WATER QUALITY PREDICTIONS AT HARDROCK MINE SITES: METHODS, MODELS, AND CASE STUDY COMPARISON

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WATER QUALITY PREDICTIONS AT HARDROCK MINE SITES
METHODS, MODELS, AND CASE STUDY COMPARISON

• James Kuipers, Kuipers Assoc
• Ann Maest, Buka Environmental

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Study Approach

• Synthesize existing reviews
• Develop toolboxes
• Evaluate methods and models
• Recommendations for improvement
• Outside peer review (Logsdon, Nordstrom, Lapakko)
• Case studies

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Specific Models Used

• Water Quantity only (18 mines)
• Near-surface processes HEC-1, HELP
• Sediment transport SEDCAD, MUSLE, RUSLE,
  R1/R3SED
• Storm hydrographs WASHMO
• Groundwater flow MODFLOW, MINEDW
• Vadose zone HYDRUS drawdown

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Specific Models Used (cont.)

• Water quantity and quality (21 mines)
• Water quantity PHREEQE (3), WATEQ (1), MINTEQ
  (5)
• Pyrite oxidation (PYROX) 3 mines
• Water balance/contam transport (LEACHM) 1 mine
• Pit water flow and quality (limited) CE-QUAL-W2
  (3), CE-QUAL-R1 (1)
• Mass balance/loading unspecified (4 mines),
  FLOWPATH
• Proprietary codes for pit lake water quality or
  groundwater quality downgradient of waste rock
  pile (4 mines)

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Sources of Uncertainty - Modeling

• Use of proprietary codes
• need testable, transparent models difficult to
  evaluate, should be avoided. Need efforts to
  expand publicly available pit lake models
  (chemistry).
• Modeling inputs
• large variability in hydrologic parameters
  seasonal variability in flow and chemistry
  sensitivity analyses (ranges) rather than
  averages/medians
• Estimation of uncertainty
• Acknowledge and evaluate effect on model outputs
  test multiple conceptual models
• there is considerable uncertainty associated
  with long-term predictions of potential impacts
  to groundwater quality from infiltration through
  waste rock...for these reasons, predictions
  should be viewed as indicators of long-term
  trends rather than absolute values.

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Summary

• Characterization methods need major
  re-evaluation, especially static and short-term
  leach tests
• Increased use of mineralogy in characterization
  make less expensive, easier to use/interpret
• Modeling uncertainty needs to be stated and
  defined
• Limits to reliability of modeling use ranges
  rather than absolute values
• Increased efforts on long-term case studies

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Comparison of Predicted and Actual Water Quality
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EIS Review

• Mines
• 183 major, 137 NEPA, 71 NEPA reviewed
• Similar in location, commodities, extraction,
  processing, operational status
• 104 EISs reviewed for 71 mines
• 16 months to obtain all documents
• Compared EIS predictions to actual water quality
  for 25 case study mines

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EIS Years
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EIS Approach to Impacts
Geochemical Information
Engineering Design
Potential Water Quality
Predicted Water Quality
Mitigation Measures
Hydrologic/ Climatic Information
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Case Study Mines

• Selection based on
• ease of access to water quality data
• variability in geographic location, commodity
  type, extraction and processing methods
• variability in EIS elements related to water
  quality (climate, proximity to water, ADP, CLP)
• Best professional judgment
• Similar to all NEPA mines, but
• more from CA and MT
• fewer Cu mines
• more with moderate ADP and CLP
• more with shallower groundwater depths

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Case Study Mines General

• States
• AK 1 - MT 6
• AZ 2 - NV 7
• CA 6 - WI 1
• ID 2
• Commodity
• Au/Ag 20 - Pb/Zn 1
• Cu/Mo 2 - PGM 1
• Mo 1

• Extraction type
• open pit 19
• underground 4
• both 2
• Processing type
• CN Heap 12
• Vat 4
• Flotation 7
• Dump Leach 2

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Case Study Mines Selected
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Comparison Results

• Mines with mining-related surface water
  exceedences 60
• estimating low impacts pre-mitigation 27
• estimating low impacts with mitigation 73
• Mines with mining-related groundwater
  exceedences 52
• estimating low impacts pre-mitigation 15
• estimating low impacts with mitigation 77
• Mines with acid drainage on site 36
• predicting low acid drainage potential 89

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Inherent Factors

• ore type and association
• climate
• proximity to water resources
• pre-existing water quality
• constituents of concern
• acid generation and neutralization potentials
• contaminant leaching potential

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Surface Water Results
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Groundwater Results
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Mines without Inherent Factors

• California desert mines
• American Girl, Castle Mountain, Mesquite
• No groundwater or surface water impacts or
  exceedences
• Delayed impacts? Climate change (but less precip
  predicted)
• Stillwater, Montana
• Close to water, low ADP, moderate/high CLP
• Unused surface water discharge permit
• Increases in nitrate (predicted from LAD by
  modeling) in Stillwater River, but no exceedences
• Mining-related exceedences in adit and
  groundwater under LAD, but related to previous
  owners?
• Inherently lucky ultramafic mineralogy

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Predicted vs. Actual Water Quality
Geochemical Information
Engineering Design
Potential Water Quality
Predicted Water Quality
Actual Water Quality
Mitigation Measures
Hydrologic/ Climatic Information
Failure at 64 of sites
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Implications

• Mines close to water with mod/high ADP/CLP need
  special attention from regulators
• Water quality impact predictions before
  mitigation in place more reliable
• These can be in error too geochemical and
  hydrologic characterization need improvement
• Why do mitigations fail so often and what can be
  done about it?

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Characterization Methods

• Geology
• Mineralogy
• Whole rock analysis
• Paste pH
• Sulfur analysis
• Total inorganic carbon

• Static testing
• Short-term leach testing
• Laboratory kinetic tests
• Field testing of mined materials

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Modeling Opportunities
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Modeling Toolbox

• Category/subcategory of code
• Hydrogeologic, geochemical, unit-specific
• Available codes
• Special characteristics of codes
• Inputs required
• Modeled processes/outputs
• Step-by-step procedures for modeling water
  quality at mine facilities

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EIS Information Reviewed

• Geology/mineralogy
• Climate
• Hydrology
• Field/lab tests
• Predictive models used

• Water quality impact potential
• Mitigation measures
• Predicted water quality impacts
• Discharge information

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Surface Water Examples

• Flambeau, WI had inherent factors but no impact
  or exceedence to date
• Stillwater, MT had an impact (0.7 mg/l nitrate in
  Stillwater River) but no exceedence
• McLaughlin, CA predicted exceedences in surface
  water and was correct

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Groundwater Examples

• Lone Tree, NV had inherent factors but no
  mining-related impact or exceedence
• baseline issue
• McLaughlin, CA had inherent factors and did have
  exceedences
• regulatory exclusion for groundwater (poor
  quality, low K), so no violations
• 92 of rocks not acid generating
• Tailings leachate flunked STLC hazardous aquifer

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McLaughlin Mine, CA Waste Rock Monitoring Well
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Case Study Mines Water Quality

• Groundwater depth
• No info 1
• gt200 3
• 50-200 4
• 0-50/springs 17
• Acid drainage potential
• No info 2
• Low 12
• Moderate 8
• High 3
• Contaminant leaching
• No info 3
• Low 8
• Moderate 10
• High 4

• Climate
• Dry/Semi-Arid 12
• Marine West Coast 1
• Humid subtropical 3
• Boreal 8
• Continental 1
• Perennial streams
• No info 1
• gt1 mi 6
• lt1 mi 7
• On site 11

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Failure Modes and Effects Analysis
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Failure Modes and Effects Analysis

• Hydrological Characterization Failures
• 7 of 22 mines exhibited inadequacies in
  hydrologic characterization
• At 2 mines dilution was overestimated
• At 2 mines the presence of surface water from
  springs or lateral flow of near surface
  groundwater was not detected
• At 3 mines the amount of water generated was
  underestimated

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Failure Modes and Effects Analysis

• Geochemical Characterization Failures
• 11 of 22 mines exhibited inadequacies in
  geochemical characterization
• Geochemical failures resulted from
• Assumptions made about geochemical nature of ore
  deposits and surrounding areas
• Site analogs inappropriately applied to new
  proposal
• Inadequate sampling
• Failure to conduct and have results for long-term
  contaminant leaching and acid drainage testing
  procedures before mining begins.
• Failure to conduct the proper tests, or to
  improperly interpret test results, or to apply
  the proper models

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Failure Modes and Effects Analysis

• Mitigation Failures
• 18 of 22 mines exhibited failures in mitigation
  measures
• At 9 of the mines mitigation was not identified,
  inadequate or not installed
• At 3 of the mines waste rock mixing and
  segregation was not effective
• At 11 of the mines liner leaks, embankment
  failures or tailings spills resulted in impacts
  to water resources

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Failure Modes Root Causes Hydrologic
Characterization

• Failures most often caused by
• Over-estimation of dilution effects
• Failure to recognize hydrological features
• Underestimation of water production quantities
• Prediction of storm events or deficiencies in
  stormwater design criteria is the most typical
  root cause of hydrologic characterization
  failures

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Failure Modes Root Causes Geochemical
Characterization

• Root causes of Geochemical Prediction Failures
  include
• Sample representation
• Testing methods
• Modeling/Interpretation
• Geochemical Characterization Failures can be
  addressed by
• Ensuring sample representation
• Adequate testing
• Interpretation

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Failure Modes Root CausesMitigation

• Hydrologic and geochemical characterization
  failures are the most common root cause of
  mitigation not being identified, inadequate or
  not installed
• Most common assumption is that oxide will not
  result in acid generation
• Mitigations are often based on what is common
  rather than on site specific characterization

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Failure Modes Root CausesMitigation

• Waste rock mixing and segregation not effective
• In most cases, no real data is available (e.g.
  tons of NAG versus tons of PAG and overall ABA
  accounting)
• Failures typically caused by
• Inadequate neutral material
• Inability to effectively isolate acid generating
  material from nearby water resources

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Failure Modes Root CausesMitigation

• Liner leak, embankment failure or tailings spill
• Mitigation frequently fails to perform and can
  lead to groundwater and surface water quality
  impacts
• Failures are typically caused by
• Design mistakes
• Construction mistakes
• Operational mistakes

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Failure Modes Root CausesRecommendations

• A more systematic and complete effort should be
  undertaken when collecting data
• Recognize the importance of thorough hydrological
  and geochemical characterization
• Utilize information in a conservative manner to
  identify and utilize mitigation measures
• Consider the likelihood and consequences of
  mitigation failures