Geochemical Pit Lake Predictive Model Revision 1 · (mean case for the pit lake model) in order to track the chemical mass associated with each hydrologic component of the water balance

Geochemical Pit Lake Predictive Model Revision 1

Rosemont Copper Project

This report presents an update to the February 2010 Geochemical Pit Lake Predictive Model report. Updates were based on a review of the February 2010 report in conjunction with results from Tetra Tech’s regional groundwater flow model.

November 2010

MONT COPPER Rosourcolul.

Memorandum

To: Bev Everson

Cc: Tom Furgason

From: Kathy A

Doc #: 045/1

Subject: Transmittal of Technical Responses and Reports

Date: November 18, 2010

Rosemont is pleased to transmit the following documents:

• Response to SRK's Technical Review Comments to Tetra Tech's Groundwater Flow Model, Technical

Memorandum, Tetra Tech, November 17, 2010

• Response to comments on February 2010 Geochemical Pit Lake Predictive Model Report, Technical

Memorandum, Tetra Tech, November 16, 2010

• Geochemical Pit Lake Predictive Model — Revision 1 (includes DSM Input/Output Files in electronic format), Tetra Tech, November 2010

Rosemont is providing three hardcopies and two disk copies for the Forest and two hardcopies and one

disk copy for SWCA of the technical memos. Copies of the reports are provided in a hardcopy format

with the disk copies enclosed in the same number.

Geochemical Pit Lake Predictive Model Revision 1 Rosemont Copper Project This report presents an update to the February 2010 Geochemical Pit Lake Predictive Model report. Updates were based on a review of the February 2010 report in conjunction with results from Tetra Tech’s regional groundwater flow model.

Prepared for:

4500 Cherry Creek South Drive, Suite 1040 Denver, Colorado 80246 (303) 300-0138 Fax (303) 300-0135

Prepared by:

3031 West Ina Road Tucson, Arizona 85741 (520) 297-7723 Fax (520) 297-7724

Tetra Tech Project No. 114-320884

November 2010

Geochemical Pit Lake Predictive Model – Revision 1 Rosemont Copper Company

Tetra Tech November 2010 i

TABLE OF CONTENTS

EXECUTIVE SUMMARY .............................................................................................................. 1 1.0 INTRODUCTION ............................................................................................................... 3

1.1 Geologic Setting .................................................................................................... 3

2.0 ROSEMONT PIT LAKE CONCEPTUAL MODEL ............................................................ 5 3.0 PIT LAKE WATER BALANCE ......................................................................................... 6

3.1 Groundwater Inflow and Outflow ........................................................................... 6 3.2 Direct Precipitation ................................................................................................ 7 3.3 Pit Wall Runoff ....................................................................................................... 8 3.4 Upgradient Drainage Runoff .................................................................................. 8 3.5 Evaporation ........................................................................................................... 8

4.0 CHEMICAL LOADING .................................................................................................... 10 4.1 Groundwater Inflow Chemistry ............................................................................ 10 4.2 Precipitation Chemistry ....................................................................................... 10 4.3 Pit Wall Runoff and Blast Zone Chemistry .......................................................... 11 4.4 Statistical Development of Pit Wall Runoff Model Input ...................................... 15 4.5 Initial Flushing of Blast Affected Pit Wall Rock .................................................... 17

5.0 DYNAMIC SYSTEMS MODEL (DSM) INTEGRATION .................................................. 18 5.1 Model Objectives ................................................................................................. 18 5.2 Model Structure and Formulation ........................................................................ 18

5.2.1 Hydrology................................................................................................. 19 5.2.2 Pit Geometry ............................................................................................ 21 5.2.3 Meteorology ............................................................................................. 22

5.3 Model Results ...................................................................................................... 23 5.3.1 Water Balance and Lake Formation ........................................................ 23 5.3.2 Lake Stratification .................................................................................... 27 5.3.3 Chemical Loading .................................................................................... 28

6.0 GEOCHEMICAL MODELING ......................................................................................... 30 6.1 Mineral Precipitation ............................................................................................ 30 6.2 Surface Complexation ......................................................................................... 30 6.3 Results ................................................................................................................ 31

7.0 DISCUSSION OF RESULTS .......................................................................................... 38 8.0 CONCLUSIONS .............................................................................................................. 41 9.0 REFERENCES ................................................................................................................ 42


Tetra Tech November 2010 ii

LIST OF TABLES

Table 4.01 Average Water Quality Parameters for Pit Area Monitor Wells ........................... 10 Table 4.02 Average Monthly Precipitation – Weighted Average pH and Major Ion

Concentrations (mg/L) from NADP Site AZ06 ..................................................... 11 Table 4.03 Average SPLP Results (mg/L) for Various Rock Types Representing

Rosemont Pit Wall Rocks .................................................................................... 14 Table 4.04 Samples Selected for the Three (3) Modeled Scenarios ..................................... 16 Table 5.01 Predicted pit lake elevation for various DSM simulations .................................... 23 Table 6.01 Potential Mineral Solubility Controls for the Rosemont Pit Lake Model ............... 30 Table 6.02 Range in Predicted Water Quality (200-Year Simulation) for the Rosemont

Pit Lake ............................................................................................................... 37 Table 7.01 Comparison of Local Groundwater with Modeled Pit Lake Water ....................... 39

LIST OF ILLUSTRATIONS

Illustration 3.01 Conceptual Hydrologic Model for the Rosemont Pit Lake .................................. 6 Illustration 3.02 Mean Monthly Pan Evaporation ......................................................................... 9 Illustration 4.01 Projected Proportions of Exposed Rock Types in the Rosemont Pit ................ 12 Illustration 5.01 Groundwater Inflow versus Lake Stage – DSM Simulation Compared to

Tetra Tech (2010) Groundwater Flow Model Output ........................................ 21 Illustration 5.02 Change in Lake Surface Area with Lake Stage Elevations ............................... 22 Illustration 5.03 Simulated Pit Lake Elevation for the 1,000-year Period of Simulation ............. 24 Illustration 5.04 Simulated Water Balance for the First 20-year Period of Simulation................ 25 Illustration 5.05 Simulated Annual Water Balance for the 1,000-year Period of Simulation

for the DSM Model and the Groundwater Flow Model ..................................... 26

LIST OF APPENDICES

Appendix A Climate Data Summary Appendix B Sample Adequacy Evaluation for Rosemont Geologic Materials Appendix C Geochemical Evaluation of Rosemont Kinetic and Short-Term Leach Test Data Appendix D Sample Probability Plots and DSM Input (Electronic) Appendix E DSM Output (Electronic) Appendix F Example PHREEQC Input File


Tetra Tech November 2010 1

EXECUTIVE SUMMARY

The Rosemont Copper Project involves developing an open pit mine over a 20-25 year period on the east side of the Santa Rita Mountains. At the end of mining, final reclamation of the site will occur, including demolition and closure of the Plant Site facilities and final regrading and revegetation of the Rosemont Ridge Landform. The Rosemont Ridge Landform is the consolidated and contoured earthen structure consisting of waste rock from the open pit, a closed Heap Leach Facility encapsulated with waste rock, and a Dry Stack Tailings Facility, also encapsulated with waste rock.

In addition to the Rosemont Ridge Landform, the open pit will remain following closure. Once mining and mineral processing activities cease, dewatering of the pit will be terminated. Tetra Tech produced a regional groundwater flow model which yielded the following general conclusions:

A pit lake is expected to form in the bottom of the open pit; and

Based on the expected inflows to the pit lake (groundwater seepage and precipitation) in relation to the annual evaporation from the pit lake surface, the pit lake will be a hydraulic sink. The overall effect of the hydraulic sink will be to draw water into the system and not allow water to exit the pit.

In addition to the hydrogeological analysis performed using the numerical groundwater flow model, the expected chemical conditions within the pit lake were analyzed by Tetra Tech. This analysis included geochemical testing of the non-ore rock expected to comprise the final pit walls, and a comparison of the results of that geochemical testing to local groundwater quality. Tetra Tech replicated the water balance simulation of the regional groundwater flow model (mean case for the pit lake model) in order to track the chemical mass associated with each hydrologic component of the water balance to create a geochemical pit lake predictive model.

The geochemical model showed the quality of the pit lake water was only slightly changed from local groundwater after 1,000 years of simulation. The conclusions of the predictive geochemical modeling effort performed for the Rosemont Copper Project can be summarized as follows:

The majority of the inflow water entering the open pit will be from groundwater sources seeping through the pit walls. Most of the water and about 95% of the chemical mass contribution to the pit lake will be from groundwater. Direct precipitation, and runoff from the pit walls, will contribute to the pit lake water balance as well. Over time, the contribution from direct precipitation will increase as a percentage of the annual water balance as the pit lake surface area increases;

The pit lake is anticipated to be similar to the local groundwater with a pH of 8, which is slightly alkaline; and

Because the pit lake is predicted to be a hydraulic sink, with water leaving only through evaporation, dissolved chemical constituents are expected to concentrate over time. At the 200-year simulation mark, the model showed evapo-concentration of some constituents about 1.1 to 1.7 times that of local groundwater, depending upon the chemical loading scenario (low, average, or high).

As indicated above, the quality of the pit lake water was only slightly changed from local groundwater after 1,000 years of model simulation. At that time, the pH of the pit lake water is



anticipated to be about 8, which is also similar to local groundwater. Sulfide minerals are largely absent from the non-ore rock at the Rosemont site and carbonate minerals, such as in limestone, are abundant. Therefore, the development of an acidic pit lake is not expected, even beyond the 1,000-year modeling period.

Modeling results indicate that most of the water reporting to the pit lake will come from local groundwater, with some resulting from direct precipitation and runoff from the pit walls and other minor upgradient areas.

Laboratory testing was conducted to determine the chemical loading terms required for the geochemical pit lake predictive model. Calculations were performed to provide low, average, and elevated chemical loading scenarios over the 1,000-year time-frame simulated in the model. This was done to provide a sensitivity evaluation of the model. These geochemical sensitivities were also coupled with a sensitivity analysis of the hydrologic inputs, thus providing a range of future water quality conditions in the pit lake.

The concentrations of some dissolved chemical constituents were shown in the elevated chemical loading scenario to increase by a factor of up to 1.7 at the 200-year simulation mark relative to local groundwater. This was due to the evaporative loss of water. However, even in the elevated chemical loading scenario, metals were shown to remain at levels in the parts per billion range (less than 1 part per million). Although water quality predictions were modeled to the 1,000-year simulation time-frame, these results should only be used for determining overall trends. Specific water quality predictions beyond the 200-year time-frame become excessively speculative based on the long simulation periods.



1.0 INTRODUCTION

The Rosemont Copper Company (Rosemont) proposes to develop an open pit copper mine and ore processing facilities on the east side of the Santa Rita Mountains, approximately 30 miles southeast of Tucson, Arizona. The operational period will be 20 to 25 years. The ore will be recovered using conventional open-pit mining techniques. Blasting operations will produce waste rock that will be placed into a designated Waste Rock Storage Area, whereas ore will be transported from the pit and processed using conventional sulfide milling or leaching procedures. Approximately 546 million tons (Mt) of sulfide ore and over 60 Mt of oxide ore are expected to be recovered from the open pit (pit) during the anticipated 20-25 year mine life.

At the end of mining, final reclamation of the site will occur, including demolition and closure of the Plant Site facilities and final regrading and revegetation of the Rosemont Ridge Landform. The Rosemont Ridge Landform is the consolidated and contoured earthen structure consisting of waste rock from the open pit, a closed Heap Leach Facility encapsulated with waste rock, and a Dry Stack Tailings Facility, also encapsulated with waste rock.

In addition to the Rosemont Ridge Landform, the open pit will remain following closure. Once mining and mineral processing activities cease, dewatering of the pit will be terminated. Tetra Tech, Inc. (Tetra Tech) produced a groundwater flow model for the Rosemont site which yielded the following general conclusions:

A pit lake is expected to form in the bottom of the open pit; and

Based on the expected inflows to the pit lake (groundwater seepage and precipitation) in relation to the annual evaporation from the pit lake surface, the pit lake will be a hydraulic sink. The overall effect of the hydraulic sink will be to draw water into the system and not allow water to exit the pit (Tetra Tech, 2010a).

Based on the pit filling data from the groundwater flow model, a geochemical pit lake predictive model was developed by Tetra Tech. The objectives of the geochemical modeling effort were:

To prepare a conceptual pit lake model of the anticipated pit lake;

To reproduce the base case pit water balance predicted by the groundwater flow model (Tetra Tech, 2010a) and incorporate the geochemical components of the pit lake model;

To describe a Dynamic Systems Model (DSM) used to integrate the various hydrologic and chemical mass components of the pit lake model;

To provide a description of the geochemical equilibrium processes used to predict pit lake water quality over time; and

To develop a range of potential pit lake water quality predictions based on uncertainties associated with hydrologic and chemical parameter estimates.

The main body of the report presents the general evaluations performed and conclusions made based on the analysis. Supporting details are provided in the appendices.

1.1 Geologic Setting A detailed description of the geology of the Rosemont region and the Rosemont Copper Project (Project) specifically is presented in the report titled Geochemical Characterization, Addendum 1 (Tetra Tech, 2007). In general, Rosemont can be classified as a skarn system. Within this class of copper deposits, the ores are primarily contained within sedimentary and/or volcanic hosts



which have been cut by weakly mineralized intrusive porphyry stocks. At Rosemont, carbonate and clastic lithologies of the Paleozoic section (Martin through Epitaph) have been altered and mineralized to varying degrees by quartz latite porphyry and quartz monzonite porphyry stocks (Daffron et al, 2007). Most of the primary sulfide mineralization at Rosemont is hosted by Horquilla Limestone, Colina Limestone, and the Epitaph Formation. Hypogene mineralization (primary mineralization formed by ascending hydrothermal solutions) is characterized by finely disseminated and vein-controlled bornite, chalcopyrite, sphalerite, molybdenite, and pyrite. Silver occurs in minor, but economically significantly quantities. Like most copper systems of southwestern North America, the gold content of the mineralized zones at Rosemont is negligible. Compared to other southwest copper systems, the total sulfide content of the Paleozoic hosts at Rosemont is generally quite low (<3%). Thus, while sulfide mineralization is present at the Rosemont site, acid neutralizing limestone (calcium carbonate) is abundant.



2.0 ROSEMONT PIT LAKE CONCEPTUAL MODEL

Upon cessation of mining activities and active pit dewatering, the open pit will begin to fill with water. The rate at which the pit fills, and the ultimate depth and stage of the pit lake, is dependent upon the water balance of the pit lake. A pit lake water balance describes how water flows into and out of the lake. Depending on the relative magnitudes of these flows, the pit will remain dry or a pit lake will form. In the case of Rosemont, the pit lake water balance as simulated by the regional groundwater flow model (Tetra Tech, 2010a) indicates that a pit lake will form.

As a result of the anticipated water level in the pit being below that of the groundwater in the adjacent bedrock, groundwater will flow towards and into the pit. Precipitation on the catchment above the pit, the pit walls, and the lake itself will also add water to the pit lake. All of the water sources that report to the pit will carry with them an associated chemical load of dissolved chemical constituents, principally as a result of water contacting local rock units.

In simplest terms, producing a prediction of water quality in the pit requires summing the total chemical load reporting to the pit lake from the various inflows and dividing it by the total volume of water in the pit. This results in an estimate of the chemical concentrations in the projected pit lake. Because the filling of the pit will take a significant length of time, modeling the changing inflows of water and their corresponding chemical loads is simulated using a series of discreet time steps.

The rate at which various water sources report to the pit changes through time. However, the chemical composition of each water source remains constant in the model. Computer software is used to simulate the changing rate of inflow of various water sources and to track the total chemical load associated with each source. The following report sections summarize the critical components of the hydrologic features of the projected pit lake, the chemical composition of each inflow, and the results of the numerical simulation.



3.0 PIT LAKE WATER BALANCE

During the post-closure stage of the Project, a lake is expected to form in the pit. The rate of pit filling and the ultimate level or stage of the pit lake will be controlled by the post-closure water balance. Conceptually, the post-closure water balance can be expressed as:

Δpit lake volume = Iprecip + Irunoff + Ipitrunoff+ GWinflow– Epit- GWoutflow

Where:

Iprecip is the inflow from direct precipitation falling on the lake surface;

Irunoff is the inflow from runoff from upgradient drainages;

Ipitrunoff is the inflow from pit wall runoff (the fraction of precipitation falling on the pit walls that ultimately reaches the pit lake);

GWinflow is the groundwater inflow to the pit lake;

Epit is the open water evaporation from the pit lake surface based on a modified pan evaporation rate; and

GWoutflow is the outflow of groundwater from the pit lake, which based upon modeled results (Tetra Tech, 2010a; Tetra Tech, 2010c) is zero.

The interaction between these parameters is presented schematically in Illustration 3.01. The components of the pit lake water balance are discussed below.

Evaporation

Direct Precipitation

Wall Runoff

Rosemont Pit Lake Groundwater Inflow

Evaporation

Direct Precipitation

Wall Runoff

Rosemont Pit Lake Groundwater Inflow

Illustration 3.01 Conceptual Hydrologic Model for the Rosemont Pit Lake

3.1 Groundwater Inflow and Outflow Groundwater inflow will contribute the majority of water to the developing pit lake (Illustration 3.01). A regional groundwater flow model was developed (Tetra Tech, 2010b) to simulate



dewatering and post-mining pit filling. The challenge of predicting the rate of pit filling is a complicated transient problem and depends on:

• the rate and duration of pit dewatering; • the depth, size, and geometry of the final pit configuration; • the geology and geologic structures proximal and distal to the pit; and • the pre-mining hydrologic regime.

Ultimately, groundwater inflow to the post-mining pit is dependant on hydraulic heads adjacent to and underneath the pit, the lake stage, and the aquifer properties of the surrounding country rock. The groundwater inflow rate is initially high and decreases with time as heads in the aquifer approach (but never reach) the lake stage.

The regional groundwater flow model (Tetra Tech, 2010a) predicts that the pit lake will be a terminal pit lake (i.e., a hydrologic sink). The hydraulic gradients will be towards the pit in perpetuity as a result of high evaporation rates and relatively low groundwater inflow rates. As a result, no groundwater flow out of the lake is predicted (Tetra Tech, 2010a; Tetra Tech 2010c).

3.2 Direct Precipitation Precipitation which falls directly onto the lake surface will contribute to the pit water balance, particularly as the surface area of pit lake increases with time. Rosemont installed an on-site monitoring station that began recording meteorological data in April 2006. This station is monitored by Applied Environmental Consultants (AEC), and the monitoring program includes data processing and instrument audits, calibrations, and maintenance. The Rosemont meteorological monitoring site is located at the center of the proposed open pit at an elevation of 5,350 feet above mean sea level (amsl). Table 3.01 summarizes the average monthly precipitation for the data recorded from April 2006 through September 2008.

Table 3.01 Average Monthly Precipitation Summary (inches)

MonthRosemont Station

(2006-2008) JAN 0.59 FEB 0.79 MAR 0.45 APR 0.45 MAY 0.51 JUNE 0.98 JULY 5.51 AUG 3.74 SEPT 1.62 OCT 0.24 NOV 1.11 DEC 1.16

TOTAL 17.12Note: Rosemont Station is at

5,350’ amsl.

Based on a detailed analysis (Tetra Tech, 2009), the data derived from the Nogales weather station was selected to represent the long-term weather conditions at the Rosemont site. Pertinent information is summarized in Appendix A. In comparison to Rosemont, the total average annual rainfall for the Nogales station is 17.37 inches (Table 3.02 Average Monthly Nogales Station Summary) which is less than a 2% difference (0.25 inches) of the Rosemont



station. Although the Nogales station is located at an elevation of 3,560 feet amsl versus 5,350 feet amsl for the Rosemont station, the Nogales station is the closest station to the Rosemont that includes more than 50 years of continuous data for both precipitation and evaporation measurements. Application of these data to the pit lake model is consistent with other hydrologic studies completed for the site (Tetra Tech, 2010a; Tetra Tech, 2010b; Tetra Tech 2010c).

Table 3.02 Average Monthly Nogales Station Summary (inches)

Month PrecipitationJAN 1.10 FEB 0.85 MAR 0.90 APR 0.39 MAY 0.22 JUNE 0.47 JULY 4.34 AUG 4.13 SEPT 1.55 OCT 1.33 NOV 0.66 DEC 1.43

TOTAL 17.37

The volume of direct precipitation which falls onto the lake each year is proportional to the surface area of the lake and the annual precipitation. Therefore, direct precipitation becomes an increasingly important component of the hydrologic water balance as the lake surface area increases with pit filling.

3.3 Pit Wall Runoff A portion of the precipitation which falls in the pit area will fall on the pit walls and contribute to the development of the pit lake as wall rock runoff (Illustration 3.01). The precise volume of runoff from pit walls is complicated to determine and is dependant on many variables such as the size of the storm, antecedent conditions, type of precipitation, hydraulic conductivity of the exposed rock, the degree to which blasting and/or compaction (due to haul trucks) have altered the material properties, etc. The volume of pit wall runoff contributing to the pit lake each year will be proportional to the total area of exposed wall rock and the annual precipitation. Consequently, the contribution of pit wall runoff to pit lake development will decrease over time, as the total area of exposed pit wall decreases with pit filling.

3.4 Upgradient Drainage Runoff The areas above the 5,100 feet amsl boundary of the pit rim are considered upgradient catchment areas. Runoff from these areas will reach the pit walls from unbermed drainages or as sheet flow.

3.5 Evaporation Direct evaporation from the pit lake surface will act to remove water from the pit lake (Illustration 3.01). Pan evaporation is measured at the Rosemont Weather Station and has been continuously collected since June 2008. However, as a result of the short period of record, the projected pan evaporation for the Rosemont Site was estimated (Tetra Tech, 2009). The Nogales station was adjusted to the Rosemont Project site based on a linear trend with each



station’s elevation (see Appendix A for summary data). The estimated monthly pan evaporation is shown in Illustration 3.02 and totals 71.52 inches per year.

The monthly average projected pan evaporation data were converted to a lake evaporation rate. This conversion coefficient accounts for the fact that an evaporation pan has far less heat-storage capacity than a large water body, no groundwater inflow, and metal sides exposed to sun and air which tends to overstate evaporation.

A common value for converting pan evaporation to lake evaporation is 0.70 (Kohler and Parmele, 1967). However, pit lakes often have lower pan evaporation coefficients than natural lakes due to their high relative depths, reduced solar radiation (due to shading), and lower wind exposures. Additionally, the volume of water which evaporates will be proportional to the surface area of the exposed lake surface. Therefore, the contribution of evaporation to the pit lake water balance will increase as the surface area of the lake increases with pit filling.

0

2

4

6

8

10

12

14

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Eva

pora

tion

(inch

es)

Nogales Station PanEvaporation: Total 91.2

Rosemont Projected PanEvaporation: Total 71.5

Illustration 3.02 Mean Monthly Pan Evaporation



4.0 CHEMICAL LOADING

Each of the Rosemont pit lake hydrologic components (Illustration 3.01) has an associated chemical mass loading component. Some of the chemical components are easily defined since they can be directly measured (i.e., groundwater quality and precipitation chemistry). Other chemical components, such as chemical loading associated with pit wall runoff, must be estimated from geochemical testing using representative samples of wall rock. The methods used to define the various chemical mass loading characteristics for each of the hydrologic components shown in Illustration 3.01 are described in the following sections.

4.1 Groundwater Inflow Chemistry In the pit lake model, the chemical composition of groundwater was represented using the averaged constituent concentrations from monitor wells PC-1 through PC-8 (M&A, 2009) located in the vicinity of the proposed Rosemont open pit (Table 4.01).

Table 4.01 Average Water Quality Parameters for Pit Area Monitor Wells

Parameter Concentration (mg/L) Aluminum <0.03 Antimony <0.0004 Arsenic 0.0037 Barium 0.042 Beryllium <0.0001 Bicarbonate 187 Cadmium <0.0001 Carbonate 4.5 Calcium 131 Chloride 8.36 Chromium <0.01 Cobalt <0.01 Copper <0.01 Fluoride 0.85 Iron 0.554 Lead 0.00092 Magnesium 20.5 Manganese 0.174 Molybdenum 0.121 Mercury <0.0002 Nickel <0.01 Nitrate-N 0.49 Potassium 3.17 Radium226+228 (pCi/L) 1.58 Selenium 0.00212 Silver <0.01 Sodium 26.0 Sulfate 300 Thallium <0.0001 Uranium 0.00419 Zinc 0.694

4.2 Precipitation Chemistry The chemical composition of precipitation was obtained from the National Atmospheric Deposition Program (NADP, 2008). Monthly precipitation chemistry was obtained for the Organ



Pipe Cactus National Monument (NADP Site AZ06), which is the nearest NADP station west of the Rosemont Project site, located at 5,151 feet amsl. Monthly average concentrations, expressed as precipitation-weighted means from weekly sample results, were averaged for each month and represent the period of record between 1980 and 2006 (Table 4.02).

Table 4.02 Average Monthly Precipitation – Weighted Average pH and Major Ion Concentrations (mg/L) from NADP Site AZ06

Month pH Calcium Magnesium Potassium Sodium Ammonia

(as N) Nitrate (as N)

Chloride Sulfate

Jan 5.53 0.24 0.06 0.03 0.35 0.10 0.11 0.58 0.63 Feb 5.64 0.33 0.13 0.04 0.71 0.11 0.10 1.22 0.732 Mar 5.60 0.19 0.09 0.04 0.69 0.09 0.10 1.17 0.53 Apr 5.96 0.62 0.19 0.07 1.31 0.07 0.14 2.12 1.10 May 5.85 0.71 0.80 0.32 4.89 0.34 0.42 8.41 4.78 Jun 5.10 1.55 0.17 0.17 0.45 0.68 0.77 0.74 4.27 Jul 5.49 0.63 0.07 0.08 0.38 0.49 0.50 0.37 1.68 Aug 5.04 0.38 0.05 0.04 0.15 0.30 0.40 0.24 1.41 Sep 5.26 0.36 0.06 0.04 0.31 0.23 0.29 0.46 1.26 Oct 5.41 0.38 0.11 0.05 0.65 0.17 0.21 1.03 1.57 Nov 5.71 0.41 0.17 0.06 1.07 0.07 0.14 1.84 0.73 Dec 5.40 0.14 0.04 0.02 0.30 0.07 0.12 0.49 0.50

4.3 Pit Wall Runoff and Blast Zone Chemistry Precipitation on the exposed pit will dissolve chemical mass from wall rocks, which subsequently becomes transported to the lake by means of pit wall runoff. Both the quantity and quality of the total pit wall runoff will be proportional to the exposed areas of the various rock types in the ultimate pit wall (Illustration 4.01). The contribution of chemical loading from pit wall runoff will decrease with time, as pit filling progresses and less wall rock area is exposed above the pit lake. Because the Rosemont open pit has not yet been developed, estimates of the magnitude of chemical release from geologic materials were generated from the results of geochemical testing.

Various types of geochemical testing have been conducted to characterize geologic materials at the Rosemont site:

Static tests;

Kinetic tests; and

Short-term leaching tests (STLTs) (Tetra Tech, 2007).

The most commonly-used static test is known as acid-base accounting (ABA), which measures the balance between the acid-producing potential and the acid-neutralizing potential of a sample. Because ABA characteristics reflect the dominant reactive mineralogic properties of the material (i.e., carbonate and sulfur mineral content), ABA results can be used to evaluate the adequacy of geochemical characterization. An evaluation of the ABA test results, performed on rock types anticipated to comprise the final pit walls, indicates that a sufficient number of samples have been collected and analyzed to support leach testing results performed on these same representative rock types (Appendix B).



Illustration 4.01 Projected Proportions of Exposed Rock Types in the Rosemont Pit

Both kinetic testing and STLTs were conducted to evaluate the potential for release of various constituents from the different rock types, and to develop source terms for chemical mass



loading to the pit lake from pit wall runoff. As part of the baseline geochemical characterization program (Tetra Tech, 2007), kinetic testing was performed on samples which were classified as either potentially-acid generating or uncertain with respect to their acid generation capacities. The kinetic tests were carried out using standard humidity cell testing (HCT) (ASTM, 1996) to evaluate the actual degree of acid production under accelerated weathering conditions. The Bolsa Quartzite was the only material which generated an acidic pH during humidity cell testing (Appendix C), although the total acidity was very low.

The STLTs included both the Synthetic Precipitation Leaching Procedure (SPLP) and the Meteoric Water Mobility Procedure (USEPA, 1986; ASTM, 2003). A comparative evaluation of kinetic and STLT data indicate that SPLP results are appropriate to represent constituent concentrations associated with the pit wall runoff for most rock types (Appendix C) as they produced leachate that closely mimicked HCT results. At Rosemont, the limited occurrence of sulfide minerals in non-ore rock, combined with the abundance of carbonate minerals, appears to eliminate the usual advantage of using HCT for mine rock characterization.

The HCT procedure was designed, and is typically used, for gauging the rate of oxidation of sulfide minerals. On the other hand, SPLP offers an approach that addresses relatively short-term contact between water and a given solid. Although SPLP testing employs a higher water-to-rock ratio (20:1) than does HCT (about 1.5:1), the SPLP test results consistently yielded higher dissolved concentrations of various chemical constituents compared to the long-term HCT tests. Thus, the water-to-rock ratio seems to have limited bearing on testing of the rock units at Rosemont. Therefore, the use of SPLP results appears to be an appropriate selection to simulate pit wall runoff. The average SPLP results for each rock type are provided in Table 4.03.

Section 4.4 contains as presentation of the statistical method used to define probable ranges of concentrations for pit wall rock runoff. In general, the statistical distributions of SPLP results for each rock type (Table 4.01) were used to represent chemical mass loading to the pit lake from the pit walls. However, both SPLP and humidity cell results were used for the Bolsa Quartzite to calculate the range of probable pit wall runoff concentrations. The Bolsa Quartzite contained enough sulfide-S to generate measurable amounts of acidity during humidity cell testing (Appendix C), which was taken into consideration when developing model source terms.

For simulating the initial flushing of blast-fractured pit walls, the first rinse from the HCTs were used to represent the chemical source terms. The HCT concentrations were generally higher than from the SPLP, which generally corresponds to rock that had had more time to weather before contacting water. If HCT first flush data were not available for a rock unit, its blast-fractured source term was established as twice the SPLP value for major constituents and three (3) times the value for trace constituents.

In all cases where a constituent was reported as below analytical detection limits, a value of one-half the detection limit was used in the model simulations. It should also be noted that both the initial rinse HCT and the SPLP results may be affected by the amount of time test solids may have weathered during storage. This is, however, a parameter that is difficult to characterize or control for projects such as Rosemont where no development has yet occurred.

Geochemical Pit Lake Predictive Mode – Revision 1 Rosemont Copper Company


Table 4.03 Average SPLP Results (mg/L) for Various Rock Types Representing Pit Wall Rocks

1 Average values were calculated by substituting 1/2 the detection limit for all non-detect results. 2 Bicarbonate concentrations were calculated from charge balance with calcium. 3 nm = not measured.

Parameter1 Abridgo Formation

Willow Canyon

Andesite

Willow Canyon Arkose

Bolsa Quartzite

Colina Limestone

Earp Formation

Epitaph Limestone

Escabrosa Limestone

Glance Conglomerate

Horquilla Limestone Martin Overburden

Quartz Monzonite Porphyry

Aluminum 0.19 0.15 0.27 0.12 <0.08 0.08 <0.08 <0.08 <0.08 0.07 <0.08 0.62 0.46 Antimony <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 Arsenic 0.01 0.02 0.02 0.009 <0.02 0.01 0.008 <0.02 <0.003 0.010 <0.02 0.03 0.01 Barium 0.002 0.003 0.007 0.003 0.019 0.006 0.015 0.002 0.018 0.017 0.003 0.063 0.019 Beryllium <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 Bicarbonate2 17.7 31.0 17.4 7.35 598 20.8 310 18.2 15.3 144 16.8 16.2 15.2 Cadmium <0.002 <0.002 <0.002 0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 <0.002 Calcium 5.81 10.17 5.71 2.41 196 6.83 102 5.95 5.0 47.2 5.50 5.30 4.97 Chloride 0.78 0.57 0.86 0.58 0.88 0.91 1.67 0.83 0.88 2.34 1.14 1.18 1.44 Chromium <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 <0.006 Copper <0.01 <0.01 0.008 0.06 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.031 Fluoride 0.26 0.29 0.26 0.25 1.28 0.42 0.93 0.42 <0.1 0.51 0.30 0.32 0.30 Iron <0.06 0.13 0.21 0.12 <0.06 <0.06 <0.06 <0.06 <0.06 <0.06 <0.06 0.33 0.11 Lead <0.01 <0.01 <0.01 <0.01 <0.0075 <0.01 <0.01 <0.01 <0.01 <0.01 <0.0075 0.02 <0.01 Magnesium 0.54 1.40 0.75 0.40 3.37 0.71 2.49 1.28 2.6 2.37 1.88 0.59 0.51 Manganese <0.004 0.004 0.003 0.14 0.004 <0.004 0.003 <0.004 <0.004 0.006 <0.004 0.0064 <0.004 Mercury 0.0002 <0.0002 0.0003 0.0001 <0.0002 <0.0002 <0.0002 <0.0002 <0.0002 <0.0002 <0.0002 <0.0002 <0.0002 Molybdenum 0.07 nm 3 nm <0.008 0.05 0.11 0.05 0.01 nm 0.12 0.02 nm Nm Nickel <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 Potassium 4.56 5.35 2.86 1.65 2.75 2.31 2.19 1.03 2.1 1.06 3.00 2.72 3.59 Selenium <0.040 <0.040 <0.040 <0.040 <0.040 <0.040 <0.040 <0.040 <0.04 <0.040 <0.040 <0.040 <0.040 Silver <0.01 <0.01 <0.01 <0.01 <0.005 <0.01 <0.01 <0.005 <0.01 <0.01 <0.005 <0.005 <0.005 Sodium 1.64 4.46 4.86 4.56 2.53 4.38 4.22 1.98 0.8 2.13 2.90 8.90 6.18 Sulfate 3.41 17.8 4.45 5.31 11.7 10.3 254 2.78 1.4 110 5.08 3.54 2.38 Thallium <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 <0.015 <0.02 Uranium <0.005 <0.001 <0.001 <0.005 <0.004 <0.004 <0.005 <0.004 nm <0.005 <0.004 nm Nm Zinc <0.01 <0.01 <0.01 0.024 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.010 <0.01



4.4 Statistical Development of Pit Wall Runoff Model Input Based on the ABA data, it was deemed that a statistically representative number of samples were collected for all non-ore rock types (Appendix B). However, for each of the tested rock types, a range of SPLP results were generated for each constituent representing the range of potential chemical loading to the pit lake from the various rock types. Rather than selecting a single SPLP test value such as the average for modeling, the variability of possible leaching chemistry inputs were better described by using a range of test values which bounded the average, allowing the predictive model to calculate a range of potential outcomes, i.e., sensitivity analysis. This reflects the natural variation in geologic materials and the inherent uncertainties associated with water quality predictions.

Two (2) key elements were considered important in selecting the SPLP test results for use in the model: 1) incorporating a range of input values based on a probability distribution of measured values; and 2) using actual sample data rather than synthetic data sets derived from the probability distribution. Ultimately, SPLP test results were selected for each rock type that corresponded to the average and approximately two (2) standard deviations above (elevated scenario) two (2) standard deviations below that average (low scenario).

Two (2) methods were used to select the range of input values for incorporation into the geochemical model. The selected method depended on whether the number of SPLP samples were equal to or greater than five (5) or less than five (5) for a given rock type.

When the number of SPLP samples was equal to or greater than five (5) (Abrigo, Bolsa, Earp, Epitaph, and Willow Canyon Arkose), the following procedure (in order) was used:

The calculated total dissolved solids (TDS) value was determined for all samples;

Probability plots of TDS were developed for each geologic material type (normal and log normal);

Based on the probability plots, the distribution type that best represented the sample distribution was selected based on the P-value. For those probability plots with P-values > 0.05, the distribution with the higher P-value was selected;

Based on the selected probability plot, the sample closest to the average and the samples closest to the plus/minus two (2) times the standard deviation (± 2σ) were identified; and

The SPLP data for each geochemical input parameter (e.g., As, Be, Ca, etc.) for the selected sample was used in the model input files.

Based on this method, three (3) model scenarios were developed based on a low (~-2σ), an average (~50th percentile), and an elevated scenario (~+2σ). These three (3) scenarios correspond to an average, a low, and an elevated chemical loading. The samples selected for each model scenario are presented in Table 4.04 and are plotted on the probability plots included in Appendix D. As seen in these probability plots, the samples closest to the +2σ and -2σ values are typically less than the actual 2σ value. Given the sample distributions, the samples selected for the elevated and low scenarios represent values closer to the 10th and 90th percentiles rather than the 2nd and 98th percentiles that a 2σ would represent.



Table 4.04 Samples Selected for the Three (3) Modeled Scenarios

Geologic Unit Distribution P-value Low ~2σ Average

(~50th percentile) Elevated ~2σ

Abrigo Formation Log-Normal 0.208 1926-02 1561-03 1916-02 Martin Formation -- -- A856-01 A878-01 A866-01 Glance Conglomerate -- -- na 1 A808-02 na Qtz Monzonite Porphyry -- -- 1926-01 A815-01 A815-02 Epitaph Formation -- -- AR2041-02 AR2034-02 A860-02 Colina Limestone -- -- AR2011-04 A815-01 A865-01 Escabrosa Limestone -- -- A814-02 1461-01 A872-01 Earp Formation Log-Normal 0.131 AR2017-02 A845-01 AR2019-03 Bolsa Quartzite Normal 0.82 AR2059-01 A780-02 AR2066-01 Horquilla Limestone -- -- AR2042-03 A842-01 1596-03 Willow Canyon Formation, Andesite -- -- AR2016-01 AR2009-03 AR2013-01

Willow Canyon Formation, Arkose Log-Normal 0.208 VABH0609-01 AR2040-01 AR2003-03

Overburden -- -- A821-01 AR2039-01 AR2039-01

Scherrer -- -- -- -- --

Tertiary Gravel -- -- 1538-02 AR2022-02 AR2022-02

Precambrian Granite -- -- A860-02 A860-02 A860-02

Concha -- -- A814-02 1461-01 A872-01 1 na = not applicable. Only a single sample of Glance Conglomerate was available for testing.

In the instances where less than five (5) SPLP samples were available for a given rock type (Martin Formation, Colina Limestone, and Escabrosa Limestone), or the P-values of both the normal and log-normal probability plots were less than 0.05 (Horquilla Limestone and Epitaph Formation), the samples with the highest, lowest, and average TDS values were selected for the elevated, low, and average model scenarios. In the instance of the Quartz Monzonite Porphyry, overburden, and Tertiary gravel, only two (2) SPLP samples were available. Thus, the sample with the highest TDS value was used for both the average and elevated scenario model runs.

No leach test data (HCT, SPLP, or MWMP) were available for the Glance Formation and for the Precambrian Granite. As a substitute, a sample of the Epitaph formation was used that had substantial concentrations of sulfate and other constituents. As these units comprise only a small part of the total exposed pit wall area, the assumptions made for these rock types were deemed to be well within the inherent error of the overall model calculation. No sample was available for the Concha Formation. However, the average net neutralizing potential (NNP), NP/AP (ratio of neutralization potential to acid potential), and the sulfur contents of the Concha, are very similar to the Escabrosa. As such, the Escabrosa values were used for the fraction of the pit walls comprised of Concha Formation.

The Scherrer rock type is comprised of quartz and dolomite. No sample was available for the Scherrer. Since the Scherrer covers a very small portion of the anticipated exposed pit walls, the precipitation chemistry (see Section 4.2) was used for runoff from this area.

For areas above the pit rim of approximately 5,100 feet amsl, the catchment runoff was assumed to have runoff characteristics of the Bolsa Formation (which represents the highest



proportion of the area) and was a conservative assumption with respect to the higher TDS and metals concentrations.

4.5 Initial Flushing of Blast Affected Pit Wall Rock The near surface rock of the anticipated ultimate pit shell is expected to be affected by blasting practices. An initial chemical flushing of the blast affected pit wall rock was incorporated into the pit lake model. The near pit wall rocks are anticipated to have altered hydraulic properties and increased fracture densities as a result of blasting and the extraction of surrounding rock. An increase in the porosity and specific yield of the near surface rock of the ultimate pit shell is expected; therefore, the surface area of potentially mineralized rock interacting with groundwater or infiltrating meteoric water is expected to increase. Where available, the chemical source terms used for flushing of the blast affected rock for each formation were developed using the averaged HCT data (Willow Canyon Andesite, Willow Canyon Arkose, Bolsa, and Earp). For formations that did not have HCT data, the concentrations of major cations and anions derived from SPLP tests were multiplied by a factor of three (3) and the trace metals were multiplied by a factor of two (2). Three (3) pore volumes of the blast affected wall rock were considered in the model for the initial flush, after which standard groundwater inflow chemistry was used during future time steps. The blast affected wall rock was considered to be affected for a distance of six (6) feet from the ultimate shell of the pit.



5.0 DYNAMIC SYSTEMS MODEL (DSM) INTEGRATION

A dynamic systems computer model (DSM) was developed in GoldSimTM (GoldSim Technology Group, 2005) to simulate the hydrologic water balance and the mixing of the chemical loads from the various hydrologic processes (e.g., groundwater inflow, pit wall runoff, precipitation) for the anticipated Rosemont pit lake. Outputs from the DSM predictive simulations were used as inputs to a final simulation model step using PHREEQC (see Section 6.0).

The first step in developing the DSM involved incorporating the system components and description of the interactions between the components that were developed as part of the regional groundwater flow model (Tetra Tech, 2010b) discussed in Section 3.0 of this report. The interactions between the system components were represented by empirical relationships derived from the analysis of the site data or from additional models of site processes.

The DSM includes both stochastic (variable) and deterministic (fixed) parameters. The stochastic parameters were used to assess the uncertainty in the predictions due to the data and analytical constraints, as well as the natural variability in the input parameters. This was accomplished by utilizing GoldSim in the Monte Carlo simulation mode. Monthly time steps and a 1,000-year period of simulation were selected for the model using Monte-Carlo sampling (for the stochastic parameters) with 100 realizations.

The selected 1,000-year timeframe is consistent with the period of time simulated in the regional groundwater flow model, during which steady-state conditions were achieved (Tetra Tech, 2010a). Although the 1,000-year period of simulation is a relatively long time to have any practical significance given the transient nature of natural systems (e.g., climate, changes in near surface geochemistry of the exposed geologic materials, groundwater elevations and quality), it was intended to assist in gauging long-term effects or trends and not specific constituent concentrations.

5.1 Model Objectives The objectives of the DSM model were to:

Reproduce the post-closure pit water balance through time as simulated in the regional groundwater flow model (Tetra Tech, 2010a);

Predict the chemical loading to the pit lake through time;

Determine the concentration of chemical constituents through time; and

Evaluate a range of potential pit water quality outcomes based on uncertainties associated with the hydrologic and chemical loading parameter estimates.

5.2 Model Structure and Formulation Each element of the regional groundwater flow model (Tetra Tech, 2010b) was incorporated into the DSM. These elements were organized into modules or containers of related elements. The DSM contains five (5) of these organizational modules:

Pit Geometry;

Meteorology;

Hydrology;

Geochemistry; and

Results.



5.2.1 Hydrology Hydrology is simulated in the DSM model to provide a mechanism to apply chemical loading from the various hydrologic inputs and to model the affects of evapo-concentration. These hydrologic processes were simulated in the regional groundwater flow model (Tetra Tech, 2010a and 2010b). The DSM was designed such that the mean outputs closely matched the base case results provided by the regional groundwater model, which incorporates the LAK2 Package (Council, 1999) to simulate the pit water balance. This was achieved by using the same inputs for the hydrologic parameters as were used in the groundwater flow model (Tetra Tech, 2010c).

Uncertainty associated with the specific hydrologic processes, including the hydraulic parameters, was addressed in a detailed sensitivity analysis of the regional groundwater flow modeling results (Tetra Tech, 2010c). The uncertainty associated with hydrologic parameters in the DSM was addressed by varying the hydrologic impacts over a range of values. Changes to the hydraulic parameters in the DSM were consistent with those used in the sensitivity analysis of the groundwater flow model (Tetra Tech, 2010c). For all of the water balance components, a uniform probability distribution function (PDF) was used for the stochastic elements associated with the input values. A uniform PDF results in each value having an equal probability of occurring in a simulation.

While this approach does not explicitly couple all of the physical processes associated with these independencies (namely how groundwater inflow is approximated), it provides a more rigorous assessment of the potential upper and lower bounds on pit lake elevation and chemistry than could be done using a reasonable number of deterministic model runs. Furthermore, this approach permits an assessment of the cumulative uncertainties with multiple parameter estimates. For example, if evaporation is lower than expected, and precipitation and groundwater inflow are higher than expected (despite the higher lake stage), then these parameters could result in a considerably higher lake stage than would be predicted from perturbation of only a single parameter. The drawback to this approach is that in the example above, the lake stage would be higher (as a result of lower evaporation and higher precipitation) which would be partially offset by lower groundwater inflow, which is approximated by the lake stage-groundwater inflow relationship from the base case regional groundwater flow model. However, due to the inherent uncertainty in groundwater inflow predictions, this is a reasonable approach. The insights gained through predicting a range of future pit water qualities based on the uncertainty in multiple parameter estimates are considered essential in bounding the potential future conditions.

5.2.1.1 Direct Precipitation to the Lake Surface The precipitation falling on the surface of the pit lake was simulated by multiplying the precipitation rate by the lake area for each time step.

5.2.1.2 Pit Wall Runoff Pit wall runoff was simulated using a stochastic element as a result of the uncertainty associated with the fraction of precipitation that ultimately reaches the pit lake. Much of the precipitation falling on the pit walls will pond in depressions (such as on haul roads) and evaporate, or will infiltrate into the blast altered rock zone of the ultimate pit walls. Based on professional experience, the stochastic element was assigned a uniform distribution between 20% and 40%. Therefore, runoff was calculated as varying between 20% and 40% of the precipitation rate for a given time step on an exposed pit wall. The pit wall runoff area



considered all wall rocks up to the 5,100 feet amsl elevation and was comprised of an area of about 561.1 acres.

5.2.1.3 Catchment Area Runoff Areas above the pit rim (approximately 5,100 feet amsl) were simulated in the DSM. The same runoff coefficient used for the pit walls was used for the simulated area above the pit (about 141.5 acres). Therefore, the total runoff area to the pit was simulated as 702.6 acres.

5.2.1.4 First Flush of Blast Affected Rock The first flush of water through blast affected rock was simulated by assuming that the higher permeability zone around the pit shell penetrates approximately six (6) feet. The specific yield of this zone was simulated using a stochastic element with a uniform distribution between 3% and 15%. Three (3) pore volumes were assumed to have the elevated dissolved chemical mass, which was simulated using the HCT results.

5.2.1.5 Groundwater Inflow and Outflows The groundwater hydrology and groundwater-lake interactions were explicitly simulated in the regional groundwater flow model (Tetra Tech, 2010a; and Tetra Tech, 2010c). The geologic complexities in the Rosemont area necessitated that the groundwater inflow predictions to the pit be completed in the regional groundwater flow model. Furthermore, the pit filling rate is also dependent on factors such as the duration of pit dewatering, depth and size of the pit, and the pre-mining flow regime (Naugle and Atkinson, 1993). These complexities can only be simulated rigorously in a groundwater flow model. As such, the DSM directly uses the groundwater inflow, as calculated from the regional groundwater inflow model, as a function of the lake stage. Thus, the groundwater inflow to the pit in the DSM was based on the lake stage simulated in the DSM during the previous time step. The range of groundwater inflow values determined from the regional groundwater flow model sensitivity analysis ranged from 85% to 107% of the base case (Tetra Tech, 2010c). This same range was simulated by using a stochastic element with a uniform distribution function.

A plot of modeled (Tetra Tech, 2010b) groundwater inflow versus lake stage relationships, simulated in the DSM versus the regional groundwater flow model output, is provided in Illustration 5.01.



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DSM Groundwater Inflow

Illustration 5.01 Groundwater Inflow versus Lake Stage – DSM Simulation Compared to Regional Groundwater Flow Model Output (Tetra Tech, 2010a)

As shown in Illustration 5.01, the values simulated in the DSM closely match the output from the regional groundwater flow model (Tetra Tech, 2010b). Groundwater inflow into the pit was simulated in the DSM using a lookup table for groundwater inflow based on the lake stage of the current time step. In the late stages of the 1,000-year simulation period, the DSM simulates higher pit lake stages than in the regional groundwater flow model (Tetra Tech, 2010b). This is the result of multiple stochastic elements. The groundwater inflow versus lake stage relationship for these higher lake stages is estimated using a linear extrapolation.

As discussed previously, no groundwater outflow is anticipated due to the terminal nature of the pit lake.

5.2.2 Pit Geometry The pit geometry provides the pit volume and area relationships used in the water balance sections of the DSM. The final pit geometry was simulated with lookup tables of the open pit elevation versus area and volume. The depth to area relationship used in the model is shown in Illustration 5.02. The lake area is critically important in determining the evaporative flux off the surface of the lake, which given the high open water evaporation rate for the site is a sensitive component of the lake water balance. The functional relationship shown in Illustration 5.02,



indicates that during initial filling of the pit lake, the lake elevation would rise quickly but the increase in area is more subtle (as a result of the steep pit shell). As lake elevations continue to rise the area begins to increase more substantially, which would result in higher lake evaporation. The entire input is shown in Illustration 5.02, when in practice the average predicted lake stage is 4287 feet amsl (Section 5.3.1) and values above about 4500 feet amsl are not used by the model.

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Illustration 5.02 Change in Lake Surface Area with Lake Stage Elevation

5.2.3 Meteorology An analysis of available meteorological data was completed as part of an effort to ensure consistency in the data being used for other design efforts at the Rosemont site. The results of this analysis are summarized in Appendix A and discussed in Section 3.0, and presented fully in a separate technical memorandum (Tetra Tech, 2009). This 2009 technical memorandum summarizes the methodology used to develop the synthetic precipitation dataset for the Rosemont site. The two (2) meteorological inputs into the DSM are precipitation and evaporation.

5.2.3.1 Precipitation The precipitation rate is determined from the input data and a stochastic element with a uniform probability distribution function (i.e., PDF) which varies precipitation between 80% and 120% of the input value to account for uncertainties associated with knowing the precise precipitation



values. The mean monthly precipitation inputs were values developed as part of the meteorological analysis (Tetra Tech, 2009) and are presented in Section 3.2.

5.2.3.2 Evaporation Lake evaporation was treated as a stochastic variable as a result of the uncertainty associated with the extrapolated pan evaporation estimates for the site and the pan evaporation coefficient. The lake evaporation has a uniform distribution between 80% and 120% of the estimated values that were determined using a pan evaporation coefficient of 0.7.

5.3 Model Results Based on the 1,000-year simulation period, model results were generated for all components of the hydrologic water balance and various chemical loads. Four (4) scenarios were simulated, correlating to the four (4) pit wall runoff scenarios outlined in Section 4.4. The mean, high, low, and Mean HCT runs were coupled with mean hydrology results, respectively. Model outputs are presented in electronic form in Appendix E.

5.3.1 Water Balance and Lake Formation In all cases, the DSM confirmed that a lake will form in the open pit upon cessation of mining activities. The predicted rate of pit lake filling and the ultimate depth of the pit lake varied between model runs since the output results are dependent on the variability of the stochastic elements. The mean, 25th, 75th percentile, and upper and lower bound estimates (as defined by the 5th and 95th percentiles) for the pit lake elevation for the final time step (1,000-year period of simulation) are shown in Table 5.01.

Table 5.01 Predicted Pit Lake Elevation for Various DSM Outcomes

Average 5th 25th 75th 95th 4,287 4,095 4,209 4,363 4,488Note: All values shown are in feet amsl



The rate of pit filling is initially controlled by the groundwater inflow rate and later by evaporation and direct precipitation as the surface area of the pit lake increases. Based on the simulated hydrology, the pit lake will fill to 90% of the final lake elevation in 215 years. The steady-state lake elevation is estimated to be achieved in approximately 1,000 years. Illustration 5.03 illustrates the predicted pit lake development through time. The mean estimates for lake area and lake volume are 218 acres and 101,700 acre-feet, respectively. There are small differences in the area and volume calculated between the regional groundwater flow model and the DSM as a result of varying degrees of vertical discretization in the models. These differences are less than 6%.

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EXPLANATION

Illustration 5.03 Simulated Pit Lake Elevation for the 1,000-year Period of Simulation



The pit lake water balance is largely controlled by the relationships between lake stage and groundwater inflow and lake stage and evaporation. The transient relationships between the components of the water balance for the first 20 years are presented in Illustration 5.04. As a result of the monthly time-step, these relationships vary greatly depending on the month of the year that was simulated. To understand the interaction of these variables over the 1,000-year simulation period, the average annual fluxes for each of the water balance components are presented in Illustration 5.05.

The lake water balance simulated by both the mean DSM simulation and LAK2 Package (Council, 1999) and the regional groundwater flow model (Tetra Tech, 2010b) are nearly identical. This is essentially the objective of the hydrology component of the DSM. The result is that the predicted chemical loading to the pit lake (based on the mean values) is consistent with the regional groundwater flow model. Based on the pre-mining groundwater elevations on the down-gradient side, and those predicted by the regional groundwater flow model, the upper bound predicted lake stage of 4,488 feet amsl would not create flow through conditions.

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Evaporation

Illustration 5.04 Simulated Water Balance for the First 20-year Period of Simulation



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Groundwater InflowPit Wall RunoffLake PrecipitationEvaporationEvaporation DSMPrecipitation DSMGroundwater Inflow DSMPit Wall RunoffLake StageLake Stage DSM

EXPLANATION

Illustration 5.05 Simulated Annual Water Balance for the 1,000-year Period of Simulation for the DSM and the Regional Groundwater Flow Model

Illustration 5.05 shows that as the lake elevation and surface area increase, so does the lake evaporation and lake precipitation. In contrast, the groundwater inflow decreases substantially, while the pit wall runoff decreases only slightly due to the geometry of the simulated pit. The simulated mean annual water balance for the last year of the simulation is presented in Table 5.02.



Table 5.02 Simulated Pit Lake Water Balance 1,000 Years After End of Mining Operations

Inflows Rate (gpm)*

Direct Precipitation 197.6

Groundwater Inflow 217.6

Pit Wall Runoff 138.3

Upgradient Runoff 0

Total Inflow 553.5

Outflows

Evaporation 552.5

Groundwater Outflow 0

Total Outflow 552.5

Inflow - Outflow 1.0

* Average annual gallons per minute.

The model estimates that the post-mining pit lake will reach a steady-state condition approximately 1,000 years after mine closure. The final pit lake level is estimated to be about 4,287 feet amsl. It is estimated that the lake rises approximately 140 feet between the 200th and 1,000th year (average of 0.17 feet per year).

5.3.2 Lake Stratification The relative depth (RD) of the predicted pit lake at the 1,000-year mark (mean pit lake stage) is approximately 35%. RD relates the maximum depth of a lake (Zm) to the width (d). Assuming an approximately circular lake, the width is a function of surface area (Ao) and can be determined from:

d = 2(A0/π)^0.5

The percent RD is defined as:

RD = (Zm/d)*100%

The estimated RD of 35% is considerably greater than 5%, which typically suggests that the lake is likely to stratify. Such stratification would result in oxidizing conditions in the upper portions of the lake and more chemically reducing (oxygen-deprived) conditions at depth. However, pit lakes that form in arid regions are unlikely to stratify, relative to lakes that form in cooler, wetter climates (Jewell, 2009). While stratification of the lake has implications for water quality at depth, the near surface waters will remain oxidizing. These near surface waters are considered the most important from a pit water quality perspective given the potential ecological risks associated with them. The water quality at depth is less important given the expected terminal nature of the pit lake.



5.3.3 Chemical Loading The hydrologic system reaches a dynamic equilibrium in the 1,000-year simulation period; however, chemical mass continues to be added to the system past this 1,000-year period. Chemical concentrations will continue to increase even when a dynamic equilibrium is achieved. This is due to continual removal of water by evaporation. The effect of evapo-concentration of the lake water is an important component affecting the chemical concentrations in the system at the end of the 1,000-year simulation period.

Model simulations were conducted to provide not only a sense of the expected case (average or mean) associated with conditions in the pit lake, but also the relative uncertainty of the predictions. Uncertainty in the model is derived from the expected uncertainty in precipitation, lake evaporation, pit wall runoff, and groundwater inflow (described in Section 3.0). Uncertainty is also associated with the range of the observed geochemical leach tests. Taken together, the hydrologic uncertainty, coupled with the uncertainty associated with how the pit wall rocks will weather, were used to generate a range of chemical loads to the pit.

Accordingly, the low chemical loading scenario couples low end leach testing with maximum water accumulation (high runoff, low evaporation) to simulate the best case water quality scenario. The high chemical loading scenario couples high end leach testing results with minimal water accumulation (low runoff, high evaporation) to estimate worst case conditions. These two (2) end members bracket the average case. Overall, the bulk of the chemical mass is found to be contributed from groundwater flowing into the pit (over 95%), with less than 4% of the mass attributed to pit wall runoff and less than 1% of the mass attributable to the first flush of the blast affected rock.

Table 5.03 below breaks down the chemical contributions to the pit lake model from groundwater, pit wall runoff, and the flushing of the pit wall blast zone. As shown, about 95% of the major elements are derived from groundwater. Alternatively, the majority of trace chemical constituents are derived from pit wall runoff and flushing of the pit wall blast zone.



Table 5.03 Chemical Contributions to Pit Lake Model

Constituent Groundwater Pit Wall Runoff Initial Flush

Ca 96.8% 2.9% 0.1% Mg 97.0% 2.6% 0.1% Na 87.7% 10.2% 0.1% K 66.6% 23.1% 0.5%

SO4 97.5% 1.7% 0.1% Cl 91.3% 5.5% 0.1% F 83.4% 16.0% 0.6%

HCO3 95.4% 4.2% 0.1% Ag 66.9% 33.1% 0.0% Al 30.2% 69.6% 0.1% As 18.1% 81.8% 0.0% Sb 57.8% 42.1% 0.1% Ba 94.9% 5.0% 0.1% Be 33.6% 66.4% 0.0% Cd 66.9% 33.1% 0.0% Cr 62.8% 37.2% 0.0% Cu 28.6% 71.2% 0.2% Fe 95.1% 4.9% 0.0% Pb 7.5% 92.5% 0.0% Hg 19.7% 80.3% 0.0% Mn 82.2% 2.7% 0.0% Mo 98.2% 1.7% 0.1% Ni 50.3% 49.7% 0.0% Se 17.7% 82.3% 0.0% Tl 4.8% 95.2% 0.0% U 90.8% 9.2% 0.0% Zn 99.6% 0.4% 0.0%



6.0 GEOCHEMICAL MODELING

In the final stage of pit lake modeling, output from the DSM for model years 75, 200, 500, and 1,000 were input into the geochemical speciation model PHREEQC (Parkhurst and Appelo, 1999), a well-established code applicable to a wide-range of geochemical conditions. PHREEQC was derived from the original PHREEQE code (Parkhurst and others, 1980) which has been continuously refined and developed for over 25 years. PHREEQC is capable of performing a variety of aqueous geochemical calculations, such as speciation and saturation index calculations, and calculations involving mixing of solutions, mineral and gas equilibria, and surface complexation reactions. The MINTEQ geochemical database (Allison and others, 1991) was used in conjunction with PHREEQC because it contains an extensive thermodynamic compilation that is adequate for addressing a broad range of geochemical conditions involving both major ions and trace elements. An example PHREEQC input file is provided in Appendix F.

6.1 Mineral Precipitation To the extent that chemical concentrations in the projected pit lake water significantly increase, mineral phases may precipitate from solution. This precipitation removes chemical mass from the pit lake and establishes a limit on the maximum dissolved concentration for the associated components of that mineral. Table 6.01 below presents potential mineral phases that may form in environments such as the proposed Rosemont pit lake.

Table 6.01 Potential Mineral Solubility Controls for the Rosemont Pit Lake Model

Mineral Name Formula Alunite KAl3(SO4)2(OH)6

Anglesite PbSO4 Barite BaSO4 Barium arsenate Ba3(AsO4)2 Calcite CaCO3 Calcium molybdate CaMoO4 Ferrihydrite Fe(OH)3(a) Fluorite CaF2 Gypsum CaSO4•2H2O Huntite CaMg3(CO3)4 Jurbanite Al4(OH)10SO4 Magnesite MgCO3 Manganite MnOOH Rhodochrosite MnCO3 Smithsonite ZnCO3 Wulfenite PbMoO4

6.2 Surface Complexation Exposure of the pit lake surface to the atmosphere will allow for free exchange of atmospheric oxygen and carbon dioxide into surface waters. The resulting oxidizing conditions at the pit lake surface will favor precipitation of hydrous ferric oxide (HFO; Fe(OH)3) with a strong affinity to adsorb certain trace elements. The PHREEQC code incorporates the Dzombak and Morel (1990) diffuse double-layer model and a non-electrostatic surface complexation model (Davis



and Kent, 1990) to simulate adsorption of the various trace metals (e.g., Cu, Pb, Zn) and oxyanions (e.g., As, Se, Mo) onto mineral surfaces. The simulation assumes that the adsorbing surface is hydrous ferric oxide (HFO), essentially ferrihydrite. The reactive properties of HFO have been well-characterized (Dzombak and Morel, 1990), and the three (3) most important properties of HFO used as model inputs are the: (1) mass of HFO, (2) surface area of HFO, and (3) density of surface adsorption sites.

PHREEQC uses the amount of ferrihydrite predicted to precipitate in the initial model simulation to define the mass of HFO available for adsorption. Two (2) types of adsorption sites are defined in the database: a strong binding site (HFO_s) and a weak binding site (HFO_w). To be consistent with the properties of HFO presented by Dzombak and Morel (1990), the model used a surface area of 5.33 x 104 m2/mole iron, a surface site density of 0.2 moles weak sites/mole iron, and 0.005 moles strong sites/mole iron. Prior to the adsorption simulation, PHREEQC equilibrates the HFO surface with the solution after mineral precipitation, without changing the composition of the solution.

6.3 Results The predicted water quality of the Rosemont pit lake is presented below in a series of graphs and tables. Illustrations 6.01 through 6.08 show the results for sulfate, calcium, total dissolved solids (TDS), bicarbonate, pH, arsenic, selenium, and zinc. These illustrations correspond to use of SPLP data with non-detect values set to one-half the laboratory detection limit for the DSM calculations. They show the raw output from the DSM as well as from the PHREEQC simulations for years 75, 200, 500, and 1,000 as taken from the start of pit recharge. Output is provided for all the modeled scenarios, i.e., average, low, elevated, including the average Bolsa Quartzite HCT. The PHREEQC data points are provided to illustrate which constituents are attenuated through direct precipitation (calcium, bicarbonate), adsorption (arsenic), or are unaffected. Table 6.02 summarizes the simulated pit lake chemical composition at 200-year simulation mark using SPLP data in the DSM with non-detect SPLP data set at one-half the laboratory reported detection limit.

The simulations were generated using the average groundwater composition, with low chemical loading, average chemical loading, and elevated chemical loading (approximately the 10th, 50th, and 90th percentiles, respectively) for the various rock types (Section 3.4). A fourth scenario was modeled to evaluate the contribution of acidity from the Bolsa Quartzite, the only rock type which produced net acidity during humidity cell testing (Appendix C). This scenario uses the same hydrologic and chemical inputs as the 50th percentile simulation, except that the Week 25 Bolsa Quartzite humidity cell data (pH = 3.37) was substituted for the SPLP results. In this last case, however, no interaction with the abundant neutralizing potential available in the pit walls was allowed. Any acid contributions from the Bolsa were only allowed to react with alkalinity in the pit lake solutions.

Illustrations 6.01 through 6.05 provide graphs of the concentration of major constituents over the 1,000-year model timeframe. Sulfate (Illustration 6.01) builds up concentration over time due to evapo-concentration and is not attenuated by the precipitation of gypsum (although saturation of gypsum is very close at the 1,000-year simulation period due to the effects of evaporation). Comparing the PHREEQC equilibrated solution for elevated loading scenario shows it to be identical to the sulfate concentration for the raw results from the DSM. The calculations do not reflect the potential dissolution of any gypsum present in pit walls. This was done because a quantitative accounting of gypsum occurrence in pit walls is impractical. To the extent that dissolution of gypsum from pit walls contributes sulfate to the pit lake, the rate of build up of sulfate concentrations (Illustration 6.01) increases. However, the magnitude of such an increase



is not anticipated to be great. As expected, other major constituents (sodium, potassium, magnesium, chloride) evapo-concentrate and are not attenuated.

On the other hand, calcium and bicarbonate (alkalinity) are attenuated due to the precipitation of calcite (calcium carbonate). These lower PHREEQC equilibrated solutions are shown on Illustrations 6.02 and 6.04. Overall, total dissolved solids (TDS) in the pit lake water are calculated to continually increase over the 1,000-year simulation period, although not as high as the raw DSM output due to the precipitation of calcite. As shown on Illustration 6.05, the pH remains alkaline due to the equilibrium (precipitation) of calcite, not due to reactions with the pit wall rock. Calcite reactions in the wall rock were not included in the simulation as excess calcite is expected to precipitate.

Illustrations 6.06 through 6.08 show the variable response of trace elements in the model system. Arsenic is attenuated due to strong adsorption onto hydrous ferric oxide (HFO), as illustrated by the location of the PHREEQC equilibrated solutions relative to the raw DSM results. Conversely, selenium and zinc are not strongly adsorbed (note that the PHREEQC equilibrated points for the elevated loading scenario are consistent with the raw DSM output). Table 6.02 summarizes the pit lake chemical composition at the 200-year simulation point. The 200-year simulation point was selected to provide a time period that was sufficiently long to provide a sense of scale consistent with several human generations.

Illustration 6.01 Simulated Pit Lake Sulfate Concentration



Illustration 6.02 Simulated Pit Lake Calcium Concentration

Illustration 6.03 Simulated Pit Lake TDS Concentration



Illustration 6.04 Simulated Pit Lake Bicarbonate (alkalinity) Concentration

Illustration 6.05 Simulated Pit Lake pH



Illustration 6.06 Simulated Pit Lake Arsenic Concentration

Illustration 6.07 Simulated Pit Lake Selenium Concentration



Illustration 6.08 Simulated Pit Lake Zinc Concentration



Table 6.02 Range in Predicted Water Quality (200-Year Simulation) for the Rosemont Pit Lake

Parameter*Low

Chemical Loading

Average Chemical Loading

Elevated Chemical Loading

Average Chemical Loading With Bolsa

HCT Data Ca 89.9 99.8 107.7 100.7 Mg 22.7 25.7 30.1 25.6 Na 31.9 35.9 38.6 35.3 K 5.1 5.7 6.3 5.4

SO4 330.6 374.1 518.5 375.8 Cl 9.9 11.1 12.5 11.1 F 1.1 1.2 1.4 1.2

HCO3 37.3 36.2 37.0 36.0 Ag 0.004 0.004 0.005 0.004 Al 0.158 0.197 0.260 0.357 As 0.004 0.005 0.000 0.003 Sb 0.003 0.003 0.003 0.003 Ba 0.000 0.000 0.009 0.000 Be 0.001 0.001 0.001 0.001 Cd 0.002 0.002 0.002 0.002 Cr 0.004 0.005 0.005 0.005 Cu 0.004 0.004 0.005 0.163 Fe 0.000 0.000 0.000 0.000 Pb 0.004 0.015 0.017 0.015 Hg 0.002 0.001 0.000 0.000 Mn 0.229 0.255 0.243 0.254 Mo 0.137 0.150 0.192 0.154 Ni 0.005 0.006 0.007 0.010 Se 0.013 0.014 0.016 0.014 Tl 0.005 0.006 0.007 0.006 U 0.005 0.006 0.006 0.006 Zn 0.745 0.847 0.959 0.862

TDS 527 589 751 590 pH 8.1 8.0 8.0 8.0

*mg/L except where noted

** Low, average, and elevated chemical loading scenarios were established using SPLP data for the anticipated pit wall rock based upon TDS (total dissolved solids) to establish a sense of low, average, and elevated total chemical loading simulations. See Section 4.4 for a discussion of this sensitivity analysis.

***Values reported as zero were not calculated to be present at concentrations corresponding to the reported three (3) decimal places.

****Note that nitrate was not simulated in the model due to the lack of laboratory leach test data for actual blasted mine rock that may contain residual from explosives.



7.0 DISCUSSION OF RESULTS

The restricted occurrence of sulfide minerals, and the predominance of limestone in the non-ore rocks associated with the Rosemont deposit, is expected to result in a pit lake with a projected chemical quality that is only slightly changed from local groundwater over a 200-year timeframe. For chemical constituents that are conservative (allowed to concentrate free from any attenuating chemical reactions, e.g., chloride), concentrations build up under the effects of evapo-concentration by a factor of about 1.5 over a time span of about 200 years. Only calcium, bicarbonate, iron, and arsenic appear to be noticeably chemically attenuated.

The observed overall abundance of acid neutralization potential of the rock types at Rosemont (Appendix C) indicates that the formation of low pH conditions (acid rock drainage) is unlikely, which is consistent with the model simulations. The Bolsa Quartzite was the only non-ore rock type that displayed a net capacity to generate acidic drainage. Although this material had a limited sulfide mineral content, the absence of acid neutralizing capacity resulted in a low pH water quality in the humidity cell tests. The resulting total acidity, however, was quite low. As a result, the abundant neutralizing capacity of all other non-ore rock produces a water quality from those materials that appears to more than adequately neutralize the effects of the Bolsa Quartzite. Therefore, alkaline conditions within the pit lake are anticipated to be maintained.

The capacity of the final pit walls to contribute chemical mass the pit lake, both from long-term precipitation runoff and the initial flush through the blast affected rock of the ultimate pit walls, is dwarfed by the chemical mass reporting to the lake from recharging groundwater. The chemical quality of the groundwater is generally good and comprises a significant portion of the water that refills the pit. The groundwater carries with it the majority of the total chemical mass reporting to the pit lake. The local groundwater is a calcium bicarbonate-sulfate type. On reporting to the projected pit lake, the local groundwater is expected to be oversaturated with respect to calcium carbonate (calcite) as compared to the pit lake water (see discussion above). The calcium carbonate is expected to precipitate, thus limiting the concentration of calcium and bicarbonate in the pit lake, even with increasing evapo-concentration.

Other than calcium and bicarbonate, chemical constituents, from the limited occurrence of sulfide minerals and other minerals, include sulfate, iron, and a range of trace metals (e.g., selenium). Due to the oxidizing conditions at the surface of the projected pit lake (exposed to air), any iron derived from sulfide minerals oxidizes and precipitates as a common oxide phase (hydrous ferric oxide, HFO). This precipitate is relatively reactive, scavenging arsenic and other trace metals, primarily by adsorption onto its surface. However, arsenic only appears to be appreciably affected by the reactivity of the HFO. The elevated chemical loading scenario results indicate lower arsenic concentrations than either the mean or low loading case (see Table 6.02). This result appears to be due in part to the increased release of iron which provides an increased amount of HFO, leading to more effective scavenging (adsorption) of trace metals.

Table 7.01 shows a comparison of the average concentrations from the 200-year geochemically equilibrated pit lake model solutions to local groundwater. This table also shows the model results obtained for the elevated and low chemical loading scenarios, which provide useful bookends for the average chemical loading scenario. The average chemical loading scenario represents an outcome that has the highest probability of occurring. The low and elevated scenarios represent outcomes that span the range of possibilities, but have lower probabilities of occurring. Overall, the various scenarios are intended to provide perspective on the average scenario and to represent a sensitivity consideration of the uncertainty associated with the average result.



Table 7.01 Comparison of Local Groundwater with Modeled Pit Lake Water

Parameter* Ambient Groundwater

Low Chemical Loading

Average Chemical Loading

Elevated Chemical Loading

Average Chemical Loading With Bolsa

HCT Data Ca 131 89.9 99.8 107.7 100.7 Mg 20.5 22.7 25.7 30.1 25.6 Na 26 31.9 35.9 38.6 35.3 K 3.17 5.1 5.7 6.3 5.4

SO4 300 330.6 374.1 518.5 375.8 Cl 8.36 9.9 11.1 12.5 11.1 F 0.85 1.1 1.2 1.4 1.2

HCO3 187 37.3 36.2 37.0 36.0 Ag NA 0.004 0.004 0.005 0.004 Al <0.03 0.158 0.197 0.260 0.357 As 0.0037 0.004 0.005 0.000 0.003 Sb <0.0004 0.003 0.003 0.003 0.003 Ba 0.042 0.000 0.000 0.009 0.000 Be <0.0001 0.001 0.001 0.001 0.001 Cd <0.0001 0.002 0.002 0.002 0.002 Cr <0.01 0.004 0.005 0.005 0.005 Cu <0.01 0.004 0.004 0.005 0.163 Fe 0.554 0.000 0.000 0.000 0.000 Pb 0.00092 0.004 0.015 0.017 0.015 Hg <0.0002 0.002 0.001 0.000 0.000 Mn 0.174 0.229 0.255 0.243 0.254 Mo 0.121 0.137 0.150 0.192 0.154 Ni <0.01 0.005 0.006 0.007 0.010 Se 0.00212 0.013 0.014 0.016 0.014 Tl NA 0.005 0.006 0.007 0.006 U 0.00419 0.005 0.006 0.006 0.006 Zn 0.694 0.745 0.847 0.959 0.862

TDS 581 527 589 751 590 pH 7.6 / 8.2# 8.1 8.0 8.0 8.0

*mg/L except where noted; # the reported pH for ambient groundwater includes field measurement average followed by the laboratory measurement average.

** Low, average, and elevated chemical loading scenarios were established using SPLP data for the anticipated pit wall rock based upon TDS (total dissolved solids) to establish a sense of low, average, and elevated total chemical loading simulations. See Section 4.4 for a discussion of this sensitivity analysis.

***Values reported as zero were not calculated to be present at concentrations corresponding to the reported three (3) decimal places.

Highlighted rows correspond to chemical constituents that were always below detection limits in laboratory leach testing.

Net positive values are shown in the table due to the use of one-half detection limit values.

As described in Section 4.4 of this report, the model scenarios were established using Total Dissolved Solids (i.e., TDS) as the basis. The low chemical loading scenario incorporated leach tests of rock that corresponded to a low capacity (relatively) to release chemical constituents and the elevated scenario incorporated leach tests of rocks that corresponded to an elevated capacity to do so (also relatively). Using TDS as the basis to set up the model scenarios allowed the ability to gauge the final pH conditions which were likely to exist in the pit lake. That basis also gauged the chemical constituents that comprised the bulk of the chemical mass in the pit



lake. Approximate concentrations of most trace elements, those at concentrations below one (1) part per million (ppm), were also obtained. However, given the very, very low concentrations of these constituents, the accuracy associated with predictive modeling of trace elements in a system as large as a pit lake is limited, especially over extended periods of time. Nonetheless, the modeled results do provide an indication of the approximate trace metal concentrations, and show that the Rosemont pit lake is not anticipated (over the 200-year model period) to build up trace element concentrations beyond one (1) ppm (for any given constituent). Note that the shaded rows in Table 7.01 correspond to constituents that were not detected in the leach tests of the non-ore rock and that the concentrations shown are related only to the evapo-concentration of leach test values being set to one-half the laboratory detection limit.

The overall dimensions of the projected pit lake typically suggest the potential for a stratified condition to occur. However, pit lakes that form in arid regions are unlikely to stratify relative to lakes that form in cooler, wetter climates (Jewell, 2009). On this basis, the Rosemont pit lake is not expected to stratify. In the event that stratification occurs, the upper portion of the lake would likely result in oxidizing conditions, owing to the associated contact with air. The lower portions of the lake would conversely become relatively reducing due to the lack of air exposure. However, modeling suggests that the difference in the chemical composition of these two (2) domains may not be significant. For example, Illustration 6.06 shows that the removal of arsenic due to scavenging by HFO (under oxidizing conditions) yields estimated concentrations (average scenario) of about 0.02 mg/L whereas the raw DSM output indicates about 0.07 mg/L, which would be the applicable value if unlikely reducing conditions prevailed.

The evaluation performed as part of this study focused on geochemical modeling (PHREEQC) with oxidizing conditions in the upper portion of the projected pit lake since only this portion of lake water will be exposed to the local environment. The anticipated terminal condition of the pit lake (Tetra Tech, 2010a) implies that deeper reaches of the pit lake will not leave the pit and will not, therefore, affect groundwater in the area.

The oxidizing conditions at the lake surface have been modeled to show a limited removal of iron and arsenic. Reducing conditions, which are possible at the deeper reaches of the projected lake, would preclude this removal mechanism, or re-dissolve the small amount of metals precipitated at near-surface depths. The modeled precipitation of calcite is not dependent on the oxidation-reduction conditions of the pit lake and is therefore not affected by any potential lake stratification. Overall, the chemical quality of the projected pit lake appears to be driven by the evapo-concentration of chemical constituents.



8.0 CONCLUSIONS

The chemical conditions within the anticipated pit lake at Rosemont were analyzed and included geochemical testing of the materials comprising the ultimate pit walls and the quality of local groundwater.

The pit lake model showed that the quality of the pit lake water was only slightly changed from local groundwater after 200 years of simulation. The conclusions of the predictive geochemical modeling effort performed for the Rosemont Copper Project can be summarized as follows:

The majority of the inflow water entering the open pit will be from groundwater sources seeping through the pit walls. Most of the water and about 95% of the chemical mass contribution to the pit lake will be from groundwater. Direct precipitation, and runoff from the pit walls, will contribute to the pit lake water balance as well. Over time, the contribution from direct precipitation will increase as a percentage of annual inflow as the pit lake surface area increases;

The pit lake water is anticipated to be similar to the local groundwater with a pH of 8, which is slightly alkaline; and

Because the pit lake is predicted to be a hydraulic sink, with water leaving only through evaporation, dissolved chemical constituents are expected to concentrate over time. At the 200-year simulation mark, the pit lake model showed evapo-concentration of some chemically conservative constituents of about 1.1 to 1.7 times that of local groundwater, depending upon model conditions (low, average, or high chemical loading as derived from laboratory leach testing).

As indicated above, the quality of the pit lake water was only slightly changed from local groundwater after 200 years of model simulation. Although there is appreciable chemical loading as calcium and bicarbonate, the precipitation of this phase removes significant chemical mass. At the 200-year simulation mark, the pH of the pit lake water is anticipated to be 8, which is similar to local groundwater. Sulfide minerals are largely absent from the non-ore rock at the Rosemont site and carbonate minerals, such as limestone, are abundant. Therefore, the development of an acidic pit lake is not expected, even beyond the 1,000-year modeling period.

The results of the pit lake model show that most of the water reporting to the pit lake will come from local groundwater, with the remaining comprised of direct precipitation and runoff from the pit walls. Similarly, the majority of chemical loading to the pit lake will also come from groundwater sources.

Laboratory testing was conducted to determine the chemical loading terms required for the pit lake model. Over the 1,000-year simulation period, calculations were performed to provide low, average, and elevated chemical loading scenarios. This was done to provide a sensitivity evaluation of the model.

At the 200-year simulation mark under the elevated chemical loading scenario, the concentrations of some dissolved chemical constituents were shown to increase by a factor of up to 1.7 relative to local groundwater due to the evaporative loss of water. Even in the elevated chemical loading scenario, metals are expected to remain at levels in the parts per billion range (less than 1 part per million). Although water quality predictions were modeled to the 1,000-year simulation time-frame, these results should only be used for determining overall trends. Specific water quality predictions beyond the 200-year time-frame become excessively speculative based on the long simulation periods.



9.0 REFERENCES

Allison, J.D., D.S. Brown, and K.J. Novo-Gradac (1991). MINTEQA2/PRODEFA2, A Geochemical Assessment Model for Environmental Systems, Version 3.0 User’s Manual. Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, GA. 106 pp.

American Society for Testing and Materials (ASTM) (1996). Standard Test Method for Accelerated Weathering of Solid Materials Using a Modified Humidity Cell. ASTM Designation D 5744-96. ASTM, West Conshohocken, PA.

ASTM (2003). Standard Test Method for Column Percolation Extraction of Mine Rock by the Meteoric Water Mobility Procedure. ASTM Designation E 2242-02. ASTM, West Conshohocken, PA.

Council, G.W., (1999). “A Lake Package for MODFLOW (LAK2) – Documentation and User’s Manual, Version 2.2. “ HSI GEOTRANS – A Tetra Tech Company.

Davis, J.A. and D.B. Kent (1990). Surface Complexation Modeling in Aqueous Geochemistry. In M.F. Hochella and A.F. White (eds.). Mineral-Water Interface Geochemistry, Reviews in Mineralogy, Volume 23, Chapter 5, p. 177-260. Mineralogical Society of America, Washington, D.C.

Daffron, W.J., R.A. Metz, S.W. Parks, and K.L. Sandwell-Weiss (2007). Geologic Report, Relogging Program at the Rosemont Porphyry Skarn Copper Deposit. Prepared for Augusta Resource Corporation.

Dzombak, D.A. and F.M.M. Morel. 1990. Surface Complexation Modeling-Hydrous Ferric Oxide. John Wiley & Sons, New York. 393 pp.

GoldSim Technology Group, (2005). User’s Guide Goldsim Probabilistic Simulation Environment, Version 9. Washington, USA.

Jewell, P.W. (2009) Stratification controls of pit mine lakes. Mining Engineering. February 2009, 40-45.

Kohler, M.A. and L.H. Parmele (1967). Generalized estimates of free-water evaporation. Water Resources Research, 3(4):997-1005.

Naugle G.D., Atkinson L.C. (1993). Estimating the rate of post-mining filling of pit lakes. Mining Engineering 45(4), 402-404.

National Atmospheric Deposition Program (NADP) (2008). NADP/NTN Monitoring Location AZ06. http://nadp.sws.uiuc.edu/sites/siteinfo.asp?net=NTN&id=AZ06. Visited May 12, 2008.

Parkhurst., D.L., D.C. Thorstenson, and L.N. Plummer (1980). PHREEQE-A Computer Program for Geochemical Calculations. U.S. Geological Survey Water-Resources Investigations Report 99-4259.



Parkhurst, D.L. and C.A.J. Appelo (1999) User’s Guide to PHREEQC (Version 2)-A Computer Program for Speciation, Batch-Reaction, One-dimensional Transport, and Inverse Geochemical Calculations. U.S. Geological Survey Water-Resources Investigations Report 99-4259.

Tetra Tech (2007). Geochemical Characterization Addendum 1. Prepared for Rosemont Copper Company. Report Dated November 2007.

Tetra Tech (2009). Rosemont Copper Project Design Storm and Precipitation Data/Design Criteria. Technical Memorandum Prepared for M3 Engineering & Technology Corp. Technical Memorandum Dated April 2009.

Tetra Tech (2010a). Predictive Groundwater Flow Modeling Results. Technical Memorandum to Kathy Arnold (Rosemont Copper Company). Technical Memorandum Dated July 30, 2010.

Tetra Tech (2010b). Groundwater Flow Model Construction and Calibration. Technical Memorandum to Kathy Arnold (Rosemont Copper Company), Technical Memorandum Dated July 26, 2010.

Tetra Tech (2010c). Groundwater Flow Model Sensitivity Analyses. Technical Memorandum to Kathy Arnold (Rosemont Copper Company). Technical Memorandum Dated August 16, 2010.

U.S. Environmental Protection Agency (USEPA) (1986). Test Methods for Evaluating Solid Wastes. 3rd Edition. SW-486. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, D.C.

WestLand Resources Inc. (2007). Mine Plan of Operations – Rosemont Project. Prepared for Augusta Resource Corporation. Report Dated July 2007.

Western Regional Climate Center (WRCC), (2008a). Arizona Climate Summaries: http://www.wrcc.dri.edu/summary/Climsmaz.html. Visited May 13, 2008.

Western Regional Climate Center (WRCC), (2008b). Average Pan Evaporation Data: http://www.wrcc.dri.edu/htmlfiles/westevap.final.html. Visited May 13, 2008.

APPENDIX A CLIMATE DATA SUMMARY


Tetra Tech November 2010 A-1

Table A1 Average Monthly Precipitation Data for the Nogales Station (Tetra Tech, 2009)

Month Precipitation (Inches) JAN 1.10 FEB 0.85 MAR 0.90 APR 0.39 MAY 0.22 JUN 0.47 JUL 4.34 AUG 4.13 SEP 1.55 OCT 1.33 NOV 0.66 DEC 1.43

TOTAL 17.37

Table A2 Estimated Average Monthly Pan Evaporation (Tetra Tech, 2009)

Month Nogales Station Pan

Evaporation 1

(inches)

Rosemont Projected Pan Evaporation

(inches) JAN 3.59 4.13 FEB 4.46 4.28 MAR 7.01 7.11 APR 9.35 8.50 MAY 11.91 10.38 JUN 13.31 10.75 JUL 10.00 4.93 AUG 8.28 2.89 SEP 8.06 4.40 OCT 7.17 6.15 NOV 4.49 4.11 DEC 3.57 3.89

TOTAL 91.20 71.52

APPENDIX B SAMPLE ADEQUACY EVALUATION FOR

ROSEMONT GEOLOGIC MATERIALS


Tetra Tech November 2010 B-1

B1.0 INTRODUCTION

An open pit copper mine and ore processing operation are planned for the Rosemont Copper Project (Rosemont) site, located approximately 30 miles southeast of Tucson, Arizona. Processing of approximately 546 million tons (Mt) of sulfide ore and over 60 Mt of oxide ore is expected to generate about 1,232 Mt of waste rock during the anticipated 20-25 year mine life. Consequently, a baseline geochemical characterization was prepared which focused on the potential water quality impacts from the various mine facilities (e.g., waste rock and dry stack tailings storage areas). One of the primary goals of the baseline characterization study was to test a representative number of samples in order to adequately characterize the geochemical behavior of the rock that would be developed from mining (Tetra Tech, 2007). The results from this geochemical testing can also be used to estimate the geochemistry of non-ore rocks in the final walls of the pit.

The exposed pit wall lithology will be dominated by arkosic rocks of the Willow Canyon Formation, the Horquilla Limestone, and Bolsa Quartzite, with less exposure of additional limestone, quartz monzonite porphyry, and andesite (Table B1). Most of the primary sulfide mineralization is hosted by the Horquilla, Colina, and Epitaph Limestones, although the total sulfide content of these Paleozoics is generally low compared to other southwest porphyry copper systems (Tetra Tech, 2007). In fact, the total sulfur content of the overlying arkosic and andesitic lithologies is generally higher than in the remainder of the deposit. A total of 226 applicable samples were subsequently submitted for standard static testing procedures.

The most commonly-used static test is known as acid-base accounting (ABA), which measures the balance between the acid-producing potential (AP) and the acid-neutralizing potential (NP) (White and others, 1999). The AP is determined by sulfur analysis and determines the sulfur content associated with pyritic sulfur. The NP is determined by acid-titration and generally represents the carbonate content of the sample. The net-neutralizing potential (NNP) is the difference between these values (NNP = NP - AP) and is typically expressed in units of kilograms of calcium carbonate (CaCO3) per ton of rock (kg CaCO3/t rock, or kg/t). The NNP, together with the NP ratio (NP/AP), is an important parameter used to classify a material as either potentially-acid generating (PAG) or inert with respect to acid generation. Because the ABA characteristics for a given sample reflect the dominant mineralogic properties of the material (i.e., carbonate and sulfur mineral content), ABA results can be used to evaluate if a material has been adequately characterized with respect to its potential effects on water quality.



B2.0 TECHNICAL OBJECTIVE

The objective of this sample adequacy evaluation was to assess the degree to which results from geochemical testing represent the overall geochemical tendencies of various rock types at Rosemont.



B3.0 TECHNICAL EVALUATION

Numerous criteria for determining an adequate sample population have been suggested as a means of obtaining representative samples of waste rock (USEPA, 1983; USEPA, 1994; Maest and Kuipers, 2005; Runnells and others, 1997). However, because it is impossible to confidently predict the degree of heterogeneity of a material, it is impossible to predict in advance how many samples will be required to representatively characterize it. In concept, a perfectly homogeneous material requires only one (1) sample. Because the degree of variability in geochemical properties of rock is unique to each site, a reasonable approach is to determine the sample requirement based upon site-specific variability.

Samples may be taken from over a reasonable volume of the rock unit under consideration and continuously characterized until no further significant variability is observed. Such a process explicitly determines the heterogeneity of geochemical characteristics and demonstrates an adequate level of characterization.

The evaluation of sample adequacy presented herein was conducted using the approach outlined by Runnells and others (1997), which utilizes statistical measures of central tendency (mean) and dispersion (standard deviation) (USEPA, 2000) to evaluate sample representation. The method uses a stepwise evaluation to evaluate the degree to which additional sample analysis improves the level of confidence for a given parameter. Once the naturally-occurring variability of a rock unit is established, specific samples may be selected for detailed characterization of water quality that results from contact with that material.

During the baseline geochemical characterization (Tetra Tech, 2007), samples of geologic materials were submitted for ABA testing in proportion to their expected occurrence in the waste rock. The previous baseline testing results can be found in Appendix A of the Geochemical Characterization, Addendum 1 report (Tetra Tech, 2007). A subsequent evaluation of these data using the approaches described above indicated that insufficient information existed for several of the rock types, and therefore additional geochemical analysis was conducted in 2008. This additional data was composited with the 2007 data for subsequent analysis.

Illustrations B1 and B2 show the spatial extent of sampling of rock within the projected pit. In this illustration, the traces of the boreholes are shown as lines and the individual sample locations are shown as a colored segment of the line. Samples were collected from a relatively large volume in the area most proximal to mineralization, where variability of the unit would be expected to be greatest. Samples were also collected, although fewer in number, at more distal positions, where less variability was anticipated.

The final composite ABA data for the rock types analyzed (Table B2) were first listed in random order, and then a moving average and standard deviation were calculated for NNP. The resulting data were graphed. This analysis evaluates potential increasing convergence toward the population mean (average), and decreasing variability about the population mean, with increasing sample size. A given rock type was considered to be adequately characterized when increasing the sample size produces a change of <10% in the average NNP, and if the slope of the standard deviation approached zero.



B4.0 RESULTS AND DISCUSSION

The statistical results (from the Geochemical Characterization, Addendum 1 report [Tetra Tech, 2007] and subsequent additional analyses) for NNP within each rock type evaluated are presented on Illustrations B3 through B15. Each illustration depicts the moving average and standard deviation of the NNP for a given rock type. Based on these results, the rock types which are anticipated to be exposed on the pit walls have been adequately characterized. Increasing the number of samples associated with these materials for analysis would yield limited or no increased definition of their chemical characteristics: therefore, no further sampling was deemed necessary.

For example, Illustration B3 shows the variation in the moving average and standard deviation for the Willow Canyon Formation arkose NNP values. For the first few samples, the running average changes by more than 10% as the sample number increases, but with increasing sample size, very little change is seen for both the moving average and the standard deviation (Illustration B3). Therefore, increasing the number of arkose samples will not change the level of confidence in the average value, indicating that an adequate number of arkose samples have been analyzed. Similar trends are apparent for the remaining rock types, which includes the rock types that are expected to dominate the final pit wall exposure.



B5.0 CONCLUSIONS

Statistical evaluation of ABA data collected from the Rosemont geologic materials provides a method for evaluating sample adequacy using site-specific geochemical parameters, rather than relying on arbitrary literature criteria developed for mine materials in general. Application of a statistical technique to the Rosemont Project site indicates that an adequate number of samples have been analyzed to characterize their central geochemical tendency.



B6.0 REFERENCES

Maest, A. S. and Kuipers, J. R. (2005) Predicting Water Quality at Hardrock Mines: Methods and Models, Uncertainties and State-of-the-Art. Independent report prepared through support from EARTHWORKS, Washington, D.C.

Runnells, D.D., M. Shields, and R.L. Jones (1997). Methodology for adequacy of sampling and mill tailings and mine waste rock. pp. 561-563, Proceedings of the Fourth International Conference on Tailings and Mine Waste. Fort Collins, CO. January 13-17, 1997. AA. Balkema, Rotterdam, Netherlands. 788 pp.

Tetra Tech (2007). Geochemical Characterization, Addendum 1. Prepared for Rosemont Copper Company. Report Dated November 2007.

U.S. Environmental Protection Agency (USEPA) (1983). Preparation of Soil Sampling Protocol: Techniques and Strategies. EPA-600/S4-83/020. Environmental Monitoring Systems Laboratory, Las Vegas, NV.

USEPA (1994). Acid Mine Drainage Prediction. EPA530-R-94-036. Office of Solid Waste, Special Waste Branch. Washington, DC.

USEPA (2000). Guidance for Data Quality Assessment: Practical Methods for Data Analysis. EPA QA/G-9, QA00 Update. EPA/600/R-96/084. Office of Environmental Information, Washington, DC.

White, W.W., K.A. Lapakko, and R.L. Cox (1999). Static-test methods most commonly used to predict acid-mine drainage: Practical guidelines for use and interpretation. In G.S. Plumlee and M.S. Logsdon (eds.) The Environmental Geochemistry of Mineral Deposits, Part A: Processes, Techniques, and Health Issues. Vol. 6A. Society of Economic Geologists, Inc.



TABLES



Table B1 Projected Exposed Areas and ABA Summary for Various Rock Types in the Rosemont Pit

Rock Type % of Exposed Area No. Samples Analyzed for ABAWillow Canyon Formation, Arkose 29.3 55

Horquilla Limestone 16 26 Bolsa Quartzite 8.1 13

Abrigo Formation 7.5 6 Epitaph Formation 7.4 16

Tertiary Gravel 6.4 5 Colina Limestone 4.8 11 Earp Formation 4.0 14

Glance Conglomerate 3.8 4 Escabrosa Limestone 3.8 10

Concha 2.9 6 Martin Formation 2.5 7

Precambrian Granodiorite 1.0 0 Willow Canyon Formation, Andesite 0.89 38

Scherrer 0.62 0 Quartz Monzonite Porphyry 0.53 9

Overburden 0.15 6 TOTAL 100 226

Table B2 Summary of ABA Data Used to Evaluate Sampling Adequacy

Sample ID Rock Type # Samples AP NP NNP NNP - Mean NNP - Std. Dev.1561-03 Abrigo 1 0.3 665 665.0 665.0 1561-01 Abrigo 2 0.3 439 439.0 552.0 159.8 1916-02 Abrigo 3 0.3 630 630.0 578.0 121.6 A818-01 Abrigo 4 0.3 693 693.0 606.8 114.8 1926-02 Abrigo 5 0.3 550 550.0 595.4 102.6 A780-01 Abrigo 6 0.3 501 501 579.7 99.5 AR2019-02 Andesite 1 0.3 44.2 44.2 44.2 AR2021-01 Andesite 2 35.3 47.3 12.0 28.1 22.8 AR2030-06 Andesite 3 0.3 33.9 33.9 30.0 16.4 AR2010-03 Andesite 4 52.2 26.6 -25.6 16.1 30.9 AR2017-06 Andesite 5 0.3 39.6 39.6 20.8 28.7 A-820 245.5 Andesite 6 18.2 87.7 69.5 28.9 32.5 AR2009-03 Andesite 7 64.1 99.1 35.0 29.8 29.7 AR2014-03 Andesite 8 29.7 71.7 42.0 31.3 27.9 AR2017-01 Andesite 9 0.3 45.3 45.3 32.9 26.5 A-816 569 Andesite 10 28.9 121 92.1 38.8 31.2 AR2028B-01 Andesite 11 29.4 75.7 46.3 39.5 29.7 AR2043-01 Andesite 12 47.5 103 55.5 40.8 28.7 AR2013-01 Andesite 13 48.4 59.7 11.3 38.5 28.7 AR2013-02 Andesite 14 33.1 70.2 37.1 38.4 27.5




Sample ID Rock Type # Samples AP NP NNP NNP - Mean NNP - Std. Dev.AR2030-05 Andesite 15 5.3 47.7 42.4 38.7 26.6 AR2011-03 Andesite 16 68.8 54.2 -14.6 35.4 28.9 AR2032-01 Andesite 17 0.3 39.8 39.8 35.6 28.0 AR2026-01 Andesite 18 14.4 48.3 33.9 35.5 27.2 AR2038-04 Andesite 19 0.3 6.2 6.2 34.0 27.3 AR2029-01 Andesite 20 0.3 105 105.0 37.5 30.9 A-882 109 Andesite 21 12 71.6 59.6 38.6 30.5 AR2020-02 Andesite 22 5.3 50.1 44.8 38.9 29.8 AR2038-01 Andesite 23 0.3 13.7 13.4 37.8 29.6 AR2030-03 Andesite 24 11.9 68.5 56.6 38.6 29.2 1535-01 Andesite 25 34.1 41 6.9 37.3 29.3 AR2022-01 Andesite 26 34.1 88.5 54.4 37.9 28.9 AR2014-02 Andesite 27 123 105 -18.0 35.9 30.3 A808-01 Andesite 28 36.9 19 -17.9 34.0 31.4 AR2038-06 Andesite 29 0.3 38.9 38.9 34.1 30.9 A817-01 Andesite 30 45 46.8 1.8 33.0 30.9 A-886 888 Andesite 31 27.6 156 128.4 36.1 34.9 AR2016-01 Andesite 32 0.3 26.2 26.2 35.8 34.4 AR2037-01 Andesite 33 36.3 80.4 44.1 36.1 33.9 AR2038-03 Andesite 34 0.3 43.5 43.5 36.3 33.4 AR2017-05 Andesite 35 0.6 25.7 25.1 36.0 32.9 AR2013-03 Andesite 36 31.9 79 47.1 36.3 32.5 AR2025-03 Andesite 37 0.3 39.1 39.1 36.3 32.0 AR2025-01 Andesite 38 30.3 37.8 7.5 35.6 32.0 AR2037-02 Arkose 1 0.3 48.9 48.9 48.9 AR2011-01 Arkose 2 0.3 10.5 10.2 29.6 27.4 A873-01 Arkose 3 0.3 78.9 78.9 46.0 34.4 1596-01 Arkose 4 0.3 65.6 65.6 50.9 29.8 AR2035-01 Arkose 5 0.3 44.8 44.5 49.6 25.9 VABH0609-01 Arkose 6 0.3 4.3 4.3 42.1 29.7 AR2009-02 Arkose 7 0.3 19.6 19.6 38.9 28.4 AR2004-01 Arkose 8 0.3 56.2 56.2 41.0 27.0 AR2036-01 Arkose 9 7.2 103 95.8 47.1 31.2 AR2020-01 Arkose 10 0.3 13.9 13.9 43.8 31.2 AR2005-01 Arkose 11 0.3 35.6 35.6 43.0 29.7 AR2002-01 Arkose 12 1.6 27.1 25.5 41.6 28.8 AR2011-02 Arkose 13 0.3 21.6 21.6 40.0 28.1 AR2026-02 Arkose 14 0.3 108 108.0 44.9 32.5 AR2017-07 Arkose 15 41.6 74.6 33.0 44.1 31.5 AR2013-05 Arkose 16 9.1 82.8 73.7 46.0 31.3 AR2003-01 Arkose 17 0.3 37.6 37.6 45.5 30.4 AR2009-01 Arkose 18 0.3 52.7 52.7 45.9 29.5 A857-01 Arkose 19 0.3 91.6 91.6 48.3 30.6 AR2040-01 Arkose 20 0.3 7.7 7.4 46.2 31.1 AR2025-01 Arkose 21 30.3 37.8 7.5 44.4 31.5




Sample ID Rock Type # Samples AP NP NNP NNP - Mean NNP - Std. Dev.AR2001-02 Arkose 22 0.3 26.1 25.8 43.5 31.0 AR2005-02 Arkose 23 29.4 34.1 4.7 41.9 31.3 AR2041-01 Arkose 24 13.1 44.3 31.2 41.4 30.7 AR2030-04 Arkose 25 0.3 63.5 63.5 42.3 30.4 AR2014-01 Arkose 26 0.3 31.1 31.1 41.9 29.9 AR2039-03 Arkose 27 20.3 24.8 4.5 40.5 30.2 AR2007-01 Arkose 28 0.3 74 74.0 41.7 30.3 AR2017-03 Arkose 29 40.9 73.2 32.3 41.4 29.8 AR2003-03 Arkose 30 20.6 31.1 10.5 40.3 29.8 AR2038-05 Arkose 31 0.3 16.6 16.6 39.6 29.6 AR2019-01 Arkose 32 0.3 11.8 11.8 38.7 29.5 AR2013-04 Arkose 33 6.9 70.2 63.3 39.4 29.4 AR2036-03 Arkose 34 5 75.2 70.2 40.3 29.4 AR2030-02 Arkose 35 0.3 97.2 97.2 42.0 30.5 AR2010-01 Arkose 36 0.3 19.6 19.6 41.3 30.3 AH4-01 Arkose 37 0.3 43.2 43.2 41.4 29.9 AR2042-02 Arkose 38 0.3 134 134.0 43.8 33.1 AR2003-02 Arkose 39 0.3 24.1 24.1 43.3 32.8 1920-01 Arkose 40 0.3 45.3 45.3 43.4 32.4 AR2039-06 Arkose 41 23.1 19.7 -3.4 42.2 32.8 AR2010-02 Arkose 42 13.8 17.6 3.8 41.3 32.9 AR2043-02 Arkose 43 32.2 175 142.8 43.7 36.0 AR2038-02 Arkose 44 0.3 100 100.0 45.0 36.6 A886-01 Arkose 45 1.9 9.1 7.2 44.1 36.6 AR2025-02 Arkose 46 15.9 71.9 56.0 44.4 36.3 A830-03 Arkose 47 0.3 17.6 17.6 43.8 36.1 Arkose (AR2054) Arkose 48 0.3 8.3 8.3 43.1 36.1 A814-01 Arkose 49 0.3 90 90.0 44.0 36.3 AR2001-01 Arkose 50 0.3 27.1 27.1 43.7 36.0 AR2025-04 Arkose 51 6.3 31.1 24.8 43.3 35.7 AR2032-02 Arkose 52 0.3 36.5 36.5 43.2 35.4 A831-01 Arkose 53 0.3 29 28.7 42.9 35.1 AR2015-01 Arkose 54 0.3 73.3 73.3 43.5 35.0 AR2000-01 Arkose 55 0.3 42.7 42.7 43.5 34.7 AR2042-04 Arkose 56 0.3 111 111.0 44.7 35.6 AR2067-01 Bolsa 1 0.3 7.3 7.3 7.3 AR2033-01 Bolsa 2 0.55 13.5 13.0 10.1 4.0 AR2059-01 Bolsa 3 2.74 2.7 0.0 6.7 6.5 VABH0608-01 Bolsa 4 0.3 2.6 2.6 5.7 5.7 AR2023-01 Bolsa 5 21.5 8.3 -13.2 1.9 9.8 A780-02 Bolsa 6 9.69 3.5 -6.2 0.6 9.4 A780-03 Bolsa 7 39.7 0.3 -39.7 -5.2 17.5 AR2066-01 Bolsa 8 15.4 38.1 22.7 -1.7 18.9 1561-02 Bolsa 9 6.03 1.5 -4.5 -2.0 17.7 AR2073-01 Bolsa 10 0.3 9.9 9.9 -0.8 17.1




Sample ID Rock Type # Samples AP NP NNP NNP - Mean NNP - Std. Dev.1561-04 Bolsa 11 0.54 4.2 3.7 -0.4 16.3 AR2072-01 Bolsa 12 5.47 10.4 4.9 0.0 15.6 AR2060-01 Bolsa 13 0.3 3.2 3.2 0.3 15.0 A852-01 Colina 1 74.8 203 128.2 128.2 A840-01 Colina 2 0.3 492 492.0 310.1 257.2 A815-01 Colina 3 0.3 453 453.0 357.7 199.7 A865-01 Colina 4 3.4 129 125.6 299.7 200.2 AR2011-04 Colina 5 1.6 299 297.4 299.2 173.4 1914-01 Colina 6 18.1 403 384.9 313.5 158.9 1528-02 Colina 7 11.9 337 325.1 315.2 145.2 A860-01 Colina 8 2.2 354 351.8 319.8 135.0 AR 2010-04 Colina 9 0.3 930 930.0 387.6 239.4 AR2002-02 Colina 10 0.6 617 616 410.4 237.0 AR2041-02 Colina 11 1.6 221 220 393.1 232.1 AR2042-01 Concha 1 0.3 432 432.0 432.0 AR2042-05 Concha 5 0.3 570 570.0 596.2 153.2 AH4-02 Concha 6 0.3 530 530.0 585.2 139.7 AR2006-01 Concha 8 0.3 740 740.0 627.6 142.1 A808-02 Concha 9 0.3 740 740.0 640.1 138.1 A804-01 Concha 11 0.3 889 889.0 651.7 150.9 AR2019-03 Earp 1 8.8 23.1 14.3 14.3 AR2030-01 Earp 2 4.4 85.2 80.8 47.6 47.0 A849-01 Earp 3 1.81 208 206.2 100.4 97.4 A830-04 Earp 4 15.3 178 162.7 116.0 85.4 1528-01 Earp 5 4.4 182 177.6 128.3 79.0 1920-02 Earp 6 1.16 249 247.8 148.2 85.8 A845-01 Earp 7 4.1 26.2 22.1 130.2 91.7 AR2035-02 Earp 8 1.9 171 169.1 135.1 86.0 AR2017-02 Earp 9 0.3 47.4 47.4 125.3 85.6 A834-02 Earp 10 5.6 109 103.4 123.1 81.0 AR2000-03 Earp 11 8.1 112 103.9 121.4 77.1 AR2000-02 Earp 12 10.9 62.2 51.3 115.6 76.2 AR2014-05 Earp 13 10 104 94.0 113.9 73.2 AR2028B-02 Earp 14 0.3 58.4 58.4 109.9 71.9 AR2009-04 Epitaph 1 17.2 80.3 63.1 63.1 A847-01 Epitaph 2 0.3 774 774.0 418.6 502.7 A828-01 Epitaph 3 5.47 165 159.5 332.2 385.6 A860-02 Epitaph 4 5 252 247.0 310.9 317.7 AR2040-02 Epitaph 5 0.3 707 707.0 390.1 327.3 A850-01 Epitaph 6 0.3 176 175.7 354.4 305.5 A860-03 Epitaph 7 0.3 405 405.0 361.6 279.5 1538-01 Epitaph 8 0.3 621 621.0 394.0 274.6




Sample ID Rock Type # Samples AP NP NNP NNP - Mean NNP - Std. Dev.AR2002-03 Epitaph 9 0.3 522 522.0 408.3 260.4 AR2034-02 Epitaph 10 0.3 928 928.0 460.2 295.4 A829-01 Epitaph 11 0.3 680 680.0 480.2 288.0 A825-01 Epitaph 12 0.3 933 933.0 517.9 304.1 A801-01 Epitaph 13 0.3 770 770 537.3 299.4 A830-01 Epitaph 14 0.3 638 638 544.5 288.9 AR2001-03 Epitaph 15 0.3 99.1 99.1 514.8 301.2 AR2014-04 Epitaph 16 0.3 584 584 519.2 291.5 A801-01 Epitaph 7 0.3 770 770.0 611.6 145.4 1580-01 Escabrosa 1 0.3 34.8 34.8 34.8 1507-01 Escabrosa 2 0.3 912 912.0 473.4 620.3 A814-02 Escabrosa 3 0.3 874 874.0 606.9 495.8 A872-01 Escabrosa 4 0.3 203 203.0 506.0 452.4 AR2004-05 Escabrosa 5 0.3 880 880.0 580.8 426.0 1926-03 Escabrosa 6 0.3 862 862.0 627.6 398.0 A812-01 Escabrosa 7 0.3 112 112.0 554.0 412.3 1461-01 Escabrosa 8 0.3 788 788.0 583.2 390.6 1506-02 Escabrosa 9 0.3 838 838.0 611.5 375.1 A871-01 Escabrosa 10 0.6 570 569.4 607.3 353.9 AR2004-02 Glance 2 0.3 722 722.0 577.0 205.1 A805-01 Glance 3 0.3 473 473.0 542.3 156.9 1596-02 Glance 4 0.3 784 784.0 602.8 176.1 A834-01 Glance 10 0.3 519 519.0 628.0 135.7 A845-02 Horquilla 1 0.3 201 201.0 201.0 A878-02 Horquilla 2 2.19 175 172.8 186.9 19.9 1530-01 Horquilla 3 32.2 202 169.8 181.2 17.2 AR2039-07 Horquilla 4 0.3 169 169.0 178.2 15.3 A809-01 Horquilla 5 0.6 219 218.4 186.2 22.4 A806-01 Horquilla 6 0.3 194 194.0 187.5 20.3 1596-03 Horquilla 7 6.25 212 205.8 190.1 19.7 A842-01 Horquilla 8 0.3 224 224.0 194.3 21.8 A866-02 Horquilla 9 0.3 766 766.0 257.9 191.6 AR2007-02 Horquilla 10 0.9 97.8 96.9 241.8 187.7 1502-01 Horquilla 11 0.3 887 887.0 300.4 263.7 AR2004-03 Horquilla 12 0.3 270 270.0 297.9 251.6 AR2043-03 Horquilla 13 0.3 449 449.0 309.5 244.5 AR2004-04 Horquilla 14 0.3 459 459.0 320.2 238.3 AR2042-03 Horquilla 15 0.3 285 285.0 317.8 229.8 AR2000-04 Horquilla 16 0.3 467 467.0 327.2 225.1 AR2017-08 Horquilla 17 0.3 251 251.0 322.7 218.8 AR2042-06 Horquilla 18 0.3 410 410.0 327.5 213.2 AR 2030-07 Horquilla 19 0.8 412 411.2 331.9 208.1 AR 2035-03 Horquilla 20 0.3 272 272.0 328.9 203.0




Sample ID Rock Type # Samples AP NP NNP NNP - Mean NNP - Std. Dev.AR 2000-05 Horquilla 21 0.3 862 862.0 354.3 229.5 AR 2015-02 Horquilla 22 0.3 874 874.0 377.9 249.9 AR 2043-05 Horquilla 23 42.7 272 229.3 371.5 246.1 AR 2006-02 Horquilla 24 0.3 167 167.0 363.0 244.3 AR 2032-03 Horquilla 25 0.3 154 154.0 354.6 242.8 AR 2004-06 Horquilla 26 0.3 590 590.0 363.7 242.3 A856-01 Martin 1 0.3 707 707.0 707.0 1916-01 Martin 2 0.3 738 738.0 722.5 21.9 A866-01 Martin 3 0.3 599 599.0 681.3 73.0 1511-01 Martin 4 0.3 863 863.0 726.8 108.6 A878-01 Martin 5 4.1 576 571.9 695.8 116.8 1506-03 Martin 6 2 489 487.0 661.0 134.8 1461-02 Martin 7 0.3 876 876.0 691.7 147.5 AR2039-02 Overburden 1 1.6 19 17.4 17.4 AR2039-05 Overburden 2 0.3 19.2 18.9 18.2 1.1 AR2039-04 Overburden 3 0.3 4.2 4.2 13.5 8.1 A821-01 Overburden 4 0.3 47.3 47.3 22.0 18.1 1485-01 Overburden 5 0.3 25.7 25.7 22.7 15.8 AR2039-01 Overburden 6 1.9 9.5 7.6 20.2 15.4 AR2036-04 QMP 1 0.3 0.3 0.0 0.0 AR2034-01 QMP 2 0.3 2.1 2.1 1.1 1.5 A855-01 QMP 3 0.3 20.6 20.6 7.6 11.3 AR2037-03 QMP 4 0.3 36.7 36.7 14.9 17.3 1503-01 QMP 5 0.3 5.6 5.6 13.0 15.5 1926-01 QMP 6 0.3 12.2 12.2 12.9 13.9 1506-01 QMP 7 0.3 9.3 9.3 12.4 12.7 AR2036-02 QMP 8 0.3 4.7 4.7 11.4 12.1 A815-02 QMP 9 0.3 10.1 10.1 11.3 11.3



ILLUSTRATIONS



Illustration B1 Drill Holes and Samples Used to Characterize Non-Ore Rock



Illustration B2 Drill Holes and Samples Used to Characterize Non-Ore Rock



Willow Canyon Formation, Arkose

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Illustration B3 Moving Average and Standard Deviation of NNP Values for Rosemont Willow Canyon Formation Arkose Samples



Horquilla Limestone

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Illustration B4 Moving Average and Standard Deviation of NNP Values for Rosemont Horquilla Limestone Samples

Bolsa Quartzite

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Illustration B5 Moving Average and Standard Deviation of NNP Values for Rosemont Bolsa Quartzite Samples



Epitaph Formation

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Illustration B6 Moving Average and Standard Deviation of NNP Values for Rosemont Epitaph Formation Samples



Abrigo Formation

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Illustration B7 Moving Average and Standard Deviation of NNP Values for Rosemont Abrigo Formation Samples

Glance Conglomerate

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Illustration B8 Moving Average and Standard Deviation of NNP Values for Rosemont Glance Conglomerate Samples



Escabrosa Limestone

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Illustration B9 Moving Average and Standard Deviation of NNP Values for Rosemont Escabrosa Limestone Samples

Earp Formation

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Illustration B10 Moving Average and Standard Deviation of NNP Values for Rosemont Earp Formation Samples



Colina Limestone

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Illustration B11 Moving Average and Standard Deviation of NNP Values for Rosemont Colina Limestone Samples

Martin Formation

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Illustration B12 Moving Average and Standard Deviation of NNP Values for Rosemont Martin Formation Samples



Quartz Monzonite Porphyry

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Illustration B13 Moving Average and Standard Deviation of NNP Values for Rosemont Quartz Monzonite Porphyry Samples

Willow Canyon Formation, Andesite

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Illustration B14 Moving Average and Standard Deviation of NNP Values for Rosemont Willow Canyon Formation Andesite Samples



Overburden

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Illustration B15 Moving Average and Standard Deviation of NNP Values for Rosemont Overburden Samples

APPENDIX C GEOCHEMICAL EVALUATION

OF ROSEMONT KINETIC AND SHORT-TERM LEACH TEST DATA


Tetra Tech November 2010 C-1

C1.0 INTRODUCTION

An open pit copper mine and ore processing operation are planned for the Rosemont Copper Project (Rosemont) site, located approximately 30 miles southeast of Tucson, Arizona. Processing of about 546 million tons (Mt) of sulfide ore and over 60 Mt of oxide ore is expected to generate up to 1,288 Mt of waste rock during the anticipated 20-25 year mine life. Consequently, a baseline geochemical characterization was prepared which focused on potential water quality impacts from the various mine facilities (e.g., waste rock and dry stack tailings storage areas). One of the primary goals of the baseline characterization study was to test a representative number of samples in order to adequately characterize the chemical behavior of rock that would be developed from mining (Tetra Tech, 2007). The geochemical baseline characterization utilized a phased approach to characterize mine materials using both kinetic testing and short-term leaching tests (STLTs). Kinetic tests were carried out using standard humidity cell tests (HCTs) (ASTM, 1996), while STLTs included the Meteoric Water Mobility Procedure (MWMP) (ASTM, 2003) and the Synthetic Precipitation Leaching Procedure (SPLP) (USEPA, 1986).



C2.0 TECHNICAL OBJECTIVE

The objective of this analysis was to evaluate the adequacy of existing kinetic and STLT data for providing the information needed to evaluate wall rock runoff source terms for use in the Rosemont pit lake model. A summary of the HCT and STLT methods used to characterize the rock types is provided. An evaluation of the data is also provided with respect to predicting chemical mass loading to the pit lake from pit wall rock runoff.



C3.0 SUMMARY OF TESTING METHODS

Geochemical testing of Rosemont materials included both kinetic testing and STLTs. This section provides a summary of the kinetic tests and STLTs which were conducted on various rock types which are anticipated to comprise the ultimate pit walls and thus contribute to chemical mass loading of the pit lake.

C3.1 Kinetic Testing Kinetic testing procedures are designed to accelerate the natural weathering processes for a material. Typically, kinetic tests are conducted to verify the extent of acid generation for samples whose acid-base accounting (ABA) results indicate uncertainty with respect to acid generation, or to determine the rates of acid production for samples which are known to be acid generating. Static ABA test results are first used to classify a sample as either inert, potentially-acid generating (PAG), or uncertain with respect to acid generation. Static ABA testing compares the acid generation potential (AGP) with the acid neutralization potential (ANP) to calculate a net neutralization potential (NNP = ANP-AGP). Theoretically, a sample would be acid generating if its NNP value is less than zero. In practice, however, the risk of acid generation is greatest for samples with NNP values less than -20 kg CaCO3/ton rock (kg/t) and is the lowest when the NNP is greater than +20 kg/t (Price, 1997). The neutralization potential ratio (NPR = ANP/AGP) can also be used to assess the risk of acid generation, where an NPR greater than three (3) is considered to indicate a low risk for acid generation, and an NPR value less than one (1) indicates a high risk of acid generation (Price, 1997). The Arizona Department of Environmental Quality (ADEQ) Best Available Demonstrated Control Technology (BADCT) Guidance Manual (ADEQ, 2004) also follows these criteria to assess the potential of a material to be acid generating.

Kinetic testing was carried out on 16 samples which displayed a range of NNP (-25.6 to 63.1 (kg CaCO3/ton) and NPR (0.1 to 94.1) (Table C1). Testing was carried out using standard humidity cell tests (HCTs) (ASTM, 1996), where samples are placed into humidity cells and exposed to alternating periods of wetting and drying, followed by periodic leaching between cycles. The HCTs were carried out for a period of 25 to 35 weeks. The leachates are analyzed for constituents of interest (typically pH, sulfate, and metals) which can then be used to calculate rates of sulfide oxidation and metals release. Kinetic testing using HCTs is particularly useful in evaluating acid generation from samples which have been classified as PAG or uncertain.



Table C1 Static Testing Summary for Rosemont HCT Materials

Sample ID Rock Type NPR NNP Total Sulfur

Pyritic Sulfur

(%)

Sulfate Sulfur

(%) Class

AR2000-02 Earp 5.7 10.5 0.55 0.66 0.19 uncertain AR2003-03 Arkose 1.5 10.5 0.92 0.66 0.26 uncertain AR2005-02 Arkose 1.2 4.7 1.21 0.94 0.27 uncertain AR2009-03 Andesite 1.5 35 2.33 2.05 0.26 uncertain AR2009-04 Epitaph 4.7 63.1 0.80 0.55 0.24 uncertain AR2010-02 Arkose 1.3 3.8 0.57 0.44 0.13 uncertain AR2010-03 Andesite 0.5 -25.6 2.01 1.67 0.34 PAG AR2011-03 Andesite 0.8 -14.6 2.51 2.2 0.31 PAG AR2013-01 Andesite 1.2 11.3 1.98 1.55 0.40 uncertain AR2013-02 Andesite 2.1 37.1 1.36 1.06 0.30 uncertain AR2013-03 Arkose 2.5 47.1 1.02 1.02 <0.01 uncertain AR2014-02 Andesite 0.9 -17.1 4.77 3.92 0.81 PAG AR2014-03 Andesite 2.4 42.1 1.22 0.95 0.23 uncertain AR2014-05 Earp 94.1 10.4 0.32 0.37 <0.01 uncertain

A780-02 COMPOSITE Bolsa 0.33 -6.1 0.42 0.29 0.13 PAG

A780-03 COMPOSITE Bolsa 0.10 -8.7 0.45 0.31 0.13 PAG

C3.2 Short-Term Leaching Tests (STLTs) The Meteoric Water Mobility Procedure (MWMP) is a common leaching test used to evaluate the potential for dissolution and mobility of selected constituents from a mine waste sample when exposed to meteoric water (ASTM, 2003). The MWMP incorporates a single-pass column deionized water leach (unspecified pH) over a 24-hr period. The ratio of solution:solid in the MWMP (1:1) is identical to the HCTs. Column leachates are filtered and analyzed for the constituents of interest. Twenty-three MWMP tests were run and included

Nine (9) samples of Willow Canyon Formation Arkose;

Six (6) samples of Willow Canyon Formation Andesite;

Two (2) samples of Horquilla Limestone;

Two (2) samples of Overburden;

One (1) sample of Abrigo Formation;

One (1) sample of Concha Limestone;

One (1) sample of Quartz Monzonite Porphyry; and

One (1) sample of Tertiary Gravel.

The Synthetic Precipitation Leaching Procedure (SPLP) is a different leaching test which was designed to determine the potential for constituent mobility from geologic materials when exposed to precipitation (USEPA, 1986). The SPLP is a batch testing procedure (as opposed to the MWMP column leach) which uses a solution:solid ratio of 20:1. The SPLP extraction



solution is adjusted to a pH of 5.0 using a nitric/sulfuric acid mixture, and the sample is extracted for approximately 18 hours. The resulting extracts are then filtered and analyzed for the constituents of interest. Sixty-seven samples of varying lithologies were submitted for SPLP testing, including

Ten (10) samples of Willow Canyon Formation Arkose;

Ten (10) samples of Horquilla Limestone;

Seven (7) samples of Abrigo Formation;

Six (6) samples of Bolsa Quartzite;

Six (6) samples of Earp Formation;

Six (6) samples of Epitaph Formation;

Four (4) sample of Willow Canyon Formation Andesite;

Four (4) samples of Colina Limestone;

Four (4) samples of Escabrosa Limestone;

Four (4) samples of Martin Formation;

Two (2) sample of Overburden;

Two (2) samples of Quartz Monzonite Porphyry;

One (1) sample of Concha Limestone; and

One (1) sample of Tertiary Gravel.



C4.0 SUMMARY AND EVALUATION OF KINETIC AND LEACHING TEST DATA

This section presents a summary of the HCT, MWMP, and SPLP results for the Rosemont geologic materials. A comparison of these results is then used to select the most appropriate testing method for defining chemical mass loading from wall rocks to the pit lake. Previous baseline testing results can be found in Appendix A of the Geochemical Characterization, Addendum 1 report (Tetra Tech, 2007).

C4.1 Materials Testing Evaluation One of the most important aspects of geochemical characterization is ensuring that the materials being tested are representative of the overall population. As part of the initial Rosemont geochemical characterization program (Tetra Tech, 2007), a number of different rock types were characterized for ABA. A statistical evaluation of these data, in conjunction with additional data collection in 2008 and 2009, has shown that the majority of the rock types expected to comprise the pit walls have been adequately characterized (Appendix B in this report).

Subsequent to the initial geochemical characterization, a subset of samples was subjected to additional kinetic tests and STLTs. These results were evaluated in Sections C4.2 through C4.4. A summary of the mean NNP for each rock type, and the corresponding range in NNP for the specific samples tested, is provided in Table C2.

Table C2 Statistical Summary for Materials Geochemical Testing

Rock Type % of Pit Wall

NNP HCT MWMP SPLP

Mean NNP Range for Test Willow Canyon Formation, Arkose 29.3 45 ± 36 4.2 to 47.1 -3.4 to 70.2 4.3 to 95.6

Horquilla Limestone 16.2 364 ± 242 NT 1 410 to 467 169 to 766 Bolsa Quartzite 8.1 -0.75 ± 14 -6.1 to –8.8 NT -6.2 to 22.7 Abrigo Formation 7.5 580 ± 100 NT 439 439 to 693 Epitaph Formation 7.4 519 ± 282 63.1 NT 175 to 928 Tertiary Gravel 6.4 41.7 ± 46.7 NT 4.0 to 17.4 4.0 to 17.4 Colina Limestone 4.8 393 ± 232 NT NT 125 to 453 Earp Formation 4.0 110 ± 72 51.3 to 94.1 NT 14.4 to 169 Glance Conglomerate 3.8 625 ± 152 NT NT NT Escabrosa Limestone 3.8 612 ± 354 NT NT 203 to 874 Concha 2.9 650 ± 168 NT 570 740 Martin Formation 2.5 692 ± 148 NT NT 572 to 876 Pre-Cambrian Granodiorite 1.0 NT NT NT NT Willow Canyon Formation, Andesite 0.9 36 ± 33 -25.6 to 42.1 -14.6 to 42.4 11.3 to 39.8

Scherrer 0.6 NT NT NT NT Quartz Monzonite Porphyry 0.5 11.3 ± 11.3 NT 0.3 10.1 to 12.2 Overburden 0.2 20.2 ± 15.4 NT 4.2 to 19 7.6 to 47

1 NT = not tested.



This statistical summary shows that the range of sample NNP values for each test was within the mean NNP plus one (1) standard deviation for all samples of a given rock type. However, certain samples were biased toward a low NNP in order to evaluate the potential for acid generation by materials classified as PAG. For tests performed on only a single sample, the NNP of each sample was most often within one (1) standard deviation of the mean. Therefore, samples which were selected for additional kinetic tests and STLTs adequately represent the overall population for their respective rock type.

C4.2 Kinetic Testing The results of weekly HCT sampling for pH, sulfate, acidity, and alkalinity for each of the 16 HCTs are shown on Illustrations C1 through C16. With the exception of the Bolsa Quartzite samples, pH levels were generally neutral to alkaline and sulfate levels gradually declined or remained stable throughout the 35-week testing period. Most metals were below detection limits in the HCT leachates; however, low concentrations of antimony, copper, manganese, arsenic, and selenium were detected in a few samples (Tetra Tech, 2007). Even though ABA testing results for these materials (Table C1) indicate that certain Rosemont rock types have the potential to generate acid, the HCT results provide no indication that sulfide oxidation and the subsequent release of metals and acidity is a significant mechanism controlling solution chemistry during weathering.

For example, one (1) sample of Willow Canyon Formation andesite (AR2010-03) displayed the most negative NNP value (-25.6 t/kt) and is classified as PAG (Table C1). The ABA results for this sample indicate that both sulfide-S (1.67%) and sulfate-S (0.34%) were present. Illustration C7 clearly shows that the pH of the humidity cell leachate remained neutral throughout the entire 35 weeks of testing. Both calcium and sulfate leaching rates fell slightly during the test period, while maintaining a calcium:sulfate ratio which is consistent with gypsum (CaSO4•2H2O) dissolution. Any acid generation that may have occurred is masked by sulfate mineral dissolution. The constant low levels of acidity and iron are also consistent with simple sulfate mineral dissolution, rather than acid generation resulting from iron sulfide oxidation.

Another sample of Willow Canyon andesite (AR2014-02) displayed the highest total-S (4.77%) and sulfide-S content (3.92%), with a corresponding negative NNP, and was also considered PAG (Table C1). Similarly, the HCT results shown on Illustration C12 show that the pH of the cell leachate remained between 7.5 and 8.5 during the 35 weeks of testing. Again, the decrease in calcium and sulfate leaching rates over time, and the low constant levels of acidity and iron, are consistent with solution chemistry that is dominated by simple mineral dissolution, rather than iron sulfide oxidation.

The Bolsa Quartzite was the only material tested which generated net acidity during humidity cell testing. Unlike the andesite, arkose, and limestone materials which contain more reactive acid-neutralizing minerals, the neutralizing silicate fraction of the Bolsa Quartzite reacts more slowly. Therefore, net acidity associated with low pH values was generated in the humidity cells (Illustrations C15 and C16). These observations are consistent with the presence of 0.13% sulfate-S in the Bolsa Quartzite samples (Table C1).

C4.3 Meteoric Water Mobility Procedure (MWMP) A comparison of the MWMP results to the HCT results provides further indication that mineral dissolution, rather than sulfide oxidation, is the dominant factor controlling constituent release from the Rosemont rocks. This conclusion is based on similarities between the leaching characteristics of short-term MWMP results as compared to long-term HCT results. Both testing procedures use a 1:1 ratio of solution:solid, which allows for a direct comparison of the two (2)



methods. However, the MWMP is a short-duration test (24 hour), compared to the long-duration (35 weeks) for HCTs.

The average calcium, magnesium, sulfate, and pH values at [at five (5) week intervals] for the HCTs representing three (3) rock types are compared to the MWMP concentrations for the same rock types in Illustrations C17 through C19. The HCT data indicated that the initial flush (Week 0) generally produced elevated and variable constituent concentrations compared to the remaining five (5) week HCT rinsing intervals. The occurrence of high concentrations in the Week 0 leachates is a commonly-observed phenomenon caused by dissolution of soluble pre-existing reaction products, which sometimes requires three (3) to five (5) weeks to be completely flushed from the system (ASTM, 1996). From Week 5 forward, however, the concentrations of calcium, magnesium, and sulfate in the HCTs decreased and showed little variability (Illustrations C17 through C19).

The concentrations of major ions and pH in the arkose and andesite HCTs were within the range observed in the MWMP extracts (Illustrations C17 and C18). In other words, the solution composition which results from a simple 24-hr leaching is essentially the same as that produced by either early (Week 5) or late (35-week) HCT leachates. These observations suggest that short-term mineral solubility is the most important factor controlling the solution composition, not long-term weathering and release of oxidation products which is more typically demonstrated by decreasing pH, and increasing concentrations of iron, sulfate, and acidity. While no MWMP results exist for the Earp Formation limestone, the Earp HCT leachate compositions were very similar to the andesite and arkose HCT leachate compositions (Illustration C19).

C4.4 Synthetic Precipitation Leaching Procedure (SPLP) The SPLP test is much more cost-effective and requires smaller samples volumes when compared to MWMP, and therefore a larger number of samples was analyzed (SPLP, N = 59 and MWMP, N =19). The SPLP is a batch leach procedure which also differs from the MWMP column test in that the SPLP utilizes a solution:solid ratio of 20:1. Therefore, the SPLP procedure has the potential to dissolve more constituent mass from a sample compared to the MWMP, but the actual solution concentrations may be lower. For example, a comparison of the SPLP and MWMP results from four (4) different rock types is shown in Table C3. Examination of the major cation (calcium, magnesium, sodium, potassium) and anion (sulfate, chloride, fluoride) data for each rock type shows that their concentrations are more often than not higher in the MWMP solutions when compared to the SPLP solutions. However, when the results are expressed on a rock mass basis, the SPLP can be shown to release more chemical mass when compared to the MWMP (Table C4).

The SPLP was developed by the USEPA to determine the mobility of solid-phase constituents and the potential for groundwater contamination. The main advantage of using SPLP for geochemical characterization is that it provides a measure of the readily-dissolvable constituents from dried mine materials (e.g., wall rocks) (Maest and Kuipers, 2005). The 20:1 water to rock testing ratio for SPLP would correspond to precipitation events contacting only a limited amount of rock, although in practice this proportion is difficult to estimate for field conditions.



C5.0 SUMMARY AND CONCLUSIONS

Geochemical characterization of the rocks at Rosemont, which are anticipated to comprise the ultimate pit wall, has been conducted using a variety of static tests, kinetic tests, and STLTs. A review of the static testing data indicates that some samples were classified as PAG based on their ABA characteristics. However, when the majority of these materials were subjected to long-term humidity cell testing, the leachate pH remains neutral and the trends in sulfate, iron, and acidity provide no indication of sulfide oxidation. Any sulfide oxidation products which may have been generated become masked by the dissolution of any existing secondary weathering products, such as jarosite.

Dissolution of Rosemont rocks is largely controlled by carbonate mineral solubility, an observation which is further supported by the chemical similarities between long-term HCT leachate compositions and those produced by selective short-term MWMP leaching. The abundance of SPLP data were subsequently chosen to simulate runoff compositions for the various wall rocks. Due to the low reactivity of acid neutralizing minerals in the Bolsa Quartzite, however, this rock type was shown to produce net acidity during humidity cell testing as a result of sulfide oxidation. Therefore, HCT data from the Bolsa Quartzite were also incorporated into the pit lake model to account for potential effects of acid generating materials.



Table C3 Solution-Basis Comparison (mg/L) of MWMP and SPLP Testing for Four Rock Types

Material Type

Willow Canyon Formation- Andesite

Willow Canyon Formation- Arkose

Horquilla Limestone Glance Conglomerate

Test MWMP SPLP MWMP SPLP MWMP SPLP MWMP SPLP Calcium 20.3 10.2 14.3 5.71 28.2 47.2 8.69 5.01

Magnesium 4.72 1.40 2.43 0.75 2.90 2.37 0.88 2.61 Sodium 15.8 4.46 15.4 4.86 10.8 2.13 5.29 0.80

Potassium 12.9 5.35 5.20 2.86 4.68 1.06 0.83 2.14 Sulfate 62.4 17.8 33.6 4.45 28.2 110 6.34 1.4

Chloride 3.09 0.57 4.05 0.86 24.4 2.34 0.88 0.88 Fluoride 1.03 0.29 0.79 0.26 1.09 0.51 0.17 0.10

Table C4 Mass-Basis Comparison (mg/kg) of MWMP and SPLP Testing for Four Rock Types

Material Type

Willow Canyon Formation- Andesite

Willow Canyon Formation- Arkose

Horquilla Limestone Glance Conglomerate

Test MWMP SPLP MWMP SPLP MWMP SPLP MWMP SPLP Calcium 20.3 204 14.3 114 28.2 944 8.69 100.2

Magnesium 4.72 28.1 2.43 15.0 2.90 47.4 0.88 52.2 Sodium 15.8 89.1 15.4 97.3 10.82 42.6 5.29 16

Potassium 12.9 107 5.20 57.2 4.68 21.2 0.83 42.8 Sulfate 62.4 357 33.6 89.1 28.2 2200 6.34 28

Chloride 3.09 11.5 4.05 17.2 24.4 46.8 0.88 17.6 Fluoride 1.03 5.78 0.79 5.3 1.09 10.2 0.17 2



C6.0 REFERENCES

ADEQ (2004). Arizona Mining Best Available Demonstrated Control Technology (BADCT) Guidance Manual. Aquifer Protection Program. Publication TB-04-01.

American Society for Testing and Materials (ASTM) (1996). Standard Test Method for Accelerated Weathering of Solid Materials Using a Modified Humidity Cell. ASTM Designation D 5744-96. ASTM, West Conshohocken, PA.

ASTM (2003). Standard Test Method for Column Percolation Extraction of Mine Rock by the Meteoric Water Mobility Procedure. ASTM Designation E 2242-02. ASTM, West Conshohocken, PA.

GoldSim Technology Group (2005). GoldSim User’s Guide. GoldSim Technology Group, Issaquah, Washington, U.S.A.

Maest, A. and J.R. Kuipers (2005). Predicting Water Quality at Hardrock Mines: Methods and Models, Uncertainties, and State-of-the-Art.

Price, W.A. (1997). Draft Guidelines and Recommended Methods for the Prediction of Metal Leaching and Acid Rock Drainage at Minesites in British Columbia. British Columbia Ministry of Employment and Investment, Energy and Minerals Division, Smithers, BC. 143 pp.

Tetra Tech (2007). Geochemical Characterization, Addendum 1. Prepared for Rosemont Copper Company. Report Dated November 2007.

U.S. Environmental Protection Agency (USEPA) (1986). Test Methods for Evaluating Solid Wastes. 3rd Edition. SW-486. USEPA, Office of Solid Waste and Emergency Response, Washington, D.C.



ILLUSTRATIONS



Earp Formation (Limestone) (AR2002-02)0.35% Sulfide-S, NNP = +51.1 kg/t

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Willow Canyon Formation Arkose (AR2003-03)0.66% Sulfide-S, NNP = +10.4 kg/t

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Willow Canyon Andesite (AR2010-03)1.67% Sulfide-S, NNP = -25.2 kg/t

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Willow Canyon Andesite (AR2013-01)1.55% Sulfide-S, NNP = +11.6 kg/t

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Earp Formation (limestone) (AR2014-05)0.32% Sulfide-S, NNP = +94.0 kg/t

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Illustration C15 Humidity Cell Test Results for Sample A780-02 Bolsa Composite

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Illustration C16 Humidity Cell Test Results for Sample A780-03 Bolsa Composite



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Illustration C17 Comparison of Major Ion and pH Data from Willow Canyon Arkose HCT and MWMP Testing



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Illustration C18 Comparison of Major Ion and pH Data from Willow Canyon Andesite HCT and MWMP Testing



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Wee

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um (m

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eek

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pH

Illustration C19 Selected Major Ion and pH Data from Earp Formation (Limestone) HCTs

APPENDIX D SAMPLE PROBABILITY PLOTS

AND DSM INPUT (ELECTRONIC)


Tetra Tech November 2010 D-1

Illustration D1 Probability Plots of Total Dissolved Solids (TDS) from SPLP Results for the Abrigo Formation



Illustration D2 Probability Plots of TDS from SPLP Results for the Willow Canyon, Arkose Formation



Illustration D3 Probability Plots of TDS from SPLP Results for the Bolsa Quartzite Formation



Illustration D4 Probability Plots of TDS from SPLP Results for the Earp Formation



Illustration D5 Probability Plots of TDS from SPLP Results for the Epitaph Formation



Illustration D6 Probability Plots of TDS from SPLP Results for the Horquilla Limestone Formation



Table D1 Chemical Inputs for Wall Rock Runoff – Average Scenario



Table D2 Chemical Inputs for Wall Rock Runoff – Average HCT Scenario



Table D3 Chemical Inputs for Wall Rock Runoff – Elevated Scenario



Table D4 Chemical Inputs for Wall Rock Runoff – Low Scenario



See Attached CD For

DSM Input File

Record # 013788

tter to F a Electronic Files unable to pint

Document Date: 2-0/o 11

Author: Tetra_ Tech.. Content of CD:

APPENDIX E DSM OUTPUT (ELECTRONIC)


Tetra Tech November 2010 E-1

See Attached CD For

DSM Output File

APPENDIX F EXAMPLE PHREEQC INPUT FILE


Tetra Tech November 2010 F-1

SOLUTION 1 Rosemont Pit - Year 200 temp 25 pe 9 units mg/l Ca 236 Mg 23.7 Na 30.0 K 6.5 S(6) 510 Cl 10.0 F 1.16 Alkalinity 496 as HCO3 Al 0.15641 As 0.0075 Sb 0.00025966 Ba 0.0533 Cu 0.00037864 Fe 0.565 Pb 0.0206 Hg 2.80E-05 Mn 0.181 Mo 0.171 Se 0.00215 U 0.00463 Zn 0.703 EQUILIBRIUM_PHASES 1 CO2(g) -3.5 Calcite 0.0 0.0 CaMoO4(C) 0.0 0.0 Ferrihydrite 0.0 0.0 Fluorite 0.0 0.0 Barite 0.0 0.0 Smithsonite 0.0 0.0 Anglesite 0.0 0.0 Al4(OH)10SO4 0.0 0.0 PbMoO4(C) 0.0 0.0 Rhodochrosite 0.0 0.0 Magnesite 0.0 0.0 Huntite 0.0 0.0 Gypsum 0.0 0.0 Ba3(AsO4)2 0.0 0.0 Manganite 0.0 0.0 Alunite 0.0 0.0 Zincite 0.0 0.0 SAVE Solution 2 SAVE Equilibrium_phases 2 END USE Solution 2 USE Equilibrium_phases 2 END SURFACE 2 equilibrate Solution 2 Hfo_wOH Ferrihydrite equilibrium_phases 0.200 5.33e4 Hfo_sOH Ferrihydrite equilibrium_phases 0.005


Tetra Tech November 2010 F-2

SELECTED_OUTPUT -reset false -file c:\rosemont_200.dat USER_PUNCH -headings Ca Mg Na K SO4 Cl F HCO3 Ag Al As Sb Ba Be Cd Cr -headings Cu Fe Pb Hg Mn Mo Ni Se Tl U Zn NO3-N Ra(pCi/L) TDS pH 10 REM Calculate concentrations as mg/L and sum for TDS 20 PUNCH TOT("Ca")*40.08*1000 30 PUNCH TOT("Mg")*24.312*1000 40 PUNCH TOT("Na")*22.9898*1000 50 PUNCH TOT("K")*39.102*1000 60 PUNCH TOT("S(6)")*96.0616*1000 70 PUNCH TOT("Cl")*35.453*1000 80 PUNCH TOT("F")*18.9984*1000 90 PUNCH MOL("HCO3-")*61.018*1000 100 PUNCH TOT("Ag")*107.868*1000 110 PUNCH TOT("Al")*26.9815*1000 120 PUNCH TOT("As")*74.9216*1000 130 PUNCH TOT("Sb")*172.772*1000 140 PUNCH TOT("Ba")*137.34*1000 150 PUNCH TOT("Be")*9.0122*1000 160 PUNCH TOT("Cd")*112.399*1000 170 PUNCH TOT("Cr")*51.996*1000 180 PUNCH TOT("Cu")*63.546*1000 190 PUNCH TOT("Fe")*55.847*1000 200 PUNCH TOT("Pb")*207.19*1000 210 PUNCH TOT("Hg")*200.59*1000 220 PUNCH TOT("Mn")*54.938*1000 230 PUNCH TOT("Mo")*95.94*1000 240 PUNCH TOT("Ni")*58.71*1000 250 PUNCH TOT("Se")*78.96*1000 260 PUNCH TOT("Tl")*204.37*1000 270 PUNCH TOT("U")*238.029*1000 280 PUNCH TOT("Zn")*65.37*1000 290 PUNCH TOT("N(5)")*14.0067*1000 300 PUNCH TOT("Ra")*226.025*1000/1.01e-9 310 A = (TOT("Ca")*40.08*1000)+(TOT("Mg")*24.312*1000) 320 B = (TOT("Na")*22.9898*1000)+(TOT("K")*39.102*1000) 330 C = MOL("HCO3-")*61.018*1000 340 D = TOT("S(6)")*96.0616*1000 350 E = TOT("Cl")*35.453*1000 360 PUNCH A+B+C+D+E 370 PUNCH -LA("H+") END

Documents

Geochemical Pit Lake Predictive Model Revision 1 · (mean case for the pit lake model) in order to track the chemical mass associated with each hydrologic component of the water balance