Session 4: Prediction of Impacts
Dr. Rob Bowell – SRK Consulting (UK)
Developing Conceptual Models
Why develop models?
• Concept validation
• Explanation
• Basis of quantification
of inputs and outputs
• Regulatory assessment
• Risk based assessment
• Management tool
Where does this fit in?
• Mine development
• Mine operation
• Closure and reclamation
Gas transferO2 CO2
Pit lake / wall rock interaction
Direct
precipitation
Lake evaporation
GW inflow during pit filling
Runoff from high wall
Mixing
Seepage to groundwater
Mineral precipitationand Adsorption
Static water level
What is a Model? Anything used in any way to represent
an other item, idea, concept or action
Qualitative or Quantitative
Analyze a system to be controlled or
optimized
Hypothesis of how the system could
work
Analyse how an unforeseeable event
could affect the system.
Determine different control approaches
in simulations.
A mathematical model is the set of
functions that describes a system using
variables and equations that establish
relationships between the variables.
Generic Concepts
Source: Figure 5-3. Chapter 5. Prediction. GARD guide
Flow Diagram
Source: Figure 5-4. Chapter 5. Prediction. GARD guide
3-Phase Approach for Assessing Baseline
Chemistry and Predicting Future Water Quality
Geological
Analytical Engineering
Management
and Mitigation Quantify Magnitude
of Impact
Define Contributing
Processes
Check List
Conceptual thinking about
the interaction of a mine with
the environment requires
tools:
• Geological
• Hydrological
• Climate
• Geochemical
characterization
• Land use
Influence of Geology on Geochemistry
Key Geological Controls
Host rock
• Limestone/Marble
• Porous vs. crystalline
• Hydrothermal alteration
Mineralogy
• Carbonates present?
• Sulfides present?
• Trace element
secondary minerals?
• Buffering silicates?
Structure
• Flow paths
Geochemistry
Source: Stillitoe, 2009. EG fig 6. v.105, pp3-41
Key Alteration Types
Alteration – hydrothermal
Contrasting mineral
assemblage from
parent rock
Contrasting element
enrichment/depletion
Essential to characterize
Source: Stillitoe, 2009. EG fig 10. v.105, pp3-41
Hydrothermal Alteration & ABA
NP
eq kg
CaCO3/t
AP. eq kg CaCO3/t
QSP KFSP
Argillic
Silica
Propylitic
Deutric
Carbonate
UNCERTAIN ZONE
NET ACID PREDICTION
NET NEUTRAL PREDICTION
Alteration control on ABA
Chemical Zonation
Chalcophile Corridor
Chalcophile corridor
• “Existence of regional
geochemical trends of
chalcophile and
associated elements”
Smith et al., 1989
Several exist in north central
Nevada
• Carlin trend
• Battle Mountain
• Getchell
• Bald Mountain
Metal Chemistry/Mineralogical Controls
0.01
0.1
1
10
100
1000
10000
100000
0 2 4 6 8 10 12
pH (su)
(Co
+N
i+C
u+
Zn
+C
d+
Pb
)mg
/L
High sulfide-Au
Porphyry
Low sulfide-Au
Carlin-type
VMS
SEDEX
Tin veins
Younger Diagram
PUMPED DEEP
GROUND WATERS
BRINES
NET ALKALINE
NET ACID
ALKALINITY100%
ACIDITY100%
SO
100%4
2- Cl100%
-
100
60
40
20
0
0 20 40 60 80 100
80
% t
ota
l as m
g/l C
aC
O 3
%S (SO +Cl ) meq/l4
2- -
High Sulfidation
Porphyry
CarbonatePb-Zn Clay pitsLow Sulfidation
CarlinShear zone Au
Geochemical Change with Time
TIME
Mass Loadingto groundwater
Process waterBuffering
50-200 years >10,000 years
Natural attenuation capacity infoundation soils
Seepage co-mingles with groundwaterNo control other than dilution
Release of Process waterhigh pH, sulfate
Chronic seepage fromreactive tailings
Tailings seepage mixeswith groundwater
Example: Tsumeb, Namibia
Polymetallic pipe-like deposit
Precambrian age
1908-1993 operation
• 5Mt Cu, 9.5 Mt Pb 2.1 Mt Zn
• Ag, Au, Cd, Ge, As, Sn, W, V,
Mo, Co, Hg, Ga, In, Sb
Current resource (post 1996)
• 5 Mt @ 4.3% Cu, 7% Pb,
• 2% Zn, 3 opt Ag, + 330 ppm
Ge,
Eh-pH Groundwaters
Upperoxide zone
SurfaceS N
Sulfide ore
Lower oxide zone
Nor
th B
reak
Fra
ctur
e Zon
e
0 1000Metre
2 4 6 8 10 12
-0.2
0
0.2
0.4
0.6
0.8
1.0
H O
H O
O
H2
2
2
2
pH
E(V
)
First oxidation zoneSecond oxidation zone
First sulfide zoneSecond sulfide zone
Mineralogy/Geochemistry, First Oxidation Zone
First oxidation zone
More resistant or
low solubility, higher pCO2
secondary minerals
Mineralogy/Geochemistry, Second Oxidation Zone
Second oxidation zone
“alkali to neutral pH” greater range
Eh-pH, therefore more
secondary minerals
Even reduced!
Water
Groundwater
• Is there water – regolith or protolith
• Fracture flow?
• Seasonal water table
• Groundwater yield from different rock types –
does it change?
• Water quality – does it change proportional
to host rock, depth, water shed?
• Relation to potential active mining zone
– particularly important with fracture flow
where water utilizes same zones as mineralization
Surface water
• Is there water – seasonal rivers?
• Relationship to land use
• Groundwater yield from different water sheds –
does it change?
• Water quality – does it change proportional
to host rock, water shed?
• Relation to mineralization/zone of mining
Background Groundwater Histogram
0
100
200
300
400
x<10 10<x<5050<x<100 x>100
Fre
qu
en
cy
.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
Frequency
Cumulative %
As, µg/L Frequency Percentage
x<10 302 37.15%
10<x<50 373 45.88%
50<x<100 69 8.49%
x>100 69 8.49%
Total 813 100%
Arsenic standard of 50 µg/L
Snowpack Precipitation
Evapotranspiration Infiltration
Ground Water in
Ground Water out
Well
Recharge
WATER BUDGET EQUATION:
SWIN + RECHARGE + GWIN = SWOUT + ET + PUMPING + GWOUT
QIN = QOUT
Water Budget
Water Balance
Water Relationship to Mining Features
Climate Control
Not political – just natural
cycles
Seasonality of climates
Promote salt production
High humidity –
greater biological activity,
greater air moisture
Diurnal variation, e.g. UV light,
influences cyanide breakdown
Environmental and Cultural Setting
Receptors – do they exist?
Evaluation level for water
Purpose of land and water resources in the area
Upstream issues
Ore zone
Pit Shell
Waste Waste
Overburden
Oxide zone
Transition zone
Sulfide zone
Pre Mining Conceptual Model
Unrecovered ore
Oxide zone
Transition zone
Sulfide zone
Waste rock
Ore
Tailings
Air + Water
ARDML
Sulfide mine Waste
Post Mining Conceptual Model
Groundwater Flowpath
Pit Lake Conceptual Model
Gas transferO2 CO2
Pit lake / wall rock interaction
Direct
precipitation
Lake evaporation
GW inflow during pit filling
Runoff from high wall
Mixing
Seepage to groundwater
Mineral precipitationand Adsorption
Static water level
Waste Rock/Heap Leach
Conceptual Model: Tailings
Precipitation
Evaporation
Gas Transfer
Surface
ponding
Groundwater flow
Cyanide + metals in entrained decant waters
Tailings drawdown and meteoric leaching
Interpretation of TMF Geochemistry
Bedrock
Core of Unreacted Tailings
Limit of Entrained Moisture
Limit of Oxygen Transfer
Surface of Tailings
Leached Layer
Precipitation
Evaporation
Alkalinity being removed
(net alkaline)
Geosynthetic Liner
Reaction Layer
Moves with time?
Leached of reactive components
(Net Acid)
Alkalinity being removed
(Net Alkaline)
Alkalinity has been consumed
(Net Acid)
Runoff
Bedrock
Core of Unreacted Tailings
Limit of Entrained Moisture
Limit of Oxygen Transfer
Surface of Tailings
Leached Layer
Precipitation
Evaporation
Alkalinity being removed
(net alkaline)
Geosynthetic Liner
Reaction Layer
Moves with time?
Leached of reactive components
(Net Acid)
Alkalinity being removed
(Net Alkaline)
Alkalinity has been consumed
(Net Acid)
Runoff
Bedrock
Core of Unreacted Tailings
Limit of Entrained Moisture
Limit of Oxygen Transfer
Surface of Tailings
Leached Layer
Precipitation
Evaporation
Alkalinity being removed
(net alkaline)
Geosynthetic Liner
Reaction Layer
Moves with time?
Leached of reactive components
(Net Acid)
Alkalinity being removed
(Net Alkaline)
Alkalinity has been consumed
(Net Acid)
Runoff
Mine Life Cycle
Early Exploration
Stage
Advanced
Exploration & Pre-
Feasibility Stage
Feasibility &
Development
Stage – Mine
Design
Operational Stage Monitoring and
Management
• Geoenvironmen-
tal models
• Geologic
characterization
• Limited number of
tests
Representative
number of static
tests to assess
AD & ML risk in
rocks
• Combine test
results with block
model
• Evaluate site
specific factors
(climate,
hydrology,
receiving waters)
• Create
appropriate
mitigation
measures
• Conduct
management
program
outlined in
permits
• Plan for
closure
• Assess /
manage impacts
to receiving
waters
• Monitor
performance of
constructed
facilities
• Periodically
assess
operations and
closure method
• Revise design
as necessary
Prediction timing
Predicting Metal Leaching Risk
in Waste Rock
Typical criteria
Degree of AD risk
Examples
• Validation of field assessment
• Predicting lag time to metal leaching
• Validation of ion specific WQ prediction
38
How Successful is Selective
Handling and Placement?
PAG criterion developed
prior to mining
Blasthole testing (1 in 10)
Modeling
Dispatch routing to controlled
placement areas
Three zones tested
• PAG
• Non-PAG
• Mixed
39
Predicting ARDML Risk
We can reliably predict ARDML risk
• Requires professional judgment –
cookbook methods don’t work
• Need to consider population
distribution – averages alone can
be misleading
41
Geochemical models
WATEQ
MINSOLV
MINTEQ
PHREEQC
GWB
PHAST
Choice, regulatory acceptance,
database value
Importance of thermodynamic database
Parameter
Observed
Value
Standard
Model
Prediction
Refined Model
Prediction
pH 6.8 7.57 7.06
Sulfate (mg/L) 419 366 424
Arsenic (mg/L) 0.96 5.33 0.83
Iron (mg/L) 0.05 0.005 0.05
Calcium (mg/L) 172 671 192
Sources: Bowell et al., 1998; Parshley et al., 2000; SRK, 2000; SRK, 2001; Bowell & Parshley, 2004
Size matters!
1012 kg in exposed pit walls
Size distribution - important
45
Degree of Saturation
46
Degree of Saturation versus GOR
47
Rfield = Rlab x SFmoist x SFsize x SFcontact x SFtemp x SFO2
Rlab = HCT leach rates
SFmoist = reduced oxidation due to low moisture
SFsize = reactivity reduction due to HCT vs field PSD
SFcontact = reduction due to unflushed mass (retained
solutes) in field vs HCT
SFtemp = rate relationship for temperature: Arrhenius
SFO2 = reactive mass reduction due to O2 diffusion
limits
Convert Laboratory data to field
scenario
Method Modified from Kempton et al., 2012
Sensitivity of inputs on results
49
Sensitivity
Scenario
Base case Sensitivities
Availability of
hydrous ferric
oxide (HFO) for
adsorption
Assumes that 25% of the potential
hydrous ferric oxide produced during
pyrite oxidation is available for solute
adsorption
10% and 50% availability of HFO used. 10%
is likely very low and therefore conservative
Oxidation rate
scaling factor
No adjustment of the combined factor 50% and 200% of the combined rate used.
200% is considered very conservative
Humidity cell
averaging
Assumes that an average of all
humidity cell weeks is representative
of the waste rock dump weathering at
any one time.
Average of last 20 weeks of humidity cell test
data used
High sulfur
humidity cell use
Assumes that using the average of
humidity cells is representative
Average of cells replaced with high sulfur
material humidity cell only
Infiltration rate
Assumes that post closure, infiltration
into the WRD will return to catchment
baseline infiltration) while the rest
reports as surface of run-off
Lower infiltration of around half base case or
10% of annual precipitation used which
would result in higher concentrations but
likely slightly lower loading due a greater
degree of mineral saturation and mass of
solutes adsorbed to mineral surfaces..
SULFIDOX modelling
Sulfidox represents the following processes
in waste rock dumps:
• Gas transport via diffusion and/or
advection;
• Oxidation of sulfide minerals
• Heat transport via thermal conduction
and/or fluid flow;
• Infiltration of water down through the
waste rock dump
50
Input Parameters
51
WRD cross-section parameters NAF PAF
2D cross-section dimensions
Height (m)
Width (m)
Slope Ratio
80
725
2:1
80
725
2:1
2D cross-sectional area (m2) 45200 45200
Mass of cross-section (tonnes) 34,804 34,804
Specific density of the rock (kg.m-3
) 2800 2800
Sulfur mass fraction (%) 0.07 3.80
Effective sulfur mass fraction (%) 0.014 0.76
Porosity 0.35 0.35
Intrinsic oxidation rate (kg.m-3
.s-1
) 9.10E-11 2.16E-08
Water infiltration rate (ma-1
) 0.5 0.5
Liquid volume fraction (%) 5 5
Atmospheric conditions and gas properties
Annual average ambient air temperature (°C) 5 5
Gas permeability (m-2
) 10E-10 10E-10
Atmospheric oxygen content kg (O2)kg -1
(air) 0.23 0.23
Prediction
52
Sulfidox results:
Uncovered PAF
oxygen distribution
(blue indicates
near zero oxygen,
red indicates
oxygen at
atmospheric
concentration)
temperature
distribution
(blue indicates
ambient
temperature, red
indicates over
28°C above
ambient)
Apply to Geochemical Predictions
December 3, 2008 NWMA - Reno, NV 53
0.1 mole/m2/yr 0.2 mole/m
2/yr 0.5 mole/m
2/yr 1 mole/m
2/yr
NAF
seepage PAF seepage
Mixed seepage/ groundwater
NAF seepage
PAF seepage Groundwater
NAF seepage
PAF seepage Groundwater
NAF seepage
PAF seepage
Groundwater
pH
7.84 4-7 7.31 7.84 4-7 7.31 7.84 4-7 7.30 7.84 4-7 7.28
Alk mg/L as CaCO3
280 <1 - 30 248 280 <1 - 30 249 280 <1 - 30 244 280 <1 - 30 234
SO4 mg/L 401 147 309 401 293 316 401 731 336 401 1448 370
Na mg/L 151 1.68 116 151 3.35 116 151 8.35 116 151 16.5 117
Ca mg/L 39.0 39.3 46.8 39.0 78.6 48.6 39.0 196 54.1 39.0 387 63.2
K mg/L 208 10.2 139 208 20.3 139 208 50.6 141 208 100 143
Mg mg/L 107 2.42 75.0 107 4.84 75.1 107 12.1 75.4 107 23.9 76.0
Al mg/L 0.0026 0.0018 0.0047 0.0026 0.0016 0.0047 0.0026 0.0054 0.0050 0.0026 0.019 0.0055
As mg/L 0.0016 0.0018 0.0012 0.0016 0.0036 0.0013 0.0016 0.0090 0.0016 0.0016 0.018 0.0020
Co mg/L 0.08 0.0027 0.052 0.08 0.0054 0.052 0.08 0.014 0.053 0.08 0.027 0.053
Cr mg/L 0.09 0.0011 0.057 0.09 0.0022 0.057 0.09 0.0055 0.058 0.09 0.011 0.058
Cu mg/L 0.014 0.0039 0.0098 0.014 0.0077 0.0100 0.014 0.019 0.011 0.014 0.038 0.011
Fe mg/L 0.00033 35.7 1.78 0.00033
67.8 3.27 0.00033 144 4.67 0.00033 271 5.22
Mn mg/L 1.3E-10 0.13 0.24 1.3E-10 0.27 0.24 1.3E-10 0.67 0.26 1.3E-10 1.32 0.29
Mo mg/L 0.41 0.0042 0.27 0.41 0.0085 0.27 0.41 0.021 0.27 0.41 0.042 0.27
Ni mg/L 0.021 0.26 0.032 0.021 0.53 0.045 0.021 1.31 0.081 0.021 2.60 0.14
Pb mg/L 0.00070 0.00067 0.00054 0.00070
0.0013 0.00057 0.00070 0.0033 0.00066 0.00070 0.0066 0.00082
Sb mg/L 0.23 0.0023 0.15 0.23 0.0046 0.15 0.23 0.012 0.15 0.23 0.023 0.15
U mg/L 0.22 0.0042 0.14 0.22 0.0083 0.14 0.22 0.021 0.14 0.22 0.041 0.14
Zn mg/L 1.08 0.037 0.72 1.08 0.075 0.72 1.08 0.19 0.72 1.08 0.37 0.73
Account for Oxygen in Predictions
54
Seepage composition Solute loading (g/yr)
Base case All weeks HCT source term Base case All weeks HCT source term
NAF seepage
PAF seepage
Seepage/ groundwater
NAF seepage
PAF seepage
Groundwater NAF seepage
PAF seepage
Groundwater
NAF seepage
PAF seepage
Groundwater
Seepage/flow (m3/yr) 252340 22324 402779 252340 22324 402779
pH 7.84 4-7 7.31 7.80 4-7 7.28
Alk mg/L as CaCO3
280 <1 - 30 248 264 <1 - 30 236
SO4 mg/L 401 147 309 1042 155 702 101101740 3275475 124405883 262866150 3450905 282835978
Na mg/L 151 1.68 116 290 2.48 200 38066954 37411 46686432 73101265 55300 80540211
Ca mg/L 39.0 39.3 46.8 62.6 39.9 62.2 9834353 877480 18859224 15800596 890370 25053767
K mg/L 208 10.2 139 286 13.2 182 52525262 226772 55844268 72127211 294857 73320969
Mg mg/L 107 2.42 75.0 169 4.24 111 27085214 54019 30212838 42631770 94633 44754093
Al mg/L 0.0026 0.0018 0.0047 0.0026 0.0015 0.0045 657 39.4 1907 645 33.5 1794
As mg/L 0.0016 0.0018 0.0012 0.011 0.0019 0.0074 400.66 40.3 499.3 2881.2 43.3 2961
Co mg/L 0.08 0.0027 0.052 0.11 0.0034 0.069 19297 60.5 21034 26651 76.7 27640
Cr mg/L 0.09 0.0011 0.057 0.25 0.0021 0.15 22136 24.5 23118 62174 45.8 62083
Cu mg/L 0.014 0.0039 0.0098 0.0096 0.0047 0.0069 3478 86.3 3941 2413 106 2777
Fe mg/L 0.00033 35.7 1.78 0.00038 35.7 1.89 84.2 795869 715330 96 795906 760430
Mn mg/L 1.3E-10 0.13 0.24 2.1E-10 0.14 0.26 3.2E-05 2980 95984 5.4E-05 3109 103449
Mo mg/L 0.41 0.0042 0.27 1.24 0.0090 0.77 103524 94.6 107916 312240 201 311506
Ni mg/L 0.021 0.26 0.032 0.13 0.25 0.10 5377 5880 13046 32453 5485 39927
Pb mg/L 0.00070 0.00067 0.00054 0.0027 0.0034 0.0019 176.6 14.9 216.5 679.7 76.6 767
Sb mg/L 0.23 0.0023 0.15 0.29 0.0022 0.18 58021 51.7 60283 73576 50.0 73343
U mg/L 0.22 0.0042 0.14 0.29 0.0048 0.18 54870 93.1 57004 73165 108 72937
Zn mg/L 1.08 0.037 0.72 0.42 0.024 0.28 271493 835 288685 105277 538 112098
Predicting ARDML Risk in
Tailings
Problem formulation
• A dry stack tailing contains
>20% pyrite, but is also high
carbonate
• Will it form acid during
operation?
• Can it be closed in a way
that prevents acidification?
55
NWMA - Reno, NV 56
NWMA - Reno, NV 57
NWMA - Reno, NV 58
NWMA - Reno, NV 59
Example: Groundwater flowpath,
Zambian pit
Conceptual Model
61
Typical Steps
in Numerical Prediction
62
Input Water Chemistry
Sample
(mg/L)
Shaft
Pit
sump
BF-2
BF-3
CON-
E3
PLS
Date 10/98 9/99 10/99 10/99 1/99 1/99
Acidity 528 -- -- -- -- --
Al 10.9 <0.2 <0.3 0.05 0.33 4,810
Cu 9.83 <0.5 0.03 0.04 0.08 1,010
Mg 574 611 161 126 14.1 7,610
pH 4.3 8.8 7.7 7.9 8 3
Se 0.001 0.12 0.054 0.02 0.0008 0.15
Sulfate 4,230 3,300 1,090 1,040 30 50,200
U 0.05 0.03 -- -- -- 0.05
Predicted chemistry:
Flood u/g workings to base of pit
UG fill Pit Lake 2550
Concentration AWQS Avian
mg/L Criteria
Al 523.4 47.1
As 0.0216 0.01 22.1
Sb 0.0003 0.006
Ba 0.0980 2 89.4
Be 0.1260 0.004
Cd 0.0489 0.005 6.23
Cr 0.1720 0.01 4.3
Cu 116.5 202.0
F 23.4 4.0 33.5
Ni 6.58 0.1 333
Pb 0.0048 0.01 16.54
Hg 0.0002 0.0005 1.93
Se 0.0028 0.05 2.15
Tl 0.0010 0.002
U 0.0026 68.8
Sulfate 6756
Predicted Pit Water Chemistry
Pit Lake 2550 Pit Lake 2765
Concentration Concentration
mg/L mg/L
pH 5.54 5.68
Al 439.8 98.0
As 0.003 0.002
Sb 0.001 0.001
Ba 0.084 0.065
Be 0.111 0.012
Cd 0.042 0.022
Cr 0.152 0.118
Cu 127.1 79.0
F 19.95 19.00
Ni 5.58 4.99
Pb 0.004 0.004
Hg 0.000 0.000
Se 0.015 0.013
Tl 0.001 0.001
U 0.079 0.068
Sulfate 5750 6000
Batch test calibration
5/19/2014 66
• North Mara Mine has Potentially Acid Generating Waste
that is highly reactive
• During High Rain storm events – significant change in
runoff chemistry from dumps
o pH ~ 3.5
o Sulfate ~ 770 mg/L
o Iron ~ 26 mg/L
o Arsenic ~ 2 mg/L
o Apluminium ~ 1.2 mg/L
• Need to capture acidic runoff or limit oxidation of sulfides
• Option of using former pit as a storage area
• Currently pit has a lake
• What is the best option
o Dry storage above water level
o Placement in the pit lake ie have a water oxygen
barrier
• Can Geochemical modelling aid engineering/management
decision making
Example: Prediction of Backfill
chemistry North Mara, Tanzania
Option 1: Conceptual Model for PAF waste disposal
in Gena pit lake (assumes that disposed material
will be unavailable for reaction)
Option 2: Conceptual Model for PAF waste disposal
above water level in Gena Pit (assumes that disposed
material will be available for reaction)
Water Balance
Groundwater flow and chemistry
Surface water chemistry
Rock leachate chemistry
• Leaching chemistry
• Geology
• Physical differences between field
and laboratory conditions “Scaling”
Climate data
Precipitation chemistry
Lake chemistry and volume
Attenuation
Mineralogy of wallrock/precipitates to
determine potential saturated phases
Define Model Inputs
Waste Rock
Geochemistry
Potentially Acid Forming
Not Acid Forming
Geochemical Rock Inputs
Scaling of Data
1012 kg in exposed pit walls
Rfield = Rlab x SFmoist x SFsize x SFcontact x SFtemp x SFO2
Rlab = HCT leach rates
SFmoist = reduced oxidation due to low moisture
SFsize = reactivity reduction due to HCT vs field PSD
SFcontact = reduction due to unflushed mass (retained solutes)
in field vs HCT
SFtemp = rate relationship for temperature: Arrhenius
SFO2 = reactive mass reduction due to O2 diffusion limits
Convert Laboratory Data
to Field Scenario
Method Modified from Kempton et al., 2012
Geochemical Calibration
Geochemical Predictions
• Against Tanzanian standards
exposed PAF waste above water
level is not recommended-
exceedences for pH and As
• Using a US BLM type SLRA
approach only issue for wildlife
is low pH and long term exposure
to As for birds
• Main control- high groundwater
dilution
• Predicted precipitation of goethite
and jarosite (ie SI>0)
• Predict high levels of attenuation
of As(V) species onto goethite
• Placement of PAF in lake shows
no geochemical impacts
Environmental Assessment
Take Home Points
Prediction of mine water chemistry requires;
• Good site knowledge of geology, hydrogeology
and mineralogy
• Good hydrogeological modelling
• Understand purpose for model
• Knowledge of all contributing geochemical sources
• Knowledge of attenuation processes
• Geochemical models require good database
• Cognisant of sensitivities in model
• Assess finite components
• Assess uncertainty in model
Top Related