Chapter 5

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5.0 Prediction

5.1 Introduction
5.2 Objectives of Prediction Program
5.3 The Acid Rock Drainage Prediction Approach
5.3.1 Acid Rock Drainage/Metal Leaching Characterization
5.3.2 Description of Phases
5.3.3 Water Quality Prediction
5.4 Prediction Tools
5.4.1 Introduction
5.4.2 Geological and Lithological Investigations
5.4.3 Hydrogeological/Hydrological Investigations
5.4.4 Geochemical Testing Methods
5.4.5 Data Management
5.4.6 Quality Assurance/Quality Control
5.4.7 Screening and Evaluation Criteria
5.4.8 Reporting
5.5 Modeling of Acid Rock Drainage, Neutral Mine Drainage, and Saline Drainage for Characterization and Remediation
5.5.1 Introduction
5.5.2 Geochemical Modeling
5.5.3 Hydrological Modeling
5.5.4 Hydrogeological Modeling
5.5.5 Gas Transport Modeling
5.5.6 Statistical Evaluation
5.6 Conclusions
5.7 References
List of Tables
List of Figures
This is the First Page: Sections 5.1, 5.2, and 5.3
Second Page: Section 5.4 Prediction Tools
Third Page: Sections 5.5, 5.6, and 5.7, Lists of Tables and Figures

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5.1 Introduction

This chapter presents an overview of the various methods available for waste characterization and the prediction of drainage water quality, with guidance as to the usefulness and limitations of the various methods. For detailed explanations, the reader should refer to the references and links provided in this chapter.

One of the main objectives of site characterization is prediction of drainage chemistry. Drainage chemistry is directly linked to mine planning, particularly in regard to water and mine waste management. The site characterization effort needs to be synchronized with overall project planning. Early characterization tends to be generic and generally avoids presumptions about the future engineering and mine design. Later site characterization and modelling needs to consider the specifics of engineering and mine design. Iteration may be required as characterization may lead to a re-evaluation of the overall mine plan. Also, the timing of the program must be synchronized with the mine development so that the findings of the characterization and prediction efforts can be linked with the mine design.

Accurate prediction of future mine discharges requires an understanding of the analytical procedures used and consideration of the future physical and geochemical conditions, external inputs and outputs, and the identity, location and reactivity of the contributing minerals (Price, 2009). All sites are unique for geological, geochemical, climatological, commodity extraction, regulatory, and stakeholder reasons. Therefore, a prediction program needs to be tailored to the site in question. Also, the objectives of prediction programs are variable. For example, objectives can include definition of water treatment requirements, selection of mitigation methods, assessment of water quality impact, or determination of reclamation bond amounts.

Predictions of drainage quality are made qualitatively and quantitatively. Qualitative predictions involve assessing whether acidic conditions might develop in mine wastes with the attendant release of metals and acidity to mine drainage. Qualitative predictions have been performed for at least 40 years and although errors have been made, often due to inadequate sampling, the predictions have been successful for many mine sites around the world. Indeed, predictions of whether acidic conditions could develop for high sulphur (often acid producing) and low sulphur (often nonacid producing) are often straightforward. Where qualitative predictions indicate a high probability of ARD production without mitigation, attention quickly turns to reviewing alternatives to prevent ARD and the prediction program is refocused to assist in the design and evaluation of potential success of that program. Significant advances in the understanding of ARD have been made over the last several decades (see Chapter 2), with corresponding advances in mine water quality prediction and use of prevention techniques. However, mine water quality prediction can be challenging because of the wide array of the reactions involved and potentially long time periods to achieve steady-state conditions related to ARD, NMD, and SD generation.

Quantitative predictions are often more successful for distinctly acid generating wastes, distinctly nonacid generating wastes, or for mitigation measures that are applied to prevent ARD and constrain the number and interaction of factors that must be considered. The understanding of equilibrium vs. kinetic controls on mineral reactions and their effect on water quality is of particular importance. Equilibrium conditions are relatively simple to simulate, but might not always be achieved in mine drainage waters under ambient conditions. Conditions governed by rate-limited reactions are common and more difficult to evaluate. However, through the use of state-of-the-art geochemical testing programs, both equilibrium conditions and rate-limited reactions can be assessed.

Despite the uncertainties associated with quantitative estimation of future mine water quality, quantitative predictions developed using a range of realistic assumptions and a recognition of associated limitations have significant value as ARD management tools and environmental impact assessment. From a risk-based perspective, the probability of a certain consequence (i.e., drainage quality) occurring is examined during the testing and prediction stage.

The following approaches have been used for predicting water quality resulting from mining activities:

  • Test leachability of waste materials in the laboratory
  • Test leachability of waste materials under field conditions
  • Geological, hydrological, chemical, and mineralogical characterization of waste materials
  • Geochemical modeling

Analog sites or historical mining wastes on the property of interest are also valuable in ARD prediction, especially those that have been thoroughly characterized and monitored for water quality and have many similar characteristics as the site in need of prediction. The development of geo-environmental models is one of the more prominent examples of the “analog” methodology. As described in Chapter 2, geo-environmental models provide a very useful way to interpret and summarize the environmental signatures of mining and mineral deposits in a systematic geologic context. Geo-environmental models can be applied to anticipate potential environmental problems at future mines, operating mines, and orphan sites.

The overall approach for ARD prediction is illustrated in Figure 5-1 and discussed in more detail in this chapter.

Figure 5-1: Generic Prediction Program Flowchart

GenericPredictionProgramFlowchart.gif

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5.2 Objectives of Prediction Program

The purpose of a prediction program is to characterize mine wastes and mine opening walls and to anticipate water-quality problems related to mine wastes and mine opening walls so that, if required, prevention measures (see Chapter 6) can be implemented in the most cost-effective manner while allowing for optimal adaptive management. The objective for most programs is not so much to predict exact outcomes; the objective is more to constrain the estimate of potential water-quality and attendant consequences of metal extraction and processing.

Predictions occur at different levels of complexity and for different reasons. In the context of pre-mine water quality prediction, the most important questions generally are: Will ARD be produced from a particular

  • Rock unit?
  • Zone of the deposit?
  • Mine facility or waste type?
  • Particular mining stage or phase?

This set of questions can usually be answered satisfactorily for most sites if an appropriate database on geochemical characteristics is available and a sound understanding of geological and mineralogical conditions has been developed. The strength of the database required reflects the variability and complexity of the ARD potential. For example, a more comprehensive database is usually required where there are significant variations in sulphur and carbonate mineral content or if the sulphur and carbonate mineral content are in close balance.

Where ARD is not predicted, the potential for metal release under near neutral pH conditions must still be assessed. Special attention is often placed on metals that can be quite soluble at neutral pH such as zinc, cadmium, selenium, and arsenic. Whole rock analysis and laboratory leach tests can be quite effective in assessing metal leaching potential.

The quantitative prediction of drainage quality is more difficult when it has been established that ARD will be generated. However, in many cases, an accurate quantitative prediction of drainage quality is not required. Instead, it may be sufficient to know for design, operational, or closure purposes whether a particular drainage will meet certain water quality standards, whether it will be an ARD, NMD, or SD type water, and what the overall volume will be. Therefore, all prediction efforts (and associated information needs and level of complexity) need to be tailored to the question at hand. As a general rule, the level of complexity selected for a model must reflect the scale at which the problem is to be addressed, the availability of information, and the level of detail, accuracy, and precision required. For problems of larger scale and with less available information, a less complex model should be employed (Maest and Kuipers, 2005).

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5.3 The Acid Rock Drainage Prediction Approach

5.3.1 Acid Rock Drainage/Metal Leaching Characterization

Figure 5-1 represents an idealized generic overview of a comprehensive ARD/ML prediction program. The program, as presented, applies to a project that advances from exploration through to mine closure. However, in the case of operating or closed sites, only certain components of this program might be of relevance and the program would be adapted.

The flowchart in Figure 5-1 assumes that ARD/ML prediction activities are performed at every stage of a project. These activities are coupled with other project planning activities and the level of detail of ARD/ML characterization activities is determined by the stage of the project. Data are accumulated as the project proceeds so that the appropriate information needed to support engineering design is available when needed.

The following six mine phases are identified in the GARD Guide:

  • Exploration
  • Mine planning, feasibility studies, and design (including environmental impact assessment)
  • Construction and commissioning
  • Operation
  • Decommissioning
  • Post-closure

The flowchart Figure 5-1 focuses on the earlier stages of mine development when most of the geochemical characterization is usually conducted. The description of mine phases in Figure 5-1 therefore differs slightly from the convention used in the GARD Guide. Both sets of nomenclature are presented.

The major “pillars” of the flowchart are as follows:

  • Typical Project Phase. Five typical major project phases of the mining cycle are included in Figure 5-1 (initial exploration, advanced exploration, prefeasibility, feasibility/permitting, and project implementation).
  • Minimum Objective of ML/ARD Program. The overall minimum objective for each project phase of the ARD/ML program is indicated on the flowchart. For each project phase, the minimum objective is typically defined based on the economic assessment of the project. These objectives are described as “minimum” requirements because project managers may choose to meet the objectives of subsequent phases to avoid delays.
  • ML/ARD Program Stage. This header indicates the level of characterization that is needed to meet the objective.
  • ML/ARD Program Activities. This element indicates the main types of prediction and characterization activities. All activities are considered cumulative. Activities occurring in earlier phases are continued here as needed to meet future objectives.

If new information becomes available during any one of the stages of the ARD/ML program (e.g., a change in mine plan, or unexpected monitoring results), re-evaluation of earlier stages may be required. These types of iterations are omitted from the flowchart in Figure 5-1 for clarity.

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5.3.2 Description of Phases

5.3.2.1 Initial Exploration/Site Reconnaissance Phase

During the initial exploration/site reconnaissance phase, the following activities take place: surface geological mapping, geophysical surveys, soil and stream sediment surveys, trenching, and wide-spaced drilling. The information acquired from these activities is used by project geologists to develop a conceptual geological model for the mineral prospect. In the context of managing existing sites, reconnaissance occurs at this stage to obtain historical and site layout information to define subsequent investigations.

The information collected during the initial exploration is not specifically interpreted for ARD/ML potential but becomes the foundation for subsequent evaluations. For example, geological mapping and mineralogical studies should consider the host or country rocks in addition to the ore. A core logging manual should be developed so that logs provide information that can be used for ARD/ML characterization. Core should be suitably stored to be available for future analyses. Rock samples should be analyzed using multi-element scans (including sulphur) in addition to the suspected commodity elements. Collection of environmental baseline data (soil, sediment, surface water, groundwater, and air) should begin during this phase.

5.3.2.2 Advanced Exploration/Detailed Site Investigation Phase

The advanced exploration/detailed site investigation phase usually involves additional drilling at narrower spacing and, where appropriate, underground development to improve delineation of the ore body, but normally a mine plan has not been developed during this phase. Specific ARD/ML characterization begins early in this phase. The geological model for the project provides a basis for design of a Phase 1 (initial or screening) ARD/ML static test program (Table 5-1 provides more detail on testing methods). The geological model also affords an opportunity for comparing the project to analogs, which may indicate a potential for drainage quality issues, and provides focus for the initial investigation. At this stage, water sampling in the area should include any existing facilities and natural weathering features (e.g., gossan seeps).

Table 5-1: Methods for Geochemical Characterization
[Table 5-1 is large and is located on its own page.]


5.3.2.3 Prefeasibility Phase

The prefeasibility phase includes development of initial mine plans (or closure plans for existing sites). During this phase, the results obtained during the Phase 1 program are coupled with the mine, waste, and water management plans to design a detailed Phase 2 ARD/ML characterization program that will lead to development of waste management criteria and water quality predictions. The Phase 2 characterization program will include static chemical and physical testing, mineralogical characterization, and implementation of laboratory and field kinetic tests specifically designed to answer questions about the geochemical performance of the individual mine and infrastructure facilities. A preliminary waste geochemical block model might be developed during this phase that can be used to initially estimate the quantities of different types of wastes.

5.3.2.4 Feasibility and Permitting Phase

The feasibility and permitting phases are not distinguished as separate phases in the flowchart because the ARD/ML characterization needs are essentially the same for feasibility and permitting, and the transition from a positive feasibility study to environmental assessment and permitting often occurs rapidly or occurs in parallel and therefore allows little time for additional studies.

The main activity in this phase is the development of source water quality predictions, which are used in the feasibility study (e.g., to determine water treatment requirements) and to evaluate the water quality effects of the project. The predictions are developed by coupling findings of the Phase 2 program with waste schedules and hydrological data for individual facilities. The predictions are used in the internal load balance for the site and as direct inputs to downstream groundwater and surface water effects assessments (see Chapter 8).

The flowchart in Figure 5-1 shows iterative loops from the source term predictions back to the Phase 2 program and show iterative loops from the effects assessment back to the source term predictions because further modeling and testing may be needed to refine water chemistry predictions. The parallel process for mine or closure planning may result in the redesign of some aspects of the mine or closure to address unacceptable effects or costs.

Following completion of an acceptable mine plan, monitoring plans are designed to inform waste management decisions (e.g., analysis of blast hole sample for waste classification) and verify water chemistry predictions (e.g., seep sampling) (see Chapters 8 and 9).

5.3.2.5 Implementation Phase

The implementation phase includes execution of the monitoring plans. Evaluation of the results indicates whether the site is performing as expected, or, as shown by iteration loops, some aspect of the project needs to be redesigned to address unacceptable performance.

Application of the approach presented in Figure 5-1 needs to be customized to account for site-specific aspects.

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5.3.3 Water Quality Prediction

Figure 5-2 provides a generalized flowchart for the prediction of potential water quality impacts at mine sites (Maest and Kuipers, 2005). This flowchart focuses primarily on the iterative process illustrated in the last two project phases in Figure 5-1.

Figure 5-2: Generalized Flowchart for the ARD Prediction Approach at Mine Sites
(Maest and Kuipers, 2005)

GeneralizedFlowchartfortheARDPredictionApproach.gif

The first step in water quality prediction is to determine the prediction objectives, the importance of which is discussed in the Section 5.1, and set up the site conceptual model discussed in Chapter 4. As site characterization progresses through collection of data (geology, hydrology, mineralogy, and mineral extraction/processing), the conceptual model continues to be refined, and may change as more data become available (Younger and Sapsford, 2006). The core of the conceptual model should be a schematic that shows the major sources of contaminants (e.g., mine portals, open pits, tailings, waste rock piles), the main means of transport (e.g., wind, surface water, groundwater), and the receptors (e.g., atmosphere, lakes, reservoirs, streams, rivers, soils, aquatic biota, terrestrial flora and fauna). Figure 5-3 is an example of a conceptual model in cartoon format, developed for the Iron Mountain Mine (California) and its receiving environment. Figure 5-3 can be made into a schematic (flowchart, flux chart or reservoir chart) with the size of the arrows proportional to flow as shown in Figure 5-4.

Figure 5-3: Conceptual Model Showing Metal and Acid Source Regions at Iron Mountain
and Downstream Transport Pathways to the Sacramento River

ConceptualModelShowingMetalandAcidSourceRegionsatIronMountain.jpg

Figure 5-4: Flowchart for Metal and Acid Source Regions at Iron Mountain and
Downstream Transport Pathways to the Sacramento River

FlowchartforMetalandAcidSourceRegionsatIronMountain.jpg

Each reservoir contains a certain mass amount and average concentration of the parameters of interest (acidity, metals, and sulphate in the case of ARD) and each arrow represents a given flux (or load) of those parameters from one reservoir to the next. Because the rates may change (e.g., with hydrologic conditions, irrigation needs, or other uses), a different set of conditions can be shown by both a range of values and, better yet, a different flowchart can have different values for different times of year.

Within each reservoir and flux, geochemical processes, such as precipitation or sorption of metals, result in more dilute solutions. It is within these parts of the flowchart that static/kinetic tests and geochemical modeling can be helpful. For a complex mine site with an open pit, underground workings, waste piles, diversions, and tailings piles, each one of these units should be identified, their rate of weathering and water transport quantified, and the consequences for receiving water bodies determined. A water balance (i.e., a numerical representation of the flowchart) should be developed for the system that takes into account precipitation, infiltration, and evapotranspiration. The effect of extreme events, such as floods and droughts, might also be assessed. For example, the timing and volume of infrequent high precipitation events are important in predicting drainage quality and quantity in quite arid environments.

All geochemical reactions of relevance to water quality prediction should be placed in a hydrogeological context through the flowchart. The main transport pathways can be shown by arrows and by flux numbers where available. Selection of the model to be used for water quality prediction (Figure 5-2) should take into account the prediction objectives.

The hydrogeochemical modeling is conducted using site-specific information to the maximum extent possible. This hydrogeochemical modeling results in prediction of contaminant concentrations at a number of predetermined locations (e.g., compliance points) or receptors. Through use of multiple input values, sensitivity analyses, and “what-if” scenarios, a range of outcomes is generated, bracketing the likely extent of water quality compositions and potential impacts.

Through a comparison of water quality predictions against relevant water quality standards, the need for mitigation measures or redesign of the mine plan can be identified (Figure 5-2). If predicted concentrations meet standards, additional mitigation measures will likely not be required. If, however, predicted concentrations exceed standards, mitigation measures will be necessary and their effectiveness should be evaluated using predictive modeling and active monitoring during and after mine operation. If the proposed mitigation measures are deemed inadequate for meeting standards, a reassessment of mitigation measures and possibly even of the mine design may be required. The prediction process then repeats itself, possibly including development of an improved conceptual model and additional data collection. Clearly, mine water quality prediction is an iterative process that can take place on an ongoing basis throughout the life of a mine, from the exploration phase through post-closure monitoring.

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