Chapter 5b

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

 

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).

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 (Error! Reference source not found.) 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.

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

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 Error! Reference source not found. needs to be customized to account for site-specific aspects.

5.3.3 Water Quality Prediction

Error! Reference source not found. 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 Error! Reference source not found.. Figure 5-2: Generalized Flowchart for the ARD Prediction Approach at Mine Sites (Maest and Kuipers, 2005)

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

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

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 (Error! Reference source not found.) 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 (Error! Reference source not found.). 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.  

5.4 Prediction Tools

5.4.1 Introduction

This Section 5.3 describes the main methods of estimating the environmental water-quality consequences of mineral extraction and processing and how these tools could be used to aid in remediation planning and remedial action. These tools build on the approaches described in Chapter 4.

The primary prediction tools discussed in this chapter include the following:

• Geological and lithological investigations
• Hydrogeological investigations
• Geochemical testing methods:

o Laboratory static and short-term methods o Laboratory kinetic methods o Field methods

• Modelling

5.4.2 Geological and Lithological Investigations

Mineral deposits are categorized according to their temperature of origin, their mineralogy, their lithology, and their structure. These categorizations are the basis for the development of geo-environmental models described in Chapter 2. A thorough understanding of the mineral deposit is critical to the characterization of mine wastes and geologic materials and the prediction of mine drainage quality. This information is typically available from the project geologist. Therefore, the characterization and prediction programs often begin with assembly of geological reports and interviews with the project geologists.

The elements likely to be of concern in water-quality assessments have a source in the rock and minerals that are exposed to weathering because of mining activities. Qualitative predictions on what those elements are can be gained from the rock type, its type and degree of alteration (e.g., hydrothermal, weathering, metasomatic), and the structural controls, including those that affect permeability and surface and groundwater flow. Examples of important geological characteristics that can affect the drainage quality, and hence the characterization program, include the following:

• The presence of a pyrite halo around the mineralized zone
• The role of alteration (e.g., potassic vs. propylitic vs. quartz-sericite-pyrite alteration in porphyry copper deposits) in the presence and distribution of sulphide and carbonate minerals
• Vein vs. disseminated deposit
• The presence and role of faults in displacing mineralized and nonmineralized zones and as conduits for water
• Depth of weathering (e.g., supergene vs. hypogene alteration)
• Sedimentary/stratigraphic sequence of coal deposits

These factors will ultimately determine the chemical composition of the mine drainage source material, which is an important step toward predicting the chemical composition of the mine drainage. An example of geological information that can be gathered by mine geologists during their exploration programs, and is relevant to ARD prediction, is presented as Table 5 2.

Table 5 2: Geologists Observations and Logging of Core for ARD Analysis

5.4.3 Hydrogeological/Hydrological Investigations

Contaminants in surface water and groundwater are driven by hydrologic and geochemical processes. The conceptual site model (as discussed in Chapter 4) of the hydrologic system includes recharge (precipitation, snowmelt, less evapotranspiration or infiltration), flow path, and discharge (springs, abstraction boreholes, seeps, portal flow, base flow to a river or stream). These water fluxes should be estimated (flux-reservoir diagram) and pump tests are usually needed to determine the geohydrological characteristics of aquifer material. Often a potentiometric surface for underground workings, waste piles, and open pit or other excavations needs to be estimated to determine the current or future potential conditions for water flow and changes in direction of that flow. Determining the groundwater table in fractured rock terrain with or without mine voids (i.e., an open pit or underground mine) can be challenging but very useful, even in a crude form.

5.4.4 Geochemical Testing Methods

5.4.4.1 Introduction to Geochemical Characterization Program

This Section 5.3.4 describes the geochemical testing methods and how the test results can be used for prediction of mine water quality. This section represents a high-level overview of available test methods rather than a detailed account of individual procedures, and focuses on the interpretive and predictive value resulting from geochemical tests. Table 5 1 provides a summary description of various test methods used globally and brief discussions of advantages and limitations of the test methods. Error! Reference source not found. (Maest and Kuipers, 2005) schematically presents the components of a typical geochemical characterization program aimed at developing water quality predictions and the general sequence in which these components should be conducted. This flowchart in Figure 5-5 provides more detail on the Phase 1 and Phase 2 testing programs illustrated in Error! Reference source not found.. Figure 5-5: Schematic Illustration of Geochemical Characterization Program (modified from Maest and Kuipers, 2005)

Phase 1 consists of a screening-level program, while Phase 2 is more detailed. In some cases, a Phase 1 program may be sufficient for mine water and waste management, whereas in more complex settings, a Phase 2 program is generally required. When a Phase 2 program is required, the results from the Phase 1 program are used to identify samples for kinetic testing or additional static testing, such as those identified in Error! Reference source not found. and Error! Reference source not found..

Therefore, not all components of the geochemical testing program may be necessary depending on site-specific characteristics and prediction needs. Individual test methods are described in more detail in the Sections 5.3.4.2 through 5.3.4.8, and are summarized in Table 5 1. Not all test methods presented in the table are appropriate for evaluation of mine wastes, even though they occasionally are requested by regulatory authorities. Such methods include the Toxicity Characteristic Leaching Procedure (TCLP) and Waste Extraction Test (WET), as explained in more detail in Table 5-1.

The geochemical characterization program starts with bench-scale testing, which generally involves whole rock analysis to determine chemical composition. The whole rock analysis identifies contaminants of potential concern, mineralogical examination, evaluation of acid generation potential, and evaluation of metal leachability through short-term leach testing. Detection limits in tests must be low enough to measure contaminants at potential concern levels. Depending on the complexity of the geology and variation in ARD potential, the results from the acid generation testing might be combined to develop a 3-dimensional representation of the quantity and geochemical characteristics of ore and waste rock. The information from the whole rock analysis is used to identify categories of rock in support of development of a waste management plan, which aims to handle mining wastes in such as manner as to prevent or minimize environmental impacts (see Chapters 6 and 9).

The next important step in the geochemical characterization program is kinetic testing, which can take the form of laboratory testing, field testing, or both laboratory and field testing, supplemented by on-site water quality monitoring. All materials involved in the kinetic testing should undergo a comprehensive characterization before the test begins, including surface area, particle size distribution, mineralogy, chemical composition, acid neutralization potential, and acid generation potential. At the completion of kinetic testing, the interpretive value of the kinetic testing program is greatly enhanced by repeating the determination of mineralogy, chemical composition, and acid generation potential.

In combination with water, and sometimes oxygen flux calculations, the results from the geochemical characterization programs are used to generate predictions regarding short-term and long-term acid generation potential, leachate quality, and loadings from individual waste type units. These predictions can be extrapolated to full-size mine facilities by incorporating a site-specific water balance based on information on hydrology, hydrogeology and climate, and a block model. Use of scaling factors may be required to account for differences in mass, surface area, rock to water ratio and temperature between testing arrangements, and mine facilities. The resulting water quality estimates can be used as inputs to geochemical models to account for geochemical processes that may affect dissolved concentrations such as mineral precipitation and dilution, sorption, and interaction with atmospheric gases. Ultimately, the findings of the geochemical characterization program contribute to development of mine waste and water management plans.

Any water quality prediction program needs to be customized for a particular situation and problem. Depending on the mine phase, commodity, climate, or mine facility, all or a subset of geochemical characterization tests may be required for the prediction effort and, although not indicated in Error! Reference source not found., multiple iterations may be required. Also, the issue of water transport might outweigh the geochemical testing as in very arid or arctic conditions with limited or infrequent generation of mine discharges. In that case, the focus of the program might be on determining the site hydrology, the site hydrogeology, or the hydraulics of the mine facility of interest rather than the range of geochemical characteristics. In general, the earlier in the life of a mine, the greater the reliance on use of laboratory tests for water quality prediction. As the mine matures, use of direct field measurements from water quality monitoring becomes feasible and is advocated. Accordingly, the comprehensive characterization program presented in Error! Reference source not found. is most appropriate for proposed operations, while characterization at inactive or orphaned mines would instead focus on observations regarding existing site water quality.

5.4.4.2 Summary of Testing Requirements In summary, the prediction of mine water quality requires an understanding of the following characteristics of the mining wastes and geologic materials:

• Physical characteristics
• Chemical characteristics
• Mineralogical characteristics
• Acid neutralization potential
• Acid generation potential
• Leaching potential

For ease of presentation in this GARD Guide, tests aimed at determining acid generation potential and leaching potential are categorized as follows:

• Laboratory static and short-term methods
• Laboratory kinetic methods
• Field methods

Sections 5.3.4.3 through 5.3.4.8 present a brief overview of the components of a comprehensive geochemical characterization program and their significance for mine water quality prediction. Useful references related to static and kinetic testing methods and their interpretation include AMIRA (2002), BCAMDTF (1989), Jambor (2003), Lapakko (2003), Maest and Kuipers (2005), Mills (1999), Morin and Hutt (1997), Price (1997), USEPA (2003), and White et al. (1999).   5.4.4.3 Physical Characteristics

The physical characteristic of most significance for water quality prediction is the particle size. Particle size distributions impact both mineral reaction rates and reaction duration by affecting the reactive surface area, the distances between potentially reactive particles, and the porosity and permeability of a solid. Porosity and permeability of a solid are particularly important with regard to movement and transport of air, water, and reaction products from weathering reactions.

The particle size distribution should be measured before any testing, both for laboratory and field-scale tests. To enable scale-up of test results, estimates of particle size distribution in mine facilities, such as waste rock repositories and heap leaches, are also required. These can be determined from direct measurement or calculated from the blasting plan. The “reactive” surface area of a material (i.e., that portion of the total surface that is actively available for chemical reaction) both in the laboratory and in the field may be significantly smaller than the surface area measured by standard techniques.

Permeability, specific gravity, and porosity should be determined in the laboratory for tailing material. The soil water characteristic curve (SWCC) and air entry value for oxygen diffusion might also be determined in the laboratory (see Chapter 6).

5.4.4.4 Chemical Characteristics

The primary purpose of determining chemical composition is the identification of constituents of interest. Determining chemical composition requires that a wide range of metals be analyzed. Fortunately, modern ICP-MS scans provide a large number of parameters at relatively low costs.

Identifying parameters that might be of concern is accomplished through comparison against average values for reference materials (e.g., crustal abundance, composition ranges for specific lithologies and soils). The soluble or leachable proportion of constituents of interest can be determined by combining the results from the chemical analysis with those from leach tests.

Other uses of chemical analyses include evaluation of sample representativeness and determination of all or part of the bulk mineralogy. Chemical analyses may also provide a surrogate for acid base accounting parameters (e.g., Ca for NP; total sulphur for AP). Table 5 3 is an example table of results from chemical analysis of various rock types, including a comparison against crustal values.   Table 5 3: Example Chemistry Table

5.4.4.5 Mineralogical Characteristics

The mineralogical characteristics of a mine waste and geologic material are often the most important control on ARD/ML and mine water quality. It is therefore critical to evaluate the mineralogical characteristics of a mine waste.

Several minerals often contain the same element of water-quality concern but minerals have different degrees of solubility, reactivity, and weatherability. As discussed in Chapter 2, “reactive” mineralogy, mineral chemistry, mineral assemblage, texture, morphology, and grain-size effects will all affect the composition of the drainage coming from the source material.

Mineralogical investigations provide valuable data that assist in interpreting other laboratory tests. The purposes of a mineralogical assessment include the following (Thompson et al., 2005):

• Confirm presence of minerals contributing to static and kinetic laboratory test results.
• Identify sulphide minerals that may contribute acidity and metals.
• Determine presence of carbonates and silicates that may consume acidity versus those that may not (e.g., calcite vs. siderite).
• Identify potential galvanic effects that may impact acid production or metal leaching
• Assess relative distribution of acid producing and consuming minerals in fractures and veins that could result in waste rock fines of different composition form the whole rock.
• Identify evidence of previous weathering and coatings.

Types of mineralogical investigations include petrographic analysis, X-ray diffraction (XRD), scanning electron microscope (SEM) and microprobe. Optical petrographic analysis conducted on thin sections is included in most metallurgical and ARD assessments.

5.4.4.6 Laboratory Static Methods

5.4.4.6.1 Acid Generation Potential

Two basic types of test for determination of acid generation are available: those that measure acid generation potential through independent determination of acid generating and neutralizing content, and those that generate a single value that can be used to indicate the likelihood of acid generation or release of stored acidity. The first type of test is collectively referred to as ABA test, while the latter type of test includes the net acid generation (NAG) test and paste pH. On a global scale, use of ABA and paste pH predominates, with the exception of Southeast Asia and Australia, where the NAG test is also used in conjunction with ABA for the prediction of ARD potential.

Both tests are relatively inexpensive and can be applied to large numbers of samples. The results from both types of test can be used for identification of samples requiring additional testing (e.g., kinetic testing) to more definitively determine acid generation potential (AP). In addition, the tests may provide operational screening criteria for mine waste classification and management. However, some differences exist in the ability of the tests to predict acid generation potential. The choice of test may therefore depend on site-specific considerations related to mineralogy, material characteristics, information requirements, or regulatory expectations.

ABA methods were initially developed for the coal mining industry and later adapted for use in metal mining. Although all methods incorporate an independent determination of AP and NP, many different protocols are available and in use. Table 5 1 presents the most common methods and summarizes advantages and limitations associated with each type of test. Results from ABA methods need to be interpreted in context with mineralogical information.

In general, the determination of the AP as part of ABA testing is conducted through analysis of one or more sulphur species. The theoretical relationship between sulphur content and AP is as follows:

AP (kg CaCO3/tonne) = 31.25 x S (%).

Sulphur species identified generally include total sulphur and pyritic (or sulphide) sulphur. Other sulphur species frequently determined (either through direct analysis or calculated by difference) include sulphate sulphur, organic (or residual) sulphur, and sulphate associated with barite. The acid potential can be calculated from total sulphur content (the most conservative approach) or the acid potential can be based on the concentration of one or more sulphur species to provide a more refined estimate of the amount of reactive sulphur present. In the case of coal, it is important to discount the proportion of sulphur associated with organics when determining AP. Similarly, sulphur occurring in the form of sulphate minerals, such as gypsum and barite, should be discounted when information on sulphur speciation is available.

The measurement of AP is relatively simple and generally not prone to significant subjectivity. However, tests developed to measure NP are not as straightforward in their interpretation because of the widely variable solubilities and reaction rates of potentially neutralizing minerals (e.g., carbonate and silicates), the differences in aggressiveness of the various methods used to determine NP, and the different reaction conditions and titration endpoints prescribed for each test. Because the resulting value for the NP is highly sensitive to test protocol, it is important that any ABA program makes use of the methodology that is most appropriate for a given objective and application. It is also important that at least one single test method is used throughout the program to ensure that the results are internally consistent. Although perhaps imperfect, the advantage of using “standard” methods for determination of NP, such as the Sobek and modified Sobek methods (see Table 5 1 for description), allows for comparison against a vast body of references values from other sites. The values for AP and NP are combined mathematically to indicate whether a sample has a stoichiometric balance that favours net acidity or net alkalinity. Table 5 4 is an example of ABA results, including summary statistics. Figure 5-6 provides an example comparison of NP calculated from total carbon measurements vs. NP using the modified Sobek method, while Figure 5-7 compares total sulphur content against sulphide sulphur content. These are just two of the many graphs that can be used to interpret ABA results.   Table 5 4: Example ABA Table

Figure 5-6: Example Plot of NP from Total Carbon vs. NP from Modified Sobek

Figure 5-7: Example Plot of Total Sulphur vs. Sulphide Sulphur

The NAG procedure uses a strong oxidant (hydrogen peroxide) to rapidly oxidize sulphide minerals in a crushed sample of the entire rock. The NP of the sample then can be directly consumed by the acidity generated by rapidly oxidising sulphide minerals. Although an overall balance is obtained directly with regard to a net acid or net alkaline potential, the test offers no indication of the individual values of AP and NP. A temporal component can be added to the NAG test by conducting a multistage NAG test (“sequential NAG”) or monitoring of diagnostic parameters (temperature, pH, EC) during reaction with the oxidant (“kinetic NAG”). Shaw (2005) recommends that the NAG test results be calibrated with other laboratory tests and that sequential NAG should be used as a check for samples with high NP or net neutralization potential (NNP). Figure 5-8 shows an example of the comparison between ABA and NAG test results.

Figure 5-8: Example Plot of ABA vs. NAG Results

Paste pH is a simple, rapid, and inexpensive screening tool that indicates the presence of readily available NP (generally from carbonate) or stored acidity. The outcome of the test is governed by the surficial properties of the solid material being tested, and more particularly, the extent of soluble minerals, which may provide useful information regarding anticipated mine water quality. For example, acidic paste pH values in combination with elevated sulphate sulphur generally suggest the presence of acidic sulphate salts that could cause short-term or long-term water quality issues.

5.4.4.6.2 Short-Term Metal Leaching

Although protocols for static (or short-term) leach tests vary widely, all tests measure readily soluble constituents of mine wastes and geologic materials. The short-term nature of static leach tests provides a snapshot in time of a material’s environmental stability. Test results depend entirely on the present disposition of the sample (e.g., unoxidized vs. oxidized; oxidation products absent vs. oxidation products present). For reactive rocks (e.g., material that contains oxidizable sulphur), the transient processes that lead to changes in solution chemistry during water-rock interactions often develop over periods of time that are much greater than is stipulated in the testing protocols. Therefore, the results from short-term leach tests generally cannot be applied to develop reaction rates and predict long-term mine water quality, but should instead be used to get an initial indication of parameters of constituents of interest. In addition, metal loadings can be calculated from short-term leach tests, as illustrated in Figure 5-9, where loading rates (in milligrams per kilogram [mg/kg]) are compared against initial sulphate content. Figure 5-9: Example Plot of Metal Loadings vs. Sulphate Content

It is important to select the method that most closely simulates the site-specific ambient environment and conditions (e.g., solution to solid ratio, nature of lixiviant, grain size, agitation). In addition, selection of a test method has to take into account the anticipated use of the leach test results (e.g., for prediction of seepage vs. runoff quality, incipient vs. terminal water quality).

Regulatory requirements and expectations may also govern selection of a particular methodology. Many jurisdictions have well-defined regulations for evaluation of metal mobility and potential impacts to water resources and in such cases use of a test with regulatory status may be compulsory. In instances where such a test is required but where the mandated protocol has no bearing on site-specific conditions (e.g., the prescribed use of acetic acid in the TCLP test), use of an additional, and more appropriate, alternative short-term leach test is recommended to allow for a more realistic estimate of future mine water quality. Similarly, modifications to standard leach test protocols should be considered to take into account site-specific considerations and improve the tests’ predictive ability.

5.4.4.7 Laboratory Kinetic Methods

Laboratory kinetic testing methods are used to validate static test methods, to estimate long-term weathering rates, and to estimate the potential for mine wastes and geologic materials to release discharges that may have impacts on the environment. Both acid generation and metal leaching can be evaluated through kinetic testing. To generate the required information within a reasonable time frame, the testing procedures are designed to accelerate the natural weathering process.

The results from kinetic testing are frequently used in combination with geochemical modeling to evaluate geochemical controls on leachate composition and conduct water quality prediction under a range of conditions. Similarly, kinetic testing results are often scaled up and used in combination with water balances for mine facilities to determine loadings and associated potential impacts to the receiving environment. Depending on the end use of the kinetic test results, results may be expressed in terms of leachate quality (mass released/unit leachate volume), mass-based loadings (mass released/total mass/unit time), or surface-area-based loadings (mass released/total surface area/unit time). For loading calculations, a water balance for the test cell and information on the mass and the surface area of the test charge is required. Geochemical reactions and reaction rates most commonly monitored throughout the testing include sulphide oxidation, depletion of neutralization potential, and mineral dissolution.

Kinetic testing procedures are complex, time-consuming, and require operator skill to generate consistent results. For any kinetic test conducted, the objectives and limitations of the method used should be acknowledged before starting the program so that it is clear what information will be delivered from the tests conducted. This will ensure accountability and value for efforts and costs expended.

There is no single test that produces all of the chemical information required to evaluate all mine wastes under all conditions of disposal. In all cases, a sample is subjected to periodic leaching and leachate is collected for analysis, but the various methods available may differ in the amount of sample used, the particle size of the sample, effluent sample volume, test duration, degree of oxygenation, or nature of the lixiviant. Therefore, it is important that the objectives of kinetic testing are clearly defined so that an appropriate test method is selected and adjusted to simulate site-specific conditions and the intended use of the data produced. By the same token, conducting standard humidity cell tests (e.g., using the ASTM protocol – see Table 5 1) is very useful to allow comparison with the significant amount of information on kinetic test results available in the literature. A second phase of kinetic testing may be implemented or field testing may be considered if it is decided that tests representing site-specific conditions are required.

Two “end-member” types of kinetic tests generally recognized are the humidity cell tests (HCT) and the column tests. HCTs represent a standardized test under fully oxygenated conditions with periodic flushing of reaction products. No standards are available for column tests, and column tests can simulate different degrees of saturation, including flooded and oxygen-deficient conditions. Column tests are typically larger scale than humidity cell tests. Figure 5-10 is a photo of a typical HCT setup. Figure 5-10: Humidity Cells

Both of the HCT and column tests may be used to estimate longer term potential for acid generation and metal leaching. HCTs are primarily intended to generate information on weathering rates of primary minerals (e.g., sulphides) only. However, HCTs can also be used to assess dissolution rates of readily soluble primary and secondary minerals present at the onset of testing (e.g., gypsum, hydrothermal jarosites). In theory, column tests provide information on combined weathering rates of primary and secondary minerals. Column tests may also be more suited to evaluation of attenuation and environmental performance of mitigation measures such as covers and amended mine wastes. Transfer of oxygen, which is not limiting in HCTs but may be in columns, must be understood in column testing. For determination of lag times to acid generation, HCTs are the preferred approach. Figure 5-11 is an example of concentration trends over time and presentation of HCT results. Figure 5-11: Example Plot of HCT Results

For both HCT and column tests, it is imperative that the test charges be characterized before kinetic testing begins and after kinetic testing has been completed. The information on the test charges may provide important constraints to assist in the interpretation of test results, and may also provide information that can be used for quality control purposes by comparing measured mass removal against calculated mass removal from the leachates.

The required duration of kinetic testing is an area of frequent controversy. Although a minimum length of 20 weeks is universally referenced, there is little technical basis for this recommendation. In actuality, the kinetic testing duration depends on the specific objective of the test. If the objective is to determine whether a sample will generate acid, kinetic tests should be conducted until acidity is produced or until depletion calculations can be used reliably to predict acid generation potential. Another common endpoint for the kinetic testing is when leachate parameters are relatively constant with time (e.g., over a 5-week period).

5.4.4.8 Field Methods

Field methods to determine acid generation and metal leaching potential range from rapid very small-scale tests to monitoring of full-size mine facilities for extended periods of time. In all cases, the advantage of the field methods is that on-site materials are used and an added benefit is that that most field tests allow for evaluation of weathering reactions under ambient conditions, including seasonal effects and discrete events such as intense storms or snowmelt. The larger the amount of material being tested, the greater the likelihood that a well-designed test is representative in terms of chemical and mineralogical composition and that the physical properties of a mine facility are being simulated. The larger amount of material will better represent particle size distribution, porosity, hydraulic conductivity, gas ingress, and transport. Disadvantages of field cells are related to the time required to generate reliable field reaction rates, the challenges with comprehensive geochemical characterization of the large test charges, and the inability to test a large number of different material types.

The simplest “field” test is the 5-minute field leaching test (FLT) recently developed by the USGS to simulate the chemical reactions that occur when geological materials are leached by water. The test is considered by the USGS a useful screening procedure that can be used as a surrogate for laboratory leach tests such as the Synthetic Precipitation Leaching Procedure (SPLP), (see Table 5 1).

Wall washing allows for evaluation of runoff quality from an isolated section of in situ rock face after application of a controlled amount of irrigation (Figure 5-12). This wall washing test is considered to represent a very useful order-of-magnitude estimate of contributions from exposed mine waste, in particular open pit walls or underground mine faces.   Figure 5-12: Wall Washing

Pilot cells (Figure 5-13), test piles, test plots (Figure 5-14), or test pads are constructed for long-term monitoring of relatively large quantities of material. Large-scale field columns (field lysimeters), to be operated under natural precipitation conditions, can also be useful.

  Figure 5-13: Test Cells for Waste Rock

Figure 5-14: Test Plot for Paste Tailings – Somincor Neves Corvo Mine, Portugal

Monitoring can be conducted under ambient field conditions, or under controlled conditions, using artificial irrigation. The large scale results in use of a more representative sample, and minimizes impacts from boundary effects, sample heterogeneity, and reduced grain size relative to laboratory tests. A comprehensive characterization of the test charge is required. In combination with a good understanding of the water balance for the test pad (achievable through meteorological monitoring or controlled application of infiltration, or both), reaction rates and loadings can be developed for extrapolation to full-scale mine facilities. Longer monitoring durations are generally required because of the reduced reactivity of field cell test charges relative to the finer-grained materials commonly included in laboratory tests. It may be advantageous to operate field tests during the complete life of mine to identify potential long-term releases.

On-site monitoring of historical and newly-constructed mine facilities (e.g., waste rock pile, tailings impoundment, pit wall, adits) can provide very useful information regarding weathering rates and discharge quality under ambient conditions. By definition, monitoring results of this nature are representative of the facility and existing conditions as a whole, but prediction of future conditions may be hindered by the sluggish rate of reaction relative to smaller scale tests. Also, a comprehensive understanding of chemical and physical material characteristics is not generally feasible, nor is a comprehensive understanding of the water balance, water movement and the role of atmospheric gases. This may limit the interpretive value of direct monitoring of mine facilities for the prediction of future water quality and potential impacts to receptors.

5.4.5 Data Management

Proper data management is critical to any geochemical characterization and mine water quality prediction effort, and setup and maintenance of a database is an integral component of such a program (Wolkersdorfer, 2008). The primary requirements for a useful and reliable database are that it should be in electronic format, it should be implemented from the very beginning of the study, and it should be maintained and augmented throughout all phases of a mining project.

A database should be managed from a central location, with routine backups. The data should be presented in a format that is readily accessible, and appropriate safeguards should be in place to maintain the integrity of the information stored in the database and prevent unauthorized use. Although most databases are designed to store numeric information, increasing use of geospatial data is incorporated by use of GIS. GIS provides a means for integrating and interpreting geochemical data within a geospatial context for land use, climate, topography, or ecosystem. The primary function of a database for geochemical data is to act as a comprehensive data repository that can be used to check and maintain data integrity (see Section 5.3.6 on QA/QC), support data manipulation and data interpretation, support and guide water quality and other monitoring programs, enable evaluation of compliance with regulatory requirements, and allow for evaluation of historical trends and prediction of future conditions.

One type of database unique to mining is the so-called block model, which is a 3-dimensionsal computerized representation of the quantity and characteristics of the pit walls, ore, and waste rock. Historically, block models have been resource focused, and have included information on ore grade, lithology, alteration types, principal minerals, fracture density and orientation, and rock competency, all of which are aimed at optimizing resource recovery. To this end, data from exploration drill holes are subjected to a variety of geostatistical analysis methods, such as kriging to quantify the 3-dimensional distribution of ore throughout the mine. However, increasingly, the same block models and geostatistical techniques are also used for environmental purposes, such as development of waste rock management plans and mine water quality prediction. Results of geochemical characterization programs are incorporated in block models, including inputs such as sulphur and sulphide content, NP, paste pH, NAG pH, NCV, carbon, and carbonate content. The combination of resource and environmental parameters in block models allows for prediction of environmental behaviour of mined materials in time and space and identification of requirements for mitigation actions in time and space. Environmental block models should be developed when a 3-dimensional understanding of ARD potential is required, and should then be maintained and refined throughout the life of mine through the ongoing acquisition of additional data. Examples of use of block models are presented in and . Figure 5-15 shows the ARD potential of a highwall remaining exposed after pit lake formation. Figure 5-16 shows the ARD potential of pit walls at the cessation of mining. In both cases, a block model incorporating ABA parameters formed the basis for the evaluations. Figure 5-15: Example of Block Model Use: ARD Potential of Pit Highwall Above Final Pit lake

  Figure 5-16: Example of Block Model Use: ARD Potential of Pit Wall after Cessation of Mining

5.4.6 Quality Assurance/Quality Control

A rigorous QA/QC program is needed to ensure that geochemical data are reliable and defensible, and that such data can be used for their intended purpose, such as mine water quality prediction.

QC is defined as the application of good laboratory practices, good measurement practices, and standard procedures for sampling. QC is also defined as sample preparation and analysis with control points within the sample flow to prevent the reporting of erroneous results. The sampling should include specifications for chain of custody procedures and documentation, sample holding time verification, drying, comminution, storage and preservation, sample labelling, and use of proper sample containers. Physical and chemical tests conducted using approved methods and accredited laboratories should produce analytical results with sufficient accuracy and precision for their intended usages. Analytical methods and their repeatability, reproducibility, quantitation, and detection limits should meet anticipated requirements (e.g., for comparison against water quality standards). Replicate samples, standards, certified reference materials, and blanks should be routinely submitted to ensure and confirm that the analytical results are of acceptable quality. QA is the process of monitoring for adherence to quality control protocols. The DQO of a quality assurance project plan (QAPP) are as follows: accuracy, precision, bias, representativeness, completeness, and comparability. A QAPP will ensure that the proper procedures are established before initiating sample collection and analysis, and that procedures are maintained throughout all stages of a geochemical program. In addition, corrective actions are prescribed through a QAPP. A defensible QA/QC program will add costs to an ARD study, but it will also enhance the confidence of operators, regulatory agencies, and other reviewers in assessing the data. A QAPP will help balance the costs of implementing a quality-assured program against the potential liabilities associated with a poorly-designed and executed geochemical characterization program.

The data validation and assessment protocols for geochemical data generated in support of prediction of ARD and metal leaching potential are similar to those used in any type of study that relies on use of analytical results, and the data validation and assessment protocols include a variety of statistical analyses and graphical tools. Geochemical modeling can be useful (e.g., through calculation of the ion balance), while cross checking using results from different types of testing also may provide insight in data quality (e.g., calcium content vs. NP, sulphur content vs. mineralogical composition, measured vs. calculated TDS, NP titration vs. TIC).

5.4.7 Screening and Evaluation Criteria

Use of screening and evaluation criteria is generally required to assess whether results from geochemical characterization studies represent a potential impact or risk to a receiving environment at a mine site. These criteria can be based on professional and empirical experience, guidance documents, and regulations promulgated for the express purpose of protecting the environment.

Screening and evaluation criteria are commonly used at mine sites for water and mine waste management. Mine waste management involves identification of potentially acid generating (PAG) and NAG waste. PAG material is either acidic or predicted to become net acidic in the future. A material will become net acidic if the rate of acid neutralization is unable to keep pace with the rate of acid generation. This inability to maintain neutral conditions may be due to a decrease in the rate of acid neutralization or an increase in the rate of acid generation, or both. NAG material is predicted to generate near-neutral or alkaline drainage in the future. Materials will be net neutral or alkaline if the rate of acid neutralization keeps pace with the generation of acidity (Price, 2009).

Site-specific operational parameters and threshold values are established for waste classification (i.e., PAG vs. NAG) based on regulatory requirements, literature, and the geochemical test program. Examples of commonly used operational parameters for waste rock management include the sulphur content, paste pH, NNP, net potential ratio (NPR), NCV, NAG value, or NAG pH and metal content.

Professional and empirical experience may be an acceptable basis for establishing a screening or evaluation criterion. For example, if a quantitative relationship can be reliably established between ARD potential and sulphur content, a sulphur cutoff can be determined to segregate between PAG and non-PAG waste rock. Similarly, if a relationship between metal leachability and metal content is identified, a metal concentration cutoff can be established to discriminate between material that will or will not affect receiving water quality. Sometimes a combination of methods is needed to classify problematic material, such as paste pH and NPR.

Guidance documents are available that provide screening criteria for evaluating geochemical test results, in particular those tests related to prediction of ARD potential: ABA and NAG (Price, 1997; AMIRA, 2002). These criteria are generally related to specific values for NNP, NPR, NAG pH, and NCV, and can be used to classify mine wastes and geologic materials in terms of their ARD potential. Special care is required when dealing with mining wastes that exhibit both low sulphur contents and low NP because small changes in analytical results can dramatically affect the calculated NPR and the mine waste classification. Therefore, the screening process should generally consider use of multiple criteria and tests, such as those based on NNP and NAG.

 is the Australian AMIRA (2002) decision tree for determining acid generation potential. Through use of a combination of results from ABA testing, NAG testing, and professional judgment, samples are categorized into a number of classes with a range of ARD potentials. Another example of guidelines widely used throughout North America is based on NPR values as follows (Price, 2009):

Figure 5-17: Decision Tree for the Determination of Acid Generation Potential (AMIRA, 2002)

Potential for ARD Initial Screening Criteria Interpretation Likely NPR <1 Likely acid generating, unless sulphide minerals are nonreactive Possible (uncertain) 1<NPR<2 Possibly acid generating if NP is insufficiently reactive or is depleted at a rate faster than sulphides Nonacid generating NPR>2 Not potentially acid generating unless significant preferential exposure of sulphides along fractures planes, or extremely reactive sulphides in combination with insufficiently reactive NP.

In Europe, an NPR value of 3 is conservatively assumed to be the threshold between potential acid generating and nonacid generating mine waste. However, use of a lower ratio is acceptable if it can be proven, based on site-specific information, that such a value is sufficiently protective. As with all screening criteria, the burden in on the proponent to prove that these criteria are appropriate and defensible based on site-specific considerations.

Worldwide regulatory jurisdictions have adapted criteria for ARD potential, and some have been promulgated into law. When such criteria exist, their application is generally mandatory, unless use of appropriate and defensible site-specific criteria is allowed under the law. The selected criteria can vary and an understanding of applicable regulations is needed when evaluating results from ABA and NAG tests for the purpose of prediction of ARD potential and identification of mine waste management requirements. Examples of such regulated criteria include an NPR threshold of 3 for nonacid generating waste in New Mexico, an NPR threshold of 1.2 in Nevada, (i.e., 20% excess base), and a three-pronged approach in Quebec based on sulphide content, NNP, and NPR. In Quebec, acid generating material is characterized by sulphide content greater than 0.3%, and, in the absence of confirmatory kinetic testing results, an NNP less than 20 kg CaCO3/tonne or an NPR less than 3. Figure 5-18 is an example plot of ABA results in which a number of screening criteria have been included, delineating the boundaries between materials with a different potential for ARD. Figure 5-18: Example Plot of ABA Results and ARD Criteria

Regulatory criteria also exist for interpretation of results from certain leach tests specifically designed for classification of waste materials and compliance with water quality standards, as indicated in Table 5 1 and (AMIRA, 2002). Examples of such tests include the TCLP, meteoric water mobility procedure (MWMP), and WET tests in the United States, the CEN-series tests in Europe, the Chinese GB tests, and the Brazilian Norma Brasileira Registrada (NBR) tests.

In general, kinetic test results need to be interpreted in the context of all available geochemical information. The following evaluation steps may be of assistance in the assessment of kinetic test results:

• Temporal trends of acidity, alkalinity, sulphate, and pH used to assess rates of acid production and consumption
• Ratio of acid production (using sulphate) vs. acid consumption (using calcium, magnesium, alkalinity) to assess relative rates
• Comparison between observed sulphate generation rate and literature values (Morin, 1997)
• Comparison between observed metal concentrations and water quality objectives (A direct comparison generally should only be used as a screening tool, and should take into account the differences in solid to liquid ratio between the test and the ambient environment.)
• Comparison between kinetic test results and findings from ABA, NAG, mineralogy, static leach testing, and field water quality
• Comparison between kinetic test results and water quality from analog sites (i.e., geo-environmental approach)
• Geochemical modeling to identify controls on leachate composition
• Development of relationships between sulphate concentrations and those of constituents of interest that can be extrapolated to field conditions through sulphide oxidation modeling or calibrated against field measurements of sulphide oxidation

In the absence of regulatory criteria, and frequently in addition to regulatory criteria, site-specific screening criteria should be developed. These criteria should be based on a thorough geochemical characterization of the material at hand. Results from ABA, NAG testing, mineralogical examination, leach testing, and kinetic testing are used to develop an internally consistent understanding of acid generating potential, culminating in identification of a small number of criteria (generally one or two) that can be used to reliably classify mining wastes and geologic materials according to their ARD potential. To be of value in an operational setting, these criteria need to be based on parameters that can be rapidly determined onsite with a high degree of confidence. Visual methods (e.g., rock type, pyrite content) and laboratory determination of total sulphur, NCV, and NAG pH are probably the most commonly used operational waste management tools.

Although development of screening criteria is commonly aimed at identifying the acid generation potential of a mine waste or geologic material, the process of evaluation of potential environmental impacts should not stop there. The material classified as nonacid generating should still be assessed for drainage quality. NMD and SD from nonacid generating material may continue to be a cause for concern even in the case of waste management strategies that include, for example, segregation of PAG from non-PAG waste rock or encapsulation of PAG rock by non-PAG rock.

5.4.8 Reporting

Reporting is an integral part of an ARD-related study. In addition to including tabulations of the analytical results, the reported information needs to be presented in a format that provides proper interpretation. This requires calculation of descriptive statistics and use of a variety of graphical representations developed for evaluation of results from ABA, NAG, and kinetic testing. Price (1997) or Wolkersdorfer (2008) provide a comprehensive overview of the most commonly used table templates, calculation sheets, and graphs.

These procedures must be documented and submitted as part of the report because the reviewer of an ARD study may not be familiar with all analytical and sampling procedures. Also important is a discussion of QA/QC aspects and their bearing on data reliability and defensibility.

At a minimum, the report needs to contain all predictions of environmental behaviour, including the approach and tools used (e.g., geochemical modeling code, statistical software), assumptions incorporated in the predictions, the prediction results, and a discussion of uncertainties and limitations associated with the predictions. Frequently, a report will also include recommendations for further activities related to data collection or evaluation, an interpretation of results in terms of potential environmental impacts, and an assessment of measures that can be used to prevent, minimize, or mitigate such potential effects.  

5.5 Modeling of Acid Rock Drainage, Neutral Mine Drainage, and Saline Drainage for Characterization and Remediation

5.5.1 Introduction

Modeling and prediction have significant value as management tools and for gaining an understanding of the geochemical, physical, and biological systems at mine and process sites (Oreskes, 2000). The primary objective of mine and process water quality prediction is to evaluate the potential for geologic materials and mine and process wastes to generate acid and contaminants, and the potential to affect water resources. As an important corollary, the need for and nature of mitigation measures is determined through prediction.

In principle, modeling can be applied to all mine and process facilities, including mine portal effluent, subsurface waters (wells or underground workings), waste dumps, process tailings piles, surface waters, pit lakes, and open pits. The type of modeling used depends on both the objectives and the type of source or pathway. A wide variety of codes are available for these various environments, but the critical factors are the quality of their databases of these codes, the inherent assumptions, and, most importantly, the knowledge and experience of the modeler.

Figure 5-19 presents a generalized approach to the development, calibration, and use of a model. The modeling process starts with a strong conceptual model and the mathematical model can then be used to update the conceptual model as necessary. Calibration of the model is a critical part of the overall process.   Figure 5-19: Generalised Model Process

The nature and sophistication of the prediction effort may vary depending on the desired outcome. A prediction exercise aimed at merely answering a “yes/no” question (for example: will the water quality criterion for arsenic be exceeded?) requires less up-front understanding of the system being evaluated, in which case the use of relatively “crude” modeling tools may suffice. In contrast, when a more quantitative answer is required (for example: what is the expected arsenic concentration), the complexity of the modeling effort may be quite significant, requiring both a detailed conceptualization of the system being modeled as well as use of advanced modeling codes. Care should therefore be exercised in selecting models so that they suit the need of the application and are compatible with the range and quality of the input data. The use of more sophisticated tools does not necessary equate to more accurate and precise modeling outcomes. According to Oreskes (2000) and Nordstrom (2004), the current computational abilities of codes and advanced computers far exceed the ability of hydrogeologists and geochemists to represent the physical, chemical, and biological properties of the system at hand or to verify the model results. In light of these considerations, the meaning of “accuracy” and “precision” in the context of mine and process water quality modeling must be reassessed on a case-by-case basis, and numeric analysis needs to be conducted to reflect the uncertainty inherent in predictive modeling. USEPA (2003) recommends the following should be submitted at a minimum to substantiate modeling used for regulatory purposes, regardless of the specific model/code being used:

• Description of the model, its basis, and why it is appropriate for the particular use
• Identification of all input parameters and assumptions, including discussion of parameter derivation (i.e., by measurement, calculation or assumption)
• Discussion of uncertainty
• Sensitivity analysis of important input parameters

The general understanding of geologic materials, mine and process wastes, and the hydrogeochemical factors that govern mine and process water quality continues to advance through the implementation of laboratory and field experiments. In particular those experiments that isolate one variable at a time to identify its effect on overall discharge water quality are of great value. Similarly, ongoing characterization and monitoring of mine and process facilities allows for development of improved scaling factors needed to extrapolate results from smaller scale tests to an operational level. Also, the tools required for geochemical, hydrological, and hydrogeological modeling already exist. Therefore, modeling can be a valuable component of mine water quality prediction.

Additional detail on geochemical, hydrological, and hydrogeological modeling, including listings of commonly-used codes, can be found here [provide Wiki link].   5.5.2 Geochemical Modeling

This Section 5.4.2 describes the conceptual, thermodynamic, and kinetic fundamentals of geochemical modeling and its application to prediction of mine water quality in support of mine site characterization and remediation. The emphasis in this section is on the basic processes that models attempt to represent with discussions of the usefulness and the limitations of modeling.

In principle, geochemical modeling can be applied to all mine and process facilities, including mine portal effluent, subsurface waters (wells or underground workings), waste dumps, process tailings piles, surface waters, pit lakes, and open pits. The type of modeling used depends on both the objectives and the type of source or pathway. Many codes are available for these various environments but the critical factors are the quality of their databases, the inherent assumptions, and, most importantly, the knowledge and experience of the modeler.

Three basic approaches have been used with geochemical data: forward geochemical modeling, inverse geochemical modeling, and geostatistical analyses.

Forward modeling is also known as simulating (i.e., potential reactions between rock and water are simulated from initial conditions of a known rock type and composition). Reactions are allowed to proceed in equilibrium or kinetic or combined modes. Changes in temperature and pressure can be invoked, changes in water flow rate can be assessed, and minerals can be allowed to precipitate as they reach equilibrium solubility or dissolve as they become undersaturated. Potential reactions can be simulated to see what the consequences are. This type of modeling is the least constrained. A great many assumptions are either invoked as input data or invoked as dictated by the program that may not apply to the specific system being simulated. This approach assumes the modeler has a significant amount of information on the ability of minerals to maintain equilibrium solubility or their rates of reaction.

Inverse modeling assumes a water flow path is known and that water samples have been analyzed along that flow path. Such data can then be converted into amounts of minerals dissolved or precipitated along that flow path. Several assumptions are still made regarding the choice of minerals and their relative proportions contributing to the water chemistry, but the calculations are constrained with actual data. Inverse modeling can also be done without any recourse to kinetic or thermodynamic data, in which case it represents a relatively simple mass balance calculation. When speciation and thermodynamic and kinetic properties are included for additional constraints, the possible reactions become quite limited and the modeling is much more meaningful.

Modeling of any type does not lead to a unique solution but the possibilities are more limited with greater amounts of carefully collected field data. Martin et al. (2005) summarized the benefits and limitations of geochemical modeling as follows:

Benefits

• Provide insight into potential future conditions.
• Determine which variables are most important in determining future conditions.
• Assess the effects of alternative approaches to ARD management.
• Assess potential effects of uncertain parameters
• Establish objectives and test conditions for field and laboratory studies
• Integrate available information.

Limitations

• Insufficient input data
• Modeling can be challenging and results misinterpreted
• Uncertain and variability of the results
• Difference between modeled and actual field conditions.

Alpers and Nordstrom (1999) and Mayer et al., (2003) provide a review of geochemical models for use in mine water quality prediction.

5.5.3 Hydrological Modeling

Generally, a hydrological model is an analog of a natural or human-modified hydrological system. This generic definition encompasses models of surface-water and groundwater systems. Scientists and engineers commonly use the term hydrological model to refer to models of surface-water systems, and consider hydrogeological models for groundwater systems as a separate subject. This Section 5.4.3 follows the latter convention, describing hydrological models in the context of surface-water systems.

Hydrological models range from simple algebraic calculations to complex reactive-transport computer codes. Physical analogs, such as stream tables, can also be useful simulations of complex surface-water systems. Hydrological models can be used to predict the fate and transport of mine drainage through a surface-water system, providing important input to human-health or ecological risk assessments. Hydrological models can also be used to estimate the water-quality and water-quantity evolution of pit lakes over time. Hydrological models can be coupled with hydrogeological and geochemical models to incorporate the interaction between surface water and groundwater into the simulation and account for geochemical reactions.

Selection of an appropriate, quantitative hydrological model depends on the type of output that is required and, critically, on the conceptual model of the system being evaluated. A robust conceptual model will identify the important physical and geochemical characteristics of the field-scale system being evaluated. Based on that identification, an appropriate hydrological model can be selected that quantitatively represents those important processes. For complex systems or to assess a range of different types of processes, multiple hydrological models can be applied to predict the fate, transport, and potential impacts of mine discharges.

5.5.4 Hydrogeological Modeling

Hydrogeological models address water flow and contaminant transport below the land surface. As with hydrological models, approaches to hydrogeological simulations range from simple to complex. The universe of hydrogeological models includes physical and electrical analogs. With the advent of powerful personal computers and high-level programming languages, these approaches are rarely used in current practice.

Much literature exists regarding hydrogeological modeling, as do a number of computer programs. Zheng and Bennett (2002) provide an excellent introduction to the topic of contaminant-transport modeling. Maest and Kuipers (2005) provide a review of hydrogeological models more directly focused on ARD prediction.

Three following basic types of hydrogeological models are available, in order from simple to more complex:

1. Analytical models of flow and contaminant transport 2. Analytic element models 3. Numerical models

As a general rule, hydrogeological models should be as simple as possible while still representing the physical system with an adequate degree of precision and accuracy. More complex models should only be selected when project needs dictate, when simpler models are demonstrably not adequate, or when suitable data are available for model parameterization and calibration.

Hydrogeological models are useful tools for predicting the potential generation and resulting impacts of ARD. Models can be used to fill data gaps, either in space or in time. They can also be used to test alternative conceptual models in an iterative process designed to understand the complex natural or human-modified subsurface system.

5.5.5 Gas Transport Modeling

Gas transport, particularly the transport of oxygen into unsaturated waste-rock piles, can be an important process affecting the generation of ARD. Principal modes of oxygen transport include diffusion and advection. Wels et al. (2003) provide a comprehensive overview of the role of gas transport in ARD generation and methods that can be used to model gas transport.

Relatively few models have been developed specifically to address gas transport in the subsurface and the application to ARD-related problems. Modeling the complete set of physical and chemical processes operating within a waste-rock pile requires a multiphase code capable of simulating gas and water flow in the unsaturated zone, chemical interactions with the solid matrix, heat generation and transfer, and chemical mass transfer in the liquid and gas phases.

5.5.6 Statistical Evaluation

The use of statistics can be helpful in finding groupings and correlations among many parameters in a large data set. For instance, water quality results may be grouped into sets that may relate to hydrogeochemical processes. However, caution should always prevail. Statistics is a form of mathematics and supports and helps to understand science and engineering. Statistical results demonstrate correlations or the lack thereof but are nondeterministic. Parameters can correlate but not be deterministically related. Correlated parameters may indicate unknown relationships that were overlooked. Several types of multivariate correlative manipulations using regression techniques are in common use (Davis, 2003), including Principal Component Analysis (PCA), Cluster Analysis (CA), Probability Distributions (PD), and Factor Analysis (FA). These and other techniques often depend on assuming certain characteristics for the data set that are not necessarily correct (e.g., data follow a normal distribution, sufficient data are available to apply statistical tests, and levels of variance are comparable among parameters being correlated). Perhaps the best uses of statistical methods are for reasonable interpolation of spatial or temporal data and for identifying potentially causal parameters that had not previously been recognized.

5.6 Conclusions

Mine water quality prediction is an integral component of any study related to ARD. As described in this Chapter 5, a standard and structured methodology is used, particularly for new mine development. National regulatory frameworks and global guidelines frequently incorporate elements of this approach. Defensible ARD/ML and water quality predictions are being developed using state-of-the-art techniques by knowledgeable practitioners.

5.7 References

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