Sorting Rejects(SPLP)
a to
a
4 O ❑Sorted Comp 2 Ore Sorting Rejects(SPLP) a ❑Sorted Comp 2 Ore Sorting Rejects(SPLP)
0.1 o Batch Flot Ro Scav Tails(MWMP) O o Batch Flot Ro Scav Tails(MWMP)
o LCT Float Tails(MWMP) ❑LCT Float Tails(MWMP)
O O
A COMP 1 Float Tails(SPLP) ❑COMP 1 Float Tails(SPLP)
o COMP 2 FLT Tails(SPLP) o COMP 2 FLT Tails(SPLP)
0.01 0.1
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
Figure 5-58: Future Tailings and OSR MWMP/SPLP Constituent Release as a Function of pH — Fluoride and Lithium
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Table 5-12: Radiochemistry on Future Tailings and OSR SPLP and MWMP Leachates
DMS Tails Ore Sorting Rejects Floatation Tails
Parameter Units Comp 1 DMS Comp 2 DMS Sorted Comp 1 Sorted Comp 2 DMZ 1ST Sorted Comp 1 Sorted Comp 2 Batch Flot Ro LCT Float COMP 1 COMP 2 FLT
Tailings Tailings DMS Tailings DMS Tailings Pass Floats Ore Sorting Ore Sorting Scav Tails Tails Float Tails Tails (SPLP)
SPLP SPLP SPLP SPLP) (MWMP) Rejects SPLP Rejects SPLP) (MWMP) (MWMP) (SPLP
Gross Alpha pCi/L 2.1 0.86 0.99 2.9 9.9 0.63 -0.11 11 7.9 --- ---
Gross Beta pCi/L 2 4.9 4.1 6.6 17 13 18 15 11 --- ---
Radium 226+Alpha Emitting Radium Isotopes* pCi/L -0.25 0.01 0 0.01 0.81 -0.12 0.04 1.1 -0.28 -0.21 0.02
Radium 228* pCi/L 4.1 8.3 3.7 -1.6 1.1 -2.7 2 -0.81 0.31 --- ---
Uranium mg/L 0.00107 0.00089 r 0.00071 0.00077 0.0046 0.00011 0.00014 0.0008 0.0014 0.0018 0.0011
•Negative values indicate the samples have less activity attributed to radium-226 and radium-228 compared to the reference standard used by the laboratory
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TCLP
The TCLP is a test method designed to simulate the leaching process that waste materials undergo
when disposed of in a landfill. The procedure is used to determine if the waste is classified as
hazardous according to RCRA criteria. TCLP Testing was undertaken on two samples: Comp 1 Float
Tailings and Comp 2 Float Tailings. Results are compared to standards in Table 5-13 and laboratory
reports are provided in Appendix G.
Table 5-13: TCLP Results for Comp 1 Float Tailings and Comp 2 Float Tailings
Material Type North Carolina DEQ/EPA Standards Comp 1 Flot Tailings Comp 2 Flot Tailings
LABID L80356-01 L80356-02
Arsenic(TCLP) 5 <0.04 <0.04
Barium(TCLP) 100 0.0401 0.0297
Cadmium(TCLP) 1 <0.008 <0.008
Chromium(TCLP) 5 <0.02 <0.02
Lead(TCLP) 5 <0.03 <0.03
Mercury(TCLP) 0.2 <0.0002 <0.0002
Selenium(TCLP) 1 <0.05 <0.05
Silver(TCLP) 5 <0.01 <0.01
Note:
All results reported in mg/L
<indicates sample result is below the laboratory detection limit
All constituents' results were below the appropriate regulatory standards. The majority of results, with
the exception of barium, were below the laboratory detection limits, indicating minimal presence of
these constituents within the tested samples.
LEAF Tests
As was the case for waste rock and ore (Section 5.1.7), LEAF tests on tailings and ore reject materials
are being undertaken to provide additional information on leachability. Two main aspects are being
assessed:
• Leachability as a function of pH (EPA Method 1313)
• Leachability as a function of contact ratio (EPA Method 1316)
Section 6 provides more detail regarding the LEAF test methods.
Results for the EPA 1313, EPA 1314, and EPA 1316 tests are summarized below. Tabulated LEAF
data are provided in Appendix E and the full laboratory repots are provided in Appendix G. Figure 5-59
and Figure 5-60 present examples of results from the LEAF testing (for aluminum, arsenic, cobalt, and
lithium — EPA 1313 and 1316, respectively). Figure 5-64 presents an example of results from EPA
1314 for aluminum, arsenic, selenium, and lithium (note that cobalt was below the limit of detection in
leachate from the tailings). The trends shown are similar to those shown by the waste rock and ore.
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Kings Mountain Tailings LEAF-EPA 1313 Kings Mountain Tailings LEAF-EPA 1313
1000 1
Comp 1 Ore Sorting —Comp 1 Ore Sorting
Rejects Rejects
100
—Comp 2 Ore Sorting 01 Comp 2Ore Sorting
Rejects Rejects
\ 10 —Comp 1 DSM Tails / Comp 1 DSM Tails
E �
E E
_0.01
E Comp 2 DMS Tails —Comp 2 DMS Tails
a 1
Comp 1-Flotation Comp 1-Flotation
Tails-Sorting 0.001 - - Tails-Sorting
0.1
Comp 2-Flotation Comp 2-Flotation
Tails-Sorting Tails-Sorting
0.01
O.0001
1 2 3 4 5 6 7 8 9 30 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13
PH(s.u.) pH(s.u.)
Kings Mountain Tailings LEAF-EPA 1313 Kings Mountain Tailings LEAF-EPA 1313
10 100
—Comp 1 Ore Sorting Comp 1 Ore Sorting
Rejects Rejects
—Comp 2 Ore Sorting Comp 2 Ore Sorting
Rejects Rejects
1 10
—Comp 1 DSM Tails Comp 1 DSM Tails
\ E
E
Comp 2 DMS Tails Comp 2 DMS Tails
0.1
Comp 1-Flotation Comp 1-Flotation
Tails-Sorting Tails-Sorting
Comp 2-Flotation —Comp 2-Flotation
Tails-Sorting Tails-Sorting
0.01 0.1
1 7 5 6 7 8 9 10 11 12 13 4 5 6 7 8 9 10 11 12 13
PH is .) pH(s.u.)
Figure 5-59: Example LEAF 1313 Test Results (Future Tailings and Ore Sorting Rejects) —Aluminum, Arsenic, Cobalt, and Lithium
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Kings Mountain Tailings LEAF-EPA 1316 Kings Mountain Tailings LEAF-EPA 1316
100 1
Comp 1 Ore Sorting Comp 1 Ore Sorting
Rejects Rejects
—Comp 2 Ore Sorting ,Comp 2 Ore Sorting
Rejects Rejects
10 0.1
Comp 1 DSM Tails ~—Comp 1 DSM Tails
E E
E
—Comp 2 DM5 Tails a Comp 2 DIMS Tails
a
1 0.01
—Comp 1-Flotation Comp 1-Flotation
Tails-Sorting Tails-Sorting
Comp 2-Flotation Camp 2-Flotation
Tails-Sorting Tails-Sorting
0.1 0.001
0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 I1
LS Ratio LS Ratio
Kings Mountain Tailings LEAF-EPA 1316 Kings Mountain Tailings LEAF-EPA 1316
1 100
—Comp 1 Ore Sorting Comp 1 Ore Sorting
Rejects Rejects
Comp 2 Ore Sorting Comp 2 Ore Sorting
Rejects Rejects
10
Comp I DSM Tails Comp 1 DSM Tails
E E
0.1 E
Comp 2 DIMS Tails _ +Corn 2 DIMS Tails
1
Comp 1-Flotation Comp 1-Flotation
Tails-Sorting Tails-Sorting
Comp 2-Flotation Comp 2-Flotation
Tails-Sorting Tails-Sorting
0.01 0.1
0 1 2 3 9 10 11 4 5 6 1 11
LS Ratio
Figure 5-60: Example EPA 1316 Test Results (Future Tailings and Ore Sorting Rejects) —Aluminum, Arsenic, Cobalt, and Lithium
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Al As
100 - 1
—�Tailings(Comp 1)
t Tailings(Comp 1)
t Tailings(Comp 2)
f Tailings(Comp 2)
••••••Unity •..•..Unity
10
0.1
E E
� 1 —
u° u°
0.01
0.1
0.01 0.001
0.1 1 10 0.1 1 10
Liquid-to-solid contact ratio(L/kg) Liquid-to-solid contact ratio(L/kg)
Se Li
1 1000
f Tailings(Comp 1) t Tailings(Comp 1)
Tailings(Comp 2) Tailings(Comp 2)
••••••Unity ••••••Unity
0.1
100
E 0.01 E
0
— 10
0.001
uo u
1
0.0001
0.00001 0.1
0.1 1 1 0.1 1 10
Liquid-to-solid contact ratio(L/kg) Liquid-to-solid contact ratio(L/kg)
Figure 5-61: Example EPA 1314 Test Results (Future Tailings)—Aluminum, Arsenic, Selenium and Lithium
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Strong pH dependent behavior (Figure 5-53) is indicative of leaching controlled by combinations of
mineral solubility and/or sorption. With respect to sorption, elements that often form cationic solution
species(aluminum, cobalt, and lithium)sorb most strongly at alkaline pH,while those that form anionic
species (arsenic)sorb more strongly at acid pH. It is notable that for lithium, pH-dependent trends are
more obvious for the tailings and OSR material than was the case for waste rock.
For the OSR samples, aluminum, arsenic, and cobalt filtered concentrations are constant irrespective
of the US contact ratio (Figure 5-60), consistent with a solubility or sorption control (i.e., filtered
concentrations are constant (and typically low) irrespective of the volume of water added in the test).
For the flotation tailings samples, these elements show trends towards lower filtered concentrations
as contact ratio increases. In the case of lithium, the data for both tailings and OSR material trend
towards lower filtered concentrations as the contact ratio increases. This trend indicates that filtered
concentrations could be limited by small,finite quantities of the host mineral (i.e.,the host mineral may
dissolve entirely in the contact solution, and filtered concentrations are therefore strongly influenced
by the volume of water added).
Figure 5-61 shows the pH measured in the EPA 1316 tests; for all samples studied, leachate pH is
relatively constant irrespective of contact ratio(typically between pH 8 and 9(OSR)and between pH 9
and 10 (tailings)). Examination of Method 1313 results suggests that at this pH range solubility or
sorption constraints could limit filtered concentrations. At the lowest contact ratios evaluated using
EPA 1316, it is noted that the filtered concentrations of arsenic, cobalt, and lithium are higher than the
pH-dependent limits that might be inferred based on the Method 1313 data. A possible explanation is
that more than one leachable source exists in the samples, and that the signal from a less abundant
source is only evident when the volume of contact water is low(lowest contact ratios evaluated).
Figure 5-64 shows plots of the cumulative mass leached as a function of US(using a logarithmic scale)
for aluminum, arsenic, selenium, and lithium for the tailings samples. These plots can be used to
determine if the data are consistent with a solubility control, where a slope of unity is indicative of a
solubility control (the dotted lines in the plot indicate an approximate range of unity associated with the
data).
The results support the findings of the EPA 1316 testwork. In most cases, parameters show some
evidence of solubility control under the conditions of the EPA 1314 test with cumulative mass leached
plotting along trends consistent with the line of unity.There were some exceptions,for example arsenic
(Comp 1 and Comp 2) and lithium (Comp 1) with slopes shallower than the line of unity. This is
indicative of`wash out' behavior associated with flushing and depletion of a small quantity of soluble
salt.
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Kings Mountain Tailings LEAF-EPA 1316
12
tComp 1 Ore Sorting
Rejects
10
t Comp 2 Ore Sorting
Rejects
8 —�
+Comp 1 DSM Tails
6
x
a
(Comp 2 DIMS Tails
4
Comp 1-Flotation
Tails-Sorting
2
—0—Comp 2-Flotation
Tails-Sorting
0
0 1 2 3 4 5 6 7 8 9 10 11
L:S Ratio
Figure 5-62: pH Variation in EPA1316 Method as a Function of US Ratio— Future Tailings and
OSR
5.2.6 Tailings Filtrate Analysis
Filtrate water from flotation test work has been analyzed for a partial set of parameters (Table 5-14).
The pH was not reported, but presumably is consistent with the high pH from tailings leachates (pH 9
to 10 range). Constituent concentrations were typically highest in the Mica Float Filtration samples.
Many parameters were consistently below analytical detection limits in the tailings filtrate, including
cadmium, chromium, cobalt, boron, silver, vanadium, total cyanide, and free cyanide. These results
represent the total metals fraction, not the filtered fraction (i.e., < 0.45 µm).
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Table 5-14: Geochemistry of Filtrate Solutions from Metallurgical test work
Parameter Units Li Flot Li Flot Li Flot Li Flot Mica Float Mica Float Mica Float Mica Float Li Float Li Float Mica Float Mica Float
Var 1 Var 2 Var 3 Var 4 Var 1 Var 2 Var 3 Var 4 Filtration F8 Filtration F9 Filtration F8 Filtration F9
pH units -- -- -- -- -- -- -- --
Alkalinity mg/L 85.2 86.5 85.1 87 110 122 103 109 85.4 87.7 118 121
Aluminum mg/L 0.0293 0.0365 0.0362 0.206 0.13 0.047 0.311 0.101 0.151 0.0749 0.294 0.677
Antimony m /L 0.0006 <0.0004 <0.0004 <0.0004 0.00361 0.00269 0.00128 0.00114 0.00381 0.0009 0.00266 0.0027
Arsenic m /L 1 0.00091 0.00076 0.00064 0.00072 0.00438 0.00283 0.00227 0.00173 0.00906 0.00266 0.0116 0.0131
Barium m /L 0.0119 0.0122 0.0123 0.0166 0.0105 0.013 0.0093 <0.009 0.013 0.0115 0.0102 0.0104
Beryllium m /L 0.00008 <0.00008 <0.00008 0.000153 <0.00008 <0.00008 <0.00008 <0.00008 <0.00008 <0.00008 <0.00008 <0.00008
Boron m /L 0.03 <0.03 <0.03 <0.03 0.034 0.03 <0.03 <0.03 0.032 <0.03 0.04 0.035
Cadmium m /L <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005 <0.00005
Chloride mg/L 17.2 16.9 16.5 17.2 17.4 17.5 17 17 17.3 17.5 17.2 17.9
Chromium mg/L <0.0005 <0.0005 <0.0005 0.00058 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005 <0.0005
Cobalt mg/L <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02
Copper mg/L 0.00408 0.00326 0.00369 0.0102 0.00767 0.00456 0.0048 0.00299 0.00238 0.00099 0.00307 0.00181
Cyanide,free mg/L <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 -- --
Cyanide,total mg/L <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 -- --
Fluoride m /L <0.15 <0.15 <0.15 <0.15 0.44 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15 <0.15
Iron m /L <0.007 <0.007 <0.007 0.0268 <0.007 <0.007 0.0225 <0.007 0.011 <0.007 0.0081 0.0111
Lead m /L <0.0001 <0.0001 <0.0001 0.00106 0.00036 0.00034 0.00026 0.00015 0.00833 0.00021 0.0001 0.00028
Lithium m /L 0.139 0.128 0.122 0.193 1.63 0.501 0.311 0.399 0.224 0.33 0.52 0.7
Manganese m /L 0.028 0.026 0.021 0.024 <0.01 <0.01 0.015 0.011 0.035 0.029 <0.01 <0.01
Mercury m /L 0.0000059 0.0000005 0.000001 0.0000011 N/A N/A N/A N/A 0.0000003 0.0000003 0.000002 0.0000003
Molybdenum m /L 0.00032 0.0002 0.00056 0.00054 0.00072 0.00048 0.00077 0.00056 0.00058 0.00046 0.0007 0.0005
Nickel mg/L 0.00043 <0.0004 0.00048 0.00057 0.00077 0.00071 0.00059 <0.0004 0.00097 <0.0004 0.00224 0.00136
TDS mg/L 168 176 192 170 188 180 260 210 430 178 210 208
TSS mg/L 28 <5 20 18 119 131 56 65 11 65 126 56
Selenium mg/L <0.0001 <0.0001 <0.0001 <0.0001 0.00019 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.00036 <0.0001
Silver mg/L <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Sulfate m /L 31.7 33.7 36.2 31.5 33 30.4 30.3 30.8 36.1 35.4 31.1 32.1
Thallium m /L <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.00013 <0.0001 <0.0001 <0.0001
Uranium m /L 0.00037 0.00028 0.00019 0.00033 0.00421 0.00078 0.00141 0.00201 0.00044 0.00031 0.00159 0.00201
Vanadium mg/L <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01
Zinc mg/L <0.006 <0.006 <0.006 0.009 <0.006 0.0074 <0.006 <0.006 <0.006 <0.006 0.0114 0.0067
N/A-not analyzed
Samples were coarse filtered using a 25 µm filter and represent the total metals fraction, not the filtered metals fraction(i.e., <0.45 µm).
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5.2.7 HCT
Six samples were submitted for kinetic testing to assess metal and metalloid leaching rates from the
future tailings materials, including OSR, flotation tailings, and DMS rejects. The changes in these
reaction rates through the course of the test can be used to estimate the magnitude of constituents
that would be mobilized from the various types of tailings material under long-term weathering and
oxidation conditions(i.e.,source term leachate chemistry as input chemistry for predictive geochemical
modeling). The six cells consist of:
• Two ore sorting reject cells (HCT-22 and HCT-23).
• Two flotation tailings cells (HCT-24 and HCT-25).
• Two DMS reject cells (HCT-27 and HCT-28).
Table 5-15 lists the samples along with key static test data. The humidity cells have been operational
for between 51 weeks (HCT-27 and HCT-28)and 66 weeks (HCT-22, HCT-23, HCT-24, and HCT-25)
at the time of reporting. HCT leachate was collected for weekly analysis of all parameters for Week 0
to Week 4. Physiochemical parameters and major ions continue to be analyzed weekly, and trace
elements are analyzed monthly (Week 8, Week 12, etc.). Figure 5-63 to Figure 5-82 provide time-
series plots of elemental release during the HCT, and Figure 5-83 to Figure 5-85 provide scatter plots
showing the relationship between pH and constituent release. The following subsections provide a
detailed description of geochemical behavior for each humidity cell, and Table 5-15 provides a
summary. Appendix H provides the laboratory reports for the HCTs.
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Table 5-15: Summary of Future Ore Sorting Rejects and Tailings HCT Static Data and Status
Material Type HCT ID Paste Sulfide Sulfate Sulfur AP(kg NP(kg TIC NNP (kg NPR1 NAG (kg NAG HCT Current HCT NP
H s.u. wt/o wt/e Total wt/e CaCO3 a /t CaCO3 a /t wt/o CaCO3 eq/t Ratio H2SO4 a /t H s.u. Status pH s.u. Remaining /o
Comp 1 Ore Sort Waste HCT-22 9.4 0.35 0.06 0.74 10.9 22 0.1 11.1 2 4 3.8 Week 43 6.3 94%
Comp 2 Ore Sort Waste HCT-23 9.4 0.16 0.02 0.24 5 8 <0.1 3 1.6 2 3.7 Week 43 6.6 89%
Comp 1 Float Tailings HCT-24 9.8 0.02 0.01 0.03 0.6 5 <0.1 4.4 8.3 <1 7.1 Week 43 6.8 87%
Comp 2 Float Tailings HCT-25 9.7 <0.01 0.01 <0.01 0.31 4 <0.1 3.7 12.9 <1 6.8 Week 43 6.9 85%
Comp 1 DMS Rejects HCT-27 10 1 <0.01 I <0.01 I <0.01 1 0.31 1 3 1 <0.1 1 2.7 9.7 8.0 1 6.1 1 Week 29 7.3 930X
Comp 2 DMS Rejects HCT-28 10 1 <0.01 I <0.01 I <0.01 1 0.31 1 4 1 <0.1 1 3.7 12.9 8.0 6.1 1 Week 29 7.4 930;-
'NPR<1 =PAG;NPR between 1-3=Uncertain;NPR>3=non-PAG
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Kings Mountain HCTs:Weekly pH(s.u.)
10
9
8 0
��9Bo8 RE B o 0 tComp 1 Ore Sort Waste(HCT-22)
7 0Oo°000° o o ❑ e0� 0 4 ❑ ❑ ° �zsA ❑ ❑
00 o O O
O O O o °000000 00000 0000 O°0 00 ° —Comp20re Sort Waste(HCT-23)
6
3 tComp 1 Float Tailings(HCT-24)
vi
x 5
G t Comp 2 Float Tailings(HCT-25)
A
`1
y 4 —& Comp 1 DMS Rejects(HCT-27)
3 o Comp 2 DIMS Rejects(HCT-28)
2
1
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-63: Future Ore Sorting Rejects and Tailings HCT Effluent pH
Kings Mountain HCTs:NP Remaining(%)
100%
0000000oo000p0O000000
90% 000000
000o0o 0°0Qi
80% 00000o�
0
pp t Comp 1 Ore Sort Waste(HCT-22)
70%
F Comp 2 Ore Sort Waste(HCT-23)
w 60%
t R Comp 1 Float Tailings(HCT-24)
50% Comp 2 Float Tailings(HCT-25)
O
a
p 40% --A—Comp 1 DMS Rejects(HCT-27)
MN>d 30% A Comp 2 DMS Rejects(HCT-28)
7
Z 20%
10%
0%
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-64: Future Ore Sorting Rejects and Tailings HCT Neutralizing Potential Remaining
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Kings Mountain HCT's:pH v.Ficklin Metals(mg/L)
10000
1000
00
E
NHigh acid, Acid. Near neutral, •Comp 1 ore Sort waste(HCT-22)
+ 100 Extreme metal Extreme metal Extreme metal
+ High acid, Acid, Near neutral, •comp 2 ore Sort Waste(HCT-23)
a High metal High metal High metal
+ •Comp 1 Float Tailings(HCT-24)
O
10
-O •Comp 2 Float Tailings(HCT-25)
V
7
V •Comp 1 DMS Rejects(HCT-27)
N 1
M
d O Comp 2 DMS Rejects(HCT-28)
C
0.1
LL 000caN806I O
H
= High acid, Acid, Near neutral,
Low metal Low metal Low metal
0.01
0 1 2 3 4 5 6 7 8 9 10
pH(s.u.)
Figure 5-65: Future Ore Sorting Rejects and Tailings HCT Ficklin Plot
Kings Mountain HCTs:Weekly Electrical Conductivity(VS/cm)
300
250
tComp 1 Ore Sort Waste(1ACT-22)
N
=L 200
? f Comp 2 Ore Sort Waste(HCT-23)
jU
t Comp 1 Float Tailings(HCT-24)
0 150
V t Comp 2 Float Tailings(HCr-25)
r0
V
V I —A—Comp 1 DMS Rejects(HCT-27)
100 ■ ■ - -
T ° ° Comp 2 DMS Rejects(HCT-28)
Y ° O
O1
so 0
3
ia° 6000000000000000000000000000000 0000000000
o°°°°° ° ❑ ❑❑❑❑❑
° ° Y o ° 8a000 00III 11111011110 !1!111'! 11 0 0
°° °o ° 000 0 000 0
0 11
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-66: Future Ore Sorting Rejects and Tailings HCT Effluent Electrical Conductivity
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Kings Mountain HCTs:Weekly Alkalinity(mg/L)
50
45
40
35 O tComp S Ore Sort Waste(HCT-22)
\
ou
Q O f Comp 2 Ore Sort Waste(HCT-23)
30 1
C O tComp 1 Float Tailings(HCT-24)
Y_ 25 O
Q 1 A t Comp 2 Float Tailings(HCT-25)
T O �
0! 20 O ❑
3 � O O --A—Comp 1 DMS Rejects(HCT-27)
El 40
1 a ��
s eAA° ❑dCIC7
C A--Comp 2 DMS Rejects(HCT-28)
O A O
10 AA 8 ❑ ■t\ O
pY
O° 00A 00�pp0p O�O
AAA
AAA
5 AAAAA ❑
O
O
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-67: Future Ore Sorting Rejects and Tailings HCT Effluent Alkalinity
Kings Mountain HCTs:Weekly Sulfate(mg/L)
100
90
80
70 f Comp 1 Ore Sort Waste(HCT-22)
tto J
tComp 2 Ore Sort Waste(HCT-23)
60
ar
tComp 1 Float Tailings(HCT-24)
w
to 50
�. —O Comp 2 Float Tailings(HCT-25)
Y
Gl 40
3 —A—Comp 1 DMS Rejects(HCT-27)
30 I A Comp 2 DMS Rejects(HCT-28)
20 ❑❑°❑❑°❑❑
10 ❑ ❑❑°pp°O°°O❑❑❑❑❑°❑❑O°❑❑❑
0
0 daaaaaaasEaSSaaaasaasaa aaAoaa oAAAA o
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-68: Future Ore Sorting Rejects and Tailings HCT Effluent Sulfate
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Kings Mountain HCTs:Iron(mg/L)
1
f Comp 1 Ore Sort Waste(HCT-22)
t Comp 2 Ore Sort Waste(HCT-23)
CD
E
C --*--Comp 1 Float Tailings(HCT-24)
0.1
T
Y —Comp 2 Float Tailings(HCT-25)
N
3 aaaaaaaaaasaaaaaaaasaaaaaaaaaaa3�xaaaaaaaaxxaaa
—A--Comp 1 DMS Rejects(HCT-27)
A Comp 2 DMS Rejects(HCT-28)
0.01
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-69: Future Ore Sorting Rejects and Tailings HCT Effluent Iron
Kings Mountain HCTs:Aluminum(mg/L)
0.8
0.7
0.6
--ill 1 Ore Sort Waste(HCT-22)
3 0.5
pip tComp 2 Ore Sort Waste(HCT-23)
E
E 0.4 tComp 1 Float Tailings(HCT-24)
C
E tComp 2 Float Tailings(HCT-25)
a 0.3
—Comp 1 DMS Rejects�HCT-27�0.2 —*—Comp2DMSRejects HCT-28
0.1
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-70: Future Ore Sorting Rejects and Tailings HCT Effluent Aluminum
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Kings Mountain HCTs:Arsenic(mg/L)
1
0.1
tromp 1 Ore Sort Waste(HCT-22)
t Comp 2 Ore Sort Waste(HCT-23)
tw
tComp 1 Float Tailings(HCT-24)
V 0.01
iComp 2 Float Tailings(HCT-25)
Q
--A—Comp 1 DMS Rejects(HCT-27)
0.001
—A—Comp 2 DMS Rejects(HCT-28)
4
IN
0.0001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-71: Future Ore Sorting Rejects and Tailings HCT Effluent Arsenic
Kings Mountain HI Antimony(mg/L)
0.01
I I I
0
O \ —0--Comp 1 Ore Sort Waste(HCT-22)
J O O
U O O t Comp 2 Ore Sort Waste(HCT-23)
E O O O O
T O �
0 0.001 I tcon"
1 Float Tailings�HCT-24�
QO t Comp 2 Float Tailings HCT-25
❑ —A—Comp 1 DIMS Rejects(HCT-27)
9
� o0 0 0 0 0 0 o a a z9 ❑ ❑ ❑ 1
A—Comp 2 DMS Rejects(HCT-28)
0.0001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-72: Future Ore Sorting Rejects and Tailings HCT Effluent Antimony
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Kings Mountain HCTs:Cadmium(mg/L)
0.1
0.01
t Comp 1 Ore Sort Waste(HCT-22)
t Comp 2 Ore Sort Waste(HCT-23)
J
"0
E Comp 1 Float Tailings(HCT-24)
0.001
•£7 0---Comp 2 Float Tailings(HCT-25)
a
m
U tComp 1 DMS Rejects(HCT-27)
■
e Comp 2 DMS Rejects(HCT-28)
0.0001
a s a a a a a a a a a a a ❑ ❑
0.00001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-73: Future Ore Sorting Rejects and Tailings HCT Effluent Cadmium
Kings Mountain HCTs:Copper(mg/L)
1
0.1 f Comp 1 Ore Sort Waste(HCT-22)
f Comp 2 Ore Sort Waste(HCT-23)
hA
E tComp 1 Float Tailings(HCT-24)
C
C
C tComp 2 Float Tailings(HCT-25)
U
e e
0.01 114E - - - - I —A—Comp 1 DMS Rejects(HCT-27)
e Comp 2 DMS Rejects(HCT-28)
0.001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-74: Future Ore Sorting Rejects and Tailings HCT Effluent Copper
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Kings Mountain HCTs:Gross Alpha(pCi/L)
35
30
25
♦Comp 1 Ore Sort Waste(HCT-22)
20 tComp 2 Ore Sort Waste(HCT-23)
V
m 15 Comp 1 Float Tailings(HCT-24)
a
Q tComp 2 Float Tailings(HCT-2S)
H
O
10 —&—Comp 1 DMS Rejects(HCT-27)
° Comp 2 DMS Rejects(HCT-28)
5
0 ❑ - ❑ yY
0 0
° o
-5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-75: Future Ore Sorting Rejects and Tailings HCT Effluent Gross Alpha
Kings Mountain HCTs:Lead(mg/L)
0.0012
0.001
f Comp 1 Ore Sort Waste(HU-22)
0.0008
t Comp 2 Ore Sort Waste(HCT-23)
J
tlp tComp 1 Float Tailings(Htt-24)
0.0006
m t Comp 2 Float Tailings(HCT-25)
—A—Comp 1 DMS Rejects(HCT-27)
0.0004
—A—Comp 2 DMS Rejects(HCT-28)
0.0002
S O O O O
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-76: Future Ore Sorting Rejects and Tailings HCT Effluent Lead
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Kings Mountain HCTs:Manganese(mg/L)
1
f Comp 1 Ore Sort Waste(HCT-22)
f Comp 2 Ore Sort Waste(HCT-23)
W
1= ai
y O tComp 1 Float Tailings(HCT-24)
N 0.1 O
C O
tComp 2 Float Tailings(HCT-25)
txo
R A
—A—Comp 1 DMS Rejects(HCT-27)
—Comp 2 DMS Rejects(HCT-28)
I
0.01 S A A A A
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-77: Future Ore Sorting Rejects and Tailings HCT Effluent Manganese
Kings Mountain HCTs:Mercury(ng/L)
4
3.5
3
t Comp 1 Ore Sort Waste(HCT-22)
2.5
f Comp 2 Ore Sort Waste(HCT-23)
00
>. 2 tComp 1 Float Tailings(HCT-24)
7
u
N Comp 2 Float Tailings(HCT-25)
1.5
--A--Comp 1 DMS Rejects(HCT-27)
1 I —A--Comp 2 DMS Rejects(HCT-28)
® O
0.5
�Y� a zs a ❑ a
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-78: Future Ore Sorting Rejects and Tailings HCT Effluent Mercury
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Kings Mountain HCTs:Nickel(mg/L)
1
tComp 1 Ore Sort Waste(HCT-22)
0.1
t Comp 2 Ore Sort Waste(HCT-23)
E tComp 1 Float Tailings(HCT-24)
d
Y
u
2 -0--Comp 2 Float Tailings(HCT-25)
t[omp 1 DMS Rejects(HCT-27)
0.01
aaaa ■ ■ ■ ■ ■ ■ ■ ■ ■ ■n ❑ ❑ ❑ 0
o Comp 2 DMS Rejects(HCT-28)
0.001
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-79: Future Ore Sorting Rejects and Tailings HCT Effluent Nickel
Kings Mountain HCTs:Selenium(mg/L)
0.0012
0.001
0.000$ t Comp 1 Ore Sort Waste(HCr-22)
tComp 2 Ore Sort Waste(HCr-23)
Hp
E
E 0.0006 t Comp 1 Float Tailings(HCT-24)
3
Gl
--*—Comp 2 Float Tailings(HCT-25)
N
� I
0.0004 —A—Comp 1 DMS Rejects(HCT-27)
a Comp 2 DMS Rejects(HCT-28)
7
0.0002
II IIII S ■ iii�s�tg �❑�fl
0
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-80: Future Ore Sorting Rejects and Tailings HCT Effluent Selenium
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Kings Mountain HCTs:Zinc(mg/L)
0.025
0.02
f Comp 1 Ore Sort Waste(HCT-22)
0.015 ♦Comp 2 Ore Sort Waste(HCT-23)
J
fb
Comp 1 Float Tailings(HCT-24)
V
C ♦Comp 2 Float Tailings(HCT-25)
N
0.01
—A—Comp 1 DMS Rejects(HCT-27)
—A—Comp 2 DMS Rejects(HCT-28)
0.005
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-81: Future Ore Sorting Rejects and Tailings HCT Effluent Zinc
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Kings Mountain HCTs:Test Week v.Carbonate Molar Ratio
1000
100
t Comp 1 Ore Sort Waste(HCT-22)
O
.m —W—Comp 2 Ore Sort Waste(HCT-23)
_m
—.0--Comp 1 Float Tailings(HCT-24)
O
10 O
O 000 000 Comp 2 Float Tailings(HCT-25)
0 0 000000000000 O 00 00 b O
0 O❑G8 p00000 00 I 00 —�Comp 1 DMS Rejects(HCT-27)
O
U ❑ ❑❑ ❑❑❑❑❑❑❑❑❑❑❑❑❑❑❑❑❑ ❑ ❑❑❑
A ❑ ❑ ❑ ❑❑❑❑❑❑ ❑❑p❑❑❑❑❑� ❑❑❑❑ ❑ ❑ A -Comp 2 DMS Rejects(HCT-28)
1 0 ��
13
i
0.1
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68
Test Week
Figure 5-82: Future Ore Sorting Rejects and Tailings HCT Effluent Carbonate Molar Ratio
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Kings Mountain HCTs:HCT pH v.Weekly Sulfate(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Aluminum(mg/L)
100 0.8
90 •
0.7 0
80
0.6 °
•Comp 1 Ore Sort Waste(HCT-22) J
70 O •Comp 1 Ore Sort Waste(HCT-22)
4! •Comp 2 Ore Sort Waste(HCT-23) £ 0.5 •
60 •Comp 2 Ore Sort Waste(HCT-23)
w �
3 •Comp 1 Float Tailings(HCT-24) L
H £ •Comp 1 Float Tailings(HCT-24)
Y
50 • ° 2 0.4
3 •Comp 2 Float Tailings(HCT-25) T ° •Comp 2 Float Tailings(HCT-25)
Y
G
40 • •Comp 1 DMS Rejects(HR-27) N
• • • 3: 0.3 O •Comp 1 DMS Rejects(HCT-27)
•
30 • • O Comp 2 DMS Rejects(HCT-28)
• • ' • • 0 O O Comp 2 DMS Rejects(HCT-28)
• 0.2
• • O
O
20 s ' ° 6 O
o ° ° 0.1 • $ Q • Y o 8 $ o o •
10 0 B o • • ° • ° • i i °o i o ® i o 8 3 °
0 • o • O o 0
6 7 8 9 6 7 8 9
HCT pH(s.u) HCT pH(s.u)
Kings Mountain HCTs:HCT pH v.Weekly Manganese(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Zinc(mg/L)
0.14 0.022
0
0.12 °
0.02 • • • • o • o 0 0 0 0 0 0 0 0 0 0 0
J
E °0.1 •Comp 1 Ore Sort Waste(HCT-22) •Comp 1 Ore Sort Waste(HCT-22)
Gl \ 0.018
(IJ O
•Comp 2 Ore Sort Waste(HCT-23)
C � •Comp 2 Ore Sort Waste(HCT-23)
r0
40 0.08
•Comp 1 Float Tailings(HCT-24) N •Comp 1 Float Tailings(HCT-24)
• ° Y 0.016
• ° •Comp 2 Float Tailings(HCT-25) •Comp 2 Float Tailings(HCT-25)
cu 0.06
O
•Comp 1 DM0.014 S Rejects�HCT-21� •Comp 1 DMS Rejects(HCT-27)
0.04 • O O O • ° OComp2DMSRejects HCT-28 O Comp 2 DMS Rejects(HCr-28)
• O
2 O • p •
0.02 • O Q�• °• = o • 0.012
O O O ®
00068 °so0000 °
0 0.01
6 7 8 9 6 7 8 9
HCT pH(s.u) HCT pH(s.u)
Figure 5-83: Future Ore Sorting Rejects and Tailings HCT Constituent Release as a Function of pH —Sulfate,Aluminum, Manganese, and Zinc
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Kings Mountain HCTs:HCT pH v.Weekly Arsenic(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Antimony(mg/L)
0.007 0.004
• o
0.006 0 0.0035
• o
• °
8 0 0 0 0.003
0.005 0 0 •Comp 1 Ore Sort Waste(HCT-22) •Comp 1 Ore Sort Waste(HCT-22)
J tCw
OD O RR O C
• O •• O O •Comp 2 Ore Sort Waste(HCT-23) 0.0025 •Comp 2 Ore Sort Waste(HCT-23)
c0.004 0 ° • 0 C • o
yam, O •Comp 1 Float Tailings(HCT-24) £ °
•Comp 1 Float Tailings(HCT-24)
Q O O O O Q 0.002 • 0
21 O O O O Comp 2 Float Tailings(HCT-25) ° •Comp 2 Float Tailings(HCT-25)
3 0.003 ° 0 •
� ° o
• O •Comp 1 DMS Rejects(HCr-27) � 0.0015 8 0 O • •Comp 1 DMS Rejects(HCT-27)
O
8 ° a • °
0.002 • • • O Comp 2 DMS Rejects(HCT-28) • • O O O
•Comp 2 DMS Rejects(HCT-28)
• 0.001 • • O
• O
• A o
0.001 • ° 0 0.0005 i 00 0 0 °0 • 00 0 8 0 0 0 0 o 0
•
• _ •
0
• o • 0 0 0 0 0 0
0 0
6 7 8 9 6 7 8 9
HCT pH(s.u) HCT pH(s.u)
Kings Mountain HCTs:HCT pH v.Weekly Selenium(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Uranium(mg/L)
0.0012 0.0025
0.001 • 0 •
0.002
•Comp 1 Ore Sort Waste(HCT-22) O •Comp 1 Ore Sort Waste(HCT-22)
pip 0.0008
•Comp 2 Ore Sort Waste(HCT-23) •Comp 2 Ore Sort Waste(HCT-23)
_ £ 0.0015
C •Comp 1 Float Tailings(HCT-24) •Comp 1 Float Tailings(HCT-24)
y 0.0006 i
N
�. •Comp 2 Float Tailings(HCT-25) >• • O •Comp 2 Float Tailings(HCT-25)
Y ° v 0.001 •
d
• •Comp 1 DMS Rejects(HCT-27) •
0.0004
3 •Comp 1 DMS Rejects(HCT-27)
•
O Comp 2 DMS Rejects(HCT-28) O Comp 2 DMS Rejects(HCT-28)
0 • 0.0005
0.0002 • O • O 0 °
• i • o • C e °o i o 0 0 0 0 0 0 0 ° a o 0
. a. • • • ° • o • e . ° ° ® ° °
°
0 0
6 7 8 9 6 7 8 9
HCT pH(s.u) HCT pH(s.u)
Figure 5-84: Future Ore Sorting Rejects and Tailings Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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Kings Mountain HCTs:HCT pH v.Weekly Fluoride(mg/L) Kings Mountain HCTs:HCT pH v.Weekly Lithium(mg/L)
0.34 7
•
0.29 0 6
0
0 0
J •Comp 1 Ore Sort Waste(HCT-22) J 5 •Comp 1 Ore Sort Waste(HCT-22)
00 0.24 DA
•Comp 2 Ore Sort Waste(HCT-23) £ •Comp 2 Ore Sort Waste(HCT-23)
N
v 2 4
7 .r •Comp l Float Tailings(HCT-24) • • O •Comp 1 Float Tailings(HCT-24)
= 0.19 • J
T
T •Comp 2 Float Tailings(HCT-25) y •Comp 2 Float Tailings(HCT-25)
4) a 3 0 0
0
• • • • O • O 0 0 O O O O O O O O O •Comp 1 DMS Rejects(HCT-27) O B •Comp 1 DMS Rejects(HCT-27)
0.14 • O O
O Comp 2 DMS Rejects(HCT-28) 2 • 0 • O Comp 2 DMS Rejects(HCT-28)
• 0 O O
0.09 •
1 0 0 0
00
0000
0.04 0
e ® oQ i '
6 7 8 9 6 7 8 9
HCT pH(s.u) HCT pH(s.u)
Figure 5-85: Future Ore Sorting Rejects and Tailings Constituent Release as a Function of pH — Fluoride and Lithium
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General Observations
Figure 5-63 presents the trends of effluent pH for each of the tailings material humidity cells, which
demonstrate that all of the cells have exhibited circum-neutral pH throughout the test to date, with
effluent pH between 5.7 and 8.0. The pH for the OSR is lower than the flotation tails or DMS rejects,
which is consistent with the static test data for the samples. As a result of the circum-neutral pH range
of the tailings and OSR HCTs,the relationship between effluent pH and constituent release is typically
not clearly defined. The exceptions include aluminum, antimony, and uranium, which show a positive
correlation with pH (i.e., higher release at higher pH values) as shown in Figure 5-83 and Figure 5-85.
In addition, sulfate and manganese typically show an inverse correlation with pH, whereby effluent
concentrations decrease at higher pH values (Figure 5-83).
The Ficklin plot presented on Figure 5-65 shows that leachates from the tailings material humidity cells
can be classified as near-neutral, low-metal waters based on effluent pH >5.5 and Ficklin metal
concentrations <1 mg/L.
The tailings humidity cells consistently report Ca+Mg/SO4 ratios above two (Figure 5-82). This
indicates that the dissolution of neutralizing minerals in the cells is greater than the dissolution
associated with buffering of acidity produced. Furthermore, the high carbonate molar ratios indicate
that oxidation of sulfide minerals is negligible.
Concentrations of barium, beryllium, cadmium, chromium, cobalt, iron, molybdenum, nickel, silver,
thallium, vanadium, and zinc are typically at or below analytical reporting limits in the tailings HCT
leachates, demonstrating a low potential for mobilization/leaching of these constituents from the
tailings and OSR materials.
Ore Sorting Rejects (HCT-22 and HCT-23)
Based on the ABA and NAG test work results (Table 5-15), the two OSR samples submitted for HCT
have an uncertain potential for acid generation.The Composite 1 sample(HCT-22)has a higher sulfide
(0.35%) and NP (22 kg CaCO3 eq/t) content than the Composite 2 sample (HCT-23; 0.16% sulfide; 8
kg CaCO3 eq/t NP).
The OSR humidity cells have exhibited circum-neutral pH during the 66-week testing period, with
effluent pH ranging between pH 5.7 and 7.1 in HCT-22 and between pH 6.0 and 7.6 in HCT-23
(Figure 5-63). HCT-22 and HCT-23 have 89% and 82% of their original NP remaining at Week 66,
respectively, as shown in Figure 5-64. An initial flush of sulfate is seen from the OSR cells.
Concentrations in HCT-22 decline from a maximum concentration of 91 mg/L at Week 1 to a minimum
concentration of 13 mg/L at Week 12. Concentrations have subsequently been increasing with a
maximum of 39 mg/L recorded at Week 65, as shown in Figure 5-68. Sulfate concentrations are lower
in HCT-23, declining from a maximum concentration of 70 mg/L at Week 1 to a minimum concentration
of 5.3 mg/L at Week 11. Concentrations have subsequently been increasing, with a maximum of 15
mg/L recorded at Week 65. The sulfate flush is also coupled with a flush in alkalinity, with effluent
alkalinity concentrations ranging between 2 mg/L and 36 mg/L in HCT-22 and between 2 mg/L and 28
mg/L in HCT-23, as shown in Figure 5-67. Calcium is showing an upward trend in HCT-22, and
manganese release is unstable for both cells (Figure 5-77).
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Flotation Tailings (HCT-24 and HCT-25)
Based on the NAG test work results (Table 5-15), the two flotation tailings samples submitted for HCT
are predicted to be non-PAG, and both have an uncertain potential for acid generation based on the
ABA results, with low sulfide contents of<0.01% and 0.02% and low NP values of 4 and 5 kg CaCO3
eq/t.
The flotation tailings humidity cells have exhibited circum-neutral pH during the 66-week testing period,
with effluent pH ranging between pH 6.4 and 7.4 in HCT-24 and between pH 6.5 and 7.7 in HCT-25
(Figure 5-63). HCT-24 and HCT-25 have approximately 80% and 79% of their original NP remaining
at Week 66,respectively, as shown in Figure 5-64.Sulfate concentrations in the tailings HCT leachates
are notably lower than the OSR samples and are consistently <5 mg/L for both tailings cells, as shown
in Figure 5-68. Manganese concentrations have been increasing for HCT-25,and have increased from
below detection at the beginning of the test to 0.13 mg/L at Week 64, as shown in Figure 5-77.
DMS Rejects (HCT-27 and HCT-28)
Based on the NAG test work results (Table 5-15), the two DMS rejects samples are predicted to be
non-PAG. Based on the ABA results, both samples have a low potential for acid generation, with
sulfide contents of <0.01% and NP values of 3 and 4 kg CaCO3 eq/t. At the time of reporting, HCT
leachate results are available for HCT-27 and HCT 28 through Week 51.
The DMS rejects humidity cells have exhibited neutral pH during the test, ranging between pH 6.8 and
7.8 in HCT-27 and between pH 6.9 and 8.0 in HCT-28 (Figure 5-63). The exception includes a single
lower pH value of 5.9 recorded for Week 19 in Cell HCT-27. The samples have approximately 88%
and 87% of their original NP remaining at Week 51, respectively, as shown in Figure 5-64. Currently,
the cells are reporting leachate conditions consistent with the predictions of the static test work results
(i.e., are non-PAG). Sulfate release from both DMS cells is low(less than analytical detection limit from
Week 4 onwards), as shown in Figure 5-68.
5.3 Cover Material
The following sections present the geochemical characterization results for the potential future cover
materials (saprolite and overburden). In addition, Section 5.3.1 presents a summary of the SWCA soil
baseline study(SWCA, 2023) as it relates to cover material geochemistry.
5.3.1 SWCA Soil Baseline Study
The SWCA(2023) soil baseline study identified the following soil horizons at Kings Mountain:
• A: Mineral soil with organic matter accumulation, loss of iron, aluminum, and clay.This horizon
is poorly developed across the site and is generally 1 to 2 inches thick.
• B: Subsurface accumulation of clay, iron, aluminum, and humus. This horizon is marked by
the accumulation of organic matter, a blocky structure, and is generally thin.
• C: Little pedogenic alteration. In the case of Kings Mountain soils, this horizon is marked by a
greater clay component and the accumulation of iron oxides.
• Cr: Weathered bedrock
The baseline study demonstrated that soil pH across the Project area ranges from pH 4.7 to 7.6, with
soil pH values predominately acidic for all horizons (Figure 5-82). Based on Natural Resources
Conservation Service (NRCS) classification, the soils in the Project area, on average, would be
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classified as moderately acid (pH 5.5 to 6.5) (Schoeneberger et al., 2012), regardless of the soil
horizon. The C horizon is strongly acid (pH 5.1 to 5.5), and shows a narrow range of pH values. The
pH of soils in the Project area is below the optimal range of pH 6.4 to 8 for plant growth (Thorup, 1984;
Tisdale et al., 1993), which may affect the availability of plant nutrients, limit plant establishment and
growth, and increase the mobility of deleterious elements (SWCA, 2023).
Soil pH
8.0
4
7.5
7.0
6.5
6.0
0
5.5
5.0
4.5
4.0 A/B C Cr F-IC
Source:SWCA,2023
Figure 5-86: Soil pH Distribution by Horizon
As described by SWCA(2023), soil profiles are consistent across the area regardless of soil mapping
unit (SMU), geology, or slope position. Of the soil forming processes, climate appears to have the
greatest influence, leaching nutrients and bases out of the AB horizon, and accumulating in the
C horizon. Based on SWCA (2023), all soils across the site, including those identified as either fill or
mine waste, can be generally handled similarly for reclamation; this is demonstrated by good regrowth
on the mine wastes associated with the former operations, indicating that revegetation at the site will
be fairly straight forward with the available soil material. The areas to be avoided for salvaging include
areas identified as alluvial or delineated as wetland.
SWCA (2023) divided the potential cover materials into the following units for the purpose of
reclamation:
• The AB horizon, despite having a poorly defined A horizon, still maintains decent nutrient
value, and can be salvaged as plant growth media for reclamation.
• The C horizon is strongly acidic, from a soils perspective, and would require amendments
such as lime and/or organic matter if to be used as plant growth media. Additionally, the
presence of iron oxides will likely bind nutrients, such as phosphorus (P), and may require
additional fertilization for successful plant growth.
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• The weathered bedrock has generally low soil fertility, although phosphorus and potassium
values are acceptable, likely from the weathering of minerals that contain these elements.This
material is suitable for general fill material.
5.3.2 ABA
ABA testing was carried out on 14 saprolite samples and 10 samples of alluvium collected as part of
the 2022 sonic drilling program. Figure 5-87 to Figure 5-91 presents the results, which have been
compared to ABA results for overburden samples collected from core as part of the waste rock and
ore characterization program. Surrogate ABA results are also presented for saprolite and alluvium
samples collected by sonic methods, and submitted for multi-element analysis as part of the
exploration program.Total sulfur and calcium concentrations were used to calculate surrogate AP and
NP, respectively.
12
t •
10
t t• t �ti•_J�}• •�� t `• •
• • • • t• • •
8 • • ••
• • • S •"•• •
s • ' Waste rock-
core
a fi • D Overburden-
m ♦ ■ core
w �
IL •Alluvium HCT-
$ core
4 ------------- ---------
•Alluvium-
sonic
•saprolite-
2 sonic
0
0.001 0.01 0.1 1 10
Total Sulfur(%)
Figure 5-87: Paste pH Plotted as a Function of Total Sulfur Content—Cover Material
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100000
10000
z • Waste rock
Cr -core
Acid O Overburden
Y 1000 Neutralizing — r1 core
a
■ Alluvium
P�eaOk HCT
100 • Saprolite-
:: i • • ••� o sonic
o E • , Eo• 4-
a, i ; ! • Alluvium-
10 i sonic
ao
N � • i��tl"CiY3 "'
r - Generating -- NPlAP=3
f`
0 �
0.1 1 10 100 1000
Acid Generating Potential(AP)(kg CaG@q/t)
Figure 5-88: NP Plotted as a Function of AP—Cover Material
100000
10000 Acid
Neutralizing
U
Waste rock-
U 1000 - a`nti.) core
o -- �� • Saprolite-
' oak expl sonic
r • `A{e
100 t Alluvium-
• • • �-
+, • � ,� • • � expl sonic
d • n Y '•'� • :!rr •
a [ � i '_ � •� '�• o • NPlAP=1
10C.
E _ i •• •• • .� ...•.
N �'• o E oho
� s�'•c � oo� ��� a a a �� • ----NPlAP=3
1 • rr • Acid
s'`I• • r •r
• Generating
c
L -
N '
0 -
0.1 1 10 100 1000
Surrogate Acid Generating Potential(AP)from Total S(kg CgGq/t)
Figure 5-89: Surrogate NP Plotted as a Function of Surrogate AP (Exploration Database
Samples)—Cover Material
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140
120 Acid
Neutralizing
100
1
v 80
a� Waste rock-
core
R 60
_ o Overburden
° -core
40 a
°B°e °
° °°�p ° G
■Alluvium
N 20 • o° o HCT-core
.i�► it
.• '� •Alluvium-
A 0 •' sonic
Area of Uncertainty
•`� +Saprolite-
_20 sonic
°
-40
Acid •
Generating
-60
0.D 01 0.01 0.1 1 10 100 1000 10000
Neutralization Potential Ratio
Figure 5-90: NNP Plotted as a Function of NPR—Cover Material
200
acid
Neutralizing •
to
V 150 •
100 — o •Saprolite-
a° Area of Uncertainty expl sonic
^N 50 •Alluvium-
_ expl sonic
d 0 • Waste rock-
a core
m
t • N
0 _cam Owe
N
-100
Acid
Generating
-150
0.001 0.01 0.1 1 10 100 100( 10000
Surrogate Neutralization Potential "
Figure 5-91: Surrogate NNP Plotted as a Function of Surrogate NPR (Exploration Database
Samples)—Cover Material
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Paste pH measurements provide an indication of the availability of readily soluble oxidation products
and salts. An acidic paste pH (<4) may indicate the presence of acidic reaction products generated by
sulfidic oxidation. An alkaline pH (>7) suggests the presence of reactive neutralizing minerals or, if
categorized as PAG, that the sample has not yet oxidized sufficiently to become acidic.
Figure 5-87 shows that the paste pH of the 14 saprolite samples is circum-neutral (ranging between
pH 5.2 and 7.9), with five samples that had a paste pH <6. The 10 alluvium samples show a similar
range in pH values to the saprolite. The alluvium and saprolite samples are typically characterized by
lower paste pH values compared to the waste rock (core) samples from the geochemical
characterization program (Figure 5-87). These lower pH values relate to the removal of primary
neutralizing minerals by weathering processes, leaving behind insoluble iron and aluminum oxide
minerals. These results are consistent with the paste pH values from the SWCA soil survey (SWCA,
2023). The alluvium and saprolite samples are characterized by low total sulfur contents (typically
<0.1 wt%),with the exception of two saprolite and two alluvium samples with total sulfur between 0.01
and 0.1 wt%. Analysis of the multi-element data in the exploration database demonstrates that 86 out
of the 91 alluvium samples (95%) and 126 out of the 134 saprolite samples (94%) have total sulfur
contents<0.1 wt%. Only four samples of saprolite in the exploration database have total sulfur contents
>1 wt%.
Figure 5-89 provides an ABA plot showing the NP as a function of AP. In addition, Figure 5-89 provides
a plot of surrogate NP plotted as a function of surrogate AP for the exploration database samples.
These plots include the divisions between the PAG and uncertain (UC)classification (black line,where
NPR equals 1)and the UC and non-PAG classification (dotted line, where NPR equals 3). Figure 5-90
shows that NPR values for all saprolite and alluvium samples were >3, indicating this material is
classified as non-acid generating. These results are supported by the surrogate ABA calculations for
the multi-element data in the exploration database. Figure 5-89 shows the surrogate NPR of the
exploration database samples and demonstrates that only 10 saprolite samples and four alluvium
samples (equating to 6% of the sample set) are classified as PAG (i.e., NPR <1). The majority (73%)
of saprolite and alluvium samples are non-PAG (73%), with the remaining 21% of samples showing
an uncertain potential for acid generation based on NPR.
Figure 5-90 shows the NNP versus NPR for the 24 samples (10 alluvium and 14 saprolite) collected
as part of the sonic drilling program. All saprolite samples are classed as having uncertain acid
generating behavior based on NNP (i.e., NNP >-20 kg CaCO3/t); This was also the case for the
10 alluvium samples. Figure 5-91 shows the surrogate NNP versus surrogate NPR of the exploration
database samples; this demonstrates that most of the saprolite samples (131 out of 134, or 98%) are
classified as either non-PAG or uncertain based on NNP. Only three samples are classified as PAG
(i.e., NNP <-20 kg CaCO3/t). All 91 alluvium samples are classed as having either uncertain or non-
PAG characteristics based on NNP, with no alluvium samples being classed as PAG.
5.3.3 NAG
NAG tests were completed on the 24 saprolite and alluvium samples collected as part of the 2022
sonic drilling program. Figure 5-92 shows the NAG value versus NAG pH for each sample.All saprolite
samples are classified as non-PAG based on NAG pH >4.5.The majority of alluvium samples are also
classed as non-PAG based and NAG test work. However, one alluvium sample is classified as PAG
based on NAG values>10 kg H2SO4 eq/t and a NAG pH below 4.5. Figure 5-93 shows NAG pH versus
total sulfur for the cover material.
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100
i
i
90 NAG pH<4.5 and
NAG '
>5 kg H,SO41t
80 Potentially Acid i
Generating i NAG pH>4.5
i Nan Acid
70 0 ' Generating
O
Waste rock-
y 60 o i core
i
c i 0 Overburden-
o
core
a �
= 50 ■AlluviumHCT-
m care
t9 i
in 40 i •Alluvium-sonic
a
r �
z 30 •Saprolite-
' sonic
20
i •%
10
------------ - --��f F
0 2 4 6 8 10 12
NAG pH(s.u.)
Figure 5-92: NAG as a Function of NAG pH —Cover Material
12
'°•S• •�• • • •
a a • °O P • • •
•
10
. e
8 '
o Waste rock-
i •. core
3R • • • i e• •
=
a 6 • •Alluvium-
O * • . sonic
--. •Sap rolite-
4 • • sonic
� o
� a
2
0
0.001 0.01 0.1 1 10
Total Sulfur(%)
Figure 5-93: NAG pH versus Total Sulfur—Cover Material
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5.3.4 Multi-Element Analysis
Multi-element analysis was completed on the 24 saprolite and alluvium samples collected as part of
the 2022 sonic drilling program to assess their bulk geochemical composition and identify potentially
elevated constituents. Table 5-16 summarizes the minimum, average, and maximum constituent
concentrations. In addition, Table 5-17 shows the minimum, average, and maximum constituent
concentrations for the exploration database samples.
For the 24 alluvium and saprolite samples collected as part of the sonic drilling program, the following
constituents were found to be enriched relative to the average crustal abundance in one or more
sample: arsenic, antimony, beryllium, bismuth, cobalt, cesium, lanthanum, lead, lithium, manganese,
molybdenum, phosphorus, rhenium, selenium, silver, tin, tantalum, tellurium, thallium, uranium,
tungsten, uranium, vanadium, yttrium, and zinc (Table 5-16).
These results are consistent with the exploration database samples, which show elevated arsenic,
antimony, beryllium, bismuth, cadmium, cobalt, chromium, copper, lanthanum, lithium, lead,
manganese, molybdenum, nickel, phosphorus, scandium, silver, tin, thallium, uranium, vanadium,
tungsten, and zinc relative to average crustal abundance in or more sample (Table 5-17).
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Table 5-16: Summary of Multi-Element Assay Data-Cover Material Sonic Samples
Cerium Cesium Gallium Germanium Indium Lanthanum
Cover Ag Al As Ba Be Bi Ca Cd Co Cr Cu Fe Hg K Li Mg Mn Mo
Statistic Ce Cs Ga Ge In La
Material Type , ,/o o
/o ppm /o ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm /o ppm ppm /o ppm ppm
Average crustal abundance(Mason, 1966) 0.07 8.13 1.8 425 2.8 0.2 3.63 0.2 60 25 100 3 55 5 15 1.5 0.08 0.1 2.59 30 20 2.09 950 1.5
No.Samples 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
Alluvium Minimum 0.01 5.89 0.3 100 1.16 0.12 0.01 0.02 5.37 6.4 2 1.08 11.6 0.87 12.95 0.11 0 0.016 0.38 1.5 6.3 0.11 75 0.16
Average 0.06 9.46 52 321 2.9 0.55 0.24 0.09 56.4 14.3 53.7 6.9 45.2 6.06 26.6 0.18 0.03 0.09 1.29 23.7 73 0.28 525 1.06
Maximum 0.34 13.3 195 440 10 1.81 2.15 0.5 1 126 31.3 131 38.6 77 11 39.5 1 0.29 0.074 0.185 2.08 73.4 510 1.18 1285 1.85
No.Samples 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
Saprolite Minimum 0.01 7.71 0.9 40 1.68 0.09 0.02 0.02 1.48 9.8 1 1.73 0.8 0.52 18.85 0.1 0 0.005 0.17 0.6 5.6 0.05 341 0.05
Average 0.03 10.18 56.2 259 9 0.38 1.1 0.15 55.4 33.6 69.9 18 55.9 7.47 26.4 0.23 0.01 0.08 1.42 40.8 544 1.65 1261 1.03
Maximum 0.07 13.4 518 770 59 1.41 6.41 0.52 143 94.2 119 100 129 10.3 33.8 0.9 0.048 0.133 3.87 335 2450 7.2 3710 6.5
<3 times average crustal concentration
3 to 6 times average crustal concentration
6 to 12 times average crustal concentration
>12 times average crustal concentration
Niobium Rubidium Rhenium Tantalum Tellurium Yttrium Zirconium
Cover Na Ni P Pb S Sb Sc Se Sri Sr Th Ti Ti U V W Zn
Statistic Nb Rb Re Ta Te Zr
Material Type % ppm ppm ppm ppm ppm ppm % ppm ppm ppm ppm ppm ppm ppm ppm % ppm ppm ppm ppm ppm ppm ppm
Average crustal abundance(Mason, 1966) 2.83 20 75 1050 13 90 0.001 0.026 0.2 22 0.05 2 375 2 0.01 7.2 0.044 0.5 1.8 135 1.5 33 70 165
No.Samples 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
Minimum 0.03 6.1 0.5 60 4.4 32.8 0.002 0.01 0.06 6.6 1 1.8 4.2 0.53 0.05 3.94 0.079 0.19 0.8 17 0.9 2.4 22 26.1
Alluvium
Average 0.63 12.8 30.1 580 24.2 69.8 0 0.02 1.69 23.3 2.5 9.3 43.2 1.11 0.25 10.7 0.68 0.5 2.73 139 26.6 20.3 62.4 93
Maximum 3.53 20.6 66.1 2370 81 103 0.003 0.04 9.99 52.1 6 55.4 165.5 3.4 1.47 18.45 1.35 1.34 5.6 335 94.1 112 155 182
No.Samples 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
Minimum 0.02 2.4 6.2 340 3.2 26.4 0.002 0.01 0.05 1.7 1 1.3 3.5 0.4 0.05 0.48 0.018 0.26 1 1 1 9.7 31 9.8
Saprolite
Average 0.65 16.5 51.8 1201 18.4 90.6 0 0.02 0.49 31.2 1.71 19.4 57.8 2.42 0.06 8.2 0.99 0.92 4.39 228 18 41.8 118.8 70
Maximum 2.96 35.4 86.7 4050 64.3 240 0.004 0.1 1.48 49.9 3 187.5 232 17.9 1 0.09 17.15 2.87 2.48 11.8 426 92.6 216 281 158
<3 times average crustal concentration
3 to 6 times average crustal concentration
6 to 12 times average crustal concentration
>12 times average crustal concentration
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Table 5-17: Summary of Multi-Element Assay Data-Cover Material from Exploration Database
Cover Ag Al As Ba Be Bi Ca Cd Co Cr Cu Fe Ga K La Li Mg Mn Mo Na Ni P Pb S Sb
Material T Statistic
Type ppm % ppm ppm ppm ppm % ppm ppm ppm ppm % ppm % ppm ppm % ppm ppm % ppm ppm ppm % ppm
Average crustal abundance(Mason, 1966) 0.07 8.13 1.8 425 2.8 0.2 3.63 0.2 25 100 55 5 15 2.59 30 20 2.09 950 1.5 2.83 75 1050 13 0.026 0.2
No. Samples 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91
Minimum 0.5 3.09 5 10 1.2 2 0.01 0.5 3 2 1 0.69 10 0.03 10 10 0.03 71 1 0.01 4 90 2 0.01 5
Alluvium
Average 0.5 8.67 37.1 288 19.5 2.41 0.85 0.51 26.1 83.2 59.7 5.65 24.5 1.46 27.3 394 0.83 1064 1.45 0.58 50.1 624 18.1 0.03 5.02
Maximum 0.5 13.9 849 760 154 12 9.47 0.9 65 274 280 12.7 40 3.84 230 4870 5.58 4420 7 3.54 299 2180 66 0.45 6
No. Samples 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134
Minimum 0.5 4.11 5 20 0.9 2 0.01 0.5 1 3 1 0.72 10 0.01 10 10 0.04 82 1 0.01 1 90 2 0.01 5
Saprolite
Average 0.51 9.11 48.6 297 5.6 2.18 0.96 0.51 42 102 72.3 6.65 24.9 1.65 26.6 322 1.52 1020 1.27 0.52 62.2 803 12.8 0.11 5
Maximum 1.6 14.3 427 910 38 6 9.19 2.1 185 390 212 13.4 40 5.16 150 2720 8.32 8630 5 5.85 288 5410 34 3.02 5
<3 times average crustal concentration
3 to 6 times average crustal concentration
6 to 12 times average crustal concentration
>12 times average crustal concentration
Cover Sc Sn Sr Th Ti TI U V W Zn
Material T Statistic
Type ppm ppm ppm ppm % ppm ppm ppm ppm ppm
Average crustal abundance (Mason, 1966) 22 2 375 7.2 0.44 0.5 1.8 135 1.5 70
No. Samples 91 91 91 91 91 91 91 91 91 91
Minimum 2 0 2 20 0.01 10 10 2 10 15
Alluvium
Average 20.1 18.9 48 20 0.65 10 10 151 10.7 78.5
Maximum 62 100 283 20 1.95 10 10 464 40 418
No. Samples 134 134 134 134 134 134 134 134 134 134
Minimum 2 0 2 20 0.02 10 10 2 10 10
Saprolite
Average 27.6 6.6 49.1 20 0.74 10 10 202 10.3 86.8
Maximum 71 40 389 20 2.13 10 10 495 50 335
<3 times average crustal concentration
3 to 6 times average crustal concentration
6 to 12 times average crustal concentration
>12 times average crustal concentration
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5.3.5 Short-Term Leach Tests
SPLP testing has been initiated on three samples of alluvium and three samples of saprolite to assess
the leaching behavior of potential future cover materials. These samples have been selected to
represent the range of geochemical behavior for the cover material based on ABA and NAG test
results.
A modified SPLP method was used for the alluvium and saprolite samples; the extraction solution
consisted of DI water, and the US ratio is 2:1. The results of the SPLP test provide a screening
assessment of constituent leaching behavior and allow identification of constituents that may be readily
leached from the cover material samples. The test uses a defined solid-to-water ratio much higher
than would be observed in nature. As such, the concentrations are not considered to represent actual
predictions of water quality.
Tabulated SPLP data for the alluvium and saprolite samples is provided in Appendix E. The laboratory
reports are provided in Appendix G. Figure 5-94 through Figure 5-96 show the release of key
constituents as a function of pH for the alluvium and saprolite samples. Also included on the graphs
are the constituent concentrations for legacy waste rock for comparison purposes.
For the alluvium and saprolite samples, the leachate pH was neutral to alkaline (pH 5 to 8.3). For the
majority of the alluvium and saprolite samples, element release was low or at the limit of detection
under neutral conditions. In general, the alluvium and saprolite samples showed overall lower
constituent release compared to the legacy waste rock samples. Two samples of saprolite showed
higher constituent release compared to the alluvium samples, including higher arsenic, manganese,
and lithium.
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1000 100
x
10
100
� � ■ E X
d E •
■Legacy Waste Rock 1 ■Legacy Waste Rock
`" •Alluvium •Alluvium
a � • � a
(A 10 •Saprolite 0. X •Saprolite
a X
`^ 0.1
X .K
•
1 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
10 10
X
� 1 1
E 0 E
y v
■Legacy Waste Rock N ■Legacy Waste Rock
ca a
°p • •Alluvium a •Alluvium
a0.1 •Saprolite 0.1 •Saprolite
J
a
N
•
Mae 0 • • 0•
0.01 x X x 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH (s.u.) SPLP pH(s.u.)
Figure 5-94: Alluvium and Saprolite SPLP Constituent Release as a Function of pH —Sulfate, Aluminum, Manganese, and Zinc
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1 1
0.1 0.1
J J
UCQ tw
C E
v T
C
N 0.01 ■Legacy Waste Rock E 0.01 ■Legacy Waste Rock
a ■ •Alluvium Q *Alluvium
N ■ •Saprolite •Saprolite
a
0.001 ■ 0.001
x
0.0001 0.0001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
1 1
i
0.1 0.1
i
J J
Oq dA
CE E
G ■ E
0.01 ■Legacy Waste Rock 0.01 ■Legacy Waste Rock
v ra
cn •Alluvium ■ —Alluvium
a
a ■ •Saprolite a •Saprolite
0.001 ■ — 0.001
■
0.0001 • 0.0001 ■
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH (s.u.) SPLP pH (s.u.)
Figure 5-95: Alluvium and Saprolite SPLP Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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10 1000
100
Joao 10
E E
y • E
0 1 ■Legacy Waste Rock t 1 ■ ■Legacy Waste Rock
0 +'
U. •Alluvium a • •Alluvium
a
�a •Saprolite 0. 0.1 • •Saprolite
0.01 • •
�� • X • •
0.1 0.001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH (s.u.) SPLP pH (s.u.)
Figure 5-96: Alluvium and Saprolite SPLP Constituent Release as a Function of pH —Fluoride and Lithium
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5.4 Legacy Mining Waste
5.4.1 ABA
Waste Rock
ABA testing was carried out on 33 samples of legacy waste rock; Table 5-18 and Figure 5-97 to
Figure 5-99 summarize and present the results. On Figure 5-97 to Figure 5-99, the characteristics of
the legacy mine waste rock have been compared to those of future waste rock and ore material as an
analogue of potential future geochemical behavior in mine waste.
Table 5-18: Summary of ABA and NAG Results - Legacy Mining Waste
Parameter Units Legacy Waste Rock(n =33) Legacy Tailings (n =23)
Average' Minimum Maximum Average' Minimum Maximum
Paste pH S.U. 6.92 4.90 11.1 7.85 6.00 9.80
Total Sulfur % 0.07 <0.01 0.92 0.24 <0.01 1.81
Pyritic Sulfur % 0.02 <0.01 0.07 <0.01 <0.01 <0.01
Ap kg CaCO3 eq/t 0.51 <0.31 2.20 <0.31 <0.31 <0.31
NIP kg CaCO3 eq/t 12.4 1.0 181 7.7 1.0 55.0
NNP kg CaCO3 eq/t 11.9 <0.2 181 7.39 0.69 54.7
NPR - 37.4 0.91 584 24.8 3.23 177
NAG pH S.U. 6.40 3.60 10.9 6.34 5.00 8.40
NAG kg H2SO4 eq/t 2.60 <1 17.0 3.78 <1 11.0
'Average values represent the arithmetic mean.
Paste pH vs.Total Sulfur
10
= Non-PAG
• ,�1 alptgo 0
1 • a §-:o
•••• •
•
~ at
• �''�7
3 ; •J*
w 0.1 •
v • • •• • •Future Waste Rock
PAG « • N ■Legacy Waste Rock
>OKX)K 0 •. «. •
0.01 XX>10K-
0.001
0 2 4 6 8 10 12
Paste pH(s.u.)
Figure 5-97: Paste pH Plotted as a Function of Total Sulfur Content- Legacy Waste Rock
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NP vs.AP
1000
Non-PA
6 T
N �
0
U 100 : Unc
U • _ •
Y10 ' _ • • ••' "A • ••�•• : • Future Waste Rock
ao • • �j ��_ ' ■ Legacy Waste Rock
no • • • •• •r •• •• •
� • • • •. • •0• •• NPR=1
• • ��• • • • - - -NPR=3
3
d
Z 1 �,
Go
PAG
•
0.1
0.1 1 10 100 1000
Acid Generating Potential(AP)(kg CaCO3 eq/t)
Figure 5-98: NP Plotted as a Function of Acid Generating Potential — Legacy Waste Rock
NPR vs. NNP
zoo
Non-PAG x
c 150
a
O x
U
m
U
Op
100 •
a
Z
• • • '
•
a 50
0 • • •f=• ' •Future Waste Rock
CL
• • • •
to • •' • • • ■Legacy Waste Rock
�••H •, t • • •• • •+
0 %••"' Uncertain
v
Z •
Z • • '
-50 •
PAG
-100
0.1 1 10 100 1000
Neutralizing Potential Ratio(NPR)
Figure 5-99: NNP Plotted as a Function of NPR— Legacy Waste Rock
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Figure 5-97 presents paste pH results. The test gives an indication of availability of readily soluble
oxidation products and salts. An acidic pH (<5) may indicate the presence of acidic reaction products
generated by sulfidic oxidation. An alkaline pH (>7) suggests the presence of reactive neutralizing
minerals or, if categorized as potentially acid generating, that the sample has not yet oxidized
sufficiently to become acidic.
The results show that the legacy waste rock samples are characterized by variable paste pH values,
ranging from moderately acid to alkaline (4.9 to 11.1). The paste pH of the legacy samples is typically
lower(i.e., more acidic) compared to the future waste rock and ore samples, most likely as a result of
exposure of the legacy waste to oxidation processes at surface for over 35 years. Despite this, acidic
conditions (pH < 5) have only developed in one sample, and, in general, the paste pH values for the
legacy mine waste samples are circum-neutral to moderately alkaline consistent with results for the
future waste rock.
The legacy waste rock samples are characterized by low sulfide sulfur contents (<0.07 wt%) and
exhibit either non-acid generating or uncertain acid generating behavior based on ABA testing. None
of the samples are classed as PAG materials. The total sulfur and sulfide sulfur content of the legacy
waste rock samples is toward the lower range of those observed in the future waste rock materials
(Figure 5-97), and the samples show an overall lower potential to generate acid (Figure 5-98 and
Figure 5-99). This is consistent with the neutral conditions observed on-site; in particular, the neutral
pH of the existing pit lake (Schafer, 2023), and may relate to the fact that the legacy waste rock
samples were collected from the TSF berm, which was likely preferentially constructed with non-PAG
waste. As such, these samples may not fully capture the range of potential acid generating behavior.
Tailings
ABA testing was carried out on 23 samples of legacy tailings. Table 5-18 and Figure 5-100 to
Figure 5-102 summarize and present the results. Sulfide sulfur is below analytical detection limits in
the legacy tailing's samples, and all samples are classed as non-acid generating based on NPR
values >3. Paste pH values are circum-neutral to alkaline (pH 6.0 to 9.8), indicating minimal
development of acidic salts on the tailings surface, despite exposure to surface oxidation processes
for several decades.
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Paste pH vs.Total 5
10
Non-PAG
•
1
3 •
•
3
w 0.1
•Future Tailings
m
c •Legacy Tailings
PAG O O
0.01 •• • G)CKMOOOO m
0.001
0 2 4 6 8 10 12
Paste PH(s.u.)
Figure 5-100: Paste pH Plotted as a Function of Total Sulfur Content—Legacy Tailings
NP vs.AP
100
O
Non-PAG
a 8 �
O
$ • -Unc
�o
v
nn •
10
a •
Z
O
• Future Tailings
.°.
0 O • Legacy Tailings
O NPR=1
'N
— — —NPR=3
3
G!
z
PAG
0.1
0.1 1 10
Acid Generating Potential(AP)(kg CaCO3 eq/t)
Figure 5-101: NP Plotted as a Function of Acid Generating Potential — Legacy Tailings
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NP vs.AP
200
c 150
ar
O
u
.a
u
m
100
a
z
z Non-PAG
0
c 50
2 •Future Tailings
0
yo O� •Legacy Tailings
c
m
0 Uncn • • O O
Y
3
d
Z
Y
d
Z -50
PAG
-100
0.1 1 10 100
Neutralizing Potential Ratio(NPR)
Figure 5-102: NNP Plotted as a Function of NPR— Legacy Tailings
5.4.2 NAG
Waste Rock
ABA testing was carried out on 33 samples of legacy waste rock. Table 5-18 and Figure 5-103 and
Figure 5-104 summarize and present the results. The majority of legacy waste rock samples (91%)
are classed as non-acid generating based on NAG testing. Only three samples show a low capacity
for acid generation, with no samples classed as having a higher capacity for acid generation
(Figure 5-103).
Compared to future waste rock materials, the legacy waste rock samples show a lower potential for
acid generation (Figure 5-103 and Figure 5-104). This is consistent with the ABA test results for the
legacy waste rock samples and the neutral conditions observed on-site. However, this is with the
caveat that the legacy samples were collected from the TSF berm and less reactive material may have
preferentially selected and these samples may not reflect the full range of potential activity.
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NAG vs.NAG pH
80
70
c 60
p PAG-Higher Capacity
SO %
s
o " Non-PAG
%
y 40 • '
w : •Future Waste Rock
Q «•
30 N X Legacy Waste Rock
w
Z •«
•N
Nt '
20 •.••
«
M« ••
N•
N
N••
.•NN
•N•
PAG-Lower Capat#t'
S_. y
2 4 6 8
NAG pH(s.u.)
Figure 5-103: NAG as a Function of NAG pH— Legacy Waste Rock
NAG pH versus Sulfide Sulfur
1�
10
•.• Non-PAG
8
•• •/•
a 6 1
z •.L• •Future Waste Rock
t
■Legacy Waste Rock
X——
4 x A� ••
.•� ti s
2
PAG
0
0.001 0.1 1 10 1000
Sulfide Sulfur(wt%)
Figure 5-104: NAG as a Function of Sulfide Sulfur— Legacy Waste Rock
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Tailings
ABA testing was carried out on 23 samples of legacy tailings. Table 5-18 and Figure 5-105 to
Figure 5-106 summarize and present the results. All samples of legacy tailings are classed as non-
acid generating materials based on ABA testing. This is consistent with the NAG results for the future
tailings material, which typically show a low potential for acid generation.
NAG vs. NAG pH
80
70
0 60
0
d
o PAG-Higher Capacity
50
Non-PAG
0
y 40
•Future Tailings
a30 Legacy Tailings
Y
41
Z
20
10 0 8
PAG-Lower Capacit: � 0
0 0000
0 2 4 6 L 10 12
NAG pH(s.u.)
Figure 5-105: NAG as a Function of NAG pH — Legacy Tailings
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NAG pH versus Sulfide Sulfur
to
9 •
88
O Non-PAG
•
7 • •
6
7
a 6 •
Z •Future Tailings
4 •Legacy Tailings
•
3 •
2
1 PAG
0
0.01 0.1 1 10
Sulfide Sulfur(wt%)
Figure 5-106: NAG as a Function of Sulfide Sulfur— Legacy Tailings
5.4.3 Multi-Element Analysis
Multi-element analysis was carried out on 33 samples of legacy waste rock and 23 samples of legacy
tailings to identify parameters present at elevated concentrations when compared to average crustal
abundance data. Table 5-19 summarizes the results, which show that the following elements are
elevated above average crustal concentrations in the legacy mining waste:
• Arsenic, antimony, beryllium, cesium, chromium, cobalt, lead, lithium, magnesium,
manganese, nickel, rubidium, selenium, sulfur, thallium, tin, tungsten, uranium, and zinc are
elevated in one or more samples of legacy waste rock.
• Arsenic, antimony, beryllium, cadmium, cesium, cobalt, lithium, rubidium, selenium, sulfur,
thallium, tin, tungsten, and uranium are elevated in one or more samples of legacy tailings.
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Table 5-19: Summary of Multi-Element Assay Data- Legacy Mining Waste
Average Legacy Waste Rock(n=33) Legacy Tailings(n=23)
Crustal
Parameter Units Abundance
(Mason, Average' Minimum Maximum Average' Minimum Maximum
1966)
Aluminum % 8.13 9.26 2.97 14.2 7.23 5.72 12.1
Antimony ppm 0.2 0.37 <0.05 1.91 0.14 0.05 1.22
Arsenic ppm 1.8 34.2 0.50 365 9.86 0.20 150
Beryllium ppm 2.8 26.2 1.42 255 103 3.47 188
Cadmium ppm 0.2 0.06 <0.02 0.46 0.14 0.02 1.41
Calcium % 3.63 2.62 0.01 8.86 1.01 0.05 8.48
Cesium ppm 3 25.8 <0.05 70.6 30.9 8.24 41.4
Chromium ppm 100 77.8 1.00 510 16.2 3.00 96.0
Cobalt ppm 25 32.1 0.20 87.3 18.9 0.30 84.5
Copper ppm 55 53.0 0.20 160 13.5 1.80 77.6
Iron % 5 5.52 0.32 12.1 1.48 0.17 9.93
Lead ppm 13 16.7 1.40 59.1 17.6 5.90 27.3
Lithium ppm 20 1464 7.1 >10,000 2728 221 5170
Magnesium % 2.09 1.45 0.01 7.09 0.12 0.03 0.53
Manganese ppm 950 1197 125 3010 615 100 2290
Molybdenum ppm 1.5 0.98 <0.05 3.76 0.73 0.12 1.81
Nickel ppm 75 43.4 0.30 260 8.38 0.90 62.2
Potassium % 2.59 1.41 0.06 4.04 2.04 0.40 2.97
Rubidium ppm 90 206 1.50 1015 623 42.8 973
Selenium ppm 0.1 1.55 1.0 5.0 1.74 1.0 3.0
Sulfur % 0.026 0.09 <0.01 0.87 0.41 0.01 4.93
Thallium ppm 0.5 1.97 <0.02 10.3 4.71 0.41 6.99
Tin ppm 2 55.3 1.4 >500 43.5 6.70 123
Tungsten ppm 1.5 27.2 0.80 170 106 0.40 500
Uranium ppm 1.8 5.10 1.60 24.0 5.77 2.10 18.1
Zinc ppm 70 103 20.0 275 62.0 16.0 178
'Average values represent the arithmetic mean
Indicates<3 times average crustal concentrations
Indicates between 3 and 6 times average crustal concentrations
Indicates between 6 and 12 times average crustal concentrations
Indicates>12 times average crustal concentrations
These findings are generally consistent with the future waste rock, ore, and tailings characterization
program presented in Sections 5.1.7 and 5.2.4. In addition, constituent concentrations in the legacy
materials are typically within the range observed for future waste rock and tailings.
5.4.4 Lysimeter Tests
TSF porewater samples were obtained from the historical TSF. The samples were collected using
suction lysimeters to obtain a water sample that was representative of the tailings pore water.
The two results showed that the TSF porewater recorded circum-neutral pH values of 6.7 and 7.5,
lower than pH results from leach testing on future tailings samples, which is reasonable as future ore
testing probably used lime salts to maintain elevated pH. The circum-neutral pH results are consistent
with the ABA results for legacy tailings material which is non-PAG, and produced circum-neutral to
mildly alkaline paste pH. Despite this, elevated sulfate was recorded in the lysimeter from the southern
legacy tailings facility, with a concentration of 1,280 mg/L recorded.
Many parameters were low or below analytical detection limits for the lysimeter tests, including
cadmium, beryllium, lead, chromium, cobalt, boron, selenium, silver, and thorium. The results showed
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concentrations of a few parameters that were higher in comparison to the leach test results of future
tailings. For example, concentrations of manganese were between 0.1 mg/L and 1.1 mg/L, and copper
was recorded between 0.002 mg/L and 0.02 mg/L. Table 5-20 shows summary results for the legacy
tailings lysimeter testing.
Table 5-20: Lysimeter Test Results (Legacy Tailings)
Parameter Units North L simeter South L simeter
KMTL23-001 KMTL23-001 KMTL23-002 KMTL23-002
Total Alkalinity m /L -- 36.1 -- 61.6
H units -- 6.7 -- 7.5
Aluminum m /L 0.0265 0.0794 0.0192 0.0051
Antimony m /L 0.001 0.00064 0.00097 0.00104
Arsenic m /L 0.00102 0.00098 0.00272 0.00154
Barium m /L 0.0942 0.0838 0.106 0.086
Beryllium m /L <0.00008 <0.00008 <0.00008 <0.00008
Cadmium mg/L 0.000277 -- 0.000138 --
Chromium m /L <0.0005 0.00083 0.00053 0.00086
Cobalt m /L 0.00138 0.000394 0.00193 0.00164
Copper m /L 0.0044 0.0188 0.00842 0.00295
Iron m /L 0.0119 0.0284 0.113 0.0131
Lead mg/L 0.00018 <0.0001 <0.0001 <0.0001
Lithium m /L 0.287 0.546 0.401 0.374
Manganese m /L 0.318 0.103 1.05 0.42
Mercury m /L <0.0002 -- <0.0002 Molybdenum m /L 0.019 0.00844 0.0373 0.0156
Nickel mg/L 0.00731 0.00642 0.00639 0.00593
Selenium m /L <0.0001 <0.0001 0.00013 <0.0001
Silver m /L I <0.0001 <0.0001 <0.0001 <0.0001
Sulfate m /L -- 11.4 -- 1280
Thallium m /L <0.0001 <0.0001 <0.0001 <0.0001
Vanadium mg/L 0.145 0.0273 0.187 0.0508
Zinc m /L 0.103 0.0936 0.0154 0.0267
Bismuth m /L <0.04 <0.04 <0.04 <0.04
Calcium m /L 1 24.1 14.4 307 534
Conductivity at 25C mhos/cm --- 208 --- 2240
Magnesium mg/L 3.45 1.95 11.7 8.44
pH units -- 6.7 -- 7.5
Phosphorus m /L 0.26 0.23 0.88 0.63
Potassium m /L 7.84 6.4 14.5 10.4
Sodium m /L 25.1 15.1 37.4 16.5
Thorium m /L <0.001 <0.001 <0.001 <0.001
Tin m /L <0.04 <0.04 <0.04 <0.04
Titanium m /L <0.005 <0.005 0.0164 0.0053
Uranium m /L 0.00013 0.00124 0.00131 0.00205
Uranium m /L 0.00013 0.00124 0.00131 0.00205
Potassium m /L 7.84 6.4 14.5 10.4
Calcium m /L 24.1 14.4 307 534
Potassium m /L 7.84 6.4 14.5 10.4
Magnesium m /L 3.45 1.95 11.7 8.44
pmhos/cm: Micromhos per centimeter
5.4.5 Short-Term Leach Tests
SPLP
Short-term leach testing was conducted on seven samples of legacy waste rock material and eight
samples of legacy tailings. These samples were selected to represent the range of geochemical
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behavior for the legacy materials based on ABA and NAG test results. The following section provides
results for the short-term SPLP tests.
A modified SPLP method was used for the waste rock and ore samples; the extraction solution
consisted of DI water, and the L/S ratio is 2:1. This method is consistent with the SPLP method used
for the future waste rock and tailings samples. The results of the SPLP test provide a screening
assessment of constituent leaching behavior and allow identification of constituents that may be readily
leached from the legacy waste rock and legacy tailings samples.The test uses a defined solid-to-water
ratio much higher than would be observed in nature. As such, the concentrations are not considered
to represent actual predictions of water quality.
Tabulated SPLP data for the legacy waste rock samples are provided in Appendix E and the laboratory
reports are provided in Appendix G. The release of key constituents, as a function of pH, is illustrated
in Figure 5-107 to Figure 5-112.
For the legacy waste rock samples, the leachate pH was variable, ranging from acidic to alkaline (pH
4.3 to 9.8). For the majority of the legacy waste rock samples, element release was low or at the limit
of detection under neutral conditions. However, samples with acidic leachates displayed higher solute
release. In particular, the legacy waste rock sample with the lowest pH (4.3) had the highest sulfate
concentrations relative to other waste rock samples (446 mg/L), and was associated with higher
concentrations for a range of parameters (e.g., barium, strontium, iron, nickel, and zinc). Elevated
release of parameters was also recorded for the sample with alkaline pH (9.8). For this sample,
chloride was measured at 310 mg/L, fluoride was measured at 1.43 mg/L, arsenic at 0.014 mg/L, and
mercury at 0.00000238 mg/L.
For the legacy tailings samples, the Ieachate pH was circum-neutral and less variable in comparison
to the legacy waste rock samples (pH 5.9 to 8.1). Constituent release was generally low or at the limit
of detection, though manganese, mercury, and zinc were detectable.
In Figure 5-107 to Figure 5-112, the leaching behavior of the legacy waste rock and tailings samples
is compared to that of the future waste rock and tailings. In general, constituent release is the same
order or magnitude for both the legacy and future waste rock and tailings samples.
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1000 100
x ■
•
■
10
100 • •
OD w
■ •
E X X ■ E
•
41
•S• 1 • • •
rn •• •Future Waste Rock •Future Waste Rock
•
a ■ ■• ■Legacy Waste Rock as • • ■Legacy Waste Rock
v� 10 • a ■ •
H
0.1
•
•
1 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
10 T 10
x
■
1 1
E x E
y u
v) _
C
N
•Future Waste Rock a •Future Waste Rock
� a •
■Legacy Waste Rock ■Legacy Waste Rock
a 0.1 ■ 0.1
J
a
X X 310 ■•a 9=411w
0.01 110- x 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
Figure 5-107: Future and Legacy Waste Rock and SPLP Constituent Release as a Function of pH —Sulfate, Aluminum, Manganese, and Zinc
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1 1
0.1 • 0.1
bb ono
E • E
u
N 0.01 • 0 0.01
Q • •Future Waste Rock E •Future Waste Rock
41
a ■ ■Legacy Waste Rock as • • ■Legacy Waste Rock
N X • vai •
0.001 x • • 0.001 PK
x
0.0001 0.0001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH (s.u.) SPLP pH(s.u.)
1 1
i
0.1 0.1
J J
U 00
E E •
E ■ E •
0.01 0.01
_v •Future Waste Rock M • •Future Waste Rock
L
■Legacy Waste Rock a ■ • ■Legacy Waste Rock
a
J � a •
a H � ■
N 0.001 • 0.001
•
•
■ •0 • • oft
o.00o1 XT 0.0001 •
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH (s.u.) SPLP pH(s.u.)
Figure 5-108:Future and Legacy Waste Rock and SPLP Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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10 10
J J
\ \
OG 00
£ • E •
G1 DK • 0 1 X• •
.M ■ •• •Future Waste Rock .2 •• •Future Waste Rock
J %0 ■Legacy Waste Rock ■Legacy Waste Rock
a • a •
�n • •N• v+ • •••
0.1 . . . . . 0.1
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-109: Future and Legacy Waste Rock SPLP Constituent Release as a Function of pH — Fluoride and Lithium
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10000 10
J J •
bb E 1000 E
Q E 1
r0 7 •
rn • E •
a100 •Future Tailings Q • •Future Tailings
H •
•Legacy Tailings a •Legacy Tailings
a • • H
• • a 0.1
10 •�
•
•
1 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
100 1
•
bb
E 10 E
• c
IV
� • a
pp J
c a
1 •Future Tailings 0.1 •Future Tailings
a a
J •Legacy Tailings •Legacy Tailings
a �
a
0.1 • •
• •• N• ••
•
•
0.01 0.01
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-110: Future and Legacy Tailings and SPLP Constituent Release as a Function of pH —Sulfate,Aluminum, Manganese, and Zinc
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1 1
J J
E 0.1 E 0.1
� C
G1 0
L E
Q
0.01 •Future Tailings Q 0.01 •Future Tailings
a • a
• • •Legacy Tailings H • •Legacy Tailings
a •
0.001 • i 0.001 • •
O •
0.0001 0.0001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH (s.u.)
1 1
J J
0.1 0.1
E E
3 7
A) L
0.01 •Future Tailings a 0.01 •Future Tailings
a J •
J a
H •Legacy Tailings • • •Legacy Tailings
a a
0.001 0.001 •• •
•
••
•
0.0001 • NCO O O 0 0.0001
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-111: Future and Legacy Tailings and SPLP Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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10 10
•
• •
•
• •
bb
E
1 • • E •
o • •
U. J •
J ~ •Future Tailings 1 •Future Tailings
a • a
H S •Legacy Tailings •Legacy Tailings
O
a a
0.1 •
•
0.01 --. . . . . . . . . 1 0.1 i . . . . . . •
0 2 4 6 8 10 12 0 2 4 6 8 10 12
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 5-112: Future and Legacy Tailings SPLP Constituent Release as a Function of pH — Fluoride, Lithium and Mercury
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TCLP
The TCLP is a test method designed to simulate the leaching process that waste materials undergo
when disposed of in a landfill. The procedure is used to determine if the waste is classified as
hazardous according to RCRA criteria.
Sixteen (16) samples were subjected to TCLP to evaluate the concentrations of eight (8) targeted
inorganic constituents: arsenic, barium, cadmium, chromium, lead, mercury, selenium, and silver.
Results are compared to standards in Table 5-21 below.
All constituents' results were below the respective regulatory standards. The majority of results were
below the laboratory detection limit, indicating minimal presence of these constituents within the tested
samples.
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Table 5-21: TCLP Results for Legacy Tailings Samples
B-
CLIENTID B426- B432- B432- B434- B434- B389- B389- B389- SANDS B410- B427- B430- B432- B434-
NC/ 005 013 007 005 013 003 010 015 TAILIN 001 017 016 024 022
EPA GS-002
LABID L77926 L77926 L77926 L77926 L77926 L77417 L77417 L77417 L77417 L77417 L77925 L77925 L77925 L77925
-01 -02 -03 -04 -05 -01 -02 -03 -04 -05 -08 -13 -18 -26
Arsenic 5 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04 <0.04
TCLP
Barium 100 <0.009 0.0102 <0.009 <0.009 <0.009 <0.009 <0.009 0.0096 <0.009 0.17 0.368 0.0173 0.0093 0.0181
TCLP
Cadmium 1 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008 <0.008
TCLP
Chromium 5 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02
TCLP
Lead 5 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03 <0.03
TCLP
Mercury <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000 <0.000
TCLP 0'2 0
2 2 2 2 .0002 2 2 2 2 2 2 2 2 2
Selenium 1 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05
TCLP
Silver 5 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01
TCLP
All results reported in mg/L
<indicates sample result is below the laboratory detection limit
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6 Kings Creek Stream Sediment
6.1 ABA
Table 6-1, Figure 6-1, and Figure 6-2 present the ABA results for the three Kings Creek stream
sediment samples. The stream sediment samples are characterized by variable sulfide contents and
acid generating behavior. The sample collected from downstream Kings Creek has non-detectable
sulfide sulfur and is classed as non-PAG based on ABA results. In contrast, the sample collected from
upstream Kings Creek is characterized by a higher sulfide sulfur content of 0.81% and is classed as
PAG based on and NPR of 0.99. Sample SWO-004 has a sulfide sulfur content of 0.14% and shows
uncertain acid generating characteristics based on ABA results. Paste pH values for the stream
sediment samples are circum-neutral (pH 6.4 to 7.8).
Table 6-1: Summary of ABA and NAG Test Results - Kings Creek Stream Sediment
Parameter Units SWO-004 Kings Creek Upstream Kings Creek Downstream
Paste pH s.u. 7.8 6.4 7.5
Total Sulfur % 0.24 0.95 <0.01
Sulfide Sulfur % 0.14 0.81 <0.01
AP kg CaCO3 eq/t 4.4 25.3 <0.31
NP kg CaCO3 eq/t 8 25 3
NNP kg CaCO3 eq/t 3.6 -0.3 3
NPR - 1.82 0.99 9.68
NAG pH S.U. 4.1 5.1 7.8
NAG value kg H2SO4 eq/t 2 2 <1
100 ,
Ndn-PAG
10 ,
m ■ SWO-004
O
UO •• `e� ♦ KingsCreek-Upstream
a O Kings Creek-Downstream
z
1 NPR=1
----NPR=3
• PAG
0.1
0.1 1 10 100
AP(kg CaCO3/0
Figure 6-1: AP versus NP— Kings Creek Stream Sediment
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100
Non-PAG
10
■SWO-004
or
a-
z
♦Kings Creek-Upstream
❑ Uncertain
1 r O Kings Creek-Downstream
PAG
0.1
0 0.2 0.4 0.6 0.8 1
Sulfide Sulfur(wt%)
Figure 6-2: Sulfide Sulfur versus NPR— Kings Creek Stream Sediment
6.1.1 NAG Testing
Table 6-2 and Figure 6-3 present the NAG results for the three Kings Creek stream sediment samples.
Based on NAG test results, the stream sediment samples collected from upstream and downstream
Kings Creek are non-PAG. In contrast, Sample SWO-004 shows lower capacity PAG characteristics.
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100
PAG-High Capacity
1
1
1
10 - - - - - - - - - - - - - - - - - - -
1
' ■S W O-004
v
o , Non-PAG O Kings Creek-Upstream
0 1
= 1 O Kings Creek-Downstream
0 ❑ � O
z '
1 ' O
PAG-Low Capacity
1
1
0.1
0 1 2 3 4 5 6 7 8 9
NAG pH(s.u.)
Figure 6-3: NAG pH versus NAG— Kings Creek Stream Sediment
6.1.2 Multi-Element Analysis
Multi-element analysis was undertaken on the Kings Creek stream sediment samples to identify any
constituents that are elevated above average crustal abundance. Table 6-2 summarize the results,
which demonstrate that beryllium, cesium, lithium, tin, and tungsten are consistently elevated above
average crustal concentrations in the stream sediment samples. In addition, arsenic, antimony,
molybdenum, sulfur, selenium, titanium, thallium, and uranium are elevated above average crustal
concentrations in one or more sample.
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Table 6-2: Summary of Multi-Element Assay Data-Stream Sediment
Parameter Units Average Crustal SWO-004 Kings Creek Kings Creek
Abundance Mason, 1966 Upstream Downstream
Aluminum % 8.13 8.77 7.5 3
Antimony ppm 0.2 0.15 1 0.67
Arsenic ppm 1.8 1.7 28.1 10.6
Beryllium ppm 2.8 25.1 17.8 61.1
Cadmium ppm 0.2 0.21 0.07 <0.02
Cesium ppm 3 25.8 16.1 9.02
Chromium ppm 100 89 90 38
Cobalt ppm 25 45 34 51.3
Copper ppm 55 48.1 41.6 8.1
Iron % 5 7.19 4.05 2.33
Lead ppm 13 8.4 13.7 8.4
Lithium ppm 20 898 284 527
Magnesium % 2.09 3.1 1.21 0.44
Manganese ppm 950 1290 1050 1280
Molybdenum ppm 1.5 5.24 1.71 0.48
Nickel ppm 75 37.3 44 11.4
Potassium % 2.59 0.87 2.13 0.65
Selenium ppm 0.1 1 1 <1
Sulfur % 0.026 0.42 1.22 0.01
Thallium ppm 0.5 2.12 0.78 0.7
Tin ppm 2 13.3 7.5 11.7
Tungsten ppm 1.5 91.3 81.7 318
Uranium ppm 1.8 2 8.1 1.3
Zinc ppm 70 139 72 39
Indicates<3 times average crustal concentrations
Indicates between 3 and 6 times average crustal concentrations
Indicates between 6 and 12 times average crustal concentrations
Indicates>12 times average crustal concentrations
6.1.3 Short-Term Leach Tests
SPLP testing was undertaken on the Kings Creek stream sediment samples to determine their
leaching behavior and assess their potential to leach constituents of concern. Appendix G presents
the laboratory reports, and Figure 5-103 to Figure 5-105 present scatter plots of constituent release
as a function of SPLP pH.
The stream sediment SPLP leachates are circum-neutral to moderately alkaline (pH 7.1 to 8.6), with
many parameters below analytical detection limits, including: beryllium, bismuth, boron, cadmium,
chromium, cobalt, mercury, nickel, silver, thallium, tin, titanium, and vanadium. This demonstrates a
low potential for these parameters to be mobilized from the stream sediments.
The samples showed detectable release of arsenic, antimony, fluoride, iron, lithium, manganese, and
sulfate during the SPLP test; however, concentrations were typically low. In addition, the sediment
sample collected from downstream Kings Creek also showed detectable release of copper, zinc, and
lead in the SPLP. No clear relationship can be established between sample location, pH, and
constituent concentration.
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120 - 0.35
O
0 ■
100 .3
0.25
80 ■S W 0-004 ■S W 0-004
E 0.2
♦Kings Creek Upstream ♦Kings Creek Upstream
m 60
E
aO Kings Creek Downstream a 0.15 O Kings Creek Downstream
a a
� d
40
0.1
O
20 ■ 0.05 CD
0 1 0
0 2 4 6 8 10 0 2 4 6 8 10
SPLP pH(s.u.) SPLP pH(sm.)
0.9 0.6
0.8
0.5 O
0.7
0.6 0SWO-004 0.4 ■SWO-004
on
E
v 0.5 °q
w O Kings Creek Upstream E ♦Kings Creek Upstream
� c
0.3
0.4 O Kings Creek Downstream J O Kings Creek Downstream
� a
a u7
a
0.3 0.2
0.2
0.1
0.1
0 0
0 2 4 6 8 10 0 2 4 6 8 10
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 6-4: Kings Creek Stream Sediment SPLP Constituent Release as a Function of pH —Sulfate, Aluminum, Manganese, and Zinc
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0.025 0.0035
0.003
0.02
0.0025
■S W 0-004 ■S W 0-004
m 0.015 m
E t 0.002
♦Kings Creek Upstream ♦Kings Creek Upstream
0
a` O Kings Creek Downstream ¢ 0.0015 O Kings Creek Downstream
a
0.01 �
n a
N
0.001
0.005
0.0005 O
1111
O
0 0
0 2 4 6 8 10 0 2 4 6 8 10
SPLP pH(s.u.) SPLP pH(s.u.)
0.00025 0.008
O 0.007
0.0002
0.006
■SWO-004 0.005 ■SWO-004
0.00015 E
E O Kings Creek Upstream E ♦Kings Creek Upstream
� � 0.004
O Kings Creek Downstream O Kings Creek Downstream
a 0.0001 O d
N 0.003
0.002
0.00005
0.001
0 0 O
0 2 4 6 8 10 0 2 4 6 8 10
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 6-5: Kings Creek Stream Sediment SPLP Constituent Release as a Function of pH —Arsenic, Antimony, Selenium, and Uranium
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0.4 1.8
0.35 O 1.6
■ 1.4
0.3
0.25 0SWO-004 1.2 0SWO-004
m �
E
E
♦Kings Creek Upstream E 1 ♦Kings Creek Upstream
0.2
8s
O Kings Creek Downstream - 0.8 •Kings Creek Downstream
d • a
N 0.15 N
0.6
0.1
0.4
0.05
0.2 •
0 0
0 2 4 6 8 10 0 2 4 6 8 10
SPLP pH(s.u.) SPLP pH(s.u.)
0.0002 0.8
O •
0.00018
0.7
0.00016
0.6
0.00014
0SWO-004 J 0.5 0SWO-004
m
0.00012
E C Kings Creek Upstream ♦Kings Creek Upstream
m 0.0001 O ■ a 0.4
a 0 Kings Creek Downstream Uo 0 Kings Creek Downstream
0.00008 a
0.3
0.00006
0.2
0.00004
0.1
0.00002
0 0
0 2 4 6 8 10 0 2 4 6 8 10
SPLP pH(s.u.) SPLP pH(s.u.)
Figure 6-6: Kings Creek Stream Sediment SPLP Constituent Release as a Function of pH - Fluoride, Lithium, Lead, and Copper
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7 Waste Rock Management
As described above, a portion of the waste rock associated with the Project has a potential to generate
acid and leach metals and active segregation and management of PAG waste rock is needed to avoid
and/or reduce the potential impacts to groundwater and surface water qualities from mining facility
drainage or runoff.
The results from the geochemical characterization program have been used to develop a site-specific
approach to classifying waste rock as non-PAG or PAG during mining as described in detail in Section
5.6.1. An estimate of the tonnage of PAG material was developed from the block model based on this
approach and was used to support mine planning and design as well as the water quality predictions
presented in SRK(2023). Other materials that will be generated during mining and processing that will
be managed in the RSFs include rejects from DMS process and ore sorting rejects and magnetic
separation rejects from the processing. In addition, non-PAG amphibole gneiss-schist tailings from dry
crushing of waste rock will also be managed with the non-PAG waste rock.
Based on the August 2023 mine plan, the Kings Mountain Project will have two separate RSFs, one
of which is designed for the storage of non-PAG waste rock(RSF-A)and one (RSF-X)for the storage
of PAG waste rock. In addition, material from the historical tailings storage facility (TSF), rejects from
the Dense Media Separation (DMS) process and non-PAG amphibole gneiss-schist tailings from dry
crushing of waste rock will be mixed with the non-PAG waste rock in RSF-A. Ore sorting rejects and
magnetic separation rejects from the processing will be mixed with the PAG waste rock in RSF-X. The
waste rock storage facilities will be developed over the life of mine as PAG and non-PAG waste rock
is produced. Prior to the construction of RSF-X, PAG waste and a small quantity of ore sorter rejects
will be temporarily stored in RSF-W(located in the pit) during the first 1 to 2 years of operations.
A flow chart showing the general waste rock management approach for each facility is provided in
Figure 7-1 and is based on the results of the material characterization program described herein and
the mine plan. The waste rock management approach for the three waste rock facilities are described
below.
• RSF-A — located in the south-western edge of the property to avoid the southern creek and
associated wetlands. This facility will accommodate all non-PAG waste rock and overburden
material, as well as historical tailings. Geochemical characterization data for these historical
tailings indicates this material is benign (SRK, 2023a).
• RSF-W— RSF-W is a temporary PAG storage facility located in the pit. It will be used during
the first 1 to 2 years of operations for the temporary storage of PAG waste and a small quantity
of ore sorter rejects prior to the construction of RSF-X.
• RSF-X — RSF-X has been designed within the footprint of the historical TSF. It is assumed
that the historical tailings and much of the embankment will be relocated prior to establishing
the site for PAG storage. RSF-X will be underlain with a synthetic liner and will accommodate
all PAG waste rock, ore sorter rejects, and magnetic separation rejects. It will also include
non-PAG amphibole gneiss-schist tailings from dry crushing of waste rock. RSF-X is assumed
to be relocated to the base of the pit void at mine closure. This is to ensure any PAG material
will sit beneath the eventual water level, minimizing the potential for ongoing acid generation.
At the end of closure activities, there will not be any PAG waste rock remaining in RSF-X to
require reclamation.
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Mine Materials
Pit Sourced Process Waste Legacy Material Alluvium/Saprolite
Waste Rock I
r DMS Rejects No Restrictions
Multi-Element Analysis I Waste Rock I
of Blastholes(Ca,S) J y
L Ore Sorting Rejects] Used as Reclamation
Tailings Material
Crushed
NNP<-1 NNP>-1 Amphibole
Gneiss Schist
PAG Waste Rock Non-PAG Waste Rock
-------------�
� V
Runoff and
RSF-W(pit) ---- RSF-X(lined) RSF-A --- Seepage to WSB-1
i
i
Relocated to ; Relocated to Pit at Covered and
RSF-X after year 1 ; Closure and Submerged Revegetated at Closure
i
W
Treatment of Runoff
and Seepage During
Operations
J
Figure 7-1: General Waste Rock Management Approach for Kings Mountain
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7.1 Site-Specific PAG Estimation
As discussed in Section 5.1.5, there is significant uncertainty about the potential for future waste rock
associated with the Project to generate acid based on standard evaluation criteria.
Existing (Legacy) waste rock in the Kings Mountain Mine area has been exposed to atmospheric
oxidation for over 35 years, and ARID conditions have not developed. This is effectively demonstrated
by the alkaline pit lake conditions and seep chemistry, as well as neutral to alkaline paste pH values
obtained from the legacy waste rock samples. The existing conditions are not consistent with the ABA
and NAG test results on drill core that indicate a portion of the future waste rock material had a potential
to generate acid. Given the apparent uniformity in the geology of the deposit with depth and of the
waste rock this points to some discrepancy between field observation and laboratory results. This
discrepancy suggests that the ABA and NAG tests may overestimate the potential for acid generation
for the Kings Mountain waste rock or rather the reactivity of waste rock.
The standard methods for predicting acid generation potential are designed for deposits where the
dominant sulfide mineral is pyrite and where neutralization is dominated by carbonate minerals calcite
or dolomite. In the presence of sulfide minerals other than pyrite, the ABA results can be misleading.
In addition, NP measurements only provide an assessment of rapidly dissolving sources of
neutralization (i.e., carbonate minerals) and may not capture silicate-based neutralization. Therefore,
the application of standard Sobek-style ABA tests on non-pyrite and non-carbonate mineralogy can
result in an erroneous assessment of acid generation potential. Pyrite and calcite are common
minerals in the Kings Mountain deposit; however, other sulfide minerals (such as pyrrhotite) and acid
buffering silicates(such as anorthite)are also present, which complicates the interpretation of the ABA
data.
In order to overcome this uncertainty, the results from the geochemical characterization program have
been used to develop site-specific estimates of PAG waste rock quantities.This information is required
to support mine planning and design, including to support the water quality predictions presented in
SRK (2023).
Albemarle initiated a comprehensive pulp testing program to develop a better understanding of the
spatial distribution of PAG material throughout the deposit. The purpose of this program was to
populate the block model total sulfur and total calcium concentrations that can be used to calculate a
surrogate AP and NP value for each block within the model. AP and NP are usually based on acid
titration and sulfide sulfur measurements, respectively. For the block model analysis,a more-economic
approach was used, where AP was based on total sulfur and NP was based on total calcium. The
calcium-based NP was predicated on the belief that minerals containing calcium (either calcium
carbonate or calcium silicates) provide the neutralization capacity at Kings Mountain. The pulp test
program was completed in February 2023, and the data were incorporated into the exploration
database and used to estimate the total sulfur and total calcium across the deposit. From the surrogate
AP and NP values, an NNP and NPR value can then be calculated, and the block can be designated
as PAG or non-PAG according to a specified NNP and/or NPR cut-off. The following sections
summarize the site-specific approach used to define PAG waste rock quantities.
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7.1.1 General Approach
Schafer, in coordination with SRK, developed the approach for site-specific PAG determination.
Specifically, a set of equations were developed for calculating AP and NP to be incorporated into the
block model for PAG estimation. These formulas were developed from an average of four cases that
represent the range of uncertainty in the NP and AP calculations.
7.1.2 Calculations of AP and NP
Four cases were evaluated that bracket the range of expected geochemical behavior of acid
generating sulfide and neutralizing minerals, as summarized in Table 7-1. The approach for each case
was incorporated into the exploration database to determine a percentage of PAG based on the
number of samples for each rock type. For all four cases, an NNP cut-off of 0 was used to determine
the PAG abundance. This cut-off was supported by the HCT program.
Table 7-1: Range of Uncertainty in AP and NP Estimation
Estimated
Case Method for Calculating NP Method for Calculating AP Percentage
of PAG2
1 Traditional Sobek Traditional Sobek 32%
Modified AP to account for reduced
2 Traditional Sobek reactivityof sulfide minerals 12.7%
3 Modified NP to account for buffering Traditional Sobek 21.2%
from calcium silicate minerals
Modified NP to account for buffering from Modified AP to account for reduced °
4 calcium silicate minerals(same as Case 3) reactivity of sulfide minerals (same 7.5/°
as Case 2)
5' 1 Average of Case 1 through 4 Average of Case 1 through 4 20%
'Recommended approach for PAG estimation
213ased on assay data from the exploration database for ore and waste grade material
Cases 1 through 4 bracket the potential range of uncertainty in the NP and AP estimates depending
upon how the data are interpreted. Figure 7-2 shows the average results for the Amphibole Gneiss-
Schist for Case 1 through 4. The most likely answer lies in the middle of the region of uncertainty
created by these end members, and therefore, SRK and Schafer propose using an average of Case
1 through 4 to define PAG quantities (Case 5), which represents the centroid of the four NNP
estimates. Additional information and context are provided for each case in the following sections.
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Hornblende Gneiss Average
100
90
80
m 70
O
U
U 60
un
`- 50
40
Q 30 O
20 —• •
10
0
0 5 10 15 20 25 30 35 40 45 50
AGP (kg/t as CaCO3)
•Case 1 •Case 2 •Case 3 •Case 4 O Case 5 (Ave 1-4)
Figure 7-2: Average Amphibole Gneiss-Schist Results Demonstrating Range of Uncertainty in
AP and NP Estimation
7.1.3 Case 1 — Traditional Sobek
For Case 1, a traditional Sobek approach for calculating both NP and AP was used. This approach
assumed that the dominant sulfide mineral was pyrite and neutralization was dominated by carbonate
minerals calcite or dolomite.
For Case 1, the AP was calculated according to Equation 1, and NP was calculated according to
Equation 2.
[Equation 1] AP (kg CaCO3 eq/t) = Total S (wt%) x 31.25
[Equation 2] NP (kg CaCO3 eq/t) = 25th Percentile of Sobek NP (by rock type)
In the absence of TOC or Sobek NP data in the exploration database, it was conservatively assumed
that the NP was equal to the 25th percentile of the Sobek NP values for each rock type from the SRK
geochemistry dataset.
Using this approach, 32% of the material was predicted to be PAG (Figure 7-3). Case 1 is considered
to represent the upper bound, or worst-case scenario for PAG estimation, because it does not take
into account the sulfide mineralogy or the potential for silicate buffering.
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PAG Abundance
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Amphibole Gneiss-Schist
Biotite Gneiss •1 '
Diabase
Granite
Mica Schist
Pegmatite WMEEW
Po Mica Schist
Shear Schist A
Spod Pegmatite
Upper Mica Schist
Overall •'
Grand Total
Figure 7-3: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 1
7.1.4 Case 2 — Adjustment of AP to Account for Reduced Reactivity of Sulfides
For the Kings Mountain deposit, pyrrhotite is present and can comprise a significant proportion of the
sulfides in the waste rock. Various studies have demonstrated that the oxidation of pyrrhotite(Fe(t-x)S,
x=0-0.2) does not result in as much acid generation as predicted by the ABA test. Based on these
studies, it is reasonable to reduce the potential contribution of pyrrhotite to AP to account for potential
passivation. For this exercise, the contribution of pyrrhotite to AP was reduced by 80% based on
empirical data from Belzile et al. (2004) and Janzen et al. (2000). Additionally, in crystalline bedrock
such as Kings Mountain, sulfides are likely to be encapsulated within silicates or iron oxide minerals
and unavailable for reaction. This is demonstrated by the available mineralogy results. Therefore,
reduction of AP associated with pyrite and chalcopyrite within the material is considered a reasonable
approach. Equation 3 shows the resulting equation for AP that accounts for the reduced reactivity of
sulfides. Table 7-2 provides the percentage of pyrite, pyrrhotite, and chalcopyrite for each rock type
that was calculated from the XRD data.
[Equation 3] AP (kg CaCO3 eq/t) = (Total S (wt%) x pyrite% x 0.5 x 31.25)+(Total S (wt%) x
chalcopyrite% x 0.5 x 31.25) + (Total S (wt%) x pyrrhotite% x 0.2 x 31.25)
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Table 7-2: Average Sulfide Content from XRD
Rock Type Percent Percent Percent Percent
Pyrite P rrhotite Chalcopyrite S halerite
Amphibole Gneiss-Schist 28.2 29.5 32.6 9.7
Biotite Gneiss 19.6 34.4 35.9 10.2
Diabase 60.0 40.0 - -
Granite 60.0 40.0 - -
Mica Schist 43.6 37.3 16.9 2.2
Overburden 100.0 - - -
Pegmatite 51.7 27.5 13.3 7.5
Po Mica Schist 22.3 49.6 21.4 6.7
Shear Schist 13.9 29.2 48.7 8.3
Silica Mica Schist 60.0 20.0 10.0 10.0
Spod Pegmatite 22.7 30.9 44.4 2.1
Upper Mica Schist 31.4 37.4 16.5 14.6
This scenario assumed that only carbonates would contribute to the buffering capacity, and the
equation for NP was the same as for Case 1 (Equation 2). Using this approach, 12.7% of the material
was predicted to be PAG (Figure 7-4).
PAG Abundance
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Amphibole Gneiss-Schist
Biotite Gneiss
Diabase
Granite
Mica Schist
Pegmatite
Po Mica Schist
Shear Schist
Spod Pegmatite
Upper Mica Schist
Overall
Grand Total
Figure 7-4: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium -Case 2
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7.1.5 Case 3 — Adjustment of NP to Account for Acid Neutralizing Calcium Silicates
Traditional Sobek NP measurements only provide an assessment of rapidly dissolving sources of
neutralization (i.e.,carbonate minerals)and may not capture the potential silicate-based neutralization.
Therefore, for Case 3, the approach used to calculate NP attempts to account for the contribution for
calcium bearing silicate minerals that are present as indicated by the XRD results.
For this case, a portion of the total calcium was assumed to be present as calcite (based on the
average NP for the rock type from the Sobek method).The remainder of the total calcium was assumed
to be present as an undefined calcium silicate. Calcium then was assumed to represent 10% of the
mass of the calcium silicate minerals. For comparison, calcium represents as little as 4% of the mass
of hornblende, and as much as 16% in anorthite. A pure calcium silicate phase would likely provide
less buffering capacity than a carbonate phase; therefore, a reduction factor was applied such that the
calcium silicate phases were assumed to provide an NP of 50 kg CaCO3/t (not 1,000 CaCO3, as is
typically done for carbonate). The end result was a calculation of NP that takes into account calcium
silicate-based neutralization, as shown in Equations 4 through 7.
[Equation 4] Carbonate NP (kg CaCO3 eq/t) = 25th Percentile of Sobek NP (kg CaCO3 eq/t)
[Equation 5] Mass proportion of silicates (kg/kg) = Total Ca (wt%) — 25th Percentile of Sobek NP
(kg CaCO3 eq/t) x (40.1/100)/ 10
[Equation 6] Silicate NP = Mass proportion of silicates (kg/kg) x 50 (kg CaCO3 eq/t)
[Equation 7] Adjusted NP (kg CaCO3 eq/t) = Carbonate NP (kg CaCO3 eq/t) + Silicate NP (kg
CaCO3 eq/t)
Using this approach, 21.2% of the material was predicted to be PAG (Figure 5-109).
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PAG Abundance
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Amphibole Gneiss-Schist
Biotite Gneiss
Diabase •'
Granite 1
Mica Schist .1 .•,
Pegmatite
Po Mica Schist
Shear Schist
Spod Pegmatite -
Upper Mica Schist
Overall
Grand Total
Figure 7-5: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 3
7.1.6 Case 4 —Adjustment of Both NP and AP Consistent with Case 2 and 3
For Case 4, both the AP and NP were adjusted and represents the lower boundary, best-case
scenario.This case used the same approach for calculating AP as Case 2,where the AP was adjusted
to account for reduced reactivity of sulfide minerals that is anticipated based on the crystalline nature
of the rocks at Kings Mountain and the presence of pyrrhotite. For NP, the same approach as Case 3
was used, where NP was adjusted to account for calcium silicate buffering. Using this two-pronged
adjustment, 7.5% of the material was predicted to be PAG (Figure 7-6).
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PAG Abundance
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Amphibole Gneiss-Schist
Biotite Gneiss
Diabase I 1'
Granite
Mica Schist
Pegmatite
Po Mica Schist
Shear Schist
Spod Pegmatite
Upper Mica Schist
Overall rpr -
Grand Total E.
Figure 7-6: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium —Case 4
7.1.7 Case 5 (Average Case 1 through 4)
The average of Case 1 through 4 represents the central tendency of the four cases and was the
recommended approach for estimating PAG from the block model. Table 7-3 summarizes the set of
equations for calculating AP and NP based on this average case that were developed for each material
type. Figure 7-7 shows the proportion of PAG for each rock type resulting from these calculations,
which indicates 20% of the material was predicted to be PAG.
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Table 7-3: AP and NP Calculations for Incorporation into Block Model
Rock Type AP Formula NP Formula
Amphibole Gneiss-Schist Tot S x 19.8 250 X (Tot Ca-0.40)/100+ 10.0
Biotite Gneiss Tot S x 19.4 250 X (Tot Ca-0.32)/100+8.0
Diabase Tot S x 21.6 250 X (Tot Ca-0.88)/100+22.0
Granite Tot S x 21.6 250 X (Tot Ca-0.16)/100+4.0
Mica Schist Tot S x 21.2 250 X (Tot Ca-0.20)/100+5.0
Overburden Tot S x 23.4 250 X (Tot Ca-0.01)/100+0.4
Pegmatite Tot S x 20.4 250 X Tot Ca-0.12/100+3.0
Po Mica Schist Tot S x 19.5 250 X Tot Ca-0.23/100+5.8
Shear Schist Tot S x 20.1 250 X Tot Ca-0.36/100+9.0
Silica Mica Schist Tot S x 20.2 250 X Tot Ca-0.40/100+ 10.0
S od Pegmatite Tot S x 21.5 250 X Tot Ca-0.12/100+3.0
Upper Mica Schist Tot S x 18.3 250 X (Tot Ca-0.16)/100+4.0
PAG Abundance
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Amphibole Gneiss-Schist
Biotite Gneiss
Diabase
Granite
Mica Schist
Pegmatite ®
Po Mica Schist
Shear Schist
Spod Pegmatite
Upper Mica Schist
Overall
Grand Total
Figure 7-7: PAG Abundance Among Exploration Pulps Assayed for Sulfur/Calcium -Case 5
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7.1.8 PAG Estimates
Albemarle developed an estimate of PAG waste rock quantities in March 2023 using a hybrid approach
where PAG tonnages were calculated using two methods, including:
1. Estimated PAG: Tonnages of PAG were estimated in Leapfrog for the main material types
using the equations in Table 7-4 for calculating AGP from total sulfur and ANP from total
calcium. These equations are based on the average of Case 1 through 4.
2. Assumed PAG:Tonnages of PAG were calculated by multiplying the total tons by an assumed
percent of PAG based on the geochemical dataset (i.e., ABA data) as described in Section 2
above. This was done for the minor material types because sufficient total sulfur and calcium
data were not available to provide a reliable estimate of PAG.
For the Estimated PAG method, an NNP cut-off of-1 was used to define PAG material, where blocks
with an NNP<-1 are considered PAG. This NNP cut-off was used to eliminate the classification of
samples with non-detectable sulfur and calcium as PAG.
The resulting tonnages and percentages of PAG for each material type are summarized in Table 7-4
and are the tonnages carried forward in the mine plan and geochemical modeling.
Table 7-4: PAG Estimates—Schafer and SRK Approach
Total Mass of Waste Rock Tonnage
Material Type Waste Rock Assumed thousand short tons Percentage
(thousand short PAG( ) Estimated' Assumed' Total' PAG(/o)
tons
Amphibole Gneiss-Schist 57,312.68 8% 2 4,470 2 0
Chlorite Schist 371.5 100% - 371 371 100
Marble 0.07 0% - - 0 0
Mica Schist 12,255.38 64% 6,367 7,807 6,367 52
Musc Pegmatite 53.52 8% - 4 4 7
Pegmatite 38.44 19% - 7 7 18
Phyllite 0.43 -- - - 0 0
Schist-Marble 157.4 0% - - 0 0
Shear Schist 5,070.89 71% 1,330 3,600 1,330 26
Silica Mica Schist 4,858.48 36% 770 1,734 770 16
Spodumene Pegmatite 28,867.60 1% 1,405 318 1,405 5
Upper Mica Schist 1,058.05 67% - 710 710 67
Po Mica Schist 3,893.84 97% 3,883 3,758 3,883 100
Overburden-WR 3,127.75 4% - 128 128 4
Overburden-WR 530.78 8% - 41 41 8
Totals 117,597 -- 13,757 1,263 15,018 13
Assumed percentage of PAG based on the ABA data from the geochemical dataset.
2Estimated refers to tonnages calculated in Leapfrog using calcium and sulfur values.
3 Refers to tonnages calculated by multiplying the total tons by an assumed percent PAG. The percent PAG for each material
type comes from the ABA database.
'Total tonnages include assumed tonnages where estimated tonnages are not available.
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7.1.9 Limitations and Uncertainties on PAG Estimation
The initial PAG estimates presented herein were based solely on a statistical analysis of the ABA data
and did not consider the spatial distribution of PAG throughout the deposit or relative reactivity of the
different materials. To account for the variation in acid generation within each lithologic unit, an
estimation tool such as Leapfrog is needed, along with the data needed to populate the model.
Albemarle has taken steps to collect data with higher density than the geochemistry dataset and
analyzed over 15,000 pulps for total sulfur and total calcium. These data have been incorporated into
the block model and used to refine the initial PAG estimates. However, the level of confidence in the
estimates is unknown and will depend upon the degree of continuity and variability within each
lithology. Additional data are being collected as part of the ongoing exploration program and will serve
to increase the level of confidence in the PAG estimates in the future.
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8 Conclusions
A baseline geochemical characterization program has been completed for the Project to provide an
assessment of the potential for ARDML to form from the waste rock, ore, and tailings materials that
will be handled during mine operations. The geochemical characterization program was designed to
support the next phase of the Project's potential advancement, including local, state, and federal
permitting. The results of the geochemical test work carried out for the Project provide a basis for
assessment of the potential for ARDML, prediction of contact water quality (i.e., runoff and
groundwater that contacts waste rock, ore, pit walls, and tailings), and evaluation of options for design,
construction, and closure of the mine facilities.
At the time of writing, geochemical characterization testing and evaluation is ongoing, and an update
to this report will be prepared upon receipt of the outstanding data and completion of the data analysis.
8.1 Future Waste Rock and Ore
The Kings Mountain geochemical characterization program involved the collection and analysis of
571 samples representative of waste rock and ore for static testing, with 22 representative samples
submitted for kinetic testing.The static and kinetic test methods selected for this Project were designed
to address the bulk geochemical characteristics of the deposit, including the total acid generating and
acid neutralizing potential of the waste rock and ore materials and the concentration of chemical
constituents in leachates that could be derived from future mine facilities.
The methods used for the Kings Mountain geochemical characterization program include ICP-MS
multi-element analysis, ABA, NAG, MWMP, SPLP, LEAF, XRD, optical mineralogy, and SEM. Kinetic
HCTs were also undertaken to define sulfide oxidation rates and metal leaching potential under
laboratory-controlled oxygen and water exposure conditions that simulate weathering in the field. The
waste rock and ore HCTs have been operational for between 66 and 74 weeks at the time of writing.
The results of the static test work demonstrate that the Kings Mountain waste rock is variable with
respect to its acid generation, while the ore is net neutralizing, with an average NPR of
approximately 17. The Amphibole Gneiss-Schist is the main waste rock type and demonstrates a low
potential for acid generation and metal leaching. Other waste rock material types, including the Upper
Mica Schist and Po Mica Schist, are PAG overall, with average NPR values of<1. However, across
all waste rock types, NPR mass balanced against relative proportions (shown in Table 3-1) yields an
overall NPR of 2.2. Taking into account several factors (NPR, NNP, and NAG pH), the static test
program shows that the ore is non-PAG, while the waste rock is variable and ranges from non-PAG to
PAG.
To date, the HCT program broadly supports the predictions of acid generation from the ABA tests,
where those samples with NNP values <0 kg CaCO3 eq/t and NPR values <1 are generating acidic
pH values in the HCT. For some of the schist materials, the HCT program suggests that the NAG test
may overpredict acid generation potential. For these samples, acidic conditions have not developed
despite having a NAG pH <4.5. The geochemical characterization program shows that some of the
waste rock associated with the Project has a potential for acid generation; however, there are currently
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no signs of acid generation at the site acid generation associated with existing facilities. This disparity
between the laboratory and field conditions may relate to:
• The presence of pyrrhotite in the Kings Mountain mineralization and the potential for pyrrhotite
to oxidize along a non-acid generating reaction pathway (i.e., to form elemental sulfur, which
does not result in acidity).
• The slow or partial oxidation of sulfide minerals, particularly pyrrhotite under field conditions
compared to laboratory tests.
• Armoring of pyrrhotite during oxidation that reduces or prevents further oxidation of sulfide
surfaces.
• Potential contribution of silicate minerals to neutralizing capacity that may not be reflected in
the short duration of the ABA test.
Bulk chemical assays indicated that several elements are enriched with respect to crustal averages in
the future waste rock and ore materials, including arsenic, beryllium, cesium, lithium, rubidium, sulfur,
selenium, tin, thallium, uranium, and tungsten. Of these, SPLP testing indicated that arsenic, lithium,
selenium, sulfur, and uranium would be leachable at detectable, though low, filtered concentrations.
LEAF testing has extended the leach datasets to a wider range of conditions (pH and US contact
ratios). Interpretation of the LEAF testing indicates that the leach behavior of many trace elements is
controlled by pH-dependent solubility and/or sorption processes. There is evidence, however, that for
some elements (notably lithium), a key control on leaching may be the presence of small, finite
quantities of a soluble mineral. In such cases, filtered concentrations are strongly influenced by the
US contact ratio.
The characterization test work indicates a portion of the waste rock associated with the Project has a
potential to generate acid and leach metals and active segregation and management of PAG waste
rock is needed to avoid and/or reduce the potential impacts to groundwater and surface water qualities
from mining facility drainage or runoff. The results from the geochemical characterization program
have been used to develop a site-specific approach to classifying waste rock as non-PAG or PAG
during mining.An estimate of the tonnage of PAG material was developed from the block model based
on this approach and was used to support mine planning and design as well as the water quality
predictions presented in SRK (2023).
8.2 Future Tailings Material
Static testing was completed on samples representative of future tailings material and process waste
streams to assess the balance of acid generating and acid neutralizing minerals for these materials.
The ABA results indicate that overall, the sulfide sulfur content was low or below detection for the
flotation tailings and DMS rejects. NP is also relatively low in the flotation tailings and DMS rejects. In
comparison, the OSR and MSR samples have higher sulfide sulfur content than other waste streams
and as such higher potential for acid generation, with some samples indicating a potential for net acid
generation despite the generally low sulfide contents (<1%).
The ABA and NAG results demonstrate that the ore sorting and magnetic separation process results
in the removal of sulfide mineral phases and concentration within the OSR and MSR materials. The
DMS rejects and flotation tailings are depleted with respect to sulfide minerals, demonstrating the
effectiveness of sulfide removal in the ore sorting process. Even flotation tailings that did not undergo
ore sorting have lower overall sulfide sulfur, indicating the magnetic separation process also removes
sulfides. Despite the presence of sulfides within the OSR and MSR material, these materials still show
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an overall low potential for acid generation, with only three of the OSR rejects being classified as PAG
based on the NAG test. Based on the current HCT data, the two OSR HCTs have maintained neutral
conditions throughout the test despite the ABA and NAG results that indicated these samples have a
potential for acid generation. The two HCTs representative of flotation tails have also maintained
neutral conditions as predicted by the static test data.
Metal(loid)s chemistry shows enrichment is associated with the higher sulfide contents in the OSR,
but overall concentrations are still relatively limited by comparison to crustal abundance. Constituents
showing some potential to be leached from the tailings material under neutral conditions include
aluminum, iron, arsenic, antimony, manganese, and zinc.
TSF porewater samples obtained from lysimeters installed in the historical TSFs are circum-neutral,
which is consistent with the static and kinetic test program. Metals concentrations are also low in the
porewater,with many parameters below analytical detection limits, including cadmium, beryllium, lead,
chromium, cobalt, boron, selenium, silver, and thorium. The results showed concentrations of a few
parameters that were higher in comparison to the leach test results of future tailings (e.g., copper and
manganese).
8.3 Cover Material
Static characterization testing was completed on 10 samples of alluvium/overburden and 14 samples
of saprolite to assess the potential for acid generation and metal leaching from potential future cover
material. These results have been supplemented with multi-element data from the Albemarle
exploration database,with total calcium and sulfur values being used as surrogates for estimating acid
neutralizing potential and acid generating potential, respectively.
A suite of static tests was completed on the samples, including ABA, NAG, and multi-element analysis.
The results can be summarized as follows:
• Most of the alluvium and saprolite samples are characterized by low sulfur contents
(<0.01 wt%) and are classified as non-PAG based ABA and NAG test results.
• Paste pH of the 14 saprolite samples is circum-neutral (ranging between pH 5.2 and 7.9),with
three samples that were pH <6. The paste pH results for the alluvium samples are similar to
the saprolite samples.
• The alluvium and saprolite samples are typically characterized by lower paste pH values
compared to the waste rock (core) samples from the geochemical characterization program.
These lower pH values relate to the removal of primary neutralizing minerals by weathering
processes, leaving behind insoluble acidic iron and aluminum oxide minerals.
• Multi-element data from the exploration database classifies the majority(73%)of the alluvium
and saprolite samples as non-PAG based on surrogate NPR. Only 6% of alluvium/saprolite
samples within the exploration database are classed as PAG, with the remainder showing
uncertain acid generating characteristics based on surrogate NPR.
• Metals that are found elevated above average crustal concentrations in the alluvium and
saprolite include: arsenic, beryllium, bismuth, cadmium, cobalt, lithium, antimony, silver, tin,
and thallium; this was consistent for both the 24 cover material samples (i.e., saprolite and
alluvium) and the exploration database samples.
Based on the soil baseline study completed by SWCA, soil properties are fairly consistent across the
Project area, where minerals and nutrients have been leached from the AB horizons and have
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accumulated in the C horizon. Cover materials samples from the sonic drilling program show a similar
range of pH as the SWCA soil samples.The results of the characterization program indicate that there
are no significant geochemical differences between the alluvium and saprolite material, and they can
be used interchangeably for reclamation purposes.
8.4 Legacy Mining Waste
Samples of legacy mine waste were collected from the existing facilities in the Project area for
geochemical characterization testing. This included 23 samples of historical tailings and 33 samples
of legacy rock dumps. The purpose of this sampling was to determine the weathering and ARDML
characteristics of legacy rock dumps and tailings that have been exposed to oxidation processes at
the surface for over 35 years. It is important to note that the legacy waste rock was only available from
the legacy TSF berm and this may represent more geochemically-geotechnically stable rock so may
not represent the full range of potential characteristics for the legacy waste rock.
8.4.1 Legacy Waste Rock
A suite of static tests was completed on the legacy waste rock samples, including ABA, NAG, and
multi-element analysis. The results can be summarized as follows:
• The samples are characterized by low sulfide sulfur contents (<0.07 wt%) and exhibit either
non-acid generating or uncertain acid generating behavior based on ABA testing. None of the
samples are classed as PAG materials.
• The total sulfur and sulfide sulfur content of the legacy waste rock samples is toward the lower
range of those observed in the future waste rock materials, and the samples show an overall
lower potential to generate acid based on ABA and NAG testing; this is consistent with the
neutral conditions observed on-site.
• The paste pH of the legacy samples is typically lower(i.e., more acidic)compared to the future
waste rock and ore samples, most likely as a result of exposure of the legacy waste to
oxidation processes at surface for over 35 years. Despite this, acidic conditions (pH <5) have
only developed in one sample, and in general the paste pH values for the legacy mine waste
samples are circum-neutral to moderately alkaline.
• Metals that are found elevated above average crustal concentrations in the legacy waste rock
include arsenic, antimony, beryllium, cesium, chromium, cobalt, lead, lithium, magnesium,
manganese, nickel, rubidium, selenium, sulfur, thallium, tin, tungsten, uranium, and zinc; this
is generally consistent with future waste rock and ore samples.
• For the SPLP results for legacy waste rock, the leachate pH was variable, ranging from acidic
to alkaline (pH 4.3 to 9.8). For the majority of the legacy waste rock samples, element release
was low or at the limit of detection under neutral conditions. Constituent concentrations for the
legacy waste rock samples were comparable to the future waste rock samples.
8.4.2 Legacy Tailings
The legacy tailings characterization test work included ABA, NAG, and multi-element analysis. The
results can be summarized as follows:
• Sulfide sulfur is below analytical detection limits in the legacy tailings samples, and all samples
are classed as non-acid generating based on ABA and NAG testing.
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• Paste pH values are circum-neutral to alkaline(pH 6.0 to 9.8), indicating minimal development
of acidic salts on the tailings surface despite exposure to surface oxidation processes for
several decades.
• Metals that are found elevated above average crustal concentrations in the legacy waste rock
include arsenic, antimony, beryllium, cadmium, cesium, cobalt, lithium, rubidium, selenium,
sulfur, thallium, tin, tungsten, and uranium; this is generally consistent with future tailings
samples.
• For the legacy tailings samples, the leachate pH was circum-neutral and less variable in
comparison to the legacy waste rock samples (pH 5.9 to 8.1). Constituent release was
generally low or at the limit of detection. There was no significant difference in constituent
concentrations for the legacy tailings samples compared to future tailings samples.
8.5 Kings Creek Stream Sediment
Three samples of stream sediment were collected from Kings Creek for geochemical characterization
testing, including ABA, NAG, and SPLP. The results can be summarized as follows:
• The stream sediments are characterized by variable sulfide contents, ranging from below
analytical detection limits (<0.01%) in downstream Kings Creek to a maximum of 0.81% in
upstream Kings Creek.
• Based on ABA and NAG test results, the stream sediments are either classed as non-acid
forming or show low potential for acid generation.
• Metals that are found elevated above average crustal concentrations in the stream sediment
samples include antimony, arsenic, beryllium, cesium, lithium, molybdenum, selenium, sulfur,
thallium, tin, tungsten, and uranium.
• SPLP testing indicated that arsenic, antimony, fluoride, iron, lithium, manganese, and sulfate
are leachable at detectable, though low, filtered concentrations from the stream sediments.
• SWO04 shows some potential for being impacted by mineralized waste (high Li content) and
may not be representative of the rest of the stream sediments.
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9 References
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Ficklin, W. H., Plumlee, G. S., Smith, K. S., and McHugh, J. B., 1992. Geochemical classification of
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International Network for Acid Prevention (INAP), 2014. Global Acid Rock Drainage Guide (GARD
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Jennings, S. R., and Dollhopf, D. J., 1995, Acid-Base Account Effectiveness for Determination of Mine
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United States Environmental Protection Agency(US EPA), 1994. Method 1312. Synthetic Precipitation
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Granular Materials Using a Semi-Dynamic Tank Leaching Procedure, July 2017.
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Disclaimer
The opinions expressed in this Report have been based on the information supplied to SRK Consulting
(U.S.), Inc. (SRK) by Albemarle Corporation (Albemarle). These opinions are provided in response to
a specific request from Albemarle to do so, and are subject to the contractual terms between SRK and
Albemarle. SRK has exercised all due care in reviewing the supplied information. Whilst SRK has
compared key supplied data with expected values, the accuracy of the results and conclusions from
the review are entirely reliant on the accuracy and completeness of the supplied data. SRK does not
accept responsibility for any errors or omissions in the supplied information and does not accept any
consequential liability arising from commercial decisions or actions resulting from them. Opinions
presented in this report apply to the site conditions and features as they existed at the time of SRK's
investigations, and those reasonably foreseeable. These opinions do not necessarily apply to
conditions and features that may arise after the date of this Report.
Copyright
This report is protected by copyright vested in SRK Consulting (U.S.), Inc. It may not be reproduced
or transmitted in any form or by any means whatsoever to any person without the written permission
of the copyright holder, SRK.
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2022 PFS Report—Kings Mountain Mining Project Appendices
Appendices
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2022 PFS Report—Kings Mountain Mining Project Appendices
Appendix A: Sample Locations and Distribution
AP/RB

MainMaterialType
■Amphibole Gneiss-Schist
■Biotite Gneiss
■Chlorite Schist
■Diabase
■Granite
Mica Schist
■Overburden
......................................................................................:..............................................................................................................................................................................;................................. .......... rt...... ...._. ..........:I.............�............ J.....,. ... ■Pegmatite
r ■Po Mica Schist
J
Gr ■Schist-Marble
i =r Shear Schist
Silica Mica Schist
} t r�" x� { ■Spod Pegmatite
mom ,01 Upper Mica Schist
r '
loor
Is or/
Ar.
1001.
100e N
Plunge+30
Azimuth 000
Looking North
0 500 1000 1500

+1294000 E +1296000 E +1299000 E MainMaterial-Fype
■Amphibole Gneiss-Schist
- .� ■BIURL Gneiss
■ChioriteSchist
- - ■Diabase
■Granite
Mica Schist
■Overburden
■Pegmatite
■Pa Mica Schist
Schist-Marble
+ Shear Schist
!Silica Mica Schist
Spad r
egmatite
+544000 N -
} Upper Mica Schist44000 N
0
+542000 N +542000 N
� 0 N
0
its 0 Plunge+90
Azimuth 000
Looking down
0 500 1000 1500 2000
+1294000 E +1296000 E +1299000 E

MainMaterialType
■Amphibole Gneiss-Schist
- ■BIURL Gneiss
Chlorite Schist
- - ■Diabase
■Granite
Mica Schist
■Overburden
■Pegmatite
■Po Mica Schist
Schist-Marble
�+ Shear Schist
` Silica Mica Schist
f ■Spod Pegmatite
Upper Mica Schist
eesft
� 0 N
0 Plunge+90
Azimuth 000
Looking down
0 500 1000 1500 2000

MainMaterialType
■Amphibole Gneiss-Schist
■Biotite Gneiss
��� ■Chlorite Schist
- - ■Diabase
■Granite
r Mica Schist
f ■Overburden
f ! ■Pegmatite
■Po Mica Schist
f Schist-Marble
Shear Schist
Silica Mica Schist
■Spod Pegmatite
Upper Mica Schist
I�
� N
Plunge+90
Azimuth 000
Looking down
0 500 1000 1500 2000