Geochemical Pit Lake Predictive Model · input to a geochemical pit lake predictive model. The geochemical model showed the quality of the pit lake water was only slightly changed

Geochemical Pit Lake Predictive Model

Rosemont Copper Project

February 2010

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A Bridge to a Sustainable Future.

Memorandum

To: Beverly Everson

Cc: Tom Furgason

From: Kathy

Doc #: 003/10 — .3.5

Subject: Transmittal of Technical Memoranda and Pit Lake Report

Date: February 8, 2010

Rosemont Copper is pleased to transmit the following twenty technical memoranda and one report:

1. Rosemont Hydrology Method Justification, a Tetra Tech memo dated January 7, 2010;

2. Barrel Only alternative —

a. Noise Analysis, a Tetra Tech memo dated January 15, 2010

b. Traffic Analysis, a Tetra Tech memo dated January 8, 2010

c. Geochemical Characterization of Facilities, a Tetra Tech memo dated January 10, 2010

d. Lighting, an M3 memo dated December 2009

3. Barrel and McCleary alternative —


b. Traffic Analysis, a Tetra Tech memo dated December 15, 2009

c. Geochemical Characterization of Facilities, a Tetra Tech memo dated December 16,

2009

d. Lighting, an M3 memo dated December 2009

4. Scholefield Tailings and McCleary Waste alternative —




d. Lighting, an M3 memo dated January 2010

5. Sycamore Tailings and Barrel Waste alternative —




d. Lighting, an M3 memo dated January 2010

6. Partial Backfill alternative —




7. Geochemical Pit Lake Predictive Model, prepared by Tetra Tech and dated February 2010

As per your request, I am transmitting three hardcopies and two disks (disks contain tech memos only)

directly to the Forest Service and two copies and one disk directly to SWCA. The Pit Lake report includes

a copy of the report on a CD on the inside of the back cover of each report.

Geochemical Pit Lake Predictive Model Rosemont Copper Project

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. 114-320777

February 2010

Rosemont Geochemical Pit Lake Predictive Model Rosemont Copper Company

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 ....................................................................................... 11 4.3 Pit Wall Runoff Chemistry ................................................................................... 11 4.4 Statistical Development of Pit Wall Runoff Model Input ...................................... 15

5.0 DYNAMIC SYSTEMS MODEL (DSM) INTEGRATION .................................................. 17 5.1 Model Objectives................................................................................................. 17 5.2 Model Structure and Formulation ........................................................................ 17

5.2.1 Pit Geometry............................................................................................ 17 5.2.2 Meteorology............................................................................................. 18 5.2.3 Hydrology................................................................................................. 19

5.3 Model Results...................................................................................................... 19 5.3.1 Water Balance and Lake Formation ........................................................ 20 5.3.2 Chemical Loading.................................................................................... 23

6.0 GEOCHEMICAL MODELING......................................................................................... 25 6.1 Mineral Precipitation............................................................................................ 25 6.2 Surface Complexation ......................................................................................... 25 6.3 PHREEQC Model Results ................................................................................... 26

7.0 DISCUSSION OF RESULTS .......................................................................................... 28 8.0 CONCLUSIONS.............................................................................................................. 31 9.0 REFERENCES................................................................................................................ 32

Tetra Tech February 2010 i


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.................................... 20 Table 5.02 Simulated Water Balance in Model Year 200...................................................... 22 Table 6.01 Potential Mineral Solubility Controls for the Rosemont Pit Lake Model............... 25 Table 6.02 Range in Predicted Water Quality (200-Year Simulation) for the

Rosemont Pit Lake .............................................................................................. 27 Table 7.01 Comparison of Local Groundwater with Modeled Pit Lake Water ....................... 29

LIST OF FIGURES

Illustration 3.01 Conceptual Hydrologic Model for the Rosemont Pit Lake ......................... 6 Illustration 3.02 Comparison of Rosemont and Santa Rita Monthly Precipitation............... 8 Illustration 3.03 Average Monthly Pan Evaporation ............................................................ 9 Illustration 4.01 Projected Proportions of Exposed Rock Types in the Rosemont

Pit ............................................................................................................ 12 Illustration 5.01 Change in Lake Surface Area with Lake Stage Elevations ..................... 18 Illustration 5.02 Simulated Pit Lake Elevation for the 200-year Period of Simulation ....... 20 Illustration 5.03 Simulated Water Balance for the First 20-year Period of

Simulation................................................................................................ 21 Illustration 5.04 Simulated Annual Water Balance for the 200-year Period of

Simulation................................................................................................ 22 Illustration 5.05 DSM Predicted Total Solids versus Time for the Average

Simulation................................................................................................ 24

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 February 2010 ii


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 Open Pit will be terminated. Montgomery & Associates (M&A) produced a groundwater flow model for the Rosemont site which yields 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 (M&A, 2009).

In addition to the hydrogeological analysis performed by M&A, 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 used M&A’s pit filling data as an input to 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 200 years of simulation. The conclusions of the predictive geochemical modeling effort performed for the Rosemont Copper Project by Tetra Tech 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. About 95 percent of the 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 is anticipated to be similar to the local groundwater with a pH of 8, which is slightly alkaline; and

Because the pit lake is expected 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 1.3 times that of local groundwater.

As indicated above, the quality of the pit lake water was only slightly changed from local groundwater after 200 years of model simulation. At that time, the pH of the pit lake water is anticipated to be 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 limestone, are abundant. Therefore, the development of an acidic pit lake is not expected, even beyond the 200 year modeling period.

Tetra Tech February 2010 1


From the results of the geochemical model, it was estimated that 95 percent 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. Therefore, 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 geochemical model. Over the 200 year time-frame simulated in Tetra Tech’s Rosemont Geochemical Pit Lake Predictive Model, calculations were performed to show low, average, and elevated chemical loading scenarios. This was done to provide a sensitivity evaluation of the model.

The concentrations of some dissolved chemical constituents were shown to increase by a factor of up to 1.3 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). All of the parameters modeled were below the primary Aquifer Water Quality Standards (AWQS) for drinking water in Arizona at the end of the 200 year simulation period.



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 Open Pit will be terminated. Montgomery & Associates (M&A) produced a groundwater flow model for the Rosemont site which yields 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 (M&A, 2009).

Based on the pit filling data from M&A’s hydrogeological model, a geochemical pit lake predictive model was developed by Tetra Tech, Inc. (Tetra Tech). The objectives of the geochemical modeling effort were:

To prepare a conceptual hydrologic model for the anticipated pit lake;

To define the specific hydrologic and 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; and

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

The main body of the report presents the general evaluations performed and conclusions made based on the analysis while the appendices contain the supporting details.

1.1 Geologic Setting A detailed description of the geology of the project region and the Rosemont project specifically is presented in the Geochemical Characterization Addendum 1 (Tetra Tech, 2007). In general, Rosemont can be classified as a wall rock porphyry system. Within this class of porphyry 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 porphyry copper systems of southwestern North America, the gold content of the mineralized zones at Rosemont is negligible. Compared to other southwest porphyry 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, the local water table will rebound from its operational, lowered condition produced by dewatering pumps. Water from the surrounding aquifer and direct precipitation will report to the proposed pit and begin to the refill it. Some direct precipitation will fall on the pit walls and flow down to the forming pit lake. All water sources that report to the pit will carry with them an associated chemical load of dissolved chemical constituents resulting from water contacting local rock units.

In simplest terms, producing a prediction of potential 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 the various water sources report to the pit changes over 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 track the total chemical mass 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 (M&A, 2009) 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


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 groundwater flow model was developed by M&A to simulate dewatering and post-



mining pit filling. The challenge of predicting the rate of pit filling is a complicated transient problem, dependant on:

the rate and duration of pit dewatering;

the depth, size, and geometry of the final pit configuration; and

the pre-mining hydrologic regime (M&A, 2009).

Ultimately, groundwater inflow to the post-mining pit is dependant on hydraulic heads adjacent to and below 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 M&A model 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. As a result, no groundwater flow out of the lake is anticipated (M&A, 2009).

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. Short-term precipitation data have been collected at the Rosemont Site since the installation of a weather station in January of 2007.

Due to the limited amount of site-specific data, average monthly precipitation data were taken from the Santa Rita Experimental Range (Santa Rita), which is located approximately eight (8) miles to the southwest at an elevation of 4,300 feet above mean sea level (amsl), compared to the Rosemont site at 5350 feet. Santa Rita precipitation data were obtained from the Western Regional Climate Center (Reno, NV) and represent the period of record from May 1, 1950 to March 31, 2006 (WRCC, 2008a, Tetra Tech, 2009). Additionally, the Nogales 6N weather station, located approximately 30 miles southeast of the site at an elevation of 3,560 feet amsl, has recorded precipitation, temperature, and pan evaporation data since 1952.

The data from these stations, which corresponds closely to the limited Rosemont site data (Illustration 3.02, Table A1), was selected for use in all evaluations, and corresponds to a maximum climatic flux of water to the site. The average annual total precipitation at Santa Rita is 22.2 inches.

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.



0

1

2

3

4

5

6

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

Month

Prec

ipita

tion

(inch

es)

Santa Rita (1950-2006)

Rosemont (2007-2008)

Illustration 3.02 Comparison of Rosemont and Santa Rita Monthly Precipitation

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 wall runoff which enters the pit 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 According to the Mine Plan of Operations (MPO; Westland Resources, 2007) the upgradient areas will be bermed and existing drainages will be diverted around the pit. Therefore, no upgradient drainage runoff was incorporated into the present model.

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 by Tetra Tech (2009). The Nogales station was adjusted to the Rosemont site based on a linear trend with the each



stations elevation. The estimated monthly pan evaporation is shown in Illustration 3.03 and totals 71.52 inches per year.

The monthly average projected pan evaporation data are converted to a lake evaporation rate using a coefficient to account for the fact that an evaporation pan has far less heat-storage capacity, no groundwater inflow, and metal sides exposed to sun and air. 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. The evaporation coefficient is discussed further in Section 5.2.2. 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 3.03 Average 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 model, the chemical composition of groundwater is represented as the average concentrations from monitor wells PC-1 through PC-8 (M&A, 2009) located in the vicinity of the proposed mine 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 Chemistry Precipitation which intercepts the pit walls 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 wall runoff will be proportional to the exposed areas of the various rock types in the pit wall (Illustration 4.01). The contribution of chemical loading from wall runoff will decrease with time, as pit filling progresses and less wall rock area is above the pit lake. Because the Rosemont mine pit has not yet been developed, estimates of the magnitude of chemical release from geologic materials must be generated from the results of geochemical testing.

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

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 results from the rock types



which comprise the pit walls indicates that a sufficient number of samples of been collected and analyzed (ABA) to support leach testing of representative materials for each rock unit comprising the pit walls (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 wall rock runoff. As part of baseline geochemical characterization (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, 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. The average SPLP results for each rock type are given in Table 4.03. 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.

In Section 4.4, the statistical method used to define probable ranges of concentrations for wall rock runoff is presented. For most rock types, the statistical distributions of SPLP results for each rock type (Table 4.01) are used to represent chemical mass loading to the pit lake from the pit wall. However, both SPLP and humidity cell results were used for the Bolsa Quartzite to calculate the range of probable wall rock runoff concentrations.

el for the Rosemont Copper Project Rosemont Copper Company


Table 4.03 Average SPLP Results (mg/L) for Various Rock Types Representing Rosemont 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 Abridge 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

nm Uranium <0.005 <0.001 <0.001 <0.005 <0.004 <0.004 <0.005 <0.004 nm <0.005 <0.004 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

Pit Lake Mod


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. These ranges in SPLP data represent a range of potential chemical loading to the pit lake from the various rock types. Rather than selecting a single SPLP test such as the average for modeling, the variability of possible leaching chemistry inputs were better described by using a range of test values bounding the average. Incorporating a range of SPLP results for all rock types allows the predictive model to calculate a range of potential outcomes. This reflects the natural variation in geologic materials and the inherent uncertainties associated with water quality predictions to produce a sensitivity analysis.

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 one (1) standard deviation above (elevated scenario) and below that average (low scenario). The following method was utilized to accomplish these objectives. 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 times the standard deviation (± 2σ) were identified;

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), a average (~50th percentile), and 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 the probability plots in Appendix D, the samples closest to the +2σ and -2σ values are typically less than the actual 2σ value. Given the sample set, the samples selected for the elevated and low scenarios represent values closer to the 10th and 90th percentiles (rather than 5th and 95th) based on the distributions. Therefore, while the aim was 2σ, the resulting values are closer to being about half-way between 1σ and 2σ, representing about 80% of the possible outcomes.



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. A single SPLP sample was available for the Glance Formation and the Precambrian Granite. Therefore, this test result was used for all runs for both of these materials. No sample was available for the Concha Formation. However, the average NNP, NP/AP, 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 pit walls, the precipitation chemistry (see Section 4.2) was used for runoff from this area.



5.0 DYNAMIC SYSTEMS MODEL (DSM) INTEGRATION

A dynamic systems computer model (DSM) for the anticipated Rosemont mine pit lake 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). The DSM outputs from the predictive simulations were used as inputs to a final simulation model using PREEQC (see Section 6.0).

The first step in developing the DSM was to incorporate the system components and description of the interactions between the components that were developed as part of the M&A conceptual groundwater flow model (M&A, 2009). The interactions between the system components were represented by empirical relationships or rules derived from the analysis of the site data or additional models of site processes.

The DSM includes both stochastic (variable) and deterministic (fixed) parameters. The stochastic parameters are used to assess the uncertainty in the predictions due to the data and analytical constraints and natural variability in the input parameters. This was accomplished by utilizing GoldSim in the Monte Carlo simulation mode. The model was allowed to run for a 200-year period using Monte-Carlo sampling (for the stochastic parameters) with 1,000 realizations. The 200-year period of simulation was determined to be the longest period with 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). The period of simulation also allows the time required for the system to approximate a hydrologic steady-state condition.

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

Predict the post-closure pit water balance through time;

Predict the chemical loading to the pit lake through time; and

Determine the concentration of chemical constituents.

5.2 Model Structure and Formulation Each element of the conceptual groundwater flow model (M&A, 2009) 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 Pit Geometry The pit geometry provides the pit volume and area relationships used in the water balance sections of the DSM. The geometry was simulated with lookup tables of the mine pit elevation versus area and volume that were based on the final pit geometry. The depth to area



relationship used in the model is shown in Illustration 5.01. From this illustration it can be seen that the surface area of the pit lake does not initially increase substantially with increasing elevation. This relationship is an important component of the DSM, given the high potential evaporation rate at the site.

3000

3500

4000

4500

5000

0 100 200 300 400 500 600 700

Surface Area (acres)

Elev

atio

n (ft

am

sl)

Illustration 5.01 Change in Lake Surface Area with Lake Stage Elevations

5.2.2 Meteorology An analysis of available meteorological data was completed as part of an effort to ensure consistency in the data being used for design efforts at the Rosemont site. The results of this analysis are summarized as Appendix A, and presented fully in the technical memorandum titled, “Rosemont Copper Project Design Storm and Precipitation Data/Design Criteria,” (Tetra Tech, 2009). The 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.

5.2.2.1 Precipitation

Deterministic (fixed) precipitation values were developed for the DSM model based on the values for the Santa Rita Station. The average monthly precipitation inputs are values developed as part of the meteorological analysis (Tetra Tech, 2009).



5.2.2.2 Evaporation

Lake evaporation was treated as a stochastic variable as a result of the uncertainty associated with the pan evaporation coefficient. The pan evaporation coefficient was simulated stochastically as a uniform distribution between 0.65 and 0.75 about the expected average of 0.70.

5.2.3 Hydrology

5.2.3.1 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.

5.2.3.2 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 blast altered rock of the pit walls. Based on professional experience the stochastic element was assigned a uniform distribution between 15% and 35%. Therefore runoff was calculated as varying between 15% and 35% of the precipitation falling on exposed pit walls.

5.2.3.3 Catchment Area Runoff

No catchment area runoff was simulated in the DSM. The MPO indicated that the pit will be bermed and upgradient runoff from catchment areas will be diverted around the pit, or will be minor compared to other inputs.

5.2.3.4 Groundwater Inflow and Outflows

The groundwater hydrology and groundwater-lake interactions were explicitly simulated in the groundwater flow model developed by M&A. The groundwater discharge to the pit varies over time depending on groundwater elevation adjacent to and below the pit and the lake stage (elevation). A simplified relationship between groundwater inflow versus lake stage was developed based on outputs from the post-mining portion of the conceptual groundwater model. The lake stage versus groundwater inflow relationship was taken exactly from the M&A model and was not critically evaluated for consistency with expected or standard pit inflow curves (M&A, 2009). This data is presented in electronic format in Appendix D.

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

5.3 Model Results Based on the 200-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 rock runoff scenarios outlined in Section 4.4. The low, average, elevated, and average HCT runs were coupled with average hydrology results, respectively. All model output is presented in electronic form in Appendix E.



5.3.1 Water Balance and Lake Formation In all cases, the DSM confirms that a pit 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 vary between model runs since the output results are dependent on the variability of the stochastic elements used in the model. The average, 25th, 75th percentile and upper and lower bound estimates for the pit lake elevation for the final time step (200 year period of simulation) are shown in Table 5.01.

Table 5.01 Predicted pit lake elevation for various DSM simulations

Average 5th 25th 75th 95th

3962 3891 3931 3993 4035

Note: All values shown are in feet above mean sea level

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 100 years. The steady-state lake elevation is estimated to be achieved in approximately 500 years, although the present study evaluated only a 200 year period as this was deemed to be the longest practical time due to the transient nature of climate and geochemical weathering of near-surface materials. Illustration 5.02 illustrates the predicted pit lake development through time. The average estimates for lake area and lake volume are 124 acres and 44,109 acre-feet, respectively.

3000

3200

3400

3600

3800

4000

4200

0 20 40 60 80 100 120 140 160 180 200

Time Since Mine Closure [Years]

Lake

Sta

ge [f

eet a

bove

mea

n se

a le

vel]

Mean

25th Percentile

75th Percentile

Illustration 5.02 Simulated Pit Lake Elevation for the 200-year Period of Simulation



The 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 5.03. As a result of the monthly time-step, these relationships vary greatly depending on the month of the year simulated. To understand the interaction of these variables over the 200-year simulation period, the average annual fluxes for each of the water balance components are presented in Illustration 5.04.

0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16 18 20

Time Since Mine Closure (Years)

Flux

(acr

e-fe

et/y

ear0

Groundwater Inflow Lake Precipitation

Lake Evaporation Pit Wall Runoff

Illustration 5.03 Simulated Water Balance for the First 20-year Period of Simulation



0

100

200

300

400

500

0 20 40 60 80 100 120 140 160 180 200

Time Since Mine Closure (years)

Flux

(acr

e-fe

et/y

ear)

600

Lake Precipiation Lake Evaporation

Groundwater Inflow Pit Wall Runoff

Illustration 5.04 Simulated Annual Water Balance for the 200-year Period of Simulation

Illustration 5.04 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 water balance does not reach steady-state conditions over the 200-year simulation. The simulated annual water balance for the last year of the simulation is presented in Table 6.

Table 5.02 Simulated Water Balance in Model Year 200


Groundwater Inflow Evaporation Groundwater

Outflow Pit Wall Runoff

229.4 139.7 517.1 0 210.9 Note: All values shown are in acre-feet / year as indicated by the last year of simulation. Net water balance is plus 62.9 acre-feet in year 200 of the simulation, indicating steady-state conditions had not been reached.

The model allows the estimation that the post-mining pit lake reaches a steady-state condition approximately 500 years after mine closure. The final pit lake level is approximately 4011 feet amsl. Therefore, it is estimated that the lake rises an additional 49 feet between the 200th and 500th year (average of 0.16 feet per year) of an extended simulation.

The relative depth of the predicted pit lake at year 200 is between 29 and 30 percent. The relative depth 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 relative depth, RD is defined as:

RD=(Zm/d)*100%

Generally, a value greater than 5% 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).

5.3.2 Chemical Loading The hydrologic system does not reach a dynamic equilibrium in the 200-year simulation period, thus chemical mass continues to be added to the system. However, 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 200 year simulation period.

Model simulations were conducted to provide not only a sense of the expected (average) case, but also the relative uncertainty. Uncertainty in the model is derived from the expected uncertainty in the runoff coefficient assigned to pit walls and the pan evaporation coefficient (both described in Section 3.0). Uncertainty is also associated with the range of observed geochemical leach tests. Taken together, the hydrologic uncertainty, coupled with the uncertainty associated with how the pit wall rocks will weather, have been 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 elevated 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 (approximately 90%), with less than 10% of the mass attributed to pit wall runoff (Illustration 5.05).



0

100

200

300

400

500

600

700

800

0 20 40 60 80 100 120 140 160 180 200

Time Since Mine Closure (Years)

Tota

l Dis

solv

ed S

olid

s (m

g/L)

Groundwater Component

Total Chemical Mass

Pit Wall Runoff Component

Illustration 5.05 DSM Predicted Total Solids versus Time for the Average Simulation



6.0 GEOCHEMICAL MODELING

In the final stage of pit lake modeling, output from the DSM for model year 200 was 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 below presents potential mineral phases which 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 Radium sulfate RaSO4 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 uses 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 PHREEQC Model Results The predicted water quality of the Rosemont Pit lake at 200 years from the start of infilling is provided in Table 6.02. The tabulated results represent the final predicted water quality from PHREEQC using the DSM output for three (3) simulations. 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 in pit walls was allowed. Any acid contributions from the Bolsa were allowed to react with alkalinity in the pit lake solutions only.



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 86.7 86.6 87.6 87.3 Mg 22.0 22.0 23.7 22.0 Na 32.7 32.9 30.0 32.8 K 5.60 5.60 6.51 5.50 SO4 317 317 511 319 Cl 10.1 10.1 10.0 10.1 F 1.22 1.22 1.16 1.22 HCO3 36.6 36.6 39.3 36.5 Al 0.18 0.16 0.16 0.19 As 0.0139 0.0143 0.0000048 0.0141 Sb ND 0.0003 0.0003 0.0003 Cu 0.0001 0.0001 0.0004 0.0292 Fe 0.0003 0.0003 0.0003 0.0003 Pb 0.0012 0.0014 0.0012 0.0013 Hg 0.0027 0.0008 0.000028 0.0008 Mn 0.19 0.19 0.18 0.19 Mo 0.13 0.12 0.16 0.12 Se 0.0022 0.0022 0.0022 0.0022 U 0.004 0.005 0.005 0.005 Zn 0.70 0.70 0.70 0.71 NO3-N 0.71 0.71 0.71 0.71 Ra(pCi/L) 0.31 0.31 0.31 0.31 TDS 511 511 708 513 pH (s.u.) 8.06 8.06 8.09 8.06

*mg/L except where noted

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



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. 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 1.3 over a time span of about 200 years. Only calcium, bicarbonate, iron, arsenic, and lead 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. The Bolsa Quartzite was the only non-ore rock type that indicated 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 (92% of projected pit walls) produces a water quality from those materials that appears to more than adequately neutralize the effects of the Bolsa Quartzite (projected to comprise about 8% of the final pit walls). 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 is dwarfed by the chemical mass reporting to the lake from recharging groundwater. The chemical quality of the groundwater is 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 (see Illustration 5.06) 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. The calcium carbonate is expected to precipitate, thus limiting the concentration of calcium and bicarbonate in the pit lake, even with increasing evapo-concentration. The net loss of calcium and bicarbonate from the pit lake are the projected precipitation of calcite.

Other chemical constituents, primarily from the limited occurrence of sulfide 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), 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, only arsenic and lead appear to be appreciably affected by the reactivity of the HFO. The elevated chemical loading scenario results (Table 6.02) indicate lower arsenic and lead concentrations than either the mean or low loading case. This result is 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.

As described in Section 4.4 of this report, the model scenarios were established using Total Dissolved Solids (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 provided an ability to gauge the final pH conditions likely to exist in the pit lake. That basis also gauges the chemical constituents that comprise the bulk of the chemical mass in the lake. Approximate concentrations of most trace elements, those at concentrations below 1 part per million (ppm), are also obtained. However, given the very, very low concentrations of these constituents, the inherent error associated with predictive modeling of trace elements in a system as large as a pit lake is limited, especially over a period of 200 years. 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 time) to build up trace element concentrations beyond 1 ppm (for any given constituent).

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 86.7 86.6 87.6 87.3 Mg 20.5 22.0 22.0 23.7 22.0 Na 26.0 32.7 32.9 30.0 32.8 K 3.17 5.60 5.60 6.51 5.50 SO4 300 317 317 511 319 Cl 8.36 10.1 10.1 10.0 10.1 F 0.85 1.22 1.22 1.16 1.22 HCO3 187 36.6 36.6 39.3 36.5 Al <0.03 0.18 0.16 0.16 0.19 As 0.0037 0.0139 0.0143 0.0000048 0.0141 Sb <0.0004 ND 0.0003 0.0003 0.0003 Cu <0.01 0.0001 0.0001 0.0004 0.0292 Fe 0.554 0.0003 0.0003 0.0003 0.0003 Pb 0.00092 0.0012 0.0014 0.0012 0.0013 Hg <0.0002 0.0027 0.0008 0.000028 0.0008 Mn 0.174 0.19 0.19 0.18 0.19 Mo 0.121 0.13 0.12 0.16 0.12 Se 0.00212 0.0022 0.0022 0.0022 0.0022 U 0.00419 0.004 0.005 0.005 0.005 Zn 0.694 0.70 0.70 0.70 0.71 NO3-N 0.49 0.71 0.71 0.71 0.71 Ra(pCi/L) 1.58 0.31 0.31 0.31 0.31 TDS 511 511 708 513 pH (s.u.) 8.06 8.06 8.09 8.06

*mg/L except where noted

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



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 unlikely 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.

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 as only this portion of lake water will be exposed to the local environment. The anticipated terminal condition of the pit lake (M&A, 2009) 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, arsenic, and lead. The 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 is driven by the evapo-concentration of chemical constituents.



8.0 CONCLUSIONS

The chemical conditions within the pit lake were analyzed and included geochemical testing of the materials comprising the final pit walls and the quality of local groundwater. The analysis used M&A’s pit filling data as an input to a geochemical pit lake predictive model (M&A, 2009).

The geochemical model showed 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 by Tetra Tech 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. About 95 percent of the 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 is anticipated to be similar to the local groundwater with a pH of 8, which is slightly alkaline; and

Because the pit lake is expected 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 1.3 times that of local groundwater.

As indicated above, the quality of the pit lake water was only slightly changed from local groundwater after 200 years of model simulation. At that time, the pH of the pit lake water is anticipated to be 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 limestone, are abundant. Therefore, the development of an acidic pit lake is not expected, even beyond the 200 year modeling period.

From the results of the geochemical model, it was estimated that 95 percent 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. Therefore, 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 geochemical model. Over the 200 year time-frame simulated in Tetra Tech’s Rosemont Geochemical Pit Lake Predictive Model, calculations were performed to show low, average, and elevated chemical loading scenarios. This was done to provide a sensitivity evaluation of the model.

The concentrations of some dissolved chemical constituents were shown to increase by a factor of up to 1.3 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). All of the parameters modeled were below the primary Aquifer Water Quality Standards (AWQS) for drinking water in Arizona at the end of the 200 year simulation period.



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.

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.

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

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

Montgomery & Associates (M&A) (2009). Groundwater Flow Modeling Conducted for Simulation of Proposed Rosemont Pit Dewatering and Post-Closure Rosemont Project Pima County, Arizona. Prepared for Rosemont Copper Company. Report Dated October 28, 2009.

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.


http://nadp.sws.uiuc.edu/sites/siteinfo.asp?net=NTN&id=AZ06


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

Tetra Tech, Inc., 2009. Technical memorandum on the Rosemont Copper Project Design Storm and Precipitation Data/Design Criteria. Prepared for M3 Engineering & Technology Corp. Dated April 2009.

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.


http://www.wrcc.dri.edu/summary/Climsmaz.html

http://www.wrcc.dri.edu/htmlfiles/westevap.final.html

APPENDIX A CLIMATE DATA SUMMARY


Table A1 Average Monthly Precipitation Data for the Santa Rita Experimental Range (Tetra Tech, 2009)

Month Precipitation (Inches) JAN 1.63 FEB 1.46 MAR 1.48 APR 0.69 MAY 0.24 JUN 0.62 JUL 4.87 AUG 4.32 SEP 2.16 OCT 1.64 NOV 1.15 DEC 1.95

TOTAL 22.18

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

Tetra Tech February 2010 A-1

APPENDIX B SAMPLE ADEQUACY EVALUATION FOR

ROSEMONT GEOLOGIC MATERIALS

Rosemont Copper Project Locator Sheet

Record # 01 2q 39 Document Date 2O 1. Document Title: 0.....vv\p1Q., fAr01 tors F.Nick_\

LttOsiL

Document Description P► ...e..e.5,NVY1Q1A4- Cie/Dvilk 46 u1DA.:)04.

-kr/A ctszltyvi,icilA 412E-4109,, VD.pfi2t).. 034 rAtv ca. t Other Notes Pcipoitii 1, I-) Cv.Q.C.Did a 012-105 -tc6Adss\c-55

This document is located in the following

[CIRCLE THE CATEGORY (from the list below) IN WHICH THIS ITEM IS FILED]

1. Project Management

a. Formal recommendations & Directions

b. Formal meeting minutes & memos

c. General Correspondence

d. Contracts, Agreements, & MOUs (Rosemont,

Udall, SWCA)

e. Other

2. Public Involvement

a. Mine Plan (including compilation)

b. Supporting Documents

c. Detailed Designs

6. Alternatives

a. Cumulative Effects Catalog

b. Connected Actions

c. Dismissed from Detailed Analysis

d. Analyzed in Detail

a. Announcements & Public Meetings Resources

b. Mailing Lists a. Air Quality & Climate Change

c. Scoping Period Comments b. Biological

d. Udall Foundation Working Group c. Dark Skies

e. Scoping Reports d. Fuels & Fire Management

f. Comments after Scoping Period e. Hazardous Materials

g. DEIS Public Comments f. Heritage

3. Agency Consultation & Permits g. Land Use

a. Army Corps of Engineers (404 permit) h. Livestock Grazing

b. US Fish & Wildlife Service (Sec. 7 T&E) i. Noise & Vibration

c. State Historic Preservation Office (Sec. 106) j. Public Health & Safety

d. Tribes (Sec. 106) k. Recreation & Wilderness

e. Advisory Council on Historic Preservation (Sec. I. Riparian

106) Socioeconomics & Environmental Justice

f. Other n. Soils & Geology

4. Communication . Transportation & Access

a. Congressional p. Visual

b. Cooperating Agencies q. Water

c. Organizations 8. Reclamation

d. Individuals 9. DEIS

e. FOIA 10. FEIS

f. Internal 11. Geospatial Analysis (GIS Data)

g. Proponent 12. FOIA Exempt Documents

5. Proposed Action 13. ROD (including BLM & ACOE)

Document Author I QA.V11101` S.A-J

APPENDIX C GEOCHEMICAL EVALUATION

OF ROSEMONT KINETIC AND SHORT-TERM LEACH TEST DATA

Record # 0 1 2.4 38 Rosemont Copper Project

Locator Sheet

Document Date 2.b lb b2-.

Document Title: (P...C) e...VtYV1.1( ?)ICI l)041enn—r Pittcwurv4 tivmSt

cot6 Document Author ‘--1-0-4-1(02-11)

Document Description EV CLW Ct..4r1eAA, a Auk. casg6uacki I

Other Notes Pi pclyvi.i C, vozyci «lie

This document is located in the following [CIRCLE THE CATEGORY (from the list below) IN WHICH THIS ITEM IS FILED] 1. Project Management

a. Mine Plan (including compilation)

a. Formal recommendations & Directions

b. Supporting Documents

b. Formal meeting minutes & memos

c. Detailed Designs

c. General Correspondence

6. Alternatives

d. Contracts, Agreements, & MOUs (Rosemont, a. Cumulative Effects Catalog

Udall, SWCA)

b. Connected Actions

e. Other

c. Dismissed from Detailed Analysis

2. Public Involvement

d. Analyzed in Detail

a. Announcements & Public Meetings

Resources

b. Mailing Lists a. Air Quality & Climate Change

c. Scoping Period Comments

b. Biological

d. Udall Foundation Working Group

c. Dark Skies

e. Scoping Reports

d. Fuels & Fire Management

f. Comments after Scoping Period

e. Hazardous Materials

g. DEIS Public Comments

f. Heritage

3. Agency Consultation & Permits

B. Land Use

a. Army Corps of Engineers (404 permit)

h. Livestock Grazing

b. US Fish & Wildlife Service (Sec. 7 T&E)

i. Noise & Vibration

c. State Historic Preservation Office (Sec. 106)

j. Public Health & Safety

d. Tribes (Sec. 106)

k. Recreation & Wilderness

e. Advisory Council on Historic Preservation (Sec. I. Riparian

106)

m. Socioeconomics & Environmental Justice

f. Other

Soils & Geology

4. Communication

o. Transportation & Access

a. Congressional

p. Visual

b. Cooperating Agencies

q. Water

c. Organizations

8. Reclamation

d. Individuals

9. DEIS

e. FOIA

10. FEIS

f. Internal

11. Geospatial Analysis (GIS Data)

g. Proponent

12. FOIA Exempt Documents

5. Proposed Action

13. ROD (including BLM & ACOE)

APPENDIX D SAMPLE PROBABILITY PLOTS

AND DSM INPUT (ELECTRONIC)


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

Tetra Tech February 2010 D-1


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


mont Geochemical Pit Lake Predictive Model Rosemont Copper Company


Table D4 Chemical Inputs for Wall Rock Runoff – Low Scenario

Rose


See Attached CD For

DSM Input File


Record #

tter to Re Electronic Files unable to pint

Appexakb -USAA

Document Date: Author: redncrech

Content of CD:

APPENDIX E DSM OUTPUT (ELECTRONIC)


See Attached CD For

DSM Output File

Tetra Tech February 2010 E-1

Record #

Letter to Fite- Electronic Files unable to pint

Appiki C - Dsikk.

Document Date: Date: Author: Tetra:Tech_

Content of CD:

APPENDIX F EXAMPLE PHREEQC INPUT FILE


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 N(5) 0.709 N(3) 0.182 Ra 3.08E-10 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 RaSO4 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

Tetra Tech February 2010 F-1


SURFACE 2 equilibrate Solution 2 Hfo_wOH Ferrihydrite equilibrium_phases 0.200 5.33e4 Hfo_sOH Ferrihydrite equilibrium_phases 0.005 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

Tetra Tech February 2010 F-2