ann maest, james kuipers, connie travers, and david atkins
DESCRIPTION
EVALUATION OF METHODS AND MODELS USED TO PREDICT WATER QUALITY AT HARDROCK MINE SITES: SOURCES OF UNCERTAINTY AND RECOMMENDATIONS FOR IMPROVEMENT. Ann Maest, James Kuipers, Connie Travers, and David Atkins Buka Environmental; Kuipers and Associates; Stratus Consulting, Inc. - PowerPoint PPT PresentationTRANSCRIPT
EVALUATION OF METHODS AND EVALUATION OF METHODS AND MODELS USED TO PREDICT WATER MODELS USED TO PREDICT WATER
QUALITY AT HARDROCK MINE SITES: QUALITY AT HARDROCK MINE SITES: SOURCES OF UNCERTAINTY AND SOURCES OF UNCERTAINTY AND
RECOMMENDATIONS FOR RECOMMENDATIONS FOR IMPROVEMENTIMPROVEMENT
Ann Maest, James Kuipers, Connie Travers, and David Ann Maest, James Kuipers, Connie Travers, and David AtkinsAtkins
Buka Environmental; Kuipers and Associates; Stratus Buka Environmental; Kuipers and Associates; Stratus Consulting, Inc.Consulting, Inc.
WMAN Conference, Worley, IDWMAN Conference, Worley, IDOctober 1, 2005October 1, 2005
Why Characterize and Predict?Why Characterize and Predict? Regulators use characterization and modeling Regulators use characterization and modeling
information to determine if a mine will be protective of information to determine if a mine will be protective of water resources during and after miningwater resources during and after mining
Will mine generate acid and contaminants?Will mine generate acid and contaminants? Future environmental liability – set bonds Future environmental liability – set bonds Cost of remediating mine sites on the National Priorities Cost of remediating mine sites on the National Priorities
List (NPL) ~$20 billionList (NPL) ~$20 billion Recent increases in the prices of precious and base Recent increases in the prices of precious and base
metals have triggered increase in new mines around the metals have triggered increase in new mines around the worldworld
~170 large hardrock mines in US in various stages of ~170 large hardrock mines in US in various stages of permitting, operation, closurepermitting, operation, closure
This StudyThis Study
Lays out framework for evaluating Lays out framework for evaluating methods and models used to predict water methods and models used to predict water quality at hardrock mine sitesquality at hardrock mine sites
Makes recommendations for improvementMakes recommendations for improvement Intended audience: regulators, citizens, Intended audience: regulators, citizens,
mine operators and managersmine operators and managers
Nature of PredictionsNature of Predictions
Forward modeling Forward modeling Timeframe of impacts Timeframe of impacts UncertaintiesUncertaintiesRegulatory authorities require predictionsRegulatory authorities require predictions
Study ApproachStudy Approach
Synthesize existing reviews Synthesize existing reviews Develop “toolboxes” Develop “toolboxes” Evaluate methods and modelsEvaluate methods and modelsRecommendations for improvementRecommendations for improvementOutside peer review (Logsdon, Nordstrom, Outside peer review (Logsdon, Nordstrom,
Lapakko)Lapakko)Case studies – NEPA/EIS StudyCase studies – NEPA/EIS Study
Characterization MethodsCharacterization Methods
Method descriptionMethod descriptionMethod referenceMethod referenceUse in water quality predictionsUse in water quality predictionsAdvantagesAdvantagesLimitationsLimitations
Characterization during different phases of Characterization during different phases of miningmining
Sources of Uncertainty - GeneralSources of Uncertainty - General
Extent/representativeness of Extent/representativeness of environmental samplingenvironmental samplingneed more environmental sampling; let need more environmental sampling; let
geologic/mineralogic variability dictate extent geologic/mineralogic variability dictate extent of sampling; define geochemical test units of sampling; define geochemical test units
Mass of Each Separate Rock Type (tonnes)
Minimum Number of Samples
<10,000 3
<100,000 8
<1,000,000 26
10,000,000 80
Recommended Minimum # Recommended Minimum # SamplesSamples
Sources of Uncertainty – StaticSources of Uncertainty – Static
Effect of mineralogy on NP and APP Effect of mineralogy on NP and APP Rely on mineralogy more than on Rely on mineralogy more than on
operationally defined lab testsoperationally defined lab tests Interpretation of static testing resultsInterpretation of static testing results
only use as initial screening technique to only use as initial screening technique to estimate total amount of AGP/ANPestimate total amount of AGP/ANP
Sources of Uncertainty – Leach Sources of Uncertainty – Leach TestsTests
Water:rock ratioWater:rock rationever known definitively; 20:1 too dilutenever known definitively; 20:1 too dilute
Use of unweathered materialsUse of unweathered materialsmust start with weathered materialsmust start with weathered materials
Interpretation of resultsInterpretation of resultsmay have limited use as scoping tool if use may have limited use as scoping tool if use
weathered rock and evaluate applicability of weathered rock and evaluate applicability of resultsresults
Sources of Uncertainty - KineticSources of Uncertainty - KineticParticle sizeParticle size
minimize amount of size reduction for minimize amount of size reduction for samples – field/lab discrepanciessamples – field/lab discrepancies
Length of testsLength of tests20 weeks is too short for kinetic tests, unless 20 weeks is too short for kinetic tests, unless
shown to be AG before then. NPshown to be AG before then. NP≥APP.≥APP. Interpretation of resultsInterpretation of results
analyze effluent for all COCs; use for short- analyze effluent for all COCs; use for short- and long-term AGP/leaching potentialand long-term AGP/leaching potential
Length of Kinetic TestsLength of Kinetic Tests
Source: Nicholson and Rinker, 2000 (ICARD).
Percent of NEPA Mines Conducting Different Types of Geochemical Characterization
None10%
Static only13%
Short-term leachonly6%
Static+short-termleach18%
Static+kinetic16%
Static+short-termleach+kinetic
37%
N = 69
Characterize geology,
alteration, mineralogy,
liberation
Define geochemical
test units; estimate volumes
Determine # samples/
unit
Bench-scale testing
Whole rock analysis of
each test unit
Static testing for each test
unit
Modify APP and ANP based on
mineralogyTailings?
Mineralogy
Yes
Aerially exposed: humidity cell
testsSubmerged: batch tests
Aerially exposed: aerobic column tests/
minewall washingSubmerged:
Continuous-flow column tests
Site-specific scaling factors
Kinetic testing for
each test unit
No
Mineralogy
Results for total amt AG+NP
material, block model, waste management
Potential COCs
Results for short/long-
term AGP and contaminant-
leaching potential
Inputs for geochemical
models
Modeling ToolboxModeling Toolbox
Category/subcategory of codeCategory/subcategory of codeHydrogeologic, geochemical, unit-specificHydrogeologic, geochemical, unit-specific
Available codesAvailable codesSpecial characteristics of codesSpecial characteristics of codes Inputs requiredInputs requiredModeled processes/outputsModeled processes/outputsStep-by-step procedures for modeling Step-by-step procedures for modeling
water quality at mine facilitieswater quality at mine facilities
Modeling OpportunitiesModeling Opportunities
water table(approximate)
Pit Outline
Waste Rock PileHeap/Dump Leach Pile
Precipitation
Evapotranspiration Precipitation
EvapotranspirationInfiltration
Runoff
Vadose Zone/Geochemical Models
Groundwater Flow and Geochemical Speciation/ Reaction Path Models
Limnologic ModelsGeochemical Speciation/Reaction Path Models
Stream/RiverModels
Precipitation
Evapotranspiration
Near SurfaceHydrologic Models
Sediment GenerationModels
Pyrite Oxidation andGeochemical Speciation/Reaction Path Models
Site-Wide Conceptual
Model
Baseline Conditions
Sources
Pathways
ProcessesMitigations
Receptors
SourcesSources
PathwaysPathways
ProcessesProcesses
Sources of Uncertainty - ModelingSources of Uncertainty - Modeling Conceptual modelConceptual model
Conceptual models are not unique and can change over timeConceptual models are not unique and can change over time Revisit conceptual models and modify mining plans and Revisit conceptual models and modify mining plans and
predictive models based on new site-specific information predictive models based on new site-specific information Use of proprietary codesUse of proprietary codes
need testable, transparent models – difficult to evaluate, should need testable, transparent models – difficult to evaluate, should be avoided. Need efforts to expand publicly available pit lake be avoided. Need efforts to expand publicly available pit lake models (chemistry).models (chemistry).
Modeling inputsModeling inputs large variability in hydrologic parameters; seasonal variability in large variability in hydrologic parameters; seasonal variability in
flow and chemistry; sensitivity analyses (ranges) rather than flow and chemistry; sensitivity analyses (ranges) rather than averages/mediansaverages/medians
Estimation of uncertaintyEstimation of uncertainty Acknowledge and evaluate effect on model outputs; test multiple Acknowledge and evaluate effect on model outputs; test multiple
conceptual modelsconceptual models “…“…there is considerable uncertainty associated with long-term there is considerable uncertainty associated with long-term
predictions of potential impacts to groundwater quality from predictions of potential impacts to groundwater quality from infiltration through waste rock...for these reasons, predictions should infiltration through waste rock...for these reasons, predictions should be viewed as indicators of long-term trends rather than absolute be viewed as indicators of long-term trends rather than absolute values.”values.”
Develop Site Conceptual
Model
Gather input data for
geochemical test units and receptors
Select appropriate model(s) for
predicting water quality
Conduct modeling to determine
concentrations at receptors/other
locations
Conduct sensitivity/uncertainty
analysis using range of input
values
Evaluate effect of
mitigations
Concentrations at receptors > standards?
Yes
EndNo
Concentrations at receptors > standards?
No
Redesign Mine Plan
Yes
Percent of NEPA Mines Using General Types of Predictive Models
No Models44%
Water Quantity26%
Water Quality1%
Water Quantity+Quality
29%
N = 69
SummarySummary Characterization methods need major re-Characterization methods need major re-
evaluation, especially static and short-term leach evaluation, especially static and short-term leach teststests
Increased use of mineralogy in characterization Increased use of mineralogy in characterization – make less expensive, easier to use/interpret– make less expensive, easier to use/interpret
Modeling uncertainty needs to be stated and Modeling uncertainty needs to be stated and defineddefined
Limits to reliability of modeling – use ranges Limits to reliability of modeling – use ranges rather than absolute valuesrather than absolute values
Increased efforts on long-term studies and Increased efforts on long-term studies and collection of site-specific data over modelingcollection of site-specific data over modeling
ConclusionConclusion
Predictive modeling is an evolving science Predictive modeling is an evolving science with inherent uncertaintieswith inherent uncertainties
Using the approaches described in this Using the approaches described in this report, predictive water quality modeling report, predictive water quality modeling and site characterization information can and site characterization information can be reliably used to design protective be reliably used to design protective mitigation measures and to estimate the mitigation measures and to estimate the costs of future remediation of hardrock costs of future remediation of hardrock mine sites.mine sites.